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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Limb Correction of Individual Infrared Channels Used in RGB Composite Products

This study demonstrates that limb-cooling can be removed from infrared imagery using latitudinally and seasonally dependent limb correction coefficients, which account for an increasing optical path length as scan angle increases. Furthermore, limb-corrected RGB composites provide multiple advantages over uncorrected RGB composites, including increased confidence in the interpretation of RGB features, improved situation awareness for operational forecasters, seamless transition between overlaid RGB composites, easy comparison of RGB products from different sensors, and the availability of high quality proxy products for the GOES-R era, as demonstrated by the case examples presented in Section 3. This limb correction methodology can also be applied to additional infrared channels used to create other RGB products, including those created from other satellite sensors, such as Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS).

RGB↗
NASA NTRS2015

The Intra-Cloud Lightning Fraction in the Contiguous United States

Lightning is dangerous and destructive; cloud-to-ground (CG) lightning flashes can start fires, interrupt power delivery, destroy property and cause fatalities. Its rate-of-occurrence reflects storm kinematics and microphysics. For decades lightning research has been an important focus, and advances in lightning detection technology have been essential contributors to our increasing knowledge of lightning. A significant step in detection technology is the Geostationary Lightning Mapper (GLM) to be onboard the Geostationary Operational Environment Satellite R-Series (GOES-R) to be launched in early 2016. GLM will provide continuous "Total Lightning" observations [CG and intra-cloud lightning (IC)] with near-uniform spatial resolution over the Americas by measuring radiance at the cloud tops from the different types of lightning. These Total Lightning observations are expected to significantly improve our ability to nowcast severe weather. It may be important to understand the long-term regional differences in the relative occurrence of IC and CG lightning in order to understand and properly use the short-term changes in Total Lightning flash rate for evaluating individual storms.

lightning↗
NASA NTRS2016

Use and Protection of GPS Sidelobe Signals for Enhanced Navigation Performance in High Earth Orbit

The application of the Global Positioning System (GPS) for navigation of spacecraft in High and Geosynchronous Earth Orbit (HEO/GEO) has crossed a threshold and is now being employed in operational missions. Utilizing advanced GPS receivers optimized for these missions, space users have made extensive use of the sidelobe transmissions from the GPS satellites to realize navigation performance that far exceeds that predicted by pre-launch simulations. Unfortunately, the official specification for the GPS Space Service Volume (SSV), developed in 2006, assumes that only signals emanating from the main beam of the GPS transmit antenna are useful for navigation, which greatly under-estimates the number of signals available for navigation purposes. As a result, future high-altitude space users may be vulnerable to any GPS design changes that suppress the sidelobe transmissions, beginning with Block III space vehicles (SVs) 11-32. This paper presents proposed changes to the GPS system SSV requirements, as informed by data from recent experiments in the SSV and new mission applications that are enabled by GPS navigation in HEO/GEO regimes. The NASA/NOAA GOES-R series satellites are highlighted as an example of a mission that relies on this currently-unspecified GPS system performance to meet mission requirements.

Navigation↗
NASA NTRS2013

Diagnosing Aircraft Icing Potential from Satellite Cloud Retrievals

The threat for aircraft icing in clouds is a significant hazard that routinely impacts aviation operations. Accurate diagnoses and forecasts of aircraft icing conditions requires identifying the location and vertical distribution of clouds with super-cooled liquid water (SLW) droplets, as well as the characteristics of the droplet size distribution. Traditional forecasting methods rely on guidance from numerical models and conventional observations, neither of which currently resolve cloud properties adequately on the optimal scales needed for aviation. Satellite imagers provide measurements over large areas with high spatial resolution that can be interpreted to identify the locations and characteristics of clouds, including features associated with adverse weather and storms. This paper describes new techniques for interpreting cloud products derived from satellite data to infer the flight icing threat to aircraft. For unobscured low clouds, the icing threat is determined using empirical relationships developed from correlations between satellite imager retrievals of liquid water path and droplet size with icing conditions reported by pilots (PIREPS). For deep ice over water cloud systems, ice and liquid water content (IWC and LWC) profiles are derived by using the imager cloud properties to constrain climatological information on cloud vertical structure and water phase obtained apriori from radar and lidar observations, and from cloud model analyses. Retrievals of the SLW content embedded within overlapping clouds are mapped to the icing threat using guidance from an airfoil modeling study. Compared to PIREPS and ground-based icing remote sensing datasets, the satellite icing detection and intensity accuracies are approximately 90% and 70%, respectively, and found to be similar for both low level and deep ice over water cloud systems. The satellite-derived icing boundaries capture the reported altitudes over 90% of the time. Satellite analyses corresponding to the time and location of several recent aviation accidents and with icing PIREPS are also presented that reveal skill in identifying severe icing conditions. These results demonstrate the utility of satellite cloud retrievals for quantitatively diagnosing the potential for icing conditions on temporal and spatial scales that should be useful to the aviation community. Plans are being developed to deliver these new satellite products to the GOES-R Proving Ground in the near future so that they can be evaluated in operational applications.

