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NASA NTRS2012

Characterization of Turbulent Latent and Sensible Heat Flux Exchange Between the Atmosphere and Ocean in MERRA

Turbulent fluxes of heat and moisture across the atmosphere-ocean interface are fundamental components of the Earth s energy and water balance. Characterizing both the spatiotemporal variability and the fidelity of these exchanges of heat and moisture is critical to understanding the global water and energy cycle variations, quantifying atmosphere-ocean feedbacks, and improving model predictability. This study examines the veracity of the recently completed NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) product with respect to its representation of the surface turbulent heat fluxes. A validation of MERRA turbulent heat fluxes and near-surface bulk variables at local, high-resolution space and time scales is achieved by making comparisons to a large suite of direct observations. Both in situ and satellite-observed gridded surface heat flux estimates are employed to investigate the spatial and temporal variability of the surface fluxes with respect to their annual mean climatologies, their seasonal covariability of near-surface bulk parameters, and their representation of extremes. The impact of data assimilation on the near-surface parameters is assessed through evaluation of incremental analysis update tendencies produced by the assimilation procedure. It is found that MERRA turbulent surface heat fluxes are relatively accurate for typical conditions but have systematically weak vertical gradients in moisture and temperature and have a weaker covariability between the near-surface gradients and wind speed than found in observations. This results in an underestimate of the surface latent and sensible heat fluxes over the western boundary current and storm track regions. The assimilation of observations mostly acts to bring MERRA closer to observational products by increasing moisture and temperature near the surface and decreasing the near-surface wind speeds. The major patterns of spatial and temporal variability of the turbulent heat fluxes produced by MERRA compare favorably to observationally based estimates. However, MERRA is distinct in terms of amplitude. These results suggest that MERRA is likely to be a valuable resource for a number of research applications though, as with all turbulent flux estimates, systematic issues should be taken into account.

Roberts, J. Brent↗

NASA NTRS2012

A Regional CO2 Observing System Simulation Experiment Using ASCENDS Observations and WRF-STILT Footprints

Knowledge of the spatiotemporal variations in emissions and uptake of CO2 is hampered by sparse measurements. The recent advent of satellite measurements of CO2 concentrations is increasing the density of measurements, and the future mission ASCENDS (Active Sensing of CO2 Emissions over Nights, Days and Seasons) will provide even greater coverage and precision. Lagrangian atmospheric transport models run backward in time can quantify surface influences ("footprints") of diverse measurement platforms and are particularly well suited for inverse estimation of regional surface CO2 fluxes at high resolution based on satellite observations. We utilize the STILT Lagrangian particle dispersion model, driven by WRF meteorological fields at 40-km resolution, in a Bayesian synthesis inversion approach to quantify the ability of ASCENDS column CO2 observations to constrain fluxes at high resolution. This study focuses on land-based biospheric fluxes, whose uncertainties are especially large, in a domain encompassing North America. We present results based on realistic input fields for 2007. Pseudo-observation random errors are estimated from backscatter and optical depth measured by the CALIPSO satellite. We estimate a priori flux uncertainties based on output from the CASA-GFED (v.3) biosphere model and make simple assumptions about spatial and temporal error correlations. WRF-STILT footprints are convolved with candidate vertical weighting functions for ASCENDS. We find that at a horizontal flux resolution of 1 degree x 1 degree, ASCENDS observations are potentially able to reduce average weekly flux uncertainties by 0-8% in July, and 0-0.5% in January (assuming an error of 0.5 ppm at the Railroad Valley reference site). Aggregated to coarser resolutions, e.g. 5 degrees x 5 degrees, the uncertainty reductions are larger and more similar to those estimated in previous satellite data observing system simulation experiments.

