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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Detecting Hail from Space: Using a Multi-Frequency Passive-Microwave Retrieval to Analyze the Global Climatology and Diurnal Cycle of Severe Hail

Severe hail poses myriad threats to society, causing extensive damage to infrastructure and agriculture. As hail is severe, relatively infrequent, and highly localized, it is difficult to measure in-situ and if left unresolved in models and precipitation retrievals, hail can cause large errors and uncertainties. The difficulty in measuring hail in situ and the inconsistency of surface-based hail reporting drive the motivation to use spaceborne remote-sensing platforms to retrieve hail and construct climatologies in a globally uniform way. We leverage the scattering signatures of severe hail in spaceborne passive-microwave datasets paired with surface hail reports to construct a multi-frequency hail retrieval using Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) data. Using coincident Global Precipitation Measurement (GPM) Ku-band precipitation radar, we assessed this retrieval and several others in the literature for their effectiveness and regional variability. We use this retrieval to construct global passive-microwave climatologies of severe hail using the TRMM, GPM, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and extend into the pre-TRMM era to the Special Sensor Microwave Imager/Sounder (SSMI/(S)) data. We also leverage the sensors in inclined orbits to assess the diurnal variability of severe hail globally and the effect the diurnal cycle has on the detection of hail by sensors in sun-synchronous orbit. The goal is to construct a robust, multi-decade multi-satellite climatology of hail. As part of the NASA Disasters Applied Sciences program, we assess these climatologies against other satellite severe weather datasets and use these climatologies in collaboration with stakeholders and end-users to help them assess risk, and improve the prediction, preparation, and response to severe storms around the world.

Sarah D Bang↗
NASA NTRS2015

A Prototype Hail Detection Algorithm and Hail Climatology Developed with the Advanced Microwave Sounding Unit (AMSU)

In previous studies published in the open literature, a strong relationship between the occurrence of hail and the microwave brightness temperatures (primarily at 37 and 85 GHz) was documented. These studies were performed with the Nimbus‐7 SMMR, the TRMM Microwave Imager (TMI) and most recently, the Aqua AMSR‐E sensor. This lead to climatologies of hail frequency from TMI and AMSR‐E, however, limitations include geographical domain of the TMI sensor (35 S to 35 N) and the overpass time of the Aqua satellite (130 am/pm local time), both of which reduce an accurate mapping of hail events over the global domain and the full diurnal cycle. Nonetheless, these studies presented exciting, new applications for passive microwave sensors. Since 1998, NOAA and EUMETSAT have been operating the AMSU‐A/B and the MHS on several operational satellites: NOAA‐15 through NOAA‐19; MetOp‐A and ‐B. With multiple satellites in operation since 2000, the AMSU/MHS sensors provide near global coverage every 4 hours, thus, offering a much larger time and temporal sampling than TRMM or AMSR‐E. With similar observation frequencies near 30 and 85 GHz and additionally three at the 183 GHz water vapor band, the potential to detect strong convection associated with severe storms on a more comprehensive time and space scale exists. In this study, we develop a prototype AMSU‐based hail detection algorithm through the use of collocated satellite and surface hail reports over the continental U.S. for a 12‐year period (2000‐2011). Compared with the surface observations, the algorithm detects approximately 40 percent of hail occurrences. The simple threshold algorithm is then used to generate a hail climatology that is based on all available AMSU observations during 2000‐11 that is stratified in several ways, including total hail occurrence by month (March through September), total annual, and over the diurnal cycle. Independent comparisons are made compared to similar data sets derived from other satellite, ground radar and surface reports. The algorithm was also applied to global land measurements for a single year and showed close agreement with other satellite based hail climatologies. Such a product could serve as a prototype for use with a future geostationary based microwave sensor such as NASA's proposed PATH mission.

