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547 results for “GPM”
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2019
<p><strong># GPM_API 2019</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2016
<p><strong># GPM_API 2016</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2018
<p><strong># GPM_API 2018</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2017
<p><strong># GPM_API 2017</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2020
<p><strong># GPM_API 2020</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
PyFLEXTRKR Global MCS Tracking Dataset using GPM MergedIR Tb and IMERG precipitation data
<p>This is the global mesoscale convective system (MCS) tracking dataset developed by <a href="https://doi.org/10.1029/2020JD034202">Feng et al. (2021) JGR</a>. It contains the MCS track data (location, time, lifecycle evolution of MCS cloud and precipitation characteristics), monthly mean and 20-year climatological MCS statistics on 0.1 degree x 0.1 degree (lat x lon) grid. All data files are in netCDF format.</p><p>The periods are from June 2000 to December 2020. The geographic coverage is 180°W-180°E, 60°S-60°N. For more detailed documentations, please refer to the README "PyFLEXTRKR_MCS_Tracking_Data_Readme.pdf".</p><p>Due to the large file size of the native 1-hourly resolution pixel-level data on the 0.1 degree x 0.1 degree grid, they are not included in this dataset. Please contact Zhe Feng (<a href="mailto:zhe.feng@pnnl.gov">zhe.feng@pnnl.gov</a>) if you are interested in obtaining the pixel-level data.</p>
Database for "A new perspective for charactering the spatio-temporal patterns of the error in GPM IMERG over mainland China"
<p>This file contains the <strong>dataset</strong> accompanying the manuscript '<strong>2020EA001232-TR'</strong> submitted to the <strong>ESS</strong> journal (https://earthandspacescience-submit.agu.org).</p> <p><strong>Title</strong>: "A new perspective for charactering the spatio-temporal patterns of the error in GPM IMERG over mainland China"</p> <p>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.</p> <p>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.</p> <p>The Shuttle Radar Topography Mission data (SRTM, with a 90-m spatial resolution) could be accessed at http://srtm.csi.cgiar.org.</p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data
<p>This repository serves as hub for the single <strong><em>GPM_API data set repositories</em></strong> related to the publication</p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <ul> <li>GPM_API 2015: <a href="https://doi.org/10.5281/zenodo.6353260">10.5281/zenodo.6353260</a></li> <li>GPM_API 2016: <a href="https://doi.org/10.5281/zenodo.6413889">10.5281/zenodo.6413889</a></li> <li>GPM_API 2017: <a href="https://doi.org/10.5281/zenodo.6413905">10.5281/zenodo.6413905</a></li> <li>GPM_API 2018: <a href="https://doi.org/10.5281/zenodo.6413907">10.5281/zenodo.6413907</a></li> <li>GPM_API 2019: <a href="https://doi.org/10.5281/zenodo.6413909">10.5281/zenodo.6413909</a></li> <li>GPM_API 2020: <a href="https://doi.org/10.5281/zenodo.6413911">10.5281/zenodo.6413911</a></li> </ul> <p> </p> <p>The related article can be found here:<br> Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
A high-resolution GPM IMERG precipitation dataset for China
<p>The database of the paper: An attention mechanism based convolutional network for satellite precipitation downscaling over China</p>
Look-up table data used in the GPM Spectral Latent Heating retrieval for the midlatitudes
