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

Author(s):  
Christina Oikonomou ◽  
Christos Pikridas ◽  
Guergana Guerova ◽  
Tsvetelina Dimitrova ◽  
Konstantinos Lagouvardos ◽  
...  
Author(s):  
H. Haralambous ◽  
C. Oikonomou ◽  
C. Pikridas ◽  
K. Lagouvardos ◽  
V. Kotroni ◽  
...  

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

Author(s):  
Nathan A. Wendt ◽  
Israel L. Jirak

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.


2014 ◽  
Vol 15 (5) ◽  
pp. 1989-1998 ◽  
Author(s):  
Francesco Di Paola ◽  
Elisabetta Ricciardelli ◽  
Domenico Cimini ◽  
Filomena Romano ◽  
Mariassunta Viggiano ◽  
...  

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.


2010 ◽  
Vol 25 (5) ◽  
pp. 1412-1429 ◽  
Author(s):  
Russ S. Schumacher ◽  
Daniel T. Lindsey ◽  
Andrea B. Schumacher ◽  
Jeff Braun ◽  
Steven D. Miller ◽  
...  

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.


2021 ◽  
Author(s):  
Santiago Gaztelumendi

<p>Although social media industry is now a very congested Marketplace, Twitter continues to maintain its status as a popular social media platform. There are 330 million monthly active users and 145 million daily active users on Twitter sending more than 6,000 tweets every second in the world. In Spain case 85% population are social media users, with around 5 million tweeter profiles for a population around 47 million. In the autonomous community of Basque country (2.17 million inhabitants) around 20% of citizens use Twitter.</p><p>Twitter is a social tool that enables users to post messages (tweets) of up to 280 characters supporting a wide variety of social communication practices including photo and video attach. The Basque Meteorology Agency @Euskalmet with more than 115,3 K followers is one of the most popular accounts in Basque Country. Twitter is not only an opportunity to instantaneous spread messages to people without intermediaries, but also as a potential platform for valuable data acquisition using tweeter API capabilities. In this contribution, we present a study of different aspects related to the operational use of Twitter data in the context of high impact weather scenarios at local level.</p><p>The most important activity in Euskalmet are actions in severe weather events. Before the event, mainly centered in forecast and communication, during the event in nowcast, surveillance and impact monitoring and after the event in post-event analysis. During all these complex processes real time tweets posted by local users offer a huge amount of data that conveniently processed could be useful for different purposes. For operational staff, working at office during severe weather episodes, is critical to understand the local effects that an adverse phenomenon is causing and the correct perception of the extent of impact and social alarm. For this purposes, among others, different information associated with posted tweets can be extracted and exploited conveniently. In this work, we present some results that demonstrate how different data mining and advances analytics techniques can be used in order to include social media data information for different tasks and particularly during high impact weather events.</p><p>In this paper we summarize our experience during a proof of concept project for automatic real time tweeter analysis and the development of an operational tool for tweeter API data exploitation in the Basque Country. We present the main challenges and problems that we have had to face, including how to deal with the lack of geolocation information, since in the case of the Basque country, as in other parts of the world, tweets containing geotags are the exception, not the rule.</p>


Author(s):  
J. Craig Prather ◽  
Michael Bolt ◽  
Haley Harrell ◽  
Tyler Horton ◽  
Mark L. Adams

