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At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Automatic cloud tracking applied to GOES and Meteosat observations

An improved automatic processing method for the tracking of cloud motions as revealed by satellite imagery is presented and applications of the method to GOES observations of Hurricane Eloise and Meteosat water vapor and infrared data are presented. The method is shown to involve steps of picture smoothing, target selection and the calculation of cloud motion vectors by the matching of a group at a given time with its best likeness at a later time, or by a cross-correlation computation. Cloud motion computations can be made in as many as four separate layers simultaneously. For data of 4 and 8 km resolution in the eye of Hurricane Eloise, the automatic system is found to provide results comparable in accuracy and coverage to those obtained by NASA analysts using the Atmospheric and Oceanographic Information Processing System, with results obtained by the pattern recognition and cross correlation computations differing by only fractions of a pixel. For Meteosat water vapor data from the tropics and midlatitudes, the automatic motion computations are found to be reliable only in areas where the water vapor fields contained small-scale structure, although excellent results are obtained using Meteosat IR data in the same regions. The automatic method thus appears to be competitive in accuracy and coverage with motion determination by human analysts.

Endlich, R. M.↗
NASA NTRS1985

Variational mesoscale satellite data assimilation and initialization

The problems of mesoscale satellite data assimilation were examined. Assimilation of satellite data to improve the forecasts made by mesoscale forecast models was undertaken. Assimilation of high resolution satellite derived temperature data into a mesoscale model with horizontal resolution of 50 to 60 km is reported. Unlike global assimilation, in which a small portion of the forecast model domain is subject to data insertion at virtually every time step, the mesoscale assimilation virtually all of the forecast model domain is subject to data insertion at one time step. The mesoscale problem lends itself naturally to intermittent data assimilation and the forecast model is reinitialized whenever a new satellite pass covers its domain with data. The satellite data assimilation as an initialization problem are discussed.

Sasaki, Y. K.↗
NASA NTRS1986

Optical imaging spectroscopy

During the recent solar maximum the combination of imaging and spectroscopy in the visible part of the spectrum became a powerful tool for observational study of flares primarily because of the development of two-dimensional charge-coupled-device (CCD) arrays. In combination with appropriate new operational methods, this has led to the ability to observe, for the first time, the preflare and impulsive-phase physical processes associated with spatially resolved features of flare loops. As a result of concurrent theoretical developments, modeling progressed from an empirical to a physical level. This made it possible to interpret imaging spectra in terms of coronal pressure and heat flux, particle beam heating, chromospheric evaporation, and explosive chromospheric dynamics at the footpoints of flare loops. There is clear potential for further advances in the near future, taking advantage of improvements in digital recording speed (approx. 10-fold), number of photosensitive elements per array (approx. 10-fold), real-time data pre-reduction (potentially 10- to 100-fold), and using multiple CCD arrays. By the time of the next solar maximum imaging spectroscopy is expected to achieve spatial resolution or approx. arc 1 arc s, temporal resolution or approx. 5 s, and simultaneous critically-sampled spectroscopy of several lines and continua. As a result, continued increase in our understanding of the physical processes and configurations of solar flares in the chromosphere, temperature minimum region, and photosphere can be anticipated. Even greater progress toward a more global understanding of flares will obviously come about when simultaneous optical, X-ray, and gamma-ray imaging spectroscopy are possible.

Canfield, R. C.↗
NASA NTRS1994

Response of a two-dimensional dual-Doppler radar wind synthesis

The wind field resulting from a two-dimensional dual-Doppler synthesis algorithms is spectrally modified from the true wind field. The effects of spatial filtering on wind fields from the processes of interpolation, the averaging of pulses, and the effect of the finite radar pulse dimension were assessed. The effect resulting from the use of different interpolation techniques was also evaluated. Of those techniques tested, the best are the Cressman distance-weighted averaging and linear distance-weighted averaging, with the closest neighbor and uniform weighting having more undesirable characteristics. The optimum influence radius is defined as the influence radius at which the ratio of the rms difference between the Fourier and least-squares responses (a measure of the aliasing) and the variance of the filtered wind field is minimized. This seeks to minimize the effect of energy aliased into scales other than the input wavelength. For the Cressman interpolation technique, the optimum influence radius is between 1.85 and 2.25 times the maximum data spacing. The range of acceptable influence radii includes consideration of the filtering by the radar of the data as it is collected, as well as the resolution of the final dataset. The optimum influence radius is dependent upon the largest data separation in the analysis domain. The absolute optimum influence radius is not significantly affected by inclusion of the radar-beam filtering effects.