Ferraro, Ralph↗
NASA NTRSDate not supplied

Challenges in Remote-Sensing of Hail: Examining the Performance and Biases of Satellite Hail Retrievals Using Aqua MODIS Visible/IR and AMSR-E Passive-Microwave Observations

Hail poses threats to myriad aspects of human life and society, infrastructure, and agriculture. Scientifically, hail can often cause large errors in precipitation retrieval and estimation, posing challenges to establishing the current climatology of severe storms and their future trend in a changing Earth system. Fortunately, hailstorms exhibit distinct signatures in spaceborne remote-sensing datasets (e.g. overshooting cloud tops in visible/IR, or brightness temperature depressions in passive-microwave imagery). Approaches that leverage these signatures, however, are not without their pitfalls,: passive-microwave channels have large footprints and exhibit non-uniform beam filling. Visible/IR instruments have fine horizontal resolution but are limited by their insensitivity to processes occurring below cloud top. Large horizontal areas of smaller scatterers may also meaningfully lower the brightness temperatures, especially if they are able to occupy large portions of the footprint. Radiative transfer simulations show that low frequencies such as 19- and 37-GHz can be scattered to extremely low brightness temperatures by high concentrations of smaller (graupel-sized) ice scatterers, especially in larger features that are more likely to occupy the footprint, which may cause climatologies to overestimate the frequency severe hail. To address this, we investigate the nearly simultaneous and colocated MODIS (visible/IR) and AMSR-E (passive-microwave) onboard the Aqua satellite to leverage both datasets together, pairing AMSR-E and MODIS signatures of severe convection with ground-based weather radar, severe weather reports, and environmental parameters defined by the MERRA-2 reanalysis over CONUS, and then explore the performance and challenges of the algorithm when we expand outside the United States into six different geographical regimes throughout the Aqua domain.

Sarah D Bang↗
NASA NTRS2008

Hail Disrometer Array for Launch Systems Support

Prior to launch, the space shuttle might be described as a very large thermos bottle containing substantial quantities of cryogenic fuels. Because thermal insulation is a critical design requirement, the external wall of the launch vehicle fuel tank is covered with an insulating foam layer. This foam is fragile and can be damaged by very minor impacts, such as that from small- to medium-size hail, which may go unnoticed. In May 1999, hail damage to the top of the External Tank (ET) of STS-96 required a rollback from the launch pad to the Vehicle Assembly Building (VAB) for repair of the insulating foam. Because of the potential for hail damage to the ET while exposed to the weather, a vigilant hail sentry system using impact transducers was developed as a hail damage warning system and to record and quantify hail events. The Kennedy Space Center (KSC) Hail Monitor System, a joint effort of the NASA and University Affiliated Spaceport Technology Development Contract (USTDC) Physics Labs, was first deployed for operational testing in the fall of 2006. Volunteers from the Community Collaborative Rain. Hail, and Snow Network (CoCoRaHS) in conjunction with Colorado State University were and continue to be active in testing duplicate hail monitor systems at sites in the hail prone high plains of Colorado. The KSC Hail Monitor System (HMS), consisting of three stations positioned approximately 500 ft from the launch pad and forming an approximate equilateral triangle (see Figure 1), was deployed to Pad 39B for support of STS-115. Two months later, the HMS was deployed to Pad 39A for support of STS-116. During support of STS-117 in late February 2007, an unusual hail event occurred in the immediate vicinity of the exposed space shuttle and launch pad. Hail data of this event was collected by the HMS and analyzed. Support of STS-118 revealed another important application of the hail monitor system. Ground Instrumentation personnel check the hail monitors daily when a vehicle is on the launch pad, with special attention after any storm suspected of containing hail. If no hail is recorded by the HMS, the vehicle and pad inspection team has no need to conduct a thorough inspection of the vehicle immediately following a storm. On the afternoon of July 13, 2007, hail on the ground was reported by observers at the VAB, about three miles west of Pad 39A, as well as at several other locations around Kennedy Space Center. The HMS showed no impact detections, indicating that the shuttle had not been damaged by any of the numerous hail events which occurred that day.

Lane, John E.↗
NASA NTRSDate not supplied

Hail Storm Risk Assessment Using Space-Borne Remote Sensing Observations and Reanalyses

