Climatology of severe hail in Finland: 1930-2006

A climatology of severe hail (2 cm in diameter or larger) in Finland was constructed by collecting news-paper, storm-spotter, and eyewitness reports. The climatology covered the warm season (1 May–14 September) during the 77-yr period of 1930–2006. Altogether, 240 severe-hail cases were found. The maximum reported severe-hail size was mainly 4 cm in diameter or less (65 % of the cases), with the number of cases decreasing as hail size increased. In a few extreme cases, 7–8-cm (baseball sized) hailstones have been reported in Finland. Most of the severe-hail cases (84%) occurred from late June through early August, with July being the peak month (almost 66 % of the cases). Most severe hail fell during the afternoon and early evening hours 1400–2000 local time (LT). Larger hailstones (4 cm or larger) tended to occur a little later (1600–2000 LT) than smaller (2–3.9 cm) hailstones (1400–1800 LT). Most severe-hail cases occurred in southern and western Finland, generally decreasing to the north, with the majority of the cases near population centers. The proportion of severe hail less than 4 cm in diameter is greatest over the agricultural area in southwestern Finland where crop damage caused by severe hail is more likely to be reported. The underreporting of hail is a particular problem across much of Finland because of the vast forest and lake areas, low population density, and relatively small hail swaths. Since the 1990s, a greater interest in severe weather among the general public and media, a storm-spotter network, improved communications technology, and an official Web site for reporting hail have increased the number of reported hail cases. According to the most recent 10 yr (1997–2006), Finland experiences an annual average of 10 severe-hail cases during 5 severe-hail days. 1

Regional Precipitation Forecast with Atmospheric InfraRed Sounder (AIRS) Profile Assimilation

Advanced technology in hyperspectral sensors such as the Atmospheric InfraRed Sounder (AIRS; Aumann et al. 2003) on NASA's polar orbiting Aqua satellite retrieve higher vertical resolution thermodynamic profiles than their predecessors due to increased spectral resolution. Although these capabilities do not replace the robust vertical resolution provided by radiosondes, they can serve as a complement to radiosondes in both space and time. These retrieved soundings can have a significant impact on weather forecasts if properly assimilated into prediction models. Several recent studies have evaluated the performance of specific operational weather forecast models when AIRS data are included in the assimilation process. LeMarshall et al. (2006) concluded that AIRS radiances significantly improved 500 hPa anomaly correlations in medium-range forecasts of the Global Forecast System (GFS) model. McCarty et al. (2009) demonstrated similar forecast improvement in 0-48 hour forecasts in an offline version of the operational North American Mesoscale (NAM) model when AIRS radiances were assimilated at the regional scale. Reale et al. (2008) showed improvements to Northern Hemisphere 500 hPa height anomaly correlations in NASA's Goddard Earth Observing System Model, Version 5 (GEOS-5) global system with the inclusion of partly cloudy AIRS temperature profiles. Singh et al. (2008) assimilated AIRS temperature and moisture profiles into a regional modeling system for a study of a heavy rainfall event during the summer monsoon season in Mumbai, India. This paper describes an approach to assimilate AIRS temperature and moisture profiles into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimensional variational (3DVAR) assimilation system (WRF-Var; Barker et al. 2004). Section 2 describes the AIRS instrument and how the quality indicators are used to intelligently select the highest-quality data for assimilation. Section 3 presents an overall precipitation improvement with AIRS assimilation during a 37-day case study period, and Section 4 focuses on a single case study to further investigate the meteorological impact of AIRS profiles on synoptic scale models. Finally, Section 5 provides a summary of the paper

Weather extremes in a changing climate: analyses for extreme precipitation, severe hail, and tornadoes

