The Global Precipitation Climatology Project (GPCP) consists of monthly satellite-gauge and associated precipitation error estimates and covers the period January 1979 to the present. The general approach is to combine the precipitation information available from each of several satellite and in situ sources into a final merged product, taking advantage of the strengths of each data type: passive Microwave estimates are based on SSMI/SSMIS data; infrared precipitation estimates are included, using GOES data and POES data; as well as other low earth orbit data and insitu observations. Data are provided on a 2.5 degree grid.

The Global Precipitation Climatology Project (GPCP) is an international project designed to provide improved long-record estimates of precipitation over the globe. The general approach is to combine the precipitation information available from several sources into a final merged product that takes advantage of the strengths of each data type. The GPCP has promoted the development of an analysis procedure for blending the various estimates together to produce the necessary global gridded precipitation fields. The currently operational procedure is based on Huffman et al. (1995) and has been used to produce the GPCP Version 2 Combined Precipitation Data Set, covering the period January 1979 through the present. The primary product in the Version 2 data set is a combined observation-only data set, that is, a gridded analysis based on gauge measurements and satellite estimates of rainfall. Beginning in October of 1996, the GPCP began producing 3-hourly merged global infrared (IR) brightness temperature (Tb) histograms on a 1 degree by 1 degree grid, which became the impetus for this product, also known as the 1 degree daily (1DD) product. The data set prepared for SAFARI 2000 has been extracted from the 1DD data set for the years 1999, 2000, and 2001.The Global Precipitation Climatology Project (GPCP) is an element of the Global Energy and Water Cycle Experiment (GEWEX) of the World Climate Research program (WCRP). The 1DD is produced by the GPCP Merge Development Centre (GMDC), located at NASA's Goddard Space Flight Center in the Laboratory for Atmospheres.

The Global Precipitation Climatology Project (GPCP) is an international project designed to provide improved long-record estimates of precipitation over the globe. The general approach is to combine the precipitation information available from several sources into a final merged product that takes advantage of the strengths of each data type. The GPCP has promoted the development of an analysis procedure for blending the various estimates together to produce the necessary global gridded precipitation fields. The currently operational procedure is based on Huffman et al. (1995) and has been used to produce the GPCP Version 2 Combined Precipitation Data Set, covering the period January 1979 through the present. The primary product in the Version 2 data set is a combined observation-only data set, that is, a gridded analysis based on gauge measurements and satellite estimates of rainfall. Beginning in October of 1996, the GPCP began producing 3-hourly merged global infrared (IR) brightness temperature (Tb) histograms on a 1 degree by 1 degree grid, which became the impetus for this product, also known as the 1 degree daily (1DD) product. The data set prepared for SAFARI 2000 has been extracted from the 1DD data set for the years 1999, 2000, and 2001.The Global Precipitation Climatology Project (GPCP) is an element of the Global Energy and Water Cycle Experiment (GEWEX) of the World Climate Research program (WCRP). The 1DD is produced by the GPCP Merge Development Centre (GMDC), located at NASA's Goddard Space Flight Center in the Laboratory for Atmospheres.

This dataset contains Version 3.2 of The Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) Combined Precipitation Data Set at monthly resolution (GPCPMON). The variables included are combined SG precipitation, combined SG precipitation random error, multi-satellite precipitation, probability of liquid phase, wind-loss adjusted gauge precipitation, satellite source index, gauge relative weighing, and quality index.

As the follow-on to the GPCP Version 2.X products, GPCP Version 3 (GPCP V3.2) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets. The GPCP V3 suite currently consists of the 0.5-degree Monthly and Daily products. A follow-on 0.1-degree 3-hourly product is expected. All GPCP V3 products are constructed to be internally consistent. The Monthly product spans 1983 to the present, with roughly quarterly updates.

