PERSIANN Precipitation Climate Data Record (PERSIANN-CDR) is a daily quasi-global precipitation product for the period of 1982 to the present (note that there is a delay in data availability due to processing and data input availability). The data covers from 60 degrees S to 60 degrees N and 0 degrees to 360 degrees longitude at 0.25 degree spatial resolution. The product is developed using Gridded Satellite (GridSat-B1) IR data that are derived from merging ISCCP B1 IR data, along with GPCP version 2.2.

NOAA NEXRAD Quantitative Precipitation Estimation (QPE) Climate Data Record (CDR) is created from the Radar Multi-Radar/Multi-Sensor (MRMS) Reanalysis to produce severe weather and precipitation products for improved decision-making capability to improve severe weather forecasts and warnings, hydrology, aviation, and numerical weather prediction. The data cover a time period from 2002-01-01 to 2011-12-31. NOAA's NEXRAD reanalysis consists of two primary components; (1) Severe weather and radar-reflectivity data generation, (2) Quantitative Precipitation Estimate (including associated precipitation variables and merged rain gauge and radar estimation). This document focuses on the second component of NOAA's NEXRAD reanalysis - the Quantitative Precipitation Estimate (QPE). The primary files generated within this data set are radar-only and radar- gauge (ROQPE, GCQPE, and MOS2D) merged precipitation products as well as ancillary information on precipitation type (PRATE and PFLAG) and radar quality (RQIND). The initial data set covers the time period from January 2002 - December 2011. Radar-only reflectivity, Gauge, Precipitation Flag, and Radar Quality Index for 5-minute data at 1km regular grid over CONUS. Radar only Radar-Gauge Quantitative Precipitation Estimates at hourly scale at 1km regular grid over CONUS. MRMS Quantitative Precipitation Estimation (QPE) uses the most advanced radar technologies and provides high-resolution information about precipitation types and amounts for the nation. The data are stored in netCDF version 4.0 files that include the necessary metadata and supplementary data fields. Data set provides information that can be useful for identification of various types of precipitation, estimation of radar reflectivity, recognition of storm patterns, forecasting technologies for rainfall estimation, and associating different phases of precipitation such as hail freezing rain and snow with radar observations.

NOAA NEXRAD Quantitative Precipitation Estimation (QPE) Climate Data Record (CDR) is created from the Radar Multi-Radar/Multi-Sensor (MRMS) Reanalysis to produce severe weather and precipitation products for improved decision-making capability to improve severe weather forecasts and warnings, hydrology, aviation, and numerical weather prediction. The data cover a time period from 2002-01-01 to 2011-12-31. NOAA's NEXRAD reanalysis consists of two primary components; (1) Severe weather and radar-reflectivity data generation, (2) Quantitative Precipitation Estimate (including associated precipitation variables and merged rain gauge and radar estimation). This document focuses on the second component of NOAA's NEXRAD reanalysis - the Quantitative Precipitation Estimate (QPE). The primary files generated within this data set are radar-only and radar- gauge (ROQPE, GCQPE, and MOS2D) merged precipitation products as well as ancillary information on precipitation type (PRATE and PFLAG) and radar quality (RQIND). The initial data set covers the time period from January 2002 - December 2011. Radar-only reflectivity, Gauge, Precipitation Flag, and Radar Quality Index for 5-minute data at 1km regular grid over CONUS. Radar only Radar-Gauge Quantitative Precipitation Estimates at hourly scale at 1km regular grid over CONUS. MRMS Quantitative Precipitation Estimation (QPE) uses the most advanced radar technologies and provides high-resolution information about precipitation types and amounts for the nation. The data are stored in netCDF version 4.0 files that include the necessary metadata and supplementary data fields. Data set provides information that can be useful for identification of various types of precipitation, estimation of radar reflectivity, recognition of storm patterns, forecasting technologies for rainfall estimation, and associating different phases of precipitation such as hail freezing rain and snow with radar observations.

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 GPCP Daily analysis is a companion to the GPCP Monthly analysis, and provides globally complete precipitation estimates at a spatial resolution of one degree latitude-longitude and daily time scale from October 1996 to the present. Although derived using both some of the same, but also different, data sets and methods than used in the GPCP Monthly analysis, the GPCP Daily "adds up" to the GPCP Monthly. The GPCP Daily V1.3 analysis is currently computed by the University of Maryland and submitted to NCEI. The routine update of the product takes place two months after the end of the month, once all input data sets become available. The data set is part of World Climate Research Program (WCRP) and GEWEX activities, being part of the array of data sets describing the water and energy cycles of the planet under the auspices of the GEWEX Data and Assessment Panel (GDAP). Details of input data sets and methods can be found in: Huffman, G.J., R.F. Adler, M. Morrissey, D.T. Bolvin, S. Curtis, R. Joyce, B McGavock, J. Susskind, 2001: Global Precipitation at One-Degree Daily Resolution from Multi-Satellite Observations. J. Hydrometeor., 2(1), 36-50.

