+ **Data Latency:** N/A + The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded GHGI) includes spatially disaggregated (0.1 deg x 0.1 deg or approximately 10 x 10 km resolution) maps of annual anthropogenic methane emissions for the contiguous United States (CONUS), consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA [Inventory of U.S. Greenhouse Gas Emissions and Sinks](https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks) (U.S. GHGI). This dataset contains methane emissions provided as fluxes, in units of molecules of methane per square cm per second, for over 25 individual emission source categories, including those from agriculture, petroleum and natural gas systems, coal mining, and waste. The data have been converted from their original NetCDF format to Cloud-Optimized GeoTIFF (COG) and scaled to Megagrams of CH4 per km2 per year (Mg/km²/yr) for use in the US GHG Center, thereby enabling user exploration of spatial anthropogenic methane emissions and their trends. + + The gridded dataset, as included in the [U.S. GHG Center Exploration Environment](https://earth.gov/ghgcenter/exploration?datasets=%5B%5D&search=U.S.%20Gridded%20Anthropogenic%20Methane%20Emissions%20Inventory), currently includes 34 data layers. The first data layer includes annual 2012-2020 gridded methane emissions fluxes from all anthropogenic sources of methane in the U.S. GHGI (excluding Land Use, Land-Use Change and Forestry (LULUCF) sources, which are not included in the gridded GHGI). The next six data layers include annual 2012-2020 gridded methane fluxes from sources within the aggregate Agriculture, Natural Gas, Petroleum, Waste, Industry, and ‘Other’ source categories. The remaining 27 data layers include annual 2012-2020 gridded methane emissions fluxes from individual emission sectors within each of the aggregate categories. For more information, see the ‘information’ icon on each data layer or refer to the data interpretation notes available under “Learn More” below. - **Data Version Details:** + ## Source Data Product Citation + Gridded GHGI Version 2 & Express Extension **(this dataset in US GHG Center)**: + McDuffie, E. E., Maasakkers, J. D., Sulprizio, M. P., Chen, C., Schultz, M., Brunelle, L., Thrush, R., Steller, J., Sherry, C., Jacob, Daniel, J., Jeong, S., Irving, B., & Weitz, M. (2023). Gridded EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory (gridded GHGI) (v1.0) [Data set]. Zenodo. [https://doi.org/10.5281/zenodo.8367082](https://doi.org/10.5281/zenodo.8367082) + + Gridded GHGI Version 1: + Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Turner, A. J., Weitz, M., Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad, L., Bloom, A. A., Bowman, K. W., Jeong, S., Fischer, M. L. (2016) A Gridded National Inventory of U.S. Methane Emissions [Data set]. Available at: [https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#data](https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions#data) + + ## Version History The gridded methane GHGI is continually updated to capture ongoing improvements and updates to the U.S. GHG Inventory. The gridded methane GHGI currently includes 2 versions, which reflect sectoral methane emissions that are consistent with different versions of the U.S. GHGI. Versions include: Current Version(s) @@ -2573,43 +2590,28 @@ layers: Previous Versions - A. Gridded methane GHGI Version 1 (0.1°×0.1° annual emission maps for 2012, consistent with the 2016 U.S. GHGI) - Data available on the Data Explorer page correspond to the V2 Express Extension dataset. + **Data available on the Data Exploration page correspond to the V2 Express Extension dataset.