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Overview of GRAPES 3DVAR Development

Overview of GRAPES 3DVAR Development. By Data Assimilation Group Reporter : Shiyu Zhuang C enter For N umerical P rediction R esearch Chinese Academy of Meteorological Sciences Wu Han Jun 1st 2005. OUTLINE. Introduction motivation  status and progress Basic scheme and system

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Overview of GRAPES 3DVAR Development

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  1. Overview of GRAPES 3DVAR Development By Data Assimilation Group Reporter : Shiyu Zhuang Center For Numerical Prediction Research Chinese Academy of Meteorological Sciences Wu Han Jun 1st 2005

  2. OUTLINE • Introduction motivation  status and progress • Basic scheme and system scheme characteristics  background error covariance  system flowchart • Data usage • Test and verification single observation test  Comparison of regional analysis with global analysis  verification of analysis statistics • Some applications pre-operational trials  ATOVS radiance assimilation  assimilating Doppler radar radial wind and echo intensity • Further development

  3. Introduction Motivation • Sparseness of conventional observation is the biggest challenge in upgrading NWP in China. • Application of satellite observation is the most effective way to solve the problem of data sparseness. • Development of new variational data assimilation system is beneficial to accommodate various unconventional observation.

  4. Status and progress • The next generation NWP system GRAPES (Global/Regional Assimilation Prediction System) has been developed since 2001. • 3DVAR is a subsystem of GRAPES • GRAPES 3DVAR is a unified grid analysis system for both global and regional configuration, with efficient algorithm of optimization，flexibility for different observational operator, modularization and feasibility in data usage. • 3DVAR system is currently in pre-operational test stage. • 4DVAR and EnKF is underway.

  5. Basic scheme Main characteristics

  6. observation covariance background covariance Background error covariance : Innovation statistics Data May 14-July 14, 2003 Forecast : model forecast Observation : radiosound Zforecast Zoobservation ZT truth Forecast error : Z-ZT=Z’ Observation error : Zo-ZT=Z’’ Innovation error : Zd＝Zo-Z

  7. Forecast error : F48-F24 or F24-F12  Calculate stream function and velocity potential via wind  Calculate balanced geopotential height with stream function by linear balance equation  obtain the unbalanced part of height by removing the balanced height  Calculate forecast error of stream function, velocity potential, unbalanced height and relative humidity  Calculate the statistics of above forecast errors and rescaling. Background error covariance : NMC method

  8. Coupling ratio of balanced height

  9. SD distribution of BE for regional version

  10. SD distribution of BE for global version

  11. Vertical correlation structure of BE

  12. Raw ATOVS DATA Preprocessing Quality Control Grapes 3D-Var System First Guess T213 Forecast GRAPES 3D-VAR Assimilation cycle (4/day) Conventional DATA Analysis Preprocessing GRAPES MODEL 48h Forecast initial Quality Control

  13. Data usage Pre-operational experiment : • Conventional observation from GTS TEMP SYNOP SHIP AIREP SATOB SATEM • NOAA16/17 ATOVS radiance amusa amsub hirs • Cloud drift wind from Geostationary Sat. Research : • Doppler radar radial wind and reflectivity • Quickscat wind etc.

  14. conventional data coverage

  15. A case of NOAA ATOVS radiance coverage

  16. Test and verifications • Test with single observation • Comparison of regional analysis with global analysis • Verification of analysis statistics

  17. Analysis with single observation at Equator With single  With single U With single V

  18. Multivariate Analysis with a single observation at (89N, 180) Shaded : geoptential height Stream line : wind

  19. Cross section at 180E / W with single  located at 45N, 180 via global version V U

  20. Comparison ofregional analysis with global analysis Regional Global

  21. Analysis verification during Jun 2004 rms mean 2004/06 statistics red : analysis ; blue: innovation

  22. Some applications • Pre-operational trials • Atovs radiance direct assimilation • Experiment with Doppler radar radial wind and reflectivity

  23. Pre-operational trials 24H Precip. Threshold score 48H Precip. Threshold score

  24. ATOVS radiance assimilation Channel selection General consideration : Channels sensitive to the surface characteristics, deep clouds and upper air (above 10 hpa) temperatures are not selected. Noaa16/17 :AMSU-A CH 5-11 AMSU-B CH 18-20 Other Channels and HIRS are also tested

  25. Data Preprocessing ATOVS radiance assimilation Collocation of data from different instruments Cloud detection Gross quality control Bias correction Following Harris，Kelly(2001): • Correction depending on scan angles： s=<dj(θ)-dj(θ=0)> • Correction depending on air mass: b=y-H(xb)-s • Predictors from the background

  26. ATOVS radiance assimilation Bias correction Red: no bias correction Blue:bias correction

  27. ATOVS radiance assimilation Quality control Gross check:the brightness temperature data outside of the interval 150-350K are rejected; Background profile check:background profile outside limits and unphysical is rejected; Innovation check:the data whose departure between the simulated observation and actual value outside certain threshold are rejected.

  28. ATOVS radiance assimilation Land surface emissivity The NOAA/NESDIS microwave land emissivity model (developed by Dr. F Weng) was introduced into the Grapes-3Dvar. However, some surface parameters are needed, which are crucial for the accuracy of calculation of the microwave land emissivity model. These surface parameters are produced from a global data assimilation system (GDAS) including a boundary layer model in NOAA/NESDIS.

  29. ATOVS radiance assimilation An adjusted parameter scheme The scheme was designed to provide the surface parameters for the microwave land emissivity model. Step 1: the land surface emissivities at AMSUA channels 1-3 are first derived from satellite brightness temperature. Step 2: certain surface parameters, which are choosed according to different surface type, are adjusted to make the calculation of the microwave land emissivity model match the derived emissivity. Step 3: the surface emissivities of all channels could be calculated on the basis of these adjusted surface parameters.

  30. Result shows that adjusted parameter scheme could get improvement for several land type (Wet land, snow et al.). But it was affected by the accuracy of retrieval of channels 1-3 greatly; For snow and ice, the scheme that combining identification of snow or ice type from satellite radiance and microwave emissivity spectra observed could obtain best result.

  31. 云娜台风登陆前(08/12/06h前后)HIRS水汽通道卫星亮温图像云娜台风登陆前(08/12/06h前后)HIRS水汽通道卫星亮温图像 ATOVS radiance assimilation NOAA16 NOAA17

  32. Impact of ATOVS : A case study on Typhoon Rananim (2004) ATOVS radiance assimilation Tracks of Typhoon Rananim 2004

  33. One case of precipitation forecast on Meiyu Front With no sat. data With sat. data

  34. Doppler radar data assimilation Observation operator for radial wind and time tendency of reflectivity: The second equation is based on the conservation of reflectivity, an assumption valid only in some cases.

  35. Further Development • 4DVAR framework development • Improvement of statistics background error covariances • More sensing data to be used • New balance constraints • Fully operational implementation

  36. The END Thank you for attention

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