an efficient ensemble data assimilation approach and tests with doppler radar data

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.