scholarly journals Typhoon rainstorm simulations with radar data assimilation on the southeast coast of China

2021 ◽  
Vol 21 (2) ◽  
pp. 723-742
Author(s):  
Jiyang Tian ◽  
Ronghua Liu ◽  
Liuqian Ding ◽  
Liang Guo ◽  
Bingyu Zhang
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Radar Data ◽  
Time Interval ◽  
Storm Events ◽  
Catchment Scale ◽  
Southeast Coast ◽  
Assimilation Time ◽  
Radar Data Assimilation

Abstract. As an effective technique to improve the rainfall forecast, data assimilation plays an important role in meteorology and hydrology. The aim of this study is to explore the reasonable use of Doppler radar data assimilation to correct the initial and lateral boundary conditions of the numerical weather prediction (NWP) systems. The Weather Research and Forecasting (WRF) model is applied to simulate three typhoon storm events on the southeast coast of China. Radar data from a Doppler radar station in Changle, China, are assimilated with three-dimensional variational data assimilation (3-DVar) model. Nine assimilation modes are designed by three kinds of radar data and at three assimilation time intervals. The rainfall simulations in a medium-scale catchment, Meixi, are evaluated by three indices, including relative error (RE), critical success index (CSI), and root mean square error (RMSE). Assimilating radial velocity at a time interval of 1 h can significantly improve the rainfall simulations, and it outperforms the other modes for all the three storm events. Shortening the assimilation time interval can improve the rainfall simulations in most cases, while assimilating radar reflectivity always leads to worse simulations as the time interval shortens. The rainfall simulations can be improved by data assimilation as a whole, especially for the heavy rainfall with strong convection. The findings provide references for improving the typhoon rainfall forecasts at catchment scale and have great significance on typhoon rainstorm warning.

scholarly journals Typhoon rainstorm simulation with radar data assimilation in southeast coast of China

2020 ◽  
Author(s):  
Jiyang Tian ◽  
Ronghua Liu ◽  
Liuqian Ding ◽  
Liang Guo ◽  
Bingyu Zhang
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Doppler Radar ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Time Interval ◽  
Storm Events ◽  
Southeast Coast ◽  
Assimilation Time ◽  
Radar Data Assimilation

Abstract. As an effective technique to improve the rainfall forecast, data assimilation plays an important role in meteorology and hydrology. The aim of this study is to explore the reasonable use of Doppler radar data assimilation to correct the initial and lateral boundary conditions of the Numerical Weather Prediction (NWP) systems. The Weather Research and Forecasting (WRF) model is applied to simulate three typhoon storm events in southeast coast of China. Radar data from Changle Doppler radar station are assimilated with three-dimensional variational data assimilation (3-DVar) model. Nine assimilation modes are designed by three kinds of radar data (radar reflectivity, radial velocity, radar reflectivity and radial velocity) and three assimilation time intervals (1 h, 3 h and 6 h). The rainfall simulations in a medium-scale catchment, Meixi, are evaluated by three indices including relative error (RE), critical success index (CSI) and root mean square error (RMSE). Assimilating radial velocity with time interval of 1 h can significantly improve the rainfall simulations and outperforms the other modes for all the three storm events. Shortening the assimilation time interval can improve the rainfall simulations in most cases, while assimilating radar reflectivity always leads to worse simulation as the time interval shortens. The rainfall simulation can be improved by data assimilation as a whole, especially for the heavy rainfall with strong convection. The findings provide references for improving the typhoon rainfall forecasts in catchment scale and have great significance on typhoon rainstorm warning.

A Velocity Dealiasing Technique Using Rapidly Updated Analysis from a Four-Dimensional Variational Doppler Radar Data Assimilation System

2010 ◽  
Vol 27 (7) ◽  
pp. 1140-1152 ◽  
Author(s):  
Eunha Lim ◽  
Juanzhen Sun
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Radar Data ◽  
Objective Analysis ◽  
Doppler Velocity ◽  
Horizontal Shear ◽  
Data Assimilation System ◽  
Radar Data Assimilation ◽  
Assimilation System

Abstract A Doppler velocity dealiasing algorithm is developed within the storm-scale four-dimensional radar data assimilation system known as the Variational Doppler Radar Analysis System (VDRAS). The innovative aspect of the algorithm is that it dealiases Doppler velocity at each grid point independently by using three-dimensional wind fields obtained either from an objective analysis using conventional observations and mesoscale model output or from a rapidly updated analysis of VDRAS that assimilates radar data. This algorithm consists of three steps: preserving horizontal shear, global dealiasing using reference wind from the objective analysis or the VDRAS analysis, and local dealiasing. It is automated and intended to be used operationally for radar data assimilation using numerical weather prediction models. The algorithm was tested with 384 volumes of radar data observed from the Next Generation Weather Radar (NEXRAD) for a severe thunderstorm that occurred during 15 June 2002. It showed that the algorithm was effective in dealiasing large areas of aliased velocities when the wind from the objective analysis was used as the reference and that more accurate dealiasing was achieved by using the continuously cycled VDRAS analysis.

