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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

The Global Impact of Clouds on the Production of MODIS Bidirectional Reflectance Model-based Composites for Terrestrial Monitoring

A global data set of cloud occurrence probability derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua gridded daily data is analyzed to investigate the probability of obtaining at least a minimum number of cloud-free observations within various compositing periods. The probabilities derived from Terra and Aqua, with morning and afternoon overpass times, respectively, are similar and increase with compositing period. Compositing both Terra and Aqua observations results is considerably higher probabilities of obtaining a sufficient number of observations for bidirectional reflectance model-based compositing. Given that the only alternative is obtaining sufficient samples to extend the observation period, which can cause significant problems when the surface state changes, it is concluded that using data from the two MODIS sensors provides the most effective way of generating composited products. Findings with respect to the availability of cloud free composites when n-day composites are generated on a temporally overlapping daily rolling basis, i.e., every day, rather than every n-days, are also discussed for regional and global applications.

Roy, D. P.↗
NASA NTRS2023

Occurrence of Large Geomagnetically Induced Currents Within the EPRI SUNBURST Monitoring Network

Space weather, a natural hazard, can adversely impact human technological assets. High-voltage electric power transmission grids constitute one of the most critical technological systems vulnerable to space weather driven geomagnetically induced currents (GICs). One of the major challenges pertaining to the study of GICs over the continental United States has been the availability of GIC measurements, which are critical for validation of geoelectric field and power flow models, for example. In this study, we analyze GIC measurements collected at 17 Electrical Power Research Institute (EPRI) SUNBURST transformer locations across the United States for which a GIC value of 10 A or greater was recorded. This data set includes 52 individual geomagnetic storms with Kp index 6 and above during the period from 2010 to 2021. The analysis confirms that there is a good correlation between the number of geomagnetic storms per year and the number of recorded GIC events. Our results also show that about 76% of the top 17 GIC events are associated with the storm main phase, while only 24% are attributed to storm sudden commencements. In addition, it is shown, for the first time, that mid-latitude positive bays can cause large GICs over the continental United States. Finally, this study shows that the largest measured GIC event in the data set was associated with a localized intense dB/dt structure, which could be attributed to substorm activity.

Chigomezyo M. Ngwira↗
NASA NTRS2006

Distributed Computing Framework for Synthetic Radar Application

We are developing an extensible software framework, in response to Air Force and NASA needs for distributed computing facilities for a variety of radar applications. The objective of this work is to develop a Python based software framework, that is the framework elements of the middleware that allows developers to control processing flow on a grid in a distributed computing environment. Framework architectures to date allow developers to connect processing functions together as interchangeable objects, thereby allowing a data flow graph to be devised for a specific problem to be solved. The Pyre framework, developed at the California Institute of Technology (Caltech), and now being used as the basis for next-generation radar processing at JPL, is a Python-based software framework. We have extended the Pyre framework to include new facilities to deploy processing components as services, including components that monitor and assess the state of the distributed network for eventual real-time control of grid resources.

synthetic aperture radar (SAR)↗
NASA NTRS1995

Evaluation of numerical schemes for the analysis of sound generation by blade-gust interaction

In this investigation three different numerical algorithms have been utilized to compute the flow about a flat plate in the presence of a transverse gust described by a sinusoidal disturbance. The three schemes include the MacCormack explicit finite difference scheme which is second order accurate in both time and space, the Gottlieb and Turkel modification of MacCormack's scheme which is fourth order accurate in space and second order accurate in time, (referred to as the 2-4 scheme), and a two step scheme developed by Bayliss et al. which has second order temporal accuracy and sixth order spatial accuracy (a 2-6 scheme). The flow field results are obtained with these schemes by using the same code with the only difference being the implementation of the respective solution algorithms. The problem is set up so that the sinusoidal disturbance is imposed at the surface of the flat plate as a surface boundary condition. Thus the problem is treated as scattering problem. The computed results include the time average of the acoustic pressure squared along grid lines five points away from the boundaries; distribution throughout the computational domain is monitored at various times. The numerical results are compared with an exact solution obtained by Atassi, Dusey, and Davis.

