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NASA NTRS2012

Evaluation of Precipitation Detection over Various Surfaces from Passive Microwave Imagers and Sounders

During the middle part of this decade a wide variety of passive microwave imagers and sounders will be unified in the Global Precipitation Measurement (GPM) mission to provide a common basis for frequent (3 hr), global precipitation monitoring. The ability of these sensors to detect precipitation by discerning it from non-precipitating background depends upon the channels available and characteristics of the surface and atmosphere. This study quantifies the minimum detectable precipitation rate and fraction of precipitation detected for four representative instruments (TMI, GMI, AMSU-A, and AMSU-B) that will be part of the GPM constellation. Observations for these instruments were constructed from equivalent channels on the SSMIS instrument on DMSP satellites F16 and F17 and matched to precipitation data from NOAA's National Mosaic and QPE (NMQ) during 2009 over the continuous United States. A variational optimal estimation retrieval of non-precipitation surface and atmosphere parameters was used to determine the consistency between the observed brightness temperatures and these parameters, with high cost function values shown to be related to precipitation. The minimum detectable precipitation rate, defined as the lowest rate for which probability of detection exceeds 50%, and the detected fraction of precipitation, are reported for each sensor, surface type (ocean, coast, bare land, snow cover) and precipitation type (rain, mix, snow). The best sensors over ocean and bare land were GMI (0.22 mm/hr minimum threshold and 90% of precipitation detected) and AMSU (0.26 mm/hr minimum threshold and 81% of precipitation detected), respectively. Over coasts (0.74 mm/hr threshold and 12% detected) and snow-covered surfaces (0.44 mm/hr threshold and 23% detected), AMSU again performed best but with much lower detection skill, whereas TMI had no skill over these surfaces. The sounders (particularly over water) benefited from the use of re-analysis data (vs. climatology) to set the a-priori atmospheric state and all instruments benefit from the use of a conditional snow cover emissivity database over land. It is recommended that real-time sources of these data be used in the operational GPM precipitation algorithms.

GPM↗

NASA NTRS2021

Operational Detection of Sun Glints in DSCOVR EPIC Images

Satellite images often feature sun glints caused by the specular reflection of sunlight from water surfaces or from horizontally oriented ice crystals occurring in clouds. Such glints can prevent accurate retrievals of atmospheric and surface properties using existing algorithms, but the glints can also be used to infer more about the glint-causing objects—for example about the microphysical properties and radiative effects of ice clouds. This paper introduces the recently released operational glint product of the Earth Polychromatic Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) spacecraft. Most importantly, the paper describes the algorithm used for generating the key component of the new product: a glint mask indicating the presence of sun glint caused by the specular reflection of sunlight from ice clouds and smooth water surfaces. After describing the glint detection algorithm and glint product, the paper shows some examples of the detected glints and discusses some basic statistics of the glint population in a yearlong dataset of EPIC images. These statistics provide insights into the performance of glint detection and point toward possibilities for using the glint product to gain scientific insights about ice clouds and water surfaces.

DSCOVR↗

NASA NTRS2012

Forest Canopy Cover and Height from MISR in Topographically Complex Southwestern US Landscape Assessed with High Quality Reference Data

This study addresses the retrieval of spatially contiguous canopy cover and height estimates in southwestern USforests via inversion of a geometric-optical (GO) model against surface bidirectional reflectance factor (BRF) estimates from the Multi-angle Imaging SpectroRadiometer (MISR). Model inversion can provide such maps if good estimates of the background bidirectional reflectance distribution function (BRDF) are available. The study area is in the Sierra National Forest in the Sierra Nevada of California. Tree number density, mean crown radius, and fractional cover reference estimates were obtained via analysis of QuickBird 0.6 m spatial resolution panchromatic imagery usingthe CANopy Analysis with Panchromatic Imagery (CANAPI) algorithm, while RH50, RH75 and RH100 (50, 75, and 100 energy return) height data were obtained from the NASA Laser Vegetation Imaging Sensor (LVIS), a full waveform light detection and ranging (lidar) instrument. These canopy parameters were used to drive a modified version of the simple GO model (SGM), accurately reproducing patterns ofMISR 672 nm band surface reflectance (mean RMSE 0.011, mean R2 0.82, N 1048). Cover and height maps were obtained through model inversion against MISR 672 nm reflectance estimates on a 250 m grid.The free parameters were tree number density and mean crown radius. RMSE values with respect to reference data for the cover and height retrievals were 0.05 and 6.65 m, respectively, with of 0.54 and 0.49. MISR can thus provide maps of forest cover and height in areas of topographic variation although refinements are required to improve retrieval precision.

