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Description

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 Older versions will no longer be available and have been superseded by Version 07.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 morphing
and 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.

Product Summary

Platforms
IMERG
Instruments
NOT APPLICABLE
Spatial Extent

N: 90 S: -90 E: 180 W: -180

Location
GLOBAL ,
GLOBAL LAND ,
GLOBAL OCEAN
Coordinate System
CARTESIAN
Granule Spatial Representation
CARTESIAN
Temporal Extent
1998-01-01 to Present
Data Partner
Goddard Earth Sciences Data and Information Services Center (formerly Goddard DAAC), Terrestrial Information Systems Laboratory, Earth Sciences Division, Science and Exploration Directorate, Goddard Space Flight Center, NASA (NASA/GSFC/SED/ESD/TISL/GESDI
Concept ID
C2723754845-GES_DISC
Data State
ACTIVE
Number of Files/Granules
496680
Processing Level
3
Published
Updated
Science Keywords
Precipitation ,
Precipitation Amount ,
Precipitation Rate ,
Snow ,
Rain

Citation

Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.

Copy Citation

Precipitation Processing System (PPS) At NASA GSFC. (2022). GPM IMERG Late Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 [Data set]. NASA Goddard Earth Sciences Data and Information Services Center. https://doi.org/10.5067/GPM/IMERG/3B-HH-L/07 Date Accessed: 2026-05-02

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Publications Citing This Dataset

