1. Institute of Heavy Rain, Wuhan, CMA Wang Yehong Cui Chunguang Zhao Yuchun Li Hongli Institute of Heavy Rain, Wuhan, CMA Assimilation of Radar Observations for Heavy Rain Numerical Prediction 1. Introduction 2. 1DVAR system and experiments for heavy rain 3.GRAPES_3DVAR system and experiments 4.4DVAR system and experiments 5.Summary and future work

2. Introduction 1998 China Flood

3. Institute of Heavy Rain, Wuhan, CMA Introduction 24h accumulate rainfall from 20BST,20July1998 to 20BST,21July 1998 forecastobservation

4. Institute of Heavy Rain, Wuhan, CMA Introduction Radiosonde station shade: 1h rainfall from 20BST to 21BST 20July 1998

5. Institute of Heavy Rain, Wuhan, CMA Only by conventional observation net and traditional initialization procedure, it is very difficult to get the accurate initial field needed in the meso-scale model, especially the meso-scale systems vital to torrential rain generation. One way improving the initial field of the meso-scale model is assimilating non-traditional data into meso-scale model with variational method. In meso- or micro-scale studies, the assimilation of Doppler radar data, which have relatively high spatiotemporal resolution and contain adequate meso-scale cloud-water, precipitation and wind information, is significant to the improvements of meso-scale initial field. Introduction

6. Institute of Heavy Rain, Wuhan, CMA 1DVAR system One-dimensional Variational Assimilation System of Radar Derived Rainfall Developed at WHIHR for the research of radar derived rainfall data assimilation Focused on initialization and forecasting of heavy rain using a regional numerical model The physics includes horizontal diffusion,large-scale condensation and evaporation and Betts convective adjustment parameterization scheme. Spatial distributions of the variables are on a E-grid with 37km horizontal resolution and 16 levels in the vertical. The vertical coordinate isη

7. Institute of Heavy Rain, Wuhan, CMA 1DVAR system Where X and X b denote the optimum values and background values of the model prognostic variables; R(X) and R o denote the observation operator logarithm of precipitation and the radar-derived rainfall respectively. B is the error covariance matrix of the background. is the standard deviation of the observation error. The 1DVAR seeks optimum values of numerical model prognostic variables by minimizing the following one-dimensional cost function : 1DVAR method Background term Observation term

8. Institute of Heavy Rain, Wuhan, CMA 1DVAR System Flow Chart Eta-model 1hr forecast 24hr forecast Data analysis radiosonde at 20LST One-dimensional variational assimilation system Initial humidity at 20LST Forecast rainfall at 21LST Radar-derived rainfall at 21LST Eta-model 1hr forecast 24hr forecast analysis rainfall observation rainfall background rainfall 1DVAR system

9. Institute of Heavy Rain, Wuhan, CMA Application to rain forecast One-Dimensional Variational Assimilation of Radar-Derived Precipitation Data for “98·7” Torrential Rain limited area meso-scale numerical model underη–coordinate with 37km resolution One dimensional variational assimilation system data: radiosonde data and reflectivity observations Retrieval of 1 hour precipitation using reflectivity observations Application: 0~24h rain forecast

10. Application to rain forecast The accumulated rainfall distribution of observation R o (a), background R b (b) and analysis R a (c) in Hubei from 20:00 to 21:00 on the 20th of July 1998. (a) (c) (b)

11. Fig. the relative humidity profiles distribution at 20LST 20 July 1998 With assimilation Without assimilation Application to rain forecast

12. The relative humidity distribution of the differences between with and without assimilation at 700hPa at 20:00 on the 20th of July 1998. Application to rain forecast

13. (b)(c) Without assimilationWith assimilation observation

14. Institute of Heavy Rain, CMA, Wuhan http://www.whihr.com.cn The 12 hours rainfall distribution of model forecast differences between with and without assimilation. The former 12 hours is from 20:00 of 20th to 08:00 of 21st(a), and the latter 12 hours is from 08:00 of 21st to 20:00 of 21st(b). (a)(b)

15. Institute of Heavy Rain, Wuhan, CMA Grapes-3dvar system GRAPeS_3DVAR system Global-Regional Assimilation and Prediction System Global-Regional Assimilation and Prediction System It is a new global and regional assimilation and prediction system being developed by CMA It is a new global and regional assimilation and prediction system being developed by CMA It is analyzed in the horizontal grid and vertical isobaric surface level, and the analysis variables include potential height, wind and humidity. It is analyzed in the horizontal grid and vertical isobaric surface level, and the analysis variables include potential height, wind and humidity. The horizontal background correlation is realized by spacial recursion filter （ finite regional version ） The horizontal background correlation is realized by spacial recursion filter （ finite regional version ） minimization The minimization of control variables is carried out by LBFGS

16. Institute of Heavy Rain, Wuhan, CMA On the Three Dimensional Variational Assimilation of Radar Wind Data related to 2003-7-8 Catastrophic Torrential Rain Application to rain forecast Advanced Regional  -coordinate Model version 2.1 GRAPES_3dvar system Retrieval wind data from Wuhan and Yichang Doppler radar

