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Verification and Model Configuration Sensitivity of Simulated Abi Radiance Forecasts With The Fv3-lam Model

Abstract

This study evaluates simulated radiance forecasts from a series of controlled experiments consisting of FV3-LAM forecasts with different configurations of model physics and vertical resolution. The forecasts were produced during the 2020 Hazardous Weather Testbed Spring Forecasting Experiments on the same forecast cases. The evaluation includes grid-point, neighborhood-based and object-based verification. The experiments include forecasts that were identical except for the physics (EMC-LAM vs. EMC-LAMx), vertical resolution (EMC-LAMx vs. NSSL-LAM), or combined initial conditions, physics and vertical resolution (GSL-LAM). It is found that the EMC-LAM generally provided better simulated radiance forecasts than the other three configurations at most forecast lead times, due to its unique physics configuration. All configurations generally over-forecasted high level clouds. EMC-LAM reduced the over-forecasting of high clouds, but also under-forecasted the coverage of mid-level clouds. In contrast, at early lead times the EMC-LAM had relatively poor performance relative to the other forecasts. Furthermore, EMC-LAM was an outlier in terms of the vertical structure of clouds. It is also found that the NSSL-LAM consistently improved upon the EMC-LAMx, which had fewer vertical levels than NSSL-LAM. Compared to EMC-LAMx, NSSL-LAM had less cloud over-forecasting bias, especially with small cloud objects, and less overall error. The differences between EMC-LAMx and GSL-LAM were generally much smaller than the differences between EMC-LAMx and EMC-LAM/NSSL-LAM. Finally, it is found that a non-linear bias correction conditioned on symmetric brightness temperature reduced the overall root-mean-square error by about a factor of 2 while improving the unrealistic vertical structure of clouds in the EMC-LAM.

Article / Publication Data
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DOI ↗
Fiscal Year
Publication Name
Earth and Space Science
Published On
March 18, 2023
Publisher Name
AGU
Submitted On
November 01, 2022
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