Air Force Global Weather Central Research Articles

Intercomparison of In-Flight Icing Algorithms. Part I: WISP94 Real-Time Icing Prediction and Evaluation Program

Abstract The purpose of the Federal Aviation Administration’s Icing Forecasting Improvement Program is to conduct research on icing conditions both in flight and on the ground. This paper describes a portion of the in-flight aircraft icing prediction effort through a comprehensive icing prediction and evaluation project conducted by the Research Applications Program at the National Center for Atmospheric Research. During this project, in- flight icing potential was forecast using algorithms developed by RAP, the National Weather Service’s National Aviation Weather Advisory Unit, and the Air Force Global Weather Center in conjunction with numerical model data from the Eta, MAPS, and MM5 models. Furthermore, explicit predictions of cloud liquid water were available from the Eta and MM5 models and were also used to forecast icing potential. To compare subjectively the different algorithms, predicted icing regions and observed pilot reports were viewed simultaneously on an interactive, real-time display. To m...

A Description of the Air Force Real-Time Nephanalysis Model

The Air Force Global Weather Central (AFGWC) Real-Time Nephanalysis (RTNEPH) is an automated cloud model that produces a 48-km gridded analysis of cloud amount, cloud type, and cloud height. Its primary input is imagery from polar-orbiting satellites. Six main programs make up the RTNEPH. These are the satellite data mapper, the surface temperature analysis and forecast model, the satellite data processor, the conventional data processor, the merge processor, and the bogus processor. The satellite data mapper remaps incoming polar-orbiter imagery to a polar-stereographic database. The surface temperature model produces an analysis and forecast of shelter and skin temperatures for comparison to satellite-measured infrared (IR) brightness temperatures. The satellite data processor reads in the new satellite data and produces a satellite-derived cloud analysis. The conventional data processor retrieves and reformats cloud information from airport observations. The merge processor combines the satellite- and conventional-derived cloud analyses into a final nephanalysis. Finally, the bogus processor allows forecasters to manually correct the nephanalysis where appropriate. The RTNEPH has been extensively redesigned, primarily to improve analyses of total and layered cloud amounts generated from IR data. Recent enhancements include the use of regression equations to calculate atmospheric water vapor attenuation, an improved definition of surface temperatures used to calculate cloud/no-cloud thresholds for IR data, and the use of Special Sensor Microwave/Imager (SSM/1) data to further improve the calculation of infrared cloud/no-cloud thresholds. Planned enhancements include the processing of geostationary satellite data, more sophisticated processing of visible data, and a higher-resolution satellite database for the archiving and processing of multispectral satellite data.

An Objective Clear-Air Turbulence Forecasting Technique: Verification and Operational Use

Abstract An objective technique for forecasting clear-air turbulence (CAT) is described. An index is calculated based on the product of horizontal deformation and vertical wind shear derived from numerical model forecast winds aloft. The forecast technique has been evaluated and is now in operational use at two forecast centers with international aviation responsibilities: the National Meteorological Center (NMC) in Washington, D.C., and the Air Force Global Weather Central (AFGWC) in Omaha, Nebraska. The index is also an operational forecast tool at the Canadian Atmospheric Environment Service (AES), and the National Aviation Weather Advisory Unit (NAWAU) in Kansas City, Missouri, both responsible for domestic aviation forecasts. The AFGWC index also includes horizontal convergence in its calculation. Thresholds were selected empirically by comparing index values with the location and intensity of observed CAT. Verification indicates that the index is quite reliable. The probability of detection (POD) va...

Updated climatological model predictions of ionospheric and HF propagation parameters

The prediction performance of ionospheric conductivity and electron density (ICED), RADAR C, and Ionospheric Communications Analysis and Predictions Program (IONCAP) climatological ionospheric models is evaluated for different regions and sunspot number inputs. With conventional sunspot numbers, based on the observed solar 10.7‐cm flux, ƒ0F2 prediction errors at Boulder for a particular month are no less than 18%. Use of the U.S. Air Force Global Weather Center (AFGWC) global sunspot number SSNE, extracted from ionosonde data for use with the ICED model, gives 15% errors. A dramatic improvement involves the use of single‐station ionospheric data to update the driving parameters of an ionospheric model for near‐real‐time (NRT) predictions of ƒ0F2 and other ionospheric and HF circuit parameters. For middle latitudes the improvement extends out thousands of kilometers from the update point to points of comparable corrected geomagnetic latitude. At very large distances an update scheme which exploits a strong global dependence on geomagnetic activity shows promise. For the purpose of NRT predictions of the entire electron density profile the use of two distinct effective sunspot numbers for the bottomside, coupled with one or more model parameters for the topside, is suggested for model updates from a single station.