Chopping, Mark↗

NASA NTRS2007

Ice Cloud Properties in Ice-Over-Water Cloud Systems Using TRMM VIRS and TMI Data

A multi-layered cloud retrieval system (MCRS) is updated and used to estimate ice water path in maritime ice-over-water clouds using Visible and Infrared Scanner (VIRS) and TRMM Microwave Imager (TMI) measurements from the Tropical Rainfall Measuring Mission spacecraft between January and August 1998. Lookup tables of top-of-atmosphere 0.65- m reflectance are developed for ice-over-water cloud systems using radiative transfer calculations with various combinations of ice-over-water cloud layers. The liquid and ice water paths, LWP and IWP, respectively, are determined with the MCRS using these lookup tables with a combination of microwave (MW), visible (VIS), and infrared (IR) data. LWP, determined directly from the TMI MW data, is used to define the lower-level cloud properties to select the proper lookup table. The properties of the upper-level ice clouds, such as optical depth and effective size, are then derived using the Visible Infrared Solar-infrared Split-window Technique (VISST), which matches the VIRS IR, 3.9- m, and VIS data to the multilayer-cloud lookup table reflectances and a set of emittance parameterizations. Initial comparisons with surface-based radar retrievals suggest that this enhanced MCRS can significantly improve the accuracy and decrease the IWP in overlapped clouds by 42% and 13% compared to using the single-layer VISST and an earlier simplified MW-VIS-IR (MVI) differencing method, respectively, for ice-over-water cloud systems. The tropical distribution of ice-over-water clouds is the same as derived earlier from combined TMI and VIRS data, but the new values of IWP and optical depth are slightly larger than the older MVI values, and exceed those of single-layered layered clouds by 7% and 11%, respectively. The mean IWP from the MCRS is 8-14% greater than that retrieved from radar retrievals of overlapped clouds over two surface sites and the standard deviations of the differences are similar to those for single-layered clouds. Examples of a method for applying the MCRS over land without microwave data yield similar differences with the surface retrievals. By combining the MCRS with other techniques that focus primarily on optically thin cirrus over low water clouds, it will be possible to more fully assess the IWP in all conditions over ocean except for precipitating systems.

Minnis, Patrick↗

NASA NTRS1974

Image selection system

An image selection (ISS) was developed for the NASA-Ames Research Center Earth Resources Aircraft Project. The ISS is an interactive, graphics oriented, computer retrieval system for aerial imagery. An analysis of user coverage requests and retrieval strategies is presented, followed by a complete system description. Data base structure, retrieval processors, command language, interactive display options, file structures, and the system's capability to manage sets of selected imagery are described. A detailed example of an area coverage request is graphically presented.

Knutson, M. A.↗

NASA NTRS1982

Landsat-D, about to be reality

The space and ground segments of the Landsat-D thematic mapper (TM) for remote sensing of a 185 km swath with 30 m resolution are described. Scanning the full earth once every 16 days from its 705 km sunsynchronous orbit, Landsat-D will relay its data on the ku-band through the TDRSS satellite to a ground station. The X-band will also be used to transmit data directly to foreign and domestic ground stations; the S-band will be reserved for MSS data only. Landsat-D comprises the multimission modular spacecraft and the instrument module, which features the MSS and TM, a wideband communication subsystem, a solar array, and an L-band global positioning system. Three computers on the ground provide a high degree of automation for reception, storage, retrieval, back-up, and transmission of data and commands. Additionally, a van is equipped with transportable equipment, including an antenna. Procedures followed for image generation from the MSS and TM are detailed, noting storage at Goddard until dispersal of tapes under the direction of the EROS data center.