Scott, J. N.↗
NASA NTRS2016

Harmonizing Landsat and Sentinel-2 Reflectances for Better Land Monitoring

When combined, Landsat and ESA Sentinel-2 observations can provide 2-4 day coverage for the global land area. A collaboration among NASA GSFC (Goddard Space Flight Center), University of Maryland, and NASA Ames has developed a processing chain to create seamless, "harmonized" reflectance products using standardized atmospheric correction, BRDF (Bidirectional Reflectance Distribution Function) adjustment, spectral bandpass adjustment, and gridding algorithms. These products point the way to a "30-m MODIS (Moderate Resolution Imaging Spectroradiometer)" capability for agricultural and ecosystem monitoring by leveraging international sensors.

GEO-GLAM↗
NASA NTRS2019

Four Years of Airborne Measurements of Wildfire Emissions in California, with a Focus on the Evolution of Emissions During the Soberanes Megafire

Biomass burning is an important source of trace gases and particles which can influence air quality on local, regional, and global scales. With wildfire events increasing due to changes in land use, increasing population, and climate change, characterizing wildfire emissions and their evolution is vital. In this work we report in situ airborne measurements of carbon dioxide (CO2), methane (CH4), water vapor (H2O), ozone (O3), and formaldehyde (HCHO) from nine wildfire events in California between 2013 and 2016, which were sampled as part of the Alpha Jet Atmospheric eXperiment (AJAX) based at NASA Ames Research Center. One of those fires, the Soberanes Megafire, began on 22 July 2016 and burned for three months. During that time, five flights were executed to sample emissions near and downwind of the Soberanes wildfire. In situ data are used to determine enhancement ratios (ERs), or excess mixing ratio relative to CO2, as well as assess O3 production from the fire. Changes in the emissions as a function of fire evolution are explored. Air quality impacts downwind of the fire are addressed using ground-based monitoring site data, satellite smoke products, and the Community Multiscale Air Quality (CMAQ) photochemical grid model.

Iraci, Laura T.↗
NASA NTRS2013

Methods and Results of a Search for Gravitational Waves Associated with Gamma-Ray Bursts Using the GEO 600, LIGO, and Virgo Detectors

In this paper we report on a search for short-duration gravitational wave bursts in the frequency range 64 Hz-1792 Hz associated with gamma-ray bursts (GRBs), using data from GEO600 and one of the LIGO or Virgo detectors. We introduce the method of a linear search grid to analyze GRB events with large sky localization uncertainties such as the localizations provided by the Fermi Gamma-ray Burst Monitor (GBM). Coherent searches for gravitational waves (GWs) can be computationally intensive when the GRB sky position is not well-localized, due to the corrections required for the difference in arrival time between detectors. Using a linear search grid we are able to reduce the computational cost of the analysis by a factor of O(10) for GBM events. Furthermore, we demonstrate that our analysis pipeline can improve upon the sky localization of GRBs detected by the GBM, if a high-frequency GW signal is observed in coincidence. We use the linear search grid method in a search for GWs associated with 129 GRBs observed satellite-based gamma-ray experiments between 2006 and 2011. The GRBs in our sample had not been previously analyzed for GW counterparts. A fraction of our GRB events are analyzed using data from GEO600 while the detector was using squeezed-light states to improve its sensitivity; this is the first search for GWs using data from a squeezed-light interferometric observatory. We find no evidence for GW signals, either with any individual GRB in this sample or with the population as a whole. For each GRB we place lower bounds on the distance to the progenitor, assuming a fixed GW emission energy of 10(exp -2)Stellar Mass sq c, with a median exclusion distance of 0.8 Mpc for emission at 500 Hz and 0.3 Mpc at 1 kHz. The reduced computational cost associated with a linear search grid will enable rapid searches for GWs associated with Fermi GBM events in the Advanced detector era.

