The Atmospheric Radiative Transfer Simulator was used to conduct several simulations of Global Precipitation Measurement Microwave Imager brightness temperatures (BTs; 10.65-183.31 +/- 7-GHz) over a severe hailstorm. Simulations were conducted to test the sensitivity of BTs to particle size distribution form and to the size, orientation, and shape of several hydrometeor types assuming constant S-band radar reflectivity. Results show an increase in BT (i.e., less scattering) at most frequencies when changing from a normalized gamma distribution (NGD) to exponential distribution (EXPD). This change causes a decrease in cumulative hydrometeor surface area, but not necessarily a decrease in number concentration, suggesting that surface area exerts a stronger influence on BTs than concentration. Simulated BTs at the highest frequencies (166.0-183.31 +/- 7 GHz) agree better with observations when using an EXPD for cloud ice. At lower frequencies, especially 36.5-89.0 GHz, using an NGD for high-density graupel and hail leads to a better match between simulated and observed BTs. No clear preference is seen for low-density graupel, liquid precipitation, or snow. The impact of changing particle shape and/or orientation depends on the hydrometeor type. Changing the orientation of cloud ice from horizontal to a random orientation increases simulated BTs, while having no effect for high-density graupel. Assuming horizontally oriented, oblate-spheroid cloud ice results in simulated BTs that match better with observed. Finally, under the fixed reflectivity constraint, increasing the diameter of hail from 0.5 to 20 cm results in an increase in minimum BT up to 1.5-cm diameter with near constant BT at all frequencies thereafter. SIGNIFICANCE STATEMENT: Accurate estimates of precipitation are important for numerous applications, and only satellite instruments can provide a global, uniform estimate of precipitation. This study seeks to use simulations to better understand how microwave radiation interacts with various hydrometeor types to improve the assumptions on which satellite-based precipitation estimates are based and ultimately improve the estimates themselves. Results indicate that an exponential distribution may be more appropriate for cloud ice and a normalized gamma distribution more appropriate for hail and high-density graupel. Other hydrometeor types (e.g., rain) show no clear preference for either distribution. Furthermore, assuming cloud ice is an oblate spheroid with horizontal orientation produces simulated brightness temperatures that better match those observed than assuming spherical ice or other orientations.

Using passive microwave brightness temperatures T b from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and hydrometeor identi fi cation (HID) data from dual-polarization ground radars, empirical lookup tables are developed for a multifrequency estimation of the likelihood a precipitation column includes certain hydrometeor types, as a function of T b . Eight years of collocated T b and HID data from the GPM Validation Network are used for development and testing of the GMI-based HID retrieval, with 2015 - 20 used for training and 2021 - 22 used for testing the GMI-based HID retrieval. The occurrence of pro fi les with hail and graupel are both slightly underpredicted by the lookup tables, but the percentage of pro fi les predicted is highly correlated with the percentage observed (0.98 correlation coef fi cient for hail and 0.99 for graupel). By having snow appear before rain in the hierarchy, the sample size for rain, without ice aloft, is fairly small, and the percentage of rain pro fi les is less than snow for all T b .

The Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) was used to investigate interannual variability of lightning from 1998 to 2014 within the 38 degrees S-38 degrees N range. Previous studies have indicated that the El Nino-Southern Oscillation (ENSO) phenomenon is one significant contributor to interannual lightning variability, potentially the dominant mechanism on the global scale. This period of 16 years contained four warm- (El Nino), eight cold(La Nina), and four neutral -phase ENSO years based on the oceanic Nino index. Large magnitude lightning anomalies were found during the warm phase of ENSO, with mean warm -phase anomalies of .10 flashes (1000 km) -2 min -1 in north -central Africa and Argentina. This includes a +35 flashes (1000 km) -2 min -1 anomaly in Argentina during the 2009 El Nino. In general, large-scale anomalies of thermodynamic properties and upper -atmospheric vertical motion coincided with the lightning anomalies observed in both Africa and South America. The anomaly over north -central Africa, however, was characterized by a 6 -week shift in the annual lightning maximum with the warm phase, a result of the more complex environmental response to ENSO over the Sahel. The most consistent ENSO anomalies with appreciable lightning were found in southeastern Africa, northwestern Brazil, central Mexico, and the southern Red Sea. Of these, all but the Mexico region had enhanced lightning with the cold phase and suppressed lightning with the warm phase.

