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NASA NTRS2011

NASA SPoRT Initialization Datasets for Local Model Runs in the Environmental Modeling System

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed several products for its National Weather Service (NWS) partners that can be used to initialize local model runs within the Weather Research and Forecasting (WRF) Environmental Modeling System (EMS). These real-time datasets consist of surface-based information updated at least once per day, and produced in a composite or gridded product that is easily incorporated into the WRF EMS. The primary goal for making these NASA datasets available to the WRF EMS community is to provide timely and high-quality information at a spatial resolution comparable to that used in the local model configurations (i.e., convection-allowing scales). The current suite of SPoRT products supported in the WRF EMS include a Sea Surface Temperature (SST) composite, a Great Lakes sea-ice extent, a Greenness Vegetation Fraction (GVF) composite, and Land Information System (LIS) gridded output. The SPoRT SST composite is a blend of primarily the Moderate Resolution Imaging Spectroradiometer (MODIS) infrared and Advanced Microwave Scanning Radiometer for Earth Observing System data for non-precipitation coverage over the oceans at 2-km resolution. The composite includes a special lake surface temperature analysis over the Great Lakes using contributions from the Remote Sensing Systems temperature data. The Great Lakes Environmental Research Laboratory Ice Percentage product is used to create a sea-ice mask in the SPoRT SST composite. The sea-ice mask is produced daily (in-season) at 1.8-km resolution and identifies ice percentage from 0 100% in 10% increments, with values above 90% flagged as ice.

Case, Jonathan L.↗
NASA NTRS2012

Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). Portions of the Northern Plains states experienced substantial increases in convective available potential energy as a result of the higher SPoRT/MODIS GVFs. These differences produced subtle yet quantifiable differences in the simulated convective precipitation systems for this event.

Bell, Jordan R.↗
NASA NTRS2011

Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

The NASA Short-term Prediction Research and Transition (SPoRT) Center develops new products and techniques that can be used in operational meteorology. The majority of these products are derived from NASA polar-orbiting satellite imagery from the Earth Observing System (EOS) platforms. One such product is a Greenness Vegetation Fraction (GVF) dataset, which is produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the new SPoRT-MODIS GVF dataset on land surface models apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. The second phase of the project is to examine the impacts of the SPoRT GVF dataset on NWP using the Weather Research and Forecasting (WRF) model. Two separate WRF model simulations were made for individual severe weather case days using the NCEP GVF (control) and SPoRT GVF (experimental), with all other model parameters remaining the same. Based on the sensitivity results in these case studies, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). The opposite was true for areas with lower GVF in the SPoRT model runs. These differences in the heating and evaporation rates produced subtle yet quantifiable differences in the simulated convective precipitation systems for the selected severe weather case examined.

Bell, Jordan R.↗
NASA NTRS2015

The Quick Fire Emissions Dataset (QFED): Documentation of Versions 2.1, 2.2 and 2.4: Technical Report Series on Global Modeling and Data Assimilation - Volume 38

Biomass burning is an important source of particulates and trace gases and a major element of the terrestrial carbon cycle. Well constrained emissions from vegetation fires are needed to model direct and indirect effects of biomass burning aerosols, to model homogeneous and heterogeneous chemistry in the atmosphere, and to perform credible Earth system analysis, and climate and air pollution studies. To improve the performance of NASA Goddard Earth Observing System Model (GEOS) in the areas of atmospheric constituent modeling with a focus on biomass burning we developed the Quick Fire Emissions Dataset (QFED). The QFED emissions are based on the fire radiative power (top-down) approach and draw on the cloud correction method developed in the Global Fire Assimilation System (GFAS). Location and fire radiative power of fires are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 fire products (MOD14 and MYD14) and the MODIS Geolocation products (MOD03 and MYD03). QFED strengths are high spatial and temporal resolutions and near-real time availability. Daily mean emissions are available at 0.3125 times 0.25 degrees and in recent versions also at 0.1 times 0.1 degrees. QFED provides emissions of black carbon, organic carbon, sulfur dioxide, carbon monoxide, carbon dioxide, PM2.5, ammonia, nitrogen oxides, methyl ethyl ketone, propylene, ethane, propane, n- and i-butane, acetaldehyde, formaldehyde, acetone and methane. Two QFED product systems are maintained by the NASA Global Modeling and Assimilation Office (GMAO): one that produces near real-time daily emissions used operationally in the GEOS-5 Data Assimilation System, and one that produces an extended historical dataset with daily emissions from March 2000 to the present. The historical dataset also provides monthly mean and monthly climatological emissions.

