Simulation of heavy rainfall events over Indian monsoon region using WRF-3DVAR data assimilation system

2009 ◽  
Vol 106 (1-2) ◽  
pp. 107-125 ◽  
Author(s):  
A. Routray ◽  
U. C. Mohanty ◽  
Dev Niyogi ◽  
S. R. H. Rizvi ◽  
Krishna K. Osuri
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Indian Monsoon ◽  
Monsoon Region ◽  
Rainfall Events ◽  
Indian Monsoon Region ◽  
Heavy Rainfall Events ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals Impact of Radiance Data Assimilation on the Prediction of Heavy Rainfall in RMAPS: A Case Study

2018 ◽  
Vol 10 (9) ◽  
pp. 1380 ◽  
Author(s):  
Yanhui Xie ◽  
Jiancheng Shi ◽  
Shuiyong Fan ◽  
Min Chen ◽  
Youjun Dou ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Precipitation Forecast ◽  
Observation Data ◽  
Rainfall Events ◽  
Satellite Radiance Data ◽  
Radiance Data ◽  
The Impact ◽  
Heavy Rainfall Events

Herein, a case study on the impact of assimilating satellite radiance observation data into the rapid-refresh multi-scale analysis and prediction system (RMAPS) is presented. This case study targeted the 48 h period from 19–20 July 2016, which was characterized by the passage of a low pressure system that produced heavy rainfall over North China. Two experiments were performed and 24 h forecasts were produced every 3 h. The results indicated that the forecast prior to the satellite radiance data assimilation could not accurately predict heavy rainfall events over Beijing and the surrounding area. The assimilation of satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) improved the skills of the quantitative precipitation forecast to a certain extent. In comparison with the control experiment that only assimilated conventional observations, the experiment with the integrated satellite radiance data improved the rainfall forecast accuracy for 6 h accumulated precipitation after about 6 h, especially for rainfall amounts that were greater than 25 mm. The average rainfall score was improved by 14.2% for the 25 mm threshold and by 35.8% for 50 mm of rainfall. The results also indicated a positive impact of assimilating satellite radiances, which was primarily reflected by the improved performance of quantitative precipitation forecasting and higher spatial correlation in the forecast range of 6–12 h. Satellite radiance observations provided certain valuable information that was related to the temperature profile, which increased the scope of the prediction of heavy rainfall and led to an improvement in the rainfall scoring in the RMAPS. The inclusion of satellite radiance observations was found to have a small but beneficial impact on the prediction of heavy rainfall events as it relates to our case study conditions. These findings suggest that the assimilation of satellite radiance data in the RMAPS can provide an overall improvement in heavy rainfall forecasting.

Added-Value of 3DVAR Data Assimilation in the Simulation of Heavy Rainfall Events Over West and Central Africa

2015 ◽  
Vol 172 (10) ◽  
pp. 2751-2776 ◽  
Author(s):  
P. Moudi Igri ◽  
Roméo S. Tanessong ◽  
D. A. Vondou ◽  
F. Kamga Mkankam ◽  
Jagabandhu Panda
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Central Africa ◽  
Added Value ◽  
Rainfall Events ◽  
West And Central Africa ◽  
Heavy Rainfall Events

scholarly journals Evaluating the extreme precipitation events using a mesoscale atmosphere model and satellite based precipitation product

2013 ◽  
Vol 1 (6) ◽  
pp. 6979-7014
Author(s):  
I. Yucel ◽  
A. Onen
Keyword(s):  
Data Assimilation ◽  
Extreme Precipitation ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Hydrologic Model ◽  
Rainfall Events ◽  
Extreme Precipitation Events ◽  
Precipitation Estimates ◽  
Heavy Rainfall Events ◽  
Precipitation Events

