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Analysis of Differences between Aeolus Wind Observations and NOAA Global Forecast Model

ESSIC/CISESS Scientists Hui Liu, Kayo Ide, Ross Hoffman, and Katherine Lukens and NOAA Scientist Keven Garrett co-authored a study published on July 5, 2022 in Atmospheric Measurement Techniques on a statistically optimal analysis of systematic differences between Aeolus horizontal line-of-sight (HLOS) Winds and NOAA’s Global Forecast System. 

The ESA Aeolus mission launched a first-of-its-kind spaceborne Doppler wind lidar in August 2018. To optimize the assimilation of the Aeolus Level-2B wind profiles, significant systematic differences between the wind observations and numerical weather prediction (NWP) background winds were accurately estimated and removed. The total least squares (TLS) regression was used to estimate speed-dependent systematic differences between the Aeolus winds and NOAA 6-hour global forecast winds. Unlike ordinary least squares (OLS) regression, TLS regression optimally accounts for random errors in both predictors and predictions, and so provides more accurate estimates of the bias. 

The figure below shows the vertical distribution of average bias estimates for various methods investigated in this study. Large, well-defined, speed-dependent differences were found in the lower stratosphere and troposphere in the tropics and Southern Hemisphere. Bias correction of Aeolus wind data using the TLS approach increases the positive impact of Aeolus data on NOAA global, medium-range weather forecasts.

Figure: Vertical distributions of average bias estimates (color scale; m s−1) for Aeolus Mie (a, c, e) and Rayleigh (b, d, f) winds as a function of Aeolus winds using one of three methods for descending orbits for all latitudes. The methods are Ordinary Least Squares using NOAA global forecast model winds as a predictor (a, b), Total Least Squares estimates (c, d), and Ordinary Least Squares using the average of Aeolus and NOAA winds as a predictor (e, f).

Liu is an associate research scientist at ESSIC/CISESS. His research focuses on impact optimization of satellite observations (Aeolus space-based lidar winds, AMVs, and radio occultations etc.) on regional and global weather forecasts.

Ide is an Associate Professor in the Department of Atmospheric & Oceanic Science, Center for Scientific Computation & Mathematical Modeling, and Institute for Physical Science & Technology, along with Earth System Science Interdisciplinary Center. With background in engineering, theoretical fluid dynamics and applied mathematics, she studies earth system sciences from interdisciplinary perspectives. Her primary research interests are scientific prediction for earth systems and mixing & transport in geophysical fluids.

Lukens is a Post Doctoral Associate at the University of Maryland/ESSIC/CISESS. Her research background in large-scale atmospheric dynamics and winter storm track impacts has prepared her for her current work that focuses on the intercomparison and verification of new and existing wind datasets (e.g., Aeolus, near-space balloon networks, AMVs, aircraft, rawinsondes) and their impact on global NWP. Additionally, she is a co-architect and manager of a novel wind archive available for public use: (check the website often for updates). 

To access the article, click here: “A statistically optimal analysis of systematic differences between Aeolus horizontal line-of-sight winds and NOAA’s Global Forecast System”.

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