methods↗
NASA NTRS2017

Monitoring Land Surface Albedo and Vegetation Dynamics Using High Spatial and Temporal Resolution Synthetic Time Series from Landsat and the MODIS BRDF/NBAR/Albedo Product

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warmingcooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500-meter Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF (Bidirectional Reflectance Distribution Function) / NBAR (Nadir BRDF-Adjusted Reflectance) / albedo products and 30-meter Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDFAlbedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30-meter Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30-meter albedos for the intervening daily time steps in this study. These enhanced daily 30-meter spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of 0.006. These synthetic time series provide much greater spatial detail than the 500 meter gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 kilometers by 14 kilometers) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF-Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30-meter resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.

Vegetation Index↗
NASA NTRS2021

Using Remote Sensing to Monitor the Spring Phenology of Acadia National Park Across Elevational Gradients

Greenup dates and their responses to elevation and temperature variations across the mountains of Acadia National Park are monitored using remote sensing data, including Landsat 8 surface reflectances (at a 30-m spatial resolution) and VIIRS reflectances adjusted to a nadir view (gridded at a 500-m spatial resolution), during the 2013–2016 growing seasons. The 30-m resolution provides a better scale for studying the phenology variation across elevational gradients than the 500-m resolution, as greenup dates monitored at 30-m scale have better agreement with leaf-out dates recorded in the field alongside the north–south-oriented hiking trails on three of the park’s tallest mountains (466 m, 418 m, and 380 m), and can provide landcover-specific analysis. The spring phenology responses to temperature and elevation vary among different spatial scales. Greenup dates of Acadia National Park monitored at 30-m scale show a weak advancing trend with higher spring temperature, while greenup dates monitored at 500 m show a weak delaying trend. The species mix within landcover at 30-m scale could weaken the advancing trend detected at field observation level. The landcover mix and elevation variation within 500-m scale could alter the spring phenology response to spring temperature variation. Greenup dates monitored at both 30-m and 500-m scales vary among different elevational zones, aspects, landcovers, and years. However, the relationship between greenup dates and elevation is rather weak.

Yan Liu↗
NASA NTRSDate not supplied

Improving Geolocation Accuracy of the Advanced Meteorological Imager on the GEO-KOMPSAT-2A

GeoNEX is a collaborative project led by scientists from NASA and many other international institutes to generate Earth monitoring products using data streams from the latest geostationary (GEO) sensors. Its consistent processing and common gridding systems can produce research-quality data products from GEO sensors and leverage GEO-GEO or GEO-LEO (low earth orbit) synergistic uses. Currently, GeoNEX has produced and disseminated L1G (geometrically corrected Level 1 products) from GOES 16/17 ABIs and Himawari-8 AHI, but a new Korean geostationary sensor (Advanced Meteorological Imager, AMI) onboard Geo-KOMPSAT-2A covering a large proportion of Asia and all of Oceania is in development. Our recent efforts on assessing geolocation accuracy in ABI and AHI suggest a nontrivial residual exists in both level 1B data with varying spatiotemporal patterns. The findings urge us to prioritize identifying and correcting geolocation residuals of AMI to generate accurate and consistent GeoNEX top-of-atmosphere (TOA) reflectance products and following processing chains. Here we implement a phase correlation correction approach to a visible band (0.64 μm, 500 m) using landmarks prepared from finer scale digital terrain models. We characterize spatiotemporal patterns (e.g., diurnal & daily) of geolocation residuals of AMI before and after correction. We then assess stability of datasets and quantify impact of unexpected geolocation errors on terrestrial monitoring. The geolocation corrected AMI data are further compared with GeoNEX AHI L1G products which are able to create unique stereo-type observations with AMI through leveraging the similarities of spectral bands and the sun-target-sensor geometry. Further, we discuss challenges in utilizing the GEO-GEO (e.g., AMI & AHI) satellite data for potential applications.