** For more information on the current data set versions, see the associated publication: [Massakkers et al., 2023.](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138) or visit the [EPA gridded inventory webpage](https://www.epa.gov/ghgemissions/us-gridded-methane-emissions). For more information on the previous version, see the associated publication: [Massakkers et al., 2016.](https://pubs.acs.org/doi/10.1021/acs.est.6b02878) - - **Temporal Extent:** 2012 - 2020 - - **Temporal Resolution:** Annual - - **Spatial Extent:** Contiguous United States - - **Spatial Resolution:** 0.1° x 0.1° - - **Data Units:** Megagrams of methane per square kilometer per year (Mg CH₄/km²/yr) - - **Data type:** Research (v2 express extension) - - **Data Latency:** N/A - - **Scientific Details:** The gridded methane GHGI is developed by spatially allocating national annual methane emissions from individual source categories from the Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI) to a 0.1 deg x 0.1 deg (~10 x 10 km) grid using a series of spatial and temporal proxy datasets at the state, county, and grid levels. Where possible, the proxy data are the same as those used to develop the GHGI so that the gridded emissions can be, as closely as possible, a spatial and temporal representation of those in the national-level U.S. GHGI Report. - - The development of the gridded GHGI enables more direct comparisons between the methane emissions reported in the annual U.S. GHGI and those derived from atmospheric methane observations, such as through inverse analyses, with the aim of improving national inventory estimates and better understanding uncertain sources of methane emissions. - - Details of the methodological development of this dataset are described in the paper Maasakkers et al., 2023: [https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138](https://pubs.acs.org/doi/full/10.1021/acs.est.3c05138) -
+ **Data Type:** Research
+ **Data Latency:** Updated yearly - **Scientific Details:** A study was conducted to show how satellite methane observations could be used to evaluate reported methane emissions. In a bottom-up approach, methane emissions from activity reports were projected through the GEOS-Chem global chemistry transport model to obtain the expected atmospheric concentrations. In a top-down approach, observed atmospheric methane concentrations from the GOSAT satellite are compared with those modeled from reports. An analytic Bayesian inversion approach is used to determine the methane flux on a 2 x 2.5 degree grid based on the differences between the two concentrations (bottom-up vs top-down). Analytic Jacobian matrices relating emissions to concentrations are derived from the GEOS-Chem model and used to calculate the prior and posterior flux error covariance. Methane fluxes are then linearly projected to emissions by sector at 1 degree resolution. The derivation used to project top-down fluxes back to emissions by region is described in [Cusworth et al. (2021)](https://doi.org/10.1038/s43247-021-00312-6). This GOSAT-based Top-down CH₄ Emissions dataset includes the total prior and posterior CH₄ emissions, wetland prior and posterior CH₄ emissions and associated uncertainties. More information is located in the [source dataset document](https://zenodo.org/record/8306874). + As part of the global stock take (GST), countries are asked to provide a record of their greenhouse gas emissions to inform decisions on how to reduce GHG emissions. The NASA Carbon Monitoring System Flux (CMS-Flux) team has used remote sensing observations from Japan’s Greenhouse gases Observing SATellite (GOSAT) to produce modeled total methane (CH₄) emissions and uncertainties on a 1 degree by 1 degree resolution grid for the year 2019. The GOSAT data is used in the model to inform total emission estimates, as well as wetland (the primary natural source of methane), and various human-related sources such as fossil fuel extraction, transport, agriculture, waste, and fires. An advanced mathematical approach is used with a global chemistry transport model to quantify annual CH₄ emissions and uncertainties. These estimates are expressed in teragrams of CH₄ per year (Tg/yr). Only the total and wetlands model-derived emissions are included in the US GHG Center. The source dataset contains emissions data for all other anthropogenic sources.
