Micro-Scale Severe Weather Prediction Based on Region Trajectories Extracted from Meteorological Radar Data

2016 ◽  
Author(s):  
Xingang Wang ◽  
Suiping Qi
Keyword(s):  
Weather Prediction ◽  
Radar Data ◽  
Severe Weather ◽  
Micro Scale ◽  
Meteorological Radar

A Case Study of Weather Radar Data Assimilation into the Harmonie Numerical Weather Prediction System

2020 ◽  
Author(s):  
Silas Michaelides ◽  
Serguei Ivanov ◽  
Igor Ruban ◽  
Demetris Charalambous ◽  
Filippos Tymvios
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Frontal Zone ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Radar Data ◽  
Severe Weather ◽  
Numerical Weather ◽  
Radar Data Assimilation

<p>Quantitative Precipitation Forecasting (QPF) is among the most central challenges of atmospheric prediction systems. The primary aim of such a task is the generation of accurate estimates of heavy precipitation events associated with severe weather, atmospheric fronts and heavy convective rainfalls. QPF is still among the most intricate challenges of Numerical Weather Prediction. The efforts in this direction are mainly concentrated on improving model formulations for microphysics and convective process and remote sensing data assimilation.</p><p>This paper describes the first results with the regional radar signal processing chain that provides the radar data assimilation (RDA) in the Harmonie convection permitting numerical model. This task is performed for a case study focusing on a wintertime frontal cyclone over the island of Cyprus. Reflectivity measurements from two weather radars, at Larnaka and Paphos, are exploited for simulations of severe weather conditions associated with this synoptic-scale system. Through the variational assimilation procedure, the model takes into account the atmospheric processes occurring in the upstream flow which can be outside the area of radar measurements. The focus is on the precipitable water vapor content and its changes during the cyclone evolution, as well as on the impact of the radar data assimilation on precipitation estimates.</p><p>The results show that the numerical experiments exhibit, in general, a suitable simulation of precipitable water at different stages of the cyclone. In particular, the bulk of the rainfall volume exhibits three stages: intensive rain on the cyclone's frontal zone, weaker precipitation immediately behind the front, and the secondary enhancement of rainfall. The largest corrections due to RDA are of up to 5 mm and occur during the approach of the cyclone frontal zone in a form of enhanced rainfall over the whole area, but more prominently in weak precipitation locations.</p>

scholarly journals Towards the Next Generation Operational Meteorological Radar

2021 ◽  
pp. 1-69
Author(s):  
Mark Weber ◽  
Kurt Hondl ◽  
Nusrat Yussouf ◽  
Youngsun Jung ◽  
Derek Stratman ◽  
...  
Keyword(s):  
Flash Flood ◽  
Beam Steering ◽  
Radar Data ◽  
Severe Weather ◽  
Flood Warning ◽  
Radar Network ◽  
Additional Improvement ◽  
Meteorological Radar ◽  
Low Altitude ◽  
Assess Data Quality

AbstractThis article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA’s future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.

scholarly journals Prediction of Convective Storms at Convection-Resolving 1 km Resolution over Continental United States with Radar Data Assimilation: An Example Case of 26 May 2008 and Precipitation Forecasts from Spring 2009

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ming Xue ◽  
Fanyou Kong ◽  
Kevin W. Thomas ◽  
Jidong Gao ◽  
Yunheng Wang ◽  
...  
Keyword(s):  
United States ◽  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Radar Data ◽  
Severe Weather ◽  
Ensemble Forecasts ◽  
University Of Oklahoma ◽  
Reflectivity Data ◽  
Radar Data Assimilation

For the first time ever, convection-resolving forecasts at 1 km grid spacing were produced in realtime in spring 2009 by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The forecasts assimilated both radial velocity and reflectivity data from all operational WSR-88D radars within a domain covering most of the continental United States. In preparation for the realtime forecasts, 1 km forecast tests were carried out using a case from spring 2008 and the forecasts with and without assimilating radar data are compared with corresponding 4 km forecasts produced in realtime. Significant positive impact of radar data assimilation is found to last at least 24 hours. The 1 km grid produced a more accurate forecast of organized convection, especially in structure and intensity details. It successfully predicted an isolated severe-weather-producing storm nearly 24 hours into the forecast, which all ten members of the 4 km real time ensemble forecasts failed to predict. This case, together with all available forecasts from 2009 CAPS realtime forecasts, provides evidence of the value of both convection-resolving 1 km grid and radar data assimilation for severe weather prediction for up to 24 hours.

