Hail Losses Research Articles

Estimation of Hail Damage Using Crop Models and Remote Sensing

Insurance agents often provide crop hail damage estimates based on their personal experience and field samples, which are not always representative of the investigated field’s spatial variability. For these reasons, farmers and the insurance market ask for a reliable, objective, and less labor-intensive method to determine crop hail losses. Integrating remote sensing and crop modeling provides a unique opportunity for the crop insurance market for a reliable, objective, and less labor-intensive method to estimate hail damage. To this end, a study was conducted on eight distinct maize fields for a total of 90 hectares. Five fields were damaged by the hailstorm that occurred on 13 July 2019 and three were not damaged. Soil and plant samples were collected to characterize the experimental areas. The Surface Energy Balance Algorithm for Land (SEBAL) was deployed to determine the total aboveground biomass and obtainable yield at harvest, using Landsat 7 and 8 satellite images. Modeled hail damages (HDDSSAT1, coupling SEBAL estimates of obtainable yield and DSSAT-based potential yield; HDDSSAT2, coupling yield map at harvest and the Decision Support System for Agrotechnology Transfer (DSSAT)-based potential yield) were calculated and compared to the estimates of the insurance company (HDinsurance). SEBAL-based biomass and yield estimates agreed with in-season measurements (−4% and +0.5%, respectively). While some under and overestimations were observed, HDinsurance and HDDSSAT1 averaged similar values (−4.9% and +3.4%) compared to the reference approach (HDDSSAT2).

Large Hail Incidence and Its Economic and Societal Impacts across Europe

Abstract By 31 December 2018, 39 537 quality-controlled reports of large hail had been submitted to the European Severe Weather Database (ESWD) by volunteers and ESSL. This dataset and the NatCatSERVICE Database of Munich RE jointly allowed us to study the hail hazard and its impacts across Europe over a period spanning multiple decades. We present a spatiotemporal climatology of the ESWD reports, diurnal and annual cycles of large hail, and indicate where and how they may be affected by reporting biases across Europe. We also discuss which hailstorms caused the most injuries and present the only case with hail fatalities in recent times. Additionally, we address our findings on the relation between hail size to the type of impacts that were reported. For instance, the probability of reported hail damage to roofs, windows, and vehicles strongly increases as hail size exceeds 5 cm, while damage to crops, trees, and greenhouses is typically reported with hailstone diameters of 2–3 cm. Injuries to humans are usually reported with hail 4 cm in diameter and larger, and number of injuries increases with increasing hail size. Using the NatCatSERVICE data, we studied economic losses associated with hailstorms occurring in central Europe and looked for long-term changes. The trend in hail losses and the annual number of hail loss days since 1990 to 2018 are compared to that of meteorological conditions favorable for large hail as identified by ESSL’s Additive Regression Convective Hazards model. Both hail loss days and favorable environments show an upward trend, in particular since 2000.

Evaluating Hail Damage Using Property Insurance Claims Data

Abstract A series of thunderstorms on 24 May 2011 produced significant hail in the Dallas–Fort Worth (DFW) metroplex, resulting in an estimated $876.8 million (U.S. dollars) in insured losses to property and automobiles, according to the Texas Department of Insurance. Insurance claims and policy-in-force data were obtained from five insurance companies for more than 67 000 residential properties located in 20 ZIP codes. The methodology for selecting the 20 ZIP codes is described. This study evaluates roofing material type with regard to resiliency to hailstone impacts and relative damage costs associated with roofing systems versus wall systems. A comparison of Weather Surveillance Radar-1988 Doppler (WSR-88D) radar-estimated hail sizes and damage levels seen in the claims data is made. Recommendations for improved data collection and quality of insurance claims data, as well as guidance for future property insurance claims studies, are summarized. Studies such as these allow insurance underwriters and claims adjusters to better evaluate the relative performance and vulnerability of various roofing systems and other building components as a function of hail size. They also highlight the abilities and limitations of utilizing radar horizontal reflectivity-based hail sizes, local storm reports, and Storm Data for claims processing. Large studies of this kind may be able to provide guidance to consumers, designers, and contractors concerning building product selections for improved resiliency to hailstorms, and give a glimpse into how product performance varies with storm exposure. Reducing hail losses would reduce the financial burden on property owners and insurers and reduce the amount of building materials being disposed of after storms.