Weather affects many aspects of our daily lives from our individual commutes to the global economy. Although much progress has been made in understanding atmospheric physics and weather forecasting, there is still a need for better in situ atmospheric data. Forecasts are based on high performance computer models which solve the differential equations that represent the dynamics of the atmosphere. In all of these models, initial conditions based on the current state of the atmosphere are ingested into the models. The initial conditions are based on data from many sources including remote sensing satellites, ground based weather stations, weather balloons and even aircraft. However, the amount of in situ atmospheric data is very limited and so often times the initial conditions for the models are not truly representative of the current atmosphere. This is especially true for severe storms such as super cell thunderstorms, tornadoes, and hurricanes. Severe weather impacts millions of people every year costing both human life and substantial resources. A better understanding of severe weather will have a significant impact on human safety and infrastructure protection. Electronics miniaturization and advances in manufacturing such as 3D printing have allowed for the development of low-cost, light-weight probes capable of providing real-time in situ information about the atmosphere which can improve forecasts models and provide a better understanding to atmospheric scientists. The probes provide temperature, relative humidity, pressure, position, and velocity data. MEMS sensors are used to monitor the ambient weather conditions and an on-board GPS receiver provides position information. The sensors are combined with a microcontroller and radio to transmit data back to a receiver on the ground. Power is provided by zinc-air batteries and antennas for both the GPS and data radio are integrated into the package. In order to ensure correct operation of the electronics, 3D printing is used to generate a custom electronics/mechanical package that is both functional and robust while maintaining low weight and high drag coefficient. The desire is for the probes to stay airborne as long as possible without any active means of propulsion or buoyancy. The probes designed are small, light-weight, and low cost. They can be deployed from aircraft, weather balloons, or dropped directly into a storm. The design of the probes was simulated through CFD to determine the optimal mechanical packaging of the device. The probes have been tested to validate the range of the probes and the accuracy of the measurements. Although most probes can be recovered after testing, designs focus on minimizing the environmental impact of unrecovered probes. This was done by utilizing 3D printing to create custom mechanical packaging for the electronics that is environmentally friendly along with using zinc air batteries which are a less hazardous battery chemistry. The devices have been designed, fabricated, and tested and the results will be presented. This paper will explain the design processes, design decisions, and testing procedures utilized along with the testing results.


Author(s):  
Thomas Kox ◽  
Catharina Lüder

AbstractThis article presents the results of a series of ethnographic observations at the Berlin fire brigade control and dispatch center during routine and severe weather situations. The weather-related challenges of a fire brigade lie between the anticipation of events and their potential consequences, and the ad hoc reactions to actual impacts of weather. The results show that decisions and actions related to high impact weather are not necessarily motivated by weather warnings alone. Instead, they are reactions to the experience of impacts, for example, an increased number of missions or emergency calls. Impacts are the main trigger for the decision making. Weather is one additional external factor that influences the operational capability of a fire brigade. While commanding officers in a fire brigade control and dispatch center experience weather primarily through technical equipment, verified by ground truth, observations showed that direct personal contact with the regional weather service and colleagues on the ground takes on a greater role in actual severe weather situations. The observations point to the need for increased interagency communication between the emergency services, the weather service, and other organizations to integrate weather information, impacts, and non-weather-related tasks into coherent weather-related decision making.


2009 ◽  
Vol 24 (1) ◽  
pp. 187-210 ◽  
Author(s):  
Kenneth A. James ◽  
David J. Stensrud ◽  
Nusrat Yussouf

Abstract Near-real-time values of vegetation fraction are incorporated into a 2-km nested version of the Advanced Research Weather Research and Forecasting (ARW) model and compared to forecasts from a control run that uses climatological values of vegetation fraction for eight severe weather events during 2004. It is hypothesized that an improved partitioning of surface sensible and latent heat fluxes occurs when incorporating near-real-time values of the vegetation fraction into models, which may result in improved forecasts of the low-level environmental conditions that support convection and perhaps even lead to improved explicit convective forecasts. Five of the severe weather events occur in association with weak synoptic-scale forcing, while three of the events occur in association with moderate or strong synoptic-scale forcing. Results show that using the near-real-time values of the vegetation fraction alters the values and structure of low-level temperature and dewpoint temperature fields compared to the forecasts using climatological vegetation fractions. The environmental forecasts that result from using the real-time vegetation fraction are more thermodynamically supportive of convection, including stronger and deeper frontogenetic circulations, and statistically significant improvements of most unstable CAPE forecasts compared to the control run. However, despite the improved environmental forecasts, the explicit convective forecasts using real-time vegetation fractions show little to no improvement over the control forecasts. The convective forecasts are generally poor under weak synoptic-scale forcing and generally good under strong synoptic-scale forcing. These results suggest that operational forecasters can best use high-resolution forecasts to help diagnose environmental conditions within an ingredients-based forecasting approach.


Sign in / Sign up

Export Citation Format

Share Document