An Hourly Climatology of Operational MRMS MESH-Diagnosed Severe and Significant Hail with Comparisons to Storm Data Hail Reports

2021 ◽  
Author(s):  
Nathan A. Wendt ◽  
Israel L. Jirak
Keyword(s):  
Population Density ◽  
Real Time ◽  
Risk Information ◽  
Severe Weather ◽  
Radar Reflectivity ◽  
Real Time Monitoring ◽  
National Weather Service ◽  
Convective Weather ◽  
The Relationship ◽  
Weather Service

AbstractThe multi-radar/multi-sensor (MRMS) system generates an operational suite of derived products in the NationalWeather Service useful for real-time monitoring of severe convective weather. One such product generated byMRMSis the maximum estimated size of hail (MESH) that estimates hail size based on the radar reflectivity properties of a storm above the environmental 0 °C level. The MRMS MESH product is commonly used across the National Weather Service (NWS), including the Storm Prediction Center (SPC), to diagnose the expected hail size in thunderstorms. Previous work has explored the relationship between the MRMS MESH product and severe hail (≥ 25.4 mm or 1 in.) reported at the ground. This work provides an hourly climatology of severe MRMS MESH across the contiguous U.S. from 2012–2019, including an analysis of how the MESH climatology differs from the severe hail reports climatology. Results suggest that the MESH can provide beneficial hail risk information in areas where population density is low. Evidence shows that the MESH can provide potentially beneficial information about severe hail occurrence during the night in locations that are climatologically favored for upscale convective growth and elevated convection. These findings have important implications for the use of MESH as a verification dataset for SPC probabilistic hail forecasts as well as severe weather watch decisions in areas of higher hail risk but low population density.

Project - Balkan-Mediterranean Real Time Severe Weather Service

2019 ◽  
Author(s):  
H. Haralambous ◽  
C. Oikonomou ◽  
C. Pikridas ◽  
K. Lagouvardos ◽  
V. Kotroni ◽  
...  
Keyword(s):  
Real Time ◽  
Severe Weather ◽  
Weather Service

Multidisciplinary Analysis of an Unusual Tornado: Meteorology, Climatology, and the Communication and Interpretation of Warnings*

2010 ◽  
Vol 25 (5) ◽  
pp. 1412-1429 ◽  
Author(s):  
Russ S. Schumacher ◽  
Daniel T. Lindsey ◽  
Andrea B. Schumacher ◽  
Jeff Braun ◽  
Steven D. Miller ◽  
...  
Keyword(s):  
Time Of Day ◽  
Severe Weather ◽  
Decision Makers ◽  
Future Research ◽  
Front Range ◽  
National Weather Service ◽  
Weather Information ◽  
Multidisciplinary Analysis ◽  
Local Decision ◽  
Weather Service

Abstract On 22 May 2008, a strong tornado—rated EF3 on the enhanced Fujita scale, with winds estimated between 136 and 165 mi h−1 (61 and 74 m s−1)—caused extensive damage along a 55-km track through northern Colorado. The worst devastation occurred in and around the town of Windsor, and in total there was one fatality, numerous injuries, and hundreds of homes significantly damaged or destroyed. Several characteristics of this tornado were unusual for the region from a climatological perspective, including its intensity, its long track, its direction of motion, and the time of day when it formed. These unusual aspects and the high impact of this tornado also raised a number of questions about the communication and interpretation of information from National Weather Service watches and warnings by decision makers and the public. First, the study examines the meteorological circumstances responsible for producing such an outlier to the regional severe weather climatology. An analysis of the synoptic and mesoscale environmental conditions that were favorable for significant tornadoes on 22 May 2008 is presented. Then, a climatology of significant tornadoes (defined as those rated F2 or higher on the Fujita scale, or EF2 or higher on the Enhanced Fujita scale) near the Front Range is shown to put the 22 May 2008 event into climatological context. This study also examines the communication and interpretation of severe weather information in an area that experiences tornadoes regularly but is relatively unaccustomed to significant tornadoes. By conducting interviews with local decision makers, the authors have compiled and chronicled the flow of information as the event unfolded. The results of these interviews demonstrate that the initial sources of warning information varied widely. Decision makers’ interpretations of the warnings also varied, which led to different perceptions on the timeliness and clarity of the warning information. The decision makers’ previous knowledge of the typical local characteristics of tornadoes also affected their interpretations of the tornado threat. The interview results highlight the complex series of processes by which severe weather information is communicated after a warning is issued by the National Weather Service. The results of this study support the growing recognition that societal factors are just as important to the effectiveness of weather warnings as the timeliness of and information provided in those warnings, and that these factors should be considered in future research in addition to the investments and attention given to improving detection and warning capabilities.