Geostationary satellite imagers provide historical and near-real-time observations of cloud-top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to Next Generation Weather Radar (NEXRAD) estimated maximum expected size of hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are made where satellites observe cold clouds, indicative of convection, located in favorable storm environments. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. Statistical distributions of convective parameters from satellite and reanalysis show separation between nonsevere and severe hailstorm classes for predictors that include overshooting cloud-top temperature and area characteristics, vertical wind shear, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a likelihood estimate with a critical success index of 0.511 and Heidke skill score of 0.407, which is exceptional among analogous hail studies. Furthermore, applications of the DNN to case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-yr Geostationary Operational Environmental Satellite (GOES) image database from GOES-12/13 to derive a hail frequency and severity climatology, which denotes the central Great Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied.

NASA’s future Earth System Observatory (ESO) will provide key information related to understanding climate change processes, mitigating natural hazards, fighting forest fires, and improving real-time agricultural processes. The Atmosphere Observing System (AOS) constellation is a key component of the ESO, providing the atmospheric part of the ESO and focusing on two of the five designated observables from the 2017 NASA Earth Science Decadal Survey: aerosols and clouds, convection, and precipitation (CCP). AOS is made up of two projects, one in an inclined orbit (referred to as AOS-I) and the other in a polar, sun synchronous orbit (AOS-P), with both projects addressing synergistic aerosol and CCP science. The constellation is expected to deliver a comprehensive suite of observations to address coupled aerosol-cloud-precipitation interactions, with science objectives focused on low and high cloud feedbacks; the dynamics and structure of convective systems and properties of the aerosol environment; phase partitioning and precipitation formation in frozen and mixed-phase clouds; aerosol microphysical and optical properties, aerosol sources, and relationships to air quality; aerosol vertical redistribution and processing by clouds and precipitation; and aerosol direct and indirect effects. AOS-I and AOS-P are expected to launch no earlier than July 2028 and December 2030, respectively. This talk will describe the science objectives of AOS and the mission architecture and measurement capabilities.

<p class="ParagraphText"><span lang="EN-US">Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) and Meteosat series, provide both historical and near-real-time observations of cloud top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to United States Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are specifically designed to make hail likelihood distinctions based on satellite-indicated points of deep convection within environments favorable for storm development. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record.</span></p> <p class="ParagraphText"><span lang="EN-US">This presentation demonstrates that statistical distributions of convective parameters from satellite and reanalysis show separation between non-severe/severe hailstorm classes for predictors including overshooting cloud top temperature and area characteristics, convective available potential energy, vertical wind shear, 500 hPa temperature, mid-level lapse rate, precipitable water, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a hail likelihood metric with a critical success index of 0.504 and Heidke skill score of 0.403, which is exceptional among recent analogous hail studies. Furthermore, applications of the DNN to select case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-year GOES-12/13 image database to derive a hail frequency and severity climatology, which denotes the Central Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied. Opportunities for training and applying DNN-based hailstorm predictions to recently developed GOES-8/10/12/13/16 and Meteosat Second Generation convective storm detection and characterization climatologies over South America and South Africa, respectively, will also be presented.</span></p>

Hailstorms are among the most destructive and damaging weather phenomena. Satellites offer a unique view of deep convective clouds generating hail, especially over regions that lack surface-based measurements (e.g., oceans and remote areas over land). The chapter reviews the main techniques used from space to detect hail, with a specific focus on active and passive microwave methodologies. Different case studies are presented to illustrate the principles underpinning the remote sensing techniques. Finally, the review identifies drawbacks and limitations of the current observing system and discusses future research avenues to be explored.

The catastrophic derecho that occurred on 10 August 2020 across the midwestern United States caused billions of dollars of damage to both urban and rural infrastructure as well as agricultural crops, most notably across the state of Iowa. This paper documents the complex evolution of the derecho through the use of low-Earth-orbit passive-microwave imager and GOES-16 satellite-derived products complemented by products derived from NEXRAD weather radar observations. Additional satellite sensors including optical imagers and synthetic aperture radar (SAR) were used to observe impacts to the power grid and agriculture in Iowa. SAR improved the identification and quantification of damaged corn and soybeans, as compared to true-color composites and normalized difference vegetation index (NDVI). A statistical approach to identify damaged corn and soybean crops from SAR was created with estimates of 1.97 million acres of damaged corn and 1.40 million acres of damaged soybeans in the state of Iowa. The damage estimates generated by this study were comparable to estimates produced by others after the derecho, including two commercial agricultural companies.