This thesis is broken into three parts, examining three distinct hazards that often accompany severe thunderstorms: extreme precipitation, severe hail, and tornados. First, observational data and an ensemble of climate change model experiments (from the Coupled Model Intercomparison Project Phase 5 (CMIP5)) are used to examine past and potential future seasonal changes in extreme precipitation event frequency over the United States. Using the extreme precipitation index as a metric for extreme precipitation event frequency change, we find key differences between models and observations. In particular, the CMIP5 models tend to overestimate the number of spring events and underestimate the number of summer events. This seasonal shift in the models is amplified in projections. These results provide a basis for evaluating climate model skill in simulating observed seasonality and changes in regional extreme precipitation. Additionally, we highlight key sources of variability and uncertainty that can potentially inform regional impact analyses and adaptation planning. Severe hail, another impactful hazard associated with severe storms, is also investigated. A radar-based hail climatology, with superior coverage and resolution, is possible using the Next-Generation Weather Radar (NEXRAD) reanalysis through the application of multi-radar multisensory (MRMS) algorithms, such as Maximum Expected Size of Hail (MESH). Using 12-years of MESH data we define a “severe hail outbreak day” and analyze characteristics of the severe hail and severe hail outbreak dataset, including an analysis of hail swaths. When comparing severe hail days in MESH to reports, we find a linear relationship between MESH and reports. Several case studies are also included to highlight the utility of MESH when studying outbreaks of severe hail, specifically regarding outbreak events that occur in lowpopulation areas. We find that severe hail days decrease while severe hail outbreak days increase over the 12-years examined. The increase in outbreaks is happening primarily in the month of June, where the number of severe hail days stays fairly constant over the 12-years. This suggests that the increase in outbreaks is mainly taking place on days when severe hail already occurs. When examining hail swath characteristics we found that there are a greater number of hail swaths, with a Major-Axis-Length (MAL) of at least 15km, on outbreak versus non-outbreak days. Additionally, hail swaths with the largest MALs occur on outbreak days. Lastly, the frequency and spatial extent of environments supportive of tornado and severe hail outbreak days, utilizing reanalysis data, are investigated over a 38-year historical period. A better understanding of the meteorological reason for observed trends in severe weather events is provided. The MESH-based severe hail dataset and tornado reports from the Storm Prediction Center storm reports database are considered ground truth. Composite parameters and thresholds, from the North American Regional Reanalysis (NARR), signifying outbreak environments are then determined. Severe hail and tornado outbreak days and areal extent of supportive environments are assessed. Specifically, the changing nature of tornado outbreaks is addressed utilizing these environmental parameters. The existence of long term trends in severe hail and tornado outbreaks, independent of reporting biases are identified. A long-term (1980-2015), statistically significant, positive trend in environments favoring severe hail and tornado outbreak days is found. This suggests that the recent short-term, positive trend in MESH based severe hail outbreak days extends further back in time. Additionally the yearly total outbreak days are positively correlated with the yearly median outbreak area. This correlation is statistically significant and supports our hypothesis that the increase in days supportive of outbreaks coincides with increases in the areal extent of outbreak environments. The variability of tornado outbreak days is also found to be increasing with time, supporting prior studies utilizing reports

Developing the framework for a risk map for mite vectored viruses in wheat resulting from pre-harvest hail damage

There is a strong economic incentive to reduce mite-vectored virus outbreaks. Most outbreaks in the central High Plains of the United States occur in the presence of volunteer wheat that emerges before harvest as a result of hail storms. This study provides a conceptual framework for developing a risk map for wheat diseases caused by mite-vectored viruses based on pre-harvest hail events. Traditional methods that use NDVI were found to be unsuitable due to low chlorophyll content in wheat at harvest. Site-level hyperspectral reflectance from mechanically hailed wheat showed increased canopy albedo. Therefore, any increase in NIR combined with large increases in red reflectance near harvest can be used to assign some level of risk. The regional model presented in this study utilized Landsat TM/ETMþ data and MODIS imagery to help gap-fill missing data. NOAA hail maps that estimate hail size were used to refine the area most likely at risk. The date range for each year was shifted to account for annual variations in crop phenology based on USDA Agriculture statistics for percent harvest of wheat. Between 2003 and 2013, there was a moderate trend (R2 ¼ 0.72) between the county-level insurance claims for Cheyenne County, Nebraska and the area determined to be at risk by the model (excluding the NOAA hail size product due to limited availability) when years with low hail claims (\u3c400 ha) were excluded. These results demonstrate the potential of an operational risk map for mite-vectored viruses due to pre-season hail events

The Influences of Sea-Surface Temperature Uncertainty on Cool-Season High-Shear, Low Cape Severe Weather Event Predictability in the Southeast United States

Environments conducive to severe weather and tornadoes occur throughout the southeastern United States, particularly during the cold-season. Throughout the cold-season, severe weather in this region predominantly occurs in environments characterized by high-shear, low-CAPE (HSLC). An important aspect to the production of severe weather in HSLC environments in the southeast United States is that air parcels that help contribute to the limited positive-buoyancy generation originate over areas such as the Gulf of Mexico, western Caribbean Sea, and western Atlantic Ocean. These relatively warm bodies of water, particularly outside of the cooler coastal shelf regions, allow the air parcels to warm and moisten via latent heat and surface sensible fluxes. It is hypothesized that the forecasts of cold-season severe weather in the southeastern United States are sensitive to the treatment of the underlying ocean surface, which influences the simulated representation of the surface heat exchange between the air and sea. We aimed to address and quantify these sensitivities by conducting numerical simulations for eight identified cold-season southeastern United States severe weather cases initialized using several different sea-surface temperature (SST) analyses. An ensemble of forecasts using varying atmospheric and SST analyses is also conducted for the case with the largest variability in forecast skill between SST initializations to quantify the contributions of initial atmospheric and SST uncertainty to subsequent forecast uncertainty. Neighborhood-based forecast verification techniques based off updraft helicity swaths are used to quantify these uncertainties

