This dataset contains Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) v5. The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the Day-1 multi-satellite precipitation product. The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2014 version of the Goddard Profiling Algorithm (GPROF2014), then gridded, intercalibrated to the GPM Combined Instrument product, and combined into half-hourly 10x10 km fields. The Global Precipitation Measurement (GPM) mission is an international network of satellites that provide the next-generation global observations of rain and snow.

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 [https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_07/summary]: 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. This dataset is the GPM Level 3 IMERG Final Daily 0.1 degree x 0.1 degree (GPM_3IMERGDF) computed from the half-hourly GPM_3IMERGHH. The dataset represents the Final Run estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in...

This dataset contains Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) v7. NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation over most of the Earth's surface. IMERG is particularly valuable over areas of Earth's surface that lack ground-based precipitation-measuring instruments, including oceans and remote areas.

IMERG fuses precipitation estimates collected during the TRMM satellite’s operation (2000 - 2015) with recent precipitation estimates collected by the GPM mission (2014 - present) creating a continuous precipitation dataset spanning over two decades. This extended record allows scientists to compare past and present precipitation trends, enabling more accurate climate and weather models and a better understanding of Earth’s water cycle and extreme precipitation events. IMERG is available in near real-time with estimates of Earth’s precipitation updated every half-hour, enabling a wide range of applications to help communities around the world make informed decisions for disasters, disease, resource management, energy production, food security, and more.

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. Level 3 data are averaged global gridded products, screened for bad data points

The Global Precipitation Measurement (GPM) mission is an international network of satellites that provide the next-generation global observations of rain and snow.

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 Final Daily 10 x 10 km (GPM_3IMERGDF) derived from the half-hourly GPM_3IMERGHH. The derived result represents the Final 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 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 Final Daily product depends on the delivery of the IMERG Final Half-Hourly product GPM_IMERGHH. Since the latter are delivered in a batch, once per month for the entire month, with up to 4 months latency, so will be the latency for the Final Daily, plus about 24 hours. Thus, e.g. the Dailies for January can be expected to appear no earlier than April 2. 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.

The Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset provides global precipitation estimates. IMERG integrates information from NASA's Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) satellites. This IMERG layer is a time-enabled image service of monthly precipitation accumulation rasters. Time Extent: June 2000 – PresentUnits: mm/monthCell Size: 0.1 degrees (~11km)Source Type: StretchedPixel Type: 32 Bit IntegerData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource:  NASA 1 Month IMERG final Run Precipitation AccumulationsLegend: Time-enabled MosaicThe IMERG layer is a time-enabled mosaic created from monthly precipitation rasters. Each raster can be accessed individually or in sequence to display the variation of precipitation over time.What can you do with this layer?The pop-up quickly displays the accumulated precipitation for a selected month and place. The layer can be used in analysis in a variety of water resources and environmental applications from the analysis of precipitation patterns to the creation of water balances.Source Data: The data behind this layer was created from GeoTIF files produced by NASA at 0.1 degrees resolution. The rasters were converted to Cloud Raster Format (CRF) and added to a mosaic dataset.Citation:Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: December 2021, 10.5067/GPM/IMERG/3B-MONTH/06Revisions:Aug 19, 2022: Image service update. Multidimensional info available.Feb 16, 2022: Image service update. Use of Cloud Raster Format (CRF) instead of Meta Raster Format (MRF).Mar 31, 2021: Official release of Feature Service offering.

GPM_3IMERGHHE Early Precipitation Rate L3 V06 (GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06 (GPM_3IMERGHHE 06)) is an image service derived from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) Early dataset.

The image service shows precipitation rate (mm/hr), approximately four hours after observation. The image service provides global coverage with a temporal span from 06/01/2000 0:00 UTC to present at 30-minute intervals. The service is updated every three hours to incorporate the new granules. To access the REST endpoint for the service, input the URL into a browser or select View just above the URL.

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 glance

Shortname: GPM_3IMERGHHE

DOI: 10.5067/GPM/IMERG/3B-HH-E/06

Version: 06

Coverage: -180.0,-90.0,180.0,90.0

Temporal Coverage: 2000-06-01 to Present

Data Resolution

Spatial: 0.1 ° x 0.1 °

Temporal: 30 minutes

Symbology

The 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 Coverage

The source dataset is in UTC time but the service is displayed in the Map Viewer in local time. The data is available in 30-minute 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.

Portal Options

    Select Modify Map to

customize the layer visualization. More information about the image service capabilities may be found in the REST endpoint. In the portal, the basemap may be changed by selecting the desired option from the Basemap menu. Further instructions on using the image service may be found at [GES DISC How-To's: How to access the GES DISC IMERG ArcGIS Image Service using the ArcGIS Enterprise Map Viewer (nasa.gov)].

