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Applications of LANCE Data at SPoRT

Short term Prediction Research and Transition (SPoRT) Center: Mission: Apply NASA and NOAA measurement systems and unique Earth science research to improve the accuracy of short term weather prediction at the regional/local scale. Goals: Evaluate and assess the utility of NASA and NOAA Earth science data and products and unique research capabilities to address operational weather forecast problems; Provide an environment which enables the development and testing of new capabilities to improve short term weather forecasts on a regional scale; Help ensure successful transition of new capabilities to operational weather entities for the benefit of society

Molthan, Andrew↗

Verification of a New NOAA/NSIDC Passive Microwave Sea-Ice Concentration Climate Record

A new satellite-based passive microwave sea-ice concentration product developed for the National Oceanic and Atmospheric Administration (NOAA)Climate Data Record (CDR) programme is evaluated via comparison with other passive microwave-derived estimates. The new product leverages two well-established concentration algorithms, known as the NASA Team and Bootstrap, both developed at and produced by the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC). The sea ice estimates compare well with similar GSFC products while also fulfilling all NOAA CDR initial operation capability (IOC) requirements, including (1) self describing file format, (2) ISO 19115-2 compliant collection-level metadata,(3) Climate and Forecast (CF) compliant file-level metadata, (4) grid-cell level metadata (data quality fields), (5) fully automated and reproducible processing and (6) open online access to full documentation with version control, including source code and an algorithm theoretical basic document. The primary limitations of the GSFC products are lack of metadata and use of untracked manual corrections to the output fields. Smaller differences occur from minor variations in processing methods by the National Snow and Ice Data Center (for the CDR fields) and NASA (for the GSFC fields). The CDR concentrations do have some differences from the constituent GSFC concentrations, but trends and variability are not substantially different.

Passive Microwave↗

Use and Assessment of Multi-Spectral Satellite Imagery in NWS Operational Forecasting Environments

NOAA's Satellite Proving Grounds have established partnerships between product developers and NWS WFOs for the evaluation of new capabilities from the GOES-R and JPSS satellite systems. SPoRT has partnered with various WFOs to evaluate multispectral (RGB) products from MODIS, VIIRS and Himawari/AHI to prepare for GOES-R/ABI. Assisted through partnerships with GINA, UW/CIMSS, NOAA, and NASA Direct Broadcast capabilities.

VIIRS↗

Magnetogram Forecast: An All-Clear Space Weather Forecasting System

Solar flares and coronal mass ejections (CMEs) are the drivers of severe space weather. Forecasting the probability of their occurrence is critical in improving space weather forecasts. The National Oceanic and Atmospheric Administration (NOAA) currently uses the McIntosh active region category system, in which each active region on the disk is assigned to one of 60 categories, and uses the historical flare rates of that category to make an initial forecast that can then be adjusted by the NOAA forecaster. Flares and CMEs are caused by the sudden release of energy from the coronal magnetic field by magnetic reconnection. It is believed that the rate of flare and CME occurrence in an active region is correlated with the free energy of an active region. While the free energy cannot be measured directly with present observations, proxies of the free energy can instead be used to characterize the relative free energy of an active region. The Magnetogram Forecast (MAG4) (output is available at the Community Coordinated Modeling Center) was conceived and designed to be a databased, all-clear forecasting system to support the operational goals of NASA's Space Radiation Analysis Group. The MAG4 system automatically downloads nearreal- time line-of-sight Helioseismic and Magnetic Imager (HMI) magnetograms on the Solar Dynamics Observatory (SDO) satellite, identifies active regions on the solar disk, measures a free-energy proxy, and then applies forecasting curves to convert the free-energy proxy into predicted event rates for X-class flares, M- and X-class flares, CMEs, fast CMEs, and solar energetic particle events (SPEs). The forecast curves themselves are derived from a sample of 40,000 magnetograms from 1,300 active region samples, observed by the Solar and Heliospheric Observatory Michelson Doppler Imager. Figure 1 is an example of MAG4 visual output