Inputs consist of the Goddard Profiling Algorithm (GPROF) applied to the 6 AM/PM (local solar time) Special Sensor Microwave Imager / Special Sensor Microwave Imager-Sounder (SSMI/SSMIS) passive microwave (PMW) orbit files that are used to calibrate the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR) infrared (IR) based precipitation in the span 60 degrees N-S, which are in turn calibrated to the monthly 2.5-degree Microwave Emission brightness Temperature Histograms (METH) product computed from the same SSMI/SSMIS data. The METH-GPROF-adjusted PERSIANN-CDR IR estimates are then climatologically adjusted to the blended Tropical Combined Climatology (TCC) at low latitudes and Merged CloudSat, Tropical Rainfall Measuring Mission (TRMM), and GPM (MCTG) climatology at higher latitudes. These climatologies are computed from shorter-term, but higher-quality precipitation datasets that are considered to provide the best global climatology of precipitation. Outside of 58 degrees N-S, estimates computed from Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder / Advanced Infrared Sounder (TOVS/AIRS) data, adjusted climatologically to the MCTG, are used. The PERSIANN-CDR / TOVS/AIRS estimates are then merged in the region between 35 degrees N-S and 58 degrees N-S, and this multi-satellite product is then merged with Global Precipitation Climatology Centre (GPCC) gauge analyses over land to obtain the complete Monthly product. In addition to the merged precipitation field, random error, Quality Index, and probability of liquid phase estimates are provided.

GPCP Version 3 was initiated under a NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) program (PI: George Huffman, NASA GSFC) and is now being upgraded under a current NASA MEaSUREs project (PI: Ali Behrangi, University of Arizona). The GPCP is computed as a contribution to the World Climate Research Program (WCRP) and Global Water and Energy Exchange (GEWEX) activity and is part of the array of data sets describing the water and energy cycles of the planet. Prior versions of the GPCP analysis have been produced by a consortium of individual scientists at various government and university institutions and most recently as part of the NOAA Climate Data Record (CDR) Program. The current GPCP Monthly SG product described here blends precipitation estimates from polar-orbit PMW imagers (SSMI, SSMIS), polar orbit IR sounders (TOVS, AIRS), and geostationary IR imagers (GOES, MeteoSat, GMS, MTSat, and Himawari); and then combines in Global Precipitation Climatology Centre (GPCC) precipitation gauge analyses, the Tropical Combined Climatology (TCC); and the Merged CloudSat, TRMM, and GPM (MCTG) climatology products.

For more information, see the GPCPMON official site at NASA GES DISC [https://disc.gsfc.nasa.gov/datasets/GPCPMON_3.2/summary].

Version 3.3 is the current version. Older versions have been superseded by Version 3.3.

Product latency/update: The products are currently paused at September 2024 because the IR input dataset from NCEI requires a new calibration scheme to extend past that point. Once NCEI irons out the calibration, we expect to return to quarterly updates.

The Global Precipitation Climatology Project (GPCP) is the precipitation component of an internationally coordinated set of (mainly) satellite-based global products dealing with the Earth's water and energy cycles, under the auspices of the Global Water and Energy Exchange (GEWEX) Data and Assessment Panel (GDAP) of the World Climate Research Program. As the follow on to the GPCP Version 1.3 One Degree Daily product, GPCP Version 3 (GPCP V3.3) seeks to continue the long, homogeneous precipitation record using modern merging techniques and input data sets. The GPCPV3 suite currently consists of the 0.5-degree Monthly and 0.5-degree Daily. Additional products may be added, which consist of (1) 0.5-degree pentad and (2) 0.1-degree 3-hourly. All GPCPV3 products will be internally consistent. Inputs consist of GPM IMERG in the span 55°N-S, and TOVS/AIRS estimates, adjusted climatologically to IMERG, outside 55°N-S. The Daily estimates are scaled to approximately sum to the Monthly value at each 0.5° grid box. In addition to the final precipitation field, probability of liquid phase estimates are provided globally.