Increasing major hail losses in the U.S.

Property losses due to hailstorms on April 13–14, 2006, resulted in Midwestern property losses that totaled $1.822 billion, an amount considerably more than the previous record high of $1.5 billion set by an April 2001 hail event. The huge April 2006 loss was largely due to multiple severe storms with frequent large hail hitting major metropolitan areas. A highly unstable air mass that developed on April 13 led to several supercell storms and they then produced large hailswaths across portions of Iowa, Illinois, Indiana, and Wisconsin during a 30-h period. This storm event and prior recent major hail losses occurred when several major hailstorms developed and then traveled for hundreds of kilometers. The nation’s top ten loss events during 1950–2006 reveal a notable temporal increase with most losses in the 1992–2006 period. Causes for the increases could be an increasing frequency of very unstable atmospheric conditions leading to bigger, longer lasting storms, and/or a greatly expanded urban society that has become increasingly vulnerable to hailstorms.

Temporal and Spatial Distributions of Damaging Hail in the Continental United States

Insurance data for hail damages to property during the period 1949-2006 were assessed to define the spatial and temporal distributions of losses in the United States. There were 876 hail events, each causing more than $1 million in losses, and the average loss was $56 million per event. The nation's annual average property losses due to hail was $852 million and greatly exceeded existing estimates of hail losses. Hail losses typically occurred within 1 state, and were most common in the High Plains where hailstorms are most frequent. When hail losses occurred with tornadoes, they were most frequent in the South and Midwest. The temporal distribution of hail losses had a flat linear trend during 1949-2006. In cases when damaging hail occurred with floods, the losses increased from 1949 to 2006, an outcome resulting from increasing floods incidences.

Hailfall: the relationship between radar-derived hail kinetic energy and hail damage to buildings

Relationships between radar-derived hail kinetic energy and hail damage to residential and agricultural buildings are presented for nine hail cells that occurred over the Swiss Mittelland (1992–1999). Hail kinetic energy ( E KINPIX) was calculated from C-band Doppler radar Constant Altitude Plan Position Indicators (CAPPIs) at a height of 1.5 km and daily hail damage claim data were available per Swiss community through several cantonal building insurance companies. In order to derive point-to-point relationships, hailfall and damage variables were triangulated on a 2×2-km grid and cross-correlation coefficients were then calculated for entire hail cells. The results show that high season hail storms (15 June–15 August) produced higher mean damage than low season storms (before and after) and weighted logistic damage functions produced correlation coefficients of 0.83 (0.86) for high (low) season storms over residential buildings and 0.79 (0.88) over agricultural buildings. The relationship between E KINPIX and total loss ratios (damage in relation to total sums insured) reveals no seasonal difference in hailstorm intensities and nonweighted logistic functions yield correlation coefficients of 0.85 (0.84) for residential (agricultural) buildings. The comparison between actual and predicted losses showed that nonweighted functions overestimated actual damage and that altered damage functions (flatter slope gradients) predicted damages that are in the range of the actual losses. Damage functions are calibrated using E KINPIX, total loss ratios and actual losses from two independent hail cells. One hail cell showed that damage induced through rainfall that followed hailfall multiplied the final loss considerably. The valuable agreement between radar-measured hailfall intensity and total loss ratios suggests that the established damage functions could be used by insurance companies to derive possible maximal hail losses to a building portfolio of interest.