Low-cost silicon solar array project environmental hail model for assessing risk to solar collectors

The probability of solar arrays being struck by hailstones of various sizes as a function of geographic location and service life was assessed. The study complements parallel studies of solar array sensitivity to hail damage, the final objective being an estimate of the most cost effective level for solar array hail protection

Developing the framework for a risk map for mite vectored viruses in wheat resulting from pre-harvest hail damage

There is a strong economic incentive to reduce mite-vectored virus outbreaks. Most outbreaks in the central High Plains of the United States occur in the presence of volunteer wheat that emerges before harvest as a result of hail storms. This study provides a conceptual framework for developing a risk map for wheat diseases caused by mite-vectored viruses based on pre-harvest hail events. Traditional methods that use NDVI were found to be unsuitable due to low chlorophyll content in wheat at harvest. Site-level hyperspectral reflectance from mechanically hailed wheat showed increased canopy albedo. Therefore, any increase in NIR combined with large increases in red reflectance near harvest can be used to assign some level of risk. The regional model presented in this study utilized Landsat TM/ETMþ data and MODIS imagery to help gap-fill missing data. NOAA hail maps that estimate hail size were used to refine the area most likely at risk. The date range for each year was shifted to account for annual variations in crop phenology based on USDA Agriculture statistics for percent harvest of wheat. Between 2003 and 2013, there was a moderate trend (R2 ¼ 0.72) between the county-level insurance claims for Cheyenne County, Nebraska and the area determined to be at risk by the model (excluding the NOAA hail size product due to limited availability) when years with low hail claims (\u3c400 ha) were excluded. These results demonstrate the potential of an operational risk map for mite-vectored viruses due to pre-season hail events

A Radar-Based Hail Climatology of Australia

In Australia, hailstorms present considerable public safety and economic risks, where they are considered the most damaging natural hazard in terms of annual insured losses. Despite these impacts, the current climatological distribution of hailfall across the continent is still comparatively poorly understood. This study aims to supplement previous national hail climatologies, such as those based on environmental proxies or satellite radiometer data, with more direct radar-based hail observations. The heterogeneous and incomplete nature of the Australian radar network complicates this task and prompts the introduction of some novel methodological elements. We introduce an empirical correction technique to account for hail reflectivity biases at C-band, derived by comparing overlapping C- and S-band observations. Furthermore, we demonstrate how object-based hail swath analysis may be used to produce resolution-invariant hail frequencies, and describe an interpolation method used to create a spatially continuous hail climatology. The Maximum Estimated Size of Hail (MESH) parameter is then applied to a mixture of over fifty operational radars in the Australian radar archive, resulting in the first nationwide, radar-based hail climatology. The spatiotemporal distribution of hailstorms is examined, including their physical characteristics, seasonal and diurnal frequency, and regional variations of such properties across the continent.Comment: Revision 1 of manuscript submitted to Monthly Weather Revie

Radar-based assessment of hail frequency in Europe

In this study we present a unique 10 year climatology of severe convective storm tracks for a large European area covering Germany, France, Belgium and Luxembourg. For the period 2005–2014, a high-resolution hail potential composite of 1×1 km$^{2}$ is produced from two-dimensional radar reflectivity and lightning data. Individual hailstorm tracks as well as their physical properties, such as radar reflectivity along the tracks, were reconstructed for the entire time period using the Convective Cell Tracking Algorithm (CCTA2D). A sea-to-continent gradient in the number of hail days per year is found to be present over the whole domain. In addition, the highest number of severe storms is found on the leeward side of low mountain ranges such as the Massif Central in France and the Swabian Jura in southwest Germany. A latitude shift in the hail peak month is observed between the northern part of Germany, where hail occurs most frequently in August, and southern France, where the maximum amount of hail is 2 months earlier. The longest footprints with high reflectivity values occurred on 9 June 2014 and on 28 July 2013 with lengths reaching up to 500 km. Both events were associated with hailstones measuring up to 10 cm diameter, which caused damage in excess of EUR 2 billion