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26 results for “Daily rainfall”
SM2RAIN-ASCAT (2007-2021) global daily satellite rainfall including aggregated values and trend parameters as 10km resolution GeoTIFFs
<p>This is a GeoTIFF version of the <a href="http://hydrology.irpi.cnr.it/download-area/sm2rain-data-sets/">SM2RAIN-ASCAT (2007-2021): global daily satellite rainfall from ASCAT soil moisture</a> data set v1.1 (Brocca et al. 2019). Conversion steps are available <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Few important notes:</p> <ul> <li>Daily values are stored as integers, whereas in the NetCDF the dataset is rounded to one decimal place.</li> <li>The NetCDF has also a Quality Flag for a better and more informed use of the data (here omitted).</li> <li>P05, P50 and P95 indicate quantiles derived per pixel.</li> </ul> <p>Includes also long-term trends (trend.logit.ols) which was produced by fitting regression models to de-seasonalized time-series as explained in this <strong><a href="https://gitlab.com/openlandmap/global-layers/-/blob/master/input_layers/MOD13Q1/03-data-access.ipynb">python tutorial</a></strong>. Basically models are fitted for <strong>each pixel</strong> and the model parameters are saved as images.</p> <p>Monthly averages and s.d. of precipitation are available in the files:</p> <ul> <li>clm_precipitation_sm2rain.*_m_10km_s0..0cm_2007..2021_v1.5.tif = monthly precipitation in mm,</li> <li>clm_precipitation_sm2rain.*_sd.10_10km_s0..0cm_2007..2021_v1.5.tif = standard deviation of precipitation in mm * 10 per month (multiplied by 10 so Integers can be used),</li> </ul> <p>Downscaled monthly averages (1 km) are also available (<a href="https://doi.org/10.5281/zenodo.1435912">https://doi.org/10.5281/zenodo.1435912</a>).</p> <p>To cite this data set please refer to the <strong><a href="https://doi.org/10.5281/zenodo.2591214">original copy</a></strong> of the data set.</p> <ul> <li>Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). <strong><a href="https://doi.org/10.5194/essd-11-1583-2019">SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations</a></strong>. Earth Syst. Sci. Data, 11, 1583–1601. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a></li> </ul>
Daily rainfall series and rainfall erosivity in Mexico for three climatic normals (1968-1997, 1978-2007, and 1988-2017)
As in many countries around the world, there are some issues in the Mexican rainfall series, such as missing values, short measurement periods, and series homogeneity (breaks due to station relocation and measurement mistakes), which further compound the challenge of using climate data. Furthermore, it is necessary to develop a complete and helpful rainfall series database by following an imputing and homogenization process of the rainfall series. This research has compiled and systematized a national dataset with daily rainfall and rainfall erosivity for three climatic normals CN (1968-1997, 1978-2007, and 1988-2017). We have used the "climatol" package to fill the data. After, we calculated daily rainfall erosivity using a power law model. As a result, we obtained 1370, 1679, and 1683 rainfall series for the CNs 1968-1997, 1978-2007, and 1988-2017, respectively. The median values of the rainfall erosivity for the three CNs were 3245, 3070, and 3327 MJ mm/ ha h yr, respectively. We are making this database available for public consultation for researchers and students, technical assistants, decision-makers, and others interested in environmental studies in Mexico.
