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NASA NTRSDate not supplied

Tracking the Hunga Tonga-Hunga Ha’apai Eruption Stratospheric Aerosol and Trace Gas Plumes Using Machine Learning

On January 15, 2022, the Hunga Tonga-Hunga Ha’apai (hereafter, Hunga Tonga) submarine volcano had an explosive eruption that thrusted ash, gases, and water vapor through the troposphere into the stratosphere and mesosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using data retrieved from low earth orbiting satellite instruments (e.g., OMPS, OMI, and CALIPSO), this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), with prompt engineering can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline using NASA Earthdata and Openscapes, establishes a framework for systematically and rapidly studying extreme events, including volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of remote sensing data, this work demonstrates how AI and open science can accelerate research and generate actionable results. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions (e.g., the Atmosphere Observing System (AOS)), and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters and extreme events in a changing world.

David M. Giles↗

NASA NTRS2002

TOMS UV Algorithm: Problems and Enhancements

Satellite instruments provide global maps of surface ultraviolet (UV) irradiance by combining backscattered radiance measurements with radiative transfer models. The models are limited by uncertainties in input parameters of the atmosphere and the surface. We evaluate the effects of possible enhancements of the current Total Ozone Mapping Spectrometer (TOMS) surface UV irradiance algorithm focusing on effects of diurnal variation of cloudiness and improved treatment of snow/ice. The emphasis is on comparison between the results of the current (version 1) TOMS UV algorithm and each of the changes proposed. We evaluate different approaches for improved treatment of pixel average cloud attenuation, with and without snow/ice on the ground. In addition to treating clouds based only on the measurements at the local time of the TOMS observations, the results from other satellites and weather assimilation models can be used to estimate attenuation of the incident UV irradiance throughout the day. A new method is proposed to obtain a more realistic treatment of snow covered terrain. The method is based on a statistical relation between UV reflectivity and snow depth. The new method reduced the bias between the TOMS UV estimations and ground-based UV measurements for snow periods. The improved (version 2) algorithm will be applied to re-process the existing TOMS UV data record (since 1978) and to the future satellite sensors (e.g., Quik/TOMS, GOME, OMI on EOS/Aura and Triana/EPIC).

Krotkov, Nickolay↗

NASA NTRS2018

Operational Applications of Global Precipitation Measurement Observations

The launch of next-generation satellites with advanced sensors, such as Global Precipitation Measurement (GPM) Core Observatory with the GPM Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) onboard, provide a unique opportunity to develop new products, tools, and capabilities to enhance operational weather forecasting. Many of the next-generation weather satellites boast of capabilities that have never been used in the operational environment. The operational environment is often fast-paced and requires forecasters to examine and make decisions based on analyzing many different types of data very quickly, particularly in rapidly evolving weather threat situations. This is a stark contrast to the research world, where researchers use a limited number of datasets to answer very specific scientific questions, taking months or years to perform analyses and arrive at conclusions. The National Aeronautics and Space Administration (NASA) Short-term Prediction Research and Transition (SPoRT) Center strives to bridge the gap between researchers and the operational community of end users to facilitate the transition of research products and capabilities to an operational environment. PRECIPITATION (METEOROLOGY); PRECIPITATION MEASUREMENT; MEASURING INSTRUMENTS; SATELLITE OBSERVATION; OPERATIONS RESEARCH; ALGORITHMS; MICROWAVE SENSORS; MICROWAVE IMAGERYMETEOROLOGICAL RADAR

LeRoy, Anita↗

NASA NTRS2019

Developing an Aircraft-Based Angular Distribution Model of Solar Reflection from Wildfire Smoke to Aid Satellite-Based Radiative Flux Estimation