Koster, Randal D.↗
NASA NTRSDate not supplied

Creating Gridded Fire Probability Maps using NASA Data

Fire is a nationally and globally significant process that strongly affects human–dominated and wild landscapes. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes, providing value by decreasing fuels at the Wildland Urban Interface (WUI) to promote safe communities. However, uncontained wildfires can devastate communities, threaten our health, and result in substantial economic losses. There has been greater than a $50B increase in wildfire insurance claims from 2017-2021, which has been exacerbated by climate change. Our partners at Kettle reinsurance are focused on building a smarter reinsurance model for protecting today’s globalized world from the catastrophic effects of climate change. Our objective is to develop the world's first grid-based wildfire probability product using multiple sources of satellite data to determine whether a ‘conflagration' (fire larger than 999+ acres) has ‘breached’ a grid cell. This will substantially decrease the time it takes for homeowners to receive payouts, from over a year to a couple months. Working with our partners at Kettle reinsurance, we use multiple satellites and ancillary data to weigh the likelihood of fire, based on a number of sources that verify a fire burning in a grid cell and the level of confidence in the data source. For example, Sentinel-2 vegetation-change indices have a higher level of confidence than VIIRS (Visible Infrared Imaging Radiometer Suite) active-fire detection data; and VIIRS active-fire detection data have a higher-level of confidence than MODIS (Moderate Resolution Imaging Spectroradiometer) active-fire detection data. The first iteration has been developed for responding to wildfires in California, with the possibility to expand nationwide and globally.

Emily Gargulinski↗
NASA NTRS1990

Accuracy of the spectral and radiometric laboratory calibration of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

The laboratory procedures, algorithms, measurements, and uncertainties associated with generation of the spectral and radiometric calibration of data acquired by AVIRIS are described. AVIRIS is an airborne sensor that obtains high-spatial-resolution image data of the earth in 224 spectral channels in four spectrometers covering the range from 400 to 2450 nm. The spectral calibration of AVIRIS agrees with the in-flight data to within two nanometers, and the absolute radiometric calibration is consistent with the in-flight verification to 10 percent over the spectral range. In-flight radiometric stability as measured by five consecutive passes over the surface calibration site is reported to be between three and five percent.

Chrien, Thomas G.↗
NASA NTRS1987

HIRIS - EOS instrument with high spectral and spatial resolution

The High-Resolution Imaging Spectrometer (HIRIS) is designed for NASA's Earth Observing System (EOS). It will have 10-nm wide spectral bands from 0.4-2.5 microns at 30 m spatial resolution over a 30 km swath. The spectral resolution allows identification of many minerals in rocks and soils, important algal pigments in oceans and inland waters, spectral changes associated with plant canopy biochemistry, composition of atmospheric aerosols, and grain size of snow and contamination by absorbing impurities. The bands will have 12-bit quantization over a dynamic range suitable for bright targets, such as snow. For targets of low brightness, such as water bodies, image-motion compensation will allow gains up to a factor of 8 to increase signal-to-noise ratios. The sensor will be able to point + or - 24 deg crosstrack and +60/-30 deg downtrack. In the 824-km orbit altitude proposed for EOS, the crosstrack pointing capability will allow 4-5 views during a 16-day revisit cycle.