Barghouty, Nasser↗

Using 3-D Numerical Weather Data in Piloted Simulations

This report describes the process of acquiring and using 3-D numerical model weather data sets in NASA Langley's Research Flight Deck (RFD). A set of software tools implement the process and can be used for other purposes as well. Given time and location information of a weather phenomenon of interest, the user can download associated numerical weather model data. These data are created by the National Oceanic and Atmospheric Administration (NOAA) High Resolution Rapid Refresh (HRRR) model, and are then processed using a set of Mathworks' Matlab(TradeMark) scripts to create the usable 3-D weather data sets. Each data set includes radar re ectivity, water vapor, component winds, temperature, supercooled liquid water, turbulence, pressure, altitude, land elevation, relative humidity, and water phases. An open-source data processing program, wgrib2, is available from NOAA online, and is used along with Matlab scripts. These scripts are described with sucient detail to make future modi cations. These software tools have been used to generate 3-D weather data for various RFD experiments.

Daniels, Taumi S.↗

Airborne Hyperspectral Sensing of Monitoring Harmful Algal Blooms in the Great Lakes Region: System Calibration and Validation

Harmful algal blooms (HABs) in Lake Erie have been prominent in recent years. The bloom in 2014 reached a severe level causing the State of Ohio to declare a state of emergency. At that time NASA Glenn Research Center was requested by stakeholders to help monitor the blooms in Lake Erie. Glenn conducted flights twice a week in August and September and assembled and distributed the HAB information to the shoreline water resource managers using its hyperspectral imaging sensor (in development since 2006), the S􀂱3 Viking aircraft, and funding resources from the NASA Headquarters Earth Science Division. Since then, the State of Ohio, National Oceanic and Atmospheric Administration (NOAA), and U.S. Environmental Protection Agency (EPA) have elevated their funding and activities for observing, monitoring, and addressing the root cause of HABs. Also, the communities and stakeholders have persistently requested NASA Glenn􀂶s participation in HAB observation. Abundant field campaigns and sample analyses have been funded by Ohio and NOAA, which provided a great opportunity for NASA to advance science and airborne hyperspectral remote sensing economically. Capitalizing on this opportunity to advance the science of algal blooms and remote sensing, NASA Glenn conducted the Airborne Hyperspectral Observation of harmful algal blooms campaign in 2015 that was, in many respects, twice as large as the 2014 campaign. Focusing mostly on Lake Erie, but also including other small inland lakes and the Ohio River, the campaign was conducted in partnership with a large number of partners specializing in marine science and remote sensing. Airborne hyperspectral observation of HABs holds promise to distinguish potential HABs from nuisance blooms, determine their concentrations, and delineate their movement in an augmented spatial and temporal resolution and under clouds􀂲all of which are excellent complements to satellite observations. Working with collaborators at several Ohio and Michigan institutions as well as one in South Dakota and one in Alabama, this effort was able to provide next-day georeferenced estimates of cyanobacteria and scum concentrations. Very prompt processing and analysis of the hyperspectral imagery is necessary for the information to be acted upon. For example, a next-day report of an overflight over the Ohio River indicated that a bloom could be present as far downstream as the Cincinnati intake, but the Ohio EPA had not received visual reports of a bloom that far downstream. Water samples were obtained at the Cincinnati water intake, based on the flight data, and detected microcystins in the source water. The flight data helped State and municipal authorities realize the potential extent of that bloom, and triggered response sampling, before the visual river-wide scums started forming. The present document describes the process that was utilized to take raw remote sensing data and create information products; this includes system calibration and validation, efforts to correct atmospheric effects, and algorithms that produce the data products. Furthermore, successful research into improved algorithms for expanding the capability to delineate in water constituents is included. Finally, comparisons that show expected relationships between ground-based measurements and hyperspectral imager version 2 (HSI2) data results are presented, giving confidence in the remote sensing products.