Aepli, T. C.↗

NASA NTRS1994

Develop advanced nonlinear signal analysis topographical mapping system

The Space Shuttle Main Engine (SSME) has been undergoing extensive flight certification and developmental testing, which involves some 250 health monitoring measurements. Under the severe temperature, pressure, and dynamic environments sustained during operation, numerous major component failures have occurred, resulting in extensive engine hardware damage and scheduling losses. To enhance SSME safety and reliability, detailed analysis and evaluation of the measurements signal are mandatory to assess its dynamic characteristics and operational condition. Efficient and reliable signal detection techniques will reduce catastrophic system failure risks and expedite the evaluation of both flight and ground test data, and thereby reduce launch turn-around time. The basic objective of this contract are threefold: (1) develop and validate a hierarchy of innovative signal analysis techniques for nonlinear and nonstationary time-frequency analysis. Performance evaluation will be carried out through detailed analysis of extensive SSME static firing and flight data. These techniques will be incorporated into a fully automated system; (2) develop an advanced nonlinear signal analysis topographical mapping system (ATMS) to generate a Compressed SSME TOPO Data Base (CSTDB). This ATMS system will convert tremendous amount of complex vibration signals from the entire SSME test history into a bank of succinct image-like patterns while retaining all respective phase information. High compression ratio can be achieved to allow minimal storage requirement, while providing fast signature retrieval, pattern comparison, and identification capabilities; and (3) integrate the nonlinear correlation techniques into the CSTDB data base with compatible TOPO input data format. Such integrated ATMS system will provide the large test archives necessary for quick signature comparison. This study will provide timely assessment of SSME component operational status, identify probable causes of malfunction, and indicate feasible engineering solutions. The final result of this program will yield an ATMS system of nonlinear and nonstationary spectral analysis software package integrated with the Compressed SSME TOPO Data Base (CSTDB) on the same platform. This system will allow NASA engineers to retrieve any unique defect signatures and trends associated with different failure modes and anomalous phenomena over the entire SSME test history across turbo pump families.

Source record↗

NASA NTRS2005

Evaluation of Aerosol Properties over Ocean from Moderate Resolution Imaging Spectroradiometer (MODIS) during ACE-Asia

The Aerosol Characterization Experiment-Asia (ACE-Asia) was conducted in March-May 2001 in the western North Pacific in order to characterize the complex mix of dust, smoke, urban/industrial pollution, and background marine aerosol that is observed in that region in springtime. The Moderate Resolution Imaging Spectroradiometer (MODIS) provides a large-scale regional view of the aerosol during the ACE-Asia time period. Focusing only on aerosol retrievals over ocean, MODIS data show latitudinal and longitudinal variation in the aerosol characteristics. Typically, aerosol optical depth (tau(sub a)) values at 0.55 micrometers are highest in the 30 deg. - 50 deg. latitude band associated with dust outbreaks. Monthly mean tau(sub a) in this band ranges approx. 0.40-70, although large differences between monthly mean and median values indicate the periodic nature of these dust outbreaks. The size parameters, fine mode fraction (eta), and effective radius (r(sub eff)) vary between monthly mean values of eta = 0.47 and r(sub eff)= 0.75 micrometers in the cleanest regions far offshore to approximately eta = 0.85 and r(sub eff) =.30 micrometers in near-shore regions dominated by biomass burning smoke. The collocated MODIS retrievals with airborne, ship-based, and ground-based radiometers measurements suggest that MODIS retrievals of spectral optical depth fall well within expected error (DELTA tau(sub a) = plus or minus 0.03 plus or minus 0.05 tau(sub a)) except in situations dominated by dust, in which cases MODIS overestimate both the aerosol loading and the aerosol spectral dependence. Such behavior is consistent with issues related to particle nonsphericity. Comparisons of MODIS-derived r(sub eff) with AERONET retrievals at the few occurrences of collocations show MODIS systematically underestimates particle size by 0.2 micrometers. Multiple-year analysis of MODIS aerosol size parameters suggests systematic differences between the year 2001 and the years 2000 and 2002, which are traced to instrumental electronic cross talk. Sensitivity studies show that such calibration errors are negligible in tau(sub a) retrievals but are more pronounced in size parameter retrievals, especially for dust and sea salt.