Smith, William L., Jr.↗
NASA NTRS2016

The NASA Applied Sciences Program: Volcanic Ash Observations and Applications

Since 2000, the NASA Applied Sciences Program has been actively transitioning observations and research to operations. Particular success has been achieved in developing applications for NASA Earth Observing Satellite (EOS) sensors, integrated observing systems, and operational models for volcanic ash detection, characterization, and transport. These include imager applications for sensors such as the MODerate resolution Imaging SpectroRadiometer (MODIS) on NASA Terra and Aqua satellites, and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NASA/NOAA Suomi NPP satellite; sounder applications for sensors such as the Atmospheric Infrared Sounder (AIRS) on Aqua, and the Cross-track Infrared Sounder (CrIS) on Suomi NPP; UV applications for the Ozone Mapping Instrument (OMI) on the NASA Aura Satellite and the Ozone Mapping Profiler Suite (OMPS) on Suomi NPP including Direct readout capabilities from OMI and OMPS in Alaska (GINA) and Finland (FMI):; and lidar applications from the Caliop instrument coupled with the imaging IR sensor on the NASA/CNES CALIPSO satellite. Many of these applications are in the process of being transferred to the Washington and Alaska Volcanic Ash Advisory Centers (VAAC) where they support operational monitoring and advisory services. Some have also been accepted, transitioned and adapted for direct, onboard, automated product production in future U.S. operational satellite systems including GOES-R, and in automated volcanic cloud detection, characterization and alerting tools at the VAACs. While other observations and applications remain to be developed for the current constellation of NASA EOS sensors and integrated with observing and forecast systems, future requirements and capabilities for volcanic ash observations and applications are also being developed. Many of these are based on technologies currently being tested on NASA aircraft, Unmanned Aerial Systems (UAS) and balloons. All of these efforts and the potential advances that will be realized by integrating them are shared in this presentation.

Murray, John J.↗
NASA NTRS2016

The NOAA Satellite Observing System Architecture Study

NOAA is beginning a study, the NOAA Satellite Observing System Architecture (NSOSA) study, to plan for the future operational environmental satellite system that will follow GOES and JPSS, beginning about 2030. This is an opportunity to design a modern architecture with no pre-conceived notions regarding instruments, platforms, orbits, etc. The NSOSA study will develop and evaluate architecture alternatives to include partner and commercial alternatives that are likely to become available. The objectives will include both functional needs and strategic characteristics (e.g., flexibility, responsiveness, sustainability). Part of this study is the Space Platform Requirements Working Group (SPRWG), which is being commissioned by NESDIS. The SPRWG is charged to assess new or existing user needs and to provide relative priorities for observational needs in the context of the future architecture. SPRWG results will serve as input to the process for new foundational (Level 0 and Level 1) requirements for the next generation of NOAA satellites that follow the GOES-R, JPSS, DSCOVR, Jason-3, and COSMIC-2 missions.

Value Model↗
NASA NTRS2017

Refining the GPS Space Service Volume (SSV) and Building a Multi-GNSS SSV

The GPS (Global Positioning System) Space Service Volume (SSV) was first defined to protect the GPS main lobe signals from changes from block to block. First developed as a concept by NASA in 2000, it has been adopted for the GPS III block of satellites, and is being used well beyond the current specification to enable increased navigation performance for key missions like GOES-R. NASA has engaged the US IFOR (Interagency Forum Operational Requirements) process to adopt a revised requirement to protect this increased and emerging use. Also, NASA is working through the UN International Committee on GNSS (Global Navigation Satellite System) to develop an interoperable multi-GNSS SSV in partnership with all of the foreign GNSS providers.