Dozier, Jeff↗
DOE OSTI2016

Patient-specific quantification of image quality: An automated method for measuring spatial resolution in clinical CT images

Purpose: To develop and validate an automated technique for evaluating the spatial resolution characteristics of clinical computed tomography (CT) images. Methods: Twenty one chest and abdominopelvic clinical CT datasets were examined in this study. An algorithm was developed to extract a CT resolution index (RI) analogous to the modulation transfer function from clinical CT images by measuring the edge-spread function (ESF) across the patient’s skin. A polygon mesh of the air-skin boundary was created. The faces of the mesh were then used to measure the ESF across the air-skin interface. The ESF was differentiated to obtain the line-spread function (LSF), and the LSF was Fourier transformed to obtain the RI. The algorithm’s ability to detect the radial dependence of the RI was investigated. RIs measured with the proposed method were compared with a conventional phantom-based method across two reconstruction algorithms (FBP and iterative) using the spatial frequency at 50% RI, f{sub 50}, as the metric for comparison. Three reconstruction kernels were investigated for each reconstruction algorithm. Finally, an observer study was conducted to determine if observers could visually perceive the differences in the measured blurriness of images reconstructed with a given reconstruction method. Results: RI measurements performed with the proposed technique exhibited the expected dependencies on the image reconstruction. The measured f{sub 50} values increased with harder kernels for both FBP and iterative reconstruction. Furthermore, the proposed algorithm was able to detect the radial dependence of the RI. Patient-specific measurements of the RI were comparable to the phantom-based technique, but the patient data exhibited a large spread in the measured f{sub 50}, indicating that some datasets were blurrier than others even when the projection data were reconstructed with the same reconstruction algorithm and kernel. Results from the observer study substantiated this finding. Conclusions: Clinically informed, patient-specific spatial resolution can be measured from clinical datasets. The method is sufficiently sensitive to reflect changes in spatial resolution due to different reconstruction parameters. The method can be applied to automatically assess the spatial resolution of patient images and quantify dependencies that may not be captured in phantom data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning

Purpose: The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques. Methods: The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based on an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images. Results: For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively. Conclusion: PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Reducing Uncertainties in Large-Scale Solar Resource Data: The Impact of Aerosols

This study examines the impact of various aerosol databases on the accuracy of irradiance predictions pertaining to the National Solar Radiation Database (NSRDB) over the Americas. Compared to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis and moderate resolution imaging spectroradiometer (MODIS) space-borne observations, the various improvements that have been made to develop the SOLSUN aerosol database are described, using a monthly temporal resolution and a 4-km spatial resolution. Relative to MERRA-2, these improvements result in lower uncertainty in the solar irradiance components. The most recent version of the NSRDB now uses hourly MERRA-2 aerosol data with bias and elevation correction. This significantly improves the frequency distribution of hourly direct irradiance while having only a small impact on the annual solar resource, as suggested by the validation undertaken here at 12 control sites.

14 SOLAR ENERGY↗
DOE OSTI2016

MO-DE-207B-07: Assessment of Reproducibility Of FDG-PET-Based Radiomics Features Across Scanners Using Phantom Imaging