SM2RAIN-ASCAT (2007-2022): global daily satellite rainfall from ASCAT soil moisture
<p><strong>SM2RAIN-ASCAT is a new global scale rainfall product</strong> obtained from ASCAT satellite soil moisture data through the SM2RAIN algorithm (<em>Brocca et al., 2014; 2019</em>). The SM2RAIN-ASCAT rainfall dataset (in mm/day) is provided over a regular grid at 0.1-degree sampling (3600x1801) on a global scale. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The SM2RAIN method was applied to the ASCAT soil moisture product (<em>Wagner et al., 2013</em>) for the period from January 2007 to December 2022 (16 years), for version 2.1.2n.</p> <p>The rainfall dataset is provided in NetCDF format. A total of 16 NetCDF files, one per year, are provided. 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. In addition to the daily accumulated rainfall value, also the rainfall noise (mm/day) is provided for every day.</p> <p><strong>Version 2.1.2 should not be used due to an error in the precipitation data. Version 2.1.2n with respect to version 2.1 is calibrated anew and extended to December 2022.</strong></p> <p>A GeoTIFF version of the dataset (v1.5) is available here: <a href="https://doi.org/10.5281/zenodo.2615278">https://doi.org/10.5281/zenodo.2615278</a></p> <p>A monthly version at 0.25- and 0.5-degree resolution (v1.4) is available here: <a href="https://doi.org/10.5281/zenodo.4570191">https://doi.org/10.5281/zenodo.4570191</a></p> <p>A sample dataset that can be used for testing SM2RAIN algorithm is available here: <a href="../record/2580285#.XLrYDugzbIU">https://zenodo.org/record/2580285</a></p> <p>Details on the dataset development and its assessment with ground and reanalysis observations are provided as:</p> <p><strong>Brocca, L.</strong>, Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. <em>Earth System Science Data</em>, 11, 1583–1601, doi:10.5194/essd-11-1583-2019. <a href="https://doi.org/10.5194/essd-11-1583-2019">https://doi.org/10.5194/essd-11-1583-2019</a>.</p> <p><strong>Simple Python and Matlab codes for the extraction of SM2RAIN-ASCAT rainfall at one and multiple station(s)\location(s) are available at (note that reader for versions <1.3 are not usable for version >1.3): </strong><a href="https://github.com/IRPIhydrology/SM2RAIN_ASCAT_reader">https://github.com/IRPIhydrology/SM2RAIN_ASCAT_reader</a></p> <p><strong>The SM2RAIN code in Python is available here</strong>: <a href="https://github.com/IRPIhydrology/sm2rain">https://github.com/IRPIhydrology/sm2rain</a><br><strong>The SM2RAIN code in Matlab is available here</strong>: <a href="https://github.com/IRPIhydrology/SM2RAIN_Matlab">https://github.com/IRPIhydrology/SM2RAIN_Matlab</a><br><strong>The SM2RAIN code in R is available here</strong>: <a href="https://github.com/IRPIhydrology/sm2rainR">https://github.com/IRPIhydrology/sm2rainR</a></p> <p> </p> <p><strong>Acknowledgements</strong></p> <ul> <li>EUMETSAT Global SM2RAIN project (contract n° EUM/CO/17/4600001981/BBo)</li> <li>EUMETSAT "Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF)" CDOP 3 (EUM/C/85/16/DOC/15).</li> </ul>
Daily Rainfall Anomalies in the Brazilian Northeast (DRA-BNE)
<p>This dataset contains the daily precipitation anomalies in the Brazilian northeast calculated from the GPCP dataset (combined sources of precipitation measurement) in the period from 1996-10-01 to 2021-07-31, in the geographic boundaries 42°W - 30°W/20.5°S - 0°. The GPCP dataset has an original resolution of 1 degree, but this dataset was interpolated by the bilinear method, obtaining a resolution of 0.25 degrees.</p>
SM2RAIN-CCI (1 Jan 1998 – 31 December 2015) global daily rainfall dataset
<p>A NEW GLOBAL SCALE RAINFALL PRODUCT obtained from satellite soil moisture data through the SM2RAIN algorithm (<em>Brocca et al., 2014</em>), at 0.25 degree/daily spatial-temporal resolution, has been delivered (Ciabatta et al., 2018). The SM2RAIN method was applied to the ESA CCI soil moisture Active and Passive products (<em>Liu et al., 2011, 2012; Wagner et al., 2012</em>) for the period from January 1998 to December 2015 (18 years).</p> <p>The CCI-derived rainfall datasets (in mm/day) is gridded over a 0.25-degree grid on a global scale. The number of dates is 6574 (1998/01/01 – 2015/12/31). The product represents the cumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. A climatological correction has been applied to the data at monthly scale.