Assimilation of Doppler Weather Radar Data in WRF Model for Simulation of Tropical Cyclone Aila

2013 ◽  
Vol 171 (8) ◽  
pp. 2043-2072 ◽  
Author(s):  
Kuldeep Srivastava ◽  
Rashmi Bhardwaj
Keyword(s):  
Tropical Cyclone ◽  
Wrf Model ◽  
Weather Radar ◽  
Radar Data ◽  
Doppler Weather Radar ◽  
Cyclone Aila

scholarly journals THE USE OF WRF MODEL TO SUPPORT CLOUD SEEDING OPERATION: A STUDY IN THE CITARUM CATCHMENT AREA

2012 ◽  
Vol 13 (1) ◽  
pp. 1 ◽  
Author(s):  
Mahally Kudsy ◽  
Ridwan Ridwan ◽  
Findy Renggono ◽  
Faisal Sunarto
Keyword(s):  
Catchment Area ◽  
Wrf Model ◽  
Weather Radar ◽  
Radar Data ◽  
Doppler Weather Radar ◽  
Cloud Seeding ◽  
West Java ◽  
Data Set

This paper presents about the use of WRF modelling to assist weather analysis for cloud seeding operation in the Citarum Catchment Area, West Java, Indonesia. In this study, WRF parameterization was carried out . The parameterized values were used to forecast precipitation during cloud seeding operation. To study the effect of variational run, WRF 3DVAR was run using GDAS data set and doppler weather radar data. The result of this study shows that precipitation can be better predicted by ingesting radar data into 3DVAR run.Makalah ini menyajikan tentang penggunaan pemodelan dengan WRF untuk membantu analisis cuaca yang dipakai dalam operasi penyemaian awan di DAS Citarum, Jawa Barat, Indonesia. Dalam kajian ini telah dilakukan parameterisasi WRF, kemudian nilaiparameter yang diperoleh dipakai untuk mendapatkan prakiraan presipitasi selama operasi penyemaian awan. Untuk mempelajari pengaruh dari run variasional, WRF 3DVAR dijalankan dengan menggunakan data GDAS dan data radar doppler. Hasil dari studi ini menunjukkan bahwa prakiraan presipitasi yang lebih baik dapat diperoleh dengan mengasimilasikan data radar ke dalam run 3DVAR.

scholarly journals WRF-model sensitivity test and assimilation studies of Cempaka tropical cyclone

2021 ◽  
Vol 893 (1) ◽  
pp. 012029
Author(s):  
Fazrul Rafsanjani Sadarang ◽  
Fitria Puspita Sari
Keyword(s):  
Tropical Cyclone ◽  
Doppler Radar ◽  
Time Integration ◽  
Wrf Model ◽  
Radar Data ◽  
Control Model ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Parameterization Schemes ◽  
Assimilation Scheme

Abstract The WRF model was used to forecast the most intensive stage of Cempaka Tropical Cyclone (TC) on 27 - 29 November 2017. This study evaluates the combination of cumulus and microphysics parameterization and the efficiency of assimilation method to predict pressure values at the center of the cyclone, maximum wind speed, and cyclone track. This study tested 18 combinations of cumulus and microphysics parameterization schemes to obtain the best combination of both parameterization schemes which later on called as control model (CTL). Afterward, assimilation schemes using 3DVAR cycles of 1, 3, 6 hours, and 4DVAR, namely RUC01, RUC03, RUC06, and 4DV, were evaluated for two domains with grid size of each 30 and 10 km. GFS data of 0.25-degree and the Yogyakarta Doppler Radar data were used as the initial data and assimilation data input, respectively. The result of the parameterization test shows that there is no combination of parameterization schemes that constantly outperform all variables. However, the combination of Kain-Fritsch and Thompson can produce the best prediction of tropical cyclone track compared to other combinations. While, the RUC03 assimilation scheme was noted as the most efficient method based on the accuracy of track prediction and duration of model time integration.

