Hourly Precipitation Data (HPD) is digital data set DSI-3240, archived at the National Climatic Data Center (NCDC). The primary source of data for this file is approximately 5,500 US National Weather Service (NWS), Federal Aviation Administration (FAA), and cooperative observer stations in the United States of America, Puerto Rico, the US Virgin Islands, and various Pacific Islands. The earliest data dates vary considerably by state and region: Maine, Pennsylvania, and Texas have data since 1900. The western Pacific region that includes Guam, American Samoa, Marshall Islands, Micronesia, and Palau have data since 1978. Other states and regions have earliest dates between those extremes. The latest data in all states and regions is from the present day. The major parameter in DSI-3240 is precipitation amounts, which are measurements of hourly or daily precipitation accumulation. Accumulation was for longer periods of time if for any reason the rain gauge was out of service or no observer was present. DSI 3240_01 contains data grouped by state; DSI 3240_02 contains data grouped by year.

Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.

Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.

Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, Tmax, and Tmin are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily Tmax and Tmin, coldest daily Tmax and Tmin , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily Tmax, Tmin, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

See included README file for more information.

Additional technical details and analyses can be found in: Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320. https://doi.org/10.1175/JCLI-D-18-0630.1

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2024 August updated -

  Annual calculations for 2022 and 2023 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.

  Note that future updates may be infrequent.

• 2022 January updated -

  Annual calculations for 2021 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

• 2021 January updated -

  Annual calculations for 2020 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

• 2020 January updated -

  Annual calculations for 2019 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.

  Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

• 2019 June updated -

  Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.

  README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).

This dataset contains Version 07 of the Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG Level 3 "Final Run" precipitation analysis at 0.1 degree, daily resolution.

From the official GPM IMERG site at NASA GES DISC: The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1 degree every half-hour beginning June 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite (for June 2014 to present) and the Tropical Rainfall Measuring Mission (TRMM) satellite (for June 2000 to May 2014) as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications, including over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of a user's application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-hour latency), same/next-day applications (Late Run, 14-hour latency), and post-real-time research (Final Run, 4-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.

In 2024, the United States saw some **** inches of precipitation. The main forms of precipitation include hail, drizzle, rain, sleet, and snow. Since the turn of the century, 2012 was the driest year on record with an annual precipitation of **** inches. Regional disparities in rainfall Louisiana emerged as the wettest state in the U.S. in 2024, recording a staggering ***** inches (*** meters) of precipitation—nearly **** inches (ca. ** centimeters) above its historical average. In stark contrast, Nevada received only **** inches (ca. ** centimeters), underscoring the vast differences in rainfall across the nation. These extremes illustrate the uneven distribution of precipitation, with the southwestern states experiencing increasingly dry conditions that experts predict will worsen in the coming years. Drought concerns persist Drought remains a significant concern in many parts of the country. The Palmer Drought Severity Index (PDSI) for the contiguous United States stood at ***** in December 2024, indicating moderate to severe drought conditions. This reading follows three years of generally negative PDSI values, with the most extreme drought recorded in December 2023 at *****.

U.S. 15 Minute Precipitation Data is digital data set DSI-3260, archived at the National Climatic Data Center (NCDC). This is precipitation data. The primary source of data for this file is approximately 2,000 mostly U.S. weather stations operated or managed by the U.S. National Weather Service. Stations are primary, secondary, or cooperative observer sites that have the capability to measure precipitation at 15 minute intervals. This dataset contains 15-minute precipitation data (reported 4 times per hour, if precip occurs) for U.S. stations along with selected non-U.S. stations in U.S. territories and associated nations. It includes major city locations and many small town locations. Daily total precipitation is also included as part of the data record. NCDC has in archive data from most states as far back as 1970 or 1971, and continuing to the present day. The major parameter is precipitation amounts at 15 minute intervals, when precipitation actually occurs.