Parker, Joel J. K.↗
NASA NTRS2016

Earth Remote Sensing for Weather Forecasting and Disaster Applications

NASA's constellation of current missions provide several opportunities to apply satellite remote sensing observations to weather forecasting and disaster response applications. Examples include: Using NASA's Terra and Aqua MODIS, and the NASA/NOAA Suomi-NPP VIIRS missions to prepare weather forecasters for capabilities of GOES-R; Incorporating other NASA remote sensing assets for improving aspects of numerical weather prediction; Using NASA, NOAA, and international partner resources (e.g. ESA/Sentinel Series); and commercial platforms (high-res, or UAV) to support disaster mapping.

Molthan, Andrew↗
NASA NTRS2016

A New Tropical Cyclone Dynamic Initialization Technique Using High Temporal and Spatial Density Atmospheric Motion Vectors and Airborne Field Campaign Data

Background: Initialization of tropical cyclones in numerical weather prediction (NWP) systems is a great challenge: Mass-wind field balance; Secondary circulation and heating; Asymmetries. There can be large adjustments in structure and intensity in the first 24 hours if the initial vortex is not in balance: Spurious gravity waves; Spin-up (model and physics). Existing mesoscale NWP model TC (Tropical Cyclone) initialization strategies: Bogus vortex, cold start from global analyses; 3DVAR or 4DVAR, possibly with synthetic observations; EnKF (Ensemble Kalman Filter); Dynamic initialization. Dynamic initialization allows vortex to have improved balance and physics spin-up at the initial time (e.g., Hendricks et al. 2013, 2011; Nguyen and Chen 2011; Fiorino and Warner 1981; Hoke and Anthes 1976). Himawari-8 geostationary satellite has capability of continuous imagery (10-minutes) over the full disk: New GOES-R satellites will have same capability. This will allow for unprecedented observations of tropical cyclones. However, current data assimila1on systems are not capable of ingesting such high temporal observations (Atmospheric Mo1on Vectors - AMVs). Hourly AMVs are produced, and thinned to 100-kilometer spacing in the horizontal. An entirely new data assimilation concept is required to utilize these observations.

remote sensing↗
NASA NTRS2018

On-orbit Validation of the Geolocation Accuracy of the GOES-16 Geostationary Lightning Mapper (GLM) Flashes Using Ground-based Laser Beacons

As part of the geolocation accuracy assessment of lightning flashes detected by the Geostationary Lightning Mapper (GLM) on the GOES-16 and GOES-17 satellites (Geostationary Operational Environmental Satellite), two satellite laser ranging stations employed laser beacon systems to generate transient light pulses that simulate natural lightning around 777.4 nm to validate the pre-launch spec of 5 km. The pulse width, repetition rate, wavelength, and power of the laser-pulses were configured to produce sufficient instrument response to be detected as synthetic lightning events by the GLM instrument. During the testing period from April 2017 to January 2018, the laser systems illuminated the GOES-16 satellite to observe diurnal variation of the GLM system response, with particular emphasis on geolocation accuracy. The final GOES-16 laser beacon tests, which used the latest updates of the geolocation algorithms implemented by the GOES-R Ground Segment, showed the offsets between the GLM geolocated location and the known laser locations were within 5 km.

Lightning↗
NASA NTRS2018

GEONEX: Challenges in Producing MODIS-Like Land Products from a New Generation of Geostationary Sensors