Chu, D. A.↗

NASA NTRS2009

Contamination Examples and Lessons from Low Earth Orbit Experiments and Operational Hardware

Flight experiments flown on the Space Shuttle, the International Space Station, Mir, Skylab, and free flyers such as the Long Duration Exposure Facility, the European Retrievable Carrier, and the EFFU, provide multiple opportunities for the investigation of molecular contamination effects. Retrieved hardware from the Solar Maximum Mission satellite, Mir, and the Hubble Space Telescope has also provided the means gaining insight into contamination processes. Images from the above mentioned hardware show contamination effects due to materials processing, hardware storage, pre-flight cleaning, as well as on-orbit events such as outgassing, mechanical failure of hardware in close proximity, impacts from man-made debris, and changes due to natural environment factors.. Contamination effects include significant changes to thermal and electrical properties of thermal control surfaces, optics, and power systems. Data from several flights has been used to develop a rudimentary estimate of asymptotic values for absorptance changes due to long-term solar exposure (4000-6000 Equivalent Sun Hours) of silicone-based molecular contamination deposits of varying thickness. Recommendations and suggestions for processing changes and constraints based on the on-orbit observed results will be presented.

Pippin, Gary↗

NASA NTRS2019

Remote Sensing of Daytime Water Leaving Reflectances of Oceans and Large Inland Lakes from EPIC onboard the DSCOVR Spacecraft at Lagrange-1 Point

The NASA’s Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) satellite has been making multiple observations of the entire sunlit Earth in a given day from the Sun-Earth Lagrangian L1 point since the summer of 2015. EPIC contains 10 narrow channels in the 317–780 nm solar spectral range. The data acquired with EPIC have already been used in a variety of scientific investigations, including the study of the global ozone levels, aerosol index and aerosol optical depth, UV reflectivity of clouds over land and ocean, cloud height over land and ocean, and vegetation indices. In this article, we report that EPIC data, particularly for the data measured with narrow channels centered near 443, 551, and 680 nm, can also have important applications in remote sensing of ocean color in different geographical regions. We have modified a version of a multi-channel atmospheric correction algorithm for Moderate Resolution Imaging Spectro Radiometer (MODIS) ocean color applications and adapted the algorithm for processing EPIC data. We present three case studies on water leaving reflectance retrievals from EPIC data acquired over a large turbid river, inland lakes, and oceans. We conclude that a future ocean color instrument on board a satellite at the L1 point, which provides continuous view of the full sunlit disk of the Earth, will complement and extend ocean color observations with the low Earth observing polar orbital and geostationary satellite instruments in both the spatial and time domains.

remote sensing↗

NASA NTRS2022

Shoshone River Water Resources: Assessing Sediment Inputs into the Shoshone River in Wyoming to Determine Areas for Protection and Restoration Practices

In 2016, a routine repair operation at the Willwood Dam released tons of built-up sediment into the Shoshone River, polluting the river and negatively impacting the ecosystem. This release greatly affected the communities that rely on the river for farming, recreation, and tourism. In partnership with the Wyoming Department of Environmental Quality (WYDEQ), Shoshone River Partners, and the United States Geological Survey (USGS) Wyoming–Montana Water Science Center, this project utilized satellite imagery and precipitation data to examine turbidity patterns in the Shoshone River between the Buffalo Bill Dam and the Willwood Dam. We used PlanetScope satellite images to assess changes in surface reflectance of the river in response to precipitation events and Global Precipitation Measurement (GPM) Integrated Multi-Spectral Retrieval (IMERG) precipitation data to estimate the lag time between rainfall events and increased turbidity. The National Land Cover Dataset (2019) was used to identify the main land cover types within each sub-basin. The end products included a turbidity analysis, land cover analysis, and precipitation analysis that provided the partners with a better understanding of sediment dynamics in the river. The results demonstrated the feasibility of using PlanetScope data to examine turbidity spatially along small rivers. Sediment plumes from tributaries were visually identified for multiple high turbidity events, and we calibrated an equation that translated reflectance to turbidity, accurately representing plume extent. Inconsistent spectral quality of PlanetScope data, however, limited our ability to assess the relative sediment contribution of the tributaries.

tributaries↗

NASA NTRS2017

Development of Multi-Sensor Global Cloud and Radiance Composites for DSCOVR EPIC Imager with Subpixel Definition

