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NASA NTRSDate not supplied

TPSAS-NF1676L-10735-DND

The Clouds and the Earth's Radiant Energy System (CERES) project has now surpassed the 10-year mark, has combined rigorous calibration, improved scene identification, and multi-satellite data fusion to produce a climate-accuracy data set of global 1° gridded radiative surface and TOA fluxes, and cloud properties. CERES provides the climate community with four observed TOA flux products. The ERBE-like product uses the same algorithms as ERBE in order to be compared with the ERBE time period. The SSF product uses the improved scene identification and radiance to flux conversion. The SYN product uses GEO derived clouds and fluxes to provide diurnally complete TOA fluxes. The EBAF product provides net balanced fluxes tied to the ocean heat storage term. This product is suited for climate modelers estimating the global mean energy budget. Each successive CERES flux product applies the improvements over the previous product. This presentation will focus on the diurnal averaging improvements by the inclusion of imager radiances from 5 geostationary (GEO) satellites at 3-hourly intervals to estimate the flux in between CERES observations. The regional monthly mean flux differences based on SYN (CERES/GEO) and SSF (CERES-only) can exceed 20 Wm^-2. CERES shortwave and longwave fluxes have been rigorously validated and are key elements in evaluating the ability of climate models to predict both past and future climate states. The CERES normalization of GEO derived fluxes in order to maintain the CERES instrument calibration and are consistent globally within 0.1 Wm^-2, even if the GEO calibration is artificially altered by ±5%, twice the anticipated calibration error. The predicted GEO TOA fluxes normalized to Terra have been compared at Aqua times to Aqua flux observations, as well as with GERB hourly fluxes over the METEOSAT domain. Similarly ground based radiometers have been used to validate the CERES radiative transfer computed surface fluxes and have confirmed that the CERES/GEO fluxes have improved the computed surface flux product. Although the GEO based fluxes are of inferior quality then the CERES observed fluxes, they capture the diurnal signal more effectively than using constant meteorology between CERES measurements. The validation of the GEO derived fluxes and clouds will be shown.

D. R. Doelling↗

NASA NTRS2020

Gila Water Resources II: Using Earth Observations to Identify Wildfire Impacts on Hydrologic Functions and Recovery in the Gila National Forest

Wildfires have the potential to cause devastating and long-lasting impacts on ecological systems. In the Gila National Forest (Gila NF), wildfire events have occurred with increasing frequency and severity over recent years. These disturbances, such as the historic Whitewater Baldy Complex Fire (2012) and Silver Fire (2013), have raised concerns over post-fire flooding, debris flows, and vegetation recovery. Understanding connections between burn events and ecological functions is crucial for developing effective land management practices within the Gila NF that ensure conservation of the watershed. The Gila Water Resources II team worked in partnership with the US Department of Agriculture (USDA) US Forest Service’s (USFS) Gila National Forest and Region 3. This project provided insight into the influence of wildfires on increased flooding events and determined if restoration efforts in the Gila NF are having a beneficial impact on vegetation regeneration. To understand recovery trends and hydrologic impact in the Gila NF between 2000-2019, this project used Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, Landsat 8 Operational Land Imager, Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM precipitation, along with ancillary data from USGS stream gauges and data provided by USDA USFS’s Gila National Forest and Region 3. Based on these data, the team identified burn areas that received restorative treatments and compared Normalized Burn Ratio for different land cover types to better inform land management decisions. Additionally, the team analyzed the relationship between precipitation and streamflow from stream gauges to investigate the impact wildfires have on hydrology within the watershed.

Water Resources↗

NASA NTRSDate not supplied

Assessing the Viability of Using GEOS-Forecast Product for Landslides Forecasting: A Step Toward Early Warning System