Objective Data Analysis Conference

The Air Force Global Weather Central (AFGWC) hosted a conference on objective data analysis 2–4 October 1985. This was a continuation in a series of conferences on data analysis. Participants included both operational meteorologists and research meteorologists from various government's numerical weather prediction (NWP) facilities. The conference consisted of each participating facility reviewing their current status and future plans. Individual topics were then presented concerning data analysis, followed by small group discussions on meteorological and computational aspects of objective data analysis. Various conclusions were developed during the conference: 1) Optimal interpolation is the analysis method most commonly used at NWP facilities and is favored for its ability to assimilate data from many different observing platforms. 2) Promising work continues with the insertion of new data into the analysis model. 3) The fundamental difference between most analyses is the difference in the imposed quality control. 4) Further improvements in data assimilation are possible if archival procedures are performed to gather covariance statistics.

Nimbus-7 SMMR Derived Global Snow Cover Parameters

Snow covers about 40 million km2 of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

Nimbus-7 SMMR Derived Global Snow Cover Parameters

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

Nimbus-7 SMMR Derived Global Snow Cover Parameters

Snow covers about 40 million km2of the land area of the Northern Hemisphere during the winter season. The accumulation and depletion of snow is dynamically coupled with global hydrological and climatological processes. Snow covered area and snow water equivalent are two essential measurements. Snow cover maps are produced routinely by the National Environmental Satellite Data and Information Service of the National Oceanic and Atmospheric Administration (NOAA/NESDIS) and by the US Air Force Global Weather Center (USAFGWC). The snow covered area reported by these two groups sometimes differs by several million km2, Preliminary analysis is performed to evaluate the accuracy of these products.Microwave radiation penetrating through clouds and snowpacks could provide depth and water equivalent information about snow fields. Based on theoretical calculations, snow covered area and snow water equivalent retrieval algorithms have been developed. Snow cover maps for the Northern Hemisphere have been derived from Nimbus-7 SMMR data for a period of six years (1978–1984). Intercomparisons of SMMR, NOAA/NESDIS and USAFGWC snow maps have been conducted to evaluate and assess the accuracy of SMMR derived snow maps. The total snow covered area derived from SMMR is usually about 10% less than the other two products. This is because passive microwave sensors cannot detect shallow, dry snow which is less than 5 cm in depth. The major geographic regions in which the differences among these three products are the greatest are in central Asia and western China. Future study is required to determine the absolute accuracy of each product.Preliminary snow water equivalent maps have also been produced. Comparisons are made between retrieved snow water equivalent over large area and available snow depth measurements. The results of the comparisons are good for uniform snow covered areas, such as the Canadian high plains and the Russian steppes. Heavily forested and mountainous areas tend to mask out the microwave snow signatures and thus comparisons with measured water equivalent are poorer in those areas.

The Air Force Global Weather Central operational boundary-layer model

A limited-area seven-layer physical-numerical model for the lower tropospheric region (surface - 1600m) is described. The grid interval, approximately 190km, is half that of the standard numerical weather-prediction grid used in the hemispheric free atmospheric operational model at the Air Force Global Weather Central (AFGWC). This model is an integral part of the complete AFGWC meso-scale (sub-synoptic) numerical analysis and prediction system and is used to provide greater horizontal and vertical resolution in both numerical analyses and forecasts. Important features of this boundary-layer model include: a completely automated objective analysis of input data; the transport of heat and moisture by three-dimensional wind flow; latent heat exchange in water substance phase changes; and eddy fluxes of heat and water vapor. Input data are conventional synoptic surface and upper air reports. Other operational AFGWC prediction models provide input in the form of horizontal wind components at the upper boundary and an estimate of cloudiness above the boundary layer. Forecasts for the lower boundary and surface layer are empirically derived. Despite some approximations which broadly simplify the real planetary boundary-layer processes, operational use for highly weather-sensitive Air Weather Service support indicates that the model is capable of producing accurate detailed forecasts for periods of up to 24h.