Purpose: Use a NEMA-IEC PET phantom to assess the robustness of FDG-PET-based radiomics features to changes in reconstruction parameters across different scanners. Methods: We scanned a NEMA-IEC PET phantom on 3 different scanners (GE Discovery VCT, GE Discovery 710, and Siemens mCT) using a FDG source-to-background ratio of 10:1. Images were retrospectively reconstructed using different iterations (2–3), subsets (21–24), Gaussian filter widths (2, 4, 6mm), and matrix sizes (128,192,256). The 710 and mCT used time-of-flight and point-spread-functions in reconstruction. The axial-image through the center of the 6 active spheres was used for analysis. A region-of-interest containing all spheres was able to simulate a heterogeneous lesion due to partial volume effects. Maximum voxel deviations from all retrospectively reconstructed images (18 per scanner) was compared to our standard clinical protocol. PET Images from 195 non-small cell lung cancer patients were used to compare feature variation. The ratio of a feature’s standard deviation from the patient cohort versus the phantom images was calculated to assess for feature robustness. Results: Across all images, the percentage of voxels differing by <1SUV and <2SUV ranged from 61–92% and 88–99%, respectively. Voxel-voxel similarity decreased when using higher resolution image matrices (192/256 versus 128) and was comparable across scanners. Taking the ratio of patient and phantom feature standard deviation was able to identify features that were not robust to changes in reconstruction parameters (e.g. co-occurrence correlation). Metrics found to be reasonably robust (standard deviation ratios > 3) were observed for routinely used SUV metrics (e.g. SUVmean and SUVmax) as well as some radiomics features (e.g. co-occurrence contrast, co-occurrence energy, standard deviation, and uniformity). Similar standard deviation ratios were observed across scanners. Conclusions: Our method enabled a comparison of feature variability across scanners and was able to identify features that were not robust to changes in reconstruction parameters.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1980

Field-flattener lens

Proposed spherical lens employs image flattening reflective surface within spherical refracting elements to focus light to flat image. Device is intended for use as optical scanner for silicon light detector. Lens can scan wide angle at low F-stops, producing high-resolution image over angle of wavelengths from 0.4 to 14 micrometers.

Vicik, F.↗
NASA NTRS1992

Geologic remote sensing - New technology, new information

Results of geologic studies using data collected by the NASA/JPL Thermal Infrared Imaging Spectrometer (TIMS), Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and the Airborne Synthetic Aperture Radar (AIRSAR) are discussed. These instruments represent prototypes for the Earth Observing System (EOS) satellite instruments ASTER, High Resolution Imaging Spectrometer (HIRIS), and EOS SAR. Integrated analysis of this data type is one of the keys to successful geologic research using EOS. TIMS links the physical properties of surface materials in the 8-12-*mm region to their composition. Calibrated aircraft data make direct lithological mapping possible. AVIRIS, an analog for HIRIS, provides quantitative information about the surface composition of materials based on their detailed visible and infrared spectral signatures (0.4-2.45 mm). Calibrated AVIRIS data make direct identification of minerals possible. The AIRSAR provides additional complementary information about the surface morphology of rocks and soils.

Kruse, F. A.↗
NASA NTRS1999

An Overview of Future NASA Missions, Concepts, and Technologies Related to Imaging of the World's Land Areas

In the near term NASA is entering into the peak activity period of the Earth Observing System (EOS). The EOS AM-1 /"Terra" spacecraft is nearing launch and operation to be followed soon by the New Millennium Program (NMP) Earth Observing (EO-1) mission. Other missions related to land imaging and studies include EOS PM-1 mission, the Earth System Sciences Program (ESSP) Vegetation Canopy Lidar (VCL) mission, the EOS/IceSat mission. These missions involve clear advances in technologies and observational capability including improvements in multispectral imaging and other observing strategies, for example, "formation flying". Plans are underway to define the next era of EOS missions, commonly called "EOS Follow-on" or EOS II. The programmatic planning includes concepts that represent advances over the present Landsat-7 mission that concomitantly recognize the advances being made in land imaging within the private sector. The National Polar Orbiting Environmental Satellite Series (NPOESS) Preparatory Project (NPP) is an effort that will help to transition EOS medium resolution (herein meaning spatial resolutions near 500 meters), multispectral measurement capabilities such as represented by the EOS Moderate Resolution Imaging Spectroradiometer (MODIS) into the NPOESS operational series of satellites. Developments in Synthetic Aperture Radar (SAR) and passive microwave land observing capabilities are also proceeding. Beyond these efforts the Earth Science Enterprise Technology Strategy is embarking efforts to advance technologies in several basic areas: instruments, flight systems and operational capability, and information systems. In the case of instruments architectures will be examined that offer significant reductions in mass, volume, power and observational flexibility. For flight systems and operational capability, formation flying including calibration and data fusion, systems operation autonomy, and mechanical and electronic innovations that can reduce spacecraft and subsystem resource requirements. The efforts in information systems will include better approaches for linking multiple data sets, extracting and visualizing information, and improvements in collecting, compressing, transmitting, processing, distributing and archiving data from multiple platforms. Overall concepts such as sensor webs, constellations of observing systems, and rapid and tailored data availability and delivery to multiple users comprise and notions Earth Science Vision for the future.