Much of the world is impacted by severe thunderstorms, but whether they become disasters depends upon resilience--our capacity to prepare, mitigate, respond, and recover. Hail is the costliest severe weather hazard for the insurance industry, generating ~70% of severe convective storm losses due to damage to assets such as homes, businesses, agriculture, and infrastructure. Most insurance companies do not reserve enough capital to cover catastrophes, so they acquire reinsurance. The reinsurance industry uses catastrophe models (CatModels) to statistically estimate risk to an insurer’s portfolio. Hail CatModels are developed with climatologies that define hailstorm frequency and severity. Hail-prone areas can be defined using hail reports from trained spotters, the media, and the general public. Extremely severe hail (2+ inch diameter) occurs nearly every day across the world. Weather radars can detect hail because hailstones strongly reflect microwave signals that they emit. However, hail climatologies are difficult to derive because hail covers small areas and there are neither hail reporting mechanisms (e.g. website or mobile app) nor radar networks in most places outside the US and Europe. This lack of ground truth on severe hail puts society and economies at risk. Hail is generated within storms by strong updrafts. These updrafts exhibit unique signatures in NASA and other agency satellite observations, offering new opportunities for hailstorm analysis. Geostationary (GEO) visible and infrared imagery has been collected for ~15-25 years across the world (region dependent) and methods have been developed at NASA Langley Research Center (LaRC) to detect hailstorm updrafts using GEO imagery. Climatological GEO updraft data has been used by Willis Towers Watson (WTW), a leader in catastrophe risk assessment for the insurance industry, and Karlsruhe Institute of Technology to develop CatModels over Europe and Australia. Hail can also be inferred with passive microwave imagery collected by low-Earth-orbiting sensors such as the GPM GMI, TRMM TMI, AMSR-E, AMSR-2, SSM/I, and SSMIS over the last 20+ years using methods developed at the Marshall Space Flight Center (MSFC). Hailstorms generate enhanced lightning flash rates that can be tracked using new GOES-R series GEO Lightning Mapping (GLM) imagery. Atmospheric reanalyses can be used to define favorable hailstorm environments for combination with the satellite-based storm detections. This presentation will describe a framework for developing continental to global hail climatologies and CatModels based on NASA satellite data and capabilities. This is a collaboration between LaRC and MSFC, WTW, and partners in Brazil, Argentina, and South Africa. This project seeks to mitigate hail disasters by aiding development of new satellite-based severe storm nowcasting tools by regional partners and developing climatologies to improve societal understanding of hail frequency. GEOO visible and infrared metrics of storm intensity, environmental conditions based on reanalyses, spotter hail reports and radar MESH observations are intercompared to quantify the detectability of hailstorms, and our ability to discriminate hailstorms from other severe storms. We are also maturing methods using land surface imaging satellite data (e.g. MODIS, Landsat, Sentinel 1 and 2) to identify hail damage to agriculture. Work with WTW will improve socioeconomic resilience through development of new CatModels. Southern Brazil, Uruguay, Paraguay, and Argentina feature some of the most intense thunderstorms on Earth. South America and South Africa are developing insurance markets of interest to WTW clients, and is similar to other regions routinely impacted by hail that do not have comprehensive hail reporting or radars to assess hailstorm frequency. Project datasets will be made available via online GIS-enabled tools developed at the LaRC Atmospheric Science Data Center (ASDC) which will visualize data and provide it in multiple formats for use in a wide range of open source and commercial tools.

Kristopher Michael Bedka↗
NASA NTRS2012

Analysis of the March 30, 2011 Hail Event at Shuttle Launch Pad 39A

The Kennedy Space Center (KSC) Hail Monitor System, a joint effort of the NASA KSC Physics Lab and the KSC Engineering Services Contract (ESC) Applied Technology Lab, was first deployed for operational testing in the fall of 2006. Volunteers from the Community Collaborative Rain, Hail, and Snow Network (CoCoRaHS) in conjunction with Colorado State University have been instrumental in validation testing using duplicate hail monitor systems at sites in the hail prone high plains of Colorado. The KSC Hail Monitor System (HMS), consisting of three stations positioned approximately 500 ft from the launch pad and forming an approximate equilateral triangle, as shown in Figure 1, was first deployed to Pad 39B for support of STS-115. Two months later, the HMS was deployed to Pad 39A for support of STS-116. During support of STS-117 in late February 2007, an unusually intense (for Florida standards) hail event occurred in the immediate vicinity of the exposed space shuttle and launch pad. Hail data of this event was collected by the HMS and analyzed. Support of STS-118 revealed another important application of the hail monitor system. Ground Instrumentation personnel check the hail monitors daily when a vehicle is on the launch pad, with special attention after any storm suspected of containing hail. If no hail is recorded by the HMS, the vehicle and pad inspection team has no need to conduct a thorough inspection of the vehicle immediately following a storm. On the afternoon of July 13, 2007, hail on the ground was reported by observers at the Vertical Assembly Building (VAB) and Launch Control Center (LCC), about three miles west of Pad 39A, as well as at several other locations at KSC. The HMS showed no impact detections, indicating that the shuttle had not been damaged by any of the numerous hail events which occurred on that day.