Geostationary Satellites↗
NASA NTRS1996

Precision and Radiosonde Validation of Satellite Gridpoint Temperature Anomalies. Part I; MSU Channel 2: MSU Channel 2 - Pt. 1

In Part 1 of this study, monthly 2.5 deg. gridpoint anomalies in the TIROS-N (Television and Infrared Operational Satellite-N) series Microwave Sounding Unit (MSU) channel 2 brightness temperatures during 1979-88 are evaluated with multiple satellites and radiosonde data for their climate temperature monitoring capability. The MSU anomalies we computed about a 10-year mean annual cycle at each grid point, with the MSUs intercalibrated to a common arbitrary level. The intercalibrations remove relative biases between instruments of up to several tenths of a degree celsius. The monthly gridpoint anomaly agreement between concurrently operating satellites reveals single-satellite precision on generally better than 0.07 C in the tropics and better than 0.15 C at higher latitudes. Monthly anomalies in radiosonde channel 2 brightness temperatures computed with the radiative transfer equation compare very closely to the MSU measured anomalies in all climate zones, with correlations generally from 0.94 to 0.98 and standard errors of 0.15 C in the tropics to 0.30 C at high latitudes. Simplification of these radiative transfer calculations to a static weighting profile applied to the radiosonde temperature profile leads to an average degradation of only 0.02 deg. in the monthly skill. In terms of a more traditionally measured quantity, the MSU channel 2 anomalies match best with either the radiosonde 100-20-kPa or 100-15-kPa layer anomalies. No significant spurious trends were found in the 10-yr satellite dataset compared to the radiosondes that would indicate a calibration drift in either system. Thus, sequentially launched, overlapping passive microwave radiometers provide a useful system for monitoring intraseasonal to interannual climate anomalies and offer hope for monitoring of interdecadal trends from space. The Appendix includes previously unpublished details of the MSU gridpoint anomaly dataset construction. Part II of this study addresses the removal from channel 2 of the temperature influence above the 30-kPa level, providing a sharper and thus potentially more useful weighting function for monitoring lower tropospheric temperatures.

Spencer, Roy W.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33601-DND

The Clouds and the Earth's Radiant Energy System (CERES) project now has over 18 years of an accurately observed top-of-the-atmosphere (TOA) flux record for climate monitoring and diagnostic studies. CERES provides the climate community the following parameters: coincident instantaneous 1? gridded CERES observed TOA fluxes, computed profile and surface fluxes, as well as MODIS cloud and aerosol retrievals. Clouds and radiation interaction one key factor that dominates climate feedbacks and is also the most difficult problem with large uncertainty. To further advance our understanding of the cloud-radiation interaction, the climate community need data with accurate fluxes and their associated cloud properties for both observational and modelling study. The new CERES FluxByCldTyp data product is produced for this purpose. The flux product combines for the first time CERES measured TOA fluxes along with the associated MODIS cloud properties, which links radiative flux directly to a specific cloud type. This was achieved by computing sub-footprint fluxes for the clear-sky and cloudy portions of the CERES footprint. The spatially distributed cloud properties within the CERES footprint were retrieved from 2-km MODIS pixels, that were stratified by cloud type. The sub-footprint fluxes were estimated from MODIS radiances based on empirically derived narrowband to broadband coefficients. The combined sub-footprint fluxes can then be validated with the observed footprint flux. This presentation will focus on the algorithm development of the flux-by-cloud-type product and its validation.