The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). EPIC delivers adequate spatial resolution imagery but only in shortwave bands (317-780 nm), while NISTAR measures the top-of-atmosphere (TOA) whole-disk radiance in shortwave and longwave broadband windows. Accurate calculation of albedo and outgoing longwave flux requires a high-resolution scene identification such as the radiance observations and cloud properties retrievals from low earth orbit (LEO, including NASA Terra and Aqua MODIS, Suomi-NPP VIIRS, and NOAA AVHRR) and geosynchronous (GEO, including GOES east and west, METEOSAT, INSAT-3D, MTSAT-2, and Himawari-8) satellite imagers. The cloud properties are derived using the Clouds and the Earth's Radiant Energy System (CERES) mission Cloud Subsystem group algorithms. These properties have to be co-located with EPIC pixels to provide the scene identification and to select anisotropic directional models (ADMs), which are then used to adjust the NISTAR-measured radiance and subsequently obtain the global daytime shortwave and longwave fluxes. This work presents an algorithm for optimal merging of selected radiance and cloud property parameters derived from multiple satellite imagers to obtain seamless global hourly composites at 5-km resolution. Selection of satellite data for each 5-km pixel is based on an aggregated rating that incorporates five parameters: nominal satellite resolution, pixel time relative to the EPIC time, viewing zenith angle, distance from day/night terminator, and probability of sun glint. To provide a smoother transition in the merged output, in regions where candidate pixel data from two satellite sources have comparable aggregated rating, the selection decision is defined by the cumulative function of the normal distribution so that abrupt changes in the visual appearance of the composite data are avoided. Higher spatial accuracy in the composite product is achieved by using the inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling.

Khlopenkov, Konstantin V.↗

NASA NTRS2012

Analysis of Fine-Mode Aerosol Retrieval Capabilities by Different Passive Remote Sensing Instrument Designs

Remote sensing of aerosol optical properties is difficult, but multi-angle, multi-spectral, polarimetric instruments have the potential to retrieve sufficient information about aerosols that they can be used to improve global climate models. However, the complexity of these instruments means that it is difficult to intuitively understand the relationship between instrument design and retrieval success. We apply a Bayesian statistical technique that relates instrument characteristics to the information contained in an observation. Using realistic simulations of fine size mode dominated spherical aerosols, we investigate three instrument designs. Two of these represent instruments currently in orbit: the Multiangle Imaging SpectroRadiometer (MISR) and the POLarization and Directionality of the Earths Reflectances (POLDER). The third is the Aerosol Polarimetry Sensor (APS), which failed to reach orbit during recent launch, but represents a viable design for future instruments. The results show fundamental differences between the three, and offer suggestions for future instrument design and the optimal retrieval strategy for current instruments. Generally, our results agree with previous validation efforts of POLDER and airborne prototypes of APS, but show that the MISR aerosol optical thickness uncertainty characterization is possibly underestimated.

Knobelspiesse, Kirk↗

NASA NTRS2015

Validation of Long-Term Global Aerosol Climatology Project Optical Thickness Retrievals Using AERONET and MODIS Data

A comprehensive set of monthly mean aerosol optical thickness (AOT) data from coastal and island AErosol RObotic NETwork (AERONET) stations is used to evaluate Global Aerosol Climatology Project (GACP) retrievals for the period 1995-2009 during which contemporaneous GACP and AERONET data were available. To put the GACP performance in broader perspective, we also compare AERONET and MODerate resolution Imaging Spectroradiometer (MODIS) Aqua level-2 data for 2003-2009 using the same methodology. We find that a large mismatch in geographic coverage exists between the satellite and ground-based datasets, with very limited AERONET coverage of open-ocean areas. This is especially true of GACP because of the smaller number of AERONET stations at the early stages of the network development. Monthly mean AOTs from the two over-the-ocean satellite datasets are well-correlated with the ground-based values, the correlation coefficients being 0.81-0.85 for GACP and 0.74-0.79 for MODIS. Regression analyses demonstrate that the GACP mean AOTs are approximately 17%-27% lower than the AERONET values on average, while the MODIS mean AOTs are 5%-25% higher. The regression coefficients are highly dependent on the weighting assumptions (e.g., on the measure of aerosol variability) as well as on the set of AERONET stations used for comparison. Comparison of over-the-land and over-the-ocean MODIS monthly mean AOTs in the vicinity of coastal AERONET stations reveals a significant bias. This may indicate that aerosol amounts in coastal locations can differ significantly from those in adjacent open-ocean areas. Furthermore, the color of coastal waters and peculiarities of coastline meteorological conditions may introduce biases in the GACP AOT retrievals. We conclude that the GACP and MODIS over-the-ocean retrieval algorithms show similar ranges of discrepancy when compared to available coastal and island AERONET stations. The factors mentioned above may limit the performance of the validation procedure and cause us to caution against a direct extrapolation of the presented validation results to the entirety of the GACP dataset.

