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Weather forecasting

Weather forecasting is the application of scientific principles, observational data, and computational models to predict atmospheric conditions, such as temperature, precipitation, wind, and severe weather events, over specific locations and time periods ranging from hours to months ahead.^[1] This process relies on collecting vast amounts of real-time data from satellites, radars, weather stations, and aircraft to analyze current weather patterns and project future states.^[1] Accurate forecasts are essential for public safety, economic planning, and disaster mitigation, generating over $30 billion annually in economic benefits across sectors like agriculture, transportation, and energy in the United States alone.^[2] The foundations of modern weather forecasting trace back to the late 19th century, when systematic observations began in the United States under the U.S. Army Signal Service in 1870, evolving into the National Weather Service (NWS) in 1970. Similar systematic efforts emerged globally, coordinated by organizations like the World Meteorological Organization (WMO) established in 1950.^[3] Early efforts focused on manual predictions using telegraphed reports, but the advent of numerical weather prediction (NWP) in the mid-20th century revolutionized the field.^[4] Pioneered by Norwegian physicist Vilhelm Bjerknes in the 1900s, who proposed using mathematical equations to model atmospheric dynamics, NWP became feasible with post-World War II computers.^[5] Key milestones include the first computer-based forecasts in 1950 using the ENIAC at Princeton University and the establishment of the Joint Numerical Weather Prediction Unit in 1954, which by 1958 provided real-time forecasts surpassing manual accuracy.^[4] Today, weather forecasting encompasses diverse methods tailored to time scales: nowcasting for immediate threats up to six hours ahead using radar and satellite imagery; short-range forecasts (up to 48 hours) for daily planning; medium-range (up to 15 days) relying on global models like the ECMWF Integrated Forecasting System; and long-range seasonal outlooks incorporating climate patterns.^[1] Core tools include NWP models run on supercomputers, such as NOAA's Global Forecast System, which assimilate observations into equations describing fluid dynamics and thermodynamics.^[6] Ensemble forecasting, introduced in the 1990s, generates multiple model runs to quantify uncertainty and improve reliability, with five-day forecasts now accurate about 90% of the time.^[4]^[7] Recent advances integrate artificial intelligence (AI) and machine learning to enhance speed and precision, particularly for high-resolution predictions in data-sparse regions.^[8] For instance, AI-driven nowcasting pilots improve precipitation forecasts using deep learning on radar data, while initiatives like ECMWF's Artificial Intelligence Forecasting System (AIFS) aim to deliver medium-range global forecasts up to 10 days ahead with reduced computational costs.^[8] These developments support international efforts, such as the World Meteorological Organization's Severe Weather Forecasting Programme, to bolster early warnings and resilience in vulnerable areas.^[9] Overall, ongoing research in data assimilation, satellite technology, and AI continues to extend forecast lead times and accuracy, underscoring weather forecasting's critical role in a changing climate.^[10]

History

Ancient and early methods

Ancient civilizations developed rudimentary weather forecasting techniques primarily through direct observations of the natural world, including celestial bodies, animal behaviors, and recurring seasonal patterns. In Mesopotamia, the Babylonians around 650 BCE attempted short-term predictions by examining cloud formations, optical phenomena such as halos around the sun or moon, and astrological alignments, believing these signs influenced atmospheric changes.^[11] Similarly, ancient Chinese astronomers integrated weather observations with celestial monitoring as early as the Zhou Dynasty (1046–256 BCE), noting how planetary positions and solar movements correlated with wind, rain, and seasonal shifts; by 300 BCE, they had formalized a calendar of 24 solar terms to anticipate agricultural weather patterns like monsoons.^[12] In ancient Greece, philosophers such as Aristotle (384–322 BCE) classified winds and weather events in his Meteorologica, while his successor Theophrastus compiled empirical signs in On Weather Signs (c. 371–287 BCE), including animal behaviors like dogs rolling on the ground to signal impending wind or birds flying low before storms. Early instruments emerged to quantify these observations, enhancing the reliability of local predictions. In ancient India, during the Mauryan Empire (322–185 BCE), rain gauges known as varsha—copper bowls approximately 18 inches wide—were systematically used to measure precipitation for agricultural and taxation purposes, as detailed in Kautilya's Arthashastra (c. 300 BCE); references to similar devices appear even earlier in Panini's Astadhyayi (c. 5th–4th century BCE).^[13] Among the Greeks, wind direction indicators evolved from simple descriptions in Aristotle's works to practical devices; the Tower of the Winds in Athens (c. 50 BCE), an octagonal structure topped with a bronze Triton figure that rotated to show wind direction, represented one of the earliest known weather vanes, aiding sailors and farmers in anticipating local conditions.^[14] In medieval Europe, weather forecasting drew heavily from accumulated folklore and proverbs passed down orally among farmers, sailors, and rural communities, often blending observation with superstition. Common sayings, such as "red sky at night, sailor's delight; red sky in morning, sailor's warning," originated from ancient mariners' experiences with atmospheric optics—red hues at sunset indicating high-pressure systems clearing the air, while morning reds signaling approaching fronts—but gained widespread use in European traditions by the Middle Ages, as evidenced in 12th–13th century texts like those of English chroniclers.^[15] Other proverbs invoked animal signs, such as cows lying down before rain (due to joint discomfort from humidity) or woolly spider webs signaling dry weather, reflecting a reliance on accessible, everyday indicators without formal measurement.^[16] These ancient and early methods, while practical for short-range, local needs like farming and navigation, were inherently limited by the absence of global data networks and a comprehensive scientific framework for atmospheric dynamics, often resulting in inconsistent accuracy tied to regional patterns and anecdotal evidence.^[17] Such approaches persisted until the 19th century, when advances in instrumentation and communication began transitioning weather prediction toward formalized scientific meteorology.^[11]

Development of scientific approaches

The transition from anecdotal weather observations to systematic scientific inquiry began in the early 19th century, building on ancient roots of empirical sky-watching to establish standardized nomenclature and measurement scales that enabled more reliable predictions.^[18] In 1803, British pharmacist and amateur meteorologist Luke Howard published "On the Modification of Clouds," introducing the first comprehensive classification system for cloud types, dividing them into genera such as cirrus, cumulus, and stratus based on their form and altitude; this nomenclature, refined over time, remains the foundation of modern cloud observation and aids in forecasting precipitation and storm development.^[19]^[20] Two years later, in 1805, Irish hydrographer Sir Francis Beaufort devised a scale for estimating wind force at sea, ranging from 0 (calm) to 12 (hurricane), using observable effects on sails and waves rather than instruments; initially for naval use, it was adopted internationally by 1874 and standardized wind reporting for forecasting gale risks.^[21]^[22] The institutionalization of meteorology accelerated mid-century with the creation of national services dedicated to data collection and analysis. The United Kingdom's Meteorological Department, later the Met Office, was established on August 1, 1854, under Vice-Admiral Robert FitzRoy within the Board of Trade, initially to improve maritime safety through coordinated weather observations following devastating storms like the 1854 Royal Charter gale that claimed over 300 lives.^[23]^[24] In the United States, Congress authorized the Weather Bureau on February 9, 1870, as part of the Army Signal Corps under President Ulysses S. Grant, tasked with synchronous telegraphic observations from 24 initial stations to track storms and issue public advisories, marking the shift to a government-led scientific enterprise.^[25] The advent of the electric telegraph in the 1860s revolutionized data sharing, allowing near-real-time reports from distant stations and enabling the first coordinated storm warnings across Europe and North America. By 1860, hundreds of U.S. telegraph stations were transmitting daily weather summaries to newspapers like the Washington Evening Star, while in Europe, networks expanded rapidly for synoptic analysis.^[25]^[26] The Netherlands issued the first telegraphic storm warnings in 1860 under C.H.D. Buys Ballot, director of the Royal Netherlands Meteorological Institute, followed by the UK's initial gale alerts in 1861 via FitzRoy's cone signals at ports, and the U.S. debut of public storm warnings on November 8, 1870, for the Great Lakes region; these efforts reduced maritime losses by providing advance notice of pressure shifts and wind patterns.^[27]^[28] A key advance in visual analysis came from Dutch meteorologist C.H.D. Buys Ballot, who in the 1860s pioneered the use of synoptic charts plotting simultaneous pressure observations across regions, revealing isobar patterns and wind circulation rules (later formalized as Buys Ballot's law); his 1868 publication of Europe's first isobaric weather map facilitated the identification of storm tracks and fronts, influencing international forecasting practices.^[27]^[29]

Emergence of numerical techniques

The concept of numerical weather prediction emerged in the early 20th century as meteorologists sought to apply mathematical models to forecast atmospheric changes systematically. Lewis Fry Richardson pioneered this approach with his 1922 book Weather Prediction by Numerical Process, where he attempted a manual computation of a six-hour weather forecast using hydrodynamic equations based on observations from the Western Front during World War I.^[30] His effort involved solving finite-difference approximations by hand, but the process proved highly impractical, requiring weeks of labor for a short forecast period and yielding erroneous results, such as an unrealistically large pressure change of 145 hectopascals over six hours due to inconsistencies in the initial observational data.^[31] Richardson himself acknowledged the computational burden, envisioning a vast "forecast factory" of 64,000 human calculators to achieve real-time predictions, highlighting the limitations of manual methods in handling the nonlinear equations of motion.^[32] Following World War II, advances in electronic computing enabled the first practical numerical forecasts. In 1950, Jule Charney, along with collaborators Agnar Fjörtoft and John von Neumann, utilized the ENIAC computer at the Institute for Advanced Study to produce the inaugural successful numerical weather predictions.^[33] This effort, detailed in their seminal paper "Numerical Integration of the Barotropic Vorticity Equation," involved integrating a simplified model over a 24-hour period, which took approximately 24 hours of processing time on the machine.^[34] The model treated the atmosphere as barotropic—assuming constant density and neglecting vertical variations—to reduce computational complexity, focusing on large-scale horizontal flows via the vorticity equation derived from the Navier-Stokes equations.^[35] Early numerical models, including Charney's, relied on such simplifications like the barotropic vorticity equation to make global predictions feasible with limited hardware, often using grid resolutions of several hundred kilometers.^[36] However, significant challenges persisted through the 1950s and into the 1960s, primarily stemming from errors in initial conditions caused by sparse and inaccurate observational networks, which amplified forecast divergences as in Richardson's case.^[5] Computational constraints further hindered progress, as early computers like ENIAC lacked sufficient speed and memory for fully three-dimensional simulations, restricting models to two-dimensional or quasi-geostrophic approximations and delaying routine operational use until more powerful systems emerged in the mid-1960s.^[37]