<h3>Overview</h3> <p>This dataset contains the look-up table (LUT) data used for the midlatitude retrieval of the Global Precipitation Measurement (GPM) Spectral Latent Heating (SLH) V07 product. The LUTs that tie heating profiles to precipitation characteristics are constructed using Local Forecast Model simulations of eight extratropical cyclones around Japan. The LUTs for the following six categories: convective, shallow stratiform, downward increasing (DI) deep stratiform, downward decreasing (DD) deep stratiform, SUB0 (where the 0°C level is near the surface) deep stratiform, and OTHER. The precipitation top height (PTH) serves as an index for LUTs for convective and shallow stratiform, while the maximum precipitation rate (PMAX) is used as an index for LUTs for three deep stratiform and OTHER types. Note that LUTs for DD, DI, and SUB0 are subdivided based on whether the PMAX appears above or at the surface.</p> <p> </p> <h3>Contents</h3> <p>There are two directories named "data" and "fortran90".</p> <p>In the directory named "data", there are the following nine files for each-type LUT:</p> <p> 1. Convective LUT: lut_convective _midlatSLH.grd</p> <p> 2. Shallow stratiform LUT: lut_shallow-stratiform _midlatSLH.grd</p> <p> 3. DD deep strtiform LUT with PMAX above the surface with: lut_DD-stratiform_pmaxABOVEsfc_midlatSLH.grd</p> <p> 4. DD deep strtiform LUT with PMAX at the surface with: lut_DD-stratiform_pmaxATsfc_midlatSLH.grd</p> <p> 5. DI deep strtiform LUT with PMAX above the surface with: lut_DI-stratiform_pmaxABOVEsfc_midlatSLH.grd</p> <p> 6. DI deep strtiform LUT with PMAX at the surface with: lut_DI-stratiform_pmaxATsfc_midlatSLH.grd</p> <p> 7. SUB0 deep strtiform LUT with PMAX above the surface with: lut_SUB0-stratiform_pmaxABOVEsfc_midlatSLH.grd</p> <p> 8. SUB0 deep strtiform LUT with PMAX at the surface with: lut_SUB0-stratiform_pmaxATsfc_midlatSLH.grd</p> <p> 9. OTHER LUT: lut_OTHER_midlatSLH.grd</p> <p>The LUT data are in the plain binary format (single-precision real number, little endian).</p> <p>The LUT values of latent heating, apparent heat source minus radiative heating, and apparent moisture sink are included in each file. Unit is K/h. </p> <p>The convective and shallow stratiform LUTs have 176 PTH bins and 176 vertical level bins, from the surface to 21.875 km at each 125 m.</p> <p>On the other hand, the deep stratiform and OTHER LUTs have 35 PMAX bins defined as follows: 0.2–0.3, 0.3–0.7, 0.7–1, 1–1.5, 1.5–2, 2–2.5, 2.5–3, 3–4, 4–5, 5–6, 6–7, 7–8, 8–9, 9–10, 10–11, 11–12, 12–13, 13–14, 14–15, 15–16, 16–17, 17–18,18–19, 19–20, 20–21, 21–22, 22–23, 23–24, 24–25, 25–26, 26–27, 27–28, 28–29, 29–30, ≥30 mm/h. There are 353 vertical level bins for altitudes standardized by the level of PMAX, PTH, and precipitation bottom height, from -8.8 to 8.8 with an interval of 0.05.</p> <p>Please see Yokoyama et al. (2024, J. Appl. Meteor. Climatol., under review) for details.</p> <p> </p> <p>In the directory named "fortran90", there are two fortran90 programs to read the LUT data. The program named “read_lut_type-pth.f90” is for the convective and shallow stratiform LUTs, while the program named “read_lut_type-pmax.f90” is for the deep stratiform and OTHER LUTs.</p> <p> </p> <p>The data would be updated. </p>
GPM-GMI full-swath precipitation type flag
<p>This project includes the full-swath GPM-GMI precipitation type flag retrieval for the year 2014. Due to file size limit, only March, April, June, July, August, November and December data are uploaded. Users are encouraged to contact the publisher for more days of retrievals. Retrieval is available at each GMI 1CR footprint as a probability. The reference paper is:</p> <p>Das, S., Y. Wang, <a href="https://science.gsfc.nasa.gov/sed/index.cfm?fuseAction=people.jumpBio&iphonebookid=40680">J. Gong</a>, <a href="https://science.gsfc.nasa.gov/sed/#">et al.</a> 2022. "A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements." <em>Remote Sensing</em>, <strong>14 </strong> <strong>(15):</strong> 3631 [<a href="http://dx.doi.org/10.3390/rs14153631">10.3390/rs14153631</a>]</p> <p>Note that in the paper, retrievals are only available over land, but we made it available globally later on and are included in these data files.</p> <p>Users are directed to read the "README" file for details on how to screen and use the data. Two plotting sample codes are provided in IDL and Python respectively.</p>
Processed GPM-DPR Convective Profiles (April 2014 - November 2023)
Open the record for dataset details and reuse information.