Impact of Doppler radar reflectivity and velocity data assimilation on the quality of precipitation forecasting in Belarus in different seasons

2020 ◽  
Author(s):  
Palina Zaiko ◽  
Siarhei Barodka ◽  
Aliaksandr Krasouski
Keyword(s):  
Data Assimilation ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Doppler Weather Radar ◽  
Velocity Data ◽  
Numerical Weather ◽  
Convective Processes ◽  
Different Seasons

<p>Heavy precipitation forecast remains one of the biggest problems in numerical weather prediction. Modern remote sensing systems allow tracking of rapidly developing convective processes and provide additional data for numerical weather models practically in real time. Assimilation of Doppler weather radar data also allows to specify the position and intensity of convective processes in atmospheric numerical models.</p><p>The primary objective of this study is to evaluate the impact of Doppler  radar reflectivity and velocity assimilation in the WRF-ARW mesoscale model for the territory of Belarus in different seasons of the year. Specifically, we focus on the short-range numerical forecasting of mesoscale convective systems passage over the territory of Belarus in 2017-2019 with assimilated radar data.</p><p>Proceeding with weather radar observations available for our cases, we first perform the necessary processing of the raw radar data to eliminate noise, reflections and other kinds of clutter. For identification of non-meteorological noise fuzzy echo classification was used. Then we use the WRF-DA (3D-Var) system to assimilate the processed radar observations from 3 Belarusian Doppler weather radar in the WRF model. Assimilating both radar reflectivity and radial velocity data in the model we aim to better represent not only the distribution of clouds and their moisture content, but also the detailed dynamical aspects of convective circulation. Finally, we analyze WRF modelling output obtained with assimilated radar data and compare it with available meteorological observations and with other model runs (including control runs with no data assimilation or with assimilation of conventional weather stations data only), paying special attention to the accuracy of precipitation forecast 12 hours in advance.</p>

A Comparison of Scan Speedup Strategies and Their Effect on Rapid-Scan Weather Radar Data Quality

2020 ◽  
Vol 37 (11) ◽  
pp. 1955-1972
Author(s):  
Andrew Mahre ◽  
Tian-You Yu ◽  
David J. Bodine
Keyword(s):  
High Resolution ◽  
Data Quality ◽  
Wrf Model ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Imaging ◽  
Strong Argument ◽  
Scan Time ◽  
Replacement System ◽  
Scanning Strategies

AbstractAs the existing NEXRAD network nears the end of its life cycle, intense study and planning are underway to design a viable replacement system. Ideally, such a system would offer improved temporal resolution compared to NEXRAD, without a loss in data quality. In this study, scan speedup techniques—such as beam multiplexing (BMX) and radar imaging—are tested to assess their viability in obtaining high-quality rapid updates for a simulated long-range weather radar. The results of this study—which uses a Weather Research and Forecasting (WRF) Model–simulated supercell case—show that BMX generally improves data quality for a given scan time or can provide a speedup factor of 1.69–2.85 compared to NEXRAD while maintaining the same level of data quality. Additionally, radar imaging is shown to improve data quality and/or decrease scan time when selectively used; however, deleterious effects are observed when imaging is used in regions with sharp reflectivity gradients parallel to the beam spoiling direction. Consideration must be given to the subsequent loss of sensitivity and beam broadening. Finally, imaging is shown to have an effect on the radar-derived mesocyclone strength (ΔV) of a simulated supercell. Because BMX and radar imaging are most easily achieved with an all-digital phased array radar (PAR), these results make a strong argument for the use of all-digital PAR for high-resolution weather observations. It is believed that the results from this study can inform decisions about possible scanning strategies and design of a NEXRAD replacement system for high-resolution weather radar data.

scholarly journals Rainfall-runoff modeling using Doppler weather radar data for Adyar watershed, India

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 49-56
Author(s):  
S.JOSEPHINE VANAJA ◽  
B.V. MUDGAL ◽  
S.B. THAMPI
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Doppler Weather Radar ◽  
Rainfall Runoff ◽  
Cyclonic Storm ◽  
Rain Gauges ◽  
Runoff Modeling

Precipitation is a significant input for hydrologic models; so, it needs to be quantified precisely. The measurement with rain gauges gives the rainfall at a particular location, whereas the radar obtains instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted into rainfall via algorithms. It has been proved that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are too inaccurate. The research focuses on a technique to improve rainfall-runoff modeling based on radar derived rainfall data for Adyar watershed, Chennai, India. A hydrologic model called ‘Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)’ is used for simulating rainfall-runoff processes. CARTOSAT 30 m DEM is used for watershed delineation using HEC-GeoHMS. The Adyar watershed is within 100 km radius circle from the Doppler Weather Radar station, hence it has been chosen as the study area. The cyclonic storm Jal event from 4-8 November, 2010 period is selected for the study. The data for this period are collected from the Statistical Department, and the Cyclone Detection Radar Centre, Chennai, India. The results show that the runoff is over predicted using calibrated Doppler radar data in comparison with the point rainfall from rain gauge stations.

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