Implementation of Mesoscale Numerical Weather Prediction for Weather-Sensitive Business Operations (original) (raw)
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Weather-sensitive business operations are primarily reactive to short-term (3 to 36 hours), local conditions (city, county, state) due to unavailability of appropriate predicted data at this temporal and spatial scale. This situation is commonplace in a number of applications including, but not limited to transportation, agriculture, energy, insurance, entertainment, construction, communications and emergency planning. Typically, what optimization that is applied to these processes to enable proactive efforts utilize either historical weather data as a predictor of trends or the results of synoptic-scale weather models. Alternatively, mesoscale (cloud-scale) numerical weather models operating at higher resolution in space and time with more detailed physics has shown "promise" for many years as a potential enabler of pro-active decision making for both economic and societal value. They may offer greater precision and accuracy within a limited geographic region for problems...
The overall performance characteristics of a limited area, hydrostatic, fine (52 km) mesh, primitive equation, numerical weather prediction model are determined in anticipation of satellite data assimilations with the model. The synoptic and mesoscale predictive capabilities of version 2.0 of this model, the Mesoscale Atmospheric Simulation System (MASS 2.0), were evaluated. The two part study is based on a sample of approximately thirty 12h and 24h forecasts of atmospheric flow patterns during spring and early summer. The synoptic scale evaluation results benchmark the performance of MASS 2.0 against that of an operational, synoptic scale weather prediction model, the Limited area Fine Mesh (LFM). The large sample allows for the calculation of statistically significant measures of forecast accuracy and the determination of systematic model errors. The synoptic scale benchmark is required before unsmoothed mesoscale forecast fields can be seriously considered.
The Mesoscale Forecasting Process: Applying the Next Generation Mesoscale Forecast
The weather forecast effort has progressed a long way past its embryonic stage of the barotropic forecast. Both computer power and our knowledge of atmospheric processes have increased substantially over the years, allowing for the classification of many weather phenomena into scales, including the global/hemispheric scale, the synoptic scale, the mesoscale, and the microscale. These scales represent the cascade of energy that occurs in the atmosphere, with hemispheric features providing energy for the synoptic scale, synoptic features providing energy for the mesoscale, and so forth. Many observation and modeling tools exist to aid the forecaster along the way, including RAOB soundings, satellite imagery, wind profiler data, radar data, lightning data, and model data, and all are useful in mesoscale forecasting. When performing a mesoscale forecast, however, it is prudent to use a mesoscale model, such as the Air Force Weather Agency's (AFWA) Weather Research and Forecasting (WRF) model.
Assessment of the Aviation Weather Center Global Forecasts of Mesoscale Convective Systems
Jour. of Applied Meteorology, 2004
"This paper examines the precision of location and top height of mesoscale convective systems, as forecast by the Aviation Weather Center (AWC). The examination was motivated by the Mediterranean Israeli Dust Experiment (MEIDEX) on the space shuttle Columbia, aimed to image transient luminous events (TLEs), such as sprites, jets, and elves, from orbit. Mesoscale convective systems offer a high probability for the occurrence of TLEs above active thunderstorms. Because the operational methodology was planned around a 24-h cycle, there was a need for a global forecast of areas with a high probability of massive thunderstorms that are prone to exhibit TLE activity. The forecast was based on the high-level significant weather (SIGWX) maps, commonly used for civil aviation, provided by the AWC on the Internet. To estimate the operational skill of this forecast for successfully detecting clouds with a high probability for producing TLEs, predictions for selected dates were compared with satellite observations. The locations of 66 mesoscale cloud systems on Significant Weather Maps, produced for eight different dates in August 2001, were compared with satellite global IR images for these dates. Operational skill was determined as the percentage of observed cloud systems found within a 58 range in the regions that appeared on the forecast maps as having the potential to contain thunderclouds and was found to be 92%. No consistent error was found in location. The predicted size of the convective system was typically larger than the observed size. Cloud-top heights of 53 systems were examined on four dates in October–November 2001, using IR radiances converted to brightness temperatures. For each convective system, the coldest cloud-top temperature was converted to height, using the NCEP–NCAR reanalysis data for the respective location and time. The standard error in the forecast heights was 2516 m. Because the purpose was to get true alerts of potential TLE occurrences, operational forecast skill was defined as the percentage of forecasts that were accurate within 1000 m or higher than observed. The 1000-m tolerance was allowed because of inevitable uncertainties underlying this method of analysis. Operational skill was found to be only 43%. During the ‘‘STS-107’’ mission flown in January 2003, the forecasted areas of main convective centers were transmitted daily to the crew and helped them in pointing the cameras and targeting thunderstorms. This ensured the success of the MEIDEX sprite observations that recorded numerous events in many different geographical locations."
Tellus A, 2011
A B S T R A C T This work evaluates several techniques to account for mesoscale initial-condition (IC) and model uncertainty in a short-range ensemble prediction system based on the Weather Research and Forecast (WRF) model. A scientific description and verification of several candidate methods for implementation in the U.S. Air Force Weather Agency mesoscale ensemble is presented. Model perturbation methods tested include multiple parametrization suites, landsurface property perturbations, perturbations to parameters within physics schemes and stochastic 'backscatter' streamfunction perturbations. IC perturbations considered include perturbed observations in 10 independent WRF-3DVar cycles and the ensemble-transform Kalman filter (ETKF). A hybrid of ETKF (for IC perturbations) and WRF-3DVar (to update the ensemble mean) is also tested. Results show that all of the model and IC perturbation methods examined are more skilful than direct dynamical downscaling of the global ensemble. IC perturbations are most helpful during the first 12 h of the forecasts. Physical parametrization diversity appears critical for boundary-layer forecasts. In an effort to reduce system complexity by reducing the number of suites of physical parametrizations, a smaller set of parametrization suites was combined with perturbed parameters and stochastic backscatter, resulting in the most skilful and statistically consistent ensemble predictions.
Forecast Performance of an Operational Meso-Gamma-Scale Modelling System for Extratropical Systems
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Super high-resolution mesoscale weather prediction
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