This study examines the angular distribution of scattered solar radiation associated with wildfire smoke aerosols observed over boreal forests in Canada during the ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) campaign. First, it estimates smoke radiative parameters (550 nm optical depth of 3.9 and single scattering albedo of 0.90) using quasi-simultaneous multiangular and multispectral airborne measurements by the Cloud Absorption Radiometer (CAR). Next, the paper estimates the broadband top-of-atmosphere radiances that a satellite instrument such as the Clouds and the Earth’s Radiant Energy System (CERES) could have observed, given the narrowband CAR measurements made from an aircraft circling about a kilometer above the smoke layer. This estimation includes both an atmospheric correction that accounts for the atmosphere above the aircraft and a narrowband-to-broadband conversion. The angular distribution of estimated radiances is found to be substantially different than the angular model used in the operational data processing of CERES observations over the same area. This is because the CERES model is a monthly average model that was constructed using observations taken under smoke-free conditions. Finally, a sensitivity analysis shows that the estimated angular distribution remains accurate for a fairly wide range of smoke and underlying surface parameters. Overall, results from this work suggest that airborne CAR measurements can bring some substantial improvements in the accuracy of satellite-based radiative flux estimates.

Varnai, Tamas↗

NASA NTRS1994

Validation of stratospheric aerosol and gas experiments 1 and 2 satellite aerosol optical depth measurements using surface radiometer data

The stratospheric aerosol measurement 2, stratospheric aerosol and gas experiment (SAGE) 1, and SAGE 2 series of solar occultation satellite instruments were designed for the study of stratospheric aerosols and gases and have been extensively validated in the stratosphere. They are also capable, under cloud-free conditions, of measuring the extinction due to aerosols in the troposphere. Such tropospheric extinction measurements have yet to be validated by appropriate lidar and in situ techniques. In this paper published atmospheric aerosol optical depth measurements, made from high-altitude observatories during volcanically quiet periods, have been compared with optical depths calculated from local SAGE 1 and SAGE 2 extinction profiles. Surface measurements from three such observatories have been used, one located in Hawaii and two within the continental United States. Data have been intercompared on a seasonal basis at wave-lenths between 0.5 and 1.0 micron and found to agree within the range of measurement errors and expected atmospheric variation. The mean rms difference between the optical depths for corresponding satellite and surface measured data sets is 29%, and the mean ratio of the optical depths is 1.09.

Kent, G. S.↗

NASA NTRS2013

A Seasonal Trend of Single Scattering Albedo in Southern African Biomass-burning Particles: Implications for Satellite Products and Estimates of Emissions for the World's Largest Biomass-burning Source

As a representative site of the southern African biomass-burning region, sun-sky data from the 15 year Aerosol Robotic Network (AERONET) deployment at Mongu, Zambia, was analyzed. For the biomass-burning season months (July-November), we investigate seasonal trends in aerosol single scattering albedo (SSA), aerosol size distributions, and refractive indices from almucantar sky scan retrievals. The monthly mean single scattering albedo at 440 nm in Mongu was found to increase significantly from approx.. 0.84 in July to approx. 0.93 in November (from 0.78 to 0.90 at 675 nm in these same months). There was no significant change in particle size, in either the dominant accumulation or secondary coarse modes during these months, nor any significant trend in the Angstrom exponent (440-870 nm; r(exp 2) = 0.02). A significant downward seasonal trend in imaginary refractive index (r(exp 2) = 0.43) suggests a trend of decreasing black carbon content in the aerosol composition as the burning season progresses. Similarly, burning season SSA retrievals for the Etosha Pan, Namibia AERONET site also show very similar increasing single scattering albedo values and decreasing imaginary refractive index as the season progresses. Furthermore, retrievals of SSA at 388 nm from the Ozone Monitoring Instrument satellite sensor show similar seasonal trends as observed by AERONET and suggest that this seasonal shift is widespread throughout much of southern Africa. A seasonal shift in the satellite retrieval bias of aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer collection 5 dark target algorithm is consistent with this seasonal SSA trend since the algorithm assumes a constant value of SSA. Multi-angle Imaging Spectroradiometer, however, appears less sensitive to the absorption-induced bias.

