Verification of Weather Running Estimate-Nowcast (WRE-N) Forecasts Using a Spatial-Categorical Method

Spatial forecasts from Numerical Weather Prediction (NWP) models of meteorological variables to support US Army operations on the battlefield have become an integral part of the products available for the Staff Weather Officer to use in providing mission planning and execution forecasts. These forec...

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description Spatial forecasts from Numerical Weather Prediction (NWP) models of meteorological variables to support US Army operations on the battlefield have become an integral part of the products available for the Staff Weather Officer to use in providing mission planning and execution forecasts. These forecasts are ingested by certain Army tactical decision aids (TDAs) and are fused with information on the operational weather thresholds, which impact the performance of Army systems and missions. Such a TDA generates spatial and temporal forecasts of these impacts for user-specified systems and/or missions. This report presents the results from applying a method to verify forecast fields of meteorological variables that have been filtered by the application of a threshold, similar to the method used by the TDA. A threshold applied to a continuous variable field becomes a categorical forecast for which there are traditional and nontraditional methods for verification. This study evaluates the ability of the NWP model to predict multiple categories of the spatial variable. Preliminary results suggest the skill of the model when predicting objects defined by lower thresholds is greater than the skill for objects defined by higher thresholds.
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subjects Atmospheric Physics
atmospheric sciences
categorical forecast
forecast
MET Series-Analysis
Meteorology
Model Evaluation Tools
mywida(My Weather Impacts Decision Aid)
numerical weather prediction
observations
TACTICAL DECISION AIDS
tda(tactical decision aid)
thresholds
verification
weather forecasting
weather impacts
title Verification of Weather Running Estimate-Nowcast (WRE-N) Forecasts Using a Spatial-Categorical Method
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