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Vision: Enable aircraft with dramatically improved energy efficiency, environmental compatibility, and economic impact for the nation. Mission: Explore and develop viable game-changing concepts, technologies, and tools to improve vehicle and propulsion system energy efficiency and environmental compatibility. Scope: Subsonic fixed-wing commercial transport aircraft.
Comprised of 17 named tropical storms, 6 of which were major hurricanes, the 2017 Atlantic hurricane season ranked as one of the most damaging and costly hurricane seasons on record. In addition to socio-economic impacts, many previous studies have shown that important coastal ecosystems like mangroves are shaped by severe storms. However, little is known about how the cumulative effects of storms over entire hurricane seasons affect mangroves across large regions. We used satellite imagery from the entire Caribbean and Gulf of Mexico region to show that 2017 resulted in disproportionate mangrove damage compared to baseline responses over the previous 8 years. Specifically, we observed 30 times more mangrove damage, via a reduction in the normalized difference vegetation index (NDVI), during 2017 compared to any of the eight previous hurricane seasons, and most (72%) of this damage persisted throughout the 7 month post-hurricane season period as indicated by no NDVI recovery. Furthermore, wind speed, rainfall, and canopy height data showed that mangrove damage primarily resulted from high maximum wind speeds, but flooding (cumulative rainfall), previous storm history, and mangrove structure (canopy height) were also important predictors of damage. While mangroves are known to be resilient to hurricane impacts, our results suggest that increasingly frequent mega-hurricane seasons in the Caribbean region will dramatically alter mangrove disturbance dynamics.
Background:Rift Valley fever (RVF) outbreaks in domestic ruminants have severe socio-economic impacts. Climate-based continental predictions providing early warnings to regions at risk for RVF outbreaks are not of a high enough resolution for ruminant owners to assess their individual risk. (2) Methods: We analyzed risk factors for RVF occurrence and severity at the farm level using the number of domestic ruminant deaths and abortions reported by farmers in central South Africa during the 2010 RVF outbreaks using a Bayesian multinomial hurdle framework. (3) Results: We found strong support that the proportion of days with precipitation, the number of water sources, and the proportion of goats in the herd were positively associated with increased severity of RVF (the numbers of deaths and abortions). We did not find an association between any risk factors and whether RVF was reported on farms. (4) Conclusions: At the farm level we identified risk factors of RVF severity; however, there was little support for risk factors of RVF occurrence. The identification of farm-level risk factors for Rift Valley fever virus (RVFV) occurrence would support and potentially improve current prediction methods and would provide animal owners with critical information needed in order to assess their herd’s risk of RVFV infection.
Landslides are extremely damaging, pervasive and cause fatalities and economic impacts. Extreme rainfall events, coupled with inopportune surface conditions, are the primary triggers of landslides around the world. Forecasting landslide events represent an area of open research. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model has been created that provides near real-time dynamic landslide characterization using Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG). However, it does not provide information on prediction of landslides into future. This study considers how global precipitation forecast data compares to satellite rainfall at different spatiotemporal scales and outlines the potential for its use in landslide hazard prediction/forecasting system. NASA Goddard Earth Observing System (GEOS)-Forecast model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The GEOS-Forecast model is initialized at 00 UTC and is evaluated with IMERG Early, using Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) precipitation product as a reference over contiguous United States (CONUS). Categorical and continuous statistics along with probability density functions and cumulative distribution functions are considered to assess the performance of the precipitation products, with a focus on landslide hotspots. Seasonality appears to influence the performance of both the GEOS-Forecast and IMERG Early product. Moreover, the global comparison between the GEOS-Forecast and IMERG Early is carried out in terms of percentile difference, correlation, and bias maps. For extreme rainfall events in regions such as Mekong, Colombia, and Tajikistan, GEOS-Forecast appears to resolve high rainfall relative to IMERG Early more frequently. Validation over landslide points reveal that for 24hr rainfall accumulations > 100mm, GEOS-Forecast and IMERG coincide. GEOS-Forecast and IMERG Early precipitation matches more closely for tropical cyclones than other types of storms. Overall, the performance varies with respect to location, rainfall intensities, and type of precipitation events.
