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

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 – 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>

scholarly journals Evolution of Radar and Lightning Variables in Convective Events in Barcelona and Surroundings for the Period 2006–2020

2020 ◽  
Vol 02 (02) ◽  
pp. 1-1
Author(s):  
Tomeu Rigo ◽  
◽  
Sergio Castillo ◽  
Keyword(s):  
Remote Sensing Data ◽  
Severe Weather ◽  
North East ◽  
The North ◽  
Weather Events ◽  
Adverse Weather ◽  
Populated Region ◽  
Surrounding Areas ◽  
Near Future

The Metropolitan Area of Barcelona is a densely populated region in the North-East of the Iberian Peninsula. Infrastructures in this area play a significant role in the economy of this part of Europe. The combination of the Mediterranean Sea and the complex topography is responsible for the occurrence of severe weather events in this location and the surrounding areas. The use of remote sensing data in an hourly resolution allows the identification and characterization of those severe episodes, helping in determining the future trends of the adverse weather. This fact is crucial in the development of new engineering projects, as well as in the maintenance of the current ones. Weather radar and lightning observations have enabled the monitoring of an increase in severe weather occurrence and, in addition, the prime characteristics of the thunderstorms responsible for producing them. Deepening vertical developments, the presence of hail, and the decrease of the rainfall efficiency are some of the characteristics that must be taken into account in the near future.

scholarly journals Development of Mental Health Classification Related to Severe Weather Events

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Alvin F. Chu ◽  
Stella Tsai ◽  
Teresa Hamby ◽  
Elizabeth Kostial ◽  
Jerald Fagliano
Keyword(s):  
Mental Health ◽  
Health Monitoring ◽  
Weather Conditions ◽  
Severe Weather ◽  
Monitoring Systems ◽  
The Public ◽  
Adverse Weather Conditions ◽  
Weather Events ◽  
Adverse Weather ◽  
Health Monitoring Systems

Real-time emergency department (ED) data are currently received from 78 of 80 New Jersey acute care and satellite EDs by Health Monitoring Systems Inc. (HMS) EpiCenter system. After the 2012 Superstorm Sandy, NJDOH initiated a plan to develop severe weather surveillance using ED data to track both health and mental health outcomes during adverse weather conditions to alert the public about emerging health hazards. Data from 68 out of a total of 80 emergency departments with dates from October 28, 2012 to November 17, 2012 were used in this analysis. Validation results for classifications were reviewed and issues are addressed.

scholarly journals Perception and Comprehension of the Extended Forecast Graphic: A Survey of Broadcast Meteorologists and the Public

2020 ◽  
Vol 101 (2) ◽  
pp. E221-E236 ◽  
Author(s):  
Jacob R. Reed ◽  
Jason C. Senkbeil
Keyword(s):  
Risk Communication ◽  
Low Temperatures ◽  
General Public ◽  
Weather Forecast ◽  
The Public ◽  
Weather Events ◽  
Public Data ◽  
Adverse Weather ◽  
High And Low Temperatures ◽  
Probability Of Precipitation

Abstract There have been multiple efforts in recent years to simplify visual weather forecast products, with the goal of more efficient risk communication for the general public. Many meteorological forecast products, such as the cone of uncertainty, storm surge graphics, warning polygons, and Storm Prediction Center (SPC) convective outlooks, have created varying levels of public confusion resulting in revisions, modifications, and improvements. However, the perception and comprehension of private weather graphics produced by television stations has been largely overlooked in peer-reviewed research. The goal of this study is to explore how the extended forecast graphic, more commonly known as the 7, 10 day, etc., is utilized by broadcasters and understood by the public. Data were gathered from surveys with the general public and also from broadcast meteorologists. Results suggest this graphic is a source of confusion and highlights a disconnect between the meteorologists producing the graphic and the content prioritized by their audiences. Specifically, timing and intensity of any precipitation or adverse weather events are the two most important variables to consider from the viewpoint of the public. These variables are generally absent from the extended forecast graphic, thus forcing the public to draw their own conclusions, which may differ from what the meteorologist intends to convey. Other results suggest the placement of forecast high and low temperatures, use of probability of precipitation, icon inconsistency, and length of time the graphic is shown also contribute to public confusion and misunderstanding.