Wang, James S.↗

NASA NTRS2019

Deep Learning Emulation of Atmospheric Correction for Geostationary Sensors

New generation geostationary satellites make reflectance observations available at a continental scale with unprecedented spatiotemporal resolution and spectral range. Generating Earth monitoring products from these observations requires retrieval of the basic parameter, surface reflectance (SR), by atmospheric correction (AC). Algorithms for atmospheric correction, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), are adapted for each sensor and are too computationally complex to be run in real time, relying instead on look-up tables with precomputed values. Machine learning methods, including convolutional neural networks, have demonstrated performance in learning complex, nonlinear mappings and extracting insight from high-dimensional remote sensing data. In this work, we present a deep learning emulator of MAIAC to retrieve both SR and cloud products. Using this adaptation of deep learning-based emulation to remote sensing, we demonstrate stable SR retrieval over a variety of land covers and viewing conditions and accurate cloud detection. Further, a comparison of computation time suggests emulation as a compelling alternative for expensive physical simulation, especially for applications benefited by near-real time data, such as agricultural management and disaster response.

Duffy, Kate↗

NASA NTRSDate not supplied

Identifying Indicators of Harmful Algal Blooms in Coastal Atacama Using Satellite Image Processing Techniques to Improve Industry and Authority Response

Over the past decade, Chile has experienced a significant rise in both the frequency and intensity of coastal Harmful Algal Blooms (HABs). These bloom events are a growing concern for the Atacama Region especially, with potential impacts on human health, aquaculture, and the environment. HABs are caused by an excess proliferation of microalgae, with certain algae species commonly found in Chilean HABs capable of producing toxins which can poison fish, toxify shellfish, and cause illness or death when ingested by humans. Collaborating with the Ministry of Health of Chile, Centro de Información de Recursos Naturales, the University of Atacama, and the Embassy of Chile’s Agricultural Office, this study identifies potential indicators of HABs in the Coastal Atacama Region using NASA Earth observations. Satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard NASA’s Aqua satellite was used for the identification of chlorophyll-a which provides direct estimates of the algae pigment’s concentration in water bodies. In addition, the spatiotemporal patterns of several other parameters were examined to identify correlations with occurrences of HABs, including sea surface temperature and normalized fluorescence line height using Aqua MODIS, Normalized Difference Turbidity Index (NDTI) using Landsat 8, and ocean wind speed measurements from the NOAA Joint Polar Satellite System. Analysis of these patterns revealed hotspots of high chlorophyll-a concentrations from the years 2014 to 2022, the most major of which occurring in the coastal zones of Chañaral, Bahía Inglesa, and Huasco. Also, through the use of a similarity search conducted, the seasonal variation of these indicators was mapped to highlight areas with great likelihood of experiencing HABs in the future. The results can guide future efforts for in-situ water sampling, toxin analysis and assessment, and HAB monitoring and prevention.

Cody O'Ferrall↗

NASA NTRS2019

Evaluation Analysis of NASA SMAP L3 and L4 and SPoRT-LIS Soil Moisture Data in the United States

Soil moisture has a critical role in the development, frequency and persistence of climatic and hydrologic extremes such as drought, heat wave and flooding events. In situ soil moisture data are uneven and sparse in time and space. This highlights the need to utilize other soil moisture sources to fill this spatiotemporal gap. The goal of this study is to validate one satellite-based and two model-based soil moisture datasets with in situ data across the United States. Soil moisture information from the Soil Moisture Active Passive (SMAP) enhanced level 3 (L3) (SMAP L3) and modeled level 4 (L4) (SMAP L4) data at 9-km resolution and Short-term Prediction Research and Transition-Land Information System (SPoRT-LIS) modeled at 3-km resolution were selected for evaluation. SPoRT-LIS is a near real-time, high resolution operational land analysis data. Ground-based data were obtained from the North American Soil Moisture Database (NASMD) for 362 stations. Seven statistical indicators including anomaly, Spearman, and Pearson correlation coefficients, the systematic error (Bias), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and normalized standard deviation (SDV) were used to evaluate the satellite- and model-derived soil moisture data. In addition, the triple collocation (TC) error model was used to measure the error among SMAP L4, SPoRT-LIS and ground-based data. This study assesses which satellite or modeled dataset is most appropriate for specific times and locations to use as a surrogate for in situ observations. Temporal and spatial analysis demonstrated that, overall, SMAP L4 performed better than SMAP L3 and SPoRT-LIS. Strong agreement was observed between SMAP L4 and in situ observations (ρ = 0.53, Bias = −0.006) in all seasons and most regions with various land covers, especially in winter and in the central regions of the United States. For croplands, SMAP L4 presented the best agreement with in situ data, analyzing all period (ρ = 0.60) and non-winter period (ρ = 0.61) separately.