Flooding is a natural event that occurs frequently with high severity worldwide, responsible for significant societal and economic impacts. Disaster managers face significant challenges managing essential information for preparedness, response, and recovery efforts. The development of an open access, global flood alerting system for effective identification of flood impacted areas, classification of potential impacts, and the formulation of effective emergency response measures requires the incorporation of a wide variety of flood models and remote sensing data sources from multiple platforms. NASA is currently funding projects focused on flood forecasting, post-event flood mapping, flood depth estimation and pre-event flood severity estimation using Earth observation (EO) datasets and derived flood products. A new initiative in the Disasters Program is underway to disseminate flood products from different hydrologic models and sensors to global stakeholders via Pacific Disaster Center’s DisasterAWARE®, NASA’s Disasters Mapping Portal and potentially other mechanisms. This initiative focuses on improving response capacity and use of EO products in near real-time by a broader community for resource planning in case of extreme events. As part of this initiative, we have deployed Model of Models (MoM) – an open-source ensemble approach, that integrates outputs from hydrologic models and EO data from optical imagery to assess flood severity daily at sub-watershed level globally. The MoM output is integrated with the incident event system of DisasterAWARE to generate flood severity risk and flood impact boundaries, which are disseminated via the DisasterAWARE platform to different stakeholders globally for decision-making and response efforts. The next step will focus on using MoM outputs to estimate flood depth and extent mapping using high-resolution Synthetic Aperture Radar imagery, impact assessment using optical imagery and population datasets, and damage estimation using critical infrastructure datasets, which would be disseminated via DisasterAWARE to decision-makers, emergency managers and first responders around the world.
Avian influenza viruses pose a threat to wildlife and livestock health. The emergence of highly pathogenic avian influenza (HPAI) in wild birds and poultry in North America in late 2021 was the first such outbreak since 2015 and the largest outbreak in North America to date. Despite its prominence and economic impacts, we know relatively little about how HPAI spreads in wild bird populations. In January 2022, we captured 43 mallards (Anas platyrhynchos) in Tennessee, USA, 11 of which were actively infected with HPAI. These were the first confirmed detections of HPAI H5N1 clade 2.3.4.4b in the Mississippi Flyway. We compared movement patterns of infected and uninfected birds and found no clear differences; infected birds moved just as much during winter, migrated slightly earlier, and migrated similar distances as uninfected birds. Infected mallards also contacted and shared space with uninfected birds while on their wintering grounds, suggesting ongoing transmission of the virus. We found no differences in body condition or survival rates between infected and uninfected birds. Together, these results show that HPAI H5N1 clade 2.3.4.4b infection was unrelated to body condition or movement behavior in mallards infected at this location during winter; if these results are confirmed in other seasons and as HPAI H5N1 continues to evolve, they suggest that these birds could contribute to the maintenance and dispersal of HPAI in North America. Further research on more species across larger geographic areas and multiple seasons would help clarify potential impacts of HPAI on waterfowl and how this emerging disease spreads at continental scales, across species, and potentially between wildlife and domestic animals.
The ENSO phenomenon has a significant global socio-economic impact and has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help improve the initialization of the thermocline, while satellite SST aids in constraining surface heat-fluxes, leading to improved coupled system sub-seasonal to seasonal forecasts. However, few studies have focused on improving the near-surface density and mixing through satellite sea surface salinity (SSS) assimilation. The few ocean models that assimilate satellite SSS, bias correct to normalize towards the near-surface Argo data for expediency. This assumption is likely inadequate in rainy regions, where buoyant water forms a fresh surface lens. In previous work, we showed that adjusting SSS to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 improves the near-surface density and mixed layer depth, leading to deeper thermocline and improved NINO3.4 SST forecasts. We now utilize the Soil Moisture and Ocean Salinity rain-corrected (SMOS_RC) SSS, available in SMOS-CATDS products, to represent Sb more accurately at the first model layer (e.g., 5 m). Rather than a diffusivity model as RIM, SMOS_RC uses a statistical correction dependent on Integrated Multi-satellitE Retrievals for GPM (IMERG) rain rates, established on observed SMOS SSS decreases related to Sb in the presence of rain (Supply et al., 2020). For all experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis additionally assimilates SMOS SSS data as is, and a separate reanalysis assimilates SMOS_RC. We assess the impact on near-surface and subsurface dynamics by validating against observations and explore how SSS assimilation (SMOS vs SMOS_RC) impacts ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (S2S-v3, Molod et al. 2020). We show that improved estimates of density and near-surface mixing led to more accurate coupled air/sea interaction and better ENSO forecasts. The increased SSS, resulting from the removal of the instantaneous rain effect, modifies the ocean state by enhancing mixing and deepening the thermocline.