Colorful Language: Investigating Public Interpretation of the Storm Prediction Center Convective Outlook

2021 ◽  
Author(s):  
Sean Ernst ◽  
Joe Ripberger ◽  
Makenzie J. Krocak ◽  
Hank Jenkins-Smith ◽  
Carol Silva
Keyword(s):  
Statistical Tests ◽  
Weather Forecast ◽  
Severe Weather ◽  
Logistic Regression Models ◽  
Public Interpretation ◽  
Severe Thunderstorms ◽  
Weather Events ◽  
The Us ◽  
Storm Prediction ◽  
Future Work

AbstractAlthough severe weather forecast products, such as the Storm Prediction Center (SPC) convective outlook, are much more accurate than climatology at day-to-week time scales, tornadoes and severe thunderstorms claim dozens of lives and cause billions of dollars in damage every year. While the accuracy of this outlook has been well documented, less work has been done to explore the comprehension of the product by non-expert users like the general public. This study seeks to fill this key knowledge gap by collecting data from a representative survey of U.S. adults in the lower 48 states about their use and interpretation of the SPC convective outlook. Participants in this study were asked to rank the words and colors used in the outlook from least to greatest risk, and their answers were compared through visualizations and statistical tests across multiple demographics. Results show that the US public ranks the outlook colors similarly to their ordering in the outlook but switch the positions of several of the outlook words as compared to the operational product. Logistic regression models also reveal that more numerate individuals more correctly rank the SPC outlook words and colors. These findings suggest that the words used in the convective outlook may confuse non-expert users, and that future work should continue to use input from public surveys to test potential improvements in the choice of outlook words. Using more easily understood words may help to increase the outlook’s decision support value and potentially reduce the harm caused by severe weather events.

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

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).

scholarly journals Severe weather over the Highveld of South Africa during November 2016

Water SA ◽  
2018 ◽  
Vol 44 (1 January) ◽  
Author(s):  
Lee-ann Simpson ◽  
Liesl L Dyson
Keyword(s):  
South Africa ◽  
Prediction Models ◽  
Weather Forecast ◽  
Severe Weather ◽  
Severe Thunderstorm ◽  
Weather Events ◽  
Circulation Patterns ◽  
Conditional Instability ◽  
Synoptic Circulation

November months are notorious for severe weather over the Highveld of South Africa. November 2016 was no exception and a large number of severe events occurred. Very heavy rainfall, large hail and tornadoes were reported. The aim of this paper is to compare the synoptic circulation of November 2016 with the long-term mean November circulation and to investigate some sounding derived parameters. Furthermore, a few of the severe weather events are described in detail. The surface temperatures and dewpoint temperatures were found to be higher than normal resulting in increased conditional instability over the Highveld. Low-level moisture originated over the warm Mozambique Channel and the 500 hPa temperature trough was located favourably over the Highveld; further east than normal. The combination of these factors and weak steering winds resulted in flash flooding on the 9th while favourable wind shear conditions caused the development of a tornado on 15 November. The favourable circulation patterns and moisture gave rise to an atmosphere in which severe weather was a possibility, and the awareness of such factors is used as one of many tools when considering the severe weather forecast. The consideration of the daily variables derived from sounding data were good precursors for the prediction of severe thunderstorm development over the Highveld during November 2016. It is recommended that an operational meteorologist incorporates upper air sounding data into the forecasting process and not to rely on numerical prediction models exclusively.

scholarly journals Navigating Monsters: Credibility in the Twittersphere

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Carrie Boettcher
Keyword(s):  
Situation Awareness ◽  
Information Behavior ◽  
Relevant Information ◽  
Severe Weather ◽  
Dynamic Information ◽  
Weather Events ◽  
Cognitive Authority ◽  
High Level ◽  
Information Landscape

The increasing use of OSM during emergency, or potentially threatening, situations creates conditions in which emergency planners and responders need a high level of investigative skill to weed through a dynamic information landscape to determine the quality of information to contribute to improved situation awareness. This weeding process transforms the big data environment of OSM to focused information retrieval. Inquiry into indicators of quality in OSM (authority, objectivity, currency, coverage, and glyphicality) during severe weather situations informs how OSM impacts the information behavior of the severe weather enterprise of the U. S. Specifically, this paper focuses on investigation into how a particular element of the severe weather enterprise in the Midwest, the integrated warning team (IWT), identifies relevant information in OSM during severe weather events. This paper describes the theoretical framework of an inquiry into information behavior of the IWT during severe weather events through the lens of cognitive authority theory (Wilson, 1983) and Bonnici’s (2016) CAF-QIS for understanding the phenomena of both credibility and trustworthiness in the Twittersphere where author is potentially unknown.