Tavakol, Ameneh↗

NASA NTRS2022

Western Sonoran Desert Water Resources: Evaluating Rock Pool Hydroperiod Fluctuation using Climate Variables to Inform Habitat Monitoring and Protection in the Western Sonoran Desert

Ephemeral freshwater rock pools, known as tinajas, have great biologic and cultural importance as sources of surface water in the western Sonoran Desert (WSD). Tinaja flooding and drying cycles, known as hydroperiods, vary based on meteorologic and climatologic conditions; however, a lack of extensive research relating climatic impacts to tinajas puts these critical ecosystems further at risk. The National Park Service (NPS) and the University of Arizona monitor the physical and ecological condition of tinajas in Organ Pipe Cactus National Monument (OPCNM), AZ, using resource-intensive strategies: in situ trail cameras and direct measurements. To aid monitoring efforts, the NASA DEVELOP team aimed to incorporate remote sensing into NPS strategies by analyzing spatiotemporal climate data and tinaja hydroperiods in OPCNM between 1979–2022. Using Aqua and Terra Moderate Resolution Imaging Spectroradiometers (MODIS), University of Idaho Gridded Surface Meteorological Dataset (gridMET), and OpenET data, the team generated climatology maps and time series for OPCNM. The team compared these data to daily in situ hydroperiod observations from the University of Arizona between 2019–2022. Climate maps and time series showed increases in temperature and solar radiation (p<0.05), while analyses of in situ data showed correlations of hydroperiods with precipitation and evapotranspiration. End products identified high-risk tinajas and demonstrated that Earth observations can successfully be correlated with in situ hydroperiod observations. These results will support NPS efforts to prioritize water resource management and inform protocols driving the conservation of tinajas in OPCNM.

Anne Britton↗

NASA NTRS2021

Southern California Health & Air Quality: Using Remote Sensing to Detect the Frequency and Drivers of Red Tide Blooms in California to Assist in the Management of Human and Marine Exposure to Algal Toxins

In 2020, the dinoflagellate species Lingulodinium polyedra was measured at unprecedented levels off the southern California coast, raising concern for local communities. At high levels, L. polyedra can cause marine life mortality, food-borne illness, and respiratory-related health risks in humans. In partnership with the California Office of Environmental Health Hazard Assessment, the National Oceanic and Atmospheric Administration Southwest Fisheries Science Center, the California Department of Public Health, and the University of California San Diego’s Scripps Institution of Oceanography, this project utilized satellite imagery to visualize and analyze spatiotemporal trends of historical red tide events associated with L. polyedra. Using the Suomi National Polar-orbiting Partnership’s (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), Aqua’s Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Change Observation Mission – Climate (GCOM-C) Second Generation Global Imager (SGLI), the team assessed the validity of using multiple sensors in detecting chlorophyll-a as a proxy for dinoflagellate dominated-algal blooms. The results suggest that VIIRS imagery processed using the Color Index algorithm from Hu et al. (2013), amongst all other algorithms and Earth observations assessed, shows the most promise in identifying L. polyedra blooms. The end products included an ArcGIS Dashboard and Google Earth Engine tool that when combined, provided users with spatial and temporal trends, interactive interfaces to analyze the effectiveness of various sensors and algorithms, and an overall contribution to aid in the management of human health and the economy impacted by harmful algal blooms.