Evolution of forecasting dissemination

The dissemination of weather forecasts began with printed media in the mid-19th century, marking the transition from elite or maritime warnings to public accessibility. In the United Kingdom, the first regular public weather forecasts were published in The Times newspaper on August 1, 1861, based on the inaugural Daily Weather Report issued by the Meteorological Department of the British Board of Trade under Robert FitzRoy. These forecasts provided simple 24- to 48-hour outlooks for regions north, south, and west of the UK, using terms like "fine" or "stormy" alongside wind directions, and were derived from telegraphic observations from coastal stations. This innovation democratized weather information, allowing newspapers to reach broader audiences beyond official notices.^[24] By the early 20th century, radio emerged as a faster medium for broadcasting forecasts, expanding reach to rural and mobile populations. The first voice-transmitted weather forecast via radio occurred on January 3, 1921, from the University of Wisconsin's experimental station 9XM, which had earlier used radiotelegraphy for coded reports since 1916. In the 1920s, U.S. Weather Bureau stations increasingly adopted radio for daily advisories, with broadcasts delivered in spoken form to improve comprehension over Morse code transmissions. This shift enabled near-real-time dissemination, particularly for agricultural and aviation users, and set the precedent for national radio networks relaying forecasts.^[25] Television revolutionized visual presentation in the post-World War II era, with weather maps becoming a staple of evening programming starting in the 1950s. In the United States, local stations like KGW in Portland aired the first dedicated TV weather segments around 1950, featuring meteorologists drawing maps on transparent boards to illustrate fronts and isotherms. National shows integrated forecasts, such as NBC's TODAY program delivering its inaugural televised weather report on January 14, 1952, with host Dave Garroway sketching conditions by hand. These broadcasts emphasized graphical elements, making complex data more engaging for home viewers and boosting public reliance on visual media.^[38]^[39] International cooperation enhanced global data sharing in the mid-20th century, underpinning reliable dissemination. The World Meteorological Organization (WMO) established the Global Telecommunication System (GTS) in 1963 as a core component of the World Weather Watch, facilitating the rapid exchange of observational data among member states via telegraph, telex, and later satellite links. This network, operationalized in the 1960s, connected meteorological centers worldwide, enabling synchronized forecasts and reducing delays in information flow. The advent of numerical weather prediction models further accelerated dissemination by generating outputs suitable for quick broadcast and print.^[40] The digital age transformed access in the late 20th and early 21st centuries, shifting from scheduled media to on-demand platforms. In 1995, the U.S. National Weather Service launched the Interactive Weather Information Network (IWIN), an early internet portal providing text-based forecasts and maps to users via dial-up connections. The European Centre for Medium-Range Weather Forecasts (ECMWF) followed with public web access to ensemble predictions in the late 1990s, offering downloadable data for researchers and media. By the 2000s, mobile apps proliferated, with WeatherBug debuting in 2000 as one of the first desktop-to-mobile tools delivering real-time alerts and radar imagery via personal computers and emerging smartphones. These platforms enabled personalized, location-based updates, vastly increasing the speed and granularity of public dissemination.^[25]^[41]^[42]

Fundamental principles

Definition and scope of weather forecasting

Weather forecasting is the application of scientific methods to predict the state of the atmosphere over short time scales, typically ranging from hours to about 10-14 days ahead, focusing on variables such as temperature, precipitation, wind speed and direction, humidity, and atmospheric pressure.^[43] Unlike climate science, which examines long-term averages and patterns of weather over decades or longer, weather forecasting targets transient conditions that directly impact daily activities and immediate risks.^[44] The scope of weather forecasting encompasses a hierarchy of time frames, from very short-range nowcasts—detailed predictions of local conditions up to 6 hours ahead using real-time observations like radar and satellite data—to medium-range forecasts extending 10-14 days, and even subseasonal to seasonal outlooks that bridge toward climate projections.^[45] However, inherent uncertainties arise from the chaotic nature of atmospheric dynamics, as illustrated by Edward Lorenz's 1963 demonstration of sensitive dependence on initial conditions, often metaphorically termed the "butterfly effect," which limits the precision of long-range predictions.^[46] The primary goals of weather forecasting include enhancing public safety by issuing timely warnings for severe events like storms or floods, and supporting economic planning in sectors such as agriculture, transportation, and energy through reliable guidance on expected conditions.^[47] Accuracy is evaluated using metrics like the Brier score, which quantifies the mean squared error between predicted probabilities and observed outcomes for events such as precipitation, rewarding well-calibrated probabilistic forecasts.^[48] Limitations persist due to the distinction between deterministic forecasts, which assume exact future states, and probabilistic approaches that account for uncertainty; the practical horizon of skillful predictability for midlatitude weather remains around 10 days, beyond which errors grow rapidly.^[49]

Key meteorological concepts

The Earth's atmosphere is structured in layers, with the troposphere being the lowest and most dynamically active layer where nearly all weather phenomena occur. Extending from the surface up to approximately 8–15 kilometers depending on latitude and season, the troposphere features decreasing temperature with altitude, strong vertical mixing due to convection, and the majority of atmospheric water vapor and aerosols. This layer's dynamics are influenced by solar heating at the surface, leading to the formation of pressure systems that drive large-scale weather patterns. High-pressure systems, or anticyclones, are regions of sinking air associated with clear skies and stable conditions, while low-pressure systems, or cyclones, involve rising air and often turbulent weather such as storms. Fronts mark the boundaries between contrasting air masses: cold fronts occur where denser cold air advances under warmer air, potentially triggering severe thunderstorms; warm fronts feature the gradual advance of warmer air over cooler air, leading to widespread cloudiness and precipitation; and occluded fronts arise when a cold front overtakes a warm front, lifting the warm air mass aloft. Jet streams, narrow bands of high-speed winds in the upper troposphere (typically 9–16 kilometers altitude and speeds exceeding 100 km/h), meander around the globe and steer these pressure systems and fronts, influencing storm tracks and temperature extremes.^[50]^[51]^[52] Thermodynamic processes in the atmosphere govern energy transfer and stability, particularly through adiabatic changes where no heat is exchanged with the surroundings. In rising or sinking unsaturated air parcels, expansion or compression causes temperature changes at the dry adiabatic lapse rate of approximately 9.8°C per kilometer, derived from the ideal gas law and hydrostatic equilibrium under gravity. This rate reflects the balance between gravitational potential energy conversion and internal energy, with the parcel cooling upon ascent due to work done against expansion. When air becomes saturated, condensation releases latent heat, reducing the lapse rate to the moist adiabatic value (typically 4–7°C per kilometer), which promotes continued convection and cloud formation. These processes determine atmospheric stability: if the environmental lapse rate exceeds the dry adiabatic rate, the atmosphere is unstable, fostering vigorous vertical motion essential for severe weather development.^[53]^[54] Atmospheric dynamics are shaped by the Coriolis effect, a fictitious force arising from Earth's rotation that deflects moving air masses to the right in the Northern Hemisphere and to the left in the Southern Hemisphere, with magnitude proportional to wind speed and latitude via the Coriolis parameter f = 2 \Omega \sin \phi, where \Omega is Earth's angular velocity and \phi is latitude. This deflection influences large-scale flows, leading to geostrophic balance in the absence of friction, where the Coriolis force counters the pressure gradient force. The geostrophic wind velocity is given by

\vec{v_g} = \frac{1}{f \rho} \hat{k} \times \nabla p,

with \rho as air density, \nabla p the horizontal pressure gradient, and \hat{k} the vertical unit vector; this approximation holds well for mid-latitude synoptic-scale motions, resulting in winds parallel to isobars with low pressure on the left in the Northern Hemisphere. Such balance explains the cyclonic circulation around lows and anticyclonic around highs, forming the basis for interpreting weather maps.^[55]^[56] Weather systems exhibit chaotic behavior as solutions to nonlinear partial differential equations governing atmospheric motion, rendering long-term prediction sensitive to initial conditions in what is known as the initial value problem. Seminal work by Edward Lorenz demonstrated this through a simplified three-variable model of convection, revealing deterministic nonperiodic flows where infinitesimal perturbations amplify exponentially, limiting predictability to about two weeks for mid-latitude weather. This sensitivity arises from the multiplicative interactions in nonlinear systems, such as those in the Navier-Stokes equations adapted for the atmosphere, underscoring why weather forecasting relies on probabilistic approaches despite deterministic physics. These concepts form the theoretical foundation for applying numerical models to predict atmospheric evolution.^[57]

Sources of weather data

Weather data for forecasting is primarily gathered through a global network of observation systems coordinated by the World Meteorological Organization (WMO), encompassing surface-based, upper-air, marine, aircraft, satellite, and radar platforms.^[58] These sources provide essential measurements of atmospheric variables such as temperature, pressure, humidity, wind, and precipitation, forming the foundational input for predictive models. The Global Observing System (GOS) includes approximately 11,500 land-based surface stations that conduct observations at least every three hours, often hourly, to capture near-surface conditions.^[59] Surface weather stations, deployed at airports, remote sites, and urban areas, utilize automated systems like the NOAA Automated Surface Observing System (ASOS) to measure key parameters continuously.^[60] Instruments include thermometers for air temperature, often housed in shaded shelters to avoid solar heating; anemometers for wind speed and direction, typically cup or propeller types mounted at standard heights of 10 meters; and barometers for atmospheric pressure, with mercury barometers serving as historical standards requiring periodic calibration against known references to account for temperature and gravity variations.^[61]^[62] Additional sensors measure humidity via hygrometers, precipitation with rain gauges, and visibility through transmissometers, ensuring comprehensive coverage of boundary-layer dynamics.^[62] Upper-air data is obtained primarily through radiosondes, lightweight instrument packages attached to helium-filled balloons launched from about 1,300 global stations, twice daily at 0000 and 1200 UTC.^[63]^[64] These probes ascend to altitudes of up to 30 kilometers, transmitting profiles of pressure, temperature, humidity, and wind speed/direction via radio telemetry until the balloon bursts, providing vertical structure critical for understanding atmospheric stability and circulation.^[64]^[59] Remote sensing platforms expand coverage over vast and inaccessible regions. Geostationary satellites, such as NOAA's GOES series, orbit at 35,800 kilometers to deliver continuous visible and infrared imagery of cloud cover and motion every 15-30 minutes over fixed areas like the Americas.^[65] Polar-orbiting satellites, including the Joint Polar Satellite System (JPSS), circle Earth at about 850 kilometers altitude, providing twice-daily global passes with advanced sounders that infer vertical profiles of temperature and moisture through infrared and microwave emissions.^[65]^[66] Weather radars, such as the U.S. NEXRAD network of 160 S-band Doppler systems, detect precipitation intensity and motion within 230 kilometers, using Doppler shifts to measure radial velocities for identifying storm rotations and wind shear.^[67]^[68] Complementary data comes from marine buoys and aircraft reports. The WMO-coordinated network includes approximately 400 moored buoys and 1,200 drifting buoys measuring sea surface temperature, pressure, winds, and waves across oceans, alongside approximately 4,000 voluntary observing ships.^[69]^[70]^[71] Aircraft-based observations, via the Aircraft Meteorological Data Relay (AMDAR) program involving over 3,000 commercial flights daily, provide real-time temperature, wind, and turbulence data at cruising altitudes, enhancing mid- and upper-tropospheric sampling.^[72]^[73] Integrating these diverse sources into forecasting systems involves data assimilation techniques, which address challenges like instrument biases through corrections—such as adjusting satellite radiances for orbital drift or aircraft readings against radiosonde benchmarks—to ensure consistency across heterogeneous observations.^[74]

Forecasting techniques

Traditional and empirical methods

Traditional and empirical methods of weather forecasting rely on simple rules of thumb, direct observations, and pattern recognition derived from historical weather behaviors, predating computational models and serving as foundational techniques for short-term predictions in stable atmospheric conditions. These approaches emphasize extrapolation from current or recent data without complex mathematical simulations, often proving effective in regions with minimal synoptic changes.^[75] Persistence forecasting, one of the simplest empirical techniques, assumes that current weather conditions will continue unchanged into the immediate future, such as predicting clear skies tomorrow if the day is sunny and calm today. This method performs best in stable environments like high-pressure systems over subtropical areas, where weather patterns exhibit low variability, and it serves as a baseline for evaluating more advanced forecasts. Historically, persistence has been used since the early days of systematic meteorology to provide reliable short-range guidance when other data is limited.^[76]^[77] The trend method extends this simplicity by extrapolating recent changes in weather elements, assuming linear continuation of observed patterns; for instance, if temperatures have risen by 2°C per day over the past two days, the forecast might project another 2°C increase. Applicable to phenomena like steady frontal movements or pressure gradients, it calculates future positions using rate multiplied by time, such as estimating a weather system's displacement based on its prior speed. This technique was particularly valuable in manual forecasting eras for tracking features on synoptic charts in regions with consistent motion.^[78]^[79] Forecasters traditionally recognized barotropic and baroclinic patterns through visual analysis of weather maps, identifying barotropic conditions—characterized by parallel isobars and isotherms with uniform temperature distributions, often in tropical or anticyclonic flows—for straightforward extrapolation of height fields, while baroclinic patterns, marked by crossing isobars and isotherms indicating thermal contrasts and fronts, signaled more dynamic evolutions requiring cautious trend adjustments. These manual recognitions guided predictions of system persistence or intensification without numerical integration, relying on empirical rules from surface and upper-air observations.^[80]^[81] Historical examples of empirical methods include farmer's almanacs, which have employed secret formulas since the 18th century to predict seasonal trends by correlating sunspot cycles, lunar phases, and historical analogs with expected weather. Basic pressure-based rules, drawn from barometer readings, further exemplify these traditions; for example, a falling mercury level often foretells stormy conditions due to rising air and instability, while steady or rising pressure indicates fair weather with sinking air, a practice rooted in 19th-century observational lore. Such rules, like associating unsettled barometer motion with variable weather, were widely used by sailors and farmers before widespread telegraphic data networks.^[82]^[83]