Abstract. Quantitative precipitation estimates are obtained with more uncertainty under the influence of changing climate variability and complex topography from numerical weather prediction (NWP) models. On the other hand, hydrologic model simulations depend heavily on the availability of reliable precipitation estimates. Difficulties in estimating precipitation impose an important limitation on the possibility and reliability of hydrologic forecasting and early warning systems. This study examines the performance of the Weather Research and Forecasting (WRF) model and the Multi Precipitation Estimates (MPE) algorithm in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in the West Black Sea Region of Turkey. Precipitations derived from WRF model with and without three-dimensional variational (3-DVAR) data assimilation scheme and MPE algorithm at high spatial resolution (4 km) are compared with gauge precipitation. WRF-derived precipitation showed capabilities in capturing the timing of precipitation extremes and in some extent the spatial distribution and magnitude of the heavy rainfall events wheras MPE showed relatively weak skills in these aspects. WRF skills in estimating such precipitation characteristics are enhanced with the application of 3-DVAR scheme. Direct impact of data assimilation on WRF precipitation reached to 12% and at some points there exists quantitative match for heavy rainfall events, which are critical for hydrological forecast.

scholarly journals IMDAA: High Resolution Satellite-era Reanalysis for the Indian Monsoon Region

2021 ◽  
pp. 1-78
Author(s):  
S. Indira Rani ◽  
Arulalan T ◽  
John P. George ◽  
E. N. Rajagopal ◽  
Richard Renshaw ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Summer Monsoon ◽  
Land Surface ◽  
Indian Monsoon ◽  
India Meteorological Department ◽  
Summer Monsoon Rainfall ◽  
Monsoon Region ◽  
Fine Grid ◽  
Indian Monsoon Region

AbstractA high resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12 km resolution reanalysis covering the satellite-era from 1979 to 2018 using 4D-Var data assimilation method and the UK Met Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some were not used in any previous reanalyses. Various aspects of this reanalysis, like quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semi-permanent summer monsoon features (e.g., Low-level Jet and Tropical Easterly Jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, inter-annual, and intra-seasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.

scholarly journals Impact of the Assimilation of Multi-Platform Observations on Heavy Rainfall Forecasts in Kong-Chi Basin, Thailand

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1497
Author(s):  
Thippawan Thodsan ◽  
Falin Wu ◽  
Kritanai Torsri ◽  
Thakolpat Khampuenson ◽  
Gongliu Yang
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Forecasting ◽  
Forecasting Model ◽  
Lead Times ◽  
Rainfall Events ◽  
Skill Scores ◽  
Accumulated Rainfall ◽  
The Mean ◽  
Heavy Rainfall Events

Data assimilation with a Numerical Weather Prediction (NWP) model using an observation system in a regional area is becoming more prevalent for local weather forecasting activities to reduce the risk of disasters. In this study, we evaluated the predictive capabilities of multi-platform observation assimilation based on a WRFDA (Weather Research and Forecasting model data assimilation) system with 9 km grid spacing over the Kong-Chi basin (KCB), where tropical storms and heavy rainfall occur frequently. Data assimilation experiments were carried out with two assimilation schemes: (1) assimilating the combined multi-platform observations of PREPBUFR data from the National Centers for Environmental Prediction (NCEP) and Automatic Weather Stations (AWS) data from the National Hydroinformatics Data Center in Thailand, and (2) assimilating the AWS data only, which are referred to as DAALL and DAAWS, respectively. Assimilation experiments skill scores with lead times of 48 h and 72 h were evaluated by comparing their accumulated rainfall and mean temperatures every three hours in the AWS for heavy rainfall events that occurred on 28 July 2017 and 30 August 2019. The results show that the DAALL improved the statistical skill scores by improving the pattern and intensity of heavy rainfall events, and DAAWS also improved the model results of near-surface location forecasts. The accuracy of the two assimilations for 3 h of accumulated rainfall with a 5 mm threshold, was only above 70%, but the threat score was acceptable. Temperature observations and assimilation experiments fitted a significant correlation with a coefficient greater than 0.85, while the mean absolute errors, even at the 48 h lead times remained below 1.75 °C of the mean temperature. The variables of the AWS observations in real-time after combining them with the weather forecasting model were evaluated for unprecedented rain events in the KCB. The scores suggested that the assimilation of the multi-platform observations at the 48 h lead times has an impact on heavy rainfall prediction in terms of the threat score, compared to the assimilation of AWS data only. The reason for this could be that fewer observations of the AWS data affected the WRFDA model.