Harmful algal bloom↗

NASA NTRS2013

Tool for Automated Retrieval of Generic Event Tracks (TARGET)

Methods have been developed to identify and track tornado-producing mesoscale convective systems (MCSs) automatically over the continental United States, in order to facilitate systematic studies of these powerful and often destructive events. Several data sources were combined to ensure event identification accuracy. Records of watches and warnings issued by National Weather Service (NWS), and tornado locations and tracks from the Tornado History Project (THP) were used to locate MCSs in high-resolution precipitation observations and GOES infrared (11-micron) Rapid Scan Operation (RSO) imagery. Thresholds are then applied to the latter two data sets to define MCS events and track their developments. MCSs produce a broad range of severe convective weather events that are significantly affecting the living conditions of the populations exposed to them. Understanding how MCSs grow and develop could help scientists improve their weather prediction models, and also provide tools to decision-makers whose goals are to protect populations and their property. Associating storm cells across frames of remotely sensed images poses a difficult problem because storms evolve, split, and merge. Any storm-tracking method should include the following processes: storm identification, storm tracking, and quantification of storm intensity and activity. The spatiotemporal coordinates of the tracks will enable researchers to obtain other coincident observations to conduct more thorough studies of these events. In addition to their tracked locations, their areal extents, precipitation intensities, and accumulations all as functions of their evolutions in time were also obtained and recorded for these events. All parameters so derived can be catalogued into a moving object database (MODB) for custom queries. The purpose of this software is to provide a generalized, cross-platform, pluggable tool for identifying events within a set of scientific data based upon specified criteria with the possibility of storing identified events into a searchable database. The core of the application uses an implementation of the connected component labeling (CCL) algorithm to identify areas of interest, then uses a set of criteria to establish spatial and temporal relationships between identified components. The CCL algorithm is used for identifying objects within images for computer vision. This application applies it to scientific data sets using arbitrary criteria. The most novel concept was applying a generalized CCL implementation to scientific data sets for establishing events both spatially and temporally. The combination of several existing concepts (pluggable components, generalized CCL algorithm, etc.) into one application is also novel. In addition, how the system is designed, i.e., its extensibility with pluggable components, and its configurability with a simple configuration file, is innovative. This allows the system to be applied to new scenarios with ease.

Clune, Thomas↗

NASA NTRSDate not supplied

All-sky Retrieval of Atmospheric Temperature, Water Vapor, Clouds, Trace Gases, and Surface Properties from Operational Hyperspectral IR Sounders

Operational IR sounders such AIRS, CrIS, and IASI provide high quality hyperspectral measurements for weather and climate applications. We will describe a new all-sky Single Field-of-view Sounder Atmospheric Product (SiFSAP). The uniqueness of this product is that it uses all available channels from hyperspectral sounders and the optimal estimation retrieval is done at a single FOV spatial resolution. The SiFSAP includes atmospheric temperature, water vapor, clouds, trace gases, surface skin, and surface emissivity and will be produced operationally at NASA GES DISC. We will describe the core component of the SiFSAP algorithm, which is the Principal Component-based Radiative Transfer Model (PCRTM), and will show example applications of the SiFSAP product for various atmospheric weather and dynamics studies. We also describe a new Climate Fingerprinting Sounder Product (ClimFiSP), which is derived from spatiotemporally averaged level-1 hyperspectral radiances directly. The ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides fast and accurate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. The ClimFiSP are being transitioned to NASA data centers for routine generations level-3 products.

pcrtm↗

NASA NTRS2014

A Regional CO2 Observing System Simulation Experiment for the ASCENDS Satellite Mission

