Sensitivity Analysis of Cumulus Parameterization in WRF Model for Simulating Summer Heavy Rainfall in South Korea

2020 ◽  
Vol 15 (4) ◽  
pp. 243-256
Author(s):  
Ga-Yeong Seo ◽  
◽  
Joong-Bae Ahn
Keyword(s):  
Sensitivity Analysis ◽  
South Korea ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Cumulus Parameterization

Ensemble-Based Sensitivity Analysis Applied to African Easterly Waves

2010 ◽  
Vol 25 (1) ◽  
pp. 61-78 ◽  
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.

Understanding the Dynamics of a Heavy Rainfall Event using Multivariate Ensemble Sensitivity Analysis 

2021 ◽  
Author(s):  
Babitha George ◽  
Govindan Kutty
Keyword(s):  
Sensitivity Analysis ◽  
Covariance Matrix ◽  
Heavy Rainfall ◽  
Initial Conditions ◽  
Wrf Model ◽  
Control Area ◽  
Rainfall Event ◽  
Heavy Rainfall Event ◽  
Ensemble Forecasts ◽  
The Impact

<p>Ensemble forecasts have proven useful for investigating the dynamics in a wide variety of atmospheric systems and they might be useful for diagnosing the source of forecast uncertainty in multi-scale flows. Ensemble Sensitivity Analysis (ESA) uses ensemble forecasts to evaluate the impact of changes in initial conditions on subsequent forecasts. ESA leads to a simple univariate regression by approximating the analysis covariance matrix with the corresponding diagonal matrix. On the contrary, the multivariate ensemble sensitivity computes sensitivity based on a more general multivariate regression that retains the full covariance matrix. The purpose of this study is to examine the performance of multivariate ensemble sensitivity over univariate by applying it to a heavy rainfall event that happened over the Himalayan foothills in June 2013. The ensemble forecasts and analyses are generated using the Advanced Research version of the Weather Research and Forecasting (WRF) model DART based Ensemble Kalman Filter. Initial results are promising and the sensitivity shows similar patterns for both univariate and multivariate methods. The reflectivity forecast for both methods are characterized by lower temperatures and increased moisture in the control area at 850 hPa level. Compared to multivariate, univariate ensemble sensitivity overestimates the magnitude of sensitivity for temperature. But the sensitivity for the moisture is the same in both methods.</p>

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

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.

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.

A parameterization of slantwise convection in the WRF model

2021 ◽  
Author(s):  
Ting-Chen Chen ◽  
Man-Kong Yau ◽  
Daniel J. Kirshbaum
Keyword(s):  
General Circulation ◽  
Wrf Model ◽  
Real Data ◽  
General Circulation Models ◽  
Added Value ◽  
Cumulus Parameterization ◽  
Moisture Loss ◽  
Precipitation Event ◽  
Cyclonic Storm ◽  
Slantwise Convection

Abstract In this study, we introduce a parameterization scheme for slantwise convection (SC) to be considered for models that are too coarse to resolve slantwise convection explicitly (with a horizontal grid spacing coarser than 15 km or less). This SC scheme operates in a locally defined 2D cross-section perpendicular to the deep-layer-averaged thermal wind. It applies momentum tendency to adjust the environment toward slantwise neutrality with a prescribed adjustment timescale. Condensational heating and the associated moisture loss are also considered. To evaluate the added value of the SC scheme, we implement it in the Weather Research and Forecasting (WRF) model to supplement the existing cumulus parameterization schemes for upright convection and test for two different numerical setups: a 2D idealized, unforced release of conditional symmetric instability (CSI) in an initially conditionally stable environment, and a 3D real-data precipitation event containing both CSI and conditional instability along the cold front of a cyclonic storm near the UK. Both test cases show significant improvements for the coarse-gridded (40-km) simulations when parameterizing slantwise convection. Compared to the 40-km simulations with only the upright convection scheme, the counterparts with the additional SC scheme exhibit a larger extent of CSI neutralization, generate a stronger grid-resolved slantwise circulation, and produce greater amounts of precipitation, all in better agreement with the corresponding fine-gridded reference simulations. Given the importance of slantwise convection in midlatitude weather systems, our results suggest that there exist potential benefits of parameterizing slantwise convection in general circulation models.

scholarly journals Sensitivity Study of WRF Numerical Modeling for Forecasting Heavy Rainfall in Sri Lanka

Atmosphere ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 378 ◽  
Author(s):  
Channa Rodrigo ◽  
Sangil Kim ◽  
Il Jung
Keyword(s):  
Sri Lanka ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Wrf Model ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Sensitivity Study ◽  
Southwest Monsoon ◽  
Northeast Monsoon ◽  
Pressure System

This study aimed to determine the predictability of the Weather Research and Forecasting (WRF) model with different model physics options to identify the best set of physics parameters for predicting heavy rainfall events during the southwest and northeast monsoon seasons. Two case studies were used for the evaluation: heavy precipitation during the southwest monsoon associated with the simultaneous onset of the monsoon, and a low pressure system over the southwest Bay of Bengal that produced heavy rain over most of the country, with heavy precipitation associated with the northeast monsoon associated with monsoon flow and easterly disturbances. The modeling results showed large variation in the rainfall estimated by the model using the various model physics schemes, but several corresponding rainfall simulations were produced with spatial distribution aligned with rainfall station data, although the amount was not estimated accurately. Moreover, the WRF model was able to capture the rainfall patterns of these events in Sri Lanka, suggesting that the model has potential for operational use in numerical weather prediction in Sri Lanka.

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