scholarly journals Ensemble Sensitivity Analysis of the Heavy Rainfall Event Occurred on 6th August 2003 over the Korean Peninsula

Atmosphere ◽  
2013 ◽  
Vol 23 (1) ◽  
pp. 23-32
Author(s):  
Namkyu Noh ◽  
Shin-Woo Kim ◽  
Ji-Hyun Ha ◽  
Gyu-Ho Lim
Keyword(s):  
Sensitivity Analysis ◽  
Korean Peninsula ◽  
Heavy Rainfall ◽  
Rainfall Event ◽  
Heavy Rainfall Event

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>

Assimilation of GPS Radio Occultation Refractivity Data with WRF 3DVAR and Its Impact on the Prediction of a Heavy Rainfall Event

2014 ◽  
Vol 53 (6) ◽  
pp. 1381-1398 ◽  
Author(s):  
Ji-Hyun Ha ◽  
Gyu-Ho Lim ◽  
Suk-Jin Choi
Keyword(s):  
Data Assimilation ◽  
Korean Peninsula ◽  
Heavy Rainfall ◽  
Radio Occultation ◽  
Rainfall Event ◽  
Moisture Distribution ◽  
Precipitation Forecast ◽  
Specific Humidity ◽  
Heavy Rainfall Event ◽  
Gps Radio Occultation

AbstractTo accommodate accurate analyses and forecasts of a heavy rainfall event over the Korean Peninsula, the authors assimilated the GPS radio occultation (RO) data by using the Weather Research and Forecasting Model (WRF) and its three-dimensional variational data assimilation system (3DVAR). The employed datasets are from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and Challenging Minisatellite Payload (CHAMP) missions. The selected case was from late October 2006, which intensively hit the northeastern part of the Korean Peninsula with record-breaking rainfall. In this study, the local refractivity observation operator was used in assimilating GPS RO soundings. The results are more pronounced for the cycling assimilation of GPS RO data than for the one-time data assimilation. From all of the parameters investigated (temperature, geopotential height, specific humidity, and winds), the GPS RO soundings highly modified the moisture distribution in the lower troposphere and also changed the wind field via the model dynamics. For the heavy rainfall forecast, the quantitative accuracy of the precipitation forecast with the GPS RO data assimilation was in good agreement with observations in terms of the maximum rainfall amount and threat scores. The improved forecast in the experiment came from the exact positioning of the low pressure system and its consequent convergence near the rainfall area. When RO data and GPS precipitable water data were assimilated simultaneously, the moisture distribution changed horizontally and vertically such that it increased the amount of rainfall, and an accurate description of the convective system development was feasible.

scholarly journals Role of land state in a high resolution mesoscale model for simulating the Uttarakhand heavy rainfall event over India

2016 ◽  
Vol 125 (3) ◽  
pp. 475-498 ◽  
Author(s):  
P V Rajesh ◽  
S Pattnaik ◽  
D Rai ◽  
K K Osuri ◽  
U C Mohanty ◽  
...  
Keyword(s):  
High Resolution ◽  
Heavy Rainfall ◽  
Mesoscale Model ◽  
Rainfall Event ◽  
Heavy Rainfall Event
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