MODIS (radiometry)↗

NASA NTRS2020

Medics: Medical Decision Support System for Long-Duration Space Exploration

The Autonomous Medical Operations (AMO) group at NASA Ames is developing a “medical decision support system” to enable astronauts on long-duration exploration missions to operate autonomously. The system will support clinical actions by providing medical interpretation advice and procedural recommendations during emergent care and clinical work performed by crew. The current state of development of the system, called MedICS (Medical Interpretation Classification and Segmentation) includes two separate aspects: a set of machine learning diagnostic models trained to analyze organ images and patient health records, and an interface to ultrasound diagnostic hardware and to medical repositories. Three sets of images of different organs and medical records were utilized for training machine learning models for various analyses, as follows: 1. Pneumothorax condition (collapsed lung). The trained model provides a positive or negative diagnosis of the condition. 2. Carotid artery occlusion. The trained model produces a diagnosis of 5 different occlusion levels (including “normal”). 3. Ocular retinal images. The model extracts optic disc pixels (image segmentation). This is a precursor step for advanced autonomous fundus clinical evaluation algorithms to be implemented in FY20. 4. Medical health records. The model produces a differential diagnosis for any particular individual, based on symptoms and other health and demographic information. A probability is calculated for each of 25 most common conditions. The same model provides the likelihood of survival. All results are provided with a confidence level. Item 1 images were provided by the US Army and were part of a data set for the clinical treatment of injured battlefield soldiers. This condition is relevant to possible space mishaps, due to pressure management issues. Item 2 images were provided by Houston Methodist Hospital, and item 3 health records were acquired from the MIT laboratory of computational physiology. The machine learning technology utilized is deep multilayer networks (Deep Learning), and new models will continue to be produced, as relevant data is made available and specific health needs of astronaut crews are identified. The interfacing aspects of the system include a GUI for running the different models, and retrieving and storing data, as well as support for integration with an augmented reality (AR) system deployed at JSC by Tietronix Software Inc. (HoloLens). The AR system provides guidance for the placement of an ultrasound transducer that captures images to be sent to the MedICS system for diagnosis. The image captured and the associated diagnosis appear in the technician’s AR visual display.

Colombano, Silvano↗

NASA NTRS2011

Subsurface Emission Effects in AMSR-E Measurements: Implications for Land Surface Microwave Emissivity Retrieval

An analysis of land surface microwave emission time series shows that the characteristic diurnal signature associated with subsurface emission in sandy deserts carry over to arid and semi-arid region worldwide. Prior work found that diurnal variation of Special Sensor Microwave/Imager (SSM/I) brightness temperatures in deserts was small relative to International Satellite Cloud Climatology Project land surface temperature (LST) variation and that the difference varied with surface type and was largest in sand sea regions. Here we find more widespread subsurface emission effects in Advanced Microwave Scanning Radiometer-EOS (AMSR-E) measurements. The AMSR-E orbit has equator crossing times near 01:30 and 13 :30 local time, resulting in sampling when near-surface temperature gradients are likely to be large and amplifying the influence of emission depth on effective emitting temperature relative to other factors. AMSR-E measurements are also temporally coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements, eliminating time lag as a source of LST uncertainty and reducing LST errors due to undetected clouds. This paper presents monthly global emissivity and emission depth index retrievals for 2003 at 11, 19, 37, and 89 GHz from AMSR-E, MODIS, and SSM/I time series data. Retrieval model fit error, stability, self-consistency, and land surface modeling results provide evidence for the validity of the subsurface emission hypothesis and the retrieval approach. An analysis of emission depth index, emissivity, precipitation, and vegetation index seasonal trends in northern and southern Africa suggests that changes in the emission depth index may be tied to changes in land surface moisture and vegetation conditions

