Simulation of monsoon depression over India using high resolution WRF Model – Sensitivity to convective 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.

Download Full-text

Sensitivity of Rainfall to Cumulus Parameterization Schemes from a WRF Model over the City of Douala in Cameroon

Journal of Extreme Events ◽

10.1142/s2345737621500214 ◽

2021 ◽

Author(s):

Roméo S. Tanessong ◽

A. J. Komkoua Mbienda ◽

G. M. Guenang ◽

S. Kaissassou ◽

Lucie A. Tchotchou Djiotang ◽

...

Keyword(s):

Heavy Rainfall ◽

Wrf Model ◽

Early Warning Systems ◽

Cumulus Parameterization ◽

Rainfall Events ◽

Parameterization Schemes ◽

K Index ◽

Cumulus Parameterization Schemes ◽

The City ◽

Heavy Rainfall Events

With the recurrence of extreme weather events in Central Africa, it becomes imperative to provide high-resolution forecasts for better decision-making by the Early warning systems. This study assesses the performance of the Weather Research and Forecasting (WRF) model to simulate heavy rainfall that affected the city of Douala in Cameroon during 19–21 August 2020. The WRF model is configured with two domains with horizontal resolutions of 15 and 5[Formula: see text]km, 33 vertical levels using eight cumulus parameterization schemes (CPSs). The WRF model performance is assessed by investigating the agreement between simulations and observations. Categorical and deterministic statistics are used, which include the probability of detection (POD), the success ratio (SR), the equitable threat score (ETS), the pattern correlation coefficient (PCC), the root mean square error (RMSE), the mean absolute error (MAE), and the BIAS. K-index is finally used to assess the capacity of the WRF model to predict the instability of the atmosphere in Douala during the above-mentioned period. It is found that (1) The POD, SR and ETS decrease when the threshold increases, showing the difficulty of the WRF model to predict and locate heavy rainfall events; (2) There are important differences in the rainfall area simulated by the eight CPSs; (3) The BIAS is negative for the eight CPSs, implying that all of the CPSs tested underestimate the rainfall over the study area; (4) Some of the CPSs have good agreement with observations, especially the new modifed Tiedtke and the Betts–Miller–Janjic schemes; (5) The K-index, an atmospheric instability index, is well predicted by the eight CPSs tested in this work. Overall, the WRF model exhibits a strong ability for rainfall simulation in the study area. The results point out that heavy rainfall events in tropical areas are very sensitive to CPSs and study domain. Therefore, sensitivity tests studies should be multiplied in order to identify most suitable CPSs for a given area.

Download Full-text

A sensitivity study of WRF model microphysics and cumulus parameterization schemes for the simulation of tropical cyclones using GPM radar data

Journal of Earth System Science ◽

10.1007/s12040-021-01682-3 ◽

2021 ◽

Vol 130 (4) ◽

Author(s):

Harish Baki ◽

Sandeep Chinta ◽

C Balaji ◽

Balaji Srinivasan

Keyword(s):

Tropical Cyclones ◽

Wrf Model ◽

Radar Data ◽

Sensitivity Study ◽

Cumulus Parameterization ◽

Parameterization Schemes ◽

Cumulus Parameterization Schemes

Download Full-text

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

Environment and Natural Resources Research ◽

10.5539/enrr.v6n2p28 ◽

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>

Download Full-text

Ensemble-Based Sensitivity Analysis Applied to African Easterly Waves

Weather and Forecasting ◽

10.1175/2009waf2222255.1 ◽

2010 ◽

Vol 25 (1) ◽

pp. 61-78 ◽

Cited By ~ 27

Author(s):

Ryan D. Torn

Keyword(s):

Sensitivity Analysis ◽

Initial Conditions ◽

Wrf Model ◽

Cumulus Parameterization ◽

Initial Condition ◽

Ensemble Forecasts ◽

Large Variability ◽

Multidisciplinary Analysis ◽

Meridional Winds ◽

Cumulus Parameterization Schemes

Abstract An ensemble Kalman filter (EnKF) coupled to the Advanced Research version of the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts of a strong African easterly wave (AEW) during the African Monsoon Multidisciplinary Analysis field campaign. Ensemble sensitivity analysis is then used to evaluate the impacts of initial condition errors on AEW amplitude and position forecasts at two different initialization times. WRF forecasts initialized at 0000 UTC 8 September 2006, prior to the amplification of the AEW, are characterized by large variability in evolution as compared to forecasts initialized 48 h later when the AEW is within a denser observation network. Short-lead-time amplitude forecasts are most sensitive to the midtropospheric meridional winds, while at longer lead times, midtropospheric θe errors have equal or larger impacts. For AEW longitude forecasts, the largest sensitivities are associated with the θe downstream of the AEW and, to a lesser extent, the meridional winds. Ensemble predictions of how initial condition errors impact the AEW amplitude and position compare qualitatively well with perturbed integrations of the WRF model. Much of the precipitation associated with the AEW is generated by the Kain–Fritsch cumulus parameterization, thus the initial-condition sensitivities are also computed for ensemble forecasts that employ the Betts–Miller–Janjić and Grell cumulus parameterization schemes, and for a high-resolution nested domain with explicit convection, but with the same initial conditions. While the 12-h AEW amplitude forecast is characterized by consistent initial-condition sensitivity among the different schemes, there is greater variability among methods beyond 24 h. In contrast, the AEW longitude forecast is sensitive to the downstream thermodynamic profile with all cumulus schemes.

Download Full-text