Given, Terence↗
NASA NTRS2002

Quantification of Surface Suspended Sediments along a River Dominated Coast with NOAA AVHRR and SeaWiFS Measurements: Louisiana, USA

The ability to quantify suspended sediment concentrations accurately over both time and space using satellite data has been a goal of many environmental researchers over the past few decades This study utilizes data acquired by the NOAA Advanced Very High Resolution Radiometer (AVHRR) and the Orbview-2 Sea-viewing wide field-of-view (SeaWiFS) ocean colour sensor, coupled with field measurements to develop statistical models for the estimation of near-surface suspended sediment and suspended solids "Ground truth" water samples were obtained via helicopter, small boat and automatic water sampler within a few hours of satellite overpasses The NOAA AVHRR atmospheric correction was modified for the high levels of turbidity along the Louisiana coast. Models were developed based on the field measurements and reflectance/radiance measurements in the visible and near infrared Channels of NOAA-14 and Orbview-2 SeaWiFS. The best models for predicting surface suspended sediment concentrations were obtained with a NOAA AVHRR Channel 1 (580-680nm) cubic model, Channel 2 (725-1100 nm) linear mod$ and SeaWiFs Channel 6 (660-68Onm) power modeL The suspended sediment models developed using SeaWiFS Channel 5 (545-565 nm) were inferior, a result that we attribute mainly to the atmospheric correction technique, the shallow depth of the water samples and absorption effects from non-sediment water constituents.

Myint, S. W.↗
NASA NTRS2005

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

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

Chu, D. A.↗
NASA NTRS2008

Enabling and Enhancing Space Mission Success and Reduction of Risk through the Application of an Integrated Data Architecture

The engineering phases of design, development, test, and evaluation (DDT and E) and subsequent planning, preparation, and operation (Ops) of space vehicles in a complex and distributed environment requires massive and continuous flows of information across the enterprise and across temporal stages of the vehicle lifecycle. The resulting capabilities at each subsequent stage depend in part on the capture, preparation, storage, and subsequent provision of information from prior stages. The United States National Aeronautics and Space Administration (NASA) is currently designing a fleet of new vehicles that will replace the Space Shuttle and expand space operations and exploration capabilities. This includes the 2 stage human rated lift vehicle Ares 1 and its associated crew vehicle the Orion, and a service module; the heavy lift cargo vehicle, Ares 5, and an associated cargo stage known as the Earth Departure Stage; and a Lunar Lander vehicle that contains a descent stage, and ascent stage, and a habitation module. A variety of concurrent assorted ground operations infrastructure including software and facilities are also being developed, assorted technology and assembly designs and development for equipment such as EVA suits, life support systems, command and control technologies are also in the pipeline. The development is occurring in a distributed manner, with project deliverables being contributed by a large and diverse assortment of vendors and most space faring nations. Critical information about all of the components, software, and procedures must be shared during the DDT and E phases and then made readily available to the mission operations staff for access during the planning, preparation, and operations phases, and also need to be readily available for system to system interactions. The Constellation Data Systems Project (CxDS) is identifying the needs, and designing and deploying systems and processes to support these needs. This paper details the steps and processes that NASA is applying within the Constellation Program to manage this data and information, and to insure that the correct information is available, correctly annotated, and can be provisioned digitally to enhance response times, and support engineering analysis and anomaly resolution.

