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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

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

  • 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


  • 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.
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.

Basic scheme

Main characteristics


observation covariance

background covariance

Background error covariance : Innovation statistics


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


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





Quality Control

Grapes 3D-Var System

First Guess

T213 Forecast


Assimilation cycle (4/day)






48h Forecast


Quality Control

data usage
Data usage

Pre-operational experiment :

  • Conventional observation from GTS


  • NOAA16/17 ATOVS radiance

amusa amsub hirs

  • Cloud drift wind from Geostationary Sat.

Research :

  • Doppler radar radial wind and reflectivity
  • Quickscat wind etc.
test and verifications
Test and verifications
  • Test with single observation
  • Comparison of regional analysis with global analysis
  • Verification of analysis statistics
analysis with single observation at equator
Analysis with single observation at Equator

With single 

With single U

With single V

multivariate analysis with a single observation at 89n 180
Multivariate Analysis with a single observation at (89N, 180)

Shaded : geoptential height

Stream line : wind

analysis verification during jun 2004
Analysis verification during Jun 2004



2004/06 statistics red : analysis ; blue: innovation

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

Pre-operational trials

24H Precip. Threshold score

48H Precip. Threshold score


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

data preprocessing
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:


  • Correction depending on air mass: b=y-H(xb)-s
  • Predictors from the background

ATOVS radiance assimilation

Bias correction

Red: no bias correction

Blue:bias correction


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.


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.


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.


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.

08 12 06h hirs

ATOVS radiance assimilation



impact of atovs a case study on typhoon rananim 2004
Impact of ATOVS : A case study on Typhoon Rananim (2004)

ATOVS radiance assimilation

Tracks of Typhoon Rananim 2004


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.

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

Thank you for attention