Search NASA⌕ Search

SEARCH · Search NASA

Results for “Climate Data”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Development of Carbon Flux Model Using ABI Data Over the Conterminous US

The satellite-driven carbon flux estimation has been playing important role to estimate continental-scale carbon budget. One of the biggest recent advances in the satellite-driven carbon flux modeling is utilization of high-frequent geostationary satellites to estimate diurnal cycle in carbon fluxes. The satellite based carbon flux estimation used the polar orbiting satellite sensors (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)), which allow us to observe target regions only once during the day. The new generation of geostationary satellite sensors provide frequent observations, often less than every 10 minutes. Here, we use GOES Advanced Baseline Imager (ABI) data to estimate hourly NEE over the conterminous US. We used the Terrestrial Observation Prediction System (TOPS) model for estimating hourly NEE. TOPS is a diagnostic ecosystem process model that simulates the fluxes of carbon and water through vegetation in response to climate variability. For the climate input, we developed hourly climate data using the same algorithm with NASA Earth Exchange Gridded Daily Meteorology (NEX-GDM) datasets based on machine learning techniques. The hourly climate data includes precipitation, maximum temperature, minimum temperature, dew point temperature, and solar radiation were derived from the Geostationary observations. The spatial patterns of ecosystem parameters used in TOPS are optimized using satellite Solar Induced Fluorescence (SIF) data. The high frequency GPP estimations from geostationary satellite sensors make it comparable to the instantaneous SIF data than daily GPP. We also used Ameriflux data for optimization of model parameters and the validation of the output. The derived data addresses the diurnal dynamics of carbon cycling at large scales and should help in reducing the uncertainties in carbon budget studies.

geostationary satellite↗
NASA NTRSDate not supplied

Evaluating Rock Pool Hydroperiod Fluctuation using Climate Variables to Inform Habitat Monitoring and Protection in the Western Sonoran Desert

As warming and drying trends continue to impact the greater American Southwest, effective ecosystem management increasingly relies upon understanding the relationships between climate and water resources. Ephemeral freshwater rock pools, known as tinajas, have great ecological and cultural importance as some of the only sources of surface water in the western Sonoran Desert (WSD). Tinaja flooding and drying cycles, known as hydroperiods, vary based on meteorologic and climatologic conditions; however, a lack of extensive research relating climatic impacts to tinajas puts these critical ecosystems further at risk. Tinajas throughout the WSD are monitored by the National Park Service (NPS) using resource intensive strategies including in situ trail camera observation and direct measurement. To aid NPS monitoring efforts, this research used remotely sensed climate data to analyze spatiotemporal climate trends and relationships between climate variables and tinaja hydroperiods in the WSD between 1979–2022. Using Aqua and Terra Moderate Resolution Imaging Spectroradiometers (MODIS), University of Idaho Gridded Surface Meteorological Dataset (gridMET), and OpenET data, the project analyzed land surface temperature, evapotranspiration, precipitation, wind velocity, and solar radiation. The team generated climate anomaly time series and climate normal maps for the WSD, identifying statistically significant spatial and temporal trends. These data were then compared to daily, qualitative in situ hydroperiod observations taken between 2019–2022. This work will be used by the NPS to inform the monitoring and protection of tinajas in the WSD. Findings contribute to a limited body of research concerning climate and tinajas, which are often overlooked despite their disproportionate ecological importance.

Annie Britton↗
NASA NTRS2004

Lagrangian Assimilation of Satellite Data for Climate Studies in the Arctic

Under this grant we have developed and tested a new Lagrangian model of sea ice. A Lagrangian model keeps track of material parcels as they drift in the model domain. Besides providing a natural framework for the assimilation of Lagrangian data, it has other advantages: 1) a model that follows material elements is well suited for a medium such as sea ice in which an element retains its identity for a long period of time; 2) model cells can be added or dropped as needed, allowing the spatial resolution to be increased in areas of high variability or dense observations; 3) ice from particular regions, such as the marginal seas, can be marked and traced for a long time; and 4) slip lines in the ice motion are accommodated more naturally because there is no internal grid. Our work makes use of these strengths of the Lagrangian formulation.