Lane, John E.↗
NASA NTRS2011

Analysis of STS-134 Hail Event at Pad 39A, March 30, 2011

During the late afternoon of March 30, 2011 at approximately 21:25 - 21:30 GMT, hail monitor stations at Pad 39A recorded rice to pea size hail. The duration of the event was approximately 5 minutes. The maximum size detected by the three hail monitors was 10 - 12 mm. The 12 mm marble size value was measured by the active impact sensor at site #2, which experienced high winds. This 12 mm measurement may be artificially higher by one or two mm due to the extra hail kinetic energy resulting from the extreme horizontal winds. High winds from the west produced a few notable long streak-like dents in the hail pads. High winds were also responsible for damage to facilities near hail monitor site #2 on the west side of pad A (a dumpster was overturned, and a picnic table roof was demolished). NWS radar volume scan (see Figure I) showed 60-65 dBZ reflectivity values in the lowest 4 scan elevations around and over the pad 39A area. Since the lowest 0.5 degree scan showed a definite 65 dBZ signature, it is unlikely that hail had an opportunity to melt before reaching the ground. Some of the larger passive hail pad dents were shallower than what would be expected from solid frozen ice hydrometeor dents. Therefore, it is possible that the larger pea size hail may have been softer than the smaller rice size hail. This would be consistent with some melting before reaching the ground.

Lane, John E.↗
NASA NTRS2009

Hail Monitor Sensor

An inexpensive and simple hail monitor design has been developed that has a single piezoelectric ceramic disc and uses a metal plate as a sounding board. The structure is durable and able to withstand the launch environment. This design has several advantages over a multi-ceramic sensor, including reduced cost and complexity, increased durability, and improvement in impact response uniformity over the active surface. However, the most important characteristic of this design is the potential to use frequency discrimination between the spectrum created from raindrop impact and a hailstone impact. The sound of hail hitting a metal plate is distinctly different from the sound of rain hitting the same plate. This fortuitous behavior of the pyramid sensor may lead to a signal processing strategy, which is inherently more reliable than one depending on amplitude processing only. The initial concept has been im proved by forming a shallow pyramid structure so that hail is encouraged to bounce away from the sensor so as not to be counted more than once. The sloped surface also discourages water from collecting. Additionally, the final prototype version includes a mounting box for the piezo-ceramic, which is offset from the pyramid apex, thus helping to reduce non-uniform response (see Figure 2). The frequency spectra from a single raindrop impact and a single ice ball impact have been compared. The most notable feature of the frequency resonant peaks is the ratio of the 5.2 kHz to 3.1 kHz components. In the case of a raindrop, this ratio is very small. But in the case of an ice ball, the ratio is roughly one third. This frequency signature of ice balls should provide a robust method for discriminating raindrops from hailstones. Considering that hail size distributions (HSDs) and fall rates are roughly 1 percent that of rainfall, hailstone sizes range from a few tenths of a centimeter to several centimeters. There may be considerable size overlap between large rain and small hail. As hail occurs infrequently at KSC, the ideal HSD measurement sensor needs to have a collection area roughly 100 times greater than a raindrop-size distribution sensor or disdrometer. The sensitivity should be such that it can detect and count very small hail in the midst of intense rainfall consisting of large raindrop sizes. The dynamic range and durability should allow measurement of the largest hail sizes, and the operation and calibration strategy should consider the infrequent occurrence of hail fall over the KSC area.