Landslides across the globe are mostly triggered by extreme rainfall events affecting infrastructure, transportation and livelihoods. The risks are rarely quantified due to lack of data, analytical skills and limited modeling techniques. Knowledge of local to global scale landslide risks provides communities and national agencies the ability to adapt disaster management practices to mitigate and recover from these hazards. In order to minimize the risks and improve characterization of community resilience to landslides, it is vital to have reliable information about the factors triggering landslides such as rainfall, well ahead in time. Forecasting potential landslide activity and impacts can be achieved through reliable precipitation forecast models. However, it is challenging because of the temporal and spatial variability of precipitation, an important factor in triggering landslides. Evaluation of the precipitation field, associated errors, and sampling uncertainties is integral for development of efficient and reliable landslide forecasting and early warning system. This study develops a methodology to assess the viability of using a precipitation field provided by a global model and its potential integration in the landslide forecasting system. The study focuses on the comparison between the IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Mission) and GEOS (NASA Goddard Earth Observing System)-Forecast product over contiguous United States (CONUS). GEOS model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The framework is tested on the GEOS-Forecast Model initialized at 00 UTC using daily IMERG early product as reference using both categorical and continuous statistics. The categorical statistics includes the probability of detection (POD), success ratio (SR), critical success index (CSI), and the hit bias. Continuous statistics such as correlation, normalized standard deviation, and root-mean-square error are also evaluated. Overall, GEOS-Forecast precipitation field over the analysis period (~1 year) show underestimation with respect to IMERG early for the daily accumulated rainfall. However, the probability distribution function and cumulative distribution function of both show similar patterns. In terms of correlations, POD, SR, CSI, hit bias, the performance varies with respect to the rainfall threshold used.

Sana Khan↗

NASA NTRSDate not supplied

Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone

A recent, interdisciplinary survey of more than 40 international remote sensing experts reviewed the strengths and limitations of current space-based observational capabilities of the Arctic Boreal Zone (ABZ) and made recommendations for improving upon these current capabilities (Duncan et al., 2019). General recommendations focused on the complementarity of sub-orbital and satellite observations, the need for measurement continuity, and the importance of stakeholder engagement. The development of a comprehensive and robust sub-orbital portion of an ABZ observing network can act to fill some temporal gaps in satellite coverage, provide detail unobtainable from space, and is necessary for validation and interpretation of satellite data. A priority across all types of observations is the continuation, enhancement, and/or creation of long-term, multi-satellite, climate-quality, and self-consistent data records of ABZ components, such as surface temperature, energy fluxes, or sea ice extent and volume, for improved determination of trends. Long-term passive satellite observations currently represent the only feasible option for monitoring change of the ABZ at broad spatial scales required to address pressing science challenges. Active sensors are particularly attractive for the ABZ as they do not depend on reflected sunlight and so join passive microwave sensors in having a significant advantage over passive visible and infrared sensors in the low-light conditions that are typical of the ABZ for several months of the year. Application of consistent retrieval algorithms to multiple data sets, as well as careful characterization of satellite instruments and their temporal evolution, helps to ensure data quality and consistency. Among the needs ranked as “Most Important” are those associated with gaining a process-based understanding of the ABZ carbon cycle and hydrologic cycle (which includes sea level rise) as they have the potential to affect a large portion of Earth’s population.

Remote sensing↗

NASA NTRSDate not supplied

Application of the Global Precipitation Forecast dataset for Global Landslide Forecasting System

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.

Sana Khan↗

NASA NTRS2020

Africa Food Security & Agriculture: Predicting the Likelihood of Human-elephant Conflict and Assessing Elephant Habitat Conditions During Extreme Drought and Crop Deficit in the Kavango-Zambezi Area

Human-wildlife conflict is increasingly more common due to human population growth, habitat fragmentation, and changing climatic conditions. This conflict is particularly evident in the Kavango-Zambezi area, where over three million people share the landscape with an abundant megafauna population. As the changing climate continues to exacerbate drought severity and subsequent food availability, conflict between humans and wildlife has become more prevalent. In the Kavango-Zambezi area, conflict between elephants and humans has resulted in crop loss, property damage, and threats to public safety. In order to manage current and future conflict, The Ecoexist Project and Connected Conservation have been working to empower farmers and conserve the natural habitat. This DEVELOP project employed Earth observations to conduct a time series analysis of vegetation health change, elephant movement, and climate conditions, from 2017 to 2020. Data were aggregated into the wet (November through April) and dry (May through October) seasons. The resulting analysis demonstrated the potential to use Landsat 8 Operational Land Imager (OLI), Global Precipitation Measurement’s Integrated Multi-satellite Retrievals for GPM (GPM IMERG), and TerraClimate data to identify potential areas of conflict under increased seasonal variability. An improved understanding of conflict drivers will help support sustainable wildlife conservation and food security in the future.

