Identifying Roles for Public Administrators to Foster Anticipatory Altruism for Severe Weather Preparednes

Will people choose behaviors that benefit all persons in the community before a severe weather event? Or, will they choose behaviors that benefit themselves only? Knowing the answer to these questions can inform public administrators about the level of anticipatory altruism in their community. With this knowledge, they can design strategic plans that leverage a willingness to coproduce community preparedness. Over four hundred people in six severe weather and tornado prone states answered survey questions inviting them to choose between severe weather preparation options that benefit the community or the individual. Overall, our findings suggest a modest level of support for community preparation options, anticipatory altruism demonstrated by a willingness to pay, and intergenerational equity in their choices. These findings are salient in a time when governments may need to cut funding and rely more and more on foundations, nonprofits, and private efforts to provide a wide range of services. Government planners and emergency managers can use our results to strategically nudge residents into making severe weather preparations as well as to estimate support for co-production efforts with citizens to prepare the community for disasters.

Evaluating the Impact of the 2020 Iowa Derecho on Corn and Soybean Fields Using Synthetic Aperture Radar

On 10 August 2020, a series of intense and fast-moving windstorms known as a derecho caused widespread damage across Iowa’s (the top US corn-producing state) agricultural regions. This severe weather event bent and flattened crops over approximately one-third of the state. Immediate evaluation of the disaster’s impact on agricultural lands, including maps of crop damage, was critical to enabling a rapid response by government agencies, insurance companies, and the agricultural supply chain. Given the very large area impacted by the disaster, satellite imagery stands out as the most efficient means of estimating the disaster impact. In this study, we used time-series of Sentinel-1 data to detect the impacted fields. We developed an in-season crop type map using Harmonized Landsat and Sentinel-2 data to assess the impact on important commodity crops. We intersected a SAR-based damage map with an in-season crop type map to create damaged area maps for corn and soybean fields. In total, we identified 2.59 million acres as damaged by the derecho, consisting of 1.99 million acres of corn and 0.6 million acres of soybean fields. Also, we categorized the impacted fields to three classes of mild impacts, medium impacts and high impacts. In total, 1.087 million acres of corn and 0.206 million acres of soybean were categorized as high impacted fields.

A comparison between 4D-VAR and cycling 3D-VAR methods for the simulation of a severe weather event in Central Italy. Preliminary results.

<p>Nowadays, the use of 4D-VAR assimilation technique has been investigated in several scientific papers with the aim of improving the localization and timing of precipitation in complex orography regions. The results show the positive impact in rainfall forecast but, the need to resolve the tangent linear and adjoint model makes the 4D-VAR computationally too expensive. Hence, it is used in operationally only in large forecast centres. To the aim of exploring a more reasonable method, a comparison between a cycling 3D-VAR, that needs less computational resources, and 4D-VAR techniques is performed for a severe weather event occurred in Central Italy. A cut-off low (992 hPa), located in western side of Sicily region, was associated with a strong south-easterly flow over Central Adriatic region, which supplied a large amount of warm and moist air. This mesoscale configuration, coupled with the Apennines mountain range that further increased the air column instability, produced heavy rainfall in Abruzzo region (Central Italy).</p><p>The numerical simulations are carried out using the Weather Research and Forecasting (WRF) model. In-situ surface and upper-air observations are assimilated in combination with radar reflectivity and radial velocity data over a high-resolution domain. Several experiments have been performed in order to evaluate the impact of 4D-VAR and cycling 3D-VAR in the precipitation forecast. In addition, a statistical analysis has been carried out to objectively compare the simulations. Two different verification approaches are used: Receiver Operating Characteristic (ROC) curve and Fraction Skill Score (FSS). Both statistical scores are calculated for different threshold values in the study area and in the sub-regions where the maximum rainfall occurred.</p>

Analysis of ionospheric disturbances caused by the severe weather event in Poland on 11th August 2017

