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NASA NTRSDate not supplied

Pyroscopegridding: Geo-Leo Aerosol Data Fusion (an Open-Source Package)

The retrieval of aerosol optical depths (AODs) from sun-synchronous polar orbiting satellites, such as MODISs, VIIRSs, OMI, TROPOMI, etc., has been widely adopted as a method for obtaining information regarding particulate matter (PM) and related atmospheric processes. However, the advent of recently launched geostationary satellites, such as GOES-16/17/18, Himawari-8/9, and Meteosat Third Generation (MTG), has led to an increased temporal resolution of AOD observations (order of 10 minutes), resulting in typically one or more images per hour during daylight hours, compared to the once-per-day observations obtained from LEO satellites. By integrating these observations, the diurnal cycle of global AOD can be characterized at local, regional, and global scales. The scientific community is still examining the novel data from geostationary satellite observations and evaluating methods for effectively merging these observations with differing spatial and temporal resolutions. This presents a significant ""Big Data"" challenge, encompassing not only data storage, but also data discoverability, accessibility, and migration within cloud computing environments. This study presents our attempts at fusing Level 2 aerosol data from six satellites, three of which are geostationary (GOES-16/17 and Himawari-8) and three of which are polar orbiting (TERRA/MODIS, AQUA/MODIS, and SNPP-VIIRS), using the Dark Target aerosol retrieval algorithm. The ability to fuse remote sensing products on demand into desired temporal and spatial domains empowers researchers and practitioners to more efficiently work with satellite and sensor data. It is our hope that through making our open-source package and accompanying functionality available, the scientific community will have improved access to aerosol data processing resources.

Jennifer Wei↗

NASA NTRSDate not supplied

Open Source Application of Fusing Aerosol Products from GEO and LEO Satellites

Retrieving aerosol optical depths (AODs) from sun-synchronous polar orbiting (aka low earth orbit, LEO) satellites, such as MODISs, and VIIRSs, OMI, TROPOMI, etc, has become well-established as a tool for extracting information on particulate matter (PM) and related processes in the atmosphere. However, with recently launched geostationary satellites (GEO), such as GOES-16/17/18, and Himawari-8/9, and Meteosat Third Generation (MTG) they provide a much higher temporal resolution (order of 10 minutes), typically an image once or more per hour during daylight compared to LEO once per day. By combining these observations, we may be able to characterize the diurnal cycle of global AOD at the local, regional and global scale. While the science community is still exploring the new data from GEO observations, we have been thinking about how to properly combine/merge/fuse those data considering differences in their spatial and temporal resolutions. However, this poses a “Big Data” challenge. The big data challenge is not just about data storage, but also about data discoverability, and accessibility, and even more, about data migration/mirroring in the cloud-computing environment. This paper is merely showing some of the efforts and approaches we have attempted in fusing six satellites’ Level 2 aerosol data (three are from GEO (GOES-16/17 and Himawari-8), and the other three are from LEO (TERRA/MODIS, AQUA/MODIS, SNPP-VIIRS) from Dark Target (DT) aerosol retrieval algorithm. Having the on-demand capability of fusing remote sensing products onto the desired temporal and spatial domain enables researchers and application practitioners to better manipulate and work with satellite and sensor data. It is our hopeWe hope that by making such an open-source package, and the accompanying functionality, the scientific community will be granted easier access to aerosol data processing resources. The MEaSUREs Program (Making Earth System Data Records for Use in Research Environments) expands our understanding of the Earth's current system through atmospheric and surface measurements. In an effort to aid the scientific research component and improve open source methods, this project developed Python code for fusing six satellite Level 2 aerosol data (three are from geostationary satellites (GEO), and the other three are from low earth orbital satellites (LEO)) from Dark Target Aerosol Retrieval Algorithm.

Jennifer Wei↗

NASA NTRS1988

Spectrum/Orbit-Utilization Program

Interferences among geostationary satellites determine allocations. Spectrum/Orbit Utilization Program (SOUP) is analytical computer program for determining mutual interferences among geostationary-satellite communication systems operating in given scenario. Major computed outputs are carrier-to-interference ratios at receivers at specified stations on Earth. Information enables determination of acceptability of planned communication systems. Written in FORTRAN.