A cold air outbreak (CAO) is an extreme weather phenomenon that has significant social and economic impacts over a large region of the midlatitudes. However, the dynamical mechanism governing the occurrence and evolution of CAO events, particularly the role of the stratosphere, is not well understood. Through an analysis of one extreme CAO episode that occurred on 27-31 January 2019 across much of the US Midwest, this presentation examines the thermodynamic structure and impact of stratospheric downward transport using single-field-view (SFOV) satellite products (with a spatial resolution of ~14 km at nadir) from the Cross-track Infrared Sounder (CrIS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite in conjunction with MERRA-2 and ERA5 reanalysis products. The results show that along the path of cold air transport, particularly near the coldest surface center, there exists a large enhancement of O 3 , deep tropopause folding, and significant downward transport of stratospheric dry air, and a warm center above the tropopause that can be observed directly using the brightness temperature (BT) observations from CrIS stratospheric sounding channels. This study provides some observational evidences from CrIS that confirm the impact of stratosphere on CAO through downward transport, and demonstrates the value of the SFOV retrieval products for CAO dynamic transport study and model evaluation.
During the past years, we have seen that the La Nina to El Nino transition has had a significant global socio-economic impact and so has been the key focus for improving coupled ocean-atmosphere forecasts. Assimilation of satellite altimetry and subsurface temperature and salinity from (mostly) Argo help to improve the initialization of the thermocline, while satellite Sea Surface Temperature (SST) aids in constraining surface heat-fluxes, leading to improved subseasonal to seasonal forecasts of the coupled system. However, few studies have focused on improving the fresh-water flux and near-surface density and mixing through assimilation of satellite sea surface salinity (SSS). For expediency, the few ocean models that do assimilate SSS bias-correct the satellite SSS data to normalize towards the near-surface Argo data. However, in rainy regions, where buoyant water sits as a fresh lens at the surface, this assumption is likely inadequate. In previous work, we have shown that adjusting SSS data to bulk salinity (Sb) using the Rain Impact Model (RIM) of Santos-Garcia et al., 2014 has improved the near-surface density and mixed layer depth, leading to deeper thermocline and improved the NINO3.4 SST forecasts. Now we utilize the Soil Moisture/Ocean Salinity, Rain Corrected (SMOS_RC) SSS product provided by the Centre Aval de Traitement des données SMOS (CATDS CPDC) to represent the Sb more accurately at first model layer (in our case 5 m). Rather than using a diffusivity model as with RIM, SMOS_RC relies on an observed relationship between the spatial heterogeneity of SMOS SSS and instantaneous rain rate (RR) (Supply et al., 2020). In order to test the impact of SMOS_RC versus SMOS, we compare two reanalyses over the period 2014 to 2021. For both reanalysis experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF scheme (Penny et al., 2013). One reanalysis additionally assimilates SMOS SSS data as is (i.e., with the fresh bias), and a separate reanalysis is performed assimilating the SMOS_RC data. We assess the impact for near-surface and subsurface dynamics within ocean reanalyses by validating against observations and explore how SSS assimilation (SMOS versus SMOS_RC) impacts dynamical ENSO forecasts using the NASA GMAO Sub-seasonal to Seasonal coupled forecast system (GEOS S2S-3, Molod et al., 2020, Hackert et al., 2023). We will show that improved SSS estimates and near-surface density and mixing led to more accurate coupled air/sea interaction and better ENSO forecasts.