aerosol absorption↗

NASA NTRS2015

A High-Resolution Merged Wind Dataset for DYNAMO: Progress and Future Plans

In order to support research on optimal data assimilation methods for the Cyclone Global Navigation Satellite System (CYGNSS), launching in 2016, work has been ongoing to produce a high‐resolution merged wind dataset for the Dynamics of the Madden Julian Oscillation (DYNAMO) field campaign, which took place during late 2011/early 2012. The winds are produced by assimilating DYNAMO observations into the Weather Research and Forecasting (WRF) three‐dimensional variational (3DVAR) system. Data sources from the DYNAMO campaign include the upper‐air sounding network, radial velocities from the radar network, vector winds from the Advanced Scatterometer (ASCAT) and Oceansat‐2 Scatterometer (OSCAT) satellite instruments, the NOAA High Resolution Doppler Lidar (HRDL), and several others. In order the prep them for 3DVAR, significant additional quality control work is being done for the currently available TOGA and SMART‐R radar datasets, including automatically dealiasing radial velocities and correcting for intermittent TOGA antenna azimuth angle errors. The assimilated winds are being made available as model output fields from WRF on two separate grids with different horizontal resolutions ‐ a 3‐km grid focusing on the main DYNAMO quadrilateral (i.e., Gan Island, the R/V Revelle, the R/V Mirai, and Diego Garcia), and a 1‐km grid focusing on the Revelle. The wind dataset is focused on three separate approximately 2‐week periods during the Madden Julian Oscillation (MJO) onsets that occurred in October, November, and December 2011. Work is ongoing to convert the 10‐m surface winds from these model fields to simulated CYGNSS observations using the CYGNSS End‐To‐End Simulator (E2ES), and these simulated satellite observations are being compared to radar observations of DYNAMO precipitation systems to document the anticipated ability of CYGNSS to provide information on the relationships between surface winds and oceanic precipitation at the mesoscale level. This research will improve our understanding of the future utility of CYGNSS for documenting key MJO processes.

Lang, Timothy J.↗

NASA NTRS2006

Offshore Radiation Observations for Climate Research at the CERES Ocean Validation Experiment

When radiometers on a satellite are pointed towards the planet with the goal of understanding a phenomenon quantitatively, rather than just creating a pleasing image, the task at hand is often problematic. The signal at the detector can be affected by scattering, absorption, and emission; and these can be due to atmospheric constituents (gases, clouds, and aerosols), the earth's surface, and subsurface features. When targeting surface phenomena, the remote sensing algorithm needs to account for the radiation associated with the atmospheric constituents. Likewise, one needs to correct for the radiation leaving the surface, when atmospheric phenomena are of interest. Rigorous validation of such remote sensing products is a real challenge. In visible and near infrared wavelengths, the jumble of effects on atmospheric radiation are best accomplished over dark surfaces with fairly uniform reflective properties (spatial homogeneity) in the satellite instrument's field of view (FOV). The ocean's surface meets this criteria; land surfaces - which are brighter, more spatially inhomogeneous, and more changeable with time - generally do not. NASA's Clouds and the Earth's Radiant Energy System (CERES) project has used this backdrop to establish a radiation monitoring site in Virginia's coastal Atlantic Ocean. The project, called the CERES Ocean Validation Experiment (COVE), is located on a rigid ocean platform allowing the accurate measurement of radiation parameters that require precise leveling and pointing unavailable from ships or buoys. The COVE site is an optimal location for verifying radiative transfer models and remote sensing algorithms used in climate research; because of the platform's small size, there are no island wake effects; and suites of sensors can be simultaneously trained both on the sky and directly on ocean itself. This paper describes the site, the types of measurements made, multiple years of atmospheric and ocean surface radiation observations, and satellite validation results.

Rutledge, Charles K.↗

NASA NTRS2005

Towards an A-train Aerosol Assimilation System: Assimilation of MODIS Aerosol Optical Thickness Retrievals into a Global Aerosol Transport and Radiation Model

To understand the role of atmospheric aerosols in Earth's climate system we ideally require a global, three-dimensional, and temporally continuous system of measurements. The view of aerosols provided by a single polar-orbiting satellite instrument (e.g., MODIS), while providing the greatest spatial coverage of any measurement system available, can offer only a single relevant observation per day of a particular location. Furthermore, the quantitative interpretation of those observations is subject to considerable uncertainty because of we do not know the aerosol optical properties or vertical profiles well enough. Ground-based and in situ measurements can provide missing detail about aerosol vertical and optical properties, but lack the spatial (and sometimes temporal) coverage desired. Chemical transport models treating the aerosol lifecycle complement this picture by providing three-dimensional, global, and continuous aerosol fields, but suffer from poor constraints on their initial conditions (e.g., the aerosol source, sink, and transformation mechanisms). Here we present a global, three- dimensional aerosol transport model and assimilation system. The model assimilates the MODIS aerosol optical thickness product to constrain its aerosol distributions and burdens. We provide an analysis of the model aerosol distributions as constrained by ground-based sun photometer measurements and aerosol products from other satellite measurements.

