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, half-hour resolution. From the official GPM IMERG site at NASA GES DISC [https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGHH_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 the 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, 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 Half-Hourly 0.1 degree x 0.1 degree (GPM_3IMERGHH). The dataset represents the Final Run estimate of the native half-hourly precipitation rate in millimeters per hour. The complete global "precipitation" data field is complete except for a few...

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 Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high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.

This file contains the dataset accompanying the manuscript '2020EA001232-TR' submitted to the ESS journal (https://earthandspacescience-submit.agu.org).

Title: "A new perspective for charactering the spatio-temporal patterns of the error in GPM IMERG over mainland China"

China Merged Precipitation Analysis data (CMPA, hourly, with the resolution of , as validation data) for China Mainland is available at website http://data.cma.cn.

Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrieval data (IMERG, half-hourly, with the resolution of , as the observed data) is available at https://pmm.nasa.gov/data-access/downloads/gpm.

The Shuttle Radar Topography Mission data (SRTM, with a 90-m spatial resolution) could be accessed at http://srtm.csi.cgiar.org.

This file contains the dataset accompanying the manuscript '2020JD032820' submitted to the JGR journal (https://jgr-atmospheres-submit.agu.org). And more details can be found in Readme.file.

The data used in this study can be download from public website, and the generated dataset is listed in the order of figures with detail information in ReadMe file.

China Merged Precipitation Analysis data (CMPA, hourly, with the resolution of , as validation data) for China Mainland is available at website http://data.cma.cn.

Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrieval data (IMERG, half-hourly, with the resolution of , as the observed data) is available at https://pmm.nasa.gov/data-access/downloads/gpm.

The Shuttle Radar Topography Mission data (SRTM, with a 90-m spatial resolution) could be accessed at http://srtm.csi.cgiar.org.

The data collection provides 0.25-deg., 6-hourly global feature-precipitation categories from 2001 to 2019. The data is generated by merging GPM-IMERG observational rainfall (V6 final version) and atmospheric features identified by multiple object-based algorithms. Classified precipitation identifiers include rainfall associated with atmospheric rivers (AR), frontal systems (FT), low-pressure systems (LPS), mesoscale convective systems (MCS), and their co-occurrences (overlapping areas of features at a given time). In addition to algorithm-identified features, precipitation contributed from deep convection, non-deep convection, stratiform, and drizzle are pixel-wise defined using thresholds of CPC MERGE-IR brightness temperature and GPM-IMERG rain rate. The dataset is supported by the Department of Energy and Environment (DOEE): DE-SC0023244. Methods The categorization of global precipitation relies on recognizing four primary atmospheric features: atmospheric rivers (ARs), fronts (FTs), mesoscale convective systems (MCSs), and low-pressure systems (LPSs). Initially, identified atmospheric features with varying temporal and spatial resolutions are harmonized into a unified framework (6-hourly and 0.25-degree). GPM-IMERG precipitation data (0.1-degree resolution) is then coarse-grained to 0.25-degree for labeling using merged feature outputs. Additionally, precipitation attributed to deep convection, non-deep convection, stratiform, and drizzle is discerned at the pixel level using MERGE-IR brightness temperature data alongside GPM-IMERG precipitation. These classifications exclusively apply to rainy pixels not aligned with the four primary features. Rainy pixels within a specific feature boundary are considered associated with that feature object. For frontal systems represented as line segments, the line-segment masks are expanded outward by 250 km to generate two-dimensional bounded features. The identification of precipitation sources is conducted independently every 6 hours over 19 years (2001-2019). Detailed methodologies and demonstrations are accessible at https://docs.google.com/document/d/1O8NQesgyjIXv2X37wLsZ1EhgBdRKtvtPR7OYNBSBdBs/edit

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.

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.