Salomonson, Vincent V.↗
NASA NTRS2012

Effects of Real-Time NASA Vegetation Data on Model Forecasts of Severe Weather

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA-EOS Aqua and Terra satellites. NASA SPoRT started generating daily real-time GVF composites at 1-km resolution over the Continental United States beginning 1 June 2010. A companion poster presentation (Bell et al.) primarily focuses on impact results in an offline configuration of the Noah land surface model (LSM) for the 2010 warm season, comparing the SPoRT/MODIS GVF dataset to the current operational monthly climatology GVF available within the National Centers for Environmental Prediction (NCEP) and Weather Research and Forecasting (WRF) models. This paper/presentation primarily focuses on individual case studies of severe weather events to determine the impacts and possible improvements by using the real-time, high-resolution SPoRT-MODIS GVFs in place of the coarser-resolution NCEP climatological GVFs in model simulations. The NASA-Unified WRF (NU-WRF) modeling system is employed to conduct the sensitivity simulations of individual events. The NU-WRF is an integrated modeling system based on the Advanced Research WRF dynamical core that is designed to represents aerosol, cloud, precipitation, and land processes at satellite-resolved scales in a coupled simulation environment. For this experiment, the coupling between the NASA Land Information System (LIS) and the WRF model is utilized to measure the impacts of the daily SPoRT/MODIS versus the monthly NCEP climatology GVFs. First, a spin-up run of the LIS is integrated for two years using the Noah LSM to ensure that the land surface fields reach an equilibrium state on the 4-km grid mesh used. Next, the spin-up LIS is run in two separate modes beginning on 1 June 2010, one continuing with the climatology GVFs while the other uses the daily SPoRT/MODIS GVFs. Finally, snapshots of the LIS land surface fields are used to initialize two different simulations of the NU-WRF, one running with climatology LIS and GVFs, and the other running with experimental LIS and NASA/SPoRT GVFs. In this paper/presentation, case study results will be highlighted in regions with significant differences in GVF between the NCEP climatology and SPoRT product during severe weather episodes.

Case, Jonathan L.↗
NASA NTRS2012

EPE The Extreme Physics Explorer

The Extreme Physics Explorer (EPE) is a mission concept that will address fundamental and timely questions in astrophysics which are primary science objectives of IXO. The reach of EPE to the areas outlined in NASA RFI NNH11ZDA018L is shown as a table. The dark green indicates areas in which EPE can do the basic IXO science, and the light green areas where EPE can contribute but will not reach the full IXO capability. To address these science questions, EPE will trace orbits close to the event horizon of black holes, measure black hole spin in active galactic nuclei (AGN), use spectroscopy to characterize outflows and the environment of AGN, map bulk motions and turbulence in galaxy clusters, and observe the process of cosmic feedback where black holes inject energy on galactic and intergalactic scales. EPE gives up the high resolution imaging of IXO in return for lightweight, high TRL foil mirrors which will provide >20 times the effective area of ASTRO-H and similar spatial resolution, with a beam sufficient to study point sources and nearby galaxies and clusters. Advances in micro-calorimeters allow improved performance at high rates with twice the energy resolution of ASTRO-H. A lower TRL option would provide 200 times the area of ASTRO-H using a micro-channel plate optic (MCPO) and a deployable optical bench. Both options are in the middle range of RFI missions at between $600M and $1000M. The EPE foil optic has direct heritage to ASTRO-H, allowing robust cost estimates. The spacecraft is entirely off the shelf and introduces no difficult requirements. The mission could be started and launched in this decade to an L2 orbit, with a three-year lifetime and consumables for 5 years. While ASTRO-H will give us the first taste of high-resolution, non-dispersive X-ray spectroscopy, it will be limited to small numbers of objects in many categories. EPE will give us the first statistically significant samples in each of these categories.