Younquist, Robert↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
NASA NTRS2017

Detection of Hail Storms in Radar Imagery Using Deep Learning

In 2016, hail was responsible for 3.5 billion and 23 million dollars in damage to property and crops, respectively, making it the second costliest weather phenomenon in the United States. In an effort to improve hail-prediction techniques and reduce the societal impacts associated with hail storms, we propose a deep learning technique that leverages radar imagery for automatic detection of hail storms. The technique is applied to radar imagery from 2011 to 2016 for the contiguous United States and achieved a precision of 0.848. Hail storms are primarily detected through the visual interpretation of radar imagery (Mrozet al., 2017). With radars providing data every two minutes, the detection of hail storms has become a big data task. As a result, scientists have turned to neural networks that employ computer vision to identify hail-bearing storms (Marzbanet al., 2001). In this study, we propose a deep Convolutional Neural Network (ConvNet) to understand the spatial features and patterns of radar echoes for detecting hailstorms.

natural hazard↗
NASA NTRS2008

Launch Pad 39 Hail Monitor Array System

Weather conditions at Kennedy Space Center are extremely dynamic, and they greatly affect the safety of the Space Shuttles sitting on the launch pads. For example, on May 13, 1999, the foam on the External Tank (ET) of STS-96 was significantly damaged by hail at the launch pad, requiring rollback to the Vehicle Assembly Building. The loss of ET foam on STS-114 in 2005 intensified interest in monitoring and measuring damage to ET foam, especially from hail. But hail can be difficult to detect and monitor because it is often localized and obscured by heavy rain. Furthermore, the hot Florida climate usually melts the hail even before the rainfall subsides. In response, the hail monitor array (HMA) system, a joint effort of the Applied Physics Laboratory operated by NASA and ASRC Aerospace at KSC, was deployed for operational testing in the fall of 2006. Volunteers from the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network, in conjunction with Colorado State University, continue to test duplicate hail monitor systems deployed in the high plains of Colorado.

Source record↗
NASA NTRS2008

Hail Size Distribution Mapping

A 3-D weather radar visualization software program was developed and implemented as part of an experimental Launch Pad 39 Hail Monitor System. 3DRadPlot, a radar plotting program, is one of several software modules that form building blocks of the hail data processing and analysis system (the complete software processing system under development). The spatial and temporal mapping algorithms were originally developed through research at the University of Central Florida, funded by NASA s Tropical Rainfall Measurement Mission (TRMM), where the goal was to merge National Weather Service (NWS) Next-Generation Weather Radar (NEXRAD) volume reflectivity data with drop size distribution data acquired from a cluster of raindrop disdrometers. In this current work, we adapted these algorithms to process data from a cluster of hail disdrometers positioned around Launch Pads 39A or 39B, along with the corresponding NWS radar data. Radar data from all NWS NEXRAD sites is archived at the National Climatic Data Center (NCDC). That data can be readily accessed at . 3DRadPlot plots Level III reflectivity data at four scan elevations (this software is available at Open Channel Software, ). By using spatial and temporal interpolation/extrapolation based on hydrometeor fall dynamics, we can merge the hail disdrometer array data coupled with local Weather Surveillance Radar-1988, Doppler (WSR-88D) radial velocity and reflectivity data into a 4-D (3-D space and time) picture of hail size distributions. Hail flux maps can then be generated and used for damage prediction and assessment over specific surfaces corresponding to structures within the disdrometer array volume. Immediately following a hail storm, specific damage areas and degree of damage can be identified for inspection crews.

Source record↗
NASA NTRS2015

Development of a Near-Real Time Hail Damage Swath Identification Algorithm for Vegetation

The Midwest is home to one of the world's largest agricultural growing regions. Between the time period of late May through early September, and with irrigation and seasonal rainfall these crops are able to reach their full maturity. Using moderate to high resolution remote sensors, the monitoring of the vegetation can be achieved using the red and near-infrared wavelengths. These wavelengths allow for the calculation of vegetation indices, such as Normalized Difference Vegetation Index (NDVI). The vegetation growth and greenness, in this region, grows and evolves uniformly as the growing season progresses. However one of the biggest threats to Midwest vegetation during the time period is thunderstorms that bring large hail and damaging winds. Hail and wind damage to crops can be very expensive to crop growers and, damage can be spread over long swaths associated with the tracks of the damaging storms. Damage to the vegetation can be apparent in remotely sensed imagery and is visible from space after storms slightly damage the crops, allowing for changes to occur slowly over time as the crops wilt or more readily apparent if the storms strip material from the crops or destroy them completely. Previous work on identifying these hail damage swaths used manual interpretation by the way of moderate and higher resolution satellite imagery. With the development of an automated and near-real time hail swath damage identification algorithm, detection can be improved, and more damage indicators be created in a faster and more efficient way. The automated detection of hail damage swaths will examine short-term, large changes in the vegetation by differencing near-real time eight day NDVI composites and comparing them to post storm imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua and Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi NPP. In addition land surface temperatures from these instruments will be examined as for hail damage swath identification. Initial validation of the automated algorithm is based upon Storm Prediction Center storm reports but also the National Severe Storm Laboratory (NSSL) Maximum Estimated Size Hail (MESH) product. Opportunities for future work are also shown, with focus on expansion of this algorithm with pixel-based image classification techniques for tracking surface changes as a result of severe weather.