DEVELOP Project Summary↗

NASA NTRS2020

Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: the Case of Rio deJaneiro, Brazil

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001-2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12-35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

IMERG↗

NASA NTRS2021

AeroCom Phase III Multi-Model Evaluation of the Aerosol Life Cycle and Optical Properties Using Ground and Space-Based Remote Sensing as Well as Surface In Situ Observations

Within the framework of the AeroCom (Aerosol Comparisons between Observations and Models) initiative, the state-of-the-art modelling of aerosol optical properties is assessed from 14 global models participating in the phase III control experiment (AP3). The models are similar to CMIP6/AerChemMIP Earth System Models (ESMs) and provide a robust multi-model ensemble. Inter-model spread of aerosol species lifetimes and emissions appears to be similar to that of mass extinction coefficients (MECs), suggesting that aerosol optical depth (AOD) uncertainties are associated with a broad spectrum of parameterised aerosol processes. Total AOD is approximately the same as in AeroCom phase I (AP1) simulations. However, we find a 50 % decrease in the optical depth (OD) of black carbon (BC), attributable to a combination of decreased emissions and lifetimes. Relative contributions from sea salt (SS) and dust (DU) have shifted from being approximately equal in AP1 to SS contributing about 2∕3 of the natural AOD in AP3. This shift is linked with a decrease in DU mass burden, a lower DU MEC, and a slight decrease in DU lifetime, suggesting coarser DU particle sizes in AP3 compared to AP1. Relative to observations, the AP3 ensemble median and most of the participating models underestimate all aerosol optical properties investigated, that is, total AOD as well as fine and coarse AOD (AODf, AODc), Ångström exponent (AE), dry surface scattering (SCdry), and absorption (ACdry) coefficients. Compared to AERONET, the models underestimate total AOD by ca. 21 % ± 20 % (as inferred from the ensemble median and interquartile range). Against satellite data, the ensemble AOD biases range from −37 % (MODIS-Terra) to −16 % (MERGED-FMI, a multi-satellite AOD product), which we explain by differences between individual satellites and AERONET measurements themselves. Correlation coefficients (R) between model and observation AOD records are generally high (R>0.75), suggesting that the models are capable of capturing spatio-temporal variations in AOD. We find a much larger underestimate in coarse AODc (∼ −45 % ± 25 %) than in fine AODf (∼ −15 % ± 25 %) with slightly increased inter-model spread compared to total AOD. These results indicate problems in the modelling of DU and SS. The AODc bias is likely due to missing DU over continental land masses (particularly over the United States, SE Asia, and S. America), while marine AERONET sites and the AATSR SU satellite data suggest more moderate oceanic biases in AODc. Column AEs are underestimated by about 10 % ± 16 %. For situations in which measurements show AE > 2, models underestimate AERONET AE by ca. 35 %. In contrast, all models (but one) exhibit large overestimates in AE when coarse aerosol dominates (bias ca. +140 % if observed AE < 0.5). Simulated AE does not span the observed AE variability. These results indicate that models overestimate particle size (or underestimate the fine-mode fraction) for fine-dominated aerosol and underestimate size (or overestimate the fine-mode fraction) for coarse-dominated aerosol. This must have implications for lifetime, water uptake, scattering enhancement, and the aerosol radiative effect, which we can not quantify at this moment. Comparison against Global Atmosphere Watch (GAW) in situ data results in mean bias and inter-model variations of −35 % ± 25 % and −20 % ± 18 % for SCdry and ACdry, respectively. The larger underestimate of SCdry than ACdry suggests the models will simulate an aerosol single scattering albedo that is too low. The larger underestimate of SCdry than ambient air AOD is consistent with recent findings that models overestimate scattering enhancement due to hygroscopic growth. The broadly consistent negative bias in AOD and surface scattering suggests an underestimate of aerosol radiative effects in current global aerosol models. Considerable inter-model diversity in the simulated optical properties is often found in regions that are, unfortunately, not or only sparsely covered by ground-based observations. This includes, for instance, the Sahara, Amazonia, central Australia, and the South Pacific. This highlights the need for a better site coverage in the observations, which would enable us to better assess the models, but also the performance of satellite products in these regions. Using fine-mode AOD as a proxy for present-day aerosol forcing estimates, our results suggest that models underestimate aerosol forcing by ca. −15 %, however, with a considerably large interquartile range, suggesting a spread between −35 % and +10 %.

space-based remote sensing↗

NASA NTRSDate not supplied

Cloud-Precipitation Hybrid Regimes and their Projection onto IMERG Precipitation Data