Precipitation estimates with high accuracy and fine spatial resolution play an important role in the field of meteorology, hydrology, and ecology. In this study, support vector machine (SVM) and back-propagation neural network (BPNN) machine learning algorithms were used to downscale the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) data at daily scale through four events selected from 2017 and 2018 by establishing the relationships between precipitation and six environmental variables over Zhejiang, Southeastern China. The downscaled results were validated by ground observations, and we found that (1) generally, the SVM-based products had better performance and finer spatial textures than the BPNN-based products, the multiple linear regression (MLR)-based products, and the original IMERG; (2) all downscaled products decreased the degree of overestimation of the original IMERG at heavy-precipitation regions to a certain extent; (3) for heavy-precipitation events in the plum rain season, the downscaled products based on SVM and BPNN both improved prediction accuracy compared to the MLR-based products and the original IMERG considering the validations against ground observations. R2 maximally increased from 0.344 to 0.615 for the SVM-based products and from 0.344 to 0.435 for the BPNN-based products compared to the original IMERG; and (4) for typhoon precipitation events, the SVM-based products still showed better accuracy with R2 maximally increased from 0.492 to 0.615 compared to the original IMERG. In contrast, the performance of BPNN-based products was not satisfying and showed no significant differences with the performance of MLR-based products. This study provided a potential solution for generating downscaled satellite-based precipitation products at meteorological scales with finer accuracy and spatial resolutions.

Precipitation estimates with high accuracy and fine spatial resolution play an important role in the field of meteorology, hydrology, and ecology. In this study, support vector machine (SVM) and back-propagation neural network (BPNN) machine learning algorithms were used to downscale the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) data at daily scale through four events selected from 2017 and 2018 by establishing the relationships between precipitation and six environmental variables over Zhejiang, Southeastern China. The downscaled results were validated by ground observations, and we found that (1) generally, the SVM-based products had better performance and finer spatial textures than the BPNN-based products, the multiple linear regression (MLR)-based products, and the original IMERG; (2) all downscaled products decreased the degree of overestimation of the original IMERG at heavy-precipitation regions to a certain extent; (3) for heavy-precipitation events in the plum rain season, the downscaled products based on SVM and BPNN both improved prediction accuracy compared to the MLR-based products and the original IMERG considering the validations against ground observations. R2 maximally increased from 0.344 to 0.615 for the SVM-based products and from 0.344 to 0.435 for the BPNN-based products compared to the original IMERG; and (4) for typhoon precipitation events, the SVM-based products still showed better accuracy with R2 maximally increased from 0.492 to 0.615 compared to the original IMERG. In contrast, the performance of BPNN-based products was not satisfying and showed no significant differences with the performance of MLR-based products. This study provided a potential solution for generating downscaled satellite-based precipitation products at meteorological scales with finer accuracy and spatial resolutions.

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 (mm/hour) in every non-missing grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before V07, in which the simple daily totals were computed even if the cell had less than 48 non-missing half-hourly observations for the day. This under-sampling is very rare in V07 except directly at the poles. Thus, in most cases users of global "precipitation" data field would not notice any difference. This change, however, is noticeable in the microwave-only data field, variable "MWprecipitation", where 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 V07, 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 so that users may gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so.

See the official GPM IMERG site at NASA GES DISC [https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_07/summary] for the complete dataset abstract and more information.

Monthly precipitation in mm based on SM2RAIN-ASCAT 2007-2018 (https://doi.org/10.5281/zenodo.2615278). Downscaled to 1 km resolution using gdalwarp (cubic splines) and an average between WorldClim (http://biogeo.ucdavis.edu/data/worldclim/v2.0/), CHELSA Climate (https://www.wsl.ch/lud/chelsa/data/climatologies/prec/) and IMERGE monthly product (ftp://jsimpson.pps.eosdis.nasa.gov/NRTPUB/imerg/gis/ see files e.g. "3B-MO-L.GIS.IMERG.20180601.V05B.tif"). Processing steps are available here. Antartica is not included. To access and visualize maps use: https://landgis.opengeohub.org If you discover a bug, artifact or inconsistency in the LandGIS maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://github.com/Envirometrix/LandGISmaps/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, precipitation = variable: precipitation, sm2rain.oct = determination method: SM2RAIN-ASCAT long-term average values for October, m = mean value, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2007..2018 = time reference: from 2007 to 2018, v0.2 = version number: 0.2, {"references": ["Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Sch\u00fcller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. submitted to Earth System Science Data.", "Karger, D. N., Conrad, O., B\u00f6hner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., ... & Kessler, M. (2017). Climatologies at high resolution for the earth's land surface areas. Scientific data, 4, 170122.", "Huffman, G. J., D. T. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, and P. Xie, (2014). NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), Algorithm Theoretical Basis Document (ATBD). https://storm- pps.gsfc.nasa.gov/storm/IMERG_ATBD_V4.pdf", "Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1\u2010km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315."]}