The W5E5 dataset was compiled to support the bias adjustment of climate input data for the impact assessments carried out in phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b).
Version 2.0 of the W5E5 dataset covers the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2019. Data sources of W5E5 are version 2.0 of WATCH Forcing Data methodology applied to ERA5 data (WFDE5; Weedon et al., 2014; Cucchi et al., 2020), ERA5 reanalysis data (Hersbach et al., 2020), and precipitation data from version 2.3 of the Global Precipitation Climatology Project (GPCP; Adler et al., 2003).
Variables (with short names and units in brackets) included in the W5E5 dataset are Near Surface Relative Humidity (hurs, %), Near Surface Specific Humidity (huss, kg kg-1), Precipitation (pr, kg m-2 s-1), Snowfall Flux (prsn, kg m-2 s-1), Surface Air Pressure (ps, Pa), Sea Level Pressure (psl, Pa), Surface Downwelling Longwave Radiation (rlds, W m-2), Surface Downwelling Shortwave Radiation (rsds, W m-2), Near Surface Wind Speed (sfcWind, m s-1), Near-Surface Air Temperature (tas, K), Daily Maximum Near Surface Air Temperature (tasmax, K), Daily Minimum Near Surface Air Temperature (tasmin, K), Surface Altitude (orog, m), and WFDE5-ERA5 Mask (mask, 1).

The Global Precipitation Climatology Project (GPCP) Version 2 data set includes global, monthly precipitation rates and associated random errors (RMSE), and a monthly precipitation climatology derived as an average from all GPCP data sets from January 1979 to December 1999. The data are derived from measured gauge data and merged with satellite estimates of rainfall. This is a portion of the version 2 GPCP data and covers the ISLSCP II period from 1986 to 1995. There are six data files included with this data set: the original precipitation rates, errors and climatology at 2.5 degrees spatial resolution, and the same data re-gridded to a 1 degree spatial resolution by the ISLSCP II staff.

NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model and observation data from 2000 to present. GLDAS-2.2 product suites use data assimilation (DA), whereas the GLDAS-2.0 and GLDAS-2.1 products are "open-loop" (i.e., no data assimilation). The choice of forcing data, as well as DA observation source, variable, and scheme, vary for different GLDAS-2.2 products.GLDAS-2.1 data products are now available in two production streams: one stream is forced with combined forcing data including GPCP version 1.3 (the main production stream), and the other stream is processed without this forcing data (the early production stream). Since the GPCP Version 1.3 data have a 3-4 month latency, the GLDAS-2.1 data products are first created without it, and are designated as Early Products (EPs), with about 1.5 month latency. Once the GPCP Version 1.3 data become available, the GLDAS-2.1 data products are processed in the main production stream and are removed from the Early Products archive. This data product is an Early Product for GLDAS-2.1 Noah 1.0 degree monthly dataset. The monthly data product was generated through temporal averaging of GLDAS-2.1 Noah 3-hourly data simulated with the Noah Model 3.6 in Land Information System (LIS) Version 7. The data product contains 36 land surface fields from January 2000 to present.The GLDAS-2.1 simulation started on January 1, 2000 using the conditions from the GLDAS-2.0 simulation. This simulation was forced with National Oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields (Derber et al., 1991), the disaggregated Global Precipitation Climatology Project (GPCP) V1.3 Daily Analysis precipitation fields (Adler et al., 2003; Huffman et al., 2001), and the Air Force Weather Agency's AGRicultural METeorological modeling system (AGRMET) radiation fields. The simulation used with GDAS and GPCP only from 2000 to February 2001, followed by addition of AGRMET for March 1, 2001 onwards.The GLDAS-2.1 products supersede their corresponding GLDAS-1 products.The GLDAS-2.1 data are archived and distributed in NetCDF format.

This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated.