TRMMPR and GPM KuPR V8 Level 2A Processed Convective Rain States 2010-2021
<p>This dataset contains Interactive Data Language (IDL) .sav files of Convective Rain States (CRS) from 2010-2021 which are created by k-means clustering on TRMMPR Version 8 and GPM KuPR Version 8 Level 2A data. It also contains IDL scripts in the TRMM.zip and GPM.zip file that were used to process the raw TRMMPR and GPM KuPR V8 data into the available CRS data. This set is part 2 in a continuation of the data available in <a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.5281_zenodo.7853386&d=DwMGaQ&c=009klHSCxuh5AI1vNQzSO0KGjl4nbi2Q0M1QLJX9BeE&r=WTOc26e3IBrEp5cDEaVnuU41G2J-MLym-VhzO4aTABg&m=dHNcst9cbr8tlYpnfdrmqG6MCbrWJa8oa0eBAL_XEyqXQY8MPg2RqBoIe8jxqaAd&s=CnET26HiWgS_mV-kbFHpR3iojlRFOREml99vwWXs4Ig&e=">https://doi.org/10.5281/zenodo.7853386</a>. Details on this clustering can be found in Elsaesser et al. (2010) <a href="https://doi.org/10.1175/2010JCLI3330.1">https://doi.org/10.1175/2010JCLI3330.1</a>.</p> <p> </p> <p>Readme:</p> <p>- Each line in the seriesFiles txt files points to the directory where the raw downloaded data was originally held. The raw TRMMPR and GPM KuPR V8 Level 2A data was downloaded from NASA EOSDIS PPS GPM public data archive.</p> <p>- There are two different total number of entries between all the variables in the processed data. This is because some missing values were saved in some variables and not in others. These missing variables are denoted by -1's in the prcid variable (as well as other variables), and can be located and removed under that condition.</p>
Evaluation of GPM DPR rain parameters with north Taiwan disdrometers
<p>The attached files contain the rainfall and raindrop size distribution parameters from the North Taiwan disdrometers and GPM DPR.</p>
Precipitation identifiers for meteorological features combining global GPM-IMERG retrievals and ERA5 reanalysis
Open the record for dataset details and reuse information.
AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2009-2015) by calibrating GPM IMERG at daily scale using APHRODITE
<p>The datasets provide a new AIMERG precipitation data (0.1°/ half-hourly, 2009-2015, Asia) with better quality, calibrated by GPM IMERG (0.1°/half-hourly) and APHRODITE (0.25°/Daily) at daily scale for the Asian applications.</p> <p><strong>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.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E-150°E, 15°S-55°N, land.</p> <p>AIMERG (0.1°/ half-hourly, 2000-2008, Asia) is available at <a href="https://doi.org/10.5281/zenodo.3609352">https://doi.org/10.5281/zenodo.3609352</a></p>
AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2000-2008) by calibrating GPM IMERG at daily scale using APHRODITE
<p>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.</p> <p><strong>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.</strong></p> <p>Data Format: GeoTIFF</p> <p>Spatial Coverage: 60°E-150°E, 15°S-55°N, land.</p> <p>AIMERG (0.1°/ half-hourly, 2009-2015, Asia) is available at <a href="http://10.5281/zenodo.3609507">http://10.5281/zenodo.3609507</a></p>
GPM+SM2RAIN (2007-2018): quasi-global 25km/daily rainfall product from the integration of GPM and SM2RAIN-based rainfall products
<p>Rainfall dataset based on the integration of IMERG-LR of the Global Precipitation Measurement Mission (GPM) with SM2RAIN-based rainfall estimates derived from ASCAT H113 H-SAF , SMOS L3 and SMAP L3 soil moisture products. The dataset is available +/-60 ° (2007-2018). More details on the grid and data can be found in the netCDF file.</p> <p>The quality flag provided with the dataset has been used to mask out low quality data, as well as the areas characterised by complex topographic, frozen soil, and presence of tropical forests.</p> <p>The associated <a href="https://www.hydrol-earth-syst-sci.net/24/2687/2020/">publication is avalaible on Hydrology and Earth System Science journal</a></p> <p> </p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2015
<p><strong># GPM_API 2015</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
Effect of Good Pain Management (GPM) Ward Program on Moderate to Severe Cancer Pain Patients
ClinicalTrials.gov study NCT03155516. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.
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