Moguo Sun↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33753-DND

The Clouds and the Earth's Radiant Energy System (CERES) project now has over 18 years of an accurately observed top-of-the-atmosphere (TOA) flux record for climate monitoring and diagnostic studies. CERES provides the climate community the following parameters: coincident instantaneous 1 gridded CERES observed TOA fluxes, computed profile and surface fluxes, as well as MODIS cloud and aerosol retrievals. Clouds and radiation interaction one key factor that dominates climate feedbacks and is also the most difficult problem with large uncertainty. To further advance our understanding of the cloud-radiation interaction, the climate community need data with accurate fluxes and their associated cloud properties for both observational and modelling study. The new CERES FluxByCldTyp data product is produced for this purpose. The flux product combines for the first time CERES measured TOA fluxes along with the associated MODIS cloud properties, which links radiative flux directly to a specific cloud type. This was achieved by computing sub-footprint fluxes for the clear-sky and cloudy portions of the CERES footprint. The spatially distributed cloud properties within the CERES footprint were retrieved from 2-km MODIS pixels, that were stratified by cloud type. The sub-footprint fluxes were estimated from MODIS radiances based on empirically derived narrowband to broadband coefficients. The combined sub-footprint fluxes can then be validated with the observed footprint flux. This presentation will focus on the algorithm development of the flux-by-cloud-type product and its validation. The paper will also discuss CERES FlxbyCldTyp simulator and its application.

Moguo Sun↗
NASA NTRS2018

Using Reanalysis in Crop Monitoring and Forecasting Systems

Weather observations are essential for crop monitoring and forecasting but they are not always available and in some cases they have limited spatial representativeness. Thus, reanalyses represent an alternative source of information to be explored. In this study, we assess the feasibility of reanalysis-based crop monitoring and forecasting by using the system developed and maintained by the European Commission- Joint Research Centre, its gridded daily meteorological observations, the biased-corrected reanalysis AgMERRA and the ERA-Interim reanalysis. We focus on Europe and on two crops, wheat and maize, in the period 1980-2010 under potential and water-imited conditions. In terms of inter-annual yield correlation at the country scale, the reanalysis-driven systems show a very good performance for both wheat and maize (with correlation values higher than 0.6 in almost all EU28 countries) when compared to the observations-driven system. However, significant yield biases affect both crops. All simulations show similar correlations with respect to the FAO reported yield time series. These findings support the integration of reanalyses in current crop monitoring and forecasting systems and point to the emerging opportunities linked to the coming availability of higher-resolution reanalysis updated at near real time.

Reanalysis↗
NASA NTRS1995

Runtime volume visualization for parallel CFD

This paper discusses some aspects of design of a data distributed, massively parallel volume rendering library for runtime visualization of parallel computational fluid dynamics simulations in a message-passing environment. Unlike the traditional scheme in which visualization is a postprocessing step, the rendering is done in place on each node processor. Computational scientists who run large-scale simulations on a massively parallel computer can thus perform interactive monitoring of their simulations. The current library provides an interface to handle volume data on rectilinear grids. The same design principles can be generalized to handle other types of grids. For demonstration, we run a parallel Navier-Stokes solver making use of this rendering library on the Intel Paragon XP/S. The interactive visual response achieved is found to be very useful. Performance studies show that the parallel rendering process is scalable with the size of the simulation as well as with the parallel computer.

Ma, Kwan-Liu↗
NASA NTRS1999

Asteroids and Comets Outreach Compilation

Contents include various different animations in the area of Asteroids and Comets. Titles of the short animated clips are: STARDUST Mission; Asteroid Castallia Impact Simulation; Castallia, Toutatis and the Earth; Simulation Asteroid Encounter with Earth; Nanorover Technology Task; Near Earth Asteroid Tracking; Champollian Anchor Tests; Early Views of Comets; Exploration of Small Bodies; Ulysses Resource Material from ESA; Ulysses Cometary Plasma Tail Animation; and various discussions on the Hale-Bopp Comet. Animation of the following are seen: the Stardust aerogel collector grid collecting cometary dust particles, comet and interstellar dust analyzer, Wiper-shield and dust flux monitor, a navigation camera, and the return of the sample to Earth; a comparison of the rotation of the Earth to the Castallia and Tautatis Asteroids; an animated land on Tautatis and the view of the motion of the sky from its surface; an Asteroid collision with the Earth; the USAF Station in Hawaii; close-up views of asteroids; automatic drilling of the Moon; exploding Cosmic Particles; and the dropping off of the plasma tail of a comet as it travels near the sun.