scholarly journals Evaluation of Operational Monsoon Moisture Surveillance and Severe Weather Prediction Utilizing COSMIC-2/FORMOSAT-7 Radio Occultation Observations

2021 ◽  
Vol 13 (15) ◽  
pp. 2979
Author(s):  
Yu-Chun Chen ◽  
Chih-Chien Tsai ◽  
Yi-Chao Wu ◽  
An-Hsiang Wang ◽  
Chieh-Ju Wang ◽  
...  
Keyword(s):  
Numerical Model ◽  
Data Assimilation ◽  
Water Resource Management ◽  
Radio Occultation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Severe Weather ◽  
Model Experiments ◽  
Typhoon Structure ◽  
Numerical Model Experiments

Operational monsoon moisture surveillance and severe weather prediction is essential for timely water resource management and disaster risk reduction. For these purposes, this study suggests a moisture indicator using the COSMIC-2/FORMOSAT-7 radio occultation (RO) observations and evaluates numerical model experiments with RO data assimilation. The RO data quality is validated by a comparison between sampled RO profiles and nearby radiosonde profiles around Taiwan prior to the experiments. The suggested moisture indicator accurately monitors daily moisture variations in the South China Sea and the Bay of Bengal throughout the 2020 monsoon rainy season. For the numerical model experiments, the statistics of 152 moisture and rainfall forecasts for the 2020 Meiyu season in Taiwan show a neutral to slightly positive impact brought by RO data assimilation. A forecast sample with the most significant improvement reveals that both thermodynamic and dynamic fields are appropriately adjusted by model integration posterior to data assimilation. The statistics of 17 track forecasts for typhoon Hagupit (2020) also show the positive effect of RO data assimilation. A forecast sample reveals that the member with RO data assimilation simulates better typhoon structure and intensity than the member without, and the effect can be larger and faster via multi-cycle RO data assimilation.

scholarly journals Comparison of Radar-Based Hail Detection Using Single- and Dual-Polarization

2019 ◽  
Vol 11 (12) ◽  
pp. 1436 ◽  
Author(s):  
Skripniková ◽  
Řezáčová
Keyword(s):  
Comparative Analysis ◽  
Heavy Rain ◽  
Radar Data ◽  
Severe Weather ◽  
Detection Methods ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Rain Events ◽  
Single Polarization ◽  
Strong Agreement

The comparative analysis of radar-based hail detection methods presented here, uses C-band polarimetric radar data from Czech territory for 5 stormy days in May and June 2016. The 27 hail events were selected from hail reports of the European Severe Weather Database (ESWD) along with 21 heavy rain events. The hail detection results compared in this study were obtained using a criterion, which is based on single-polarization radar data and a technique, which uses dual-polarization radar data. Both techniques successfully detected large hail events in a similar way and showed a strong agreement. The hail detection, as applied to heavy rain events, indicated a weak enhancement of the number of false detected hail pixels via the dual-polarization hydrometeor classification. We also examined the performance of hail size detection from radar data using both single- and dual-polarization methods. Both the methods recognized events with large hail but could not select the reported events with maximum hail size (diameter above 4 cm).

scholarly journals The MJO in a Coarse-Resolution GCM with a Stochastic Multicloud Parameterization

2015 ◽  
Vol 72 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Qiang Deng ◽  
Boualem Khouider ◽  
Andrew J. Majda
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Deterministic Model ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Radar Data ◽  
Equation Model ◽  
Death Process ◽  
Relevant Parameter ◽  
Convective Systems