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.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 mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before "07", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared (and rain gauge in the final) dataset, variable "precipitation", and appears in higher latitudes. Thus, in most cases users of global "precipitation" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable "MWprecipitation", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version "07", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. 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 14 hours, the daily should appear no earlier than 14 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHL), please see the Documentation (Related URL). The daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have Pdaily_mean = SUM{Pi * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]Where:Pdaily_cnt = SUM{1[Pi valid]}Pi - half-hourly input, in (mm/hr)Nf - Number of half-hourly files per day, Nf=481[.] - Indicator function; 1 when Pi is valid, 0 otherwisePdaily_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 Pi=0 is a valid value.Pdaily_cnt are provided in the data files as variables "precipitation_cnt" and "MWprecipitation_cnt", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. There are various ways the daily error could be estimated from the source half-hourly random error (variable "randomError"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly "randomError" for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):Perr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err * 24}^0.5, i=[1,Nf]Ncnt_err = SUM( 1[Perr_i valid] )where:Perr_i - half-hourly input, "randomError", (mm/hr)Perr_daily - Magnitude of the daily error, (mm/day)Ncnt_err - Number of valid half-hour error estimatesAgain, the sum of squared "randomError" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.

Typical annual rainfall data were summarized from monthly precipitation data and provided in millimeters (mm). The monthly climate data for global land areas were generated from a large network of weather stations by the WorldClim project. Precipitation and temperature data were collected from the weather stations and aggregated across a target temporal range of 1970-2000.

Weather station data (between 9,000 and 60,000 stations) were interpolated using thin-plate splines with covariates including elevation, distance to the coast, and MODIS-derived minimum and maximum land surface temperature. Spatial interpolation was first done in 23 regions of varying size depending on station density, instead of the common approach to use a single model for the entire world. The satellite imagery data were most useful in areas with low station density. The interpolation technique allowed WorldClim to produce high spatial resolution (approximately 1 km2) raster data sets.

This dataset provides values for PRECIPITATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The GPM Ground Validation Naval Research Laboratory (NRL) Near-Real Time Rain Rates IFloodS data product was created for the GPM Iowa Flood Studies (IFloodS) field campaign from April 23, 2013 through June 30, 2013. The IFloodS field campaign was a ground measurement campaign that took place in eastern Iowa. The goals of the campaign were to collect detailed measurements of precipitation at the Earth’s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. This NRL real time rain rates data product was produced using the Probability Matching Method with rain gauge, Defense Meteorological Satellite Program (DMSP) F15 Special Sensor Microwave - Imager (SSM/I), and DMSP F16 Special Sensor Microwave - Imager/Sounder (SSMIS) data. This data product includes rain rate estimates and files are available in netCDF-4 and binary formats, as well as corresponding browse imagery in JPG format.

The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.The GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov

The California Department of Water Resources (DWR), Northern Region Office (NRO), maintains 33 precipitation stations that were installed starting in 1944. Stations record total annual precipitation. This information can help inform annual water budgets or track climate-related trends in annual precipitation. The CSV file contains total annual precipitation data in inches. The PDF file contains a description of stations and methods for data collection.

This data set contains daily precipitation data from Costa Rica. Data are from 45 sites within Costa Rica covering the time period January 1991 to December 1994, and January to December 1997.

This dataset, also known as the Long-term Alpine Precipitation Reconstruction (LAPrec), provides gridded fields of monthly precipitation for the Alpine region (eight countries). The dataset is derived from station observations and is provided in two issues:

LAPrec1871 starts in 1871 and is based on data from 85 input series; LAPrec1901 starts in 1901 and is based on data from 165 input series.

This allows user flexibility in terms of requirements defined by temporal extent or spatial accuracy. LAPrec was constructed to satisfy high climatological standards, such as temporal consistency and the realistic reproduction of spatial patterns in complex terrain. As the dataset covers over one-hundred years in temporal extent, it is a qualified basis for historical climate analysis in a mountain region that is highly affected by climate change. The production of LAPrec combines two data sources:

HISTALP (Historical Instrumental Climatological Surface Time Series of the Greater Alpine Region) offers homogenised station series of monthly precipitation reaching back into the 19th century.

APGD (Alpine Precipitation Grid Dataset) provides daily precipitation gridded data for the period 1971–2008 built from more than 8500 rain gauges.

