Exploring the differences in SPC convective outlook interpretation using categorical and numeric information

While previous work has shown the Storm Prediction Center (SPC) convective outlooks accurately capture meteorological outcomes, evidence suggests stakeholders and the public may misinterpret the categorical words currently used in the product. This work attempts to address this problem by investigating public reactions to alternative information formats that include numeric information: (1) numeric risk levels (i.e., “Level 2 of 5”) and (2) numeric probabilities (i.e., “a 5% chance”). In addition, it explores how different combinations of the categorical labels with numeric information may impact public reactions to the product. Survey data comes from the 2020 Severe Weather and Society Survey, a nationally representative survey of US adults. Participants were shown varying combinations of the information formats of interest, and then rated their concern about the weather and the likelihood of changing plans in response to the given information. Results indicate that providing numeric information (in the form of levels or probabilities) increases the likelihood of participants correctly interpreting the convective outlook information relative to categorical labels alone. Including the categorical labels increases misinterpretation, regardless of whether numeric information was included alongside the labels. Finally, findings indicate participants’ numeracy (or their ability to understand and work with numbers) had an impact on correct interpretation of the order of the outlook labels. Although there are many challenges to correctly interpreting the SPC convective outlook, using only numeric labels instead of the current categorical labels may be a relatively straightforward change that could improve public interpretation of the product.

Tornados in Deutschland: Räumliche und zeitliche Verteilung sowie typische Wetterlagen

<p>Tornados stellen eine Bedrohung für Leib und Leben dar und die durch sie verursachten Schäden können erhebliche wirtschaftliche Auswirkung haben. Welche Folgen ein Tornado in besiedelten Gebiet anrichten kann, zeigte nicht zuletzt der Tornado von Hodonín am 24. Juni 2021 mit sechs Todesopfern. Aber auch in Deutschland haben Tornados in jüngster Vergangenheit teils beträchtliche Schäden verursacht, wie beispielsweise in Bützow am 05.05.2015.</p> <p> </p> <p>Auf Basis der Europäischen Unwetterdatenbank (ESWD – European Severe Weather Database) wurden Statistiken über die zeitliche und räumliche Verteilung von Tornados erstellt. Die Daten reichen zurück bis in das Jahr 689. Aufgrund der Entwicklung moderner Kommunikations- und Dokumentationstechniken ist ab dem Jahr 2000 eine ausreichend gesicherte Datenbasis verfügbar, sodass der Zeitraum 2000-2020 für detailliertere Analysen herangezogen wurde. Mittels statistischer Extrapolation lässt sich zeigen, dass im Jahr durchschnittlich 67 Tornados (inkl. 17 Wasserhosen) in Deutschland auftreten, die vornehmlich im Mai und Juni in den späten Nachmittagsstunden zu beobachten sind. Bezüglich der räumlichen Verteilung starker Tornados (F2+) lassen sich bevorzugte Regionen finden, wo diese gehäuft auftreten. Eine mögliche Ursache für die Häufungen stellt die Orographie in Wechselwirkung mit der Anströmungsrichtung dar.</p> <p> </p> <p>Zusätzlich zu den statistischen Betrachtungen wurden auch typische synoptische und mesoskalige Bedingungen für das Auftreten von Tornados analysiert. Für diese Untersuchungen wurden alle Lagen mit starken Tornados (F2+) zwischen 2013 und 2020 herangezogen. Neben bevorzugten Großwetterlagen wurden auch andere Charakteristika beleuchtet, wie die Art der Konvektion oder die Konvektionszutaten. Daraus ergaben sich viele interessante Erkenntnisse, wie die Tatsache, dass es häufig im Vorfeld eines Tornadoereignisses Niederschlag gab oder, dass bestimmte Gewitter wiederholt Tornados hervorbringen können.</p> <p> </p> <p>Die gewonnenen Erkenntnisse können helfen, das Bewusstsein von Vorhersagemeteorologen für potentielle Tornadolagen zu stärken.</p> <p> </p>

Applications of satellite imageries for prediction of severe weather events along Maitri, Antarctica