The new generation geostationary (GEO) remote sensors (GOES-R ABI, Himawari AHI, and FY4 AGRI) provide high frequency (5-15 minute) observations spatially/spectrally similar to MODIS/VIIRS for land monitoring. These new features of GEO satellite sensors make producing MODIS like land products for terrestrial monitoring possible. The NASA Earth Exchange (NEX) team developed the GEONEX pipeline that is containerized, deployable on NASA Pleiades supercomputer as well as public cloud platforms (e.g. AWS). The processing pipeline is designed to take Himawari Standard Data (HSD) and GOES-16 L1b to generate surface reflectance (SR) and other high-level land remote sensing products. In order to produce low-Earth-orbiting (LEO) remote sensing compatible land products, inter-comparison between Himawari AHI and MODIS Terra/Aqua has been conducted in this research work. Comparisons of TOA reflectance and surface reflectance between AHI and Terra/Aqua are presented. Ray-Matching method was used to locate the co-located pixels, where GEO and LEO sensors look at the land target with similar Viewing Zenith Angle (VZA) and Viewing Azimuth Angle (VAA) simultaneously. Here, we address challenges associated with the selection of qualified pixels of similar solar illumination condition and atmosphere path. We used strict criterion to constrain the pixel selection: the time difference between GEO and LEO observations is less than +-2.5 mins, the cosine of VZA difference is less than 1%, and the VAA difference is less than 10 deg. We also discuss the strong radiometric consistency that the new generation GEO sensors along with the popular LEO sensors would benefit the environmental remote sensing community.

Li, Shuang↗
NASA NTRS2018

A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments

In the near future, there will be several new instruments measuring atmospheric composition from geostationary orbit over North America, East Asia, and Europe. This constellation of satellites will provide high resolution, time resolved measurements of trace gases and aerosols for monitoring air quality and tracking pollution sources. This paper describes a detailed, fast, and accurate (less than 1.0% uncertainty) method for calculating synthetic top of the atmosphere (TOA) radiances from a global simulation with a mesoscale free running model, the GEOS-5 Nature Run, for remote sensing instruments in geostationary orbit that measure in the ultraviolet-visible spectral range (UV-Vis). Generating these synthetic observations is the first step of an Observing System Simulation Experiment (OSSE), a framework for evaluating the impact of a new observation or algorithm. This paper provides details of the model sampling, aerosol and cloud optical properties, surface reflectance modeling, Rayleigh scattering calculations, and a discussion of the uncertainties of the simulated TOA radiance. An application for the simulated TOA radiance observations is demonstrated in the manuscript. Simulated TEMPO (Tropospheric Emissions: Monitoring of Pollution) and GOES-R (Geostationary Operational Environmental Satellites) observations were used to show how observations from the two instruments could be combined to facilitate aerosol type discrimination. The results demonstrate the viability of a detailed instrument simulator for radiance measurements in the UV-Vis that is capable of accurately simulating high resolution, time-resolved measurements with reasonable computational efficiency.

Castellanos, Patricia↗
NASA NTRS2019

Joint 3D-Wind Retrievals with Stereoscopic Views from MODIS and GOES

Atmospheric motion vectors (AMVs), derived by tracking patterns, represent the winds in a layer characteristic of the pattern. AMV height (or pressure), important for applications in atmospheric research and operational meteorology, is usually assigned using observed IR brightness temperatures with a modeled atmosphere and can be inaccurate. Stereoscopic tracking provides a direct geometric height measurement of the pattern that an AMV represents. We extend our previous work with multi-angle imaging spectroradiometer (MISR) and GOES to moderate resolution imaging spectroradiometer (MODIS) and the GOES-R series advanced baseline imager (ABI). MISR is a unique satellite instrument for stereoscopy with nine angular views along track, but its images have a narrow (380 km) swath and no thermal IR channels. MODIS provides a much wider (2330 km) swath and eight thermal IR channels that pair well with all but two ABI channels, o e ring a rich set of potential applications. Given the similarities between MODIS and VIIRS, our methods should also yield similar performance with VIIRS. Our methods, as enabled by advanced sensors like MODIS and ABI, require high-accuracy geographic registration in both systems but no synchronization of observations. AMVs are retrieved jointly with their heights from the disparities between triplets of ABI scenes and the paired MODIS granule. We validate our retrievals against MISR-GOES retrievals, operational GOES wind products, and by tracking clear-sky terrain. We demonstrate that the 3D-wind algorithm can produce high-quality AMV and height measurements for applications from the planetary boundary layer (PBL) to the upper troposphere, including cold-air outbreaks, wildfire smoke plumes, and hurricanes.