Miller, Edward F.↗

NASA NTRS1971

VHF Ranging and Position Fixing Experiment Using ATS Satellites: Final Report on Phases 1 and 2, 25 November 1968 - 1 May 1971

The testing program with the ATS-1 and ATS-3 spacecraft showed that geostationary satellites can provide superior communications and position surveillance for mobile craft. Inexpensive modifications to conventional mobile communications equipment aboard the craft can provide reliable, high quality voice and digital communications with distant ground stations and other vehicles, and automatic surveillance of the positions of all the craft by a ground facility. The tests also demonstrated the location and automatic readout of remote data collection platforms. Frequency modulation signals with the narrow audio and radio frequency bandwidths of terrestrial mobile radio communications were relayed through the VHF transponders of the geostationary satellites. The voice and digital communications were far superior in reliability and quality to long-distance mobile communications by other means. It was shown that one satellite can provide nearly uniform high quality performance over approximately one-third of the earth's surface. Position fixes by range measurement from the two satellites were accurate to approximately one nautical mile, except near the equator and the poles.

Source record↗

NASA NTRS2012

Globally Gridded Satellite (GridSat) Observations for Climate Studies

Geostationary satellites have provided routine, high temporal resolution Earth observations since the 1970s. Despite the long period of record, use of these data in climate studies has been limited for numerous reasons, among them: there is no central archive of geostationary data for all international satellites, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multi-satellite climate studies. The International Satellite Cloud Climatology Project set the stage for overcoming these issues by archiving a subset of the full resolution geostationary data at approx.10 km resolution at 3 hourly intervals since 1983. Recent efforts at NOAA s National Climatic Data Center to provide convenient access to these data include remapping the data to a standard map projection, recalibrating the data to optimize temporal homogeneity, extending the record of observations back to 1980, and reformatting the data for broad public distribution. The Gridded Satellite (GridSat) dataset includes observations from the visible, infrared window, and infrared water vapor channels. Data are stored in the netCDF format using standards that permit a wide variety of tools and libraries to quickly and easily process the data. A novel data layering approach, together with appropriate satellite and file metadata, allows users to access GridSat data at varying levels of complexity based on their needs. The result is a climate data record already in use by the meteorological community. Examples include reanalysis of tropical cyclones, studies of global precipitation, and detection and tracking of the intertropical convergence zone.

Knapp, Kenneth R.↗

NASA NTRS2012

Precipitation from Space: Advancing Earth System Science

Of the three primary sources of spatially contiguous precipitation observations (surface networks, ground-based radar, and satellite-based radar/radiometers), only the last is a viable source over ocean and much of the Earth's land. As recently as 15 years ago, users needing quantitative detail of precipitation on anything under a monthly time scale relied upon products derived from geostationary satellite thermal infrared (IR) indices. The Special Sensor Microwave Imager (SSMI) passive microwave (PMW) imagers originated in 1987 and continue today with the SSMI sounder (SSMIS) sensor. The fortunate longevity of the joint National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Tropical Rainfall Measuring Mission (TRMM) is providing the environmental science community a nearly unbroken data record (as of April 2012, over 14 years) of tropical and sub-tropical precipitation processes. TRMM was originally conceived in the mid-1980s as a climate mission with relatively modest goals, including monthly averaged precipitation. TRMM data were quickly exploited for model data assimilation and, beginning in 1999 with the availability of near real time data, for tropical cyclone warnings. To overcome the intermittently spaced revisit from these and other low Earth-orbiting satellites, many methods to merge PMW-based precipitation data and geostationary satellite observations have been developed, such as the TRMM Multisatellite Precipitation Product and the Climate Prediction Center (CPC) morphing method (CMORPH. The purpose of this article is not to provide a survey or assessment of these and other satellite-based precipitation datasets, which are well summarized in several recent articles. Rather, the intent is to demonstrate how the availability and continuity of satellite-based precipitation data records is transforming the ways that scientific and societal issues related to precipitation are addressed, in ways that would not be otherwise possible. These developments have taken place in parallel with the growth of an increasingly interconnected scientific environment. Scientists from different disciplines can easily interact with each other via information and materials they encounter online, and collaborate remotely without ever meeting each other in person. Likewise, these precipitation datasets are quickly and easily available via various data portals and are widely used. Within the framework of the NASA/JAXA Global Precipitation Measurement (GPM mission, these applications will become increasingly interconnected. We emphasize that precipitation observations by themselves provide an incomplete picture of the state of the atmosphere. For example, it is unlikely that a richer understanding of the global water cycle will be possible by standalone missions and algorithms, but must also involve some component of data, where model analyses of the physical state are constrained alongside multiple observations (e.g., precipitation, evaporation, radiation). The next section provides examples extracted from the many applications that use various high-resolution precipitation products. The final section summarizes the future system for global precipitation processing.