We extend and enhance the concept of the Cloud Regimes (CRs) developed from two-dimensional joint histograms of cloud optical thickness and cloud top pressure from the Moderate Resolution Imaging Spectroradiometer (MODIS), by adding precipitation information in order to better understand cloud-precipitation relationships. Taking advantage of the high-resolution Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation dataset, cloud-precipitation “hybrid” regimes are derived by implementing the k-means clustering algorithm with advanced initialization and objective measures to determine the most optimal clusters. By expressing precipitation rates within 1-degree grid cell as histograms and making choices on the relative weight of cloud and precipitation, we could obtain several editions of hybrid cloud-precipitation regimes (CPRs), and examine their characteristics. In the deep tropics, when precipitation is weighted weakly, the cloud part of the hybrid entroids resembles the centroid of cloud-only regimes, but still tightens the cloud-precipitation relationship by decreasing the precipitation variability of each regime. As precipitation weight progressively increases, the shape of the cloudy part of the hybrid centroids becomes blunter, while the precipitation part of the centroids sharpens. In the case where cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct features in the precipitation parts of the centroids, which allows us to project them onto the 30-minly IMERG domain. Such a projection can be used to overcome the temporal sparseness of MODIS cloud observations, which leads to great application potential for various convection-focused studies, including diurnal cycle analysis.

cloud-precipitation↗

NASA NTRS2020

Africa Food Security & Agriculture: Predicting the Likelihood of Human-elephant Conflict and Assessing Elephant Habitat Conditions During Extreme Drought and Crop Deficit in the Kavango-Zambezi Area

DEVELOP Tech Paper↗

NASA NTRS2021

Western Montana Ecological Forecasting: Modeling Habitat Suitability of Mustelid Species to Guide Detection Dog Surveys for Contaminants Monitoring, via Collected Scats in River Systems of Western Montana

Environmental contaminants are becoming increasingly prevalent in riverine ecosystems. The status of contaminants in western Montana’s relatively pristine river systems is largely unknown. Monitoring for heavy metals, brominated flame-retardants (BFRs), and pharmaceuticals is important due to their negative effects on ecosystems. Exposure to these contaminants can have significant endocrine, neurological, and reproductive effects. Contaminants easily travel up the food chain and bioaccumulate in apex predators. As predators with a largely aquatic diet, American mink (Mustela vison) and North American river otter (Lontra canadensis) serve as reliable indicator species of environmental health and the status of contaminants. Analysis of scat from these species is a noninvasive method to measure contaminant levels, and detection dogs from Working Dogs for Conservation (WD4C) have been used to locate these scat samples. To aid in the search of these samples, habitat suitability models were created for mink and otter for the years 2013-2020 and projected to 2040 using the random forest algorithm in the Software for Assisted Habitat Modeling (SAHM). Predictor variable data were acquired from Landsat 8 Operational Land Imager (OLI), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (GPM IMERG), Shuttle Radar Topography Mission (SRTM), and Soil Moisture Active Passive (SMAP). Within these models, the most important variable for mink and otter habitat was distance to river. Suitable habitat also corresponded with emergent herbaceous land cover and deeper river locations. These habitat suitability models will inform sampling site section for further contaminant analysis.

Anna Winter↗

NASA NTRS2021

Improved Estimates of Pentad Precipitation through the Merging of Independent Precipitation Datasets

Three independent, quasi-global, gridded datasets of precipitation (a rain gauge-based dataset, the satellite-only component of the NASA Integrated Multi-satellitE Retrievals for Global Precipitation Measurement mission [IMERG] Final Run precipitation product, and precipitation estimates derived from NASA Soil Moisture Active Passive [SMAP] soil moisture retrievals), are objectively combined into a single pentad precipitation dataset at 36-km resolution using a unique approach based on extended triple collocation. The quality of each of the four datasets is then evaluated against independent observations. When a global land surface model at 36-km resolution is integrated four times, once utilizing the merged precipitation forcing and once with each of the three contributing datasets, the near-surface soil moisture variations produced with the merged forcing validate best against independent satellite-based soil moisture fields. In addition, the merged dataset is found to be more consistent, relative to each contributor, with estimates of air temperature variations across the globe. The merged dataset thus appears to draw successfully on the complementary strengths of each contributor: the particularly high quality of the rain gauge-based dataset in areas of high gauge density, the more uniform accuracy across the globe of the IMERG data, and the moderate accuracy, particularly in semi-arid regions, of the soil moisture retrieval-based data. Plain Language Summary Obtaining measurements of precipitation across the globe can be challenging. Rain gauges in some ways provide the most accurate measurements, but gauges are absent in many parts of the world, and even where they exist, they only measure precipitation at the gauge itself and therefore may not provide an accurate large-scale average. Satellite-based estimates of precipitation largely overcome these problems, but such data have their own issues, notably a “snapshot” (rather than a time-average) character of the measurements and difficulty associated with interpreting the measured radiances in the presence of complex land surfaces. In the present paper, we use a novel approach to generate a “merged” dataset, one that optimally combines the gauge precipitation information and the satellite-based precipitation information with a third set of estimates derived from soil moisture retrievals. The merged precipitation dataset and each of the three contributors (aggregated here to 5-day averages at a spatial resolution of about 36-km) are then evaluated for consistency with independent geophysical fields. The merged dataset is found to perform best, a clear indication that it takes proper advantage of the complementary strengths of each contributor and, accordingly, that the presented approach for merging the different contributors is indeed viable.