Observational and nowcasting approaches

Observational approaches in weather forecasting rely on the direct interpretation of real-time data from ground-based, airborne, and space-based instruments to provide immediate insights into current conditions and short-term developments. These methods emphasize manual or semi-automated analysis of visible and measurable phenomena, such as cloud formations, pressure gradients, and precipitation patterns, without invoking complex computational models. By focusing on localized, observable trends, forecasters can issue timely warnings for rapidly evolving weather events, particularly in the absence of advanced numerical predictions.^[84] Nowcasting represents a core observational technique, defined as the detailed analysis of current weather followed by extrapolation up to six hours ahead, often leveraging radar and satellite imagery to track phenomena like thunderstorms. This method involves deriving motion vectors from sequential radar scans or satellite images to predict the path and intensity of precipitation systems, such as convective cells, by assuming short-term continuity in their movement and evolution. For instance, thunderstorm nowcasting uses Doppler radar to estimate storm speeds and directions, enabling predictions of hail or heavy rain impacts within 0-2 hours, which is critical for aviation and urban safety. Early applications of nowcasting were pioneered through simple extrapolation of radar echoes for thunderstorm forecasting, evolving into integrated systems that fuse multiple data streams for higher accuracy.^[85]^[86]^[87]^[88] Synoptic analysis complements nowcasting by providing a broader spatial view through the manual interpretation of weather maps compiled from simultaneous observations across a region, typically supporting forecasts from 12 to 48 hours. Forecasters examine isobar patterns, front positions, and pressure tendencies on these maps to anticipate synoptic-scale changes, such as the approach of a low-pressure system or frontal passages, using tools like surface weather charts to identify convergence zones or vorticity. This technique, rooted in the standardized collection of data at synoptic hours (every six hours), allows for qualitative assessments of mid-tropospheric flow influences on surface weather, though it requires experienced judgment to resolve ambiguities in sparse data areas.^[89]^[90] Key instruments underpin these observational methods, with barometers providing essential data on atmospheric pressure trends that signal impending changes, such as falling pressure indicating an approaching storm front. A steady decrease in barometric readings over hours can prompt forecasters to expect gusty winds or precipitation, as pressure gradients drive air mass movements. Similarly, ceilometers measure cloud base heights by emitting laser pulses and detecting backscatter from aerosols or cloud droplets, yielding vertical profiles crucial for assessing low-level stability and fog formation risks. These active remote-sensing devices offer continuous, automated cloud height data up to several kilometers, aiding in the nowcasting of ceiling conditions for air traffic control.^[91]^[92]^[93] In practice, observational and nowcasting approaches are vital for addressing immediate hazards, particularly flash floods triggered by intense, localized rainfall from thunderstorms. Radar-based nowcasting systems extrapolate rainfall rates to forecast streamflow surges within urban watersheds, allowing emergency managers to activate barriers or evacuations hours in advance, as demonstrated in projects integrating quantitative precipitation estimates with hydrological models. For thunderstorms, these techniques track cell mergers or intensification using satellite-derived cloud motion vectors, providing lead times for severe wind or lightning alerts in vulnerable areas like mountainous regions. Such applications have proven effective in reducing flood-related casualties by bridging the gap between detection and response in high-impact scenarios.^[94]^[95]^[96]^[97]

Analog and statistical methods

The analog method in weather forecasting involves identifying historical weather patterns that closely resemble the current atmospheric state to predict future conditions based on past outcomes. This empirical approach relies on comparing key meteorological fields, such as 500 hPa geopotential height maps, which represent mid-tropospheric circulation patterns, to select analogous past events from a database of observations. For instance, forecasters might select analogs by minimizing differences in spatial patterns over a region, then average the subsequent evolutions of those historical cases to generate a forecast. Historically, the method was widely used in the mid-20th century but has since become less common for direct predictions due to advances in numerical models, though it remains valuable for probabilistic guidance and verification of dynamical forecasts.^[98]^[99] Statistical models complement analogs by establishing mathematical relationships between predictors—often derived from observations or model outputs—and forecast variables through techniques like multiple linear regression. In Model Output Statistics (MOS), a post-processing method introduced in the 1970s, regression equations are developed using historical data to correct biases in numerical model guidance; for example, predicted temperature T_{\text{pred}} = a + b \cdot P + c \cdot W, where P is surface pressure and W is wind speed as predictors, with coefficients a, b, c fitted via least squares. This approach has been applied extensively for variables like temperature and precipitation, improving forecast accuracy by accounting for systematic errors in raw model outputs. Observational data, such as surface measurements, serve as inputs for developing these relationships.^[100]^[101]^[102] Modern extensions integrate machine learning and artificial intelligence to enhance pattern recognition in analog and statistical methods, particularly for medium- to subseasonal forecasts. Neural networks trained on reanalysis datasets like ERA5, which provide a consistent 40-year record of global atmospheric states, enable more sophisticated analog selection by learning nonlinear similarities beyond simple spatial correlations. Post-2020 advances, such as AI-informed hybrid analogs, have demonstrated improved skill in subseasonal predictions by combining deep learning with traditional analog techniques, outperforming climatology baselines in tasks like sea surface temperature forecasting. Verification of these methods often employs correlation coefficients, such as the anomaly correlation coefficient (ACC), to quantify pattern similarity between analogs and the current state, with values above 0.6 typically indicating high-quality matches.^[103]^[104]

Numerical weather prediction

Atmospheric modeling basics

Atmospheric modeling in numerical weather prediction relies on dividing the atmosphere into discrete grid points to simulate its evolution over time. Models are broadly categorized into global and regional types. Global models, such as the European Centre for Medium-Range Weather Forecasts' Integrated Forecasting System (ECMWF IFS), cover the entire planet and typically operate at coarser resolutions to balance computational demands with broad coverage.^[105] In contrast, regional models like the Weather Research and Forecasting (WRF) model focus on limited areas with higher resolution for detailed local predictions, often employing nested grids that refine from coarser outer domains to finer inner ones.^[106] For example, ECMWF IFS achieves a horizontal resolution of approximately 9 km, while WRF configurations commonly use 9 km meshes that can nest down to 3 km or 1 km for enhanced detail in specific regions.^[107]^[108] The core of these models is governed by the fundamental equations of fluid dynamics, starting from the Navier-Stokes equations that describe the motion of viscous fluids in three dimensions. These are simplified for atmospheric applications due to the atmosphere's thin layer relative to Earth's radius and the predominance of horizontal flows, leading to the primitive equations.^[109] The primitive equations consist of conservation laws for momentum, mass, energy, and moisture, incorporating effects like the Coriolis force for rotational influences on large-scale motions. A key simplification is the hydrostatic approximation, which assumes vertical accelerations are negligible compared to gravitational forces, yielding the relation:

\frac{\partial p}{\partial z} = -\rho g

where p is pressure, z is height, \rho is density, and g is gravitational acceleration.^[110] This approximation reduces computational complexity while maintaining accuracy for synoptic-scale phenomena.^[111] Since numerical grids cannot resolve all atmospheric scales, sub-grid processes—those smaller than the grid spacing—are handled through parameterizations, which approximate their effects statistically or empirically. Convection, for instance, represents unresolved vertical transports of heat and moisture in cumulonimbus clouds; the Kain-Fritsch scheme is a widely used cumulus parameterization that triggers deep convection based on moisture convergence and instability, then relaxes it over an estimated cloud timescale.^[112] Radiation parameterizations account for solar and terrestrial radiative fluxes interacting with clouds and gases; common schemes like the Rapid Radiative Transfer Model for GCMs (RRTMG) compute broadband fluxes using correlated-k methods to efficiently handle absorption and emission spectra.^[113] These parameterizations are tuned to match observed climatologies and ensure energy balance in the model.^[114] Initial and boundary conditions are critical for model accuracy, particularly through data assimilation, which integrates observations into the model state. The four-dimensional variational (4D-Var) method optimizes an initial state over a time window by minimizing a cost function J that quantifies discrepancies between model predictions and observations, weighted by their uncertainties.^[115] Specifically, J measures the mismatch as J = ( \mathbf{x} - \mathbf{x_b} )^T \mathbf{B}^{-1} ( \mathbf{x} - \mathbf{x_b} ) + ( \mathbf{y} - \mathcal{H}(\mathbf{x}) )^T \mathbf{R}^{-1} ( \mathbf{y} - \mathcal{H}(\mathbf{x}) ), where \mathbf{x} is the analysis state, \mathbf{x_b} the background, \mathbf{y} observations, \mathcal{H} the observation operator, and \mathbf{B}, \mathbf{R} error covariance matrices.^[116] For global models, lateral boundary conditions are often derived from coarser global analyses, while regional models inherit them from global outputs to maintain consistency.^[117]

Computational methods and models

Computational methods in numerical weather prediction (NWP) involve discretizing the governing atmospheric equations on spatial and temporal grids to enable simulation on computers. Finite difference methods approximate derivatives by differences between grid points, making them straightforward for regional models with irregular boundaries, while spectral methods represent fields using global basis functions like spherical harmonics or Fourier series, offering higher accuracy for smooth global flows at lower computational cost per degree of freedom.^[118]^[119] Time integration in these methods often employs schemes like the leapfrog method, a second-order accurate, centered difference approach that uses three time levels to advance the solution while conserving energy in certain linearized systems.^[120] This scheme is widely used in operational NWP due to its stability and efficiency when combined with filters to suppress computational modes.^[121] Prominent operational models exemplify these approaches. The Global Forecast System (GFS), developed by the National Oceanic and Atmospheric Administration (NOAA), operates at a horizontal resolution of approximately 13 km for forecasts up to 10 days, extending to coarser resolutions for outlooks up to 16 days, and relies on finite difference methods for its dynamical core.^[122] The United Kingdom Met Office's Unified Model, a flexible system supporting both weather and climate predictions, uses a semi-Lagrangian semi-implicit dynamical core with spectral methods in the horizontal, achieving global resolutions around 10 km for medium-range forecasts typically spanning 7 to 15 days.^[123]^[124]^[125] High-performance computing is essential for running these complex simulations, particularly for ensemble predictions that require multiple model integrations. Graphics processing units (GPUs) accelerate key computations, such as those in the Weather Research and Forecasting (WRF) model, achieving speedups of up to 20 times compared to traditional CPU-based systems by parallelizing matrix operations and transforms.^[126] Cloud resources further enable scalable ensemble runs, allowing meteorological centers to burst computations on demand for high-resolution global ensembles without dedicated supercomputer expansions.^[127] Post-processing refines raw model outputs to enhance forecast reliability. Model Output Statistics (MOS), a statistical technique pioneered by NOAA, corrects systematic biases by regressing historical model predictions against observed weather, producing calibrated local forecasts for variables like temperature and precipitation.^[101]^[128] This method integrates predictors from the numerical model to downscale and debias outputs, significantly improving skill over raw guidance in operational settings.^[129]