Lindsay, Ronald W.↗
NASA NTRSDate not supplied

A Convolutional Neural Network for Removing GOES-17 Image Anomalies to Improve CERES Broadband Flux Measurement

Background - CERES provides satellite-based global climate data record of Earth's radiation budget and clouds - CERES = Clouds and the Earth's Radiant Energy System - Measurement anomalies impact cloud retrieval - Incorrect Cloud Phase = Incorrect Flux - Unmitigated bad scanlines will impact climate data records - GOES-17 ABI cooling system anomaly = many bad scanlines at night (~10:30 - 16:30 UTC) - Cleaning imagery of bad scanlines is laborious but necessary - A convolution neural network (CNN) can identify and clean bad scanlines as effectively as a human

Benjamin Scarino↗
NASA NTRS2018

NASA Earth eXchange (NEX) App Store

NASA Earth Exchange (NEX), and her public cloud version OpenNEX, have become platforms supporting scientific collaboration, knowledge sharing and research for the entire Earth science community. To date, a number of custom tools and capabilities have been integrated into the platforms. However, such integration has to undergo a case-by-case manual process thus lacks scalability. This timely project builds an App Store onto OpenNEX as a building block. Climate data analytics tools/programs can be easily uploaded, shared, organized, searched, and recommended like photos and videos on the YouTube. The foundation of our App Store is a provenance server, which not only records metadata but also execution history of climate data analytics apps including the input data and parameters, output data and products, who runs the app for which purpose, and how apps may be chained into workflows. Researchers can thus understand, reproduce, and repurpose existing apps and workflows. Machine learning approaches are applied to mine provenance to provide recommend-as-you-go services for Earth scientists, such as to recommend suitable apps and workflow snippets. A browser-based workflow tool is also provided for researchers to explore the provenance server and design value-added workflows. Scalability, sustainability, extensibility, usability, adaptability, security and privacy are considered in the App Store.

eXchange↗
NASA NTRS2010

Shorebird Migration Patterns in Response to Climate Change: A Modeling Approach

The availability of satellite remote sensing observations at multiple spatial and temporal scales, coupled with advances in climate modeling and information technologies offer new opportunities for the application of mechanistic models to predict how continental scale bird migration patterns may change in response to environmental change. In earlier studies, we explored the phenotypic plasticity of a migratory population of Pectoral sandpipers by simulating the movement patterns of an ensemble of 10,000 individual birds in response to changes in stopover locations as an indicator of the impacts of wetland loss and inter-annual variability on the fitness of migratory shorebirds. We used an individual based, biophysical migration model, driven by remotely sensed land surface data, climate data, and biological field data. Mean stop-over durations and stop-over frequency with latitude predicted from our model for nominal cases were consistent with results reported in the literature and available field data. In this study, we take advantage of new computing capabilities enabled by recent GP-GPU computing paradigms and commodity hardware (general purchase computing on graphics processing units). Several aspects of our individual based (agent modeling) approach lend themselves well to GP-GPU computing. We have been able to allocate compute-intensive tasks to the graphics processing units, and now simulate ensembles of 400,000 birds at varying spatial resolutions along the central North American flyway. We are incorporating additional, species specific, mechanistic processes to better reflect the processes underlying bird phenotypic plasticity responses to different climate change scenarios in the central U.S.

Smith, James A.↗
NASA NTRSDate not supplied

Changes in Characteristics of Future Climate Across the U.S.: Time Series Analysis of Climate Model Data by NASA POWER

NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the energy, agricultural, and building heating/cooling design industries. POWER packages solar and meteorological data at various temporal levels from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). Data users can access these data either through an intuitive data viewer, image services fully integrable with GIS analysis, connections in the cloud through an Amazon Web Services S3 Bucket, or fully customizable access through an API. Data provided by POWER has been used to remotely monitor solar array fields and integrated in a sizing tool for off-grid solar and storage systems. POWER data has also been coupled with key building decision tools to support design and retrofitting of building energy systems for energy efficiency and reduction of greenhouse gases. POWER is now developing capabilities to provide time series of the projected future evolution of surface quantities important to future energy production and use, such as heating/cooling degree days, temperature, wind speed, and downwelling solar flux. We present here a range of possible future changes in these quantities at locations throughout the continental United States. We show how both average and extreme values of the quantities will evolve from present-day to future climate conditions. We plan to provide these projections for users in the energy and sustainable energy communities.