Colarco, P.↗

NASA NTRS2007

Evaluating Model Performance of an Ensemble-based Chemical Data Assimilation System During INTEX-B Field Mission

We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSFINCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (-140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrate the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements. The work described above also brought to light several short-comings of the data assimilation approach for CO profiles. Because of the limited vertical resolution of the measurement, the retrievals at different altitudes are correlated which can lead to problems with numerical error and overall efficiency. This has resulted in a manuscript that is about to be submitted to JGR:

Arellano, A. F., Jr.↗

NASA NTRS2010

Direct Retrieval of Sulfur Dioxide Amount and Altitude from Spaceborne Hyperspectral UV Measurements: Theory and Application

We describe the physical processes by which a vertically localized absorber perturbs the top-of-atmosphere solar backscattered ultraviolet (UV) radiance. The distinct spectral responses to perturbations of an absorber in its column amount and layer altitude provide the basis for a practical satellite retrieval technique, the Extended Iterative Spectral Fitting (EISF) algorithm, for the simultaneous retrieval of these quantities of a SO2 plume. In addition, the EISF retrieval provides an improved UV aerosol index for quantifying the spectral contrast of apparent scene reflectance at the bottom of atmosphere bounded by the surface and/or cloud; hence it can be used for detection of the presence or absence of UV absorbing aerosols. We study the performance and characterize the uncertainties of the EISF algorithm using synthetic backscattered UV radiances, retrievals from which can be compared with those used in the simulation. Our findings indicate that the presence of aerosols (both absorbing and nonabsorbing) does not cause large errors in EISF retrievals under most observing conditions when they are located below the SO2 plume. The EISF retrievals assuming a homogeneous field of view can provide accurate column amounts for inhomogeneous scenes, but they always underestimate the plume altitudes. The EISF algorithm reduces systematic errors present in existing linear retrieval algorithms that use prescribed SO2 plume heights. Applying the EISF algorithm to Ozone Monitoring Instrument satellite observations of the recent Kasatochi volcanic eruption, we demonstrate the successful retrieval of effective plume altitude of volcanic SO2, and we also show the improvement in accuracy in the corresponding SO2 columns.

Yang, Kau↗

NASA NTRSDate not supplied

Analysis of CO Transport from Australia Fires and Its Impact to Ozone, Water Vapor and Clouds Using CrIS Single-Field-of-View Sounder Products and Other Remote Sensing and MERRA-2 Reanalysis Data

Australia’s unprecedented fire disasters at the end of 2019 to early 2020 emitted huge amounts of carbon monoxide (CO) and fire aerosol particles, and the long-range transport of the smoke plumes poses great impact to climate change, such as altering the Antarctic ozone (O3) hole. Spaceborne observations from satellites have been proven successful in monitoring CO from wildfires in the past decade and can be used to examine the associated CO transport. The primary remote sensing data used in this study is the Single Field of View (SFOV) Sounder Atmospheric Product (SiFSAP) derived from the Cross-track Infrared Sounder (CrIS) on SNPP, a new product that will be generated at NASA and released to the public in a few months. SiFSAP has a high spatial resolution of ~14 km and includes atmospheric profiles of temperature, water vapor, CO, O3, as well as cloud products, and as the SiFSAP algorithm performs both day- and night-time retrievals, it provides an additional observation for night-time in comparison to the day-time-only solar polar satellite instruments such as MOPPIT, OMPS, and TROPOMI. Most recent significant improvements in the quality of this product plus its good spatial resolution (better than most global weather and climate models) enable us to conduct process-oriented analysis of CO transport, and the variation of water vapor, O3 and clouds (optical depth, effective radius and cloud height) within and around the fire plumes. The wind fields from MERRA-2 reanalysis will be used to help characterize the transport. Other remote sensing data, such as CO from AIRS, MOPPIT and TROPOMI, O3 from OMPS and TROPOMI, as well as the UV aerosol index from OMI and OMPS, will be used and compared with SiFSAP sounder products. Cross-validation of CO and O3 from these different sensors with the corresponding MERRA-2 products will also be carried out. Along the plume transport path, some enhancement of O3 and correlation between fire aerosol, cloud optical depth and water vapor have been found but need further investigation. Using the transport of fire plumes over the Southern Pacific Ocean as a unique testbed, this study will demonstrate the trace species monitoring capability of current satellite observations, in particular, for CO and O3, and hopefully, it will provide some insight on the impacts on fire emissions on air quality and climate.