Relationships between lightning flashes and thunderstorm kinematics and microphysics are important for applications such as nowcasting of convective intensity. These relationships are influenced by cloud electrification structures and have been shown to vary in anomalously electrified thunderstorms. This study addresses transitional relationships between active charge structure and lightning flash location in the context of kinematic and microphysical updraft characteristics during the development of an anomalously electrified supercell thunderstorm in the Tennessee Valley on 10 April 2009. The initial charge structure within the updraft was characterized as an anomalous dipole in which positive charge was inferred in regions of precipitation ice (i.e., graupel and hail) and negative charge was inferred in regions of cloud ice (i.e., aggregates and ice crystals). During subsequent development of the anomalous charge structure, additional minor charge layers as well as evidence of increasing horizontal complexity were observed. Microphysical and kinematic characteristics of the charge structure also evolved to include increasing observations of negative charge in precipitation ice regions, indicating the emergence of more prominent normal charging alongside dominant anomalous charging. Simultaneously, lightning flash initiation locations were also increasingly observed in regions of faster updrafts and stronger horizontal gradients in updraft speed. It is suggested that continuous variability in charging behavior over mesogamma spatial scales influenced the evolution of lightning flash locations with respect to the updraft structure. Further work is necessary to determine how this variability may impact lightning flash relationships, including lightning flash rate, with bulk microphysical and kinematic characteristics and related applications.

The objective of the Hurricane Imaging Radiometer (HIRAD) is to produce wide-swath images of hurricane wind and rain fields during a single pass from a high-altitude aircraft. This instrument could be a prototype for the next generation of airborne hurricane remote sensors that operate on NOAA/USAF surveillance flights over named storms and hurricanes. The improved two-dimensional surface wind field measurements provided by the HIRAD approach are crucial to improved forecasts and warnings. For almost a decade, HIRAD has been used in research flights over hurricanes; however, because of various hardware issues, the scientific potential of its measurements has not been fulfilled. This paper presents a reanalysis of HIRAD measurements over Hurricane Gonzalo on 17 October 2014 that demonstrate remarkable results. The basis for this novel approach is to use coincident surface wind speed (WS) and rain rate (RR) measurements from another source to calibrate the HIRAD brightness temperature measurements. As a result, the HIRAD retrievals of WS and RR are in excellent agreement with the accompanying airborne remote sensors and in situ surface wind speed measurements, which validates the HIRAD technique proof of concept.

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Electrical, Kinematic, and Microphysical Contrasts between Supercells Exhibiting Normal and Anomalous Charge Structures in the Southeastern United StatesAuthorsSarahStoughiDLawrenceCareyiDChristopherSchultzDanielCecilSee all authors Sarah StoughiDCorresponding Author• Submitting AuthorThe University of Alabama in HuntsvilleiDhttps://orcid.org/0000-0003-4407-9622view email addressThe email was not providedcopy email addressLawrence CareyiDThe University of Alabama in HuntsvilleiDhttps://orcid.org/0000-0003-2255-877Xview email addressThe email was not providedcopy email addressChristopher SchultzNASA Marshall Space Flight Centerview email addressThe email was not providedcopy email addressDaniel CecilNASA Marshall Space Flight Centerview email addressThe email was not providedcopy email address

Hypotheses regarding favorable conditions for anomalous charging have primarily resulted from studies within the Great Plains region of the United States, where the efficiency of warm precipitation processes is thought to be fundamental. Rare observations of anomalous charge structures in the Southeastern region challenge existing conceptual models used to explain anomalous charging. As a rigorous evaluation of conditions that support anomalous charge structures, environmental characteristics and bulk kinematic and microphysical properties of two normal and two anomalous supercell thunderstorms observed in the Southeast were compared. Within the anomalous supercells, greater quantities of precipitation ice were identified at higher altitudes and colder temperatures, suggesting a greater depth of riming growth and increased vertical transport of rimed hydrometeors. Deeper anomalous supercell updrafts were larger and stronger in the upper mixed‐phase and glaciated regions. However, normal supercells were characterized by more robust low‐level updrafts, resulting in comparable warm cloud residence times that suggested warm precipitation processes were not necessarily less efficient in the anomalous supercells. Indications of enhanced mixed‐phase liquid water content in favor of anomalous charging were observed in the anomalous supercells, though contrasts in related environmental parameters were not as large as observed in other comparative studies. Anomalous supercell environments were characterized by increased instability, shallower warm cloud depth, as well as lower relative humidity in the 700–500 mb layer. Evidence of impacts from dry air in anomalous storm structures suggested that water vapor content may have affected particle‐scale charge transfer in support of anomalous charge structure development.