</p> <p>The rainfall dataset is provided in netCDF format. A total of 18 netCDF files, one per year, are provided.</p> <p>The rainfall dataset is obtained by applying the SM2RAIN algorithm to the ESA CCI soil moisture Active and Passive products at version 03.1 separately. Then, an integration procedure based on a weighted average is applied in order to obtain the rainfall estimate. The algorithm has been calibrated during three different periods (1998-2001, 2002-2006 and 2007-2013) against the Global Precipitation Climatology Centre Full-Data daily dataset (GPCC-FDD, Schamm et al., 2015). The quality flag provided within the raw soil moisture observations has been used to mask out low quality data, as well as the areas characterized by high topographic complexity, high frozen soil and snow probability and presence of tropical forests.</p> <p><strong>References</strong></p> <p>Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. <em>Journal of Geophysical Research</em>, 119(9), 5128-5141, doi:10.1002/2014JD021489.</p> <p>Ciabatta, L., Massari, C., Brocca, L., Gruber, A., Reimer, C., Hahn, S., Paulik, C., Dorigo, W., Kidd, R., and Wagner, W.: SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture, Earth Syst. Sci. Data, 10, 267-280, https://doi.org/10.5194/essd-10-267-2018, 2018.</p> <p>Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436, doi:10.5194/hess-15-425-2011.</p> <p>Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M., Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014.</p> <p>Schamm, K., Ziese, M., Raykova, K., Becker, A., Finger, P., Meyer-Christoffer, A., Schneider, U. (2015). GPCC Full Data Daily Version 1.0 at 1.0°: Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data. DOI: 10.5676/DWD_GPCC/FD_D_V1_100.</p> <p>Wagner, W., Dorigo, W., de Jeu, R., Fernandez, D., Benveniste, J., Haas, E., Ertl, M. (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321.</p>
Bisley daily rainfall (Bisley weekly environmental data)
Data set includes all available daily, weekly, and monthly rainfall from several climate stations in the northeast section of the Luquillo Experimental Forest. These stations are surround the Bisley Experimental watersheds and the Sabana Field Station are are operated by the USFS and the USGS. Weekly canopy throughfall is also collected weekly from the Bisley experimental watersheds. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
DeepRainForest Output Data : Simulated daily rainfall output (2001-2020) under observed tree cover and no deforestation scenarios in South America
<p>This dataset deposited contains simulation data related to the analysis of forest-rainfall relationships and the impact of historical deforestation on rainfall patterns in South America. The data includes outputs from a spatiotemporal neural network model, DeepRainForest, developed to simulate rainfall based on vegetation and climate inputs in South America. This dataset is the data necessary to recreate the figures that appear in an accepted (but yet to be published manuscript) in Global Change Biology titled "Assessing the impact of past and ongoing deforestation on rainfall patterns in South America". When the manuscript is accepted then the article will be linked from here.</p> <p><strong><em>DeepRainForest_daily_rainfall_with_observed_treecover.nc:</em></strong> contains simulated daily rainfall data spanning from 2001 to 2020, considering the observed tree cover. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_2000_treecover.nc: </strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 2000 onwards. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_1982_treecover.nc:</strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 1982 onwards.</p>
Daily Rainfall Dataset for the Entire African Continent (1998-2018)
<p>This is the daily rainfall dataset for the entire African continent. The result is given in (mm/day). It is based on the Global Historical Climatology Network Daily (GHCNd) weather stations covering the whole continent. However, there are missing values in the original GCHNd, which may be challenging for applications or studies requiring continuous time series. Therefore, the Tropical Rainfall Measuring Mission (TMPA 3B42_Daily)) replaced GCHNd values. TRMM is a combination of satellite rainfall estimates & gauged observations and is reliable as a proxy for gauged observations. This dataset will be helpful for hydrologic modeling, climate change adaptation, prediction, and simulation</p>
