N: 60 S: -60 E: 180 W: -180
Description
These data originate from NOAA/NCEP.
The NOAA Climate Prediction Center/NCEP/NWS is making the data available originally in binary format, in a weekly rotating archive. The NASA GES DISC is acquiring the binary files as they become available, converts them into CF (Climate and Forecast) -convention compliant netCDF-4 format, and stores the product in a permanent archive. The original record started from February, 2000, but in June, 2025 it was extended back to January, 1998.
The leading edge of data availability is delayed by about 24 hours from real-time to abide by international data exchange agreements between NOAA and EUMETSAT (the METEOSAT data providers).
The data contain globally-merged (60°S-60°N) 4-km pixel-resolution IR brightness temperature data (equivalent blackbody temps), merged from the European, Japanese, and U.S. geostationary satellites over the period of record (GOES-8/9/10/11/12/13/14/15/16/17/18/19, METEOSAT-5/7/8/9/10/11, and GMS-5/MTSat-1R/2/Himawari-8/9).
The global geo-IR are dynamically calibrated to GOES East, using a 35 day trailing inter-calibration using time/space-matched IR Tb’s at the mid-point between sub-satellite positions. In the event of duplicate data in a grid box, the value with the smaller zenith angle is taken. The data have been corrected for "zenith angle dependence", in which IR temperatures for locations far from satellite nadir are erroneously cold due to a combination of geometric effects and radiometric path extinction effects (Joyce et al. 2001). Finally, the data are re-navigated for parallax, which shifts the geo-location of the GEO-IR footprints to approximately account for the cloud tops that the IR “sees” being displaced away from their actual geographic location when viewed along a slanted path. These corrections allow for the merging of the IR data from the various GEO-satellites with greatly reduced discontinuities at GEO-satellite data boundaries. In the event of duplicate data in a grid box, the value with the smaller zenith angle is taken.
The NASA GES DISC is curating these data in a self-documenting, CF-compliant, netCDF-4 format, which allows a broad range of applications to access the data directly, without the need to cope with the original binary data format. In addition to the direct download of netCDF-4 data, the GES DISC provides data download in binary, ASCII, and netCDF-3 formats using the OPeNDAP interface which also provides remote data access.
Similarities with the original
As in the original binaries, every netCDF-4 file covers one hour, and contains two half-hourly grids, at 4-km grid cell resolution.
Differences from the original
-
The data in the netCDF-4 files are already converted to physical values of Brightness Temperatures in Kelvin. Because the original data values are round with no decimal precision, the data type in the netCDF-4 files has been changed to 2-byte signed integer, a transition that took place in mid-August, 2025. This reduces the file size and speeds up data download and remote access. There is no need to further scale these data. The netCDF-4 format is machine-independent and users need not worry about the endian-ness of their machines.
-
To meet the requirements of collection spatial metadata, the grid is re-ordered from the original and now goes from -180 (West) to 180 (East). It is also starting from -60 (South).
The data and time units are reflected in the corresponding "units" attributes, and grid dimensions are described by longitude ("lon"), latitude ("lat") and "time" vectors. Thus, any CF-compliant tool should automatically understand the setup in the data files and the starting time for each half-hourly grid. Even without such tools, simple "ncdump" or "h5dump" command line tools will easily disclose the netCDF-4 files configuration.
Acknowledgements
The creation of the original data at NOAA/NCEP is supported by funding from the NOAA Office of Global Programs for the Global Precipitation Climatology Project (GPCP) and by NASA via the Tropical Rainfall Measuring Mission (TRMM).
The permanent archive at GES DISC is supported by NASA's HQ Earth Science Data Systems (ESDS) Program.