Precipitation↗

NASA NTRS2021

Investigating the potential of a global precipitation forecast to inform landslide prediction

Extreme rainfall events within landslide-prone areas can be catastrophic, resulting in loss of property, infrastructure, and life. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides routine near-real time estimates of landslide hazard using Integrated Multi-Satellite Precipitation Retrievals for the Global Precipitation Mission (IMERG). However, it does not provide information on potential landslide hazard in the future. Forecasting potential landslide events at a global scale presents an area of open research. This study compares a global precipitation forecast provided by NASA's Goddard Earth Observing System (GEOS) with near-real time satellite precipitation estimates. The Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) reference is used to assess the performance of both satellite and model-based precipitation products over the contiguous United States (CONUS). The forecast lead time of 24hrs is considered, with a focus on extreme precipitation events. The performance of IMERG and GEOS-Forecast products is assessed in terms of the probability of detection, success ratio, critical success index and hit bias as well as continuous statistics. The results show that seasonality influences the performance of both satellite and model-based precipitation products. Comparison of IMERG and GEOS-Forecast globally as well as in several event case studies (Colombia, southeast Asia, and Tajikistan) reveals that GEOS-Forecast detects extreme rainfall more frequently relative to IMERG for these specific analyses. For recent landslide points across the globe, the 24hr accumulated precipitation forecast >100 mm corresponds well with near-real time daily accumulated IMERG precipitation estimates. GEOS-Forecast and IMERG precipitation match more closely for tropical cyclones than for other types of storms. The main intention of this study is to assess the viability of using a global forecast for landslide predictions and understand the extent of the variability between these products to inform where we would expect the landslide modeling results to most prominently diverge. Results of this study will be used to inform how forecasted precipitation estimates can be incorporated into the LHASA model to provide the first global predictive view of landslide hazards.

S. Khan↗

NASA NTRS2021

Delaware Ecological Forecasting: Assessing Land Cover and Soil to Identify Suitable Sites for Tidal Marsh Migration in Delaware

Tidal wetlands provide vital resources for the state of Delaware, crucial not only for maintaining important ecosystem functions, but also for providing human populations with substantial services. Healthy wetland networks offer protection from severe weather, reduce flooding, improve water quality, and provide opportunities for education and recreation. However, human activities in combination with natural events, continue to cause substantial loss of wetland cover and damage wetland health. Over the last thirty years, the state of Delaware has experienced a net loss of roughly 5,000 acres of wetland. In collaboration with the Delaware Department of Natural Resources and Environmental Control (DNREC), the team used NASA Earth observations including Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Precipitation Measurement Integrated Multi-Satellite Retrievals (GPM IMERG) to develop a methodology to monitor recent changes in wetland cover and forecast landward marsh migration due to sea-level rise, changes to climate, and human development. Trend analysis of current and past climate conditions in precipitation and temperature revealed an overall increase in both metrics. Using Land Change Modeler in TerrSet and Suitability Modeler in ArcGIS Pro, the team visualized landcover shifts over the last 20 years, indicating a general pattern of net wetland loss and identified locations where marsh migration could potentially occur in the future. These observations will enable better planning for restoration activities and in form decision-making to preserve wetland health and ecosystem functions.

