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>

scholarly journals NUMERICAL SENSITIVITY EXPERIMENTS ON PRECIPITATION IN A HEAVY RAINFALL EVENT SIMULATED BY USING THE WRF MODEL

2014 ◽  
Vol 70 (4) ◽  
pp. I_541-I_546 ◽  
Author(s):  
Yuji SUGIHARA ◽  
Sho IMAGAMA ◽  
Yohei OHKUMA ◽  
Nobuhiro MATSUNAGA ◽  
Yukiko HISADA ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Wrf Model ◽  
Rainfall Event ◽  
Heavy Rainfall Event ◽  
Sensitivity Experiments

scholarly journals Impact of radar data assimilation for the simulation of a heavy rainfall case in central Italy using WRF–3DVAR

2014 ◽  
Vol 7 (9) ◽  
pp. 2919-2935 ◽  
Author(s):  
I. Maiello ◽  
R. Ferretti ◽  
S. Gentile ◽  
M. Montopoli ◽  
E. Picciotti ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Central Italy ◽  
Wrf Model ◽  
Forecast Accuracy ◽  
Radar Data ◽  
Heavy Rainfall Event ◽  
Dwr Data

Abstract. The aim of this study is to investigate the role of the assimilation of Doppler weather radar (DWR) data in a mesoscale model for the forecast of a heavy rainfall event that occurred in Italy in the urban area of Rome from 19 to 22 May 2008. For this purpose, radar reflectivity and radial velocity acquired from Monte Midia Doppler radar are assimilated into the Weather Research Forecasting (WRF) model, version 3.4.1. The general goal is to improve the quantitative precipitation forecasts (QPF): with this aim, several experiments are performed using the three-dimensional variational (3DVAR) technique. Moreover, sensitivity tests to outer loops are performed to include non-linearity in the observation operators. In order to identify the best initial conditions (ICs), statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used. The results show that the assimilation of DWR data has a large impact on both the position of convective cells and on the rainfall forecast of the analyzed event. A positive impact is also found if they are ingested together with conventional observations. Sensitivity to the use of two or three outer loops is also found if DWR data are assimilated together with conventional data.

Initial Condition Sensitivity of Western Pacific Extratropical Transitions Determined Using Ensemble-Based Sensitivity Analysis

2009 ◽  
Vol 137 (10) ◽  
pp. 3388-3406 ◽  
Author(s):  
Ryan D. Torn ◽  
Gregory J. Hakim
Keyword(s):  
Sensitivity Analysis ◽  
Tropical Cyclone ◽  
Initial Conditions ◽  
Forecast Error ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Forecast Errors ◽  
Initial Condition ◽  
Ensemble Forecasts ◽  
The Relationship

Abstract An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.

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.

Numerical Experiments on Spatially Averaged Precipitation in Heavy Rainfall Event Using the WRF Model

2015 ◽  
Vol 10 (3) ◽  
pp. 436-447 ◽  
Author(s):  
Yuji Sugihara ◽  
◽  
Sho Imagama ◽  
Nobuhiro Matsunaga ◽  
Yukiko Hisada ◽  
...  
Keyword(s):  
Numerical Experiments ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Analysis Data ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Heavy Rainfall Event ◽  
Spatial Averaging ◽  
Hourly Rainfall ◽  
Precipitation Analysis

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.

Sign in / Sign up

Export Citation Format

Share Document