Abstract The representation of the Madden–Julian oscillation (MJO) is still a challenge for numerical weather prediction and general circulation models (GCMs) because of the inadequate treatment of convection and the associated interactions across scales by the underlying cumulus parameterizations. One new promising direction is the use of the stochastic multicloud model (SMCM) that has been designed specifically to capture the missing variability due to unresolved processes of convection and their impact on the large-scale flow. The SMCM specifically models the area fractions of the three cloud types (congestus, deep, and stratiform) that characterize organized convective systems on all scales. The SMCM captures the stochastic behavior of these three cloud types via a judiciously constructed Markov birth–death process using a particle interacting lattice model. The SMCM has been successfully applied for convectively coupled waves in a simplified primitive equation model and validated against radar data of tropical precipitation. In this work, the authors use for the first time the SMCM in a GCM. The authors build on previous work of coupling the High-Order Methods Modeling Environment (HOMME) NCAR GCM to a simple multicloud model. The authors tested the new SMCM-HOMME model in the parameter regime considered previously and found that the stochastic model drastically improves the results of the deterministic model. Clear MJO-like structures with many realistic features from nature are reproduced by SMCM-HOMME in the physically relevant parameter regime including wave trains of MJOs that organize intermittently in time. Also one of the caveats of the deterministic simulation of requiring a doubling of the moisture background is not required anymore.

scholarly journals Analysis of Catania Flash Flood Case Study by Using Combined Microwave and Infrared Technique

2014 ◽  
Vol 15 (5) ◽  
pp. 1989-1998 ◽  
Author(s):  
Francesco Di Paola ◽  
Elisabetta Ricciardelli ◽  
Domenico Cimini ◽  
Filomena Romano ◽  
Mariassunta Viggiano ◽  
...  
Keyword(s):  
Real Time ◽  
Flash Flood ◽  
Weather Prediction ◽  
Rapid Evolution ◽  
Cell Formation ◽  
Winter Season ◽  
National Research Council ◽  
Severe Weather ◽  
Synoptic Situation

Abstract In this paper, the analysis of an extreme convective event atypical for the winter season, which occurred on 21 February 2013 on the east coast of Sicily and caused a flash flood over Catania, is presented. In just 1 h, more than 50 mm of precipitation was recorded, but it was not forecast by numerical weather prediction (NWP) models and, consequently, no severe weather warnings were sent to the population. The case study proposed is first examined with respect to the synoptic situation and then analyzed by means of two algorithms based on satellite observations: the Cloud Mask Coupling of Statistical and Physical Methods (MACSP) and the Precipitation Evolving Technique (PET), developed at the National Research Council of Italy. Both of the algorithms show their ability in the near-real-time monitoring of convective cell formation and their rapid evolution. As quantitative precipitation forecasts by NWP could fail, especially for atypical convective events like in Catania, tools like MACSP and PET shall be adopted by civil protection centers to monitor the real-time evolution of deep convection events in aid to the severe weather warning service.

The Bow and Arrow Mesoscale Convective Structure

2013 ◽  
Vol 141 (5) ◽  
pp. 1648-1672 ◽  
Author(s):  
Kelly M. Keene ◽  
Russ S. Schumacher
Keyword(s):  
Heavy Rainfall ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warm Season ◽  
Severe Weather ◽  
Cold Pool ◽  
Horizontal Shear ◽  
Convective Systems ◽  
Wind Speeds ◽  
Bow And Arrow

Abstract The accurate prediction of warm-season convective systems and the heavy rainfall and severe weather associated with them remains a challenge for numerical weather prediction models. This study looks at a circumstance in which quasi-stationary convection forms perpendicular to, and above the cold-pool behind strong bow echoes. The authors refer to this phenomenon as a “bow and arrow” because on radar imagery the two convective lines resemble an archer’s bow and arrow. The “arrow” can produce heavy rainfall and severe weather, extending over hundreds of kilometers. These events are challenging to forecast because they require an accurate forecast of earlier convection and the effects of that convection on the environment. In this study, basic characteristics of 14 events are documented, and observations of 4 events are presented to identify common environmental conditions prior to the development of the back-building convection. Simulations of three cases using the Weather Research and Forecasting Model (WRF) are analyzed in an attempt to understand the mechanisms responsible for initiating and maintaining the convective line. In each case, strong southwesterly flow (inducing warm air advection and gradual isentropic lifting), in addition to directional and speed convergence into the convective arrow appear to contribute to initiation of convection. The linear orientation of the arrow may be associated with a combination of increased wind speeds and horizontal shear in the arrow region. When these ingredients are combined with thermodynamic instability, there appears to be a greater possibility of formation and maintenance of a convective arrow behind a bow echo.

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