Source record↗
NASA NTRS2016

Consistency Between Sun-Induced Chlorophyll Fluorescence and Gross Primary Production of Vegetation in North America

Accurate estimation of the gross primary production (GPP) of terrestrial ecosystems is vital for a better understanding of the spatial-temporal patterns of the global carbon cycle. In this study,we estimate GPP in North America (NA) using the satellite-based Vegetation Photosynthesis Model (VPM), MODIS (Moderate Resolution Imaging Spectrometer) images at 8-day temporal and 500 meter spatial resolutions, and NCEP-NARR (National Center for Environmental Prediction-North America Regional Reanalysis) climate data. The simulated GPP (GPP (sub VPM)) agrees well with the flux tower derived GPP (GPPEC) at 39 AmeriFlux sites (155 site-years). The GPP (sub VPM) in 2010 is spatially aggregated to 0.5 by 0.5-degree grid cells and then compared with sun-induced chlorophyll fluorescence (SIF) data from Global Ozone Monitoring Instrument 2 (GOME-2), which is directly related to vegetation photosynthesis. Spatial distribution and seasonal dynamics of GPP (sub VPM) and GOME-2 SIF show good consistency. At the biome scale, GPP (sub VPM) and SIF shows strong linear relationships (R (sup 2) is greater than 0.95) and small variations in regression slopes ((4.60-5.55 grams Carbon per square meter per day) divided by (milliwatts per square meter per nanometer per square radian)). The total annual GPP (sub VPM) in NA in 2010 is approximately 13.53 petagrams Carbon per year, which accounts for approximately 11.0 percent of the global terrestrial GPP and is within the range of annual GPP estimates from six other process-based and data-driven models (11.35-22.23 petagrams Carbon per year). Among the seven models, some models did not capture the spatial pattern of GOME-2 SIF data at annual scale, especially in Midwest cropland region. The results from this study demonstrate the reliable performance of VPM at the continental scale, and the potential of SIF data being used as a benchmark to compare with GPP models.

photosynthesis model↗
NASA NTRS2018

The Use of Gridded Fossil Fuel CO2 Emissions (FFCO2) Inventory for Climate Mitigation Applications: Errors, Uncertainties, and Current and Future Challenges

Emission Inventory (EI) is a fundamental tool to monitor global compliance of greenhouse gases (GHGs) emissions reduction actions. Inventory guidelines provide a best practice to help EI compilers to make comparable national emission estimates, in spite of the differences in data availability across countries and regions. There are a variety of sources of errors and uncertainties, however, that originate beyond what the inventory guidelines define. For example, spatially-explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes, and there are no specific guidelines to disaggregate emission estimates from country scale. On top of that, EIs are fundamentally prone to systematic biases due to the simple calculation methodology and thus an objective evaluation (e.g. atmospheric top-down estimates) is needed to assure the accuracy of the estimates. ODIAC is a global high-resolution (1x1 km) fossil fuel carbon dioxide (CO2) gridded EI that is now often used in atmospheric CO2 modeling. ODIAC is based on disaggregation of national emission estimates made by CDIAC, which is the well accepted standard in the community. The ODIAC emission data product is updated on an annual basis using best available statistical data. Subnational spatial emission patterns are estimated using power plant profiles and satellite-observations of nighttime lights. In addition to the conventional CDIAC gridded data product, ODIAC carries international bunker emissions (shipping and aviation), which allows flux inversion modelers to accurately impose the global total fossil fuel emissions and their horizontal and vertical distribution. We have extensively evaluated ODIAC emissions using fine-grained EIs as well as a high-resolution atmospheric model simulation across different scales (national, subnational/regional, and urban policy relevant) with a focus on the uncertainties associated with the emission disaggregation. We have examined the use of NASA's Black Marble Suomi-NPP/VIIRS nightlight data.

Oda, Tomohiro↗