Bradley M. Hegyi↗
NASA NTRSDate not supplied

Remote Sensing of Atmospheric Temperature, Water Vapor, Trace Gases, Cloud, and Surface Properties on Daily and Decadal Time Scales

Hyperspectral IR sounders such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C 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. The SiFSAP algorithm performs one retrieval for each FOV using an all-sky optimal estimation approach. The core of the SiFSAP algorithm is an accurate and fast Principal Component-based Radiative Transfer Model (PCRTM), which can calculate hyperspectral radiance spectra under both clear and cloudy conditions. The PCRTM was developed in the past decade using consistent reference line-by-line radiative transfer model and spectroscopy for hyperspectral sounders such as AIRS, CrIS, IASI, NAST-I, and S-HIS. The 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. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

Xu Liu↗
NASA NTRSDate not supplied

Development of Radiative Transfer Models and Retrieval Algorithms for Satellite Remote Sensors

Hyperspectral IR sounders such as AIRS on Aqua, CrIS on S-NPP, NOAA20 and JPSS-2, IASI on Metop A, B, and C 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. The SiFSAP algorithm performs one retrieval for each FOV using an all-sky optimal estimation approach. The core of the SiFSAP algorithm is an accurate and fast Principal Component-based Radiative Transfer Model (PCRTM), which can calculate hyperspectral radiance spectra under both clear and cloudy conditions. The PCRTM was developed in the past decade using consistent reference line-by-line radiative transfer model and spectroscopy for hyperspectral sounders such as AIRS, CrIS, IASI, NAST-I, and S-HIS. The 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. Both SiFSAP and ClimFiSP will be available at NASA GES DISC data center for public access.

Xu Liu↗
NASA NTRS2006

An Overview of Inter-comparison Methodologies for Terra and Aqua MODIS Calibration

With increasing efforts on data fusion and long-term climate data records (CDR) using observations made by multiple sensors, on the same or different platforms, their cross-calibration and validation work has become more and more important. The uncertainties of the climate models and data records depend not only on the calibration quality of individual sensors but also on their calibration consistency. This paper provides an overview of methodologies used by the MODIS Characterization Support Team (MCST) at NASA GSFC for the inter-comparison studies of Terra and Aqua MODIS on-orbit calibration. Each MODIS was built with a set of onboard calibrators (OBC) that include a blackbody (BB), a solar diffuser (SD), and a solar diffuser stability monitor (SDSM). The BB is primarily used for the thermal emissive bands (TEB) calibration and the SD/SDSM system for the reflective solar bands (RSB) calibration. Although the-instrument design and calibration approach are nearly identical for both Terra and Aqua MODIS and they all went through an extensive and similar pre-launch calibration and characterization process, still their on-orbit calibration consistency needs to be carefully examined and validated as many science products have been generated from observations made by both instruments. Methodologies discussed in this paper include inter-comparison studies using the Moon, a third sensor, and ground targets. Our results show that Terra and Aqua reflective solar bands and thermal emissive bands have been calibrated consistently with excellent long-term stabilities. For the 11 and 12 micrometers sea surface temperature (SST) bands, the calibration difference of Terra and Aqua MODIS is less than 0.2K.

Xiong, X.↗
NASA NTRS2011

Uncertainty Assessment of the SeaWiFS On-Orbit Calibration

Ocean color climate data records require water-leaving radiances with 5% absolute and 1% relative accuracies as input. Because of the amplification of any sensor calibration errors by the atmospheric correction, the 1% relative accuracy requirement translates into a 0.1% long-term radiometric stability requirement for top-of-the atmosphere radiances. The rigorous on-orbit calibration program developed and implemented for SeaWiFS by the NASA Ocean Biology Processing Group (OBPG) Calibration and Validation Team (CVT) has allowed the CVT to maintain the stability of the radiometric calibration of SeaWiFS at 0.13% or better over the mission. The uncertainties in the resulting calibrated top-of-the-atmosphere (TOA) radiances can be addressed in terms of accuracy (biases in the measurements), precision (scatter in the measurements), and stability (repeatability of the measurements). The calibration biases of lunar observations relative to the USGS RObotic Lunar Observatory (ROLO) photometric model of the Moon are 2-3%. The biases from the vicarious calibration against the Marine Optical Buoy (MOBY) are 1-2%. The precision of the calibration derived from the solar calibration signal-tonoise ratios are 0.16%, from the lunar residuals are 0.13%, and from the vicarious gains are 0.10%. The long-term stability of the TOA radiances, derived from the lunar time series, is 0.13%. The stability of the vicariouslycalibrated TOA radiances, incorporating the uncertainties in the MOBY measurements and the atmospheric correction, is 0.30%. These results allow the OBPG to produce climate data records from the SeaWiFS ocean color data.