The Lightning Imaging Sensor (LIS) was launched to the International Space Station (ISS) in February 2017, detecting optical signatures of lightning with storm ‐ scale horizontal resolution during both day and night. ISS LIS data are available beginning 1 March 2017. Millisecond timing allows detailed intercalibration and validation with other spaceborne and ground ‐ based lightning sensors. Initial comparisons with those other sensors suggest fl ash detection ef fi ciency around 60% (diurnal variability of 51 – 75%), false alarm rate under 5%, timing accuracy better than 2 ms, and horizontal location accuracy around 3 km. The spatially uniform fl ash detection capability of ISS LIS from low ‐ Earth orbit allows assessment of spatially varying fl ash detection ef fi ciency for other sensors and networks, particularly the Geostationary Lightning Mappers. ISS LIS provides research data suitable for investigations of lightning physics, climatology, thunderstorm processes, and atmospheric composition, as well as real ‐ time lightning data for operational forecasting and aviation weather interests. ISS LIS enables enrichment and extension of the long ‐ term global climatology of lightning from space and is the only recent platform that extends

The Lightning Imaging Sensor (LIS) was launched to the International Space Station (ISS) in February 2017, detecting optical signatures of lightning with storm-scale horizontal resolution during both day and night. ISS LIS data are available beginning 1 March 2017. Millisecond timing allows detailed intercalibration and validation with other spaceborne and ground-based lightning sensors. Initial comparisons with those other sensors suggest flash detection efficiency around 60% (diurnal variability of 51-75%), false alarm rate under 5%, timing accuracy better than 2 ms, and horizontal location accuracy around 3 km. The spatially uniform flash detection capability of ISS LIS from low-Earth orbit allows assessment of spatially varying flash detection efficiency for other sensors and networks, particularly the Geostationary Lightning Mappers. ISS LIS provides research data suitable for investigations of lightning physics, climatology, thunderstorm processes, and atmospheric composition, as well as realtime lightning data for operational forecasting and aviation weather interests. ISS LIS enables enrichment and extension of the long-term global climatology of lightning from space, and is the only recent platform that extends the global record to higher latitudes (± 55). The global spatial distribution of lightning from ISS LIS is broadly similar to previous datasets, with globally averaged seasonal/annual flash rates about 5-10% lower. This difference is likely due to reduced flash detection efficiency that will be mitigated in future ISS LIS data processing, as well as the shorter ISS LIS period of record. The expected land/ocean contrast in the diurnal variability of global lightning is also observed.

The Geostationary Lightning Mapper (GLM) is an instrument designed to continuously monitor lightning. It is on the GOES-16 and GOES-17 satellites, viewing much of the Western Hemisphere equatorward of 55 degrees. Besides recording lightning-flash information, it transmits background visible-band images of its field of view every 2.5 min. The background images are not calibrated or geolocated, and they only have similar to 10-km grid spacing, but their 2.5-min sampling can potentially fill temporal gaps between full-disk imagery from the GOES satellites' Advanced Baseline Imager. This paper applies an initial calibration and geolocation of the GLM background images and focuses on animations for two cases: a volcanic eruption in Guatemala and a severe thunderstorm complex in Argentina. Those locations typically have 10-min intervals between full-disk scans. Prior to April 2019, the interval was 15 min. Despite coarse horizontal resolution, the rapid updates from GLM background images appear to be useful in these cases. The 3 June 2018 eruption of Fuego Volcano appears in the GLM background imagery as an initial darkening of the pixels very near the volcano and then an outward expansion of the dark ash cloud. The GLM background imagery lacks horizontal textural detail but compensates for this lack with temporal detail. The ash cloud resembles a dark blob steadily expanding from frame to frame. Animation of the severe thunderstorm scene reveals vertical wind shear, with northerly low-level flow across a growing cumulus field and west-northwesterly upper-level flow at anvil level. Convective initiation is seen, as are propagating outflow boundaries and overshooting convective cloud tops.