It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high-resolution models. This paper presents model verification results of a case study period from June to August 2008 over the southeastern United States using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the National Aeronautics and Space Administration’s (NASA) Land Information System (LIS) and sea surface temperatures (SSTs) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction’s (NCEP) 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spinup run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS–MODIS data substantially impact surface and boundary layer properties. The Developmental Testbed Center’s Meteorological Evaluation Tools package is employed to produce verification statistics, including traditional gridded precipitation verification and output statistics from the Method for Object-Based Diagnostic Evaluation (MODE) tool. The LIS–MODIS initialization is found to produce small improvements in the skill scores of 1-h accumulated precipitation during the forecast hours of the peak diurnal convective cycle. Because there is very little union in time and space between the forecast and observed precipitation systems, results from the MODE object verification are examined to relax the stringency of traditional gridpoint precipitation verification. The MODE results indicate that the LIS–MODIS-initialized model runs increase the 10 mm h−1 matched object areas (“hits”) while simultaneously decreasing the unmatched object areas (“misses” plus “false alarms”) during most of the peak convective forecast hours, with statistically significant improvements of up to 5%. Simulated 1-h precipitation objects in the LIS–MODIS runs more closely resemble the observed objects, particularly at higher accumulation thresholds. Despite the small improvements, however, the overall low verification scores indicate that much uncertainty still exists in simulating the processes responsible for airmass-type convective precipitation systems in convection-allowing models.

The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities. We focus on the recent DL upsurge in East Africa from October 2019 though June 2020, utilizing known locust observations from the United Nations Food and Agriculture Organization (FAO). We compare this information to results from the current literature and combine the two datasets to create “optimal thresholds” of breeding conditions. When considering the most optimal conditions (all thresholds met), the soil texture combined with modeled soil moisture content predicted the estimated DL egg-laying period 62.5% of the time. Accounting for the data errors and uncertainties, a 3 × 3 pixel buffer increased this to 85.2%. By including soil moisture, the areas of optimal egg laying conditions decreased from 33% to less than 20% on average.

Several dynamically downscaled climate simulations with various spatial resolutions (24, 12, and 4 km) and spectral nudging strengths (0, 600, and 2000 km) have been run over the contiguous United States from 2000 to 2009 using the high-resolution NASA Unified Weather and Research Forecasting (NU-WRF) regional model initialized and constrained by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). This paper summarizes the authors’ efforts on the development of a model performance metric and its application to assess summer precipitation over the U.S. Great Plains (USGP) in these downscaled climate simulations. A new model performance metric T was first developed that uses both the linear correlation coefficient and mean square error and is consistent with other commonly used metrics, but gives a bigger separation between good and bad simulations. This metric T was then applied to the summer mean precipitation spatial pattern, diurnal Hovmöller diagram, and diurnal spatial pattern over the USGP from the simulations focusing on the summer precipitation diurnal cycle related to mesoscale convective systems (MCSs). The metric T skill scores increase significantly from the control simulation to the nudged simulations and from the nudged simulations with shorter wavelengths to the nudged simulations with longer wavelengths, but do not change much from MERRA-2 to the downscaled simulations or between the various downscaled simulations with different spatial resolutions. Thus, there is some credibility, but no significant value added compared to MERRA-2, of the downscaled climate simulations of the summer precipitation over the USGP.

Some of the most intense thunderstorms on Earth occur in the Hindu Kush Himalaya (HKH) region of southern Asia—where many organizations lack the capacity needed to predict, observe, and/or effectively respond to the threats associated with high-impact convective weather. As a result, a disproportionately large number of casualties and damage often occur with premonsoon severe thunderstorms in this region. To address this problem, we combined ensemble numerical weather prediction (NWP), satellite-based precipitation products, and land-imagery techniques into a High-Impact Weather Assessment Toolkit (HIWAT) customized for HKH. In 2018 and 2019 demonstrations, a regional convection-allowing ensemble NWP system was configured to provide real-time probabilistic guidance of thunderstorm hazards over HKH, applying ensemble techniques developed for U.S.-focused experiments. Case studies of damaging wind, large hail, lightning, a rare Nepalese tornado, and landfalling tropical cyclone events show how HIWAT efficiently packages ensemble output into products that are readily interpreted by forecasters in HKH. Precipitation and total lightning flash verification reveal the highest skill occurred where deep convection was most frequently observed in Bangladesh and northeastern India, and verification scores exceeded global ensemble scores for heavy precipitation rates. These results demonstrate that plausible forecasts of thunderstorm hazards can be attained with relatively low computational resources, thereby facilitating advancements in extreme weather forecasting services in historically underserved regions such as HKH. In early 2022, a custom version of HIWAT was installed at the Bangladesh Meteorological Department using in-house computational resources, providing regional ensemble forecast guidance in real time.