Hyperspectral Imaging↗

Results of a Longer Term NDACC Measurements Comparison Campaign at Mauna Loa Observatory

Between November, 2015 and January, 2015, the Goddard Space Flight Center (GSFC) operated a pair of lidar instruments at the NOAA (National Oceanic and Atmospheric Administration) facility at Mauna Loa on the Big Island of Hawaii (Latitude 19.5 degrees North, Longitude 155.5 degrees West, altitude 3.397 kilometers). Measurements were made during six different four week periods during this time period by both the NASA GSFC Stratospheric Ozone Lidar (STROZ) and the Aerosol and Temperature (ATL) lidar. Also making measurements were the JPL Stratospheric Ozone Lidar and the NOAA Aerosol and Water Vapor Lidar. All instruments participate and archive data with the Network for the Detection of Atmospheric Composition Change (NDACC). Measurement comparisons were made among various instruments in accordance with the standard intercomparison protocols of the NDACC.

McGee, Thomas J.↗

Contribution of Asteroid Generated Tsunami to the Impact Hazard

The long-standing uncertainty about the importance of asteroid-generated tsunami was addressed at a workshop in August 2016, co-sponsored by NASA and NOAA. Experts from NASA, NOAA, the DoE tri-labs (LLNL, SNL, and LANL), DHS, FEMA, and academia addressed the hazard of tsunami created by asteroid impacts, focusing primarily on NEAs with diameter less than 250m. Participants jointly identified key issues and shared information for nearly a year to coordinate their results for discussion at the workshop. They used modern computational tools to examine 1) Near-field wave generation by the impact; 2) Long-distance wave propagation; 3) Damage from coastal run-up and inundation, and associated hazard. The workshop resulted in broad consensus that the asteroid impact tsunami threat is not as great as previously thought.

Asteroid↗

Operational Implementation of Sea Ice Concentration Estimates from the AMSR2 Sensor

An operation implementation of a passive microwave sea ice concentration algorithm to support NOAA's operational mission is presented. The NASA team 2 algorithm, previously developed for the NASA advanced microwave scanning radiometer for the Earth observing system (AMSR-E) product suite, is adapted for operational use with the JAXA AMSR2 sensor through several enhancements. First, the algorithm is modified to process individual swaths and provide concentration from the most recent swaths instead of a 24-hour average. A latency (time since observation) field and a 24-hour concentration range (maximum-minimum) are included to provide indications of data timeliness and variability. Concentration from the Bootstrap algorithm is a secondary field to provide complementary sea ice information. A quality flag is implemented to provide information on interpolation, filtering, and other quality control steps. The AMSR2 concentration fields are compared with a different AMSR2 passive microwave product, and then validated via comparison with sea ice concentration from the Suomi visible and infrared imaging radiometer suite. This validation indicates the AMSR2 concentrations have a bias of 3.9% and an RMSE of 11.0% in the Arctic, and a bias of 4.45% and RMSE of 8.8% in the Antarctic. In most cases, the NOAA operational requirements for accuracy are met. However, in low-concentration regimes, such as during melt and near the ice edge, errors are higher because of the limitations of passive microwave sensors and the algorithm retrieval.

Arctic↗

An Overview of the Design and Development of the Geostationary Operational Environmental Satellite R-Series (GOES-R) Space Segment