Post-monsoon open-field stubble burning in northwestern (NW) India—a key agricultural region known as the “breadbasket”—is a longstanding practice used to clear fields. Satellite observations spanning over two decades have revealed significant upward trends in crop production, vegetative greenness, and the frequency of post-harvest fires, with this last contributing to hazardous air quality during the peak burning season (mid-October to mid-November). Since 2022, thermal anomaly data from Aqua-MODIS and SNPP-VIIRS sensors have shown a sharp decline in reported fire events—an observation that contrasts starkly with the concurrent rise in regional aerosol loading detected from space. This apparent discrepancy became particularly pronounced in 2024–2025, prompting a closer examination using high-temporal-resolution imagery from the Advanced Meteorological Imager (AMI) on the geostationary satellite GEO-KOMPSAT-2A. These observations revealed a clear spike in fire-related signals occurring around and after 4:00 p.m. local time, i.e., outside the typical noon to 2:00 p.m. detection window of the MODIS and VIIRS. A fire detection algorithm exploiting the fire-sensitive shortwave-infrared 3.8 μm signal and its contrast to 11.2 μm infrared observations is designed to adopt AMI observations and applied to its multi-year observations (2019–2025). The resulting fire dataset unambiguously shows a gradual shift in stubble burning activity toward the late afternoon hours beginning in 2022 which is underreported by polar-orbiting satellites. The orbital drift of NASA’s MODIS sensor on the Aqua platform allows detection of some of the gradually shifting fires during afternoon hours, but the MODIS still misses a large number of fires occurring around and after 4 p.m. The AMI’s relatively coarse spatial resolution (~4 km), a consequence of its slant viewing geometry over NW India, imposes inherent limitations on quantifying the full extent of fire occurrences. The operational air quality forecasting models currently assimilate satellite fire detections predominantly captured during early afternoon overpasses of the MODIS and VIIRS. The temporal shift in fire activity complicates such forecast, leading to a substantial underestimation of emissions. Intense stubble burning and the resulting air pollution highlight the need for effective crop residue management practices for mitigating the frequency of open biomass burning and thereby reducing episodic degradation of air quality and its associated public health and economic impacts.
The multidisciplinary studies explore and evaluate the impact of the meteorological satellite and the concomitant impact of the data derived from it on various user groups. As expected, the primary impact related to those who would use satellite data for weather prediction and related purposes. A secondary impact was in the area of international concerns where GARP and other international meteorological activities were affected and international law was developed. A tertiary impact was exemplified by satellite photographs utilized in advertisements and related materials. The case studies, supporting studies, and independent studies all emphasize the potential of the meteorological satellite.
Management and use meteorological satellite data to detect severe storms and crop disease
Studies are presented on mesoscale weather monitoring, analysis, and dissemination of weather forecasts for the Wisconsin area.
An assessment of the technological impact of modern satellite weather forecasting for the United States is presented. Topics discussed are: (1) television broadcasting of weather; (2) agriculture (crop production); (3) water resources; (4) urban development; (5) recreation; and (6) transportation.
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A nuclear war using less than 1% of the current global nuclear arsenal, which would inject 5 Tg of soot into the stratosphere, could produce climate change unprecedented in recorded human history and significant impacts on agricultural productivity and the economy. These effects would be most severe for the first five years after the nuclear war and may last for more than a decade. This paper calculates how food availability would change by employing the Environmental Impact and Sustainability Applied General Equilibrium model. Under a robust world trading system, global food availability would drop by a few percentage points. If the war would destabilize trade, it would magnify by several times the negative ramifications of land productivity shocks on food availability. If exporting countries redirect production to domestic consumption at the expense of importing countries, it would lead to the destabilization of international trade. The analysis suggests that economic models aiming to inform policymakers require both economic behavior analysis and biophysical drivers. Policy lessons derived from a crop model can be significantly nuanced when coupled with economic feedback derived from economic models. Through the impact on yield, farmers could shift production among crops and reallocate land use to maximize profits, showing the importance of general equilibrium effects such as product and input substitution and international trade. Although the global impact on corn and soybean production would be significant when just considering crop production, it could be considerably smaller under the economic model. However, this would be at the expense of other sectors, including livestock. In addition, the costs borne from disruptions to climate would vary significantly across regions, with significant adverse effects in high latitude regions. The severity of the shocks in the high-latitude areas would marginalize the farmers' product and input substitution ability.
Conference report on technology utilization impact on economic growth