Using DOE-ARM and Space-Based Assets to Assess the Quality of Air Force Weather 3D Cloud Analysis and Forecast Products

Abstract:

Air Force Weather (AFW) has documented requirements for global cloud analysis and forecasting to support DoD missions around the world. To meet these needs, AFW utilizes a number of cloud products. Cloud analyses are constructed using 17 different near real time satellite sources. Products include analysis of the individual satellite transmissions at native satellite resolution and an hourly global merge of all 17 sources on a 24km grid. AFW has also recently started creation of a time delayed global cloud reanalysis to produce a ‘best possible’ analysis for climatology and verification purposes. Forecasted cloud products include global short-range cloud forecasts created using advection techniques as well as statistically post processed cloud forecast products derived from various global and regional numerical weather forecast models. All of these cloud products cover different spatial and temporal resolutions and are produced on a number of different grid projections. The longer term vision of AFW is to consolidate these various approaches into uniform global numerical weather modeling (NWM) system using advanced cloudy-data assimilation processes to construct the analysis and a licensed version of UKMO’s Unified Model to produce the various cloud forecast products.

In preparation for this evolution in cloud modeling support, AFW has started to aggressively benchmark the performance of their current capabilities. Cloud information collected from so called ‘active’ sensors on the ground at the DOE-ARM sites and from space by such instruments as CloudSat, CALIPSO and CATS are being utilized to characterize the performance of AFW products derived largely by passive means. The goal is to understand the performance of the 3D cloud analysis and forecast products of today to help shape the requirements and standards for the future NWM driven system.

This presentation will present selected results from these benchmarking efforts and highlight insights and observations between passively and actively derived observations and the impacts of varying spatial and temporal depictions of clouds.