McKenna Brahler↗

NASA NTRS2021

Argentina Food Security & Agriculture: Crop Monitoring and Forecasting for Argentina using NASA Satellite Observations

Early harvest information helps guide agricultural commodity assessments in Argentina, providing valuable planning information to identify potentially food-insecure regions, anticipate transportation and storage demands, predict price fluctuations, and project commodity trends. However, crop yield estimates are currently subjective, based on interviews with qualified informants (i.e., farmers, agribusiness actors). In partnership with the Buenos Aires Grain Exchange, we leveraged Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Soil Moisture Active Passive (SMAP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM IMERG) NASA Earth observations to develop a Google Earth Engine (GEE) toolset to monitor vegetation growth. The first component of the toolset produces spatial and temporal maps of temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI), allowing users to visualize the influence of the region’s climate and weather. Next, we developed an autoregressive model to predict NDVI several months in advance. Lastly, we created a linear regression model of crop yield and NDVI for soybeans, corn, and wheat, and input the forecasted NDVI to generate a predicted crop yield output. The NDVI forecasting model produced accurate predictions at two, four, and six months when examining the most recent growing season. In the crop yield model, soybeans exhibited moderately strong correlation, wheat had consistent weak correlation, and corn varied from weak to strong correlation depending on zone. This information is vital for vegetation growth monitoring by identifying areas of high growth and allocating resources to areas of lower growth to efficiently maximize crop yields.

DEVELOP Tech Paper↗

NASA NTRS2021

Investigating the Potential of A Global Precipitation Forecast to Inform Landslide Prediction

Extreme rainfall events within landslide-prone areas can be catastrophic, resulting in loss of property, infra-structure, and life. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides routine near-real time estimates of landslide hazard using Integrated Multi-Satellite Precipitation Retrievals for the Global Precipitation Mission (IMERG). However, it does not provide information on potential landslide hazard in the future. Forecasting potential landslide events at a global scale presents an area of open research. This study compares a global precipitation forecast provided by NASA’s Goddard Earth Observing System (GEOS) with near-real time satellite precipitation estimates. The Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) reference is used to assess the performance of both satellite and model-based precipitation products over the contiguous United States (CONUS). The forecast lead time of 24hrs is considered, with a focus on extreme precipitation events. The performance of IMERG and GEOS-Forecast products is assessed in terms of the probability of detection, success ratio, critical success index and hit bias as well as continuous statistics. The results show that seasonality influences the performance of both satellite and model-based precipitation products. Comparison of IMERG and GEOS-Forecast globally as well as in several event case studies (Colombia, southeast Asia, and Tajikistan) reveals that GEOS-Forecast detects extreme rainfall more frequently relative to IMERG for these specific analyses. For recent landslide points across the globe, the 24hr accumulated precipitation forecast >100 mm corresponds well with near-real time daily accumulated IMERG precipitation estimates. GEOS-Forecast and IMERG precipitation match more closely for tropical cyclones than for other types of storms. The main intention of this study is to assess the viability of using a global forecast for landslide predictions and understand the extent of the variability between these products to inform where we would expect the landslide modeling results to most prominently diverge. Results of this study will be used to inform how forecasted precipitation estimates can be incorporated into the LHASA model to provide the first global predictive view of landslide hazards.

S. Khan↗

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 NTRSDate not supplied

Detection of Drizzle and Light Snowfall Over the Southern Ocean Using the GPM Combined Algorithm

Following the 2014 launch of the Global Precipitation Measurement Mission (GPM), an unprecedented combination of coincident active and passive microwave observations are available for state of the art precipitation retrieval. The GPM Combined Algorithm forms the backbone of this effort, optimizing geophysical variables for agreement with the multi-spectral information content. Combined retrievals are then utilized, along with a radiative transfer model, as a database applied for retrievals across a constellation of passive microwave radiometers of varying frequencies and viewing geometries. By keeping such retrievals related through the transfer standard of the combined algorithm, level 3 products such as the Integrated Multi-satellitE Retrievals for GPM (IMERG) are able to provide consistent global products for users at the higher temporal resolution required for hydrological applications on the global scale. In the current version of the combined product, precipitation retrievals are carried out only in the presence of a signal from the active radar. As a result, light precipitation and drizzle below the threshold of radar sensitivity are not included in any of the products down the chain from the constellation to IMERG. In this work, the effects of enhancing the retrievals with an optimal estimation-type (OE) water vapor and cloud retrieval using the passive observations are explored over the Southern Ocean on a regional scale. Non-convergence of the OE in areas with no detectable radar signal is used to identify areas with high probability of light precipitation and drizzle. Microphysical scale characterization of the light precipitation will be explored using information derived from the higher sensitivity CloudSat mission along with model information to identify and associate the related atmospheric state and dynamics. This is a physically-based approach requiring radiometric consistency with all available multi-spectral observations. Retrieved drizzle can then be included in the constellation databases continuing across all scales through to the level 3 products. The technique, successfully demonstrated for the Southern Ocean, can be easily adapted for light precipitation retrieval over other areas and surfaces leading to a global climatology of light precipitation.

Sarah Rinegerud↗