Carr, James L.↗
NASA NTRS2019

Transfer Learning to Generate True Color Images from GOES-16

Along with scientific applications, Geostationary imagery is often used to learn about weather patterns through true color visualizations. NOAA/NASA's GOES-R series of satellites uses the advanced baseline imager with 16-bands which, unlike previous generations, does not include the green wavelength (500-565 nm) and hence cannot directly generate true color images. However, Himawari, Japan's geostationary satellite, uses a similar 16-band advanced Himawari imager that does include a green band (but missing cirrus). In this work, we show how transfer learning with convolutional neural networks can be applied across satellites to generate "virtual sensors". We apply this approach to generate a green band for GOES-16 and present near true color images.

transfer learning↗
NASA NTRSDate not supplied

TPSAS-NF1676L-21193-DND

Researchers at NASA Langley Research Center have been developing an automated pattern recognition algorithm to identify overshooting convective cloud tops (OTs) in support of the GOES-R satellite program. This algorithm identify regions of overshooting at the individual 1-4 km geostationary satellite pixel scale using visible (during daytime only) and infrared channel imagery and numerical weather analysis data. The algorithm has been developed based upon analysis of 0.25-1 km spatial resolution Aqua MODIS imagery, using a database of over 2000 manually identified OT features throughout the world in storms with varying intensity and morphology. The OT database includes storms ranging from small, warm topped cells in Alaska and Mongolia, tornadic supercells over the U.S. Central Plains and Europe, large tropical mesoscale convective systems, and overshooting in the eyewalls and spiral bands of category 5 tropical cyclones. This database is available for use by the research community. The algorithm is designed to operate on data from any current and historical satellite imager, allowing for development of a highly accurate global OT detection climatology that extends back into the 1990's at up to a 15-30 min temporal resolution throughout the diurnal cycle. As members of the McIDAS Users Group, NASA LaRC has immediate access to the full global archive of geostationary imager data which would allows rapid development of OT climatologies and short-term databases. This type of capability has never been available within the weather and climate research community. Regional geostationary OT databases have been already developed over CONUS during SEAC4RS, for 18-years over the Eastern U.S., and for 5-10 years over Australia, Europe, Southeast Asia, and East Africa among many other regions. Some of these datasets are being used by climate researchers and private industry to examine UTLS-penetrating storm spatial distributions and their temporal variability, in addition to weather hazards associated with these storms at unprecedented spatial detail. This presentation will describe the OT pattern recognition algorithm and highlight recent product applications.

Kristopher Bedka↗
NASA NTRS2019

Assessment of GOES-16/ABI middle wave infrared band using references of Himawari-8/AHI and Aqua/MODIS

GOES-16 is the first of the GOES-R series of Geostationary Operational Environmental Satellites (GOES) and was launched on November 19, 2016. The spacecraft was initially in a test position of 89.5° West and reached its operational position (75.2° West) on December 11, 2017. The Himawari-8 spacecraft was launched on October 7, 2014 and is located at 140.7º East. The similar design and similar calibration algorithm between the Advanced Baseline Imager (ABI) on-board GOES-16 and the Advanced Himawari Imager (AHI) on board Himawari-8 makes the importance of inter-comparison. Due to their locations, double difference is an appropriate method for their comparison and Aqua MODIS is one of good references. However, ABI (AHI) midwave-infrared (MWIR) band 7 does not have good matching with Aqua MODIS. In this work, the ABI-AHI comparison and ABI assessment for MWIR band 7 is performed using Aqua bands 20, 22, and 23. The ocean sites under ABI (AHI) at nadir are used for inter-comparison with Aqua MODIS. To enhance the comparison accuracy, a few procedures and corrections have been applied. For MWIR band 7, the ABI-AHI difference is about -0.39K over ocean scene, with the ABI measurement precision being slightly better than that of AHI. This double difference method is also being used for the assessment of ABI consistency before and after re-location on November 30, 2017. Two ocean scenes are selected for the ABI re-location assessments, with measurement precision at nadir providing better measurements than non-zero view angles. The ABI MWIR measurement over the ocean scene at the same view angle before and after re-location shows that the precisions are comparable. The MWIR band brightness temperature (BT) measurement over ocean scene shows a 0.04K difference, while the measurement precision before and after re-location is consistent.

Inter-comparison↗