scholarly journals Impact of Convective Parameterization Schemes on the Quality of Rainfall Forecast over Tanzania Using WRF-Model

2014 ◽  
Vol 06 (10) ◽  
pp. 691-699 ◽  
Author(s):  
Alfred L. Kondowe
Keyword(s):  
Wrf Model ◽  
Convective Parameterization ◽  
Parameterization Schemes ◽  
Rainfall Forecast

scholarly journals Simulation of monsoon depression over India using high resolution WRF Model – Sensitivity to convective parameterization schemes

MAUSAM ◽  
2021 ◽  
Vol 62 (3) ◽  
pp. 305-320
Author(s):  
D.R. PATTANAIK ◽  
ANUPAM KUMAR ◽  
Y.V.RAMA RAO ◽  
B. MUKHOPADHYAY
Keyword(s):  
High Resolution ◽  
Heavy Rainfall ◽  
Forecast Error ◽  
Wrf Model ◽  
Monsoon Depression ◽  
Cumulus Parameterization ◽  
Initial Condition ◽  
Convective Parameterization ◽  
Parameterization Schemes ◽  
Cumulus Parameterization Schemes

The monsoon depression of September 2008, which crossed Orissa coast near Chandbali on 16th had contributed heavy rainfall over Orissa, Chhattisgarh and northern India along the track of the system. The sensitivity of three cumulus parameterization schemes viz., Kain-Fritch (KF) scheme, Grell-Devenyi (GD) scheme and Betts-Miller-Janjic (BMJ) Scheme are tested using high resolution advanced version (3.0) Weather Research Forecasting (WRF) model in forecasting the monsoon depression. The results of the present study shows that the genesis of the system was almost well captured in the model as indicated in 48hr forecast with all three convective parameterization schemes. It is seen that the track of monsoon depression is quite sensitive to the cumulus parameterization schemes used in the model and is found that the track forecast using three different cumulus schemes are improved when the model was started from the initial condition of a depression stage compared to that when it started from the initial condition of low pressure area. It is also seen that when the system was over land all the schemes performed reasonably well with KF and GD schemes closely followed the observed track compared to that of BMJ track. The performance of KF and GD schemes are almost similar till 72 hrs with lowest landfall error in KF scheme compared to other two schemes, whereas the BMJ scheme gives lowest mean forecast error upto 48 hr and largest mean forecast error at 72 hr. The overall rainfall forecast associated with the monsoon depression is also well captured in WRF model with KF scheme compared to that of GD scheme and BMJ scheme with observed heavy rainfall over Orissa, Chhattisgarh and western Himalayas is well captured in the model with KF scheme compared to that with GD scheme and BMJ scheme.

scholarly journals Combinational Optimization of the WRF Physical Parameterization Schemes to Improve Numerical Sea Breeze Prediction Using Micro-Genetic Algorithm

2021 ◽  
Vol 11 (23) ◽  
pp. 11221
Author(s):  
Ji Won Yoon ◽  
Sujeong Lim ◽  
Seon Ki Park
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Land Surface ◽  
Wrf Model ◽  
Longwave Radiation ◽  
Sea Breeze ◽  
Meteorological Variables ◽  
Parameterization Schemes ◽  
Micro Genetic Algorithm ◽  
Physical Parameterization

This study aims to improve the performance of the Weather Research and Forecasting (WRF) model in the sea breeze circulation using the micro-Genetic Algorithm (micro-GA). We found the optimal combination of four physical parameterization schemes related to the sea breeze system, including planetary boundary layer (PBL), land surface, shortwave radiation, and longwave radiation, in the WRF model coupled with the micro-GA (WRF-μGA system). The optimization was performed with respect to surface meteorological variables (2 m temperature, 2 m relative humidity, 10 m wind speed and direction) and a vertical wind profile (wind speed and direction), simultaneously for three sea breeze cases over the northeastern coast of South Korea. The optimized set of parameterization schemes out of the WRF-μGA system includes the Mellor–Yamada–Nakanishi–Niino level-2.5 (MYNN2) for PBL, the Noah land surface model with multiple parameterization options (Noah-MP) for land surface, and the Rapid Radiative Transfer Model for GCMs (RRTMG) for both shortwave and longwave radiation. The optimized set compared with the various other sets of parameterization schemes for the sea breeze circulations showed up to 29 % for the improvement ratio in terms of the normalized RMSE considering all meteorological variables.

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.

scholarly journals Assessment of a Regional-Scale Weather Model for Hydrological Applications in South Korea

2016 ◽  
Vol 6 (2) ◽  
pp. 28
Author(s):  
Yong Jung ◽  
Yuh-Lang Lin
Keyword(s):  
Weather Prediction ◽  
Regional Scale ◽  
Wrf Model ◽  
Domain Size ◽  
Cumulus Parameterization ◽  
Precipitation Forecast ◽  
Mountainous Region ◽  
Parameterization Schemes ◽  
Plain Region ◽  
Cumulus Parameterization Schemes

<p class="1Body">In this study, a regional numerical weather prediction (NWP) model known as the Weather Research Forescasting (WRF) model was adopted to improve the quantitative precipitation forecasts (QPF) by optimizing combined microphysics and cumulus parameterization schemes. Four locations in two regions (plain region for Sangkeug and Imsil; mountainous region for Dongchun and Bunchun) in Korean Peninsula were examined for QPF for two heavy rainfall events 2006 and 2008. The maximum Index of Agreement (IOA) was 0.96 at Bunchun in 2006 using the combined Thompson microphysics and the Grell cumulus parameterization schemes. Sensitivity of QPF on domain size at Sangkeug indicated that the localized smaller domain had 55% (from 0.35 to 0.90) improved precipitation accuracy based on IOA of 2008. For the July 2006 Sangkeug event, the sensitivity to cumulus parameterization schemes for precipitation prediction cannot be ignored with finer resolutions. In mountainous region, the combined Thompson microphysics and Grell cumulus parameterization schemes make a better quantitative precipitation forecast, while in plain region, the combined Thompson microphysics and Kain-Frisch cumulus parameterization schemes are the best.</p>

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