Die aktualisierten Daten finden Sie unter: http://dx.doi.org/10.5445/IR/1000127274 Base dataset: Integrated Multi-satellitE Retrievals for GPM (IMERG) V06 dataset (doi:10.5067/GPM/IMERG/3B-HH/06) Data format: netCDF Time resolution: daily (365 calendar days) Latitude band: 40°S–40°N Spatial resolution: 0.1°x0.1° (800x3600 points) Content: Parameters (prob, shape, scale) of a fitted Bernoulli-gamma distribution derived from +/-15 days windows around each calendar day of the years 2001–2018. Value: The EPC dataset provides statistical distributions of daily rainfall for the entire global tropics with a high spatial resolution using one of the most accurate satellite-based rainfall estimates. This can be used for a climatological characterization and as a probabilistic reference forecast against which weather forecasts generated with computer or statistical models can be evaluated. Application example: Vogel et al. (2018, doi:10.1175/WAF-D-17-0127.1) Reference: The research leading to these results has been accomplished within project C2 "Statistical-dynamical forecasts of tropical rainfall" of the Transregional Collaborative Research Center SFB/TRR 165 Waves to Weather funded by the German Science Foundation (DFG).

TechnicalRemarks: Die aktualisierten Daten finden Sie unter: http://dx.doi.org/10.5445/IR/1000127274 Base dataset: Integrated Multi-satellitE Retrievals for GPM (IMERG) V06 dataset (doi:10.5067/GPM/IMERG/3B-HH/06) Data format: netCDF Time resolution: daily (365 calendar days) Latitude band: 40°S–40°N Spatial resolution: 0.1°x0.1° (800x3600 points) Content: Parameters (prob, shape, scale) of a fitted Bernoulli-gamma distribution derived from +/-15 days windows around each calendar day of the years 2001–2018. Value: The EPC dataset provides statistical distributions of daily rainfall for the entire global tropics with a high spatial resolution using one of the most accurate satellite-based rainfall estimates. This can be used for a climatological characterization and as a probabilistic reference forecast against which weather forecasts generated with computer or statistical models can be evaluated. Application example: Vogel et al. (2018, doi:10.1175/WAF-D-17-0127.1) Reference: The research leading to these results has been accomplished within project C2 "Statistical-dynamical forecasts of tropical rainfall" of the Transregional Collaborative Research Center SFB/TRR 165 Waves to Weather funded by the German Science Foundation (DFG).

In this dataset, the MODIS vegetation index and land surface temperature products are processed into NDVI and LST monthly time series with a spatial resolution of 1 km, and the final precipitation data of GPM IMERG are downscaled, unified at a spatial resolution of 1 km. And after a standardization process, using the spatial distance model, a remote sensing drought monitoring dataset in China from 2001 to 2020 was produced based on the Temperature Vegetation Precipitation Dryness Index. For the specific construction process of this data, please refer to https://linkinghub.elsevier.com/retrieve/pii/S0034425720303278

This dataset is an update of our previous dataset published on doi:10.1594/PANGAEA.912597. Based on 11 well-acknowledged global-scale microwave remote sensing-based surface soil moisture products, and with 9 main quality impact factors of microwave-based soil moisture retrieval incorporated, we developed the Remote Sensing-based global Surface Soil Moisture dataset (RSSSM, 2003~2020) through a complicated neural network approach. The spatial resolution of RSSSM is 0.1°, while the temporal resolution is approximately 10 days. The original dataset covered 2003~2018, but now it has been updated to 2020. RSSSM dataset is outstanding in terms of temporal continuity, and has full spatial coverage except for snow, ice and water bodies. The comparison against the global-scale in-situ soil moisture measurements indicates that RSSSM has a higher spatial and temporal accuracy than most of the frequently-used global/regional long-term surface soil moisture datasets. In addition, although RSSSM is remote sensing based, without the incorporation of any precipitation data or records, its interannual variation generally conforms with that of precipitation (e.g., the GPM IMERG precipitation data) and Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, RSSSM can also reflect the impact of human activities, e.g., urbanization, cropland irrigation and afforestation on soil moisture changes to some degree. The data is in 'Tiff' format, and the size after compression is 2.44 GB. The relevant data describing paper has been published in the Journal 'Earth System Science Data' in 2021.