The first of the National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite R-series (GOES-R) satellites was launched in November 2016. GOES-R has been developed by NOAA in partnership with the National Aeronautics and Space Administration (NASA). The satellite represents a quantum leap in the state of the art for geostationary weather satellites by providing data from a suite of six new instruments. All instruments were developed expressly for this mission, and include two Earth-observing instruments (the Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM)), two solar-viewing instruments (Solar Ultraviolet Imager (SUVI) and Extreme ultraviolet and X-ray Irradiance Sensors (EXIS)) and two in situ instruments (Space Environment In-Situ Suite (SEISS) and a magnetometer pair). In addition to hosting the instruments, GOES-R also accommodates several communication packages designed to collect and relay data for weather forecasting and emergency management. Accommodating the six instruments and four communication payloads imposed challenging and competing constraints on the satellite, including requirements for extremely stable earth and solar pointing, high-speed and nearly error-free instrument data transmission, and a very quiet electromagnetic background. To meet mission needs, GOES-R employed several technological innovations, including low-thrust rocket engines that allow instrument observations to continue during maneuvers, and the first civilian use of Global Positioning System-based orbit determination in geostationary orbit. This paper will provide a brief overview of the GOES-R satellite and its instruments as well as the developmental challenges involved in accommodating the instruments and communications payloads.

SUVI↗

An Overview of the Design and Development of the GOES R-Series Space Segment

The first of the National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite R-series (GOES-R) satellites was launched in November 2016. GOES-R has been developed by NOAA in partnership with the National Aeronautics and Space Administration (NASA). The satellite represents a quantum leap in the state of the art for geostationary weather satellites by providing data from a suite of six new instruments. All instruments were developed expressly for this mission, and include two Earth-observing instruments (the Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM)), two solar-viewing instruments (Solar Ultraviolet Imager (SUVI) and Extreme ultraviolet and X-ray Irradiance Sensors (EXIS)) and two in situ instruments (Space Environment In-Situ Suite (SEISS) and a magnetometer pair). In addition to hosting the instruments, GOES-R also accommodates several communication packages designed to collect and relay data for weather forecasting and emergency management. Accommodating the six instruments and four communication payloads imposed challenging and competing constraints on the satellite, including requirements for extremely stable earth and solar pointing, high-speed and nearly error-free instrument data transmission, and a very quiet electromagnetic background. To meet mission needs, GOES-R employed several technological innovations, including low-thrust rocket engines that allow instrument observations to continue during maneuvers, and the first civilian use of Global Positioning System-based orbit determination in geostationary orbit. This paper will provide a brief overview of the GOES-R satellite and its instruments as well as the developmental challenges involved in accommodating the instruments and communications payloads.

GOES-16↗

Towards A Representation of Vertically Resolved Ozone Changes in Reanalyses

The Solar Backscatter Ultraviolet Radiometer (SBUV) instruments on NASA and NOAA spacecraft provide a long-term record of total-column ozone and deep-layer partial columns since about 1980. These data have been carefully processed to extract long-term trends and offer a valuable resource for ozone monitoring. Studies assimilating limb-sounding observations in the Goddard Earth Observing System (GEOS) data assimilation system (DAS) demonstrate that vertical ozone gradients in the upper troposphere and lower stratosphere (UTLS) are much better represented than with the deep-layer SBUV observations. This is exemplified by the use of retrieved ozone from the EOS Microwave Limb Sounder (EOS-MLS) instrument in the MERRA-2 reanalysis, for the period after 2004. This study examines the potential for extending the use of limb-sounding observations at earlier times and into the future, so that future reanalyses may be more applicable to the study of long-term ozone changes.Historical data are available from NASA instruments: the Limb Infrared Monitor of the Stratosphere (LIMS: 1978-1979); the Upper Atmospheric Research Satellite (UARS: 1991-1995); Sounding of the Atmosphere using Broadband Emission Radiometry (SABER: 2000-onwards). For the post EOS-MLS period, the joint NASA-NOAA Ozone Monitoring and Profiling Suite Limb Profiler (OMPS-LP) instrument was launched on the Suomi-NPP platform in 201x and is planned for future platforms. This study will examine two aspects of these data pertaining to future reanalyses. First, the feasibility of merging the EOS-MLS and OMPS-LP instruments to provide a long-term record that extends beyond the potential lifetime of EOS-MLS. If feasible, this would allow for long-term monitoring of ozone recovery in a three-dimensional reanalysis context. Second, the skill of the GEOS DAS in ingesting historical data types will be investigated. Because these do not overlap with EOS-MLS, use will be made of system statistics and evaluation using independent datasets. Impacts of using a complete ozone chemistry module will also be considered.