SERVIR-hosted precipitation data from the Integrated Multi-satellitE Retrievals (IMERG) for Global Precipitation Mission (GPM). The IMERG algorithm intercalibrates, merges and interpolates “all” satellite passive microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, monthly precipitation gauge analyses, and potentially other precipitation estimators at fine time and space scales for the TRMM and GPM eras over the entire globe. The hosted datasets will include a 30 minute precipitation intensity, as well as a 1 day precipitation accumulation. The precipitation data are measured in tenths of a millimeter (mm x 10^1).

Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.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.The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), 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 to correct known biases.The half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) 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 KF morphing (quasi-Lagrangian time interpolation) scheme.The KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields 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 vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. 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. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.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 morphingand once after the monthly gauge analysis is received:"Final", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.In V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, 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, precipitation, is the data field of choice for most users.Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure.

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 provid

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 Final Daily 10 x 10 km (GPM_3IMERGDF) derived from the half-hourly GPM_3IMERGHH. The derived result represents the Final 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 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 Final Daily product depends on the delivery of the IMERG Final Half-Hourly product GPM_IMERGHH. Since the latter are delivered in a batch, once per month for the entire month, with 2-3 months latency, so will be the latency for the Final Daily, plus about 24 hours. Thus, e.g. the Dailies for January can be expected to appear no earlier than April 2. 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=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 Pi=0 is a valid value. Pdaily

Cloud-Precipitation Hybrid Regimes with MODIS C6.1 2D joint histogram of CTP and COT (total 42 bins) and IMERG v06B precipitation histogram (total 6 bins) derived in 15S-15N domain. There are 4 kinds of regime sets: 1) Cloud only (42bin), 2) Cloud+Precipitation with weight 1 (Cld42+Pr6x1), 3) Cloud+Precipitation with weight 3 (Cld42+Pr6x3), 4) Cloud+Precipitation with weight 7 (Cld42+Pr6x7; equal weight). File list: - MODIS_t+a_cld_hist_15S-15N_CR_set.Cld42.nc - MODIS_t+a_cld+pr6x1_hist_15S-15N_CPR_set.Cld42+Pr6x1.nc - MODIS_t+a_cld+pr6x3_hist_15S-15N_CPR_set.Cld42+Pr6x3.nc - MODIS_t+a_cld+pr6x7_hist_15S-15N_CPR_set.Cld42+Pr6x7.nc - MODIS_t+a_cld+pr6x7_hist_15S-15N_CPR_projected2IMERGdomain.Cld42+Pr6x7.nc

Version 05 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 05. 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).

The Integrated Multi-satellite Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the Day-1 multi-satellite precipitation product for the U.S. GPM team. The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2014 version of the Goddard Profiling Algorithm (GPROF2014), then gridded, intercalibrated to the GPM Combined Instrument product, and combined into half-hourly 10 x 10 km fields.

These are provided 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 "even-odd" geo-IR fields and forward them to PPS for use in the CMORPH-KF Lagrangian time interpolation scheme and the PERSIANN-CCS computation routines.

The PERSIANN-CCS estimates are computed (supported by an asynchronous re-calibration cycle) and sent to the CMORPH-KF Lagrangian time interpolation scheme. The CMORPH-KF Lagrangian time interpolation (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates.

The IMERG system is run twice in near-real time: "Early" multi-satellite product, ~4 hr after observation time, and "Late" multi-satellite product ~12 hr after observation time. After that, the IMERG system does one "Final" run, once the monthly gauge analysis is received. The "Final" satellite-gauge product has a latency of ~2 months after the observation month.

The baseline for the near real-time Late and Late half-hour estimates is to be calibrated with climatological coefficients that vary by month and location, while in the Final post-real-time run the multi-satellite half-hour estimates are adjusted so that they sum to a 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.

The following describes the derivation 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 Pi=0 is a valid value.

On occasion, the 0.5-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the "true" daily total. These events are easily detectable using "counts" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which Pdaily_cnt less than Nf.

There are various ways the accumulated daily error could be estimated from the source 0.5-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 0.5-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 0.5 is finally applied:

Perr_daily = 0.5 * { SUM[ (Perr_i * 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf] Ncnt_err = SUM( 1[Perr_i valid] )

where: Perr_daily - Magnitude of the daily accumulated error power, (mm) Ncnt_err - The counts for the error variable

Thus computed Perr_daily represents the worst case scenario that assumes the error in the 0.5-hourly source data, which is given in mm/hr, is accumulating within the 0.5-hourly period of the source data and then during the day. These values, however, can easily be conveted to root mean square error estimate of the rainfall rate:

rms_err = { (Perr_daily/0.5) ^2 / Ncnt_err }^0.5 (mm/hr)

This estimate assumes that the error given in the 0.5-hourly files is representative of the error of the rainfall rate (mm/hr) within the 0.5-hour window of the files, and it is random throughout the day. Note, this should be interpreted as the error of the rainfall rate (mm/hr) for the day, not the daily accumulation.

