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The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and...

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The Sensitivity of a Real- Time Four-Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming Hsu and Yubao Liu NCAR/RAP
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Page 1: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.

The Sensitivity of a Real-Time Four-Dimensional Data Assimilation Procedure to Weather Research and Forecast Model

Simulations: A Case Study

Hsiao-ming Hsu and Yubao Liu

NCAR/RAP

Page 2: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.

MotivationsWRF real-case initialization schemes:

SI – interpolation from other models3DVAR – hopeful, but 3D, simplified balance 4DVAR – bright future

Hereby, we look into a method to initialize WRF with a four-dimensional dynamically and physically consistent analysis, which incorporates all available synoptic and asynoptic observations.

NCAR/ATEC MM5-based RT-FDDA system provides this kind of analysis to initialize WRF forecast.

Page 3: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.

NCAR/ATEC RTFDDA Built around MM5 (Jennifer et al. 2001, Liu et al. 2002)

Continuous observation nudging (Stauffer and Seaman 1994)

Multi-grids (1 km fine meshes) 3 hourly-cycling Operated at 5 ATEC ranges and support several special tasks (CO-fire,

Olympics…)

Coldstart t

Forecasts

FDDA

Day NDay 0

Page 4: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.
Page 5: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.

During Year-2002 Winter Olympics at SLC, RTFDDA was operational for 2 months. There was a snow storm event during March 13. A pair of contrast experiments of 12-hr WRF forecasts with different initial conditions were conducted, started at 00Z, March 13.

CASE

Page 6: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.

• EXP1- “Cold start” WRF WRF initial condition was generated by re-

analysis of ETA forecast with available observations at 00Z, March 13.

• EXP2 - “Warm start” WRF WRF initial condition was obtained from the

RTFDDA analysis which had been running continuously from a “cold start” 84 hours ago.

• EXP3 - “Warm start” MM5 Same as EXP2, but with MM5

(from op-RTFDDA).

Experiment Design

Page 7: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.

Domain configuration

82 x 70

dx = 36 km

36 layers

12 levels in 1 km AGL

Coarse mesh only

Page 8: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.

Hourly Precipitation of 1 – 12 Forecasts

Cold startWarm start

Page 9: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.

Subjective verification of 1 hour precipitation at 3-h forecast

A

A

A

B B

BB

C C

C

C

OBSIR

OBSRadar

Warmstart

Coldstart

Page 10: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.
Page 11: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.

WRF Forecast at 03ZRTFDDA forecast at 03Z

Page 12: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.

Summary

• Significant differences were observed between the “cold start” and the “warm start” WRF forecasts.

• The “warm-start” WRF run compares more favorable to observations.

• The “Warm start” WRF results are very similar to those from RTFDDA (MM5) during the first few fours of forecasts.

• It is evident that reasonable benefit of reduced dynamical and cloud/precipitation “spin-up” during first few hours can be obtained by interfacing MM5 RTFDDA process to WRF initialization.

Page 13: The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.

Future Work

• Comparison study on higher resolutions and severe weather cases.

• Ingesting RTFDDA cloud/precipitation analyses into “warm start” WRF.

• Implement “warm start” WRF in the same operational environment of RTFDDA MM5

• Quantitative verification of “warm-start” WRF against various observations for a longer-term parallel tests with MM5.


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