scholarly journals The Estimation of Chemical Oxygen Demand of Erhai Lake Basin and Its Links with DOM Fluorescent Components Using Machine Learning

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3629
Author(s):  
Yuquan Zhao ◽  
Jian Shen ◽  
Jimeng Feng ◽  
Zhitong Sun ◽  
Tianyang Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Chemical Oxygen Demand ◽  
Real Time ◽  
Oxygen Demand ◽  
Organic Pollution ◽  
Lake Basin ◽  
Erhai Lake ◽  
Real Time Monitoring ◽  
The Relationship

Water quality estimation tools based on real-time monitoring are essential for the effective management of organic pollution in watersheds. This study aims to monitor changes in the levels of chemical oxygen demand (COD, CODMn) and dissolved organic matter (DOM) in Erhai Lake Basin, exploring their relationships and the ability of DOM to estimate COD and CODMn. Excitation emission matrix–parallel factor analysis (EEM–PARAFAC) of DOM identified protein-like component (C1) and humic-like components (C2, C3, C4). Combined with random forest (RF), maximum fluorescence intensity (Fmax) values of components were selected as estimation parameters to establish models. Results proved that the COD of rivers was more sensitive to the reduction in C1 and C2, while CODMn was more sensitive to C4. The DOM of Erhai Lake thrived by internal sources, and the relationship between COD, CODMn, and DOM of Erhai Lake was more complicated than rivers (inflow rivers of Erhai Lake). Models for rivers achieved good estimations, and by adding dissolved oxygen and water temperature, the estimation ability of COD models for Erhai Lake was significantly improved. This study demonstrates that DOM-based machine learning can be used as an alternative tool for real-time monitoring of organic pollution and deepening the understanding of the relationship between COD, CODMn, and DOM, and provide a scientific basis for water quality management.

The United States National Weather Service Real-Time Flood Forecasting

2017 ◽  
Author(s):  
Pedro J. Restrepo
Keyword(s):  
Real Time ◽  
Snow Water Equivalent ◽  
Weather Forecast ◽  
The United States ◽  
Flood Forecasting ◽  
Federal State ◽  
National Weather Service ◽  
Precipitation Temperature ◽  
Snow Water ◽  
Weather Service

The U.S. National Weather Service (NWS) is the agency responsible for flood forecasting. Operational flow forecasting at the NWS is carried out at the 13 river forecasting centers for main river flows. Flash floods, which occur in small localized areas, are forecast at the 122 weather forecast offices. Real-time flood forecasting is a complex process that requires the acquisition and quality control of remotely sensed and ground-based observations, weather and climate forecasts, and operation of reservoirs, water diversions, and returns. Currently used remote-sense observations for operational hydrologic forecasts include satellite observations of precipitation, temperature, snow cover, radar observations of precipitation, and airborne observations of snow water equivalent. Ground-based observations include point precipitation, temperature, snow water equivalent, soil moisture and temperature, river stages, and discharge. Observations are collected by a number of federal, state, municipal, tribal and private entities, and transmitted to the NWS on a daily basis. Once the observations have been checked for quality, a hydrologic forecaster uses the Community Hydrologic Prediction System (CHPS), which takes care of managing the sequence of models and their corresponding data needs along river reaches. Current operational forecasting requires an interaction between the forecaster and the models, in order to adjust differences between the model predictions and the observations, thus improving the forecasts. The final step in the forecast process is the publication of forecasts.