+ **Temporal resolution:** Daily and Monthly + **Spatial extent:** Global + **Spatial resolution:** 0.5° x 0.5°
+ **Data units:** Grams of methane per meter squared per day(g CH₄/m²/day) and grams of methane per meter squared per month (g CH₄/m²/mon)
+ **Data type:** Research
+ **Data Latency:** Updated bimonthly with a ~6 week latency
+ + Methane (CH₄) emissions from vegetated wetlands are estimated to be the largest natural source of methane in the global CH₄ budget, contributing to roughly one third of the total of natural and anthropogenic emissions. Wetland CH₄ is produced by microbes breaking down organic matter in the oxygen deprived environment of inundated soils. Due to limited data availability, the details of the role of wetland CH₄ emissions have thus far been underrepresented. Using the Earth Observation SIMulator version (LPJ-EOSIM) of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) global CH₄ emissions from wetlands are estimated at 0.5 x 0.5 degree spatial resolution. By simulating wetland extent and using characteristics of inundated areas, such as wetland soil moisture, temperature, and carbon content, the model provides estimates of CH₄ quantities emitted into the atmosphere. This dataset shows concentrated methane sources from tropical and high latitude ecosystems. The LPJ-EOSIM Wetland Methane Emissions dataset consists of global daily and monthly model estimates of terrestrial wetland methane emissions from 1990 to the present, with data added bimonthly. The estimates are regularly used in conjunction with NASA’s Goddard Earth Observing System (GEOS) model to simulate the impact of wetlands and other methane sources on atmospheric methane concentrations, to compare against satellite and airborne data, and to improve understanding and prediction of wetland emissions. This is a new version and replaces the LPJ-wsl dataset previously available in the GHG Center. + +
+ **Temporal Resolution:** Daily and Monthly
+ **Spatial Extent:** Global
+ **Spatial Resolution:** 0.1° x 0.1°
+ **Data Units:** Grams of Carbon per square meter per day (g Carbon/m²/day) and Grams of Carbon per square meter per month (g Carbon/m²/month)
+ **Data Type:** Research
+ **Data Latency:** Less than a year, typically 6 months
+ + This dataset presents a variety of carbon flux parameters derived from the Más Informada Carnegie-Ames-Stanford-Approach (MiCASA) model. The model’s input data includes air temperature, precipitation, incident solar radiation, a soil classification map, and several satellite derived products. All model calculations are driven by analyzed meteorological data from NASA’s Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA-2). The resulting product provides global, daily and monthly data at 0.1 degree resolution from January 2001 through December 2023. It includes carbon flux variables expressed in units of grams of carbon per square meter per day (g Carbon/m²/day) and grams of carbon per square meter per month (g Carbon/m²/month) from net primary production (NPP), heterotrophic respiration (Rh), wildfire emissions (FIRE), fuel wood burning emissions (FUEL), net ecosystem exchange (NEE), and net biosphere exchange (NBE). The latter two are derived from the first four (see Scientific Details below). MiCASA is an extensive revision of the CASA – Global Fire Emissions Database, version 3 (CASA-GFED3) product. CASA-GFED3 and earlier versions of MERRA-driven CASA-GFED carbon fluxes have been used in several atmospheric carbon dioxide (CO₂) transport studies, serve as a community standard for priors of flux inversion systems, and through the support of NASA’s Carbon Monitoring System (CMS), help characterize, quantify, understand and predict the evolution of global carbon sources and sinks. + +
+ **Data Type:** Operational
+ **Data Latency:** Updated annually - - **Temporal Extent:** 1976 - present, varies by station - - **Temporal Resolution:** Irregular, varies by station - - **Spatial Extent:** United States - - **Spatial Resolution:** Point location samples - - **Data Units:** Parts CH₄ per billion (ppb) - - **Data Type:** Operational - - **Data Latency:** Updated annually - - **Scientific Details:** CH₄ measurements are formally reported in mole fraction units of nanomol (10-9 mole) of CH₄ per mol of dry air. However, for clarity here we refer to mole fraction as “concentration” in units of parts per billion (ppb). The CH₄ data set provides CH₄ dry air mole fractions from more than 80 NOAA GML GGGRN sites across the globe. Measurements have been made using a highly-calibrated gas chromatography with a flame ionization (GC/FID, before August 2019) system or a cavity ring-down spectrometer (CRDS, after August 2019). + The Global Greenhouse Gas Reference Network (GGGRN) for the Carbon Cycle and Greenhouse Gases (CCGG) Group is part of NOAA'S Global Monitoring Laboratory (GML) in Boulder, CO. The Reference Network