Product Summary
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
Documents
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Convective Organization in African Easterly Waves Observed During the | ColonBurgos, D., Bell, M. M. | Precipitation, Brightness Temperature | |
| Nighttime thunderstorms over southwestern Amazon basin: characterization and preconditioning large-local scales ingredients | Nunes, Ana Maria Pereira, da Silva Dias, Maria A. F., da Rocha, Rosmeri | Precipitation, Brightness Temperature | |
| Mesoscale Convective Systems in Northeastern North America: identification and evaluation with the convection-permitting version of the Canadian Regional Climate ... | Alpizar, Milena, Di Luca, Alejandro, Gachon, Philippe, Roberge, Francois | Precipitation, Brightness Temperature | |
| UpperLevel Turbulence in the North American and Asian Summer Monsoon Regions Sampled in Recent Aircraft Campaigns | Atlas, Rachel, Ueyama, Rei, Kim, SooHyun, Bui, Paul, DeanDay, Jonathan, Li, Yaowei, Dykema, John, Keutsch, Frank, Weinzierl, Bernadett, Dollner, Maximilian, Podglajen, Aurelien | Precipitation, Brightness Temperature | |
| Tropical Cyclone OutertoInner Brightness Temperature Ratio: A New SizeAdaptive Parameter Reflecting Storm Intensity and Its Change | Tang, Shi, Huang, Xiaogang, Fei, Jianfang, Cheng, Xiaoping, Yang, Lu, Shao, Shiyu | Precipitation, Brightness Temperature | |
| A derecho climatology (20042021) in the United States based on machine learning identification of bow echoes | Li, Jianfeng, Geiss, Andrew, Feng, Zhe, Leung, L. Ruby, Qian, Yun, Cui, Wenjun | Precipitation, Brightness Temperature | |
| 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 | |
| 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 | |
| Characteristics of Warm-Season Mesoscale Convective Systems with Different Movement Paths Generated over the Tibetan Plateau and Their Subseasonal ... | Qu, Lianglu, Yang, Ben, Huang, Anning, Tang, Jianping, Xu, Xin, Yang, Yuncheng, Wang, Yixiao, Dai, Guoqing, Zhang, Yaocun | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature | |
| Application of stream function in tracking a quasi-closed circulation and its characteristics in developing and non-developing tropical cyclones over the North Indian Ocean | Emmanuel, R., Deshpande, Medha, T.S., Anandh, Toumi, Ralf, Mano Kranthi, Ganadhi, Ingle, S.T. | Precipitation, Brightness Temperature | |
| Key drivers and predictability of the unprecedented 2024 United Arab Emirates flood | Wang, Jingyu, Wang, Xianfeng, Sun, Shuyu, Wen, Yonggang, Pathak, Raju, Dong, Luojie, Park, Edward, Hoteit, Ibrahim | Precipitation, Brightness Temperature | |
| Initial polarimetric radio occultation results from Spire's nanosatellite constellation: Independent assessment and potential applications | Padulles, Ramon, Cardellach, Estel, Paz, Antia, Burger, Thomas | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Extreme local mesoscale convective systems over the South China coast | Wang, Chenli, Chen, Xingchao, Zhao, Kun | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Extreme Sahelian Rainfall Continues to Rise Despite Stable Storm | Spat, Dorian, Biasutti, Michela, Voigt, Aiko | Precipitation, Brightness Temperature, Geopotential Height, Altitude, Surface Temperature, Upper Air Temperature, Dew Point Temperature, Air Temperature, Cloud Top Temperature, Atmospheric Winds, Surface Winds, U/V Wind Components, Upper Level Winds, U/V Wind Components, Vertical Wind Velocity/Speed, Atmospheric Pressure, Sea Level Pressure, Cloud Top Pressure, Sea Level Pressure, Surface Pressure, Specific Humidity, Total Precipitable Water, Cloud Liquid Water/Ice, Atmospheric Water Vapor, Atmospheric Ozone, Oxygen Compounds, Boundary Layer Winds, Skin Temperature, Vertical Profiles, Ozone Profiles, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Identifying the hotspot regions of emerging triple risk due to pre-monsoon convective storms over Kerala, India | E. K., Krishna Kumar, S., Abhilash, C. S., Abhiram Nirmal, Prabath H., Kurup | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Brightness Temperature, Atmospheric Water Vapor | |
| Impact of convectively coupled tropical waves on the composition, vertical structure of the atmosphere, and tropical cyclogenesis in the region of Cabo Verde in ... | Jonville, Tanguy, Borne, Maurus, Flamant, Cyrille, Cuesta, Juan, Bock, Olivier, Bosser, Pierre, Lavaysse, Christophe, Fink, Andreas, Knippertz, Peter | Precipitation, Brightness Temperature | |