17. Institute of Heavy Rain, Wuhan, CMA retrieval wind from Wuhan and Yichang Doppler radar (vector vane) wind detected by radiosonde (barb) Application to rain forecast 武汉 宜昌 700hPa

18. Institute of Heavy Rain, Wuhan, CMA 3DVAR experiments for “ 7·8 ” heavy rain Observed fields radiosondeRetrieval wind from Wuhan Doppler radar Retrieval wind from Yichang Doppler radar Control experiment √ Experiment 1 √√√ Experiment 2 √√ Experiment 3 √√

19. initial fields improvement by assimilation of retrieval wind without assimilation of retrieval wind with assimilation of retrieval wind

20. without assimilation of retrieval wind with assimilation of retrieval wind

21. The differences of specific humidity at 850hPa Vertical-latitude cross section of the differences of specific humidity along 111 0 E

22. 大庸大庸 石门 大悟大悟 Without assimilation observation 大庸 379mm 石门 182mm 大悟 154mm With assimilation

23. Institute of Heavy Rain, Wuhan, CMA observation Test 1 Control test observation Test 1 Control test evolution of accumulate rainfallevolution of 1h rainfall

24. Institute of Heavy Rain, Wuhan, CMA The prescribed results show that in the simulation of an extremely heavy rain on July 8 in middle Yangtze river, the assimilation of retrieved wind from Wuhan and Yichang Doppler radar by 3DVAR greatly improve the initial field which is consistent with model and conducive for strong precipitation generation both in thermodynamics and dynamics with the results that the model reproduces 24h accumulated and hourly rainfall close to observation in the middle Yangtze River. It proves that effective usage of retrieved wind data from Doppler radar can make positive contributions to numerical simulation and forecasting.

25. 大庸大庸 石门 大悟大悟 observation

26. Institute of Heavy Rain, Wuhan, CMA 4DVAR system Grapes-3dvar system MM5V1 and Adjoint-model Assimilation System. The main physical processes include nonhydrostatic equilibrium scheme, Grell cumulus parameterization shceme, Blackadar high-resolution planetary boundary scheme, simple ice explicit moisture scheme, simple cooling atmosphere radiation scheme and time-dependent flowing-in/out lateral boundary condition. NCEP data, regular observations and simple-Doppler radar data. The initial time is 00h 23th June,2004. The total integration time is 24h. The center of model is located at (113ºE,29ºN). The number of horizontal grid is 61×61. The grid length is 15km.

27. Institute of Heavy Rain, Wuhan, CMA A study on 4-dimensional variational assimilation of single-Doppler radar wind data ---a heavy rainfall in middle reach of the Yangtze River Scheme I: Sigh the initial field made by the model when using NCEP data and regular observations as A field. Scheme II: First, the model analysis field of the initial time serves as the model first-guess field. Then the regular observations of 06h is input into assimilation model. And finally by adjusting the first- guess field through restrict conditions we have the optimal initial field as B field. Scheme III: We use radar data retrieved with the variational method to replace the value of A field so as to obtain C field that includes radar data, Scheme IV: It is similar with scheme II. The regular observations and retrieved radar data of 06h is input into assimilation model. And finally by adjusting the first-guess field through restrict conditions we have the optimal initial field as D field.

28. Institute of Heavy Rain, Wuhan, CMA radiosonde + retrieval wind from Wuhan Doppler radar radiosonde 4DVAR:radiosonde 4DVAR:radiosonde + retrieval wind from Wuhan Doppler radar observed rainfall field

29. Institute of Heavy Rain, Wuhan, CMASummary The established 1DVAR assimilation system can effectively assimilate radar-derived rainfall data and the heavy rain forecast can be improved by adjusting humidity profiles of the initial field. After retrieved wind from Doppler radar has been assimilated by Grapes_3dvar system, more meso-scale information is presented in the initial wind field, humidity field and potential height field in such a manner that rainfall prediction can be greatly improved. Effectively using radar wind field information in 4DVAR assimilation system can improve rain belt simulation.

30. Institute of Heavy Rain, Wuhan, CMA Future work Three-dimensional variational assimilation of radar retrieved wind field data in heavy rain forecasts should be studied on the basis of Doppler radar wind field data collected as much as possible. Real-time forecasting experiments using retrieved wind data from Doppler radar should be conducted step by step in the light of meso-scale operational forecasting model AREM and Grapes_3dvar system. Direct variational assimilation of radial velocity of Doppler radar should be studied. On the basis of LAPS developed by FSL, several kinds of local observation data such as radar data, satellite data, wind profiler data and etc, should be combined into meso- scale numerical models (AREM, GRAPES, MM5) in order to improve the initial field and better rainfall forecast.

31. 中国气象局武汉暴雨研究所 http://www.whihr.com.cn THE END THANKS ！