scholarly journals A Scheme to Assimilate “No Rain” Observations from Doppler Radar

2018 ◽  
Vol 33 (1) ◽  
pp. 71-88 ◽  
Author(s):  
Shibo Gao ◽  
Juanzhen Sun ◽  
Jinzhong Min ◽  
Ying Zhang ◽  
Zhuming Ying
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Prediction Models ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Grid Point ◽  
Three Dimensional ◽  
Radar Data ◽  
Wind Fields ◽  
Radar Data Assimilation

Abstract Radar reflectivity observations contain valuable information on precipitation and have been assimilated into numerical weather prediction models for improved microphysics initialization. However, low-reflectivity (or so-called no rain) echoes have often been ignored or not effectively used in radar data assimilation schemes. In this paper, a scheme to assimilate no-rain radar observations is described within the framework of the Weather Research and Forecasting Model’s three-dimensional variational data assimilation (3DVar) system, and its impact on precipitation forecasts is demonstrated. The key feature of the scheme is a neighborhood-based approach to adjusting water vapor when a grid point is deemed as no rain. The performance of the scheme is first examined using a severe convective case in the Front Range of the Colorado Rocky Mountains and then verified by running the 3DVar system in the same region, with and without the no-rain assimilation scheme for 68 days and 3-hourly rapid update cycles. It is shown that the no-rain data assimilation method reduces the bias and false alarm ratio of precipitation over its counterpart without that assimilation. The no-rain assimilation also improved humidity, temperature, and wind fields, with the largest error reduction in the water vapor field, both near the surface and at upper levels. It is also shown that the advantage of the scheme is in its ability to conserve total water content in cycled radar data assimilation, which cannot be achieved by assimilating only precipitation echoes.

scholarly journals Doppler Radar Data Assimilation for Mesoscale Numerical Rainfall Prediction

10.29007/h6dv ◽  
2018 ◽  
Author(s):  
Jiyang Tian ◽  
Jia Liu ◽  
Chuanzhe Li ◽  
Fuliang Yu
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Wrf Model ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Rainfall Prediction ◽  
Doppler Radar Data ◽  
Radar Data Assimilation

Hydrological prediction needs high-resolution and accurate rainfall information, which can be provided by mesoscale Numerical Weather Prediction (NWP) models. However, the predicted rainfall is not always satisfactory for hydrological use. The assimilation of Doppler radar observations is found to be an effective method through correcting the initial and lateral boundary conditions of the NWP model. The aim of this study is to explore an efficient way of Doppler radar data assimilation from different height layers for mesoscale numerical rainfall prediction. The Weather Research and Forecasting (WRF) model is applied to the Zijingguan catchment located in semi-humid and semi-arid area of Northern China. Three-dimensional variational data assimilation (3-DVar) technique is adopted to assimilate the Doppler radar data. Radar reflectivity and radial velocity are assimilated separately and jointly. Each type of radar data are divided into seven data sets according to the observation heights: (1) <500m; (2) <1000m; (3) <2000m; (4) 500~1000m; (5) 1000~2000m; (6) >2000m; (7) all heights. Results show that the assimilation of radar reflectivity leads to better results than radial velocity. The accuracy of the predicted rainfall deteriorates as the rise of the observation height of the assimilated radar data. Conclusions of this study provide a reference for efficient utilisation of the Doppler radar data in numerical rainfall prediction for hydrological use.

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

Doppler radar data assimilation with a local SVD-En3DVar method

2012 ◽  
Vol 26 (6) ◽  
pp. 717-734 ◽  
Author(s):  
Daosheng Xu ◽  
Aimei Shao ◽  
Chongjian Qiu
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Radar Data ◽  
Doppler Radar Data ◽  
Radar Data Assimilation

scholarly journals Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

2018 ◽  
Author(s):  
Serguei Ivanov ◽  
Silas Michaelides ◽  
Igor Ruban
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Radar Data ◽  
Numerical Weather Prediction Model ◽  
Precipitation Prediction ◽  
Cell Patterns ◽  
Reflectivity Data ◽  
Rain Rates ◽  
Pattern Presentation ◽  
Radar Data Assimilation

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5–3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

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