Galantowicz, John F.↗

NASA NTRS2007

Comparison of Cloud Properties from CALIPSO-CloudSat and Geostationary Satellite Data

Cloud properties are being derived in near-real time from geostationary satellite imager data for a variety of weather and climate applications and research. Assessment of the uncertainties in each of the derived cloud parameters is essential for confident use of the products. Determination of cloud amount, cloud top height, and cloud layering is especially important for using these real -time products for applications such as aircraft icing condition diagnosis and numerical weather prediction model assimilation. Furthermore, the distribution of clouds as a function of altitude has become a central component of efforts to evaluate climate model cloud simulations. Validation of those parameters has been difficult except over limited areas where ground-based active sensors, such as cloud radars or lidars, have been available on a regular basis. Retrievals of cloud properties are sensitive to the surface background, time of day, and the clouds themselves. Thus, it is essential to assess the geostationary satellite retrievals over a variety of locations. The availability of cloud radar data from CloudSat and lidar data from CALIPSO make it possible to perform those assessments over each geostationary domain at 0130 and 1330 LT. In this paper, CloudSat and CALIPSO data are matched with contemporaneous Geostationary Operational Environmental Satellite (GOES), Multi-functional Transport Satellite (MTSAT), and Meteosat-8 data. Unlike comparisons with cloud products derived from A-Train imagers, this study considers comparisons of nadir active sensor data with off-nadir retrievals. These matched data are used to determine the uncertainties in cloud-top heights and cloud amounts derived from the geostationary satellite data using the Clouds and the Earth s Radiant Energy System (CERES) cloud retrieval algorithms. The CERES multi-layer cloud detection method is also evaluated to determine its accuracy and limitations in the off-nadir mode. The results will be useful for constraining the use of the passive retrieval data in models and for improving the accuracy of the retrievals.

Nguyen, L.↗

NASA NTRS2011

The Global Precipitation Measurement (GPM) Mission: Overview and U.S. Status

The Global Precipitation Measurement (GPM) Mission is an international satellite mission specifically designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. Building upon the success of the U.S.-Japan Tropical Rainfall Measuring Mission (TRMM), the National Aeronautics and Space Administration (NASA) of the United States and the Japan Aerospace and Exploration Agency (JAXA) will deploy in 2013 a GPM "Core" satellite carrying a KulKa-band Dual-frequency Precipitation Radar (DPR) and a conical-scanning multi-channel (10-183 GHz) GPM Microwave Imager (GMI) to establish a new reference standard for precipitation measurements from space. The combined active/passive sensor measurements will also be used to provide common database for precipitation retrievals from constellation sensors. For global coverage, GPM relies on existing satellite programs and new mission opportunities from a consortium of partners through bilateral agreements with either NASA or JAXA. Each constellation member may have its unique scientific or operational objectives but contributes microwave observations to GPM for the generation and dissemination of unified global precipitation data products. In addition to the DPR and GMI on the Core Observatory, the baseline GPM constellation consists of the following sensors: (1) Special Sensor Microwave Imager/Sounder (SSMIS) instruments on the U.S. Defense Meteorological Satellite Program (DMSP) satellites, (2) the Advanced Microwave Scanning Radiometer- 2 (AMSR-2) on the GCOM-Wl satellite of JAXA, (3) the Multi-Frequency Microwave Scanning Radiometer (MADRAS) and the multi-channel microwave humidity sounder (SAPHIR) on the French-Indian Megha-Tropiques satellite, (4) the Microwave Humidity Sounder (MHS) on the National Oceanic and Atmospheric Administration (NOAA)-19, (5) MHS instruments on MetOp satellites launched by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), (6) the Advanced Technology Microwave Sounder (ATMS) on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), (7) ATMS instruments on the NOAA-NASA Joint Polar Satellite System (JPSS) satellites, and (8) a microwave imager under planning for the Defense Weather Satellite System (DWSS).

Hou, Arthur Y.↗