Ensemble and probabilistic forecasting

Ensemble prediction systems (EPS) represent a core advancement in numerical weather prediction, designed to quantify uncertainty by generating multiple forecasts from a single model run. These systems initiate simulations with slightly perturbed initial conditions and model parameters to sample the possible range of atmospheric evolutions, thereby capturing the inherent unpredictability due to chaotic dynamics. A prominent example is the European Centre for Medium-Range Weather Forecasts (ECMWF) EPS, which produces 50 perturbed forecasts alongside a control run, extending up to 15 days ahead, to estimate the probability distribution of future weather states.^[130] Probabilistic outputs from EPS provide forecasters with measures of forecast reliability beyond deterministic predictions. The ensemble spread, defined as the variability among member forecasts, indicates the degree of uncertainty at specific locations and times; for instance, a wide spread in temperature forecasts suggests higher confidence intervals. Probability maps derived from these ensembles visualize the likelihood of events, such as a 40% chance of precipitation at a given site, calculated by determining the fraction of ensemble members predicting rainfall above a threshold (e.g., from a histogram of accumulated precipitation across members). These outputs enable risk-based decision-making in sectors like agriculture and emergency management.^[131] Perturbations in EPS are generated through methods that mimic error growth in the atmosphere. The breeding of growing modes (BGM) technique, developed at the National Meteorological Center (now NCEP), rescales differences between forecast pairs to simulate the evolution of analysis errors, focusing on the fastest-growing instabilities without assuming specific error structures. This approach has been widely adopted, as seen in operational systems where bred vectors capture synoptic-scale error growth over 24-48 hour cycles. Complementing initial condition perturbations, stochastic physics schemes introduce randomness in sub-grid scale processes, such as convection and turbulence, to represent unresolved model variability; for example, multiplicative noise in boundary layer parameterizations enhances ensemble diversity and improves medium-range skill by accounting for structural model uncertainties.^[132]^[133] Verification of EPS relies on specialized metrics to assess calibration and sharpness. Rank histograms, also known as Talagrand diagrams, evaluate ensemble reliability by ranking observations against the sorted ensemble members; a flat histogram indicates unbiased forecasts with well-calibrated spread, while U- or inverted-U shapes reveal under- or over-dispersion. The continuous ranked probability score (CRPS) measures overall probabilistic accuracy by integrating the squared difference between the forecast cumulative distribution function and the observed outcome, rewarding ensembles that are both reliable and informative; lower CRPS values signify superior performance, with operational targets aiming for spread-error ratios near unity. These tools guide ongoing improvements in ensemble design.^[134]^[135]

Communication and public dissemination

Forecast formats and media

Weather forecasts are commonly presented in graphical formats to visualize atmospheric conditions and predictions spatially. Isobar maps, which depict lines of equal atmospheric pressure, help illustrate pressure systems, fronts, and wind patterns, enabling users to infer weather trends like approaching storms or high-pressure ridges associated with clear skies.^[136] Spaghetti plots display multiple ensemble model trajectories overlaid on a single map, showing the range of possible outcomes for storm paths or pressure centers to convey forecast uncertainty.^[137] Radar overlays integrate real-time precipitation data with forecast elements, such as projected rain coverage, to provide dynamic views of evolving weather on digital maps.^[138] Textual formats offer detailed, narrative descriptions tailored to specific areas, making them accessible for quick reference. The National Weather Service (NWS) issues zone forecasts that cover regions like counties or sub-counties, detailing expected sky conditions, precipitation probabilities and types, temperature ranges, wind speeds, and visibility over periods such as 7 days.^[139] These are often supplemented by simple icons representing conditions like sunny skies (a sun symbol), cloudy (cloud outline), or rainy (raindrop), which standardize visual summaries in bulletins and apps for broad comprehension.^[140] Digital media have expanded forecast accessibility through interactive platforms. Mobile apps like AccuWeather deliver hyperlocal predictions, including hourly details, radar animations, and severe weather notifications, often powered by APIs that pull data from national services for real-time updates.^[141] Websites integrate these with customizable dashboards, while voice assistants such as Amazon's Alexa provide audio briefs on current conditions, daily highs and lows, precipitation chances, and extended outlooks via natural language queries.^[142] To ensure international consistency, the World Meteorological Organization (WMO) establishes codes and standards for encoding forecast data, facilitating global exchange of meteorological information in formats like alphanumeric messages and binary universal form for representation (BUFR).^[143] These WMO guidelines, outlined in the Manual on Codes, define symbols and abbreviations for phenomena such as weather types and intensities, allowing seamless integration across national systems.^[144]

Severe weather warnings and alerts

Severe weather warnings and alerts are critical mechanisms for communicating imminent threats to public safety, distinguishing between preparatory and immediate action stages. A watch is issued when atmospheric conditions are favorable for the development of severe weather, providing lead time—typically 4 to 6 hours for tornado watches—to allow individuals and communities to prepare.^[145] In contrast, a warning indicates that severe weather is occurring or imminent, requiring immediate protective actions; for example, a tornado warning is issued when a tornado has been detected by radar or spotters and is expected to impact the area within minutes, with average lead times of 13 to 14 minutes.^[146] These alerts are designed to balance timeliness with accuracy, leveraging nowcasting techniques like real-time radar data to issue warnings as conditions evolve.^[147] In the United States, the National Weather Service (NWS) administers the primary system for severe weather watches and warnings, issuing them for hazards such as tornadoes, severe thunderstorms, and flash floods through a coordinated network of forecast offices.^[148] These alerts are disseminated via multiple channels, including the Emergency Alert System (EAS) and Wireless Emergency Alerts (WEA) to mobile devices. A key component is NOAA Weather Radio, which broadcasts official warnings 24 hours a day using Specific Area Message Encoding (SAME) to target specific geographic areas with attention-grabbing tones, ensuring rapid notification to equipped receivers in homes, vehicles, and public facilities.^[149]^[150] Internationally, similar systems adapt to regional needs while incorporating standardized protocols for interoperability. In the European Union, the Common Alerting Protocol (CAP)—an XML-based format developed by the Organization for the Advancement of Structured Information Standards (OASIS)—facilitates the exchange of weather warnings among national meteorological services, enabling automated dissemination across media like television, radio, and mobile apps for hazards including storms and floods.^[151] In Japan, the J-Alert system, operated by the government, provides nationwide instantaneous alerts for severe weather events such as typhoons, transmitting warnings from the Japan Meteorological Agency through satellites to loudspeakers, televisions, and mobile devices to urge immediate evacuation or sheltering.^[152]^[153] Verification metrics highlight ongoing challenges in alert accuracy, particularly for tornado warnings, where historical false alarm ratios have hovered around 75%, meaning three-quarters of warnings do not verify with an actual tornado touchdown.^[154] This rate has improved in some offices; for instance, the Birmingham NWS office reduced its false alarm ratio by 31% for tornado warnings since April 2011 through enhanced radar interpretation and forecaster training.^[155] Lead times remain a focus for refinement, with efforts like the Warn-on-Forecast initiative aiming to extend warnings to 30 minutes or more by integrating high-resolution models, thereby enhancing public response without excessively inflating false alarms.^[156]

Specialized public advisories

Specialized public advisories provide targeted guidance to the general population on non-severe weather conditions that can impact daily life, health, and property, such as extreme temperatures, air quality, and seasonal risks. These advisories are issued by national meteorological services like the National Weather Service (NWS) in the United States to help individuals prepare for gradual environmental hazards without the urgency of immediate threats. Unlike broad forecasts, they emphasize specific thresholds and protective actions, drawing on observational data, models, and historical patterns to communicate risks effectively. Heat index advisories address the combined effects of high temperature and humidity, which can lead to heat-related illnesses. The NWS issues a Heat Advisory when the heat index is expected to reach 100–105°F (38–41°C) for at least 3 hours during the day, varying by region to account for local climate sensitivity. An Extreme Heat Warning is triggered for more severe conditions, such as a heat index of at least 105°F (41°C) for more than 3 hours per day over 2 consecutive days, or higher thresholds in arid areas where dry heat poses risks.^[157] These advisories incorporate the NWS HeatRisk tool, a color-coded index from 0 (little risk) to 4 (extreme risk), which factors in acclimatization, duration, and vulnerability to forecast health impacts over 24-hour periods. Public recommendations include staying hydrated, avoiding outdoor exertion, and seeking air-conditioned spaces to mitigate effects. Cold weather advisories warn of dangerously cold conditions that accelerate heat loss from exposed skin. The NWS defines wind chill using a formula applicable for air temperatures at or below 50°F (10°C) and wind speeds above 3 mph (5 km/h), calculated as WC = 35.74 + 0.6215T - 35.75(V^{0.16}) + 0.4275T(V^{0.16}), where T is temperature in °F and V is wind speed in mph; however, alerts are now issued based on either temperature or wind chill values. A Cold Weather Advisory is issued when wind chill or temperatures are expected to drop to 0 to -19°F (-18 to -28°C) in many areas, or lower thresholds like -5°F (-21°C) in the Mid-Atlantic, to alert the public to risks like frostbite (with regional variations). Extreme Cold Warnings apply for more extreme values, such as -20°F (-29°C) or below in the Northeast, urging precautions like layering clothing and limiting exposure.^[158] Regional variations ensure advisories align with local physiology and infrastructure resilience. Air quality forecasts integrated with weather advisories monitor pollutants like ozone, particulate matter, and nitrogen dioxide, which are influenced by temperature, wind, and sunlight. The Environmental Protection Agency (EPA) and NWS collaborate on the Air Quality Index (AQI), a scale from 0 (good) to 500 (hazardous) that reports daily and forecast levels to guide sensitive groups on outdoor activities. NOAA's National Air Quality Forecast Capability provides guidance up to 48 hours ahead, using models like the Community Multiscale Air Quality (CMAQ) system to predict AQI based on emissions, meteorology, and chemistry. Advisories are issued for AQI above 100 (unhealthy for sensitive groups), recommending reduced exertion or indoor stays, particularly on hot, stagnant days when pollution concentrates. UV index forecasts, embedded in routine weather reports, quantify ultraviolet radiation exposure risks from the sun, aiding skin protection decisions. The NWS and EPA compute the UV Index on a scale of 1 (low) to 11+ (extreme), using forecasted ozone levels, cloud cover, surface reflectivity, and solar elevation via radiative transfer models. Forecasts are issued daily for major cities, with values above 3 prompting advisories for sunscreen, hats, and shade during peak hours (10 a.m. to 4 p.m.). Integration with weather data highlights how clear skies and high temperatures amplify UV levels, with historical validation showing forecast accuracy within 1 unit on average. Frost and freeze warnings target public protection against cold snaps that can damage plumbing, plants, and outdoor items, extending beyond agricultural concerns. The NWS issues a Frost Advisory for minimum temperatures of 33–36°F (1–2°C) on clear, calm nights during the growing season, advising residents to cover pipes or drain systems. A Freeze Warning is activated for temperatures at or below 32°F (0°C), or 28–32°F (-2 to 0°C) in sensitive areas, to prevent bursting pipes and structural ice buildup. These products are seasonal, typically from September to May, and use short-term model guidance for 12–24 hour outlooks, emphasizing actions like insulating exposed fixtures. Seasonal outlooks offer probabilistic guidance on temperature and precipitation over 3-month periods, helping the public plan for extended trends. NOAA's Climate Prediction Center (CPC) releases these outlooks monthly, showing equal chances, above-normal, or below-normal probabilities in terciles (e.g., 40–50% chance of above-average temperatures in a region). For instance, the November–December–January outlook uses dynamical models like the Climate Forecast System (CFSv2) and statistical methods to predict anomalies influenced by phenomena such as El Niño-Southern Oscillation. These outlooks inform energy use, travel, and water management, with verification showing skill scores above climatology for temperature in 60–70% of cases.