Top-down estimates of the spatiotemporal variations in emissions and uptake of CO2 will benefit from the increasing measurement density brought by recent and future additions to the suite of in situ and remote CO2 measurement platforms. In particular, the planned NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) satellite mission will provide greater coverage in cloudy regions, at high latitudes, and at night than passive satellite systems, as well as high precision and accuracy. In a novel approach to quantifying the ability of satellite column measurements to constrain CO2 fluxes, we use a portable library of footprints (surface influence functions) generated by the WRF-STILT Lagrangian transport model in a regional Bayesian synthesis inversion. The regional Lagrangian framework is well suited to make use of ASCENDS observations to constrain fluxes at high resolution, in this case at 1 degree latitude x 1 degree longitude and weekly for North America. We consider random measurement errors only, modeled as a function of mission and instrument design specifications along with realistic atmospheric and surface conditions. We find that the ASCENDS observations could potentially reduce flux uncertainties substantially at biome and finer scales. At the 1 degree x 1 degree, weekly scale, the largest uncertainty reductions, on the order of 50 percent, occur where and when there is good coverage by observations with low measurement errors and the a priori uncertainties are large. Uncertainty reductions are smaller for a 1.57 micron candidate wavelength than for a 2.05 micron wavelength, and are smaller for the higher of the two measurement error levels that we consider (1.0 ppm vs. 0.5 ppm clear-sky error at Railroad Valley, Nevada). Uncertainty reductions at the annual, biome scale range from 40 percent to 75 percent across our four instrument design cases, and from 65 percent to 85 percent for the continent as a whole. Our uncertainty reductions at various scales are substantially smaller than those from a global ASCENDS inversion on a coarser grid, demonstrating how quantitative results can depend on inversion methodology. The a posteriori flux uncertainties we obtain, ranging from 0.01 to 0.06 Pg C yr-1 across the biomes, would meet requirements for improved understanding of long-term carbon sinks suggested by a previous study.

remote sensing↗

NASA NTRS2014

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data. Part II: Using Maximum Covariance Analysis to Effectively Compare Spatiotemporal Variability of Satellite and AERONET Measured Aerosol Optical Depth

Moderate Resolution Imaging SpectroRadiometer (MODIS) and Multi-angle Imaging Spectroradiomater (MISR) provide regular aerosol observations with global coverage. It is essential to examine the coherency between space- and ground-measured aerosol parameters in representing aerosol spatial and temporal variability, especially in the climate forcing and model validation context. In this paper, we introduce Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition analysis as an effective way to compare correlated aerosol spatial and temporal patterns between satellite measurements and AERONET data. This technique not only successfully extracts the variability of major aerosol regimes but also allows the simultaneous examination of the aerosol variability both spatially and temporally. More importantly, it well accommodates the sparsely distributed AERONET data, for which other spectral decomposition methods, such as Principal Component Analysis, do not yield satisfactory results. The comparison shows overall good agreement between MODIS/MISR and AERONET AOD variability. The correlations between the first three modes of MCA results for both MODIS/AERONET and MISR/ AERONET are above 0.8 for the full data set and above 0.75 for the AOD anomaly data. The correlations between MODIS and MISR modes are also quite high (greater than 0.9). We also examine the extent of spatial agreement between satellite and AERONET AOD data at the selected stations. Some sites with disagreements in the MCA results, such as Kanpur, also have low spatial coherency. This should be associated partly with high AOD spatial variability and partly with uncertainties in satellite retrievals due to the seasonally varying aerosol types and surface properties.

aerosols↗

NASA NTRS2022

Quantifying Energy Release in Solar Flares and Solar Eruptive Events

Solar flares and the often associated solar eruptive events serve as an outstanding laboratory to study the magnetic reconnection and the associated energy release and conversion processes under plasma conditions difficult to reproduce in the laboratory, and with considerable spatiotemporal details not possible elsewhere in the universe. In the past decade, thanks to advances in multiwavelength imaging spectroscopy, as well as developments in theories and numerical modeling, significant progress has been made in improving our understanding of solar flare/eruption energy release. In particular, broadband imaging spectroscopy at microwave wavelengths offered by the Expanded Owens Valley Solar Array (EOVSA) has enabled the revolutionary capability of measuring the time-evolving coronal magnetic fields at or near the flare reconnection region. However, owing to EOVSA’s limited dynamic range, imaging fidelity, and angular resolution, such measurements can only be done in a region around the brightest source(s) where the signal-to-noise is sufficiently large. In this white paper, after a brief introduction to the outstanding questions and challenges pertinent to magnetic energy release in solar flares and eruptions, we will demonstrate how a next-generation radio facility with many (∼100–200) antenna elements can bring the next revolution by enabling high dynamic range, high fidelity broadband imaging spectropolarimetry along with a sub-second time resolution and arcsecond level angular resolution. We recommend to prioritize the implementation of such a ground-based instrument within this decade. We also call for facilitating multi-wavelength, multi-messenger observations and advanced numerical modeling in order to achieve a comprehensive understanding of the “system science” of solar flares and eruptions.