Subset of Global Sub-Daily Rainfall (GSDR) dataset
<p>A subset of the Global Sub-Daily Rainfall (GSDR) dataset, including the countries marked as 'open' in Table A2 of the accompanying paper (DIO: 10.1175/JCLI-D-18-0143.1).</p> <p>The dataset includes archive data, raw data, quality control flags and quality controlled data and raw data. Example quality control files (with headers), raw data processing scripts and INTENSE Python code are also included.</p>
Daily satellite and gauge observed rainfall dataset (1980-2019) for Oman at 1km spatial resolution in GeoTIFFs
<p>This is a re-gridded daily TRMM datasets. It has been resampled to 1km spatial resolution. The datasets have also been projected to UTM 40N</p>
Northern Cameroon daily interpolated rainfall, 1948-2022, obtained via ordinary kriging using Spherical variogram models
<p><strong>Description:</strong> This dataset provides daily interpolated rainfall maps for Northern Cameroon, including North and Extreme North provinces, for the period 1948-2022, at 0.01° resolution, derived from daily rain gauge data observations.</p> <p><strong>Dataset Preparation Methods:</strong> These maps were obtained by performing interpolation from daily rain gauge data (<a href="../doi/10.5281/zenodo.10156437">NoCORA - Northern Cameroon Observed Rainfall Archive</a> dataset) , using ordinary kriging with fitting of <strong>Spherical</strong> variogram models. Resolution of interpolation is 0.01°. The daily results were assembled into daily geotiff files.</p> <p><strong>Funding:</strong> This project was funded by the DESIRA INNOVACC project.</p> <p><strong>Authors Contributions:</strong></p> <ul> <li>Data treatment: Clara Knops.</li> <li>Documentation: Jérémy Lavarenne, Clara Knops.</li> </ul> <p><strong>Changelog:</strong> </p> <ul> <li>v1.0.0 : initial submission</li> </ul>
Northern Cameroon daily interpolated rainfall, 1948-2022, combined dataset obtained via ordinary kriging
<p><strong>Description:</strong> This dataset provides daily interpolated rainfall maps for Northern Cameroon, including North and Extreme North provinces, for the period 1948-2022, at 0.01° resolution, derived from daily rain gauge data observations.</p> <p><strong>Dataset Preparation Methods:</strong> This dataset was created by combining maps obtained by performing interpolation from daily rain gauge data (<a href="../doi/10.5281/zenodo.10156437">NoCORA - Northern Cameroon Observed Rainfall Archive</a> dataset) , using ordinary kriging with fitting of Circular variogram models and ordinary kriging with fitting of Spherical variogram models. The maps produced by the Spherical variogram models were taken as base of the combined dataset. Dates that could not be produced by the Spherical variogram models, but were produced by the Circular variogram models, were added to the combined dataset. Equally, dates produced by the Spherical variogram models containing negative values, but containing positive values when produced by the Circular variogram models, were replaced. The Mean Error calculated for both variogram models showed a similar bias and a paired t-test showed no significant difference. Resolution of interpolation is 0.01°. The daily results were assembled into daily geotiff files.</p> <p><strong>Funding:</strong> This project was funded by the DESIRA INNOVACC project.</p> <p><strong>Authors Contributions:</strong></p> <ul> <li>Data treatment: Clara Knops.</li> <li>Documentation: Jérémy Lavarenne, Clara Knops.</li> </ul> <p><strong>Changelog:</strong> </p> <ul> <li>v1.0.0 : initial submission</li> </ul>
Daily rainfall totals (mm), Sly Park Creek - Caldor Fire
<p>Sly Park Creek unheated tipping bucket rain gage. Latitude: 38.731593; Longitude: -120.501016. Daily rainfall totals (mm) from 2022-06-07 to 2023-12-09. Non-telemetered rain gage deployed for post-fire monitoring by the Burned Watershed Geohazards Program, California Geological Survey.</p>
Empirical orthogonal functions of daily rainfall measurements from TRMM
<p>(see README.md)</p>
Northern Cameroon daily interpolated rainfall, 1948-2022, obtained via ordinary kriging using Circular variogram models