ML↗

Continuation of Long-Term Global SO2 Pollution Monitoring from OMI to OMPS

Over the past 20 years, advances in satellite remote sensing of pollution-relevant species have made space-borne observations an increasingly important part of atmospheric chemistry research and air quality management. This progress has been facilitated by advanced UV-vis spectrometers, such as the Ozone Monitoring Instrument (OMI) on board the NASA Earth Observing System (EOS) Aura satellite, and continues with new instruments, such as the Ozone Mapping and Profiler Suite (OMPS) on board the NASA-NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite. In this study, we demonstrate that it is possible, using our state-of-the-art principal component analysis (PCA) retrieval technique, to continue the long-term global SO2 pollution monitoring started by OMI with the current and future OMPS instruments that will fly on the NOAA Joint Polar Satellite System (JPSS) 1, 2, 3, and 4 satellites in addition to SNPP, with a very good consistency of retrievals from these instruments. Since OMI SO2 data have been primarily used for (1) providing regional context on air pollution and long-range transport on a daily basis and (2) providing information on point emission sources on an annual basis after data averaging, we focused on these two aspects in our OMI-OMPS comparisons. Four years of retrievals (2012-2015) have been compared for three regions: eastern China, Mexico, and South Africa. In general, the comparisons show relatively high correlations (r = 0. 79-0.96) of daily regional averaged SO2 mass between the two instruments and near-unity regression slopes (0.76-0.97). The annual averaged SO2 loading differences between OMI and OMPS are small (< 0.03 Dobson unit (DU) over South Africa and up to 0.1 DU over eastern China). We also found a very good correlation (r = 0. 92-0.97) in the spatial distribution of annual averaged SO2 between OMI and OMPS over the three regions during 2012-2015. The emissions from ∼ 400 SO2 sources calculated with the two instruments also show a very good correlation (r = ∼ 0.9) in each year during 2012-2015. OMPS-detected SO2 point source emissions are slightly lower than those from OMI, but OMI-OMPS differences decrease with increasing strength of source. The OMI-OMPS SO2 mass differences on a pixel by pixel (daily) basis in each region can show substantial differences. The two instruments have a spatial correlation coefficient of 0.7 or better on < ∼ 50 % of the days. It is worth noting that consistent SO2 retrievals were achieved without any explicit adjustments to OMI or OMPS radiance data and that the retrieval agreement may be further improved by introducing a more comprehensive Jacobian lookup table than is currently used.

satellite remote sensing of pollution↗

The Evolution of Gridded NUCAPS: An Overview of Research to Operations Activities

The next-generation S-NPP and NOAA-20 Cross-track Infrared Sounder (CrIS) temperature and moisture profiles can provide valuable observations; (1) Where conventional radiosonde observations are sparse, (2) Between radiosonde launches. CrIS observations are combined with the Advanced Technology Microwave Sounder (ATMS) to produce high quality vertical soundings in clear and partly cloudy conditions. NUCAPS (NOAA Unique Combined Atmospheric Processing System) is the operational algorithm for processing combined hyperspectral infrared and microwave measurements. NUCAPS Soundings are operationally available in AWIPS as Skew-T plots. The capability to visualize the data in plan view or cross section would be valuable to maximize the benefits of NUCAPS data in AWIPS. A multi-organizational collaboration through the JPPS Soundings Applications Initiative developed the capability for plan view and cross section displays of NUCAPS in AWIPS (i.e., Gridded NUCAPS).