Brummett, Robert C.↗
NASA NTRS2016

A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description, Validation, and Case Study

In- land surface models, which are used to evaluate the role of vegetation in the context ofglobal climate change and variability, LAI and FAPAR play a key role, specifically with respect to thecarbon and water cycles. The AVHRR-based LAIFAPAR dataset offers daily temporal resolution,an improvement over previous products. This climate data record is based on a carefully calibratedand corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitablefor climate studies. It spans from mid-1981 to the present. Further, this operational dataset is availablein near real-time allowing use for monitoring purposes. The algorithm relies on artificial neuralnetworks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparisonwith MODIS products and in situ data show the dataset is consistent and reliable with overalluncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect isobserved in the broadleaf forest biomes with high LAI (greater than 4.5) and FAPAR (greater than 0.8) values.

MODIS↗
NASA NTRS2018

Development of the GEOS-MIT gcm Atmosphere-Ocean Model for Coupled Data Assimilation

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MIT-GCM (General Circulation Model)) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies. Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (approximately 1/8 degrees) configuration of the GEOS atmosphere to a latitude-longitude-cap-1080 (approximately 1/12 degrees) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

The Development of the New GEOS-MITgcm Atmosphere-Ocean Model for Coupled Data Assimilation System

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8 deg) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12 deg) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

A New Atmosphere-Ocean Model for Studying Air-Sea Interactions and Coupled Data Assimilation

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8°) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12°) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

The Development of the New GEOS-MITgcm Atmosphere-Ocean Model for Coupled Data Assimilation System

During the last two plus decades, The Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology (MIT) modeling groups have developed, respectively, atmosphere-only and ocean-only global general circulation models. These two models (GEOS and MITgcm) have demonstrated their data assimilation capabilities with the recent releases of the Modern Era Reanalysis for Research Applications, Version 2 (MERRA-2) atmospheric reanalysis and the Estimating the Circulation and Climate of the Ocean, Version 4 (ECCO-v4) ocean (and sea ice) state estimate. Independently, the two modeling groups have also produced global atmosphere-only and ocean-only simulations with km-scale grid spacing which proved invaluable for process studies and for the development of satellite and in-situ sampling strategies.Recently, a new effort has been made to couple these two models and to leverage their data-assimilation and high resolution capabilities (i.e., eddy-permitting ocean, cloud-permitting atmosphere). The focus in the model development is put on sub-seasonal to decadal time scales. In this talk, I discuss the new coupled model and present some first coupled simulation results. This will include a high-resolution coupled GEOS-MIT simulation, whereby we have coupled a cubed-sphere-720 (~ 1/8°) configuration of the GEOS atmosphere to a lat-lon-cap-1080 (~ 1/12°) configuration of the MIT ocean. We compare near-surface diagnostics of this fully coupled ocean-atmosphere set-up to equivalent atmosphere-only and ocean-only simulations. In the comparisons we focus in particular on the differences in air-sea interactions between sea surface temperature (SST) and wind for the coupled and uncoupled simulations.

Strobach, Ehud↗
NASA NTRS2018

Learning Model Structural Uncertainty with Gaussian Processes

The advent of commercially available quantum computers has marked the beginning of quantum computing as a reality. Both quantum gate and annealing computers have been released by major computer hardware companies. In this work, the D-Wave 2XTM quantum annealing computer housed at the NASA Advanced Systems computational facility is investigated to accelerate Machine Learning (ML) for image registration. NASA collects large amounts of images over the globe remotely using space-based monitoring. Images of a fixed areas of the land surface are taken over time. Due to the orbit of the sensors, the viewing angles deviate slightly, and it is necessary to align or register the images precisely to create image time series over the land surface. Unaligned images can lead to substantial analysis errors. These time-series are then used in modeling Earth Systems models such as hydrological, weather, and carbon monitoring models. In this work, we consider the Moderate Resolution Image Spectrometer (MODIS) data collected by the NASA's terra satellite. Artificial Neural Networks (ANNs) is a natural fit for ML modelling of images. Several successes have been reported using machine learning related to image processing. We investigate the use of ML to register MODIS images. ANNs are investigated in combination with a Restricted Boltzmann Machines (RBM) as an auto-encoder. We will present results showing the accuracy and efficiency of this approach.The D-Wave 2XTM quantum annealer samples the ground-state wave-function of a spin-Ising systems with quadratic interactions between qubits and a Chimera connectivity. The system sits in a ~15 mK thermal bath. One can think of the system as being placed in the ground state initially and subject to thermal excitations governed by Boltzmann statistics. If this is assumed true, one can use the statistics from the D-Wave 2XTM to train RBMs. Generating statistics for training Boltzmann machines is an NP-hard problem and constitutes the largest compute cost. We investigate the use of the D-Wave 2XTM to accelerate the training of the RBMs in our ANNs and report on the results.