GPM IMERG Early Precipitation Rate L3 V07 (GPM IMERG Early Precipitation L3 Daily aggregated 0.1 degree x 0.1 degree V07 (GPM_3IMERGDL 07)) is an image service derived from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) Early dataset.The image service shows accumulated precipitation rate (mm/day), approximately one day after observation. The image service provides global coverage with a temporal span from 06/01/2000 0:00 UTC to present at 1-day intervals.IMERG is an algorithm that estimates precipitation rate from multiple passive microwave sensors in the GPM constellation, the GPM Dual-Frequency Radar, and infrared (IR) sensors mounted on geostationary satellites. Currently, the near-real-time Early estimates have no concluding calibration. Briefly describing the Early Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the Combined Radar-Radiometer Algorithm (CORRA) product (because it is presumed to be the best snapshot Tropical Rainfall Measuring Mission (TRMM)/GPM estimate after adjustment to the monthly Global Precipitation Climatology Project Satellite-Gauge (GPCP SG)), then "forward morphed" and combined with microwave precipitation-calibrated geo-IR fields to provide half-hourly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is computed using analyses of surface temperature, humidity, and pressure.Dataset at a GlanceShortname: GPM_3IMERGDLDOI: 10.5067/GPM/IMERGDL/DAY/07Version: 07Coverage: -180.0,-90.0,180.0,90.0Temporal Coverage: Subset is from 2025-07-01 to PresentData ResolutionSpatial: 0.1 ° x 0.1 °Temporal: 1 daySymbologyThe default symbology in the Map Viewer may be changed to accommodate other color schemes using the settings in the Image Display panel from the layer settings menu. NoData values, and values less than 0.03 mm/hr (the current threshold value for the IMERG algorithm) have been removed. Ensure that pop-ups are enabled to view pixel values (select Modify Map first).Temporal CoverageThe source dataset is in UTC time but the service is displayed in the Map Viewer in local time. The data is available in daily intervals, and the map visualization may be modified by opening the Time Slider Settings menu from the icon on the time slider bar. The total temporal coverage may be limited to the desired range and the time interval may also be changed. The options in the time interval units are based on the total time range input, so a shorter time range will enable shorter time units to be selected from the time interval drop-down menu. If the time settings are set to more than 30-minute intervals, the first time slice in the time interval is visible.Suggested Use:Access GES DISC How-To's for additional portal option information.Data Download:https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3IMERGDL.07/2025/07/

The Global Precipitation Climatology Centre (GPCC), which is operated by the Deutscher Wetterdienst (National Meteorological Service of Germany), is a component of the Global Precipitation Climatology Project (GPCP) with the main emphasis on the treatment of the global in-situ observations. The GPCC simultaneously contributes to the Global Climate Observing System (GCOS) and other international research and climate monitoring projects. This rain gauge-only data set was acquired from GPCC and resampled to 0.5 degree grid boxes for use in the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II. The GPCC collects precipitation data which are locally observed at rain gauge stations and distributed as CLIMAT and SYNOP reports via the Global Telecommunication System of the World Weather Watch (GTS) of the World Meteorological Organization (WMO). The Centre acquires additional monthly precipitation data from meteorological and hydrological networks which are operated by national services.

New release of 1948-2008 data available as of July 3, 2011. A global, 50-year, dataset of meteorological forcings has been developed that can be used to drive models of land surface hydrology. The dataset is constructed by combining a suite of global observation-based datasets with the NCEP/NCAR reanalysis. Known biases in the reanalysis precipitation and near-surface meteorology have been shown to exert an erroneous effect on modeled land surface water and energy budgets and are thus corrected using observation-based datasets of precipitation, air temperature and radiation. Corrections are also made to the rain day statistics of the reanalysis precipitation which have been found to exhibit a spurious wave-like pattern in high-latitude wintertime. Wind-induced undercatch of solid precipitation is removed using the results from the World Meteorological Organization (WMO) Solid Precipitation Measurement Intercomparison. Precipitation is disaggregated in space to 1.0 degree by statistical downscaling using relationships developed with the Global Precipitation Climatology Project (GPCP) daily product. Disaggregation in time from daily to 3-hourly is accomplished similarly, using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly real-time dataset. Other meteorological variables (downward short- and longwave, specific humidity, surface air pressure and wind speed) are downscaled in space with account for changes in elevation. The dataset is evaluated against the bias-corrected forcing dataset of the second Global Soil Wetness Project (GSWP-2). The final product provides a long-term, globally-consistent dataset of near-surface meteorological variables that can be used to drive models of the terrestrial hydrologic and ecological processes for the study of seasonal and inter-annual variability and for the evaluation of coupled models and other land surface prediction schemes.