Applications and specialist forecasting

Aviation and marine sectors

Weather forecasting plays a critical role in the aviation sector by providing specialized products that enhance flight safety and operational efficiency, particularly through advisories for hazardous conditions such as turbulence and icing. Significant Meteorological Information (SIGMETs) are unscheduled advisories issued for non-convective weather phenomena that pose potential hazards to all aircraft, including severe turbulence, severe icing, and widespread dust or sandstorms covering an area of at least 3,000 square miles. These SIGMETs are valid for four hours for turbulence and icing events, or six hours for volcanic ash, and are disseminated by meteorological watch offices under the World Meteorological Organization (WMO) framework to alert pilots and air traffic control in real time. Terminal Aerodrome Forecasts (TAFs) offer concise, site-specific predictions for airports, covering a 24- to 30-hour period within a 5-statute-mile radius of the runway, including details on wind, visibility, weather phenomena, and cloud layers to support takeoff, landing, and ground operations. In the United States, the Federal Aviation Administration (FAA) utilizes the Corridor Integrated Weather System (CIWS), a nowcasting and short-term forecasting tool developed by MIT Lincoln Laboratory, to detect and predict convective hazards like thunderstorms, enabling route adjustments.^[159]^[160]^[161]^[162]^[163] In the marine sector, forecasting focuses on sea state and wind conditions to safeguard navigation and prevent vessel damage, with wave height predictions derived from spectral wave models that simulate energy distribution across wave frequencies and directions. The WAVEWATCH III model, developed and operated by the National Centers for Environmental Prediction (NCEP), generates global and regional forecasts of significant wave height, peak period, and direction up to 10 days ahead, using input from atmospheric models to propagate wave energy accurately for open ocean and coastal areas. Gale warnings are issued by national meteorological services, such as the U.S. National Weather Service (NWS), when sustained winds of 34 to 47 knots (39 to 54 mph) are expected in marine zones, excluding tropical cyclones, to prompt captains to alter course or prepare for rough seas. These warnings are broadcast via marine VHF radio, satellite, and online platforms to cover coastal, offshore, and high seas regions.^[164]^[165]^[166]^[167] Integration of aviation and marine forecasting benefits from real-time observational data and international standards, enhancing overall accuracy. The Aircraft Meteorological Data Relay (AMDAR) program, coordinated by the WMO, collects automated upper-air observations—including temperature, wind speed, direction, and turbulence—from commercial aircraft sensors during ascent, cruise, and descent, contributing over 700,000 reports daily (as of 2017) to improve global forecast models.^[168]^[169] For marine operations, the International Maritime Organization (IMO) recommends weather routing under Resolution A.528(13), which advises ship masters to use forecast services for optimal route planning, avoiding adverse conditions while complying with safety regulations like those in the SOLAS Convention. Numerical models provide the foundational precision for these sector-specific products, enabling high-resolution simulations of atmospheric dynamics.^[170] A practical application of advanced forecasting in aviation involves avoiding clear-air turbulence (CAT), an invisible hazard often encountered at cruising altitudes, through probabilistic ensemble outputs that quantify uncertainty in turbulence potential. Ensemble prediction systems, such as those from the European Centre for Medium-Range Weather Forecasts (ECMWF), combine multiple model runs to produce calibrated indices like the Ellrod technique, which integrates vertical wind shear and deformation diagnostics to forecast CAT with skill scores exceeding 0.5 for moderate-or-greater events up to 36 hours ahead. This allows dispatchers and pilots to select smoother flight levels or reroute.^[171]^[172]

Agriculture, energy, and utilities

Weather forecasting plays a crucial role in agriculture by enabling farmers to mitigate risks from extreme events and optimize resource use. Frost risk models, which predict the likelihood of damaging low temperatures during critical growth stages, integrate numerical weather prediction data with crop-specific hardiness thresholds to issue timely alerts. For instance, these models assess variables such as air temperature, humidity, and wind speed to forecast radiative or advective frost events, helping protect crops like fruits and vegetables from yield losses estimated at billions annually in vulnerable regions.^[173]^[174] Public advisories for frost often draw from these models to provide broader guidance. Irrigation scheduling in agriculture relies heavily on evapotranspiration (ET) forecasts derived from weather data, allowing precise water application to enhance efficiency and reduce waste. The Penman-Monteith equation, standardized by the Food and Agriculture Organization (FAO), calculates reference evapotranspiration (ETo) as a function of net radiation, soil heat flux, temperature, wind speed, and vapor pressure deficit:

ET_o = \frac{0.408 \Delta (R_n - G) + \gamma \frac{900}{T + 273} u_2 (e_s - e_a)}{\Delta + \gamma (1 + 0.34 u_2)}

where \Delta is the slope of the saturation vapor pressure curve, R_n is net radiation, G is soil heat flux, \gamma is psychrometric constant, T is air temperature, u_2 is wind speed at 2 m height, and e_s - e_a is saturation vapor pressure deficit. This equation underpins tools for forecasting crop water needs, enabling adjustments based on predicted solar radiation and humidity to support sustainable farming practices.^[175] In the energy sector, accurate weather forecasts are essential for predicting renewable power generation, particularly from wind and solar sources, to balance grid supply and demand. Wind power predictions use forecasted wind speeds and directions from numerical models to estimate turbine output, while solar forecasts focus on cloud cover and irradiance to project photovoltaic performance. For example, 48-hour ahead global horizontal irradiance (GHI) forecasts, often at resolutions of 2-5 km, support day-ahead market bidding and reduce curtailment costs, with improvements from ensemble methods achieving up to 20% better accuracy over deterministic approaches.^[176]^[177] Utilities leverage weather forecasts for demand-side management, anticipating peaks in heating or cooling loads that strain infrastructure. Temperature-based degree-day metrics, such as heating degree days (HDD) and cooling degree days (CDD), inform short-term load forecasts; for instance, a predicted cold snap can signal a 10-20% surge in natural gas demand for residential heating. Drought monitoring through the Standardized Precipitation Index (SPI), which standardizes precipitation anomalies over 1-48 month timescales, aids water utilities in planning reservoir releases and irrigation allocations, with SPI values below -1 indicating moderate drought risk to agricultural and urban supplies.^[178]^[179] Commercial services like those from The Weather Company provide customized forecasting models tailored to agriculture, energy, and utilities, integrating proprietary high-resolution data with client-specific needs such as site-level wind profiles or regional ET projections. These services, powered by advanced AI and global models like GRAF, deliver hyper-local predictions to optimize operations, such as scheduling solar farm maintenance during low-irradiance windows or adjusting energy trading based on 72-hour demand forecasts.^[180]^[181]

Military and other institutional uses

Weather forecasting plays a critical role in military operations by providing environmental intelligence that informs tactical decisions, enhances force protection, and supports mission execution. In the United States, the Air Force Weather Agency (AFWA), part of the 557th Weather Wing, delivers global weather analyses, forecasts, and warnings to Department of Defense (DoD) decision-makers and joint forces at over 350 installations worldwide.^[182] This includes battlefield weather support for troop movements, where AFWA's operational weather squadrons provide tailored briefings to Army, Air Force, and allied units, enabling commanders to adjust operations based on conditions like visibility, wind, and precipitation that affect mobility and logistics.^[182] The United Kingdom's Met Office similarly supports defense needs through specialized meteorological services for the Ministry of Defence (MOD) and NATO allies, focusing on weather impacts on equipment, sensors, and operational effectiveness.^[183] These services include tailored forecasts for land, sea, and air missions, helping to mitigate risks in contested environments by predicting conditions that could degrade weapon performance or troop safety.^[183] In the Pacific and Indian Oceans, the Joint Typhoon Warning Center (JTWC), operated jointly by the U.S. Navy and Air Force, issues tropical cyclone forecasts primarily to safeguard U.S. military assets, including ships, aircraft, and installations.^[184] Established in 1959, JTWC's warnings enable fleet commanders to reroute operations and avoid disasters, underscoring the need highlighted by historical events like Typhoon Cobra in World War II, which devastated naval forces.^[185] Historically, weather forecasting proved decisive in World War II, particularly for the D-Day landings on June 6, 1944, when Allied meteorologist Group Captain James Stagg advised General Dwight D. Eisenhower to delay the invasion from June 5 due to forecasted storms, identifying a narrow window of acceptable conditions based on barometric data.^[186] This decision, informed by combined British and U.S. forecasting efforts, prevented potential catastrophe from high winds and low visibility, allowing paratroop drops and naval assaults to proceed under marginally suitable weather.^[186] Beyond terrestrial weather, military institutions integrate space weather forecasting to protect satellite operations and communications. The U.S. Air Force's Weather Enterprise Squadron Operations (WESO) provides 24/7 space weather alerts and support for satellite launches and orbits, mitigating risks from solar flares and geomagnetic storms that could disrupt DoD assets.^[187] The Defense Meteorological Satellite Program (DMSP), operational for over 50 years, contributes by delivering environmental data that informs space weather models, aiding in the prediction of ionospheric disturbances affecting military GPS and radar systems.^[188] For institutional emergency management, the Federal Emergency Management Agency (FEMA) relies on hurricane track forecasts from the National Hurricane Center to guide evacuation planning and response.^[189] Tools like the Hurricane Evacuation Decision Support System (HURREVAC) incorporate real-time track data to help state and local managers assess storm surge, wind, and flooding hazards, enabling timely decisions on sheltering and resource allocation during threats.^[189] Classified military forecasting emphasizes secure data handling and tactical nowcasts in denied environments. U.S. Army tools such as the Weather Running Estimate-Nowcast (WRE-N), based on the Weather Research and Forecasting model, generate short-term predictions for battlefield awareness, supporting soldier survivability and sustainment without relying on vulnerable networks.^[190] Air Force doctrine outlines operations in data-denied scenarios, where forecasters use limited observations and modeling to deny adversaries weather insights while maintaining internal tactical superiority.^[191]