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.

The datasets provide a new AIMERG precipitation data (0.1°/ half-hourly, 2000-2008, Asia) with better quality, calibrated by GPM IMERG (0.1°/half-hourly) and APHRODITE (0.25°/Daily) at daily scale for the Asian applications.

How to cite: Ma, Z., Xu, J., Zhu, S., Yang, J., Tang, G., Yang, Y., Shi, Z., and Hong, Y.: AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2000–2015) by calibrating GPM IMERG at a daily scale using APHRODITE, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-250, 2020.

Data Format: GeoTIFF

Spatial Coverage: 60°E-150°E, 15°S-55°N, land.

AIMERG (0.1°/ half-hourly, 2009-2015, Asia) is available at http://10.5281/zenodo.3609507

the research data contain IMERG data and rain gauge data in Sichuan, China. They are daily scale from April 1, 2014 to December 31, 2017 and hourly from June 22 to 25, 2015.

The Republic of the Marshall Islands (RMI) is a sovereign Small Island State in the tropical central North Pacific Ocean. RMI is a nation of more than thirty atolls and islands, most of which are inhabited, dispersed across an exclusive economic zone (EEZ) over 2 million square kilometers. This data release contains files of daily precipitation estimates beginning in 2001 for 23 inhabited sites in the RMI derived from Integrated Multi-satellitE Retrievals for GPM (IMERG; https://gpm.nasa.gov/data/imerg). The files contain either "Late IMERG" data or "Final IMERG" data and are in millimeter per day. These data were compiled to support a 2022-2023 U.S. Geological Survey project to develop methods to apply Earth Observation to monitor the health of vegetation in the RMI, funded by the U.S. Geological Survey and the U.S. Agency for International Development Bureau for Humanitarian Assistance.

Precipitation estimates with fine quality and spatio-temporal resolutions play significant roles in understanding the global and regional cycles of water, carbon, and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at fine resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation products are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. Focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in the Tropical Rainfall Measuring Mission (TRMM) era (finished in July 2019), which were only calibrated at a monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0◦/monthly), we aim to propose a new calibration algorithm for IMERG at a daily scale and to provide a new AIMERG precipitation dataset (0.1◦/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 0.25◦/daily) at the daily scale for the Asian applications. Considering the advantages from both satellite-based precipitation estimates and the ground observations, AIMERG performs better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over mainland China.

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.

This item contains processed datasets for the published, peer-reviewed manuscript “Validation of GPM IMERG extreme precipitation in the Maritime Continent by station and radar data".The datasets were originally output from the Integrated Multi-Satellite Retrieval for GPM, a radar of precipitation in Subang (Western Peninsular Malaysia), and the Global Historical Climatological Network - daily dataset. Met Office’s Unified Model coupled to the Global Ocean Mixed Layer 3.0 configuration (MetUM-GOML3.0). The datasets were then processed to investigate the skill of IMERG in reproducing extreme precipitation over the Maritime Continent. The datasets can be used to reproduce all the figures and tables in the manuscript. Each NetCDF (.nc) file contains data used to draw a specific figure/table in the manuscript.

Accurate rainfall estimates are required to predict when and where rain-triggered landslides will occur. In regions with sparse region gauge networks, satellite rainfall products, owing to their easy availability, high temporal resolution, and improved spatial variability, could be used as an alternative. This study compares the utility of rain gauge and satellite rainfall data for assessing landslide distribution in a data-sparse region: Idukki, along the Western Ghats, India. The GPM IMERG-L (Global Precipitation Mission Integrated Multi-satellitE Retrievals for GPM – Late) daily rainfall product was compared with rain gauge measurements, and it was found that the satellite rainfall observations were underpredicting the rainfall. A conditional merging algorithm was applied to the GPM data to develop a product that combines rain gauge measures' accuracy and the satellite data's spatial variability. A comparison of the ability of the data products to capture the spatial spread of landslides was then carried out. The study area was divided into zones of influences corresponding to the rain gauge stations, and the landslides were classified according to their location within each zone. 5-day antecedent rainfall values were computed from both the rainfall products. Relying solely on the rain gauge derived values created many false positives and false negatives in landslide prediction. A total of 10.2% of the landslides fell in the true-positive category, while 51.3% was the overall false-negative rate. The study proposes using satellite products with improved spatial resolution and a denser rain gauge network to have reliable inputs for landslide prediction models.