measures the atmospheric distribution and trends of the three main long-term drivers of climate change, carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N2O), as well as carbon monoxide (CO) and many other trace gases which help interpretation of the main GHGs. The Reference Network measurement program includes continuous in-situ measurements at 4 baseline observatories (global background sites) and 8 tall towers, as well as flask-air samples collected by volunteers at over 50 additional regional background sites and from small aircraft conducting regular vertical profiles. The air samples are returned to GML for analysis where measurements of about 55 trace gases are completed. + This dataset contains CH4 concentration measurements in units of parts per billion (ppb) made from surface in-situ and tower sites and from surface flask air samples. The surface in-situ and tower instrumentation measures CH4 continuously (hourly) while the flask air samples are non-continuous measurements (frequency varies by station). Due to the high data volume of hourly tower measurements, daily and monthly averages are generated for display in the US GHG Center. + NOAA's GGGRN maintains the World Meteorological Organization international calibration scales for CO₂, CH₄, CO, N2O, and SF6 in air. The measurements from the GGGRN serve as a comparison with measurements made by many other international laboratories, and with regional studies. They are widely used in modeling studies that infer space-time patterns of emissions and removals of greenhouse gases that are optimally consistent with the atmospheric observations, given wind patterns. These data serve as an early warning for climate "surprises". The measurements are also helpful for the ongoing evaluation of remote sensing technologies. +
+ **Spatial Extent:** Global + **Spatial Resolution:** Point location samples + **Data Units:** Parts CO₂ per million (ppm)
+ **Data Type:** Operational
+ **Data Latency:** Updated annually + + The Global Greenhouse Gas Reference Network (GGGRN) for the Carbon Cycle and Greenhouse Gases (CCGG) Group is part of NOAA'S Global Monitoring Laboratory (GML) in Boulder, CO. The Reference Network measures the atmospheric distribution and trends of the three main long-term drivers of climate change, carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N2O), as well as carbon monoxide (CO) and many other trace gases which help interpretation of the main GHGs. The Reference Network measurement program includes continuous in-situ measurements at 4 baseline observatories (global background sites) and 8 tall towers, as well as flask-air samples collected by volunteers at over 50 additional regional background sites and from small aircraft conducting regular vertical profiles. The flask air samples are returned to GML for analysis where measurements of about 55 trace gases are completed. - - **Temporal Extent:** 1968 - 2022, varies by station - - **Temporal Resolution:** Varies by station, can be daily up to weekly - - **Spatial Extent:** United States - - **Spatial Resolution:** Point location samples - - **Data Units:** parts CO₂ per million (ppm) - - **Data Type:** Operational - - **Data Latency:** Updated annually + This dataset contains CO₂ concentration measurements in units of parts per million (ppm) made from surface in-situ and tower sites and from surface flask air samples. The surface in-situ and tower instrumentation measures CO2 continuously (hourly) while the flask air samples are non-continuous measurements (frequency varies by station). Due to the high data volume of hourly tower measurements, daily and monthly averages are generated for display in the US GHG Center. - **Scientific Details:** CO₂ measurements are formally reported in mole fraction units of micromol (10-6 mole) of CO₂ per mol of dry air. However, for communicating with broader audiences we refer to mole fraction as “concentration” in units of parts per million (ppm). This CO₂ data set provides CO₂ dry air mole fractions from more than 80 NOAA GML GGGRN sites across the globe. Measurements have been made using a highly-calibrated nondispersive infrared absorption analyzer (NDIR, before August 2019) or a cavity ring-down spectrometer (CRDS, since August 2019). + NOAA's GGGRN maintains the World Meteorological Organization international calibration scales for CO₂, CH₄, CO, N2O, and SF6 in air. The measurements from the GGGRN serve as a comparison with measurements made by many other international laboratories, and with regional studies. They are widely used in modeling studies that infer space-time patterns of emissions and removals of greenhouse gases that are optimally consistent with the atmospheric observations, given wind patterns. These data serve as an early warning for climate "surprises". The measurements are also helpful for the ongoing evaluation of remote sensing technologies.
