Impact Assessment of Aeolus Winds on NOAA Global Forecast

2021 ◽  
Author(s):  
Hui Liu ◽  
Kevin Garrett ◽  
Kayo Ide ◽  
Ross Hoffman ◽  
Kathrine Luekens
Keyword(s):  
Data Assimilation ◽  
Impact Assessment ◽  
Tropical Cyclones ◽  
Bias Correction ◽  
Western Coast ◽  
Global Data Assimilation System ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System ◽  
Heavy Rainfalls

<p>Recent efforts have focused on evaluation of the reprocessed Aeolus Level 2B (L2B) wind data with ESA M1 bias correction and its impact on NOAA global forecast. Aeolus wind quality especially the remaining biases vs NOAA global model background is examined. As a result, a revised bias correction taking account of noises in both Aeolus and GFS winds is implemented in the NOAA global data assimilation system to improve Aeolus wind assimilation.  In this study we will present impact from Aeolus wind on NOAA global forecast, focusing on synoptic and mesoscale scale events, e.g., tropical cyclones track and intensity in Eastern Pacific, and heavy rainfalls over the Western Coast of US.</p>

Assessment of the Impact of Simulated Satellite Lidar Wind and Retrieved 183 GHz Water Vapor Observations on a Global Data Assimilation System

1990 ◽  
Vol 118 (12) ◽  
pp. 2513-2542 ◽  
Author(s):  
Ross N. Hoffman ◽  
Christopher Grassotti ◽  
Ronald G. Isaacs ◽  
Jean-Francois Louis ◽  
Thomas Nehrkorn ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Global Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

Forecast Impact of Assimilating Aircraft WVSS-II Water Vapor Mixing Ratio Observations in the Global Data Assimilation System (GDAS)

2017 ◽  
Vol 32 (4) ◽  
pp. 1603-1611 ◽  
Author(s):  
Brett T. Hoover ◽  
David A. Santek ◽  
Anne-Sophie Daloz ◽  
Yafang Zhong ◽  
Richard Dworak ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Global Data Assimilation System ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

Abstract Automated aircraft observations of wind and temperature have demonstrated positive impact on numerical weather prediction since the mid-1980s. With the advent of the Water Vapor Sensing System (WVSS-II) humidity sensor, the expanding fleet of commercial aircraft with onboard automated sensors is also capable of delivering high quality moisture observations, providing vertical profiles of moisture as aircraft ascend out of and descend into airports across the continental United States. Observations from the WVSS-II have to date only been monitored within the Global Data Assimilation System (GDAS) without being assimilated. In this study, aircraft moisture observations from the WVSS-II are assimilated into the GDAS, and their impact is assessed in the Global Forecast System (GFS). A two-season study is performed, demonstrating a statistically significant positive impact on both the moisture forecast and the precipitation forecast at short range (12–36 h) during the warm season. No statistically significant impact is observed during the cold season.

Introduction of the GSI into the NCEP Global Data Assimilation System

2009 ◽  
Vol 24 (6) ◽  
pp. 1691-1705 ◽  
Author(s):  
Daryl T. Kleist ◽  
David F. Parrish ◽  
John C. Derber ◽  
Russ Treadon ◽  
Wan-Shu Wu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Environmental Modeling ◽  
Analysis Scheme ◽  
Global Data Assimilation System ◽  
Analysis System ◽  
Data Assimilation System ◽  
Global Data ◽  
Statistical Interpolation ◽  
New System ◽  
Assimilation System

Abstract At the National Centers for Environmental Prediction (NCEP), a new three-dimensional variational data assimilation (3DVAR) analysis system was implemented into the operational Global Data Assimilation System (GDAS) on 1 May 2007. The new analysis system, the Gridpoint Statistical Interpolation (GSI), replaced the Spectral Statistical Interpolation (SSI) 3DVAR system, which had been operational since 1991. The GSI was developed at the Environmental Modeling Center at NCEP as part of an effort to create a more unified, robust, and efficient analysis scheme. The key aspect of the GSI is that it formulates the analysis in model grid space, which allows for more flexibility in the application of the background error covariances and makes it straightforward for a single analysis system to be used across a broad range of applications, including both global and regional modeling systems and domains. Due to the constraints of working with an operational system, the final GDAS package included many changes other than just a simple replacing of the SSI with the new GSI. The new GDAS package contained an upgrade to the Global Forecast System model, including a new vertical coordinate, as well as new features in the GSI that were never developed for the SSI. Some of these new features included changes to the observation selection, quality control, minimization algorithm, dynamic balance constraint, and assimilation of new observation types. The evaluation of the new system relative to the SSI-based system was performed for nearly an entire year of analyses and forecasts. The objective and subjective evaluations showed that the new package exhibited superior forecast performance relative to the old SSI-based system. The new system has been shown to improve forecast skill in the tropics and substantially reduce the short-term forecast error in the extratropics. This implementation has laid the groundwork for future scientific advancements in data assimilation at NCEP.

The Canadian Global Data Assimilation System: Description and Evaluation

1993 ◽  
Vol 121 (5) ◽  
pp. 1467-1492 ◽  
Author(s):  
Herschel L. Mitchell ◽  
Cécilien Charette ◽  
Steven J. Lambert ◽  
Jacques Hallé ◽  
Clément Chouinard
Keyword(s):  
Data Assimilation ◽  
System Description ◽  
Global Data Assimilation System ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System
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