Bell, Jordan R.↗
NASA NTRS2015

Development of a Near Real-Time Hail Damage Swath Identification Algorithm for Vegetation

Every year in the Midwest and Great Plains, widespread greenness forms in conjunction with the latter part of the spring-summer growing season. This prevalent greenness forms as a result of the high concentration of agricultural areas having their crops reach their maturity before the fall harvest. This time of year also coincides with an enhanced hail frequency for the Great Plains (Cintineo et al. 2012). These severe thunderstorms can bring damaging winds and large hail that can result in damage to the surface vegetation. The spatial extent of the damage can relatively small concentrated area or be a vast swath of damage that is visible from space. These large areas of damage have been well documented over the years. In the late 1960s aerial photography was used to evaluate crop damage caused by hail. As satellite remote sensing technology has evolved, the identification of these hail damage streaks has increased. Satellites have made it possible to view these streaks in additional spectrums. Parker et al. (2005) documented two streaks using the Moderate Resolution Imaging Spectroradiometer (MODIS) that occurred in South Dakota. He noted the potential impact that these streaks had on the surface temperature and associated surface fluxes that are impacted by a change in temperature. Gallo et al. (2012) examined at the correlation between radar signatures and ground observations from storms that produced a hail damage swath in Central Iowa also using MODIS. Finally, Molthan et al. (2013) identified hail damage streaks through MODIS, Landsat-7, and SPOT observations of different resolutions for the development of a potential near-real time applications. The manual analysis of hail damage streaks in satellite imagery is both tedious and time consuming, and may be inconsistent from event to event. This study focuses on development of an objective and automatic algorithm to detect these areas of damage in a more efficient and timely manner. This study utilizes the MODIS sensor aboard the NASA Aqua satellite. Aqua was chosen due to an afternoon orbit over the United States when land surface temperatures are relatively warm and improve the contrast between damaged and undamaged areas. This orbit is also similar to the orbit of the Suomi-National Polar-orbiting Partnership (NPP) satellite. The Suomi NPP satellite hosts the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, which is the next generation of a MODIS-like sensor in polar orbit.

MODIS VIIRS↗
NASA NTRSDate not supplied

Spaceborne Passive-Microwave Hail Detection: Global Climatologies, Validation, and Challenges Going Forward

In addition to the myriad threats that severe hailstorms pose to society, infrastructure and agriculture, severe hail is difficult to measure in situ, and surface-based hail reporting and detection methods are inconsistent and vulnerable to nonmeteorological biases. This motivates the use of spaceborne remote-sensing platforms to retrieve hail and construct climatologies in a globally uniform way. We have developed a hail detection algorithm that leverages the sensitivity of spaceborne passive-microwave radiometers to scattering by hail, particularly in the channels from 10 to 89 GHz. We use this retrieval to construct global passive-microwave climatologies of severe hail using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement Mission (GPM) Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and are working to extend into the pre-TRMM era to the Special Sensor Microwave Imager/Sounder (SSMI/(S)) data. Using coincident Global Precipitation Measurement (GPM) Ku-band precipitation radar, we assessed this retrieval and several others in the literature for their effectiveness and regional variability. We find that this retrieval, which leverages a signature in the Minimum 19-GHz polarization corrected temperature (PCT) combined with the 37-GHz PCT depression normalized by tropopause height constrains the radar reflectivity most tightly, and gives the least appearance of regional biases compared to other passive-microwave approaches in the literature. Satellite platforms offer consistent observations, even in remote, data-sparse, and oceanic regions that ground-based networks exclude. There are, however, potential disconnects between the processes identified aloft by the satellite and the resultant weather at the ground, leading to uncertainties in the retrievals that may propagate into satellite-based climatologies, particularly in the Tropics, where there are abundant strong - but not necessarily hailing - storms that are strongly represented in the current satellite climatologies. We will discuss ongoing efforts to assess and mitigate the contributing factors to these uncertainties, chiefly among them the effects of non-uniform beam filling in the passive-microwave footprint, and the relationships between the size distributions of hailstones aloft and the dynamic processes and environments with which they interact throughout their trajectories.