The present paper is an attempt to educate the weather forecaster at Indian Scientific research Station, Maitri, Antarctica in forecasting severe weather (blizzards) using satellite images of various satellites operating in the world. The Polar stationary satellite ‘Trianna’ has been discussed .The availability of the various types of images has also been spelt. The characteristics of satellite images has been described along with overlaying of Automatic Weather station (AWS) data explaining the occurrence of Katabatic winds which also causes blizzards. Blizzard conditions often develop on the northwest side of an intense storm system the difference between the lower pressure in the storm and the higher pressure to the west creates a tight pressure gradient, which in turn results in very strong winds or blizzards. The paper also discusses about the development that took place in Antarctic satellite meteorology since beginning.

Demographic profiles and environmental drivers of variation relate to individual breeding state in a long-lived trans-oceanic migratory seabird, the Manx shearwater

Understanding the points in a species breeding cycle when they are most vulnerable to environmental fluctuations is key to understanding interannual demography and guiding effective conservation and management. Seabirds represent one of the most threatened groups of birds in the world, and climate change and severe weather is a prominent and increasing threat to this group. We used a multi-state capture-recapture model to examine how the demographic rates of a long-lived trans-oceanic migrant seabird, the Manx shearwater Puffinus puffinus, are influenced by environmental conditions experienced at different stages of the annual breeding cycle and whether these relationships vary with an individual’s breeding state in the previous year (i.e., successful breeder, failed breeder and non-breeder). Our results imply that populations of Manx shearwaters are comprised of individuals with different demographic profiles, whereby more successful reproduction is associated with higher rates of survival and breeding propensity. However, we found that all birds experienced the same negative relationship between rates of survival and wind force during the breeding season, indicating a cost of reproduction (or central place constraint for non-breeders) during years with severe weather conditions. We also found that environmental effects differentially influence the breeding propensity of individuals in different breeding states. This suggests individual spatio-temporal variation in habitat use during the annual cycle, such that climate change could alter the frequency that individuals with different demographic profiles breed thereby driving a complex and less predictable population response. More broadly, our study highlights the importance of considering individual-level factors when examining population demography and predicting how species may respond to climate change.

Storm Warnings: Time Sensitive Proximity

Weather-predictive tasks during high risk severe weather events are carried out for the common good of the community by virtual teams of weather professionals. Severe weather predictors are responsible for producing the early warnings that inform people in harms way and potentially save lives. Should we be concerned with the use of “other-generated” information from social media used by these professionals? Teams extend understanding of an event by looking to external sources of situationally relevant information such as storm spotters, publicly generated photos and comments posted to online social media (OSM), and communication with community partners. Situationally relevant OSM, specifically Twitter, provides insight to the information behavior of the team. Here we examine the role of proximity and how it impacts decisions on potentially life-saving information sharing in time sensitive information environments: proximity within the team (shared knowledge state) and proximity to the event (hashtag) specifically are addressed.

The Societal Echo of Severe Weather Events: Ambient Geospatial Information (AGI) on a Storm Event

The given article focuses on the benefit of harvested Ambient Geographic Information (AGI) as complementary data sources for severe weather events and provides methodical approaches for the spatio-temporal analysis of such data. The perceptions and awareness of Twitter users posting about severe weather patterns were explored as there were aspects not documented by official damage reports or derived from official weather data. We analysed Tweets regarding the severe storm event Friederike to map their spatio-temporal patterns. More than 50% of the retrieved >23.000 tweets were geocoded by applying supervised information retrievals, text mining, and geospatial analysis methods. Complementary, central topics were clustered and linked to official weather data for cross-evaluation. The data confirmed (1) a scale-dependent relationship between the wind speed and the societal echo. In addition, the study proved that (2) reporting activity is moderated by population distribution. An in-depth analysis of the crowds’ central topic clusters in response to the storm Friederike (3) revealed a plausible sequence of dominant communication contents during the severe weather event. In particular, the merge of the studied AGI and other environmental datasets at different spatio-temporal scales shows how such user-generated content can be a useful complementary data source to study severe weather events and the ensuing societal echo.