<p>The coupling of the ionosphere with the tropospheric processes is a complex problem and the necessity of its resolve is highlighted in numerous publications. They mainly focus on lightning, hurricanes, tornadoes, as well as tsunamis, which induce disturbances in the ionosphere. Current works suggest that they are generated by the two major mechanisms: electrical effects during lightning, and atmospheric gravity waves propagated vertically and horizontally. However, these mechanisms are still not precisely examined.</p><p>The aim of this study is investigation of the coupling of severe weather event with ionosphere. This phenomenon, which can be classified as derecho occurred on 11th August 2017 in Poland. It was a 300 km length bow echo heavy storm, characterized by wind gusts of about 150 km/h, lightning, strong rain and hail drops. All these factors may have caused disturbances not only in the troposphere but also affect the ionosphere. In order to investigate a coupling mechanism and determination of morphological characteristics of the ionospheric disturbances, we used a dense network of GNSS receivers. Using GPS and GLONASS observations, we estimated total electron content (TEC) variations with 30-second interval. This has allowed to obtain high spatial and temporal resolution maps of ionospheric disturbances which have been compared with other data derived from in situ meteorological measurements, weather radars, and the Weather Research and Forecasting (WRF) numerical weather model. We investigated that during the main phase of the storm the wavy-like ionospheric disturbances occurred for some of the observed satellite with magnitude of about 0.2 TECU. In this work, we present detailed analysis of this event and discussion about troposphere-ionosphere coupling.</p>

Explicit Ensemble Prediction of Hail in 19 May 2013 Oklahoma City Thunderstorms and Analysis of Hail Growth Processes with Several Multimoment Microphysics Schemes

Hail forecast evaluations provide important insight into microphysical treatment of rimed ice. In this study we evaluate explicit 0–90-min EnKF-based storm-scale (500-m horizontal grid spacing) hail forecasts for a severe weather event that occurred in Oklahoma on 19 May 2013. Forecast ensembles are run using three different bulk microphysics (MP) schemes: the Milbrandt–Yau double-moment scheme (MY2), the Milbrandt–Yau triple-moment scheme (MY3), and the NSSL variable density-rimed ice double-moment scheme (NSSL). Output from a hydrometeor classification algorithm is used to verify surface hail size forecasts. All three schemes produce forecasts that predict the coverage of severe surface hail with moderate to high skill, but exhibit less skill at predicting significant severe hail coverage. A microphysical budget analysis is conducted to better understand hail growth processes in all three schemes. The NSSL scheme uses two-variable density-rimed ice categories to create large hailstones from dense, wet growth graupel particles; however, it is noted the scheme underestimates the coverage of significant severe hail. Both the MY2 and MY3 schemes produce many small hailstones aloft from unrimed, frozen raindrops; in the melting layer, hailstones become much larger than observations because of the excessive accretion of water. The results of this work highlight the importance of using a MP scheme that realistically models microphysical processes.

Characterizing severe weather potential in synoptically weakly forced thunderstorm environments

Weakly forced thunderstorms (WFTs), short-lived convection forming in synoptically quiescent regimes, are a contemporary forecasting challenge. The convective environments that support severe WFTs are often similar to those that yield only non-severe WFTs and, additionally, only a small proportion of individual WFTs will ultimately produce severe weather. The purpose of this study is to better characterize the relative severe weather potential in these settings as a function of the convective environment. Thirty-one near-storm convective parameters for > 200 000 WFTs in the Southeastern United States are calculated from a high-resolution numerical forecasting model, the Rapid Refresh (RAP). For each parameter, the relative odds of WFT days with at least one severe weather event is assessed along a moving threshold. Parameters (and the values of them) that reliably separate severe-weather-supporting from non-severe WFT days are highlighted. Only two convective parameters, vertical totals (VTs) and total totals (TTs), appreciably differentiate severe-wind-supporting and severe-hail-supporting days from non-severe WFT days. When VTs exceeded values between 24.6 and 25.1 ∘C or TTs between 46.5 and 47.3 ∘C, odds of severe-wind days were roughly 5× greater. Meanwhile, odds of severe-hail days became roughly 10× greater when VTs exceeded 24.4–26.0 ∘C or TTs exceeded 46.3–49.2 ∘C. The stronger performance of VT and TT is partly attributed to the more accurate representation of these parameters in the numerical model. Under-reporting of severe weather and model error are posited to exacerbate the forecasting challenge by obscuring the subtle convective environmental differences enhancing storm severity.