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. Introduction2. 1DVAR system and experiments for heavy rain3. GRAPES_3DVAR system and experiments

4. 4DVAR system and experiments5. Summary and future work

Introduction 1998 China Flood

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Introduction

24h accumulate rainfall from 20BST,20July1998 24h accumulate rainfall from 20BST,20July1998 to 20BST,21July 1998to 20BST,21July 1998

forecastobservation

Institute of Heavy Rain, Wuhan, CMAIntroduction

Radiosonde station

shade: 1h rainfall from 20BST to 21BST 20July 1998

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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

Institute of Heavy Rain, Wuhan, CMA1DVAR 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

Institute of Heavy Rain, Wuhan, CMA1DVAR system

2

0

01

21

21

RXRXXBXXXJ bTb

Where X and Xb denote the optimum values and background values of the model prognostic variables; R(X) and Ro 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 method1DVAR method

o

Background termObservation term

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1DVAR System Flow ChartEta-model1hr forecast

24hr forecast

Data analysisradiosonde at 2

0LST

One-dimensional variational assimilation system

Initial humidity at 20LST

Forecast rainfall at 21LST

Radar-derived rainfall at 21LST

Eta-model1hr forecast 24hr forecast

analysis rainfall

observation rainfall

background rainfall

1DVAR system

Institute of Heavy Rain, Wuhan, CMAApplication to rain forecast

One-Dimensional Variational Assimilation of Radar-Derived Precipitation Data for “98·7” Tor

rential 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

Application to rain forecast

The accumulated rainfall distribution of observation Ro (a), background Rb (b) and analysis Ra (c) in Hubei from 20:00 to 21:00 on the 20th of July 1998.

(a)

(c)

(b)

Fig. the relative humidity profiles distribution at 20LST

20 July 1998

With assimilation

Without assimilation

Application to rain forecast

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

Application to rain forecast

(b)

(c)

Without assimilation With assimilation

observation

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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)

Institute of Heavy Rain, Wuhan, CMAGrapes-3dvar system

GRAPeS_3DVAR systemGRAPeS_3DVAR system GGlobal-lobal-RRegional egional AAssimilation and ssimilation and PPrreediction diction SSystemystem It is a new global and regional assimilation and predictioIt is a new global and regional assimilation and predictio

n system being developed by CMAn system being developed by CMA It is analyzed in the horizontal grid and vertical isobaric sIt is analyzed in the horizontal grid and vertical isobaric s

urface level, and the analysis variables include potential urface level, and the analysis variables include potential height, wind and humidity.height, wind and humidity.

The horizontal background correlation is realized by spacThe horizontal background correlation is realized by spacial recursion filterial recursion filter （（ finite regional versionfinite regional version ））

The minimizationminimization of control variables is carried out by LBFGS

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On the Three Dimensional Variational AssimilatiOn the Three Dimensional Variational Assimilation of Radar Wind Data related to 2003-7-8 Cataon of Radar Wind Data related to 2003-7-8 Cata

strophic Torrential Rainstrophic 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

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retrieval wind from Wuhan and Yichang Doppler radar (vector vane)wind detected by radiosonde (barb)

Application to rain forecast

武汉宜昌

700hPa

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3DVAR experiments for “73DVAR experiments for “7··8” heavy rain8” heavy rain

Observed fields

radiosonde Retrieval wind from Wuhan Doppler

radar

Retrieval wind from Yichang Dop

pler radarControl

experiment √

Experiment 1 √ √ √

Experiment 2 √ √

Experiment 3 √ √

initial fields improvement by initial fields improvement by assimilation of retrieval windassimilation of retrieval wind

without assimilation of retrieval wind

with assimilation of retrieval wind

without assimilation of retrieval wind

with assimilation of retrieval wind

The differences of specific humidity at

850hPa

Vertical-latitude cross section of the

differences of specific humidity along 1110E

大庸石门

大悟

Without assimilation

observation 大庸 379mm 石门 182mm大悟 154mm

With assimilation

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observationTest 1Control test

observationTest 1Control test

evolution of accumulate rainfallevolution of 1h rainfall

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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.

大庸石门

大悟

observation

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4DVAR system4DVAR systemGrapes-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.

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A study on 4-dimensional variational assimilation A study on 4-dimensional variational assimilation of single-Doppler radar wind data of single-Doppler radar wind data ---a heavy rainfall in middle reach of ---a heavy rainfall in middle reach of the Yangtze the Yangtze RiverRiver

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.

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radiosonde + retrieval wind from Wuhan Doppler radar

radiosonde4DVAR:radiosonde

4DVAR:radiosonde + retrieval wind from Wuhan Doppler

radar

observed rainfall field

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SummarySummary 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.

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Future workFuture 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.

中国气象局武汉暴雨研究所 http://www.whihr.com.cn

THE ENDTHE END

THANKS ！