This study examines 11 years of lightning-initiated wildfires (LIWs) that burned 1000+ acres over the conterminous United States spanning 2010–20 to identify the associated rainfall and land surface characteristics. The U.S. Forest Service database is used to examine LIWs along with Multi-Radar Multi-Sensor 1-km resolution hourly rainfall, a NASA Land Information System observations-driven simulation on a 3-km grid for historical soil moisture, 4-km resolution satellite-derived green vegetation fraction, and 5-km resolution evaporative stress index (ESI) of live vegetation health to examine land surface conditions surrounding LIWs. The study objectives are to quantify antecedent land surface conditions, compare how conditions vary between wildfires reported the same day (immediate) versus smoldering LIWs reported one or more days after ignition (holdover wildfires), and determine rainfall and land surface characteristics that favor LIWs. Composite results indicate that a steady decline in antecedent soil moisture occurred for the 3 months leading up to LIWs, with similar but not as pronounced declines in evaporative demand given by ratios of evapotranspiration (ET) to potential ET (PET) and ET/PET anomalies (ESI). Rain rates were considerably lower at flash locations igniting wildfires compared to noninitiating flash locations (p = 100%). Additionally, rainfall tended to be highly associated with holdover LIWs than in immediate LIWs. Antecedent soil moisture and ET/PET were incrementally higher preceding holdover wildfires. Shallow soil moisture was modestly drier (wetter) for lightning flashes igniting wildfires (holdover wildfires), deep soil moisture was slightly wetter in LIWs, but few statistically significant differences were found in the ET/PET and ESI results. Significance Statement: This study examines the importance of antecedent land surface properties in conjunction with rain rates associated with lightning-initiated wildfires in the United States. Steadily declining soil moisture and evaporative stress of live vegetation preceded lightning-initiated wildfires. Rain rates were the most important factors in determining whether lightning will ignite wildfires; however, drier surface soil moisture and slightly wetter deep soil moisture occurred at lightning flash locations that initiated wildfires. Rain rates and soil moisture were notably higher for holdover wildfires that were identified at least a day or more after lightning ignition, whereas vegetation was less stressed on average in holdover wildfires.

Some of the most intense thunderstorms and extreme weather events on Earth occur in the Hindu Kush Himalaya (HKH) region of Southern Asia. The need to provide end users, stakeholders, and decision makers with accurate forecasts and alerts of extreme weather is critical. To that end, a cutting edge weather modeling framework coined the High Impact Weather Assessment Toolkit (HIWAT) was created through the National Aeronautics and Space Administration (NASA) SERVIR Applied Sciences Team (AST) effort, which consists of a suite of varied numerical weather prediction (NWP) model runs to provide probabilities of straight-line damaging winds, hail, frequent lightning, and intense rainfall as part of a daily 54 h forecast tool. The HIWAT system was first deployed in 2018, and the recently released model archive hosted by the Global Hydrometeorology Resource Center (GHRC) Distributed Active Archive Center (DAAC) provides daily model outputs for the years of 2018–2022. With a nested modeling domain covering Nepal, Bangladesh, Bhutan, and Northeast India, the HIWAT archive spans the critical pre-monsoon and monsoon months of March–October when severe weather and flooding are most frequent. As part of NASA’s Transformation To Open Science (TOPS), this data archive is freely available to practitioners and researchers.