+ **Temporal Resolution:** Annual
+ **Spatial Extent:** Global
+ **Spatial Resolution:** 1° x 1°
+ **Data Units:** Grams of carbon dioxide per square meter per year (g CO₂/m²/yr)
+ **Data Type:** Research
+ **Data Latency:** N/A + Inverse modeling (“top-down”) is used to enable estimation of annual net carbon dioxide (CO₂) emissions and removals (uptake) in units of grams of CO₂ per square meter per year (g CO₂/m²/yr). This Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP) Top-down carbon dioxide (CO₂) Budget dataset is the result of a collaboration between an international group of over 60 researchers and atmospheric modelers to study the impact of assimilating OCO-2 retrieval data into atmospheric inverse models. While the dataset is attributed to NASA, it was only made possible with significant global community effort. This dataset contains annual net fluxes of CO₂ on a 1-degree grid for a six-year period (2015-2020), including: net carbon exchange (NCE), fossil fuel emissions (FF), and lateral fluxes due to crop trade, wood trade, and river export. Data is provided in units of grams of carbon dioxide per square meter per year (g CO₂/m2/yr). The following CO₂ emission variables and their uncertainties are displayed: net biosphere exchange (NBE), net carbon exchange (NCE) and net land carbon stock loss estimated by the LNLGIS ensemble (estimated using a combination of land OCO-2 dry-air mole fraction retrievals (XCO₂) and in situ CO₂ measurements); lateral crop flux, fossil fuel and cement emissions, lateral river flux, and lateral wood flux. Accurate accounting of net carbon exchange (NCE) is critical for supporting the Paris agreement, and this data aims to inform countries’ carbon budgets while supporting the [Global Stocktake](https://unfccc.int/topics/global-stocktake). - - **Temporal Extent:** 2015 – 2020 - - **Temporal Resolution:** Annual - - **Spatial Extent:** Global - - **Spatial Resolution:** 1° x 1° - - **Data Units:** Grams of carbon dioxide per square meter per year (g CO₂/m²/yr) - - **Data Type:** Research - - **Data Latency:** N/A - - **Scientific Details:** Leveraging the OCO-2 MIP, the surface-atmosphere CO₂ fluxes are estimated from four standardized experiments using: - - - IS: in situ CO₂ measurements in the National Oceanic and Atmospheric Administration (NOAA) Observation Package ([ObsPack](https://gml.noaa.gov/ccgg/obspack/)) - - LNLG: column-averaged CO₂ dry air mole fraction (XCO₂) retrievals over land from NASA’s Orbiting Carbon Observatory-2 (OCO-2) Land Nadir and Land Glint data - - LNLGIS: both in situ CO₂ and OCO-2 land XCO₂ data - - LNLGOGIS: in situ CO₂, OCO-2 land XCO₂, and OCO-2 ocean XCO₂ data combined - - The full dataset with all layers and derived national totals can be accessed on the [CEOS website](https://doi.org/10.48588/npf6-sw92). -
+ **Spatial extent:** Global + **Spatial resolution:** 0.5° x 0.625° + **Data units:** Parts per million (ppm) + **Data type:** Research
+ **Data Latency:** 2-3 months + In July 2014, NASA successfully launched the first dedicated Earth remote sensing satellite to study atmospheric carbon dioxide (CO₂) from space. The Orbiting Carbon Observatory-2 (OCO-2) is an exploratory science mission designed to collect space-based global measurements of atmospheric CO₂ with the precision, resolution, and coverage needed to characterize sources and sinks (fluxes) on regional scales (≥1000 km). This dataset provides global gridded, daily column-averaged carbon dioxide (XCO₂) concentrations from January 1, 2015 - February 28, 2022. The data are derived from OCO-2 observations that were input to the Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS), a modeling and data assimilation system maintained by NASA’s Global Modeling and Assimilation Office (GMAO). Concentrations are measured in moles of carbon dioxide per mole of dry air (mol CO₂/mol dry) at a spatial resolution of 0.5° x 0.625°. Data assimilation synthesizes simulations and observations, adjusting modeled atmospheric constituents like CO₂ to reflect observed values. With the support of NASA’s Carbon Monitoring System (CMS) Program and the OCO Science Team, this dataset was produced as part of the OCO-2 mission which provides the highest quality space-based XCO₂ retrievals to date. - **Scientific Details:** Though the OCO-2 mission provides high quality space-based XCO₂ retrievals, the data are characterized by large gaps in coverage due to the narrow 10 km OCO-2 ground track and an inability to measure through clouds and thick aerosols. Several different methods have been explored to produce spatially complete L3 (gridded) XCO₂ fields from the satellite data, including averaging, kriging, and data assimilation. Data assimilation synthesizes simulations and observations, adjusting the state of atmospheric constituents like CO₂ to reflect observed values, thus gap-filling the observations when and where none are unavailable based on previous observations. Compared to other methods, data assimilation has the advantage that it results in estimates based on the collective scientific understanding, notably of the Earth’s carbon cycle and atmospheric transport. For additional details on how this data was derived, please refer to Section 2 of the [OCO-2 GEOS L3 XCO₂ Product User’s Guide](https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/OCO2_GEOS_L3_User_Guide.pdf).