References

[1]
Forecast Process - National Weather Service
NWS meteorologists across the country create forecasts for a wide variety of weather elements such as rainfall, snow storms, severe weather and hurricanes.
[2]
Revolutionizing Weather Forecasting: How LEO Satellites ... - NESDIS
Jun 28, 2023 · Generating more than $30 billion annually in economic benefits, weather forecasts are relied upon by nearly every sector of the U.S. economy ...
[3]
The National Weather Service at 150: A Brief History - NOAA VLab
Feb 6, 2020 · The NWS began as a resolution in 1870, became the Weather Bureau in 1891, moved to Commerce in 1940, and became the NWS in 1970.
[4]
Numerical Weather Prediction - NOAA VLab
Jan 21, 2020 · From the National Weather Service's very beginnings in the early 1870's, a key goal was to predict the weather to protect lives and property ...
[5]
The History of Numerical Weather Prediction - NOAA
Oct 31, 2023 · Numerical weather prediction involves the use of mathematical models of the atmosphere to predict the weather. Manipulating the huge datasets ...
[6]
Numerical Weather Prediction
NWP computer models process current weather observations to forecast future weather. Output is based on current weather observations, which are assimilated into ...
[7]
How Reliable Are Weather Forecasts? | NESDIS - NOAA
A seven-day forecast can accurately predict the weather about 80 percent of the time and a five-day forecast can accurately predict the weather approximately 90 ...
[8]
Forecasting the Future: The Role of Artificial Intelligence in ...
Oct 13, 2025 · AI is rapidly reshaping weather and climate prediction across timescales, from nowcasting through to medium-range and subseasonal-to-seasonal ( ...
[9]
Severe Weather Forecasting Programme (SWFP)
The Severe Weather Forecasting Programme (SWFP) helps countries improve their ability to predict dangerous weather, such as storms, heavy rain, ...
[10]
Three ways NOAA Research works to improve our weather forecasts
Mar 5, 2025 · NOAA scientists study severe storms to provide meteorologists with the knowledge and capabilities to communicate accurate, timely, and ...
[11]
Weather Forecasting Through the Ages - NASA Science
Feb 25, 2002 · Around 650 B.C., the Babylonians tried to predict short-term weather changes based on the appearance of clouds and optical phenomena such as ...Missing: ancient celestial animal behavior
[12]
https://oxfordre.com/climatescience/display/10.1093/acrefore/9780190228620.001.0001/acrefore-9780190228620-e-728
[13]
Hydrology and water resources management in ancient India - HESS
Oct 5, 2020 · The Arthashastra and Astadhyayi of Panini (700 BCE) mention the rain gauges (Nair, 2004), which were introduced by the Mauryan rulers in the ...Missing: 5th | Show results with:5th
[14]
Tower of the Winds | Ancient, Timepiece, Weathervane - Britannica
Sep 27, 2025 · The Horologium was surmounted by a weather vane in the form of a bronze Triton and contained a water clock (clepsydra) to record the time when ...
[15]
Some Weather Proverbs and their Justification - Wikisource
Sep 29, 2018 · THIS thousand-year-old observation by England's wisest ruler recognizes the fact that fine weather induces good tempers, and therefore amply ...Missing: European folklore
[16]
Weathering the weather – weather predictions in folklore
Jul 7, 2021 · According to folklore, the weather following St Swithin's Day is dictated by the characteristics of the weather on the day.
[17]
From Babylon to Google: a history of weather forecasting
Sep 29, 2021 · As far back as 650BC, the Babylonians, in modern-day Iraq and Syria, tried to divine the weather based on cloud patterns and astrology. By 350BC ...
[18]
The Useful Pursuit of Shadows | American Scientist
Figure 1. Clouds were first described systematically by Luke Howard, a London pharmacist and amateur meteorologist, in an 1802 address to the Askesian Society.This Article From Issue · September-October 2003 · Clouds, Philosophy And Art
[19]
On The Modification Of Clouds - Met Office
Howard, an amateur meteorolgist and professional pharmacist, devised the cloud classification scheme the basis of which is still in use today.
[20]
The Four Core Types of Clouds - NOAA
Mar 28, 2023 · From his Essay of the Modifications of Clouds (1803), Luke Howard divided clouds into three categories: cirrus, cumulus, and stratus, plus a ...Missing: source | Show results with:source<|separator|>
[21]
Beaufort Wind Scale - National Weather Service
He developed the scale in 1805 to help sailors estimate the winds via visual observations. The scale starts with 0 and goes to a force of 12.
[22]
[PDF] National Meteorological Library and Archive Factsheet 6 - Met Office
Admiral Beaufort's 'Scale of wind force'. Francis Beaufort devised his scale of wind force in 1805, when serving aboard HMS Woolwich, and first mentioned it ...
[23]
Met Office History
The Met Office was founded in 1854 in response to an international drive to improve knowledge and understanding of maritime meteorology.
[24]
Robert FitzRoy and the early Met Office
The Met Office was founded in 1854 under the leadership of Captain, (later Vice-Admiral), Robert FitzRoy.
[25]
History of the National Weather Service
October 1, 1890: The weather service is first identified as a civilian agency when Congress, at the request of President Benjamin Harrison, passes an act ...
[26]
Thomas Jefferson and the telegraph: highlights of the U.S. weather ...
May 17, 2018 · By 1860, several hundred stations were furnishing daily telegraphic weather reports to the Washington Evening Star. In the years following ...
[27]
An Economic History of Weather Forecasting – EH.net
Observer-sergeants in the Signal Service recorded their first synchronous observations November 1, 1870, 7:35 a.m. Washington time at twenty-four stations. The ...
[28]
The birth of the weather forecast - BBC News
Apr 30, 2015 · FitzRoy's storm warnings began in 1860 and his general forecasts followed the next year - stating the probable weather for two days ahead.
[29]
(PDF) Dutch Skies, Global Laws - ResearchGate
May 9, 2025 · Buys Ballot's law was actually not the ﬁrst so-called meteorological law. During the ﬁrst half of the nineteenth century, the Berlin ...
[30]
[PDF] Weather Prediction by Numerical Process
In 1922 Richardson presented such a forecast to the world. He calculated a change of atmospheric pressure, for a particular place and time, of 145 hPa in 6 ...
[31]
a retrospective view ol Richardson's book on weather prediction*
In 1922 Lewis F. Richardson published a comprehensive numerical method of weather prediction. He used height rather than pressure as vertical coordinate but ...
[32]
Imagining Using 64000 Human Computers to Predict the Weather
In this work Richardson described a fantasy weather forecast “factory” of sixty-four thousand human computers working in “a large hall like a theatre,” ...Missing: manual impracticality<|separator|>
[33]
Jule Charney, Agnar Fjörtoff & John von Neumann Report the First ...
The paper reported the first weather forecast by electronic computer. It took twenty-four hours of processing time on the ENIAC Offsite Link to calculate a ...
[34]
[PDF] Numerical Integration of the Barotropic Vorticity Equation
A method is given for the numerical solution of the barotropic vortiLity equation over a limited area of the earth's surface. The lack of a natural boundary ...
[35]
M‐ENIAC: A Physics‐Informed Machine Learning Recreation of the ...
May 22, 2024 · In 1950 the first successful numerical weather forecast was obtained by solving the barotropic vorticity equation using the Electronic Numerical ...Introduction · Previous Work · The Barotropic Vorticity... · Conclusions
[36]
[PDF] From Richardson to early numerical weather prediction
Arrangements were made to compute a solution of a simple equation, the barotropic vorticity equation (BVE), on the only computer available, the ENIAC.
[37]
The ENIAC Compulations ol 1950 Gateway to Numerical weather ...
The script for this lengthy performance was written by John von Neumann and by Jule Charney, who also was one of the five actors on the scene.
[38]
See TODAY's first weather forecast in 1952 — hand-drawn by host ...
Feb 7, 2017 · Dave Garroway, the first TODAY anchor, did not have fancy forecasting screens or flashy animations when he first reported the weather as it was happening ...Missing: 1950s Tonight
[39]
A History Of Broadcast Meteorology – And A Peek Ahead
Mar 28, 2025 · During the 1920s, forecasting improvements occurred with the launching of weather balloons from various locations around the nation. Temperature ...
[40]
The World Weather Watch at 50
In its first decades, WMO observed considerable progress in many countries in expanding and improving both observational and telecommunications capabilities.
[41]
History - ECMWF
The first real-time medium-range forecasts were made in June 1979, and ECMWF has been producing operational medium-range weather forecasts since 1 August 1979.Missing: internet | Show results with:internet
[42]
WeatherBug - Earth Networks
In 2000, the WeatherBug brand became the forefront of Earth Networks consumer business with the WeatherBug desktop application. This application, which ...
[43]
Weather Analysis and Forecasting - American Meteorological Society
Weather forecasting uses observations, numerical models, and scientific theory. It starts with analysis of the atmosphere, ocean, and land surface.
[44]
What is the difference between weather and climate?
Jun 16, 2024 · Weather reflects short-term conditions of the atmosphere while climate is the average daily weather for an extended period of time at a certain location.
[45]
Application Area: 2.3 Nowcasting / Very Short-Range Forecasting
Nowcasting, as outlined by the World Meteorological Organization (WMO), involves detailed forecasting of local weather, utilizing any method to predict ...
[46]
Deterministic Nonperiodic Flow in - AMS Journals
A simple system representing cellular convection is solved numerically. All of the solutions are found to be unstable, and almost all of them are nonperiodic.
[47]
[PDF] NOAA Weather, Water, and Climate Strategy FY 2023-2027
Dec 7, 2022 · Through our climate products and services, NOAA aims to enhance public safety, protect ecosystems, and continue fostering sustainable economic ...
[48]
Comparing Probabilistic Forecasting Systems with the Brier Score in
This article considers the Brier score for verifying ensemble-based probabilistic forecasts of binary events.
[49]
What Is the Predictability Limit of Midlatitude Weather?
Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing ...
[50]
Weather systems and patterns - NOAA
Feb 25, 2025 · High in the atmosphere, narrow bands of strong wind, such as the jet streams, steer weather systems and transfer heat and moisture around the ...Missing: structure troposphere dynamics source
[51]
Air Masses | National Oceanic and Atmospheric Administration
Jun 5, 2023 · Fronts are identified by a change of temperature based upon their motion. With a cold front, a colder air mass is replacing a warmer air mass. A ...Missing: troposphere dynamics<|separator|>
[52]
The Jet Stream | National Oceanic and Atmospheric Administration
Dec 9, 2024 · Jet streams are relatively narrow bands of strong wind in the upper levels of the atmosphere, typically occurring around 30,000 feet (9,100 ...Missing: dynamics source
[53]
Static Stability— An Update - American Meteorological Society
lapse rate of 9.8°C km-1. A temperature inversion, where dT/dz > 0, is also ... c) Neutral: those regions of adiabatic lapse rate that are not unstable.<|separator|>
[54]
[PDF] 3 Thermodynamics - UBC EOAS
Rising air cools at the dry adiabatic lapse rate of. Γd = 9.8 °C km–1 . Since ... )-atmosphere lapse rate of 6.5°C/km. Heat Flux. 0. F max. 0. 30. –50. 0. 5. 10.
[55]
[PDF] Chapter 7 Forces and Force Balance
due to the rotation of the earth. The Coriolis force is due to conservation of angular momentum and the effects of the centrifugal force.
[56]
[PDF] Geostrophic balance | Weather and Climate Laboratory
Let's postulate a balance between the Coriolis force and the pressure-gradient force di- rected from equator to pole associated with the tilted isobaric ...Missing: fρ) k primary source
[57]
[PDF] lorenz-1963.pdf
Non- periodic trajectories are of course representations of deterministic nonperiodic flow, and form the principal subject of this paper. Periodic trajectories ...
[58]
Observation components of the Global Observing System
The GOS observation components are: Surface, Upper-air, Marine, Aircraft-based, Satellite, Weather Radar, and Other observation platforms.
[59]
The Global Observing System
Today the surface-based observing systems of GOS includes about 11 500 land stations making at least three-hourly and often hourly observations of ...
[60]
NOAA Observation Systems
Radiosonde sensors measure upper-air conditions such as atmospheric pressure, temperature and humidity, wind speed and direction. The data are important for ...
[61]
Weather observations - NOAA
Jun 24, 2025 · Weather forecasting ... Each day in the United States over 210 million weather observations are processed and used to create weather forecasts.
[62]
Surface Weather Observations
Sep 7, 2023 · TEMPERATURE LOGGERS; WET BULB THERMOMETERS; BAROMETERS; ALTIMETERS; HYGROMETERS; VISUAL OBSERVATIONS; WIND VANES; ANEMOMETERS; RAIN GAUGES.
[63]
Upper-air Observations Program - National Weather Service
Radiosondes provide upper-air data that are essential for weather forecasts, research, and historical climatology, and are launched twice daily.Missing: 30km WMO
[64]
Radiosondes | National Oceanic and Atmospheric Administration