Kouatchou, Jules↗
NASA NTRS2020

Interannual variations in meltwater input to the Southern Ocean from Antarctic ice shelves

Ocean-driven basal melting of Antarctica’s floating ice shelves accounts for about half of their mass loss in steady state, where gains in ice-shelf mass are balanced by losses. Ice-shelf thickness changes driven by varying basal melt rates modulate mass loss from the grounded ice sheet and its contribution to sea level, and the changing meltwater fluxes influence climate processes in the Southern Ocean. Existing continent-wide melt-rate datasets have no temporal variability, introducing uncertainties in sea level and climate projections. Here, we combine surface height data from satellite radar altimeters with satellite-derived ice velocities and a new model of firn-layer evolution to generate a high-resolution map of time-averaged (2010–2018) basal melt rates and time series (1994–2018) of meltwater fluxes for most ice shelves. Total basal meltwater flux in 1994 (1,090 ± 150 Gt/yr) was similar to the steady-state value (1,100 ± 60 Gt/yr), but increased to 1,570 ± 140 Gt/yr in 2009, followed by a decline to 1,160 ± 150 Gt/yr in 2018. For the four largest ‘cold-water’ ice shelves, we partition meltwater fluxes into deep and shallow sources to reveal distinct signatures of temporal variability, providing insights into climate forcing of basal melting and the impact of this melting on the Southern Ocean.

Susheel Adusumilli↗
NASA NTRSDate not supplied

Time Domain Boundary Element Method Prediction of Noise Shielding by a NACA 0012 Airfoil

As aircraft noise constraints become more stringent and the number/mixture of aircraft configurations grows, it becomes more important to understand the interaction of individual aircraft noise sources with nearby aircraft structures. Understanding these interactions and exploring possible approaches to mitigate or exploit their acoustic impact is essential for overcoming key noise barriers. This paper describes the further validation of a time domain boundary element approach for the prediction of the interactions between incident noise sources and nearby aircraft structures. Predictions were completed for multiple source locations and comparisons of these results with measured data are presented. Overall, very good agreement between the predicted and measured quantities was obtained in both the pressure time histories and pressure spectra. The effects of surface mesh resolution and source waveform are also presented. The very promising results demonstrate the capabilities of the time domain methodology employed in this study and provide further confidence in its continued development and application in future studies.

TD-FAST↗
NASA NTRSDate not supplied

GeoNEX-SUBSETS: Cutouts of geostationary satellite data over long-term monitoring sites

Satellite remote sensing data are important tool for extrapolating the knowledge obtained at the Fluxnet towers. We introduce our GEO-NEX geostationary satellite subset products, which make it easy to compare between ground observation and Geo-NEX products. The MODIS subset has been frequently used for validating ecosystem models developed at flux towers and upscaling the observed flux data to regional scales. However, MODIS on the polar orbiting satellites can observe target regions only once a day, while the Fluxnet eddy-covariance data are compiled as sub-hourly datasets. As a results, summarizing the sub-hourly flux data into daily statistics is necessary for the comparison between MODIS and Flux data. Here, the new generation geostationary satellite sensors (GOES-16/17 ABI and Himawari-8/9 AHI) has the high-frequent observation feature (10 minutes) in addition to similar spectral band and spatial resolution with MODIS. The high frequent observation allows us to compare the flux diurnal cycle with geostationary satellite sensor data. Some studies have already shown the effective utilization of time series of geostationary satellite data for ecosystem modeling. We are producing NEX Level-1G products, which is the gridded Top-of-Atmosphere reflectance and brightness temperature data of geostationary satellite sensors. We cut out the NEX Level-1G data using the same file format with the MODIS subset except for the projection. The other data products (e.g., surface reflectance, land surface temperature, vegetation indices and climate data) will be also added upon their availability. We selected the ground observation sites from Fluxnet, Phenocam, and AERONET networks. The NEX subset data will be provided through NASA NEX data portal.