BalkanMed real time severe weather service: Progress and prospects in Bulgaria

2020 ◽  
Vol 66 (12) ◽  
pp. 2844-2853 ◽  
Author(s):  
Guergana Guerova ◽  
Tsvetelina Dimitrova ◽  
Keranka Vassileva ◽  
Martin Slavchev ◽  
Krasimir Stoev ◽  
...  
Keyword(s):  
Real Time ◽  
Severe Weather ◽  
Weather Service

Data Mining Approaches to the Real-Time Monitoring and Early Warning of Convective Weather Using Lightning Data

2019 ◽  
pp. 83-101
Author(s):  
Stephan Stephany ◽  
Cesar Strauss ◽  
Alan James Peixoto Calheiros ◽  
Glauston Roberto Teixeira de Lima ◽  
João Victor Cal Garcia ◽  
...  
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Early Warning ◽  
Real Time Monitoring ◽  
The Real ◽  
Convective Weather ◽  
Monitoring And Early Warning

scholarly journals Seasonal Variations in Severe Weather Forecast Skill in an Experimental Convection-Allowing Model

2017 ◽  
Vol 32 (5) ◽  
pp. 1885-1902 ◽  
Author(s):  
Ryan A. Sobash ◽  
John S. Kain
Keyword(s):  
Warm Season ◽  
Weather Forecast ◽  
Severe Weather ◽  
Cool Season ◽  
Limited Sample ◽  
Seasonal Behavior ◽  
Severe Convection ◽  
Convective Weather ◽  
The Relationship ◽  
Do So

Abstract Eight years of daily, experimental, deterministic, convection-allowing model (CAM) forecasts, produced by the National Severe Storms Laboratory, were evaluated to assess their ability at predicting severe weather hazards over a diverse collection of seasons, regions, and environments. To do so, forecasts of severe weather hazards were produced and verified as in previous studies using CAM output, namely by thresholding the updraft helicity (UH) field, smoothing the resulting binary field to create surrogate severe probability forecasts (SSPFs), and verifying the SSPFs against observed storm reports. SSPFs were most skillful during the spring and fall, with a relative minimum in skill observed during the summer. SSPF skill during the winter months was more variable than during other seasons, partly due to the limited sample size of events, but was often less than that during the warm season. The seasonal behavior of SSPF skill was partly driven by the relationship between the UH threshold and the likelihood of obtaining severe storm reports. Varying UH thresholds by season and region produced SSPFs that were more skillful than using a fixed UH threshold to identify severe convection. Accounting for this variability was most important during the cool season, when a lower UH threshold produced larger SSPF skill compared to warm-season events, and during the summer, when large differences in skill occurred within different parts of the continental United States (CONUS), depending on the choice of UH threshold. This relationship between UH threshold and SSPF skill is discussed within the larger scope of generating skillful CAM-based guidance for hazardous convective weather and verifying CAM predictions.

BeRTISS project: Balkan-Mediterranean real-time severe weather service

2018 ◽  
Author(s):  
Christina Oikonomou ◽  
Christos Pikridas ◽  
Guergana Guerova ◽  
Tsvetelina Dimitrova ◽  
Konstantinos Lagouvardos ◽  
...  
Keyword(s):  
Real Time ◽  
Severe Weather ◽  
Weather Service

scholarly journals Tornado Casualties Depend More on Storm Energy Than Population

Eos ◽  
2017 ◽  
Author(s):  
Katherine Kornei
Keyword(s):  
National Weather Service ◽  
Service Data ◽  
The Relationship ◽  
Weather Service

National Weather Service data from nearly 900 tornadoes and a principle of economics reveal the relationship between storm energy, population, and casualty count.

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