Xiaozhen (Shawn) Xiong↗

NASA NTRS2021

Analysis of Near-Cloud Changes in Atmospheric Aerosols Using Satellite Observations and Global Model Simulations

This paper examines cloud-related variations of atmospheric aerosols that occur in partly cloudy regions containing low-altitude clouds. The goal is to better understand aerosol behaviors and to help better represent the radiative effects of aerosols on climate. For this, the paper presents a statistical analysis of a multi-month global dataset that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite instruments with data from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis. Among other findings, the results reveal that near-cloud enhancements in lidar backscatter (closely related to aerosol optical depth) are larger (1) over land than ocean by 35%, (2) near optically thicker clouds by substantial amounts, (3) for sea salt than for other aerosol types, with the difference from dust reaching 50%. Finally, the study found that mean lidar backscatter is higher near clouds not because of large-scale variations in meteorological conditions, but because of local processes associated with individual clouds. The results help improve our understanding of aerosol-cloud-radiation interactions and our ability to represent them in climate models and other atmospheric models

aerosol↗

NASA NTRSDate not supplied

Using the Diurnal Variability in GeoNEX TOA Reflectances for Earth Monitoring

Observations from the third-generation geostationary satellite instruments (GOES 16/17 ABI, Himawari 8/9 AHI, and etc.) have spatial resolution and spectral band configurations comparable to flagship LEO sensors (e.g., MODIS/VIIRS). More importantly, these data are acquired at very high temporal resolution, faithfully recording the variations of the full disk of Earth at every 5-10 minutes. They thus provide unique information about Earth’s atmosphere and surface. In order to explore the unique information content of geostationary data, this study systematically analyzes the diurnal variability in the GeoNEX L1G TOA reflectance products and compares them to simulated results by state-of-the-art radiative transfer codes. Our results show that • The smoothness of the TOA reflectance diurnal cycle provides a convenient and reliable way to identify stable atmospheric conditions and filter out passing clouds/shadows. • The diurnal variability of the blue band (0.47µm) reflectance is regulated mainly by atmospheric optical conditions over a majority of land cover types. As such, the diurnal variability of the blue band data allows us to retrieve AOD without invoking the use of spectral band ratios (SRC) as in previous algorithms. • In comparison, the diurnal variability of the short-wave infrared band (2.2µm) BRFs is mainly regulated by surface reflectance and the sun-target-satellite geometry. This information allows us to test and, if suitable, retrieve surface BRDF parameters. • Spectral band ratios, especially those between the 2.2µm and 0.47µm bands, are not constant but vary by locations and sun-target-satellite geometries. Our analysis clearly demonstrates that the information provided in high-frequent geostationary observations is unique and complementary to LEO sensors. Therefore, a synergy of GEO and LEO (and other) sensors has the great potential to improve existing remote sensing models and algorithms for better Earth monitoring.