GeoNEX↗
NASA NTRSDate not supplied

Ruggedizing a Commercial Depth Camera for Novel Lunar Exploration.

The novel contribution of this instrument is to take the first depth images on the moon. A commercial Kinect camera from Microsoft has been ruggedized for a CLPS mission to the Lunar South Pole. The 12-megapixel color camera is combined with a 1-megapixel color time-of-flight (ToF) depth sensor. The solid-state depth sensor/LIDAR provides greater resolution, a wider field of view, pixel binning, and reduced power consumption. The high-resolution data collected from the mission can be used to construct a near-field virtual environment of the lunar surface for scientific applications. The depth camera can provide a 360-degree view of the target area by combining ToF data with RGB imagery and rover turning. The unit was evaluated for space flight compliant materials and parts at NASA Ames. The microphone array, RF shield, front face, and outer body parts were removed to reduce mass. Plastic parts were replaced with vacuum-compatible materials; manufactured cables were added to properly interface with the host rover, built by Lunar Outpost. The environmental testing was performed on the Kinect with Random Vibration/Sine Testing (Fig 1a), per GEVS (NASA’s General Environmental Verification Standard for spaceflight launch survival). Thermal Vacuum testing cycled the instrument between expected hot (+ 85 C) and cold survival temperatures, as well as +50 C and -25 C operational temps (Fig 1b). The instrument functioned nominally at the conclusion of vibration and thermal cycling tests. The Azure Kinect is manifested on the Nova-C lander which flies on the Intuitive Machines mission IM-2, landing at the lunar south pole for a mission duration of 14 days (one lunar daylight cycle). The unit will be integrated to the MAPP (Mobile Autonomous Prospecting Platform) rover at Lunar Outpost. The Azure Kinect with ToF feature will improve the resolution of lunar geology data near the south pole and enable ground-based VR experience of details of the lunar surface (Fig 1c).

V. Jha↗
NASA NTRSDate not supplied

Two Decades of Atmospheric and Surface Temperature, Water Vapor, and Cloud Trends Derived from Satellite Remote Sensors

Satellite remote sensor such as Atmospheric Infrared Sounder (AIRS), Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI) provide high-quality atmospheric temperature, water, vapor, and greenhouse gas vertical profiles. Additionally, they provide atmospheric cloud properties, surface emissivity, and surface skin temperatures. We have developed two algorithms which can consistently derive these products from multiple IR sounders. The first one is a Single Field-of-view Sounder Atmospheric Product (SIFSAP) algorithm and the second one is a Climate Fingerprinting Sounder Product (ClimFiSP) algorithm. Compared to current operational AIRS and CrIS Level-2 (L2) algorithms, which perform one retrieval for each 3 by 3 field of views (FOVs) using a cloud-clearing approach, the SiFSAP algorithm, on the other hand, performs one retrieval for each FOV using an all-sky optimal estimation approach. The SiFSAP algorithm retrieves all the above-mentioned atmosphere and surface properties simultaneously including cloud properties with 3-time higher spatial resolution and 9-times more products. Climate products are typically derived by performing spatial and temporal averaging of L2 products. It is a time-consuming process to generate L2 data products since AIRS, CrIS, and IASI have millions of observations each day with thousands of spectral channels for each observation. Additionally, differences in L2 retrieval algorithms for different satellite sensors can lead to errors in the climate products. Our ClimFiSP algorithm, which performs retrievals from spatiotemporally averaged L1 hyperspectral radiances directly, will be orders of magnitude faster than traditional method. he ClimFiSP algorithm uses consistent radiative kernels and a robust spectral fingerprinting method. It provides accurate data climate data fusion products from multiple satellite sensors. We have applied this method to both AIRS and CrIS (on SNPP and on NOAA 20) data and generated two decades climate data records for atmospheric temperature, water vapor, cloud, trace gases, and surface skin temperature. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

remote sensing↗