Sarah D. Bang↗
NASA NTRSDate not supplied

Detecting Hail from Space: Algorithms, Climatologies, and Challenges Going Forward

In addition to the myriad threats that severe hailstorms pose to society, infrastructure and agriculture, severe hail is difficult to measure in situ, and surface-based hail reporting and detection methods are inconsistent and subject to geographical or societal biases. This motivates the use of spaceborne remote-sensing platforms to retrieve hail and construct climatologies in a globally uniform way. We have developed a hail detection algorithm that leverages the sensitivity of spaceborne passive-microwave radiometers to scattering by hail, particularly in the channels from 10 to 89 GHz. We use this retrieval to construct global climatologies of severe hail using several different spaceborne sensors: the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement Mission (GPM) Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and are working to extend into the late 1980’s using the Special Sensor Microwave Imager/Sounder (SSMI/(S)) data. Using coincident Global Precipitation Measurement (GPM) Ku-band precipitation radar, we assessed this retrieval and several others in the literature for their effectiveness and regional variability. We developed a passive-microwave algorithm that corresponds tightly to radar reflectivity and gives the least appearance of regional biases compared to other passive-microwave approaches in the literature. Satellite platforms offer consistent observations, even in remote, data-sparse, and oceanic regions that ground-based networks exclude. There are, however, potential disconnects between the processes identified aloft by the satellite and the resultant weather at the ground, leading to uncertainties in the retrievals that may propagate into satellite-based climatologies, particularly in the Tropics, where there are abundant strong - but not necessarily hailing - storms that are strongly represented in the current satellite climatologies. We will discuss ongoing efforts to assess and mitigate the contributing factors to these uncertainties, chiefly among them the effects of non-uniform beam filling in the passive-microwave footprint, and the relationships between the size distributions of hailstones aloft and the dynamic processes and environments with which they interact throughout their trajectories.

Sarah D Bang↗
NASA NTRSDate not supplied

Remote Sensing of Hail from Space: Retrievals, Climatologies, and Challenges Going Forward

In addition to the myriad threats that severe hailstorms pose to society, infrastructure and agriculture, severe hail is difficult to measure in situ, and surface-based hail reporting and detection methods are inconsistent and subject to geographical or societal biases. This motivates the use of spaceborne remote-sensing platforms to retrieve hail and construct climatologies in the most globally consistent way. Passive-microwave algorithms leverage the sensitivity of spaceborne passive-microwave radiometers to scattering by hail, particularly in the channels from 10 to 89 GHz. These retrievals are used to construct global climatologies of severe hail. The Bang and Cecil (2019) retrievals has been applied to several different spaceborne sensors: the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement Mission (GPM) Microwave Imager, Advanced Microwave Scanning Radiometer for EOS (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors and used to construct [near] global climatologies of severe hail. This retrieval, and others, have been tested using Global Precipitation Measurement (GPM) Ku-band precipitation radar, to assess their effectiveness and regional variability. A successful retrieval and climatology are those that correspond tightly to radar reflectivity and give minimal appearance of regional biases, especially with latitude. Satellite platforms offer consistent observations, even in remote, data-sparse, and oceanic regions that ground-based networks exclude. There are, however, potential disconnects between the processes identified aloft by the satellite and the resultant weather at the ground, leading to uncertainties in the retrievals that may propagate into satellite-based climatologies, particularly in the Tropics, where there are abundant strong - but not necessarily hailing - storms that are strongly represented in the current satellite climatologies. There are ongoing efforts to assess and mitigate the contributing factors to these uncertainties, chiefly among them the effects of non-uniform beam filling in the passive-microwave footprint, and the relationships between the size distributions of hailstones aloft and the dynamic processes and environments with which they interact throughout their trajectories.

Sarah D Bang↗