Simulation of Severe Convective Weather Events over Southern India Using WRF Model

Abstract Convection-allowing configurations of the Weather Research and Forecast (WRF) model were evaluated during the 2004 Storm Prediction Center–National Severe Storms Laboratory Spring Program in a simulated severe weather forecasting environment. The utility of the WRF forecasts was assessed in two different ways. First, WRF output was used in the preparation of daily experimental human forecasts for severe weather. These forecasts were compared with corresponding predictions made without access to WRF data to provide a measure of the impact of the experimental data on the human decision-making process. Second, WRF output was compared directly with output from current operational forecast models. Results indicate that human forecasts showed a small, but measurable, improvement when forecasters had access to the high-resolution WRF output and, in the mean, the WRF output received higher ratings than the operational Eta Model on subjective performance measures related to convective initiation, evolution, and mode. The results suggest that convection-allowing models have the potential to provide a value-added benefit to the traditional guidance package used by severe weather forecasters.

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The Potential Impact of Assimilating Synthetic Microwave Radiances Onboard a Future Geostationary Satellite on the Prediction of Typhoon Lekima Using the WRF Model

Remote Sensing ◽

10.3390/rs13050886 ◽

2021 ◽

Vol 13 (5) ◽

pp. 886

Author(s):

Yuanbing Wang ◽

Jieying He ◽

Yaodeng Chen ◽

Jinzhong Min

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Wrf Model ◽

Geostationary Satellite ◽

Weather Research And Forecasting ◽

Observational System ◽

Weather Events ◽

High Impact Weather ◽

New Perspective ◽

Microwave Radiances

Geostationary meteorological satellites can provide continuous observations of high-impact weather events with a high temporal and spatial resolution. Sounding the atmosphere using a microwave instrument onboard a geostationary satellite has aroused great study interests for years, as it would increase the observational efficiency as well as provide a new perspective in the microwave spectrum to the measuring capability for the current observational system. In this study, the capability of assimilating future geostationary microwave sounder (GEOMS) radiances was developed in the Weather Research and Forecasting (WRF) model’s data assimilation (WRFDA) system. To investigate if these frequently updated and widely distributed microwave radiances would be beneficial for typhoon prediction, observational system simulation experiments (OSSEs) using synthetic microwave radiances were conducted using the mesoscale numerical model WRF and the advanced hybrid ensemble–variational data assimilation method for the Lekima typhoon that occurred in early August 2019. The results show that general positive forecast impacts were achieved in the OSSEs due to the assimilation of GEOMS radiances: errors of analyses and forecasts in terms of wind, humidity, and temperature were both reduced after assimilating GEOMS radiances when verified against ERA-5 data. The track and intensity predictions of Lekima were also improved before 68 h compared to the best track data in this study. In addition, rainfall forecast improvements were also found due to the assimilation impact of GEOMS radiances. In general, microwave observations from geostationary satellites provide the possibility of frequently assimilating wide-ranging microwave information into a regional model in a finer resolution, which can potentially help improve numerical weather prediction (NWP).

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Initial results of the project SINOPTICA (Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM)

10.5194/ems2021-189 ◽

2021 ◽

Author(s):

Laura Esbri ◽

Maria Carmen Llasat ◽

Tomeu Rigo ◽

Massimo Milelli ◽

Vincenzo Mazzarella ◽

...

Keyword(s):

Weather Prediction ◽

Wrf Model ◽

Remote Sensing Data ◽

Weather Forecast ◽

Relevant Information ◽

Air Traffic ◽

Severe Weather ◽

Radar Images ◽

Weather Events ◽

Adverse Weather

<p>In the framework of the SINOPTICA project (EU H2020 SESAR, 2020 &#8211; 2022), different meteorological forecasting techniques are being tested to better nowcast severe weather events affecting Air Traffic Management (ATM) operations. Short-range severe weather forecasts with very high spatial resolution will be obtained starting from radar images, through an application of nowcasting techniques combined with Numerical Weather Prediction (NWP) model with data assimilation. The final goal is to integrate compact nowcast information into an Arrival Manager to support Air Traffic Controllers (ATCO) when sequencing and guiding approaching aircraft even in adverse weather situations. The guidance-support system will enable the visualization of dynamic weather information on the radar display of the controller, and the 4D-trajectory calculation for diversion coordination around severe weather areas. This meteorological information must be compact and concise to not interfere with other relevant information on the radar display of the controller.</p><p>Three severe weather events impacting different Italian airports have been selected for a preliminary radar analysis. Some products are considered for obtaining the best radar approach to characterize the severity of the events for ATM interests. Combining the Vertical Integrated Liquid and the Echo Top Maximum products, hazard thresholds are defined for different domains around the airports. The Weather Research and Forecasting (WRF) model has been used to simulate the formation and development of the aforementioned convective events. In order to produce a more accurate very short-term weather forecast (nowcasting), remote sensing data (e.g. radar, GNSS) and conventional observations are assimilated by using a cycling three-dimensional variational technique. This contribution presents some preliminary results on the progress of the project.</p>

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Study of GOES-R Thermodynamic Indices for Short-Term Forecasting of Convective Weather Events Using Machine Learning

Pure and Applied Geophysics ◽

10.1007/s00024-021-02889-7 ◽

2021 ◽

Author(s):

Caroline Menegussi Soares ◽

Gutemberg Borges França ◽

Manoel Valdonel de Almeida ◽

Vinícius Albuquerque de Almeida

Keyword(s):

Machine Learning ◽

Short Term ◽

Weather Events ◽

Short Term Forecasting ◽

Convective Weather

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A Simple and Flexible Method for Ranking Severe Weather Events

Weather and Forecasting ◽

10.1175/waf959.1 ◽

2006 ◽

Vol 21 (6) ◽

pp. 939-951 ◽

Cited By ~ 44

Author(s):