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Title Year Author Topic
Intercomparison and sensitivity analysis of WRF parameterization schemes for convection-permitting modeling of precipitation distribution along the Yarlung Zangbo River Bian, Qingyun, Wang, Shu, Yang, Heng, Zheng, Hui Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Long-term assessment of cyclonic disturbances over the North Indian Ocean (18912019) using a cloud-based platform with special reference to cyclone Fani Chatterjee, Soumen, Biswas, Biplab Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Global risk pooling mitigates financial risk from drought in hydropower-dependent countries Cuppari, Rosa Isabella, Pavelsky, Tamlin M., Characklis, Gregory W. Snow Cover, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Emissivity, Land Surface Temperature, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Flood pulse monitoring in wetlands with multi-temporal Sentinel-1 interferometric coherence data: Application to the Okavango Delta (Botswana) Gaudare, Louis, Corgne, Samuel, Jolivet, Marc, Dauteuil, Olivier, Doubre, Cecile, Wolski, Piotr, Grandin, Raphael, Doin, Marie-Pierre, Durand, Philippe Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
How armed conflict shapes environmental trends in Yemen? Al-Yaari, Amen, Govind, Ajit Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Joint calibration of multi-scale hydrological data sets using probabilistic water balance data fusion: methodology and application to the irrigated Hindon River Basin ... Mourad, Roya, Schoups, Gerrit, Rajendran, Vinnarasi, Bastiaanssen, Wim Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Milk Matters: Enhancing Early Childhood Nutrition Through Dairy in Central Madagascar Ramahaimandimby, Zoniaina, Shiratori, Sakiko, Rafalimanantsoa, Jules, Sakurai, Takeshi Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Performance Evaluation of a Distributed Hydrological Model Using Satellite Data over the Lake Kastoria Catchment, Greece Papadimos, Dimitris, Papamichail, Dimitris Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Paleoglacial coverage and paleoclimatic reconstruction of MIS 3b and the gLGM in the TurgenAsgat Catchment, Altai Mountains Liu, Chang, Liu, Liang, Zhai, Yijie, Han, Shuting, Yang, Zheng, Zhang, Wei Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Assessment of Groundwater Potential in North-Central Palawan using Remote Sensing and Geophysical Analysis of Fractured Basement Aquifers Cuevas, Joshua Godwin, Cari, John Esteban E., Principe, Jeark A., Tamondong, Ayin M., Dimalanta, Carla B., Armada, Leo T. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Daily and Monthly Scale Comparisons of Three Gridded Precipitation Datasets over the British Columbia Province, Canada Ogawa, Riki, Iseri, Yoshihiko, Kavvas, M. Levent, Duren, Angela M. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Response of Terrestrial Water Storage to Climate: Global Spatial Patterns and Driving Mechanism Zeng, Zhaozhao, Chen, Haonan, Liang, Yuyun, Yu, Long, Chen, Jia, Huang, Bingqing, Li, Jun Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Surface Soil Moisture Drydown over the Tibetan Plateau from SMAP: Dong, Shiyu, Zhu, Zhongli, Zhang, Jinsong, Liu, Ziqi, Wu, Qingxia Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Widespread forest disturbance from windthrow in central African rainforests Negron-Juarez, Robinson, Feng, Yanlei, Sheil, Douglas, Keller, Michael, Ordway, Elsa M., Magnabosco Marra, Daniel, Urquiza-Munoz, Jose D. Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow
20 Years of MCSs simulations over South America using a convection-permitting model Rehbein, Amanda, Prein, Andreas F., Ambrizzi, Tercio, Ikeda, Kyoko, Liu, Changhai, Rasmussen, Roy M. Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain
A dataset of tracked mesoscale precipitation systems in the tropics Russell, James, Rajagopal, Manikandan, Veals, Peter, Skok, Gregor, Zipser, Edward, TinocoMorales, Michell Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
A Framework to Attribute Tropical Multiscale Precipitation Extremes to Carenso, M., Fildier, B., Roca, R., Fiolleau, T. Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain
A multi-method and multi-duration trend analysis of temperature and precipitation in Istanbul, Turkey, by using meteorological records, MERRA-2 reanalysis, and IMERG estimations Sam, Sina, Ozger, Mehmet Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Integration of PhysicsBased and DataDriven Approaches for Landslide Susceptibility Assessment Han, Yi, Semnani, Shabnam J. Soil Depth, Soil Horizons/Profile, Soil Water Holding Capacity, Soil Texture, Soil Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Intensifying tropical cyclones in the Arabian Sea replenish depleting aquifers Saleh, Hassan, Sultan, Mohamed, Yan, Eugene, Save, Himanshu, Elhaddad, Hesham, Karimi, Hadi, Abdelmohsen, Karem, Emil, Mustafa K., Qamshouai, Sara Al Terrestrial Water Storage, Ground Water, Glacier Mass Balance/Ice Sheet Mass Balance, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Making earth observations model-friendly: An interoperable tool enabling Wang, Linji, Rajib, Adnan, Merwade, Venkatesh Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Evapotranspiration, Latent Heat Flux, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Mapping the spatio-temporal distribution of burned areas in the Amazon Abid, Mohamed, Gonzalez, Jonatan A., de Rivera, Oscar Rodriguez, Moraga, Paula Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Land Use/Land Cover Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Air Temperature, Specific Humidity, Evapotranspiration, Wind Speed, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Cover, Snow Depth, Snow Water Equivalent, Runoff, Burned Area, Emissivity
Monitoring Water From Space: An Illustration in Death Valley, California Buzzanga, B., Hamlington, B. D., Bekaert, D. P. S., Pavelsky, T., Handwerger, A., Bonnema, M., Lee, C. Land Use/Land Cover, MECHANICAL DISTURBANCE, DISTURBANCE, Rivers/Streams, Surface Water Processes/Measurements, Lakes/Reservoirs, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Mesoscale Convective Systems over Ecuador: Climatology, Trends and Robaina, Leandro, Campozano, Lenin, Villacis, Marcos, Rehbein, Amanda Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Feng, Zhe, Prein, Andreas F., Kukulies, Julia, Fiolleau, Thomas, Jones, William K., Maybee, Ben, Moon, Zachary L., Nunez Ocasio, Kelly M., Dong, Wenhao, Molina, Maria J., Albright, Mary Grace, Rajagopal, Manikandan, Robledo, Vanessa, Song, Jinyan, Song, Fengfei, Leung, L. Ruby, Varble, Adam C., Klein, Cornelia, Roca, Remy, Feng, Ran, Mejia, John F. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Atmospheric Water Vapor, RADAR, Brightness Temperature
Multiscale Remote Sensing Data Integration for Gully Erosion Monitoring in Southern Brazil: Case Study Breunig, Fabio Marcelo, Mancuso, Malva Andrea, Coimbra, Ana Clara Amalia, Santos, Leonardo Jose Cordeiro, Hempe, Tais Cristina, Frick, Elaine de Cacia de Lima, Nascimento, Edenilson Roberto do, Sampaio, Tony Vinicius Moreira, Gaida, William, Berra, Elias Fernando, Trentin, Romario, Othman, Arsalan Ahmed, Liesenberg, Veraldo Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Muted Amazon Rainfall Response to Deforestation in a Global Yoon, Arim, Hohenegger, Cathy Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Neighborhood-scale reductions in heatwave burden projected under a 30% minimum tree cover scenario Endreny, Theodore A., Ciolfi, Marco, Endreny, Anna, Chiocchini, Francesca, Calfapietra, Carlo Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Increasing temporal stability of global tropical cyclone precipitation Deng, E, Xiang, Qian, Chan, Johnny C. L., Dong, Yue, Tu, Shifei, Chan, Pak-Wai, Ni, Yi-Qing Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock Krishna, R. Phani Murali, Kumar, Siddharth, Prajeesh, A. Gopinathan, Bechtold, Peter, Wedi, Nils, Roy, Kumar, Ganai, Malay, Reddy, B. Revanth, Tirkey, Snehlata, Goswami, Tanmoy, Kanase, Radhika, Sarkar, Sahadat, Deshpande, Medha, Mukhopadhyay, Parthasarathi Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Integrating remote sensing and deep learning forecasting model: A fluid-environment interface study Hassanian, Reza, Cavallaro, Gabriele, Riedel, Morris Reflectance, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
InSAR Reveals Recovery of Stressed Aquifer Systems in Parts of Delhi Kumar, Hrishikesh, Syed, Tajdarul Hassan, Amelung, Falk, Mirzaee, Sara, Venkatesh, A. S., Agrawal, Ritesh Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Influence of Soil Moisture on the Development of Organized Convective Paccini, Laura, Schiro, Kathleen A. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Improvement of Summer Precipitation Simulation Over the Tibetan Plateau: Zhang, Feimin, Cui, Hao, Wang, Hao Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Impacts of the Madden-Julian Oscillation on Widespread Heavy Rainfall LopezBravo, Clemente, Vincent, Claire L., Huang, Yi, Lane, Todd P. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Long-Term and Seasonal Drivers of Organic Matter in the Clearwater Kurek, Martin R., Muniz, Rafael, Moura, Jose M. S., PeuckerEhrenbrink, Bernhard, Holmes, Robert M., McKenna, Amy M., Spencer, Robert G. M. Land Use/Land Cover Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Future soil erosion trends in Canadian agricultural lands from runoff and sustainability impacts Amiri, Afshin, Ebtehaj, Isa, Soltani, Keyvan, Gumiere, Silvio Jose, Bonakdari, Hossein Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Flash Floods Impact in the Upper Citarum Watershed: A Hydrological and Hydraulic Simulation Approach Sapan, E G A, Susanti, W D, Santosa, B H, Wardhani, F A, Widiatmoko, N, Yuvhenmindo, M R, Ridwansyah, I, Triwisesa, E, Pravitasari, A E Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Impacts of Global Warming on Severe Drought in Northern Taiwan: A Future Huang, ShihMing, Lee, TsungYu, Lin, ChuanYao, Lin, YiYing, Hsu, HuangHsiung Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Impacts of Northerly Low-Level Jets on Mesoscale Convective Systems East Mu, Ye, Jones, Charles, Carvalho, Leila M. V., Kukulies, Julia, Prein, Andreas F., Xue, Lulin, Liu, Changhai Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain
Impacts of resolution on heavyprecipitating storms in climate model hindcasts Wu, WenYing, Ma, HsiYen Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Impact assessment of 3D-var data assimilation on simulation of tropical cyclones using WRF Makar, Pragnya, Kumar Singh, Sanjeev, Mitra, Debashis, Kant, Yogesh Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Impact of antecedent rainfall and soil saturation on widespread debris flows in the northern Western Ghats during the 2021 extreme rainfall Islam, Sharib, Thanveer, Jiyadh, Yunus, Ali P., Beetan, Yuvika, Umrikar, Bhavana, Arya, Dhyan Singh, Siva Subramanian, Srikrishnan Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Impact of Dynamic Downscaling on the Simulation of Tropical Easterly DeLaune, Connor, Misra, Vasubandhu, Jayasankar, C. B. Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Impacts of climate trends on the heavy precipitation event associated with Typhoon Doksuri in Northern China Yan, Ziyu, Wang, Zhuo, Peng, Melinda Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Observation of Gravity Waves Generated by Convection and the ``Moving Corcos, Milena, Bramberger, Martina, Alexander, M. Joan, Hertzog, Albert, Liu, Chuntao, Wright, Corwin Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
nextGEMS: entering the era of kilometer-scale Earth system modeling Segura, Hans, Pedruzo-Bagazgoitia, Xabier, Weiss, Philipp, Muller, Sebastian K., Rackow, Thomas, Lee, Junhong, Dolores-Tesillos, Edgar, Benedict, Imme, Aengenheyster, Matthias, Aguridan, Razvan, Arduini, Gabriele, Baker, Alexander J., Bao, Jiawei, Bastin, Swantje, Baulenas, Eulalia, Becker, Tobias, Beyer, Sebastian, Bockelmann, Hendryk, Bruggemann, Nils, Brunner, Lukas, Cheedela, Suvarchal K., Das, Sushant, Denissen, Jasper, Dragaud, Ian, Dziekan, Piotr, Ekblom, Madeleine, Engels, Jan Frederik, Esch, Monika, Forbes, Richard, Frauen, Claudia, Freischem, Lilli, Garcia-Maroto, Diego, Geier, Philipp, Gierz, Paul, Gonzalez-Cervera, Alvaro, Grayson, Katherine, Griffith, Matthew, Gutjahr, Oliver, Haak, Helmuth, Hadade, Ioan, Haslehner, Kerstin, ul Hasson, Shabeh, Hegewald, Jan, Kluft, Lukas, Koldunov, Aleksei, Koldunov, Nikolay, Kolling, Tobias, Koseki, Shunya, Kosukhin, Sergey, Kousal, Josh, Kuma, Peter, Kumar, Arjun U., Li, Rumeng, Maury, Nicolas, Meindl, Maximilian, Milinski, Sebastian, Mogensen, Kristian, Niraula, Bimochan, Nowak, Jakub, Praturi, Divya Sri, Proske, Ulrike, Putrasahan, Dian, Redler, Rene, Santuy, David, Sarmany, Domokos, Schnur, Reiner, Scholz, Patrick, Sidorenko, Dmitry, Spat, Dorian, Sutzl, Birgit, Takasuka, Daisuke, Tompkins, Adrian, Uribe, Alejandro, Valentini, Mirco, Veerman, Menno, Voigt, Aiko, Warnau, Sarah, Wachsmann, Fabian, Wacawczyk, Marta, Wedi, Nils, Wieners, Karl-Hermann, Wille, Jonathan, Winkler, Marius, Wu, Yuting, Ziemen, Florian, Zimmermann, Janos, Bender, Frida A.-M., Bojovic, Dragana, Bony, Sandrine, Bordoni, Simona, Brehmer, Patrice, Dengler, Marcus, Dutra, Emanuel, Faye, Saliou, Fischer, Erich, van Heerwaarden, Chiel, Hohenegger, Cathy, Jarvinen, Heikki, Jochum, Markus, Jung, Thomas, Jungclaus, Johann H., Keenlyside, Noel S., Klocke, Daniel, Konow, Heike, Klose, Martina, Malinowski, Szymon, Martius, Olivia, Mauritsen, Thorsten, Mellado, Juan Pedro, Mieslinger, Theresa, Mohino, Elsa, Pawowska, Hanna, Peters-von Gehlen, Karsten, Sarre, Abdoulaye, Sobhani, Pajam, Stier, Philip, Tuppi, Lauri, Vidale, Pier Luigi, Sandu, Irina, Stevens, Bjorn Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Does Warming of the Tibetan Plateau Intensify or Weaken the Xu, Gan, Shi, Huijie, Shu, Shoujuan, Chen, Xuesong, Gu, Jiabei, Li, Weijun Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Erratum: wet bias of summer precipitation in the northwestern Tibetan Plateau in ERA5 is linked to overestimated lower-level southerly wind over the Plateau Ou, Tinghai, Chen, Deliang, Tang, Jianping, Lin, Changgui, Wang, Xuejia, Kukulies, Julia, Lai, Hui-Wen Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
Escherichia coli in the Niger River: Links to Boubacar Moussa, Moussa, Abdourhamane Toure, Amadou, Lartiges, Bruno, Kergoat, Laurent, Robert, Elodie, Mamane, Aliko, Ribolzi, Olivier, Rochelle-Newall, Emma, Grippa, Manuela Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain
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Variables