scholarly journals Internet of Things Based Weather Forecast Monitoring System

2018 ◽  
Vol 9 (3) ◽  
pp. 555 ◽  
Author(s):  
Atul Kulkarni ◽  
Debajyoti Mukhopadhyay
Keyword(s):  
Weather Forecasting ◽  
Weather Forecast ◽  
Severe Weather ◽  
Weather Data ◽  
Partial Explanation ◽  
Significant Function ◽  
Weather Events ◽  
Weather Stations ◽  
Communication Methods ◽  
Cost Components

<p>Weather forecasting is a significant function in meteorology and has been one of the most systematically challenging troubles around the world.This scheme deals with the structure of a weather display method using small cost components so that any electronics hobbyist can construct it. As a replacement for using sensors to collect the weather data, the development gets the information from weather stations placed around the world through a global weather data supplier. Severe weather phenomena challengedifficult weather forecast approach with the partial explanation. Weather events have numerous parameters that are not possible to detail and compute. Growing on communication methods enables weather predictsspecialist systems to combine and share possessions and thus hybrid systems have emerged. Still, though these improvements on climate predict, these expert systems can’t be entirely reliable while weather forecast is central problem.</p>

scholarly journals The NOAA/CIMSS ProbSevere Model: Incorporation of Total Lightning and Validation

2018 ◽  
Vol 33 (1) ◽  
pp. 331-345 ◽  
Author(s):  
John L. Cintineo ◽  
Michael J. Pavolonis ◽  
Justin M. Sieglaff ◽  
Daniel T. Lindsey ◽  
Lee Cronce ◽  
...  
Keyword(s):  
Lead Time ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Severe Weather ◽  
Short Term ◽  
Lightning Detection ◽  
Empirical Probability ◽  
Storm Cells ◽  
Total Lightning ◽  
Meteorological Satellite

Abstract The empirical Probability of Severe (ProbSevere) model, developed by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS), automatically extracts information related to thunderstorm development from several data sources to produce timely, short-term, statistical forecasts of thunderstorm intensity. More specifically, ProbSevere utilizes short-term numerical weather prediction guidance (NWP), geostationary satellite, ground-based radar, and ground-based lightning data to determine the probability that convective storm cells will produce severe weather up to 90 min in the future. ProbSevere guidance, which updates approximately every 2 min, is available to National Weather Service (NWS) Weather Forecast Offices with very short latency. This paper focuses on the integration of ground-based lightning detection data into ProbSevere. In addition, a thorough validation analysis is presented. The validation analysis demonstrates that ProbSevere has slightly less skill compared to NWS severe weather warnings, but can offer greater lead time to initial hazards. Feedback from NWS users has been highly favorable, with most forecasters responding that ProbSevere increases confidence and lead time in numerous warning situations.

scholarly journals Detection of Severe Weather Events in a High-Resolution Ensemble Prediction System Using the Extreme Forecast Index (EFI) and Shift of Tails (SOT)

2018 ◽  
Vol 33 (4) ◽  
pp. 901-908
Author(s):  
Laure Raynaud ◽  
Benoît Touzé ◽  
Philippe Arbogast
Keyword(s):  
Reference Model ◽  
Weather Forecast ◽  
Ensemble Forecast ◽  
Severe Weather ◽  
Ensemble Prediction ◽  
False Alarms ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Wind Gusts ◽  
Weather Events

Abstract The extreme forecast index (EFI) and shift of tails (SOT) are commonly used to compare an ensemble forecast to a reference model climatology, in order to measure the severity of the current weather forecast. In this study, the feasibility and the relevance of EFI and SOT computations are examined within the convection-permitting Application of Research to Operations at Mesoscale (AROME-France) ensemble prediction system (EPS). First, different climate configurations are proposed and discussed, in order to overcome the small size of the ensemble and the short climate sampling length. Subjective and objective evaluations of EFI and SOT for wind gusts and precipitation forecasts are then presented. It is shown that these indices can provide relevant early warnings and, based on a trade-off between hits and false alarms, optimal EFI thresholds can be determined for decision-making.

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