Kucera, Paul A.↗

NASA NTRS2011

Online Visualization and Analysis of Global Half-Hourly Infrared Satellite Data

nfrared (IR) images (approximately 11-micron channel) recorded by satellite sensors have been widely used in weather forecasting, research, and classroom education since the Nimbus program. Unlike visible images, IR imagery can reveal cloud features without sunlight illumination; therefore, they can be used to monitor weather phenomena day and night. With geostationary satellites deployed around the globe, it is possible to monitor weather events 24/7 at a temporal resolution that polar-orbiting satellites cannot achieve at the present time. When IR data from multiple geostationary satellites are merged to form a single product--also known as a merged product--it allows for observing weather on a global scale. Its high temporal resolution (e.g., every half hour) also makes it an ideal ancillary dataset for supporting other satellite missions, such as the Tropical Rainfall Measuring Mission (TRMM), etc., by providing additional background information about weather system evolution.

Liu, Zhong↗

NASA NTRS2012

Aerosol Daytime Variations over North and South America Derived from Multiyear AERONET Measurements

This study analyzes the daytime variation of aerosol with seasonal distinction by using multi-year measurements from 54 of the Aerosol Robotic Network (AERONET) sites over North America, South America, and islands in surrounding oceans. The analysis shows a wide range of daily variability of aerosol optical depth (AOO) and Angstrom exponent depending on location and season. Possible reasons for daytime variations are given. The largest AOO daytime variation range at 440 nm, up to 75%, occurs in Mexico City, with maximum AOO in the afternoon. Large AOO daily variations are also observed in the polluted mid-Atlantic U.S. and U.S. West Coast with maximum AOO occurring in the afternoon in the mid-Atlantic U.S., but in the morning in the West Coast. In South American sites during the biomass burning season (August to October), maximum AOO generally occurs in the afternoon. But the daytime variation becomes smaller when sites are influenced more by long-range transported smoke than by local burning. Islands show minimum AOO in the morning and maximum AOO in the afternoon. The diverse patterns of aerosol daytime variation suggest that geostationary satellite measurements would be invaluable for characterizing aerosol temporal variations on regional and continental scales. In particular, simultaneous measurements of aerosols and aerosol precursors from a geostationary satellite would greatly aid in understanding the evolution of aerosol as determined by emissions, chemical transformations, and transport processes.

Zhang, Yan↗

NASA NTRS1978

A national voice network with satellite and small transceivers

A geostationary satellite utilizing a large multiple-beam UHF antenna is shown to be potentially capable of providing tens of thousands of voice channels for hundreds of thousands of mobile ground terminals using hand-held or vehicular-mounted transceivers with whip antennas. Inclusion of on-board network switching facilities permits full interconnection between any terminal pair within the continental United States (CONUS). Configuration tradeoff studies at selected frequencies from 150 to 1500 MHz, with antenna diameters ranging from 20 to 200 m, and CONUS-coverage multiple beams down to 0.3 deg beamwidth, establish that monthly system user costs in the range of $90 to $150, including leased and maintained ground equipment, are feasible.