Berndt, Emily↗

Comparison of the MODIS and VIIRS On-Board SD and SDSM Performance

The MODIS reflective solar bands (RSB) are calibrated on-orbit using a solar diffuser (SD) with its on-orbit degradation, or change in the bi-directional reflectance factor (BRF) monitored using a solar diffuser stability monitor (SDSM). By performing alternate observations of direct sunlight via an attenuation screen and of sunlight reflected diffusely off the SD, the SDSM monitors the on-orbit degradation of the SD. The MODIS SDSM has 9 detectors, covering wavelengths from 0.41 to 0.94 microns. Both Terra and Aqua MODIS instruments have successfully operated for more than 16 years on-orbit, with the SD experiencing significant degradation at the shortest wavelength (about 50 percent for Terra MODIS and about 20 percent for Aqua MODIS at 0.41 microns). The first VIIRS instrument on the Suomi NPP spacecraft was launched in October 2011 and the follow-on instrument was launched in November 2017 on the JPSS-1 spacecraft (now NOAA-20). Both the VIIRS instruments carry a MODIS-like SD and SDSM system with an improved design based on the lessons learned from MODIS. Unlike MODIS, the VIIRS SDSM collects data using 8 detectors covering a similar wavelength range as MODIS. A similar wavelength dependent SD degradation pattern is also observed in both VIIRS instruments. This paper provides a comparison of the on-orbit performance of the four instruments in terms of the on-orbit changes in the SDSM detector responses and on-orbit degradations of their SDs. The NOAA-20 VIIRS instrument is still in its first year of operation and hence the early performance of the Terra and Aqua MODIS and SNPP VIIRS is discussed to provide a perspective comparison.

VIIRS↗

Geonex: Land Surface Monitoring from a New Generation of Geostationary Sensors

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement. Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GEONEX pipeline including choice CPUs, storage media, and automation. By making the GEONEX pipeline available on the cloud, we hope to engage a broad community of Earth scientists from around the world in utilizing this new source of data for Earth monitoring.

Nemani, Ramakrishna R.↗

Trend Quality Ozone from NPP OMPS: The Version 2 Processing

A version 2 processing of data from two ozone monitoring instruments on Suomi NPP, the OMPS nadir ozone mapper and the OMPS nadir ozone profiler, has now been completed. The previously released data were useful for many purposes but were not suitable for use in ozone trend analysis. In this processing, instrument artifacts have been identified and corrected, an improved scattered light correction and wavelength registration have been applied, and soft calibration techniques were implemented to produce a calibration consistent with data from the series of SBUV/2 instruments. The result is a high-quality ozone time series suitable for trend analysis. Total column ozone data from the OMPS nadir mapper now agree with data from the SBUV/2 instrument on NOAA 19 with a zonal average bias of −0.2 % over the 60° S to 60° N latitude zone. Differences are somewhat larger between OMPS nadir profiler and N19 total column ozone, with an average difference of −1.1 % over the 60° S to 60° N latitude zone and a residual seasonal variation of about 2 % at latitudes higher than about 50°. For the profile retrieval, zonal average ozone in the upper stratosphere (between 2.5 and 4 hPa) agrees with that from NOAA 19 within ±3 % and an average bias of −1.1 %. In the lower stratosphere (between 25 and 40 hPa) agreement is within ±3 % with an average bias of +1.1 %. Tropospheric ozone produced by subtracting stratospheric ozone measured by the OMPS limb profiler from total column ozone measured by the nadir mapper is consistent with tropospheric ozone produced by subtracting stratospheric ozone from MLS from total ozone from the OMI instrument on Aura. The agreement of tropospheric ozone is within 10 % in most locations.

OMPS nadir ozone mapper↗

Earth Observations from Geostationary Satellites

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GeoNEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GeoNEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement. Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GeoNEX pipeline including choice CPUs, storage media, and automation. By making the GEONEX pipeline available on the cloud, we hope to engage a broad community of Earth scientists from around the world in utilizing this new source of data for Earth monitoring.

Earth↗