Diurnal Variability↗

NASA NTRS2004

Evaluation of GOME Satellite Measurements of Tropospheric NO2 and HCHO using Regional Data from Aircraft Campaigns in the Southeastern United States

We compare tropospheric measurements of nitrogen dioxide (NO2) and formaldehyde (HCHO) from the Global Ozone Monitoring Experiment (GOME) satellite instrument with in situ measurements over eastern Texas and the southeast United States. On aveiage, the GOME and in situ measurements of tropospheric NO2 and HCHO columns are consistent despite pronounced sampling differences. The geometric mean in situ to GOME ratios over the campaign are 1.08 for NO2 and 0.84 for HCHO, with corresponding geometric standard deviations of 1.27 and 1.38. The correlation of the observed column spatial variability between the two NO2 measurement sets is encouraging before (r2 = 0.54, n = 18) and after (r2 = 0.67, n = 18) correcting for a sampling bias. Mean relative vertical profiles of HCHO and NO2 calculated with a global three-dimensional model (GEOS-CHEM) and used in the GOME retrieval are highly consistent with in situ measurements; differences would affect the retrieved NO2 and HCHO columns by a few percent. GOME HCHO columns over eastern Texas include contributions from anthropogenic volatile organic compound (VOC) emissions but are dominated by biogenic VOC emissions at the regional scale in August-September when HCHO columns are within 20% of those over the southeastern United States. In situ measurements show that during summer the lowest 1500 m (the lower mixed layer) contains 75% of the tropospheric NO2 column over Houston and Nashville, and 60% of the HCHO column over Houston. Future validation of space-based measurements of tropospheric NO2 and HCHO columns over polluted regions should include coincident in situ measurements that span the entire satellite footprint, especially in the heterogeneous mixed layer.

Martin, R. V.↗

NASA NTRS2021

How fire patterns reveal uneven stabilization at the end of conflict: examining Syria's unusual fire year in 2019

In 2019, Syria experienced its largest outbreak of fires since 2003, when fire occurrence began to be monitored via the MODIS satellite instruments. Here, we combine remotely sensed data on fire, local climatic conditions, and vegetation with spatial data on violence, territorial control, and return of internally displaced persons (IDPs) to explore the underlying causes of Syria's fires at the sub-district, or ADM-3, level. We find evidence for a nuanced story: precipitation and stabilization in Kurdish-controlled territory fueled a bountiful wheat-growing year in 2019. This agricultural resurgence coincided with high fire activity, possibly revealing that people were attempting to jump-start agricultural livelihoods that were lost during the civil war. Restoration of labor forces from IDP returns was not a significant factor in this agricultural recovery. Meanwhile, most sub-districts outside Kurdish-controlled territory were free of fires, which explains the absence of a relationship between vegetation and fire. Thus, Syrian fire patterns in 2019 may be a signal of uneven stabilization, rather than destabilization.

Fire patterns↗

NASA NTRS2012

Joint Polar Satellite System (JPSS) Instrument Transition to NASA and Development Status

The Joint Polar Satellite System (JPSS) Program is a cooperative program between the National Aeronautics and Space Agency (NASA) and the National Oceanic and Atmospheric Administration (NOAA) to design, develop, and fly the next suite of US civilian polar orbiting environmental sensing instruments. The JPSS Program is a product of the restructuring of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Program, which occurred in 2010. With the transition to NASA, the JPSS instruments have undergone significant review with numerous updates to the designs as well as made significant progress toward delivering a superior capability to the Nation. This paper will discuss the program transition as it relates to the instruments and the associated transition review efforts, key findings, important changes to the instruments for JPSS and their current development status. The VIIRS instrument will be presented separately.

Brann, Christopher W.↗

NASA NTRS1989

An intercomparison of longwave measurements by ERBE radiometers on the NOAA-9 and ERBS satellites

Two instrument modules of each satellite on which the Earth Radiation Budget Experiment (ERBE) is orbiting observe components of the earth radiation budget with three different scales of earth view. An intercomparison of longwave measurements by these instruments provides relative information concerning radiometric performance at satellite altitude, techniques of estimating upwelling exitances, and an end-to-end evaluation of the data processing system. Results indicate that the ERBE radiometers are mildly sensitive to varying thermal loads from the spacecraft and/or the earth-space environment. Radiometric variations at the satellite and methods of data interpretation contribute about equally to the uncertainty of radiant exitances from the earth.

House, Frederick B.↗