Impact of Combined Assimilation of Radar and Rainfall Data on Short-Term Heavy Rainfall Prediction: A Case Study

2020 ◽  
Vol 148 (5) ◽  
pp. 2211-2232 ◽  
Author(s):  
Juanzhen Sun ◽  
Ying Zhang ◽  
Junmei Ban ◽  
Jing-Shan Hong ◽  
Chung-Yi Lin
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Numerical Models ◽  
Radar Data ◽  
Rainfall Data ◽  
Data Sources ◽  
Short Term ◽  
Rainfall Prediction ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Radar and surface rainfall observations are two sources of operational data crucial for heavy rainfall prediction. Their individual values on improving convective forecasting through data assimilation have been examined in the past using convection-permitting numerical models. However, the benefit of their simultaneous assimilations has not yet been evaluated. The objective of this study is to demonstrate that, using a 4D-Var data assimilation system with a microphysical scheme, these two data sources can be assimilated simultaneously and the combined assimilation of radar data and estimated rainfall data from radar reflectivity and surface network can lead to improved short-term heavy rainfall prediction. In our study, a combined data assimilation experiment is compared with a rainfall-only and a radar-only (with or without reflectivity) experiments for a heavy rainfall event occurring in Taiwan during the passage of a mei-yu system. These experiments are conducted by applying the Weather Research and Forecasting (WRF) 4D-Var data assimilation system with a 20-min time window aiming to improve 6-h convective heavy rainfall prediction. Our results indicate that the rainfall data assimilation contributes significantly to the analyses of humidity and temperature whereas the radar data assimilation plays a crucial role in wind analysis, and further, combining the two data sources results in reasonable analyses of all three fields by eliminating large, unphysical analysis increments from the experiments of assimilating individual data only. The results also show that the combined assimilation improves forecasts of heavy rainfall location and intensity of 6-h accumulated rainfall for the case studied.

scholarly journals Evaluating a mesoscale atmosphere model and a satellite-based algorithm in estimating extreme rainfall events in northwestern Turkey

2014 ◽  
Vol 14 (3) ◽  
pp. 611-624 ◽  
Author(s):  
I. Yucel ◽  
A. Onen
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Wrf Model ◽  
Early Warning Systems ◽  
Hydrologic Model ◽  
Rainfall Events ◽  
Extreme Precipitation Events ◽  
Precipitation Estimates ◽  
Heavy Rainfall Events

Abstract. Quantitative precipitation estimates are obtained with more uncertainty under the influence of changing climate variability and complex topography from numerical weather prediction (NWP) models. On the other hand, hydrologic model simulations depend heavily on the availability of reliable precipitation estimates. Difficulties in estimating precipitation impose an important limitation on the possibility and reliability of hydrologic forecasting and early warning systems. This study examines the performance of the Weather Research and Forecasting (WRF) model and the Multi Precipitation Estimates (MPE) algorithm in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in the western Black Sea region of Turkey. Precipitation derived from WRF model with and without the three-dimensional variational (3DVAR) data assimilation scheme and MPE algorithm at high spatial resolution (5 km) are compared with gauge precipitation. WRF-derived precipitation showed capabilities in capturing the timing of precipitation extremes and to some extent the spatial distribution and magnitude of the heavy rainfall events, whereas MPE showed relatively weak skills in these aspects. WRF skills in estimating such precipitation characteristics are enhanced with the application of the 3DVAR scheme. Direct impact of data assimilation on WRF precipitation reached up to 12% and at some points there is a quantitative match for heavy rainfall events, which are critical for hydrological forecasts.

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