Sep 16, 2025 · Radiosonde observations technically provide only pressure, temperature, and relative humidity data; the tracked position of a radiosonde is ...
[65]
Satellites - National Weather Service
Polar orbiting satellites provide imagery and atmospheric soundings of temperature and moisture data over the entire Earth. Geostationary satellites are in ...
[66]
Polar Operational Environmental Satellites (POES) | NESDIS - NOAA
The Polar Operational Environmental Satellites (POES) satellite system makes 14 nearly polar orbits per day approximately 520 miles above Earth.
[67]
Next Generation Weather Radar (NEXRAD)
The Next Generation Weather Radar (NEXRAD) system is a network of 160 high-resolution S-band Doppler weather radars jointly operated by the National Weather ...Missing: WMO | Show results with:WMO
[68]
Radar Images: Velocity - NOAA
Aug 10, 2023 · Velocity is the second of the three base products that are produced by pulsed Doppler radars and is used to indicate the motion and speed of targets.Missing: WMO | Show results with:WMO
[69]
5.4: Collecting Weather Data and Forecasting Weather
Oct 17, 2024 · According to the WMO, weather information is collected from 15 satellites, 100 stationary buoys, 600 drifting buoys, 3,000 aircraft, 7,300 ...
[70]
A Review of the Impact of Automated Aircraft Wind and Temperature ...
Apr 1, 2016 · This paper reviews the impact of World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) observations on operational numerical ...
[71]
From Observations to Service Delivery: Challenges and Opportunities
Effective use of multiple sources of data to initialize numerical forecasts requires that various biases in the observations be corrected prior to or ...
[72]
[PDF] noaa_12442_DS1.pdf - the NOAA Institutional Repository
methods based on the barotropic and two-parameter baroclinic models." J. ... Weather Analysis and Prediction Division, 1968: "Precipitation fore- casting ...Missing: traditional | Show results with:traditional
[73]
https://journals.ametsoc.org/view/journals/bams/97/4/bams-d-14-00055.1.xml
[74]
[PDF] A. The Snellman funnel B. Basic forecasting strategies II. The ...
The Snellman Funnel organizes forecast information by scale: hemispheric, synoptic, and mesoscale. Basic strategies include persistence, past similar patterns, ...
[75]
Trend Technique for weather forecasting
The Trend Technique. If a phenomenon is in steady state, or is moving at constant speed, the trend technique can be used. rate x time = distance ...
[76]
[PDF] Weather Forecasting
Steady state (trend) forecast: Assume that the weather feature of interest will continue to move in the same direction and at the same speed as previously ...
[77]
[PDF] Analysis and Prognosis Trainee Workbook - National Weather Service
Feb 1, 2000 · general rules and principles of analysis, which in turn, guide the forecast/prognosis process. ... baroclinic and a barotropic system is that the ...Missing: traditional | Show results with:traditional
[78]
BAROTROPIC AND BAROCLINIC DEFINED - The Weather Prediction
Barotropic has uniform temperature and no fronts, like the tropics. Baroclinic has distinct air masses, fronts, and density gradients, like mid-latitude ...
[79]
Farmers' Almanacs Use Centuries-Old Forecasting Formulas
Dec 10, 2012 · Instead, he used sunspots and other astronomical cycles to create a formula for forecasting the weather. According to the Old Farmer's Almanac, ...
[80]
Air Pressure | National Oceanic and Atmospheric Administration
Dec 18, 2023 · If the motion of the mercury be unsettled, expect unsettled weather. · If it stands at "MUCH RAIN" and rises to "CHANGEABLE" expect fair weather ...Missing: traditional based
[81]
Nowcasting Guidelines – A Summary
Nowcasting as forecasting with local detail, by any method, over a period from the present to six hours ahead, including a detailed description of the present ...
[82]
Nowcasting - an overview | ScienceDirect Topics
Nowcasting is defined as the practice of high-frequency contemporaneous forecasting of variables by utilizing frequently released data, such as unemployment ...
[83]
Insights from Nowcasting and Mesoscale Research Working Group ...
The objective of the workshops for developing countries was to provide a high-level perspective of the meaning and utility of severe weather warning services.
[84]
[PDF] Nowcasting: The Next Revolution in Weather Prediction
The earliest work on nowcasting was mainly limited to the use of time extrapolation of meteorological radar data for short-term prediction of thunderstorm.
[85]
Thunderstorm nowcasting by means of lightning and radar data
Oct 28, 2008 · The authors present an algorithm developed for tracking high intensity storm cells by means of radar and lightning data. Application to northern ...
[86]
9.2: Synoptic Weather Maps - Geosciences LibreTexts
Dec 9, 2022 · Weather observations that were taken synoptically (ie, simultaneously) at many weather stations worldwide can be drawn on a weather map.
[87]
[PDF] 9 WEATHER REPORTS & MAP ANALYSIS - UBC EOAS
Mar 30, 2015 · In this chapter you will learn how to interpret weather reports, and how to analyze surface weather maps. 9.1. SEA-LEVEL PRESSURE REDUCTION.
[88]
[PDF] Guide to Meteorological Instruments and Methods of Observation
One of the purposes of the World Meteorological. Organization (WMO) is to coordinate the activities of its 188 Members in the generation of data and.
[89]
Cloud Height Indicator - an overview | ScienceDirect Topics
A ceilometer is defined as an instrument that measures backscatter intensity and is primarily used for determining cloud base height, but has also become ...
[90]
TOP 12 Weather Instruments For Modern Meteorology - News
Jun 18, 2025 · Barometers measure atmospheric pressure, an essential factor in predicting weather changes. ... Ceilometers detect cloud base height and ...
[91]
Rainfall Nowcasts - National Weather Service
Quantitative Precipitation Nowcasting for Flash Floods. A Radar Nowcasting Demonstration Project to Improve Flash Flood Forecast and Warning ServicesMissing: applications thunderstorms
[92]
The Use of an Automated Nowcasting System to Forecast Flash ...
Several storm nowcast systems use radar to provide quantitative precipitation forecasts that can potentially afford great benefits to flood warning and short- ...
[93]
Toward Street‐Level Nowcasting of Flash Floods Impacts Based on ...
Oct 20, 2023 · The main goal of this research is to make a step to move toward the implementation of an effective flash flood nowcasting system.
[94]
Nowcasting thunderstorms in the Mediterranean region using ...
This method has been implemented for the use in real-time now-casting and is used in the EU FLASH project to seek and track areas of thunderstorm risk according ...
[95]
[PDF] 7. Analogues - Climate Prediction Center
The analogue method was fairly widely used for weather forecasting at one time (Schuurmans 1973) but currently is rarely used for forecasts persé (for all ...
[96]
6-10 and 8-14 Day Prognostic Discussions - Climate Prediction Center
CA - CONSTRUCTED ANALOG - A LINEAR COMBINATION OF PAST OBSERVED ANOMALY PATTERNS SUCH THAT THE COMBINATION IS AS CLOSE AS DESIRED TO THE INITIAL STATE. A ...
[97]
[PDF] Glahn and Lowry - National Weather Service
Model Output Statistics (MOS) is an objective weather forecasting technique which consists of deter- mining a statistical relationship between a predictand and ...<|control11|><|separator|>
[98]
Model Output Statistics - MDL - Virtual Lab - NOAA VLab
Model Output Statistics (MOS) is a type of statistical post-processing, a class of techniques used to improve numerical weather models' ability to forecast.
[99]
[PDF] STATISTICAL WEATHER FORECASTING* - Harry R. Glahn - ECMWF
All of the statistical models presented in the following sec- tions, such as scatter diagrams or regression, can be used with any of the techniques discussed ...
[100]
[PDF] AI-Informed Model Analogs for Subseasonal-to-Seasonal Prediction
This method of optimized analog forecasting was first successfully applied to annual-to-decadal sea surface temperature prediction (Rader and Barnes 2023) and.
[101]
[PDF] Skill Scores and Correlation Coefficients in Model Verification
The anomaly correlation coefficient measures potential skill, not actual skill, and may overestimate model performance by ignoring biases and errors in scale.
[102]
Global numerical modelling at the heart of ECMWF's forecasts
Apr 7, 2022 · ECMWF's global numerical model includes atmosphere, ocean, waves, land, and sea ice, representing processes at different scales, and is key to ...
[103]
Chapter 5: WRF Model - NCAR/MMM
A two-way nested run is a run where multiple domains at different grid resolutions are run simultaneously and communicate with each other: The coarser domain ...Installing WRF · Real Data Case · Two-Way Nested Runs · Global Run
[104]
ECMWF Weather Forecast API - Open-Meteo.com
The ECMWF IFS model runs every 6 hours at a 9 km resolution. Open-Meteo delivers the full native 9 km IFS forecasts on the reduced Gaussian grid O1280 ...
[105]
Figure 1: WRF grid configuration. a) WRF 9-/3-/1-km resolution grids,...
The INFLUX WRF configuration consists of three nested grids with 9-/3-/1- km horizontal resolutions (Figure 1a), with the focus on the 1-km grid. The ...
[106]
[PDF] Models, Numerical Methods, and Data Assimilation
Charney made the first successful numerical weather forecast with barotropic potential vorticity equations (Charney et al. 1950). Specifically, between the ...<|separator|>
[107]
Weather prediction equations (Chapter 2)
The hydrostatic approximation consists in neglecting vertical acceleration and leads to what are called the primitive equations (as opposed to the filtered ...Missing: governing | Show results with:governing
[108]
[PDF] 7) Numerical Weather Prediction
Due to their complexity, the primitive equations must be solved numerically using algebraic approximations, rather than calculating complete analytic solutions.
[109]
[PDF] The Kain-Fritsch Convective Parameterization - NCAR/MMM
Convective parameterization continues to be one of the most challenging aspects of numerical modeling of the atmosphere, especially for numerical weather ...
[110]
WRF Physics — WRF Users Guide documentation - NCAR/MMM
WRF radiation schemes obtain cloud properties from the microphysics scheme, and then compute an atmospheric temperature tendency profile, as well as surface ...
[111]
A Comprehensive Radiation Scheme for Numerical Weather ...
A comprehensive scheme for the parameterization of radiative transfer in numerical weather Prediction (NWP) models has been developed.
[112]
Linearised physics: the heart of ECMWF's 4D-Var
The system works by minimising a cost function, which measures the departure of the analysis from observations and the short-range forecast. In this ...Missing: numerical | Show results with:numerical
[113]
4D‐Var for numerical weather prediction - Bonavita
Oct 17, 2020 · The full nonlinear cost function (thick blue line) is minimised through the minimisation of successive quadratic cost functions (thin black ...
[114]
Numerical Weather Prediction Model - ScienceDirect.com
Global models usually have a coarse resolution and do not allow for a detailed mapping of small-scale features, although resolution has increased rapidly during ...
[115]
Potential Numerical Techniques and Challenges for Atmospheric ...
Sep 1, 2019 · Currently, traditional approaches, including the finite difference method (FDM), finite volume method (FVM), spectral method (SM), and finite ...
[116]
A time-spectral approach to numerical weather prediction - NASA ADS
Finite difference methods are traditionally used for modelling the time domain in numerical weather prediction (NWP). Time-spectral solution is an ...
[117]
A Numerical Method Based on Leapfrog and a Fourth-Order Implicit ...
A time-stepping scheme is proposed. It is based on the leapfrog method and a fourth-order time filter. The scheme requires only one evaluation per time step.
[118]
TIME FILTERS FOR NUMERICAL WEATHER PREDICTION
Sep 30, 2016 · Abstract. The Robert-Asselin (RA) time filter combined with leapfrog scheme is widely used in numerical models of weather and climate.
[119]
Documentation - GFS - Virtual Lab - NOAA VLab
The current operational horizontal resolution is T1534 (T574), or approximately 13 km (34km) at the equator for days 0-10 (days 10-16) forecasts. In the ...
[120]
Numerical Weather Prediction model data - Met Office
A range of model data for expert users. ... The Met Office Unified Model is the numerical modelling system developed and used at the Met Office.
[121]
Unified Model - Met Office
The model is suitable for numerical weather prediction (NWP), seasonal forecasting and climate modelling with forecast times ranging from a few days to hundreds ...
[122]
GPU acceleration of numerical weather prediction - IEEE Xplore
The paper shows the promise of this approach by demonstrating a 20 times speedup for a computationally intensive portion of the Weather Research and Forecast ( ...
[123]
Weather Forecasts and Applications within Cloud Computing ...
This article provides a review of cloud terminology of possible interest to the meteorological community, and focuses specifically on the use of infrastructure ...
[124]
The Use of Model Output Statistics (MOS) in Objective Weather ...
Model Output Statistics (MOS) is an objective weather forecasting technique that determines a statistical relationship between a predictand and variables ...
[125]
Model output statistics (MOS) applied to Copernicus Atmospheric ...