The use of weather radars to estimate hail damage to automobiles: an exploratory study in Switzerland

As the first of its kind, this study presents damage functions between two damage variables of hail-damaged automobiles and radar-derived hail kinetic energy for a total of 12 severe hailstorms that have occurred over the Swiss Mittelland (1992–1998). Hail kinetic energy is calculated from C-band Doppler radar CAPPIs at low storm level (1.5 km MSL) and is integrated per radar element ( E KINPIX) for entire hail cells. Hail damage claim data were available per Swiss community on a daily basis and transformed (Delaunay triangulation) along with E KINPIX to a regular 3×3 km grid, thereafter allowing cross-correlation between the variables. The results show nonlinear relationships between E KINPIX and both loss ratios and mean damages per hail-damaged car, differing between high hail season storms (15 June–15 August) and storms that occurred during the low season (before and after). A weighted logistic function provides correlation coefficients between E KINPIX and loss ratios of 0.71 (0.79) for high (low) season storms and 0.76 (0.40) for mean damages of high (low) season hailstorms. Maximally possible loss ratios reach 60% (40%) in high (low) season storms with maximum mean damages of CHF 6000 (CHF 3000) and average values around CHF 3100 (CHF 2100). Seasonal differences in hailfall intensities are discussed in terms of atmospheric conditions favoring convective activity and the likelihood of higher numbers of large hailstones (>20 mm in diameter) that induce more severe damage to cars during the high storm season. The results suggest that radar-derived hail kinetic energy could be used by insurance companies in the future to (1) assess hail damage to cars immediately after a storm has passed over a radar observation area and (2) to estimate potential maximal hail losses to car portfolios for parts of central Europe.

A Method for Estimating Crop Losses from Hail in Uninsured Periods and Regions

Abstract The insurance industry, insurance regulatory bodies, and scientists investigating climate change all desire long records of hail losses. Existing loss records for some states cover the 1948–present period; this span is helpful but is not long enough to define trends, possible fluctuations, and extremes adequately. The only other hail data with much longer records are the frequencies of hail days collected at National Weather Service stations since 1901, and a newly developed database for the major hail-loss states that contains hail-day data for 910 cooperative stations for 1901–94. This study tested two methods for estimating the historical loss values using hail-day data; one method produced modified hail-day values found to relate closely to loss values in the nation’s 21 primary hail-loss states. The method involved modifying a station’s hail-day values for each of the crop-season months using insurance-derived monthly hail intensity indices, resulting in an annual hail-intensity-weighted val...

Hailstorm Loss Modelling and Risk Assessment in the Sydney Region, Australia

This paper summarises work undertaken at the Natural Hazards ResearchCentre with regard to the compilation, climatological analysis and lossmodelling application of hailstorm data for the greater Sydney region. Itdiscusses the results from two associated research areas: ENSO-relatedclimatological analysis of hailstorm data, and insurance sector orientedrisk assessment of hail losses in the Sydney basin. The two complementaryprojects led to the development of HailAUS, the first hail loss model for amajor urban area in Australia. The hazard occurrence module of the hailloss model is based on a comprehensive, good quality hail data set, whoseinterannual variability has previously been analysed with regard to the ENSOsignal. The paper presents the major results of the local hail data climatology,and outlines the structure of the hail loss model. The important practicalapplications that result from the collection and analysis of good qualityclimatological data are noted.