The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.

For questions on a specific variable, please use the Earthdata Forum.

Name Description Units Data Type Fill Value Valid Range Scale Factor Offset
Grid/Intermediate/IRinfluence Grid/Intermediate/IRinfluence N/A int16 -9999 N/A N/A N/A
Grid/Intermediate/IRprecipitation Grid/Intermediate/IRprecipitation mm/hr float32 -9999.900390625 N/A N/A N/A
Grid/Intermediate/MWobservationTime Grid/Intermediate/MWobservationTime minutes int16 -9999 N/A N/A N/A
Grid/Intermediate/MWprecipitation Grid/Intermediate/MWprecipitation mm/hr float32 -9999.900390625 N/A N/A N/A
Grid/Intermediate/MWprecipSource Grid/Intermediate/MWprecipSource N/A int16 -9999 N/A N/A N/A
Grid/Intermediate/precipitationUncal Grid/Intermediate/precipitationUncal mm/hr float32 -9999.900390625 N/A N/A N/A
Grid/lat Grid/lat degrees_north float32 N/A N/A N/A N/A
Grid/lat_bnds Grid/lat_bnds degrees_north float32 N/A N/A N/A N/A
Grid/lon Grid/lon degrees_east float32 N/A N/A N/A N/A
Grid/lon_bnds Grid/lon_bnds degrees_east float32 N/A N/A N/A N/A
Grid/precipitation Grid/precipitation mm/hr float32 -9999.900390625 N/A N/A N/A
Grid/precipitationQualityIndex Grid/precipitationQualityIndex N/A float32 -9999.900390625 N/A N/A N/A
Grid/probabilityLiquidPrecipitation Grid/probabilityLiquidPrecipitation percent int16 -9999 N/A N/A N/A
Grid/randomError Grid/randomError mm/hr float32 -9999.900390625 N/A N/A N/A
Grid/time Grid/time seconds since 1980-01-06 00:00:00 UTC int32 N/A N/A N/A N/A
Grid/time_bnds Grid/time_bnds seconds since 1980-01-06 00:00:00 UTC int32 N/A N/A N/A N/A
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