Under the background of global warming, the frequency and intensity of drought are increasing. The lack of water resources, food crisis and ecological deterioration (such as desertification) caused by drought disasters directly threaten the national food security and social and economic development. The technical level of drought disaster risk assessment and emergency management needs to be improved. One belt, one road area has one belt, one road area is fragile, agricultural land is concentrated and drought is frequent. Monitoring the drought level and its temporal and spatial changes in large areas by using remote sensing satellites is of great scientific and practical significance for scientifically grasping the drought pattern, regional differentiation characteristics and its impact on agricultural land in the "one belt and one road" area. The percentage of precipitation anomaly reflects the deviation degree between the precipitation of a certain period and the average state of the same period, expressed as a percentage. Based on the daily rainfall data of GPM imerg final run (GPM), the precipitation of corresponding area is calculated. The distribution characteristics of drought of different grades are analyzed by using the grade evaluation index of precipitation anomaly percentage. The spatial resolution is 200m. The data area is 34 key nodes of Pan third pole (Abbas, Astana, Colombo, Gwadar, Mengba, Teheran, Vientiane, etc.).

Precipitation is a key parameter in the water cycle process, which is of great significance for the study of regional water cycle processes, water resources, and ecological environment assessment. This precipitation dataset is downscaled from precipitation of the TMPA-3B42 and GPM-IMERG. By introducing high-resolution geostationary satellite observation data, terrain data, and vegetation index parameters, a spatial downscaling model of precipitation data was constructed to achieve spatial downscaling of the TMPA-3B42 and GPM-IMERG precipitation data. Finally, a precipitation dataset with spatial resolution of 0.05 °× 0.05 ° and temporal resolution of 3 hours was produced for the Tibetan Plateau from 2000 to 2020. The data from 2000 to 2019 was downscaled from the TMPA-3B42, while the precipitation in the year of 2020 was downscaled from the GPM-IMERG data. The downscaled precipitation dataset was compared and verified with the daily cumulative precipitation of rain gauges in the Tibetan Plateau for the whole year of 2014. The root mean square error of this dataset on a daily scale is 5.53mm/day. This dataset can be used to analyze the spatial distribution characteristics and temporal trends of precipitation in the Tibetan Plateau. Besides, the dataset can also be used in water resource assessment in the Tibetan Plateau.

Based on 11 well-acknowledged global-scale microwave remote sensing-based surface soil moisture products, and with 9 main quality impact factors of microwave-based soil moisture retrieval incorporated, we developed the Remote Sensing-based global Surface Soil Moisture dataset (RSSSM, 2003~2020) through a complicated neural network approach. The spatial resolution of RSSSM is 0.1°, while the temporal resolution is approximately 10 days. The original dataset covered 2003~2018, but now it has been updated to 2020. RSSSM dataset is outstanding in terms of temporal continuity, and has full spatial coverage except for snow, ice and water bodies. The comparison against the global-scale in-situ soil moisture measurements indicates that RSSSM has a higher spatial and temporal accuracy than most of the frequently-used global/regional long-term surface soil moisture datasets. In addition, although RSSSM is remote sensing based, without the incorporation of any precipitation data or records, its interannual variation generally conforms with that of precipitation (e.g., the GPM IMERG precipitation data) and Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, RSSSM can also reflect the impact of human activities, e.g., urbanization, cropland irrigation and afforestation on soil moisture changes to some degree. The data is in ‘Tiff’ format, and the size after compression is 2.48 GB. The relevant data describing paper has been published in the Journal ‘Earth System Science Data’ in 2021.