LOSSES CAUSED BY WEATHER AND CLIMATE EXTREMES: A NATIONAL INDEX FOR THE UNITED STATES

An annual index measuring past national losses and capable of measuring future loss from weather and climate extremes was developed to help measure future shifts in climate, a subject of great concern to the global warming issue and the insurance industry. Results from climate models indicate that a changed climate owing to global warming would alter and increase many extremes, and the objective of this study was to develop a national-scale index to monitor change in monetary losses and costs from weather and climate extremes. Forty-four historical variables addressing various aspects of weather and climate extremes and their effects on the U.S. economy since the late 1940s were assessed as potential input to such an index. Frequencies of most severe weather conditions from 1950 to 1997 did not correspond well with their financial impacts. However, hurricane losses did correspond with the frequency of intense hurricanes, and tornado losses corresponded well with the number of violent tornadic storms. Quality insurance loss data for several major storm types (thunder, hail, winter, and wind storms) were available, plus quality data on flood and hurricane losses, and all were adjusted to 1997 dollars. Techniques were developed to assess the effect of seasonal climate extremes on major crop yields and costs for electricity and natural gas consumed. Trends were upward for certain key variables between 1950 and 1997, including the incidence and losses associated with winter storms, flood losses, crop losses, and incidence of heavy rains. Trends were downward for other weather-driven loss variables including hurricane losses, energy costs, thunderstorm losses, wind storm losses, and hail losses. Nine loss variables were selected to develop the U.S. annual loss index, and in rank order, based on their average annual loss values, were energy costs, followed by losses from hurricanes, floods, severe convective storms, crops, tornadoes, winter storms, hail, and wind storms. The variables chosen also will be available in future years to allow a continuing assessment using the index. The 1950–1997 average annual index value was $17.47 billion (1997 dollars), with annual values ranging from a high of $54.4 billion in 1972 to a low of $2.4 billion in 1963. The 48-year trend of the index was unchanging with three notable high loss periods: 1950–1954, 1970–1974, and 1990–1994. [Key words: climate change, storms, natural hazards, weather extremes.]

The potential impact of global warming on hail losses to winter cereal crops in New South Wales

This study was undertaken to determine the impact of potential global warming on the magnitude of hail losses to winter cereal crops within two areas situated on the western slopes of New South Wales, Australia. A model relating historical crop hail losses to climatic variables was developed for each area. These models included seasonal measures of vertical instability, low-level moisture and the height of the freezing level. In both areas, windshear was not found to be an important factor influencing seasonal crop hail losses. The two crop hail loss models were then used in conjunction with upper-air climatic data from three single mixed-layer global climate models (GCMs). Each GCM was run for 1 × CO2 conditions and for 2 × CO2 conditions. The enhanced greenhouse effect on climatic variables was taken to be the difference between their values for these two runs. Changes to climatic variables were then translated directly into changes in the percentage value of the winter cereal crop lost due to hail. In both areas, the three GCMs agreed concerning the direction of change in each of the variables used in the crop hail loss model. GCM simulations of the greenhouse effect resulted in a decline in winter cereal crop hail losses, with the exception of one GCM simulation at one location where losses increased slightly. None of the changes due to the enhanced greenhouse effect, however, were significant owing to a large observed seasonal variability of crop hail losses. Also, the simulated seasonal variability of crop hail losses did not change significantly due to the enhanced greenhouse effect. These results depended on two important assumptions. Firstly, it was assumed that the dominant relationships between climatic variables and crop hail losses in the past would remain the same in a future climate. Secondly, it was assumed that the single mixed-layer GCMs used in the study were correctly predicting climate change under enhanced greenhouse conditions.

Climatological relevance of major USA weather losses during 1991–1994

Two insurance indices, which integrate storm types with their losses from 1948 to 1994, were examined to assess the frequency and intensity of damaging weather in the USA from 1991 to 1994. These indices adjust for changing socio‒economic conditions, allowing meaningful temporal assessment of 1991–1994 conditions against those in earlier years. Catastrophes, as defined by property loss insurers, were assessed for two groups, those 707 storms with insured losses between $10 and $100 million and those 189 with greater than $100 million. Both categories experienced their greatest frequency and caused more loss during the 1991–1994 period than in any prior period. However, the temporal behaviour of annual frequency and losses for both categories were very different, with steady increases found for the $10 to $100 million catastrophes, while the number of >$100 million catastrophes exhibited a high‒low‒high distribution and the annual cost distribution was flat with a few isolated peaks. For both categories the temporal behaviour of storm intensity was similar, with moderate intensities found in the recent period and the highest intensities in the 1950s. The crop‒hail losses were high in 1992–1994, and the annual loss costs for the 1948–1994 period had a U‒shaped time distribution similar to that of the >$100 million catastrophes. Weather conditions (USA mean temperature and surface cyclone frequency) were found to largely define: (i) when major, highly expensive weather events occur (and their annual losses), and (ii) how intense catastrophic storm conditions and crop‒damaging hailstorms are during each year. Conversely, the shifting target (population as an index) for damaging (non‒hurricane) storms across the USA is the major factor in ‘creating’ storms with losses reaching into the low ($10 million) to moderate ($100 million) range. © 1998 Royal Meteorological Society.