The Mean Layer Temperature - NOAA CDR V5.0 is a monthly global dataset with 2.5°×2.5° grid resolution covering the period from November 1978 to present. The dataset measures mean layer atmospheric temperatures from the lower-troposphere to the lower-stratosphere. The dataset was inter-calibrated and merged from three generations of microwave sounders, MSU, AMSU-A, and ATMS, with 16 polar-orbiting satellites including TIROS-N, NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-10, NOAA-11, NOAA-12, NOAA-14, NOAA-15, NOAA-18, NOAA-19, MetOp-A, Aqua, SNPP, and NOAA-20. The dataset includes temperature mid-troposphere (TMT, MSU channel 2 merged with AMSU-A channel 5 and ATMS channel 6), temperature upper-troposphere (TUT, MSU channel 3 merged with AMSU-A channel 7 and ATMS channel 8), temperature lower-stratosphere (TLS, MSU channel 4 merged with AMSU-A channel 9 and ATMS channel 10), and temperature lower-troposphere (TLT, derived from combinations of TMT, TUT, and TLS). TLT, TMT, TUT, and TLS measure layer temperatures peaking roughly at 3km, 5km, 10km, and 17km, respectively, above the Earth's surface. Features in the dataset development include a use of backward merging approach, development of an observation- and semi-physically-based algorithm for diurnal drift adjustment, and removal of spurious calibration drifting errors in NOAA-15, NOAA-14, NOAA-12, and NOAA-11 through recalibration. Satellite microwave sounding observations in stable sun-synchronous orbits (Aqua, MetOp-A, SNPP, NOAA-20) were used as a reference in the backward merging process. Bias corrections and satellite recalibration have resulted in inter-consistent CDR records for reliable climate change investigation.

The Mean Layer Temperature - NOAA CDR V5.0 is a monthly global dataset with 2.5°×2.5° grid resolution covering the period from November 1978 to present. The dataset measures mean layer atmospheric temperatures from the lower-troposphere to the lower-stratosphere. The dataset was inter-calibrated and merged from three generations of microwave sounders, MSU, AMSU-A, and ATMS, with 16 polar-orbiting satellites including TIROS-N, NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-10, NOAA-11, NOAA-12, NOAA-14, NOAA-15, NOAA-18, NOAA-19, MetOp-A, Aqua, SNPP, and NOAA-20. The dataset includes temperature mid-troposphere (TMT, MSU channel 2 merged with AMSU-A channel 5 and ATMS channel 6), temperature upper-troposphere (TUT, MSU channel 3 merged with AMSU-A channel 7 and ATMS channel 8), temperature lower-stratosphere (TLS, MSU channel 4 merged with AMSU-A channel 9 and ATMS channel 10), and temperature lower-troposphere (TLT, derived from combinations of TMT, TUT, and TLS). TLT, TMT, TUT, and TLS measure layer temperatures peaking roughly at 3km, 5km, 10km, and 17km, respectively, above the Earth's surface. Features in the dataset development include a use of backward merging approach, development of an observation- and semi-physically-based algorithm for diurnal drift adjustment, and removal of spurious calibration drifting errors in NOAA-15, NOAA-14, NOAA-12, and NOAA-11 through recalibration. Satellite microwave sounding observations in stable sun-synchronous orbits (Aqua, MetOp-A, SNPP, NOAA-20) were used as a reference in the backward merging process. Bias corrections and satellite recalibration have resulted in inter-consistent CDR records for reliable climate change investigation.

This dataset contains gridded daily surface reflectance and brightness temperatures derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors onboard NOAA polar orbiting satellites. Surface reflectance from VIIRS channels I1, I2, and I3 (at 640, 865, and 1610 nm) are a NOAA Climate Data Record (CDR). The dataset spans from 2014 to 10 days before the present, and was processed from the VIIRS 375m and 750m Earth view Sensor Data Record (SDR) datasets. VIIRS surface reflectance observations are packaged into data arrays with latitude and longitude dimensions of 3600 x 7200 covering the globe at 0.05 degree spatial resolution. This dataset is one of the Land Surface CDR products produced by the NASA Goddard Space Flight Center (GSFC) and the University of Maryland (UMD). Other Land Surface CDR products include the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The dataset is in the netCDF-4 file format following ACDD and CF Conventions. The dataset is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.