This data set consists of a subset of the Global Historical Climatology Network (GHCN) Version 1 database for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., longitude 85 to 30 degrees W, latitude 25 degrees S to 10 degrees N). There are three files available, one each for precipitation, temperature, and pressure data. Within this subset the oldest data date from 1832 and the most recent from 1990.The GHCN V1 database contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which was to produce, maintain and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea-level pressure stations, and 1800 station-pressure stations. Each station has at least 10 years of data; 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara desert, Greenland, and Antarctica. The earliest station data are from 1697; the most recent are from 1990. The database was created from 15 source data sets including:The National Climatic Data Center's (NCDC's) World Weather Records,CAC's Climate Anomaly Monitoring System (CAMS),NCAR's World Monthly Surface Station Climatology,CIRES' (Eischeid/Diaz) Global precipitation data set,P. Jones' Temperature data base for the world, andS. Nicholson's African precipitation database. Quality Control of the GHCN V1 database included visual inspection of graphs of all station time series, tests for precipitation digitized 6 months out of phase, tests for different stations having identical data, and other tests. This detailed analysis has revealed that most stations (95% for temperature and precipitation, 75% for pressure) contain high-quality data. However, gross data-processing errors (e.g., keypunch problems) and discontinuous inhomogeneities (e.g., station relocations and instrumentation changes) do characterize a small number of stations. All major data processing problems have been flagged (or corrected, when possible). Similarly, all major inhomogeneities have been flagged, although no homogeneity corrections were applied.LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. LBA was a cooperative international research initiative led by Brazil and NASA was a lead sponsor for several experiments. More information about LBA and links to other LBA project sites can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.

Weather data were collected by the National Weather Service in International Falls, Minnesota. International Falls is about 80 miles from the SNF, but the weather data is representative of the area. Total solar insolation measurements were made at Fall Lake Dam in Winton, Minn. by Prof. Donald Baker of the Department of Soil Science at the University of Minnesota, St. Paul. Insolation values were measured using a Yellow Springs solar cell calibrated against an Eppley Pyranometer.

Weather data were collected by the National Weather Service in International Falls, Minnesota. International Falls is about 80 miles from the SNF, but the weather data is representative of the area. Total solar insolation measurements were made at Fall Lake Dam in Winton, Minn. by Prof. Donald Baker of the Department of Soil Science at the University of Minnesota, St. Paul. Insolation values were measured using a Yellow Springs solar cell calibrated against an Eppley Pyranometer.

The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.

Version 06 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 06.

This dataset is the GPM Level 3 IMERG Late Daily 10 x 10 km (GPM_3IMERGDL) derived from the half-hourly GPM_3IMERGHHL. The derived result represents a Late expedited estimate of the daily accumulated precipitation. The dataset is produced at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) by simply summing the valid precipitation retrievals for the day in GPM_3IMERGHHL and giving the result in (mm). The latency of the derived late daily product is a couple of minutes after the last granule of GPM_3IMERGHHL for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHL is 12 hours, the daily should appear about 12 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHL), please see the Documentation (Related URL).

In the original IMERG algorithm, the precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean and tropical land to correct known biases.

The half-hourly intercalibrated merged PMW estimates are then input to both the Climate Prediction Center (CPC) Morphing-Kalman Filter (CMORPH-KF) Lagrangian time interpolation scheme and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the CMORPH-KF morphing (quasi-Lagrangian time interpolation) scheme.

The CMORPH-KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. The motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours of the vertically integrated vapor (TQV) provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time.

