Details:

Personal Author:
Ma, Zaizhong ; Riishojgaard, Lars Peter ; Masutani, Michiko ; Woollen, John S. ; Emmitt, G. D.
Ma, Zaizhong ; Riishojgaard, Lars Peter ; Masutani, Michiko ; Woollen, John S. ; Emmitt, G. D. Less -
Corporate Authors:
National Centers for Environmental Prediction (U.S.)
NOAA Program & Office:
NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction)
NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction) Less -
Description:
The Global Wind Observing Sounder (GWOS) concept, which has been developed by NASA in response to the National Research Council (NRC) Decadal Survey, is expected to provide global wind profile observations with high vertical resolution, precision, and accuracy when realized. The assimilation of Doppler wind LIght Detection And Ranging (LIDAR) data anticipated from the GWOS is being conducted as a series of Observing System Simulation Experiments (OSSEs) at the Joint Center for Satellite and Data Assimilation (JCSDA). A companion paper (Riishojgaard et al., 2012) describes the realistic simulation of this lidar wind data and evaluates the impact on global Numerical Weather Prediction (NWP) of the baseline GWOS using a four-telescope configuration to provide independent line-of-sight wind speeds, while this paper sets out to assess the NWP impact of GWOS equipped with alternative paired configurations of telescopes.. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) and Global Forecast System (GFS) were used, at a resolution of T382 with 64 layers, as the assimilation system and forecast model, respectively, in these lidar OSSEs. A set of 45-day assimilation and forecast experiments from July 2 to August 15, 2005 have been set up and executed. The same strategies for observational errors discussed in Riishojgaard et al. (2012) were used in this paper. In this OSSE study, a control simulation utilizing all of the data types assimilated in the operational GSI/GFS system was compared to three OSSE simulations which added lidar wind data from the different configuration of telescopes (one-, two-, and four-looks), respectively. First, the Root-Mean Squared Error (RMSE) of vector wind from analyses is compared against the Nature Run. A significant reduction of the stratospheric RMSE of vector wind analyses is found for all latitudes when lidar wind profiles are used in the assimilation system. The forecast impacts of lidar data on the wind and temperature fields are also presented. In addition, the anomaly correlations (AC) of geopotential height forecasts at 500 hPa were evaluated to compare the control and different GWOS telescope configuration experiments. The results show that the assimilation of lidar data from the GWOS (one-, two-, or four-'look') can improve the NCEP GFS wind and mass field forecasts. The addition of the simulated Lidar wind observations leads to a statistically significant increase in AC scores at day five (120h) of approximately 0.3 (for one-look), 0.7 (for two-looks) and 1.4 (for four-looks) in the northern hemisphere, while of approximately 0.9 (for one-look), 1.8 (for two-looks) and 1.8 (for four-looks) in the southern hemisphere. Larger benefits are found in the Southern Hemisphere, although a significant positive impact is also found in the Northern Hemisphere.
Content Notes:
Zaizhong Ma, Lars Peter Riishojgaard, Michiko Masutani, John S. Woollen, G. David Emmitt.

"November 2013."

System requirements: Adobe Acrobat Reader.

Includes bibliographical references (pages 21-23).


Keywords:
[+]
Atmospheric Circulation Numerical Weather Forecasting Radar Meteorology Simulation Methods Weather Forecasting Wind Forecasting
Series:
Office note (National Centers for Environmental Prediction (U.S.)) ; 475
Document Type:
Office Note
Place as Subject:
United States
Rights Information:
Public Domain
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:5abb47b40a477f12282bd92ba02c83af754d2480c5ae569d7f06cad315fe1b0b
Download URL:
https://repository.library.noaa.gov/view/noaa/6962/noaa_6962_DS1.pdf
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