Convection-Permitting Simulation of a Heavy Rainfall Event in Armenia Using the WRF Model

It is difficult to forecast hourly rainfall locally even using the latest meteorological models, although hourly rainfall averaged spatially to some extent can be used for calculating practical rainfall. This study conducts numerical experiments with triple nesting on the 2012 heavy rainfall event in northern Kyushu using the weather research and forecasting (WRF) model and examines the features of hourly rainfall averaged spatially. The dependence of rainfall is averaged spatially on a spatial averaging scale and clarified by comparing rainfall calculated by simulation using the WRF model with radar/AMeDAS precipitation analysis data. This study’s findings indicate the effective spatial averaging scale making relative error of calculated values to the observed ones minimum.

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

10.5194/egusphere-egu21-11954 ◽

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>

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Synoptic Analysis of a Heavy Rainfall Event over Southeast Region of Bangladesh Using WRF Model

Journal of Scientific Research ◽

10.3329/jsr.v5i3.13171 ◽

2013 ◽

Vol 5 (3) ◽

pp. 421-434 ◽

Cited By ~ 3

Author(s):

M. N. Ahasan ◽

M. A. Mannan ◽

S. K. Debsarma

Keyword(s):

Wind Shear ◽

Heavy Rainfall ◽

Large Scale ◽

Model Simulation ◽

Wrf Model ◽

Rainfall Event ◽

Horizontal Wind ◽

Heavy Rainfall Event ◽

Southeast Region ◽

Synoptic Analysis

Synoptic analysis of the heavy rainfall event of 7 September 2011 was carried out using the Weather Research and Forecasting (WRF) Model. This extraordinary rainfall event was localized over the southeast region of Bangladesh and recorded rainfall was 331 mm at Chittagong and 226 mm at Sandwip within a span of 24-h. The model was run at 9 km horizontal resolution using KF CPS with YSU PBL scheme. The model performance was evaluated by examining the different predicted parameters like mean sea level pressure, upper and lower level circulations, wind shear, relative vorticity, convergence, moisture and rainfall. The rainfall was validated with TRMM 3B42RT and observed rainfall data. The results indicate that the WRF model was able to simulate the heavy rainfall event, and associated synoptic features reasonably well, though there are some biases in the rainfall pattern. The results suggest that the highly localized heavy rain over southeast Bangladesh was the result of an interaction of the large scale monsoon system with cyclonic disturbances and required moisture have been supplied from the Bay of Bengal. The low level flow facilitated strong convergence over the region due to horizontal wind shear, which resulted in maintenance of the storms. Keywords: Heavy Rainfall; WRF Model; Simulation; Validation; TRMM. © 2013 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. doi: http://dx.doi.org/10.3329/jsr.v5i3.13171 J. Sci. Res. 5 (3), 421-434 (2013)

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The role of orographic effects on occurrence of the heavy rainfall event over Central Vietnam in November 1999

Tạp chí Khoa học và Công nghệ Biển ◽

10.15625/1859-3097/17/4b/12989 ◽

2017 ◽

Vol 17 (4B) ◽

pp. 31-36

Author(s):

Dang Hong Nhu ◽

Nguyen Xuan Anh ◽

Nguyen Binh Phong ◽

Nguyen Dang Quang ◽

Hiep Van Nguyen

Keyword(s):

Heavy Rainfall ◽

Wrf Model ◽

Rainfall Event ◽

Heavy Rainfall Event ◽

Monsoon Circulation ◽

Northeast Monsoon ◽

Strong Wind ◽

Orographic Effects ◽

Central Vietnam

In this study, the WRF model is used to investigate the role of Central Vietnam terrain on occurrence of the heavy rainfall event in November 1999 over Central Vietnam. Two model experiments with and without terrain were performed to examine the orographic blocking effects during the event. In the terrain experiment, the results from a three-day simulation show that the model reasonably well captures northeast monsoon circulation, tropical cyclones and the occurrence of heavy rainfall in Central Vietnam. The topography causes a high pressure anomaly intensifying northeast monsoon. When the terrain is removed, the three-day accumulated rainfall decreases approximately 75% in comparison with that in the terrain experiment. The terrain blocking and lifting effects in strong wind and moisture laden conditions combined with convergence circulation over open ocean are the main factors for occurrence of the heavy rainfall event.

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