The IMERG system is run twice in near-real time:

"Early" multi-satellite product ~4 hr after observation time using only forward morphing and "Late" multi-satellite product ~14 hr after observation time, using both forward and backward morphing and once after the monthly gauge analysis is received:

"Final", satellite-gauge product ~3.5 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.

Currently, the near-real-time Early and Late half-hourly estimates have no concluding calibration, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitationCal, is the data field of choice for most users.

The following describes the derivation of the Daily in more details.

The daily accumulation is derived by summing valid retrievals in a grid cell for the data day. Since the 0.5-hourly source data are in mm/hr, a factor of 0.5 is applied to the sum. Thus, for every grid cell we have

Pdaily = 0.5 * SUM{Pi * 1[Pi valid]}, i=[1,Nf] Pdaily_cnt = SUM{1[Pi valid]}

where: Pdaily - Daily accumulation (mm) Pi - 0.5-hourly input, in (mm/hr) Nf - Number of 0.5-hourly files per day, Nf=48 1[.] - Indicator function; 1 when Pi is valid, 0 otherwise Pdaily_cnt - Number of valid retrievals in a grid cell per day.

Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files. Note that

An historical monthly precipitation dataset for global land areas from 1900 to 1996, gridded at two different resolutions (2.5 degrees latitude by 3.75 degrees longitude and 5 degrees latitude/longitude).

This data set is a subset of a 0.5-degree gridded temperature and precipitation data set for South America (Willmott and Webber 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), defined as 10° N to 25° S, 30° to 85° W. The data are in ASCII GRID format.

The data consist of the following:

Monthly mean air temperature time series (1960-1990), in degrees C: monthly mean air temperatures for 1960-1990 cross validation errors associated with time series monthly mean air temperatures for 1960-1990, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation time series

Monthly mean air temperature climatology, in degrees C: climatic means of monthly and annual air temperatures cross validation errors associated with climatic means climatic means of monthly and annual mean air temperatures, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation climatic means

Monthly total precipitation time series (1960-1990), in millimeters: monthly precipitation totals for 1960-1990 cross validation errors associated with time series monthly precipitation totals for 1960-1990, climatologically aided interpolation cross validation errors associated with climatologically aided interpolation time series

Monthly total precipitation climatology, in millimeters: climatic means of monthly and annual precipitation totals cross validation errors associated with climatic means More information about the full data set can be found at "Willmott, Matsuura, and Collaborators' Global Climate Resource Pages" (http://climate.geog.udel.edu/~climate) at the University of Delaware. To obtain the original documentation and data, follow the link for "Available Climate Data," register or sign in, and follow the link for "South American Climate Data."

Information on the LBA subset can be found at ftp://daac.ornl.gov/data/lba/physical_climate/willmott/comp/willmott_readme.pdf.

This dataset provides modeled estimates of monthly hydrological fluxes at 0.25-degree resolution over Alaska and Canada for the years 1979-2018. The estimates were derived from the Variable Infiltration Capacity (VIC) macroscale hydrological model version 4.1.2 with water and energy balance schemes at 0.25-degree spatial and daily temporal resolution for this 38-year period. The gridded output data products are monthly average water balance variables including precipitation (P), evapotranspiration (E), 'P minus E', evaporation, soil moisture in three soil layers, base flow and runoff, snow depth, snow water equivalent (SWE), and snow sublimation, and energy balance variables including surface temperature, albedo, latent and sensible heat flux, ground heat flux, short- and long-wave and other radiative fluxes. The daily modeled values for precipitation and evapotranspiration were also aggregated to water years and precipitation was also aggregated to a 30-year climate normal average.

This data set contains hourly and daily meteorological data from 23 meteorological stations across Canada from January 1975 to January 1997. The surface meteorology parameters include: date, time, temperature, precipitation, snow, snow depth, sea level pressure, station pressure, dew point, wind direction, wind speed, dry and wet bulb temperature, relative humidity, cloud opacity and cloud amount.