Garcia, Michael↗
NASA NTRS2017

Cloud and Sun-Glint Statistics Derived from GOES and MODIS Observations Over the Intra-Americas Sea for GEO-CAPE Mission Planning

Knowledge of cloud cover, frequency, and duration is not only important to study cloud dynamics, but also critical in determining when and where to take ocean measurements from geostationary orbits such as the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission due to the challenges in achieving complete hemispheric coverage of coastal oceans, estuaries, and inland waters at hourly frequency. Using GOES hourly measurements at 4 km nadir resolution between 2006 and 2011, the number of cloud-free hourly observations per day (N(sub cf)) for solar zenith angle Theta(sub 0) less than 80 degrees was estimated for each 0.1 degree location of the Intra-Americas Sea. The number of Sun-glint-affected hourly observations per day [Ns(sub sg)] was also calculated based on the planned GEO-CAPE observation geometry and realistic wind speed. High-latitude and equatorial oceans showed the lowest N(sub cf) (less than 2.4) in all climatological months, and highest N(sub cf) was observed in the Gulf of Mexico (GoM) and Caribbean (greater than 4.5). Different regions showed differences in seasonality of cloud-free conditions and also showed differences in the hour of a day at which the satellite observations would have the maximal cloud-free and glint-free probability (Temperature maximum). Cloud cover from Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km measurements are greater than 10 degrees higher than those from the MODIS 250 m measurements, supporting ocean color missions at subkilometer resolutions to enhance both spatial coverage and temporal frequency. These findings provide valuable information for GEO-CAPE mission planning to maximize its science value through minimizing the impacts of clouds and Sun glint.

Feng, Lian↗
NASA NTRS2017

GEO-LEO Reflective Band Inter-Comparison with BRDF and Atmospheric Scattering Corrections

The inter-comparison of the reflective solar bands (RSB) between the instruments onboard a geostationary orbit satellite and a low Earth orbit satellite is very helpful in assessing their calibration consistency. Himawari-8 was launched 7 October 2014 and GOES-R was launched on 19 November 2016. Unlike previous GOES instruments, the Advanced Himawari Imager (AHI) on Himawari-8 and the Advanced Baseline Imager (ABI) on GOES-R have onboard calibrators for the RSB. Independent assessment of calibration is nonetheless important to enhance their product quality. MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) can provide good references for sensor calibration. In this work, the inter-comparison between AHI and VIIRS is performed over a pseudo-invariant target. The use of stable and uniform calibration sites provides comparison with accurate adjustment for band spectral difference, reduction of impact from pixel mismatching, and consistency of BRDF (Bidirectional Reflectance Distribution Function) and atmospheric correction. The site used is the Strzelecki Desert in Australia. Due to the difference in solar and view angles, two corrections must be applied in order to compare the measurements. The first is the atmospheric scattering correction applied to the top of atmosphere reflectance measurements. The second correction is applied to correct the BRDF effect. The atmospheric correction is performed using a vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) model and the BRDF correction is performed using a semi-empirical model. Our results show that AHI band 1 (0.47 microns) has a good agreement with VIIRS band M3 within 0.15 percent. AHI band 5 (1.61 microns) shows the largest difference (5.09 percent) with VIIRS band M10, while AHI band 5 shows the least difference (1.87 percent) in comparison with VIIRS band I3. The methods developed in this work can also be directly applied to assess GOES-16/ABI (Geostationary Operational Environment Satellite16 / Advanced Baseline Imager) calibration consistency, a topic we will address in the future.

The inter-comparison of the reflective solar bands↗