Sep 8, 2022 · Model output statistics (MOS) are statistical techniques used to correct raw forecasts, and can substantially improve O3 forecasts.
[126]
[PDF] The ECMWF Ensemble Prediction System
The ECMWF EPS creates 50 forecasts from slightly different initial conditions and models to estimate forecast uncertainty, using a spread of these forecasts.
[127]
Interpretation of Rank Histograms for Verifying Ensemble Forecasts in
The ensemble is then typically used to generate a probabilistic forecast; for example, if 20 of 50 ensemble members forecast rain at a grid point, and if the ...
[128]
Ensemble forecasting at NMC and the breeding method
The breeding method simulates the development of growing errors in the analysis cycle. A difference field between two nonlinear forecasts is carried forward ( ...
[129]
Stochastic Physics - Earth Prediction Innovation Center - NOAA
Stochastic physics is a component of the UFS Weather Model (WM) and contains a variety of stochastic schemes. The schemes add valuable scientific and numerical ...
[130]
[PDF] Ensemble Verification Metrics - ECMWF
Flat rank histogram does not necessarily indicate a skillful forecast. Rank histogram shows conditional/unconditional biases BUT not full picture. • Only ...
[131]
Section 12.B Statistical Concepts - Probabilistic Data
Nov 12, 2024 · The Brier Score (BS) is a measure of how good forecasts are in matching observed outcomes. Where: BS = 0 the forecast is wholly accurate;; BS = ...
[132]
How to read Surface Weather Maps - NOAA
Sep 23, 2025 · Weather maps come in a myriad of styles, each providing different levels of information. However, there are some common features typically found in all of ...Missing: spaghetti overlays
[133]
NCEP Ensemble Weather Maps - Physical Sciences Laboratory
Ensemble Means, 500z 'Spaghetti' Plots, Mean SLP/ 1000:500mb Thickness, Standard Deviation, 500mb Height Mean Anomaly, 850mb Temp. Mean Anomaly, 850mb Temp.NCEP Ensemble Plot Animation · All Times · 500 mb SPAG Movie · Animation
[134]
National Weather Service - Graphical Forecast
Current graphical forecast maps, rivers, marine, offshore and high seas, hurricanes, aviation weather, climatic outlook.Missing: standard formats textual
[135]
[PDF] Forecast Products - National Weather Service
A zone forecast highlights the expected sky condition, probability and type of precipitation, visibility restrictions, and temperature affecting various zone ...
[136]
How to Read a Weather Forecast: Key Symbols and Terms Explained
Feb 11, 2025 · Cloud: Overcast or partly cloudy conditions. Raindrop: Indication of rain showers. Snowflake: Signifies snowfall or wintry precipitation.
[137]
About Us - AccuWeather
AccuWeather is the most recognized and most used source of weather forecasts and warnings in the world, known to billions, and is proven and verified to be the ...Weather Matters · AccuWeather Network · Dr. Joel N. Myers · Contact
[138]
Get better weather forecasts on your Alexa speaker - CNET
Jul 17, 2017 · Alexa's built-in weather forecasts is suitable for some users. But if you want more in-depth information, you'll need to use this skill.
[139]
Standards and Recommended Practices
WMO is the international standardization organization in the fields of meteorology, hydrology, climatology and related environmental disciplines.
[140]
Manual on Codes (WMO-No. 306), Volume I.2
Aug 21, 2025 · Coded messages are used for the international exchange of meteorological information comprising observational data provided by the WMO Integrated Global ...
[141]
Severe Weather 101: Tornado Forecasting
If conditions develop that are favorable for tornadoes, SPC forecasters issue a severe thunderstorm or tornado watch that typically lasts four to six hours.
[142]
Improving Tornado Warnings - Weatherology
Back in the late 70s and early 80s, warning lead times only averaged about 3 minutes, whereas today they are closer to 13 to 14 minutes. Even with this ...<|separator|>
[143]
Watch Warning Advisory Explained - National Weather Service
A warning is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. A warning means weather conditions pose a threat to life or ...
[144]
Watch/Warning/Advisory Definitions - National Weather Service
A Severe Thunderstorm Warning is issued when severe thunderstorms are occurring or imminent in the warning area. Severe thunderstorms are defined as follows:.Winter Storm Watch · Winter Weather Advisory · Snow Squall Warning
[145]
NOAA Weather Radio All-Hazards
Sep 8, 2023 · NWR broadcasts your area's official Weather Service warnings, watches, forecasts and other hazard information 24 hours a day, 7 days a week.
[146]
"NOAA WEATHER RADIO NETWORK"
In the most sophisticated alerting system, NOAA Weather Radio Specific Area Message Encoding (SAME), digital coding is employed to activate only those ...Missing: tones | Show results with:tones
[147]
EMERGENCY COMMUNICATION AND PUBLIC WARNING SYSTEMS
TheOASIS Emergency Management TCcreated theCommon Alerting Protocol (CAP), used by crisis responders, weather prediction agencies and emergency management ...
[148]
Emergency Warning System - Japan Meteorological Agency
JMA issues various warnings to alert people to possible catastrophes caused by extraordinary natural phenomena such as heavy rain, earthquakes, tsunami and ...
[149]
J-Alert: disaster warning technology in Japan
Mar 30, 2016 · J-Alert is a satellite based system that allows authorities to quickly broadcast alerts to local media and to citizens directly via a system of loudspeakers.
[150]
[PDF] Cry Wolf Effect? Evaluating the Impact of False Alarms on Public ...
75% of tornado warnings are false alarms, and concerns exist that this may desensitize the public, but study suggests this may be overblown.
[151]
False Alarm Reduction Research - National Weather Service
Reduced Tornado Warning False Alarm Rate by 31% Since April 2011. The primary mission of the National Weather Service is to save lives and property through the ...
[152]
Warn-on-Forecast - NOAA National Severe Storms Laboratory
The Warn-on-Forecast project proposes one promising way to increase warning lead time -- to harness high-resolution numerical weather prediction models and high ...WoFS Home · What is a Warning? · Potential Benefits · Case Studies
[153]
Aviation Weather Center
Web site of the NWS Aviation Weather Center, delivering consistent, timely and accurate weather information for the world airspace system.Observations · Aviation Weather Testbed · Terminal Weather Dashboard · Winds
[154]
ZSE SIGMETs - National Weather Service
SIGMETs are Inflight Weather Advisories for Significant Meteorological hazards. A SIGMET is "widespread" in that it covers an area of at least 3,000 square ...Missing: sources | Show results with:sources
[155]
Terminal Area Forecast (TAF) - Federal Aviation Administration
Jan 21, 2025 · The Terminal Area Forecast (TAF) is the official FAA forecast of aviation activity for US airports. It contains active airports in the National Plan of ...
[156]
TAF Data - Aviation Weather Center
Raw and decoded airport forecasts. ... Prog charts TAF map Forecast Discussions · METAR/TAF data PIREP data Wind/temp data ITWS data · WAFS grids SigWx charts ...
[157]
[PDF] Corridor Integrated Weather System - MIT Lincoln Laboratory
The Corridor Integrated Weather System. (CIWS) is a fully automated weather analysis and forecasting system designed to support the development and execution of ...
[158]
WAVEWATCH III Model Description
WAVEWATCH III (Tolman 1997, 1999a, 2009) is a third generation wave model developed at NOAA/NCEP in the spirit of the WAM model.
[159]
WAVEWATCH III Development - National Weather Service
The core of the framework consists of the WAVEWATCH III third-generation wave model (WAVE-height, WATer depth and Current Hindcasting), developed at NOAA/NCEP.
[160]
Marine Definitions - National Weather Service
Gale Warning: A warning of sustained winds in the range 34 to 47 knots (KT) inclusive either predicted or occurring not associated with tropical cyclones.
[161]
Marine Weather Warnings
NOAA's National Weather Service (NWS) issues marine weather watches, advisories, and warnings for hazardous winds and sea conditions.
[162]
What is AMDAR - World Meteorological Organization WMO
AMDAR is a global system that automatically transmits meteorological data from aircraft to improve weather forecasts, using existing aircraft systems.
[163]
Other Observing Systems - National Weather Service
AMDAR The Aircraft Meteorological Data Relay (AMDAR) collects and disseminates upper-air data measured from commercial aircraft.
[164]
Ships' routeing - International Maritime Organization
Weather routeing. Weather conditions can also affect a ship's navigation, and in 1983 IMO adopted resolution A.528(13) on Recommendation on Weather Routeing, ...Missing: forecasting | Show results with:forecasting
[165]
Forecasting clear-air turbulence - ECMWF
A calibrated CAT parameter for the ECMWF Integrated Forecasting System (IFS) that serves the purpose of aviation forecasting as well as research in turbulence.Missing: avoidance | Show results with:avoidance
[166]
Evaluation of Multimodel-Based Ensemble Forecasts for Clear-Air ...
Seven global numerical weather prediction (NWP) model outputs are used to construct the multimodel-based ensemble forecasts for clear-air turbulence (CAT).
[167]
Stay ahead of frost with new prediction tools developed ... - Penn State
Apr 18, 2018 · The team developed algorithms that combine a variety of weather variables to determine the likelihood that frost will form in an area of ...
[168]
Evaluation of Frost Protection as an Adaptation Strategy
We will estimate the risk of cold damage associated with spring freezes for apple and blueberry, two representative regional fruit crops, at several locations ...
[169]
Evapotranspiration-based irrigation scheduling or water-balance ...
This method is usually referred to as weather-based or evapotranspiration (ET c ) - based irrigation scheduling or water balance method.
[170]
Solar Forecasting 2 | Department of Energy
Dec 19, 2017 · Solar Forecasting 2 aims to improve solar energy forecasting for grid operators, focusing on 24-48 hour day-ahead and 1-6 hour real-time ...
[171]
[PDF] A Gridded Solar Irradiance Ensemble Prediction System Based on ...
Mar 16, 2023 · NWPs represent a component of most solar and wind power prediction systems, providing reliable wind speed and solar irradiance forecasts up.
[172]
Degree-days - U.S. Energy Information Administration (EIA)
The U.S. Energy Information Administration (EIA) uses population-weighted degree days to model and forecast energy consumption for the United States and for ...
[173]
U.S. Gridded Standardized Precipitation Index (SPI) from nClimGrid ...
The Standardized Precipitation Index, or SPI, is a drought index that captures how observed precipitation deviates from the climatological average over a given ...
[174]
Weather Data APIs: Real-Time & Historical Weather Data Insights
The Weather Company's Weather Data API delivers real-time and historical data, plus forecasts, to power business operations and data-driven decision-making.
[175]
Global High-Resolution Atmospheric Forecasting System (GRAF)
Unlock precise decision-making with The Weather Company's GRAF weather model, delivering 6x faster and 3x more accurate forecasts than competitors ...
[176]
Air Force Weather Agency > 557th Weather Wing > Display
... Air Force Weather Weapon System that strengthens AFWA's ability to conduct weather ops and supports worldwide DoD operations. They also delivers state-of ...
[177]
Defence services - Met Office
Weather services and solutions to support military operations. In the context of the National Security Strategy, we support and enable the protection, security, ...
[178]
Joint Typhoon Warning Center (JTWC)
This website is the only official public source for JTWC tropical cyclone products. Please consult your national meteorological agency.Best Track Archive · Warning Graphic Legend · Annual Tropical Cyclone Reports
[179]
Joint Typhoon Warning Center Marks 50 Years of Service - DVIDS
Jul 3, 2025 · The Joint Typhoon Warning Center ensures that today's military forces in the Pacific will never again experience a disaster like Typhoon Cobra.
[180]
How a weather forecast made history - the D-Day Landings
It is a little-known fact that perhaps one of the most important weather forecasts ever made was the one for D-Day, the Allied invasion of France.
[181]
Weather from Above – 5 years of supporting the Space Force
Dec 20, 2024 · USAF WESO Airmen support space launches, provide satellite support, deliver space alerts, and operate a 24/7 space weather center, ensuring ...
[182]
Defense Meteorological Satellite Program - Space Force
The Defense Meteorological Satellite Program (DMSP) has been collecting weather data for US military operations for more than five decades.Missing: nowcasts | Show results with:nowcasts
[183]
Hurricane Preparedness and Evacuation Planning | FEMA.gov
Oct 27, 2025 · The Hurricane Evacuation Decision Support Tool (HURREVAC) is a free web-based platform available to government emergency managers. HURREVAC ...
[184]
WRF-based Weather Running Estimate -- Nowcast Tool for Army ...
The WRE-N will provide critical battlefield weather information that can enhance Soldier survivability, force sustainment, situational awareness.Missing: classified | Show results with:classified
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[PDF] WEATHER OPERATIONS - Air Force Doctrine
Sep 26, 2025 · In a limited data or data-denied environment, weather personnel should utilize any available environmental data combined with forecasting skills.Missing: nowcasts | Show results with:nowcasts