| From cause to consequence: examining the historic April 2024 rainstorm in the United Arab Emirates through the lens of climate change | Francis, Diana, Fonseca, Ricardo, Nelli, Narendra, Cherif, Charfeddine, Yarragunta, Yesobu, Zittis, George, Jan de Vries, Andries | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain, Aerosols, Aerosol Extinction, Aerosol Optical Depth/Thickness, Angstrom Exponent, Aerosol Particle Properties, Carbonaceous Aerosols, Dust/Ash/Smoke, Organic Particles, Sulfate Particles, Sulfur Oxides, Sulfur Compounds, Sulfate, Sulfur Dioxide, Sulfur Oxides, Particulate Matter, Dimethyl Sulfide, Black Carbon, Sea Salt, PARTICULATE MATTER (PM 2.5), PARTICULATE MATTER (PM 1.0), PARTICULATE MATTER (PM 10), Geopotential Height, Altitude, Surface Temperature, Upper Air Temperature, Dew Point Temperature, Air Temperature, Cloud Top Temperature, Atmospheric Winds, Surface Winds, U/V Wind Components, Upper Level Winds, U/V Wind Components, Vertical Wind Velocity/Speed, Atmospheric Pressure, Sea Level Pressure, Cloud Top Pressure, Sea Level Pressure, Surface Pressure, Specific Humidity, Total Precipitable Water, Cloud Liquid Water/Ice, Atmospheric Water Vapor, Atmospheric Ozone, Oxygen Compounds, Boundary Layer Winds, Skin Temperature | |
| Highlighting the Impact of Anthropogenic OCS Emissions on the | Gurganus, Colin, Rollins, Andrew, Waxman, Eleanor, Pan, Laura L., Smith, Warren P., Ueyama, Rei, Feng, Wuhu, Chipperfield, Martyn P., Atlas, Elliot L., Schwarz, Joshua P., DeLone, Samantha, Thornberry, Troy | Precipitation, Brightness Temperature, Atmospheric Carbon Dioxide, Atmospheric Carbon Monoxide, Methane, Atmospheric Ozone, Particulate Matter, Aerosol Optical Depth/Thickness, Aerosol Extinction, Aerosol Backscatter, Sulfate Particles, Dew Point Temperature, Nitrogen Dioxide, Nitrate Particles, Nitrogen Oxides, Organic Particles, Volatile Organic Compounds, Formaldehyde, Relative Humidity, Wind Speed, Wind Direction, Photolysis Rates, Aerosol Forward Scatter, Vapor Pressure, Saturation Vapor Pressure, Platform Characteristics, Atmospheric Temperature, Atmospheric Pressure, Static Pressure, Atmospheric Water Vapor, Peroxyacetyl Nitrate, ETHANE, Halocarbons And Halogens, Chlorofluorocarbons, Hydrochlorofluorocarbons, Hydrofluorocarbons, Aerosol Concentration, Aerosol Size Distribution, AEROSOL ABSORPTION, AEROSOL SINGLE SCATTERING ALBEDO, Chemical Composition, Organic Particles, Black Carbon, AMMONIUM AEROSOLS, Oxygen Compounds, Hydrogen Compounds, Nitrogen Compounds, Nitrous Oxide, Glyoxal, Sulfur Dioxide | |
| 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 | |
| Environmental conditions affecting global mesoscale convective system occurrence | Muetzelfeldt, Mark R., Plant, Robert S., Christensen, Hannah M., Zhang, Zhixiao, Woollings, Tim, Feng, Zhe, Li, Puxi | Precipitation, Brightness Temperature | |
| Evolution and Characteristics of Mesoscale Convective Systems over the Congo Basin | Dong, Zeyao, Washington, Richard | Precipitation, Brightness Temperature, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Examining Clustered MCSs and Their Precipitation Significance Over | Hu, Huancui, Feng, Zhe, Leung, L. Ruby, Marquis, James | Precipitation, Brightness Temperature | |
| Drivers and Trends of Summertime Convection Over the Southeastern Arabian Peninsula | Nelli, Narendra, Francis, Diana, Fonseca, Ricardo, Delle Monache, Luca, Al Mandous, Abdulla | Precipitation, Brightness Temperature | |
| Convection-permitting climate simulations over South America: | Liu, Changhai, Ikeda, Kyoko, Prein, Andreas, Scaff, Lucia, Dominguez, Francina, Rasmussen, Roy, Huang, Yongjie, Dudhia, Jimy, Wang, Wei, Chen, Fei, Xue, Lulin, Fita, Lluis, Lagos-Zuniga, Miguel, Lavado-Casimiro, Waldo, Masiokas, Mariano, Puhales, Franciano, Yepes, Leidy Johanna | Precipitation, Brightness Temperature | |
| Origin, size distribution, and hygroscopic properties of marine aerosols in the southwestern Indian Ocean: results of six campaigns of shipborne observations | Dournaux, Meredith, Tulet, Pierre, Pianezze, Joris, Brioude, Jerome, Metzger, Jean-Marc, Thyssen, Melilotus, Athier, Gilles | Precipitation, Brightness Temperature |
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 Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| lat | lat | degrees_north | float32 | N/A | N/A | N/A | N/A |
| lon | lon | degrees_east | float32 | N/A | N/A | N/A | N/A |
| Tb | Tb | K | float32 | -9999 | N/A | N/A | N/A |
| time | time | days since 1970-01-01 00:00:00 | float64 | N/A | N/A | N/A | N/A |