An Exploratory Analysis of Crop Hail Insurance Data for Evidence of Cloud Seeding Effects in North Dakota

The basis for the cloud seeding operations of the North Dakota Cloud Modification Project (NDCMP) is first outlined. Then the multiresponse permutation procedures are applied in an analysis of crop hail insurance data for the NDCMP target area and for an upwind control area in eastern Montana. A historical analysis of the annual hail insurance loss ratios for the target area indicates lower hail-loss experience during the NDCMP operational years 1976‐88. A corresponding analysis for the control area shows no indication of a difference during those years, suggesting the absence of any significant climatological variation. Analysis of a target‐ control scatterplot of the loss ratios also indicates that the target area experienced relatively smaller hail losses during the NDCMP period. An inference that the difference can be attributed to the NDCMP seeding operations appears to be justified, and the reduction in hail insurance loss ratios in the target area during the NDCMP years is estimated to be about 45%.

Simulated Hail Research in Sunflower: Defoliation1

AbstractAccurate loss procedure and compensation due to hail losses are a major concern to both the producer and the insurance company. Field experiments simulating leaf loss such as that which would occur as a result of a hail storm were conducted on hybrid sunflower (Helianthus annuus L.) at Carrington and Fargo, ND. The objective of this study was to investigate the agronomic response of sunflower to various levels of defoliation applied at different stages of plant development. Defoliation treatments (0, 25, 50, 75, and 100% of the leaves randomly removed with a knife) were applied at nine stages of plant development from V4 through R7. Fifty, 75, and 100% defoliation averaged across all developmental stages decreased seed yield significantly compared to the check. Seventy‐five and 100% defoliation decreased seed yield significantly compared to the check when treatments were applied at the R1 through R6 stages. Plant death often resulted when treatments of 100% defoliation were applied at stages R3 and R4. Oil percentage decreased significantly with 100% defoliation at the R4 through R7 stages. One‐hundred‐percent defoliation averaged over all stages of plant development decreased oil percentage an average of 11.9%. One‐hundred‐percent defoliation averaged across all stages of plant development decreased head diameter, plant height, percent stem breakage; days to R5.5, and achene weight. Complete removal of leaves at the reproductive (R) stages of plant development affected the variables measured to the greatest degree. As defoliation percentage decreased, values of variables measured were affected to a lesser degree.

More on the La Porte Anomaly: A Review

Abstract The anomalous behavior of the precipitation in northwestern Indiana (the La Porte anomaly) since the late 1930s has been extensively studied and debated. Local records suggested an upward shift in warm season rainfall, thunderstorms, and hail during the 1935–65 period. The possible causes for this included changed station exposure, a poor observer, urban influences on the atmosphere due to nearby Chicago, and/or shifts in the general circulation patterns. Most debate has centered on the observer error versus urban effects explanation, but the La Porte anomaly has become a cause celebre in the interwoven areas of climate change, air pollution, weather modification, and the quality of climatic records. A variety of recent studies of rainfall conditions and their areas of impact (streamflow, crop yields, and hail losses) show that the anomaly in the La Porte area began to shift locale in the 1950s and then disappeared in the 1960s. Taken in totality, it seems likely that the anomalous precipitation ...