+ **Temporal Resolution:** Monthly
+ **Spatial Extent:** Global
+ **Spatial Resolution:** 1 km x 1 km
+ **Data Units:** Tons of carbon per 1 km x 1 km cell (monthly total)
+ **Data Type:** Research
+ **Data Latency:** Updated annually, following the release of an updated [BP Statistical Review of World Energy report](https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html) - **Scientific Details:** The emission spatial disaggregation uses multiple spatial proxy data, such as geographical location of point sources, satellite observations of nightlights, and aircraft and ship fleet tracks. The emissions seasonality was taken from the CDIAC monthly gridded data product (Andres et al. 2011) and the Carbon Monitor product (2020-2021, https://carbonmonitor.org/). The year 2019 CDIAC country-level estimates were projected for the recent years (2020 and 2021) using fuel consumption data reported by the latest BP statistical review of the world (BP, 2022). More details about the ODIAC approach, methodology, etc. are described in the Oda et al. (2018) publication. + The Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) data product is a monthly high-resolution global data product of modeled fossil fuel carbon dioxide (CO₂) emissions. A complex model incorporates and combines space-based nighttime light data and individual power plant emission/location profiles from the latest country fossil fuel CO₂ estimates (2000-2019) made by the Carbon Dioxide Information Analysis Center (CDIAC) team at the Appalachian State University (CDIAC at AppState, Gilfillan et al. 2021, Hefner et al. 2022). The ODIAC estimated global spatial extent of fossil fuel CO₂ emissions is produced on a 1 km by 1 km grid that details variations in urban regions where emissions are most intense. The ODIAC CO₂ emission data is widely used by the international research community for applications such as CO₂ flux inversion, urban emission estimation, and observing system design experiments. The ODIAC product was first created in 2009 by Dr. Tomohiro Oda with support from the National Institute for Environmental Studies (NIES) GOSAT project. The ODIAC team is now supported by NASA Goddard Space Flight Center, NASA Carbon Monitoring System program, the NASA Orbiting Carbon Observatory mission and NIES. The US GHG Center displays the ODIAC 2022 version containing monthly data from January 2000 to December 2021 that replaces all previous versions.
+ **Temporal Resolution:** Annual, every 5 years + **Spatial Extent:** Global + **Spatial Resolution:** 30 arc-seconds (~1 km at equator) + **Data Units:** Number of persons per square kilometer (persons/km²) + **Data Type:** Research
+ **Data Latency:** 5 years - **Scientific Details:** - Population estimates are distributed to a 30 arc-second (~1km) grid using an areal-weighting method. The input geographic boundaries were obtained from multiple sources including national statistical offices, national mapping agencies, and the Global Administrative Areas, version 2.0 (GADMv2) dataset. A water mask was also applied to ensure that areas of water and permanent ice were not included in distributed population counts. The annual exponential growth rates applied to determine population estimates in the target years (2000, 2005, 2010, 2015, and 2020) were defined using the population count from the previous census, the population count from the current census, and the number of years between the two records. Methodology details are available in the [GPWv4 Revision 11 documentation](https://sedac.ciesin.columbia.edu/binaries/web/sedac/collections/gpw-v4/gpw-v4-documentation-rev11.pdf). Additional FAQs about the data can be found on the [SEDAC User Feedback site](https://sedac.uservoice.com/knowledgebase/topics/110829-gpwv4). + The Socioeconomic Data and Applications Center (SEDAC) Gridded Population of the World (GPW), version 4, revision 11 source dataset contains a Population Density product that provides estimates of population density at five year intervals for the years 2000, 2005, 2010, 2015, and 2020 on a 30 arc-second (~1 km at the equator) grid. The dataset can be used for assessing disaster impacts, risk mapping, and any other applications that include a human dimension. Population density is obtained by dividing the population count grid for a given target year by the gridded land area. The GPW population count is generated using input from tabular census data that identifies the number of persons by administrative area and digital census administrative boundary data. The density values are based on the 2010 census results extrapolated to other years based on annual population growth rates. This dataset (v4.11) was built upon previous versions of the SEDAC global population dataset.