The adopted reconstruction method, Reduced Space Optimal Interpolation (RSOI), establishes a linear model between station and grid data, calibrated over the period when both are available. RSOI involves a Principal Component Analysis (PCA) of the high-resolution grid data, followed by an Optimal Interpolation (OI) using the long-term station data. The LAPrec dataset is updated on a two-year basis, by no later than the end of February each second year. The latest version of the dataset will extend until the end of the year before its release date. LAPrec has been developed in the framework of the Copernicus Climate Change Service in a collaboration between the national meteorological services of Switzerland (MeteoSwiss, Federal Office of Meteorology and Climatology) and Austria (ZAMG, Zentralanstalt für Meteorologie und Geodynamik). For more information on input data, methodical construction, applicability, versioning and data access, see the product user guide in the Documentation tab. The latest version of the dataset will temporally extend until the end of the year before its release date.

The GPM Ground Validation Met One Rain Gauge Pairs OLYMPEX dataset contains precipitation amount and precipitation rate data collected during the Global Precipitation Measurement mission (GPM) Ground Validation (GV) Olympic Mountains Experiment (OLYMPEX). The OLYMPEX field campaign took place between November 2015 and January 2016, with additional ground sampling continuing through February 2016, on the Olympic Peninsula in the Pacific Northwest of the United States. The purpose of the campaign was to provide ground-validation data for the measurements taken by instrumentation aboard the GPM Core Observatory satellite. The Met One Rain Gauge Pairs are tipping bucket precipitation gauges which collect precipitation amounts and calculate precipitation rates. This dataset contains two ASCII-tsv files per rain gauge and two rain gauges are located on each station platform. The Met One Rain Gauge Pairs OLYMPEX dataset files are available from January 1, 2015 through June 20, 2016 in ASCII-tsv format.

The Cooperative Observer Program (COOP) Hourly Precipitation Data (HPD) consists of quality controlled precipitation amounts, which are measurements of hourly accumulation of precipitation, including rain and snow for approximately 2,000 observing stations around the country, and several U.S. territories in the Caribbean and Pacific from the National Weather Service (NWS) Fischer-Porter Network. This new version of COOP HPD with faster automations due updated stations will result in faster access for the public. The data are from 1940 to present, depending upon when each station was installed. These stations, nearly all of which were part of HPD version 1, also known as DSI-3240, were gradually upgraded from paper punch tape data recording systems to a more modern electronic data logger system from 2004-2013. The 15-min gauge depth time series are processed at NCEI via automated quality control and filtering algorithms to identify and remove spurious observations from noise and malfunctioning equipment, and also those due to natural phenomena such as evaporation and the necessary occasional emptying of the gauge. Hourly precipitation totals are then computed from the 15-min data and are quality controlled by a suite of automated algorithms that combine checks on the daily and hourly time scale. Data and metadata are ingested on a daily basis and combined in a single integrated dataset. As with the legacy punch paper instrumentation, the electronic loggers record rain gauge depth every 15 minutes. Monthly site visits to each station are still performed, but instead of collecting punched paper (that would subsequently need conversion to a digital record via a MITRON reader), data are downloaded from the station's datalogger to a memory stick and centrally collected at the local Weather Forecast Office (WFO) for all stations in the WFO area. The WFO subsequently combines all data into a single tar file and transfers the data to NCEI via ftp upload nominally each month. This updated HPD includes the historical data from the punch paper era and the recent digital era in order to provide the full period of record for each location. These data are formatted consistent with practices for NCEI Global In-situ datasets.

The National Forest Climate Change Maps project was developed to meet the need of National Forest managers for information on projected climate changes at a scale relevant to decision making processes, including Forest Plans. The maps use state-of-the-art science and are available for every National Forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation and air temperature, including both Alaskan and lower 48 datasets. Data from the lower 48 were downloaded from here: https://www.fs.usda.gov/rm/boise/AWAE/projects/national-forest-climate-change-maps.html, and Alaskan data came from here: https://www.snap.uaf.edu/tools/data-downloads. Historical data are compared with RCP 8.5 projections from the 2080s.A Raster Function Template is available in this service that will classify the data as originally intended by OSC. The RFT currently works in AGOL but not in ArcGIS Pro.