Reilly, N. B.↗

NASA NTRS1990

Servicing communication satellites in geostationary orbit

The econmic benefits of a LEO space station are quantified by identifying alternative operating scenarios utilizing the space station's transportation facilities and assembly and repair facilities. Particular consideration is given to the analysis of the impact of on-orbit assembly and servicing on a typical communications satellite is analyzed. The results of this study show that on-orbit servicing can increase the internal rate of return by as much as 30 percent.

Russell, Paul K.↗

NASA NTRS1997

Advanced Propulsion for Geostationary Orbit Insertion and North-South Station Keeping

Solar electric propulsion technology is currently being used for geostationary satellite station keeping. Analyses show that electric propulsion technologies can be used to obtain additional increases in payload mass by using them to perform part of the orbit transfer. Three electric propulsion technologies are examined at two power levels for geostationary insertion of an Atlas IIAS class spacecraft. The onboard chemical propulsion apogee engine fuel is reduced in this analysis to allow the use of electric propulsion. A numerical optimizer is used to determine the chemical burns that will minimize the electric propulsion transfer times. For a 1550-kg Atlas IIAS class payload, increases in net mass (geostationary satellite mass less wet propulsion system mass) of 150-800 kg are enabled by using electric propulsion for station keeping, advanced chemical engines for part of the transfer, and electric propulsion for the remainder of the transfer. Trip times are between one and four months.

Oleson, Steven R.↗

NASA NTRS1982

Differential spacecraft charging on the geostationary operational environmental satellites

Subsystems aboard the Geostationary Operational Environmental Satellites 4 and 5 showed instances of anomalous changes in state corresponding to false commands. Evidence linking the anomalous changes to geomagnetic activity, and presumably static discharges generated by spacecraft differential charging induced by substorm particle injection events is presented. The anomalies are shown to be correlated with individual substorms as monitored by stations of the North American Magnetometer Chain. The relative frequency of the anomalies is shown to be a function of geomagnetic activity. Finally a least squares fit to the time delay between substorm initiation and spacecraft anomaly as a function of spacecraft local time is shown to be consistent with injected electron populations with energy in the range 10 keV to 15 keV, in agreement with present understanding of the spacecraft charging mechanism. The spacecraft elements responsible for the differential charging were not satisfactorily identified. That question is currently under investigation.

Farthing, W. H.↗

NASA NTRS2014

New Approach to Monitor Transboundary Particulate Pollution over Northeast Asia

A new approach to more accurately monitor and evaluate transboundary particulate matter (PM) pollution is introduced based on aerosol optical products from Korea's Geostationary Ocean Color Imager (GOCI). The area studied is Northeast Asia (including eastern parts of China, the Korean peninsula and Japan), where GOCI has been monitoring since June 2010. The hourly multi-spectral aerosol optical data that were retrieved from GOCI sensor onboard geostationary satellite COMS (Communication, Ocean, and Meteorology Satellite) through the Yonsei aerosol retrieval algorithm were first presented and used in this study. The GOCI-retrieved aerosol optical data are integrated with estimated aerosol distributions from US EPA Models-3/CMAQ (Community Multi-scale Air Quality) v4.5.1 model simulations via data assimilation technique, thereby making the aerosol data spatially continuous and available even for cloud contamination cells. The assimilated aerosol optical data are utilized to provide quantitative estimates of transboundary PM pollution from China to the Korean peninsula and Japan. For the period of 1 April to 31 May, 2011 this analysis yields estimates that AOD as a proxy for PM2.5 or PM10 during long-range transport events increased by 117-265% compared to background average AOD (aerosol optical depth) at the four AERONET sites in Korea, and average AOD increases of 121% were found when averaged over the entire Korean peninsula. This paper demonstrates that the use of multi-spectral AOD retrievals from geostationary satellites can improve estimates of transboundary PM pollution. Such data will become more widely available later this decade when new sensors such as the GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI-2 are scheduled to be launched.

