Improvement of Monsoon Depressions Forecast with Assimilation of Indian DWR Data Using WRF-3DVAR Analysis System

被引:27
|
作者
Routray, Ashish [1 ]
Mohanty, U. C. [2 ]
Osuri, Krishna K. [2 ]
Prasad, S. Kiran [2 ]
机构
[1] Natl Ctr Medium Range Weather Forecasting, Noida, India
[2] Indian Inst Technol, Ctr Atmospher Sci, Delhi, India
关键词
Doppler weather radar; monsoon depression; variational data assimilation; Indian monsoon; DOPPLER RADAR OBSERVATIONS; MICROPHYSICAL RETRIEVAL; HEAVY RAINFALL; CLOUD MODEL; IMPACT; PREDICTION; ADJOINT; REGION; WIND;
D O I
10.1007/s00024-013-0648-z
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
An attempt is made to evaluate the impact of Doppler Weather Radar (DWR) radial velocity and reflectivity in Weather Research and Forecasting (WRF)-3D variational data assimilation (3DVAR) system for prediction of Bay of Bengal (BoB) monsoon depressions (MDs). Few numerical experiments are carried out to examine the individual impact of the DWR radial velocity and the reflectivity as well as collectively along with Global Telecommunication System (GTS) observations over the Indian monsoon region. The averaged 12 and 24 h forecast errors for wind, temperature and moisture at different pressure levels are analyzed. This evidently explains that the assimilation of radial velocity and reflectivity collectively enhanced the performance of the WRF-3DVAR system over the Indian region. After identifying the optimal combination of DWR data, this study has also investigated the impact of assimilation of Indian DWR radial velocity and reflectivity data on simulation of the four different summer MDs that occurred over BoB. For this study, three numerical experiments (control no assimilation, with GTS and GTS along with DWR) are carried out to evaluate the impact of DWR data on simulation of MDs. The results of the study indicate that the assimilation of DWR data has a positive impact on the prediction of the location, propagation and development of rain bands associated with the MDs. The simulated meteorological parameters and tracks of the MDs are reasonably improved after assimilation of DWR observations as compared to the other experiments. The root mean square errors (RMSE) of wind fields at different pressure levels, equitable skill score and frequency bias are significantly improved in the assimilation experiments mainly in DWR assimilation experiment for all MD cases. The mean Vector Displacement Errors (VDEs) are significantly decreased due to the assimilation of DWR observations as compared to the CNTL and 3DV_GTS experiments. The study clearly suggests that the performance of the model simulation for the intense convective system which influences the large scale monsoonal flow is significantly improved after assimilation of the Indian DWR data from even one coastal locale within the MDs track.
引用
收藏
页码:2329 / 2350
页数:22
相关论文
共 42 条
  • [21] Assimilation of Doppler Weather Radar Radial Velocity and Reflectivity Observations in WRF-3DVAR System for Short-Range Forecasting of Convective Storms
    S. Abhilash
    A. K. Sahai
    K. Mohankumar
    John P. George
    Someshwar Das
    Pure and Applied Geophysics, 2012, 169 : 2047 - 2070
  • [22] A WRF/WRF-Hydro Coupled Forecasting System with Real-Time Precipitation-Runoff Updating Based on 3Dvar Data Assimilation and Deep Learning
    Liu, Yuchen
    Liu, Jia
    Li, Chuanzhe
    Liu, Lusan
    Wang, Yu
    WATER, 2023, 15 (09)
  • [23] Simulation of heavy rainfall event along east coast of India using WRF modeling system: impact of 3DVAR data assimilation
    Shilpi Kalra
    Sushil Kumar
    A. Routray
    Modeling Earth Systems and Environment, 2019, 5 : 245 - 256
  • [24] Assimilation of radar radial velocity data with the WRF Hybrid ETKF-3DVAR system for the prediction of Hurricane Ike (2008)
    Shen, Feifei
    Min, Jinzhong
    Xu, Dongmei
    ATMOSPHERIC RESEARCH, 2016, 169 : 127 - 138
  • [25] An Examination of WRF 3DVAR Radar Data Assimilation on Its Capability in Retrieving Unobserved Variables and Forecasting Precipitation through Observing System Simulation Experiments
    Sugimoto, Soichiro
    Crook, N. Andrew
    Sun, Juanzhen
    Xiao, Qingnong
    Barker, Dale M.
    MONTHLY WEATHER REVIEW, 2009, 137 (11) : 4011 - 4029
  • [26] Comparisons of next-day solar forecasting for Singapore using 3DVAR and 4DVAR data assimilation approaches with the WRF model
    Huva, Robert
    Verbois, Hadrien
    Walsh, Wilfred
    RENEWABLE ENERGY, 2020, 147 : 663 - 671
  • [27] Assimilation of FY-3D MWHS-2 Radiances with WRF Hybrid-3DVAR System for the Forecast of Heavy Rainfall Evolution Associated with Typhoon Ampil
    Sun, Wei
    Xu, Youping
    MONTHLY WEATHER REVIEW, 2021, 149 (05) : 1419 - 1437
  • [28] Analysis and Evaluation of Observing System Simulation Experiments (OSSEs) forecast data for Indian Summer Monsoon (ISM)
    Deshpande, Medha
    Mukhopadhyay, P.
    Masutani, Michiko
    Ma, Zaizhong
    Riishojgaard, Lars Peter
    Hardesty, Michael
    Emmitt, Dave
    Krishnamurti, T. N.
    Goswami, B. N.
    REMOTE SENSING AND MODELING OF THE ATMOSPHERE, OCEANS, AND INTERACTIONS VI, 2016, 9882
  • [29] Assimilation of Radar Radial Velocity Data with the WRF Hybrid Ensemble-3DVAR System for the Prediction of Hurricane Ike (2008)
    Li, Yongzuo
    Wang, Xuguang
    Xue, Ming
    MONTHLY WEATHER REVIEW, 2012, 140 (11) : 3507 - 3524
  • [30] Assimilation of MWHS radiance data from the FY-3B satellite with the WRF Hybrid-3DVAR system for the forecasting of binary typhoons
    Xu, Dongmei
    Min, Jinzhong
    Shen, Feifei
    Ban, Junmei
    Chen, Peng
    JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 2016, 8 (02): : 1014 - 1028