This dataset provides global estimates of precipitation based on satellite observations. Precipitation is the main component of water transport from the atmosphere to the Earth’s surface within the hydrological cycle. It varies strongly, depending on geographical location, season, synopsis, and other meteorological factors. The supply with freshwater through precipitation is vital for many subsystems of the climate and the environment, but there are also hazards related to extensive precipitation or the lack of precipitation. The present dataset allows the investigation and quantification of these aspects of precipitation, possibly in conjunction with other datasets covering components of the hydrological cycle. The data represent the current state-of-the-art for satellite-based precipitation climate data record production in Europe, which is in line with the “Systematic observation requirements for satellite-based products for climate” as defined by GCOS (Global Climate Observing System). Spaceborne passive microwave (MW) imagers and sounders, available on different Low Earth Orbit (LEO) platforms, provide the most effective measurements for the remote sensing of precipitation because of the sensitivity of the MW upwelling radiation to the cloud microphysical properties, especially the emission and scattering of precipitation-size hydrometeors (solid and liquid). However, they are available at low spatial and temporal resolution, due to the limited number of overpasses per day (depending on latitude and number of platforms) at each location. A further ECV Precipitation product only based on MW observations, COBRA, is also available in the CDS. On the other hand, infrared (IR) sensors onboard geostationary (GEO) platforms, provide only information on the upper-level cloud structure, but at much higher temporal and spatial resolution, for example improving the representative sampling of intermittent precipitation. Since precipitation is not directly observed in the infrared, these measurements are often merged with microwave-based precipitation estimates. This precipitation data record and its processing chain are called Global Interpolated RAinFall Estimate (GIRAFE). GIRAFE provides a global 1° gridded daily accumulated precipitation amount together with uncertainty estimates coming from the sampling, and a global 1° gridded monthly mean of daily accumulation. In the above sense, GIRAFE optimizes the sampling of precipitation by merging observations by LEO MW imagers and sounders (Level-2 data) with GEO-Ring IR brightness temperatures (Level-1 data). The daily accumulated precipitation is also aggregated to monthly mean precipitation. This dataset has been produced by the EUMETSAT Satellite Application Facility on Climate Monitoring.

Instantaneous precipitation maps over IODC area generated combining geostationary (GEO) IR images from operational geostationary satellites 'calibrated' by precipitation measurements from MW images on Low Earth Orbit (LEO) satellites, processed soon after each acquisition of a new image from GEO. The blending algorithm ('Rapid Update’) generates precipitation estimates combining the equivalent blackbody temperatures (TBB) at 10.8 μm with rain rates from all available Passive MW measurements. A separate treatment is performed for convective precipitation: the morphologic information and the enhancement of precipitation estimate is done by the use of NEFODINA software.

The Global Precipitation Climatology Project (GPCP) pentad version 1 precipitation data set includes global precipitation rates for 5-day, or pentad, periods. The data sets are derived from measured rain gauge data and merged with satellite estimates of rainfall. This is a portion of the version 1 GPCP pentad data set and covers the ISLSCP II period from 1986 to 1995. The original precipitation rates at 2.5 degrees were re-gridded to a 1 degree spatial resolution by the ISLSCP II staff.

The GPM Ground Validation Met One Rain Gauge Pairs Wallops Flight Facility (WFF) dataset contains rain rate data from 4 rain gauge networks located in Virginia and Maryland near the Wallops Flight Facility (WFF): Nassawadox, Pocomoke, HalfDeg and Wallops Flight Facility (WFF) Assorted Gauges. These data were collected in support of the Global Precipitation Mission (GPM) Ground Validation (GV) campaign. The Met One Rain Gauge Pairs are tipping bucket precipitation gauges which collect precipitation amounts and calculate rain rates. The dataset contains 3 products: formatted gauge tips (GAG), interpolated one-minute rain rates for a year (GMIN), and interpolated one-minute rain rates for a month (2A56). Data are available in ASCII format for the period of April 10, 2012 through October 1, 2018.