+ **Temporal Resolution:** Monthly
+ **Spatial Extent:** Global + **Spatial Resolution:** 1° x 1° + **Data Units:** Grams of methane per square meter per year (g CH₄/m²/year) + **Data Type:** Research
+ **Data Latency:** Approximately 2 years - The atmospheric inverse model was run at 3° (longitude) x 2° (latitude) globally to estimate monthly surface emissions. The atmospheric inversion problem is under-constrained, and therefore inversely estimated emissions cannot be interpreted at the grid scale. Therefore, to present 1°x1° emission maps for the U.S. GHG Center, prior 1°x1° emissions were scaled to match the posterior continental and global totals for each source type and year. + Surface methane (CH₄) emissions are derived from atmospheric measurements of methane and its ¹³C carbon isotope content. Different sources of methane contain different ratios of the two stable isotopologues, ¹²CH₄ and ¹³CH₄. This makes normally indistinguishable collocated sources of methane, say from agriculture and oil and gas exploration, distinguishable. The National Oceanic and Atmospheric Administration (NOAA) collects whole air samples from its global cooperative network of flasks (https://gml.noaa.gov/ccgg/about.html), which are then analyzed for methane and other trace gases. A subset of those flasks are also analyzed for ¹³C of methane in collaboration with the Institute of Arctic and Alpine Research at the University of Colorado Boulder. Scientists at the National Aeronautics and Space Administration (NASA) and NOAA used those measurements of methane and ¹³C of methane in conjunction with a model of atmospheric circulation to estimate emissions of methane separated by three source types, microbial, fossil and pyrogenic. Microbial emissions are produced by microbial decomposition of present-day organic matter, such as in wetlands, agricultural fields, livestock and landfills. Fossil emissions come from the breakdown of ancient carbonaceous matter deep underground, such as natural gas, coal bed methane, and small amounts of naturally occurring geologic seeps. Finally, pyrogenic emissions come from the burning of present-day organic matter, such as from wildfires and biofuels. These three sources of methane come with slightly different ¹³C content, and therefore can be separated given enough atmospheric measurements. This dataset presents monthly methane emissions from microbial, fossil and pyrogenic sources, along with a layer of total methane emissions from all three sources combined, at 1° resolution from 1999 to 2016.
@@ -273,11 +248,11 @@ featured: true
- ## What’s Coming Next?
+ ## **What’s Coming Next?**
- Additions to the US GHG Center will occur in the coming months, including some new features and capabilities that will improve data discovery, exploration and analysis.
+ Additions to the US GHG Center will occur in the coming year, including some new features and capabilities that will improve data discovery, exploration and analysis.
- - Improvements to Center design and function of the interface and tools
+ - Improvements to the Center design and function of the interface and tools
- More and different kinds of data, such as data from airborne campaigns, new instruments, and other selected data products
- More Jupyter notebooks to help you use the Center data
- GIS or STAC data download for when you want to take the data into another application
@@ -290,8 +265,8 @@ featured: true
- ## We Want Your Help! Please Share Your Ideas and Feedback!
-
+ ## We Want Your Help!
+ Please Share Your Ideas and Feedback!
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