An Efficient Ensemble Data Assimilation Approach and Tests with Doppler Radar Data Jidong Gao Ming Xue Center for Analysis and Prediction of Storms, University of Oklahoma, Norman
Jan 20, 2016
An Efficient Ensemble Data Assimilation Approach and Tests
with Doppler Radar Data
Jidong Gao Ming Xue
Center for Analysis and Prediction of Storms,
University of Oklahoma, Norman
Research Goals
• To develop an efficient ensemble Kalman filter (EnKF) method for high-resolution NWP, by using a dual resolution approach.
• To evaluate the efficiency and accuracy of the method through OSSEs, with simulated radar radial velocity data for a supercell storm.
Introduction• EnKF was first introduced by Evensen (1994)
and has become very popular in recent years
• Recently, the EnKF method has been successfully applied to the radar data assimilation problem (e.g., Snyder and Zhang 2003; Zhang et al. 2004; Dowell et al. 2004; Tong and Xue 2005).
• Effective assimilation of radar data is essential for initializing convective-scale NWP models
Radar Data Assimilation• The EnKF data assimilation method is especially
suitable for radar data assimilation because
– Radar only observes Vr and Z, and data coverage is usually incomplete
– All other variables have to be ‘retrieved’– EnKF ‘retrieves’ the unobserved variables via background
error covariance obtained through a forecast ensemble
• But, EnKF is expensive, because of the need for running a usually rather large ensemble of forecasts and analyses
• In this work, we propose a dual-resolution (DR) hybrid ensemble DA strategy, with the goal of improving the EnKF efficiency
• With the method, an ensemble of forecasts and analyses is run at a lower resolution (LR), while a single system of analysis and forecast is performed at a higher resolution (HR)
• The LR forecast ensemble provides estimated background error covariance for the HR analysis
• The HR forecast is used to replace or partially adjust the mean of the LR analysis ensemble
The Methodology
LR
EnK
F A
nalysis
LR E
nKF
Analysis
LR E
nKF
Analysis
HR EnKF
Single higher-resolution analysis and forecast
Lower-resolution analysis and forecast ensemble
covarian
ce rep
lace m
ean
covarian
ce co
varianc
e
replace
mean
replace
mean
HR EnKFHR EnKF
OSSEs with a Simulated Supercell Storm
• A truth simulation is created using ARPS with the Del City supercell sounding, at x = 2 km
• The model domain: 92 x 92 x 16 km3.
• LR has x=4 km, HR has x=2 km
• z = 500 m.
•Vr data collected at grid point locations are assimilated, at 5 min intervals
•20 ensemble members are used
List of EnKF OSSEs
Experiment Description
EXP1 Single-reslution EnKF at HR (2 km)
EXP2 Single-resolution EnKF at LR (4 km)
EXP3 Dual-resolution hybrid EnKF (2 & 4 km)
RMS Errors of the Analyses for the Three Experiments
HR EnKF (EXP1)
LR EnKF (EXP2)
DR EnKF (EXP3)
’(contours), Z(color shades) and Vh (vectors) at Surface
Truth
EXP2
LR-EnKF
EXP1
HR-EnKF
EXP3
DR-EnKF
’, Z and Vh at Surface after 80 min assimilation
Truth EXP1
HR-EnKF
EXP2
LR-EnKF
EXP3
DR-EnKF
W at 6 km AGL after 80 min assimilation
Truth
EXP2
LR-EnKF
EXP1
HR-EnKF
EXP3
DR-EnKF
2-h Forecasts of ’, Z and Vh at surface
Truth
EXP2
LR
EXP1
HR
EXP3
DR
2-h Forecasts of w at 6 km AGL
Truth
EXP2
LR
EXP1
HR
EXP3
DR
Summary and Discussion• A new efficient dual-resolution (DR) approach for
EnKF is proposed and tested with simulated radar data for a supercell storm.
• It is shown that the EnKF analysis using DR is almost as good as the HR analysis, but is much better than the LR analysis.
• For this case, we save CPU 3-4 times. However, depending on the resolution one choose, the method have the potential to save CPU 10-50 times more than Original EnKF methods.
Summary and Discussion
• My new experiments: using Dx =Dy= 4km with model EnKF run, to provide error structure for Dx =Dy= 1km, single model run. The result is also very positive.