Data Assimilation↗

NASA NTRSDate not supplied

Communicating Metrics of Land Surface Temperature Variability Using Multi-sensor Machine Learning

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Further, time series clustering approaches are examined with the objective of identifying key indicators of change in diurnal cycling and extremes on a continental scale. Communicating LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Duffy↗

NASA NTRSDate not supplied

LEO Sensor to GEO Sensor Algorithm Transfer Models for Land Surface Temperature

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Communicating diurnal LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Marie Duffy↗

NASA NTRS1985

Tropical upper tropospheric motion field

Upper tropospheric cloud motion vectors and commercial aircraft wind reports have enabled a detailed definition of the motion field. These two data sets have enhanced our observational capability since the launch of 4 to 5 geostationary satellites around the globe and the implementation of the ASDAR program. A sample is illustrated of recent commercial aircraft wind reports at the 300, 250, and 200 mb surfaces over parts of the Pacific Ocean, North America, and Atlantic Ocean. This data coverage is impressive. The rms difference between these data and those obtained from rawinsonde ascents (colocated) is around + or - 4 m/s. In a recent study of the cloud winds, from geostationary satellites, Johnson (1984) noted almost 15 m/s vector wind differences between estimates from GMS, GOES, and METEOSAT with respect to the rawinsonde observations. These results are illustrated based on the assessment of Johnson (1984). Overall, the commercial aircraft winds are the more accurate of these two observing systems. However, it is apparent that because of the sparcity of airline routes across southern oceans, one has to rely rather heavily on the cloud winds as a primary data source over the tropical southern oceans. The operational rawinsonde coverage over the tropics is generally very poor. The overall data coverage based primarily on the collections at the ECWMF are shown.

Krishnamurti, T. N.↗

NASA NTRSDate not supplied

Earth Radiation Budget Climate Record Composed of Multiple Satellite Observations

Effects of cloud diurnal cycle on top-of-atmosphere (TOA) and surface regional monthly mean irradiances, climatological mean, and anomalies are analyzed using CERES derived TOA irradiances and surface irradiances computed with MODIS derived cloud properties. Cloud properties derived from Terra and Aqua MODIS are sufficient to capture cloud diurnal cycle to compute regional monthly mean surface irradiances. While missing cloud diurnal cycle leads to a biased TOA and surface regional irradiances for regions with a strong cloud diurnal cycle, monthly regional TOA and surface anomalies derived from one sun-synchronous orbit agrees well with those derived from two sun-synchronous orbits. Based on these results, the algorithm to produce Edition 4.2 CERES EBAF product is developed. Regional TOA and surface climatological means derived from one sun-synchronous orbit are adjusted to match corresponding climatological means derived from Terra+Aqua observations. This climatological adjustment approach is used to merge the Terra only period to the Terra+Aqua period and to extend the Terra and Aqua record by merging NOAA20 observations. Two additional differences of Edition 4.2 EBAF algorithm to compute surface irradiances compared to the earlier version are: 1) no geostationary satellite derived cloud properties are used and 2) temperature and humidity from MERRA-2 instead of GEOIS-5.4.1 are used. Once surface monthly regional mean irradiances are compared with surface observations, the agreement is equivalent to the agreement with the earlier version. However, because surface irradiances are not affected by geostationary satellite artifacts, regional surface irradiance anomaly time series is significantly improved, especially for longwave irradiances.

Seiji Kato↗

NASA NTRS1995

Electric propulsion for geostationary orbit insertion

Solar electric propulsion (SEP) technology is already being used for geostationary satellite stationkeeping to increase payload mass. By using this same technology to perform part of the orbit transfer additional increases in payload mass can be achieved. Advanced chemical and N2H4 arcjet systems are used to increase the payload mass by performing stationkeeping and part of the orbit transfer. Four mission options are analyzed which show the impact of either sharing the orbit transfer between chemical and SEP systems or having either complete the transfer alone. Results show that for an Atlas 2AS payload increases in net mass (geostationary satellite mass less wet propulsion system mass) of up to 100 kg can be achieved using advanced chemical for the transfer and advanced N2H4 arcjets for stationkeeping. An additional 100 kg can be added using advanced N2H4 arcjets for part of a 40 day orbit transfer.

Oleson, Steven R.↗