Bin Chen↗

NASA NTRS2020

Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors

Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.

Jeff W. Atkins↗

NASA NTRS2019

The Global Fire Atlas of Individual Fire Size, Duration, Speed and Direction

Natural and human-ignited fires affect all major biomes, altering ecosystem structure, biogeochemical cycles and atmospheric composition. Satellite observations provide global data on spatiotemporal patterns of biomass burning and evidence for the rapid changes in global fire activity in response to land management and climate. Satellite imagery also provides detailed information on the daily or sub-daily position of fires that can be used to understand the dynamics of individual fires. The Global Fire Atlas is a new global dataset that tracks the dynamics of individual fires to determine the timing and location of ignitions, fire size and duration, and daily expansion, fire line length, speed, and direction of spread. Here, we present the underlying methodology and Global Fire Atlas results for 2003–2016 derived from daily moderate-resolution (500 m) Collection 6 MCD64A1 burned-area data. The algorithm identified 13.3 million individual fires over the study period, and estimated fire perimeters were in good agreement with independent data for the continental United States. A small number of large fires dominated sparsely populated arid and boreal ecosystems, while burned area in agricultural and other human-dominated landscapes was driven by high ignition densities that resulted in numerous smaller fires. Long-duration fires in boreal regions and natural landscapes in the humid tropics suggest that fire season length exerts a strong control on fire size and total burned area in these areas. In arid ecosystems with low fuel densities, high fire spread rates resulted in large, short-duration fires that quickly consumed available fuels. Importantly, multiday fires contributed the majority of burned area in all biomass burning regions. A first analysis of the largest, longest and fastest fires that occurred around the world revealed coherent regional patterns of extreme fires driven by large-scale climate forcing. Global Fire Atlas data are publicly available through http://www.globalfiredata.org (last access: 9 August 2018) and https://doi.org/10.3334/ORNLDAAC/1642, and individual fire information and summary data products provide new information for benchmarking fire models within ecosystem and Earth system models, understanding vegetation–fire feedbacks, improving global emissions estimates, and characterizing the changing role of fire in the Earth system.

global fire atlas↗

NASA NTRSDate not supplied

20-Years of Atmospheric Temperature, Water Vapor, Cloud, and Surface Temperature Anomalies and Trends Derived From Operational Hyperspectral Ir Sounders

Hyperspectral IR sounders such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. Compared to current operational AIRS and CrIS Level-2 (L2) algorithms, which perform one retrieval for each 3 by 3 field of views (FOVs) using a cloud-clearing approach, the SiFSAP algorithm, on the other hand, performs one retrieval for each FOV using an all-sky optimal estimation approach. The SiFSAP algorithm retrieves all the above-mentioned atmosphere and surface properties simultaneously including cloud properties with 3-time higher spatial resolution and 9-times more products. The core of the SiFSAP algorithm is an accurate and fast Principal Component-based Radiative Transfer Model (PCRTM), which can calculate hyperspectral radiance spectra under both clear and cloudy conditions. The PCRTM was developed in the past decade using consistent reference line-by-line radiative transfer model and spectroscopy for hyperspectral sounders such as AIRS, CrIS, IASI, NAST-I, and S-HIS. The SiFSAP retrieval algorithm also uses the same climatology a priori and associated covariances, which makes it ideal for generating high quality products for both weather and climate applications. Climate products are typically derived by performing spatial and temporal averaging of L2 products. It is a time-consuming process to generate L2 data products since AIRS, CrIS, and IASI have millions of observations each day with thousands of spectral channels for each observation. Additionally, differences in L2 retrieval algorithms for different satellite sensors can lead to errors in the climate products. Our ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