C. A. Doswell ◽

R. Edwards ◽

R. L. Thompson ◽

J. A. Hart ◽

K. C. Crosbie

Keyword(s):

Secular Trends ◽

Severe Weather ◽

Weather Events ◽

Multiple Variables ◽

Convective Weather ◽

The Impact ◽

Severe Weather Event ◽

Intended Use ◽

Made In ◽

Tornado Outbreak

Abstract The notion of an “outbreak” of severe weather has been used for decades, but has never been formally defined. There are many different criteria by which outbreaks can be defined based on severe weather occurrence data, and there is not likely to be any compelling logic to choose any single criterion as ideal for all purposes. Therefore, a method has been developed that uses multiple variables and allows for considerable flexibility. The technique can be adapted easily to any project that needs to establish a ranking of weather events. The intended use involves isolating the most important tornado outbreak days, as well as important outbreak days of primarily nontornadic severe convective weather, during a period when the number of reports has been growing rapidly from nonmeteorological factors. The method is illustrated for both tornadic and primarily nontornadic severe weather event day cases. The impact of the secular trends in the data has been reduced by a simple detrending scheme. The effect of detrending is less important for the tornado outbreak cases and is illustrated by comparing rankings with and without detrending. It is shown that the resulting rankings are relatively resistant to secular trends in the data, as intended, and not strongly sensitive to the choices made in applying the method. The rankings are also consistent with subjective judgments of the relative importance of historical tornado outbreak cases.

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Verifying Supercellular Rotation in a Convection-Permitting Ensemble Forecasting System with Radar-Derived Rotation Track Data

Weather and Forecasting ◽

10.1175/waf-d-16-0121.1 ◽

2017 ◽

Vol 32 (2) ◽

pp. 781-795 ◽

Cited By ~ 7

Author(s):

Logan C. Dawson ◽

Glen S. Romine ◽

Robert J. Trapp ◽

Michael E. Baldwin

Keyword(s):

Ensemble Forecasting ◽

Velocity Shear ◽

Doppler Velocity ◽

Model Ensemble ◽

Model Data ◽

Weather Events ◽

Report Data ◽

Forecasting System ◽

Supercell Thunderstorm ◽

Convective Weather

Abstract The utility of radar-derived rotation track data for the verification of supercell thunderstorm forecasts was quantified through this study. The forecasts were generated using a convection-permitting model ensemble, and supercell occurrence was diagnosed via updraft helicity and low-level vertical vorticity. Forecasts of four severe convective weather events were considered. Probability fields were computed from the model data, and forecast skill was quantified using rotation track data, storm report data, and a neighborhood-based verification approach. The ability to adjust the rotation track threshold for verification purposes was shown to be an advantage of the rotation track data over the storms reports, because the reports are inherently binary observations whereas the rotation tracks are based on values of Doppler velocity shear. These results encourage further pursuit of incorporating observed rotation track data in the forecasting and verification of severe weather events.

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Planning arrival flows during convective weather events using the strategic arrivals recommendation tool (START)

2016 Integrated Communications Navigation and Surveillance (ICNS) ◽

10.1109/icnsurv.2016.7486394 ◽

2016 ◽

Author(s):

Jon Cunningham ◽

Lara Shisler ◽

George Hunter

Keyword(s):

Weather Events ◽

Convective Weather

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A Real-Time Convection-Allowing Ensemble Prediction System Initialized by Mesoscale Ensemble Kalman Filter Analyses

Weather and Forecasting ◽

10.1175/waf-d-15-0013.1 ◽

2015 ◽

Vol 30 (5) ◽

pp. 1158-1181 ◽

Cited By ~ 41

Author(s):

Craig S. Schwartz ◽

Glen S. Romine ◽

Morris L. Weisman ◽

Ryan A. Sobash ◽

Kathryn R. Fossell ◽

...

Keyword(s):

Kalman Filter ◽

Real Time ◽

Ensemble Kalman Filter ◽

Wrf Model ◽

Severe Weather ◽

Ensemble Prediction ◽

Limited Area ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Weather Events

Abstract In May and June 2013, the National Center for Atmospheric Research produced real-time 48-h convection-allowing ensemble forecasts at 3-km horizontal grid spacing using the Weather Research and Forecasting (WRF) Model in support of the Mesoscale Predictability Experiment field program. The ensemble forecasts were initialized twice daily at 0000 and 1200 UTC from analysis members of a continuously cycling, limited-area, mesoscale (15 km) ensemble Kalman filter (EnKF) data assimilation system and evaluated with a focus on precipitation and severe weather guidance. Deterministic WRF Model forecasts initialized from GFS analyses were also examined. Subjectively, the ensemble forecasts often produced areas of intense convection over regions where severe weather was observed. Objective statistics confirmed these subjective impressions and indicated that the ensemble was skillful at predicting precipitation and severe weather events. Forecasts initialized at 1200 UTC were more skillful regarding precipitation and severe weather placement than forecasts initialized 12 h earlier at 0000 UTC, and the ensemble forecasts were typically more skillful than GFS-initialized forecasts. At times, 0000 UTC GFS-initialized forecasts had temporal distributions of domain-average rainfall closer to observations than EnKF-initialized forecasts. However, particularly when GFS analyses initialized WRF Model forecasts, 1200 UTC forecasts produced more rainfall during the first diurnal maximum than 0000 UTC forecasts. This behavior was mostly attributed to WRF Model initialization of clouds and moist physical processes. The success of these real-time ensemble forecasts demonstrates the feasibility of using limited-area continuously cycling EnKFs as a method to initialize convection-allowing ensemble forecasts, and future real-time high-resolution ensemble development leveraging EnKFs seems justified.

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