Hailstorms in Southern Saskatchewan

Abstract This article represents a first attempt at a climatological investigation of the hail problem in the Canadian province of Saskatchewan. Initial results from a 5-year study in southern Saskatchewan, carried out through a volunteer observing network, indicate that the hailstorms of this section of the Great Plains are comparable in hailfall characteristics to those of the High Plains. Stone size distributions, hailfall intensity values and durations of hail are really no different. These indices of severity bear out loss costs (loss-to-risk ratio) for crop-hail insurance that are only slightly lower than those for Alberta, and lend credence to the estimate that hail losses in Saskatchewan are approximately equal to one-seventh of those in the entire United States.

Variety and Row Spacing Effects on Recoverability of Soybeans from Simulated Hail Injury1

AbstractTwo soybean [Glycine max (L.) Merr.] cultivars were grown in 100‐ and 50‐cm rows to evaluate the influence of cultivar and row spacing on recoverability from hail injury. ‘Hawkeye’ and ‘Hark’ soybeans were treated at three stages of plant development with 0, 25, and 50% stand reduction and 0, 25, and 50% stem cut‐off at ½ height.The experiment was conducted at Ames, Iowa for 3 consecutive years. The percentage yield loss averaged across years and treatments was the same (7%) for both row spacings of Hark and only 1 percentage point different (3% for 100‐cm and 4% for 50‐cm rows) for Hawkeye. No significant row spacings ✕treatments or row spacings ✕treatments ✕cultivars interactions were observed. Row spacing does not appear to warrant consideration in adjustment of soybeans for recoverability from hail injury.Average yield loss for Hark (7.0%) was more than for Hawkeye (3.5%) when averaged across years, treatments, and row spacings. The cultivars ✕stem cut‐offs interaction was not significant, but the cultivars ✕stand reductions interaction was significant at the 1% probability level. Hark had greater yield loss due to stand reduction than Hawkeye, primarily at the 50% level of stand reduction. Although small differences for recoverability did occur between cultivars, the differences were not great enough to justify the evaluation of all commercial soybean cultivars for their recovery potential. Such an evaluation would be necessary if cultivar is to be considered in the adjustment of hail losses.

Quantification of Crop-Hail Losses by Aerial Photography

Experiments designed to evaluate the potential of infrared (IR) color and standard color aerial photographs to measure crop-hail losses were performed. Detailed post-storm field measurements of loss (by standard adjusting techniques) and actual harvested yields (control data) were used for comparisons. Studies were made from a damaging hailstorm and from simulated hail damage on corn test plots. Stereoscopic analysis of standard color film provided estimates of average field loss for corn and soybeans that were better than those derived from the “best” field adjusting that involved detailed sampling of 1 point per 5 acres. Badly damaged areas had unique “signature” on the photographs consisting of semi-circular areas of loss that suggest a hail-wind related series of vortices of 100–500 ft in diameter. Measurements of IR film density for the simulated hail-damaged corn at the 14-leaf and later growth stages also showed a good relationship with the amount of harvested corn loss, but a weaker relationship existed at the 10-leaf stage (at hailstorm time and in the simulated fields), and none was apparent at the 6-leaf stage. The IR film density values for storm-damaged soybeans did not relate well to actual harvested losses. Corn-hail losses ≤30% per field at the 9- to 11-leaf stage were poorly ascertained by standard point assessment, stereoscopic analysis, and the IR film density technique. In this loss range, the IR photographs may have been taken too late after the storm or the physiological damage insufficient to be sensed in the infrared range. On standard color film, the stand reduction was too slight to be detected stereoscopically. Since the standard assessment method provided poor estimates of corn and bean losses when they were in the 1–30% range, either the causes for such low loss are not understood or succeeding weather conditions greatly alter the apparent loss at assessment time. If the latter is true, early season hail losses should be assessed at harvest. The very dense measurements of loss revealed amazing spatial variability in loss per field. Also, these dense measurements predicted final harvested field losses better and provided generally lower estimated losses than those obtained by insurance adjustors with their sampling methods.