A statistically optimal analysis of systematic differences between Aeolus horizontal line-of-sight winds and NOAA's Global Forecast System

Autor: H. Liu, K. Garrett, K. Ide, R. N. Hoffman, K. E. Lukens
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Atmospheric Measurement Techniques, Vol 15, Pp 3925-3940 (2022)
Druh dokumentu: article
ISSN: 1867-1381
1867-8548
DOI: 10.5194/amt-15-3925-2022
Popis: The European Space Agency Aeolus mission launched a first-of-its-kind spaceborne Doppler wind lidar in August 2018. To optimize the assimilation of the Aeolus Level-2B (B10) horizontal line-of-sight (HLOS) winds, significant systematic differences between the observations and numerical weather prediction (NWP) background winds should be removed. Total least squares (TLS) regression is used to estimate speed-dependent systematic differences between the Aeolus HLOS winds and the National Oceanic and Atmospheric Administration (NOAA) Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS) 6 h forecast winds. Unlike ordinary least squares regression, TLS regression optimally accounts for random errors in both predictors and predictands. Large, well-defined, speed-dependent systematic differences are found in the lower stratosphere and troposphere in the tropics and Southern Hemisphere. Correction of these systematic differences improves the forecast impact of Aeolus data assimilated into the NOAA global NWP system.
Databáze: Directory of Open Access Journals
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