Xu Liu↗

NASA NTRSDate not supplied

Two Decades of Atmospheric and Surface Temperature, Water Vapor, and Cloud Trends Derived from Satellite Remote Sensors

Satellite remote sensor such as Atmospheric Infrared Sounder (AIRS), Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI) provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. Compared to current operational AIRS and CrIS Level-2 (L2) algorithms, which perform one retrieval for each 3 by 3 field of views (FOVs) using a cloud-clearing approach, the SiFSAP algorithm, on the other hand, performs one retrieval for each FOV using an all-sky optimal estimation approach. The SiFSAP algorithm retrieves all the above-mentioned atmosphere and surface properties simultaneously including cloud properties with 3-time higher spatial resolution and 9-times more products. Climate products are typically derived by performing spatial and temporal averaging of L2 products. It is a time-consuming process to generate L2 data products since AIRS, CrIS, and IASI have millions of observations each day with thousands of spectral channels for each observation. Additionally, differences in L2 retrieval algorithms for different satellite sensors can lead to errors in the climate products. Our ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

remote sensing↗

NASA NTRS2024

Extending the Utility of Space-Borne Snow Water Equivalent Observations Over Vegetated Areas With Data Assimilation

Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24° x 37° domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

Justin Pflug↗

NASA NTRS2019

Surface erythemal UV irradiance in the continental United States derived from ground-based and OMI observations: quality assessment, trend analysis and sampling issues

Surface full-sky erythemal dose rate (EDR) from the Ozone Monitoring Instrument (OMI) at both satellite overpass time and local noon time is evaluated against ground measurements at 31 sites from the US Department of Agriculture's (USDA) UV-B Monitoring and Research Program (UVMRP) over the period of 2005–2017. We find that both OMI overpass and solar noon time EDR are highly correlated with the measured counterparts (with a linear correlation coefficient of 0.90 and 0.88, respectively). Although the comparison statistics are improved with a longer time window (0.5–1.0 h) for pairing surface and OMI measurements, both OMI overpass and local noon time EDRs have 7 % overestimation that is larger than 6 % uncertainty in the ground measurements and show different levels of dependence on solar zenith angle (SZA) and to lesser extent on cloud optical depth. The ratio of EDR between local noon and OMI overpass time is often (95 % in frequency) larger than 1 with a mean of 1.18 in the OMI product; in contrast, the same ratio from surface observation is normally distributed with 22 % of the times less than 1 and a mean of 1.38. This contrast in part reflects the deficiency in the OMI surface UV algorithm that assumes constant atmospheric conditions between overpass and noon time. The probability density functions (PDFs) for both OMI and ground measurements of noontime EDR are in statistically significant agreement, showing dual peaks at ∼20 and ∼200 mW/sq. m, respectively; the latter is lower than 220 mW/ sq. m, the value at which the PDF of daily EDR from ground measurements peaks, and this difference indicates that the largest EDR value for a given day may not often occur at local noon. Lastly, statistically significant positive trends of EDR are found in the northeastern US in OMI data, but opposite trends are found within ground-based data (regardless of sampling for either noontime or daily averages). While positive trends are consistently found between OMI and surface data for EDR over the southern Great Plains (Texas and Oklahoma), their values are within the uncertainty of ground measurements. Overall, no scientifically sound trends can be found among OMI data for aerosol total and absorbing optical depth, cloud optical depth and total ozone to explain coherently the surface UV trends revealed either by OMI or ground-based estimates; these data also cannot reconcile trend differences between the two estimates (of EDR from OMI and surface observations). Future geostationary satellites with better spatiotemporal resolution data should help overcome spatiotemporal sampling issues inherent in OMI data products and therefore improve the estimates of surface UV flux and EDR from space.

Ozone Monitoring Instrument (OMI)↗