<p><strong>Description:</strong> This dataset provides daily interpolated rainfall maps for Northern Cameroon, including North and Extreme North provinces, for the period 1948-2022, at 0.01° resolution, derived from daily rain gauge data observations.</p> <p><strong>Dataset Preparation Methods:</strong> These maps were obtained by performing interpolation from daily rain gauge data (<a href="../doi/10.5281/zenodo.10156437">NoCORA - Northern Cameroon Observed Rainfall Archive</a> dataset) , using ordinary kriging with fitting of Circular variogram models. Resolution of interpolation is 0.01°. The daily results were assembled into daily geotiff files.</p> <p><strong>Funding:</strong> This project was funded by the DESIRA INNOVACC project.</p> <p><strong>Authors Contributions:</strong></p> <ul> <li>Data treatment: Clara Knops.</li> <li>Documentation: Jérémy Lavarenne, Clara Knops.</li> </ul> <p><strong>Changelog:</strong> </p> <ul> <li>v1.0.0 : initial submission</li> </ul>
Daily inflow and rainfall data
<p>Daily inflow and rainfall time series used within the simulation of a small hydropower plant at Achelous river basin. These data are used in the paper titled " Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data", by Korina-Konstantina Drakaki, Georgia-Konstantina Sakki, Ioannis Tsoukalas, Panagiotis Kossieris, and Andreas Efstratiadis, published in Advance in Geosciences ( https://doi.org/10.5194/adgeo-1-1-2022).</p>
IMD New High Spatial Resolution (0.25X0.25 degree) Long Period (1901-2022) Daily Gridded Rainfall Data Set Over India: Matlab script for merging into single netcdf/mat file with datetime stamping
Open the record for dataset details and reuse information.
Feldman et al. 2024 "Large global scale vegetation sensitivity to daily rainfall variability"
<p>These data and scripts are a part of the Feldman et al. manuscript accepted in principle titled "Large global scale vegetation sensitivity to daily rainfall variability".</p> <p>The data and scripts here can be used to generate all steps of the analysis. It includes:</p> <p>(1) The final generated outputs of the analysis and the python script to generate the figures in the main text, extended data, and supplemental information. This figure generation script is: "MainScript_GenerateFigures_V4.py"</p> <p>(2) The python script to conduct the main analysis in an example region in Africa, which is named: "MainScript_SatelliteVegetationProcessing_Example.py". </p> <p>(3) The python script to conduct the main analysis across the globe (to generate figure 1), which is named: "MainScript_SatelliteVegetationProcessing_V2.py". Input data for this script are available at a separate Zenodo account here: https://zenodo.org/records/10947071.</p> <p>(4) Scripts and input data to conduct rainfall trend analyses and mechanism analyses.</p> <p>(5) Scripts to show how the gridded datasets were processed into their one degree gridding to be input into other components of the analysis.</p> <p>Due to their size, the full global datasets to conduct the global analysis are available on a separate Zenodo account here: https://zenodo.org/records/10947071.</p> <p>Please consult the README.txt file for more information about python and names of files and scripts being used. </p> <p>For any use of the datasets and scripts, please cite (1) the Feldman et al "Large global scale vegetation sensitivity to daily rainfall variability" publication. Additionally, (2) please acknowledge the use of the scripts and/or datasets in the acknowledgements section of your publication. The use of any scripts or data are not allowed unless the paper is in print. For questions, please contact Andrew Feldman at andrew.feldman@nasa.gov. </p>
FIGURE 14. Average daily rainfall from 17 January 2011 in A new cryptic species of glassfrog (Centrolenidae: Nymphargus) from Reserva Las Gralarias, Ecuador
FIGURE 14. Average daily rainfall from 17 January 2011 to 1 July 2011, measured in mm. Calling males of Nymphargus griffithsi were observed from 27 February 2011 until 14 April 2011; calling males of N. lasgralarias sp. nov. were observed from 17 March 2011 until 1 July 2011. Rainfall was measured using a weather station on site at the reserve.
Spatiotemporal variability of current and future sub-daily rainfall in Japan using state-of-the-art high-quality datasets
<p>Attached are the rainfall return levels based on peaks-over-threshold.</p>
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