Eplee, Robert E., Jr.↗
NASA NTRS1989

SSM/I radiances correlated with 15 minute rain gauge data

The relationship between rain rate and the 85.5 and 37 GHz brightness temperature (BT) observations of the Special Sensor Microwave Imager (SSM/I) is examined to develop a BT-rain rate relationship for quantitative mapping of global precipitation using SSM/I data. SSM/I observations of a long-lived rain event over the south-central U.S. in 1987 are compared with digitized rain gage data from the National Climate Data Center. The results show that, for the squall line system studied, the rain gage amounts were well correlated with SSM/I polarization corrected 85.5 GHz BT.

Spencer, Roy W.↗
NASA NTRS1991

NSSDC data listing

The purpose here is to identify, in a highly summarized way, data available from the National Space Science Data Center (NSSDC). Most data are maintained as offline data sets gathered from individual instruments carried on spacecraft; these comprise the Satellite Data Listing. Descriptive names, time spans, data form, and quality of these data sets are identified in the listing, which is sorted alphabetically, first by spacecraft name and then by the principal investigator's or team leader's last name. Several data sets not associated with individual spaceflight instruments are identified in separate listings following the Satellite Data Listing. These include composite spacecraft data sets, ground based data, models, and computer routines. NSSDC also offers data via special services and systems in a number of areas, including the Astronomical Data Center, Coordinated Data Analysis Workshops, NASA Climate Data System, Pilot Land Data System, and Crustal Dynamics Data Information System.

Horowitz, Richard↗
NASA NTRS1999

Interpreting Aircraft-Derived Ice Sheet Elevation Change Using Climate Station Data

This year repeat elevation surveys in the southern half of Greenland were made using the Airborne Topographic Mapper (ATM). The intent of these surveys is to compare present elevations to those measured in 1993 and determine the magnitude and spatial distributions of thickening and thinning rates. In order to effectively interpret any observed changes, it is important to understand the processes that affect these changes. Moreover, because the surveys are made over a brief period (2-4 weeks) during the spring or summer, it is also important to understand the effects of seasonal and interannual elevation variability, in relation to the timing of these surveys. Toward that end we are examining data from weather stations along the coast of Greenland along with data from GC-Net automatic weather stations (AWS's) on the ice sheet. The objectives are to assess: a) the importance of the timing of the flights in relation to natural processes that affect surface heights, namely accumulation and melt, and b) the temperature characteristics of the region in the five years that separated the two sets of surveys (1993-1998), in relation to the past 19 years.

Abdalati, Waleed↗
NASA NTRS2014

Independent Verification of Mars-GRAM 2010 with Mars Climate Sounder Data

The Mars Global Reference Atmospheric Model (Mars-GRAM) is an engineering-level atmospheric model widely used for diverse mission and engineering applications. Applications of Mars-GRAM include systems design, performance analysis, and operations planning for aerobraking, entry, descent and landing, and aerocapture. Atmospheric influences on landing site selection and long-term mission conceptualization and development can also be addressed utilizing Mars-GRAM. Mars-GRAM's perturbation modeling capability is commonly used, in a Monte Carlo mode, to perform high-fidelity engineering end-to-end simulations for entry, descent, and landing. Mars-GRAM is an evolving software package resulting in improved accuracy and additional features. Mars-GRAM 2005 has been validated against Radio Science data, and both nadir and limb data from the Thermal Emission Spectrometer (TES). From the surface to 80 km altitude, Mars-GRAM is based on the NASA Ames Mars General Circulation Model (MGCM). Above 80 km, Mars-GRAM is based on the University of Michigan Mars Thermospheric General Circulation Model (MTGCM). The most recent release of Mars-GRAM 2010 includes an update to Fortran 90/95 and the addition of adjustment factors. These adjustment factors are applied to the input data from the MGCM and the MTGCM for the mapping year 0 user-controlled dust case. The adjustment factors are expressed as a function of height (z), latitude and areocentric solar longitude (Ls).

Justh, Hilary L.↗