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23 results for “rainfall observations”
Observational rainfall data of the 2021 mid-July flood event in Belgium – Part 1. Rain gauges observations
<p>From July 13th to 16th 2021, a long period of sustained and heavy rainfall affected Central Europe producing extreme rainfall amounts in western Germany, eastern Belgium, Luxembourg and The Netherlands. In Belgium, this unusual event induced massive flooding on a large part of the country and was responsible for 39 fatalities and strong damages to buildings and infrastructures.</p><p>Such extremely rare event needs to be documented as much as possible and data must be made available for further studies in hydrology, in urban planning and, more generally, in all multi-disciplinary studies aiming at identifying and understanding all factors leading to such disaster.</p><p>The observational rainfall data available for Belgium during the period from July 13th to July 16th 2021 are here shared with the scientific community. These data are twofold and provided in 2 parts:</p><p><br><strong>Part 1. </strong><a href="https://doi.org/10.5281/zenodo.7739983"><strong>Observations from high-quality rain gauges</strong></a></p><p>The dataset includes daily precipitation accumulation recorded by 323 weighing and manual rain gauges in Belgium as well as 5-min precipitation data recorded by 168 weighing rain gauges. These data were checked for possible errors and inconsistencies.</p><p>The rain gauges observations are provided in csv format in 2 files:</p><ul><li>RainGaugesData_FLOOD21_daily.csv</li><li>RainGaugesData_FLOOD21_5min.csv</li></ul><p><br><strong>Part 2. </strong><a href="https://doi.org/10.5281/zenodo.7740059"><strong>Radar-based quantitative precipitation estimation (RADFLOOD21)</strong></a></p><p>This product provides a quantitative precipitation estimation of the event at high spatial (i.e., 1 km) and temporal (i.e., 5 min and hourly) resolutions. It is obtained after a careful processing of the weather radar measurements and a merging with rain gauge measurements. The data is provided in hdf5 format. In addition, an animation of the 5-min RADFLOOD21 data is also made available.</p><p> </p><p>These data are exposed and discussed in <a href="https://hess.copernicus.org/articles/27/3169/2023/">https://hess.copernicus.org/articles/27/3169/2023/</a>. In particular, several analyses of these data are performed to describe the spatial and temporal distribution of rainfall during the event and to illustrate its exceptional character.</p><p> </p>
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>
Mechanisms for a record-breaking rainfall in the coastal metropolitan city of Guangzhou, China: observation analysis and nested very-large-eddy simulation with the WRF Model
<p>A video shows the processes of a record-breaking rainfall in the coastal metropolitan city of Guangzhou, China simulated by WRF nested very-large-eddy simulation.</p>
Fig. 1. Pelomedusa galeata from the Ratelfontein farm near Calvinia observed directly after rainfall, 16 February 2019 in Mind the gap-Is the distribution range of Pelomedusa galeata really disjunct in western South Africa?
Fig. 1. Pelomedusa galeata from the Ratelfontein farm near Calvinia observed directly after rainfall, 16 February 2019. For the location of the farm, see Fig. 2 (locality 1). Photos: C.A. van Niekerk.
Data set: Huai et al. (2021). JAMC. Quantifying rainfall in Greenland: a combined observational and modelling approach
<p>Abstract Paper. This paper estimates rainfall totals at 17 Greenland meteorological stations, subjecting data from in-situ precipitation gauge measurements to seven different precipitation phase schemes to separate rain- and snowfall amounts. To correct the resulting snow/rain fractions for undercatch, we subsequently use a Dynamic Correction Model (DCM) for Automatic Weather Stations (AWS, Pluvio gauges) and a regression analysis correction method for staffed stations (Hellmann gauges). With observations ranging from 5% to 57% for cumulative totals, rainfall accounts for a considerable fraction of total annual precipitation over Greenland’s coastal regions, with the highest rain fraction in the south (Narsarsuaq). Monthly precipitation and rainfall totals are used to evaluate the regional climate model RACMO2.3. The model realistically captures monthly rainfall and total precipitation (R=0.3-0.9), with generally higher correlations for rainfall for which the undercatch correction factors (1.02-1.40) are smaller than those for snowfall (1.27-2.80), and hence the observations more robust. With a horizontal resolution of 5.5 km and simulation period from 1958-present, RACMO2.3 therefore is a useful tool to study spatial and temporal variability of rainfall in Greenland, although further statistical downscaling may be required to resolve the steep rainfall gradients.</p> <p>The dataset contains:<br> Automatic weather station data:<br> AWS-daily.zip: per station daily values of snowfall and rain fall derived from raw precipitation data and for 7 methods to divide between rain and snowfall<br> AWS-factork.zip: per station the factor with which the data is corrected for undercatch<br> AWS-script.zip: the scripts used for the analyses</p> <p>Staffed weather stations:<br> Meteo-daily.zip: per station daily values of snowfall and rain fall derived from the precipitation data and for 7 methods to divide between rain and snowfall<br> Meteo-factork.zip: per station the factor with which the data is corrected for undercatch<br> Meteo-script.zip: the scripts used for the analyses</p> <p> </p>
Observational rainfall data of the 2021 mid-July flood event in Belgium – Part 2. Radar product RADFLOOD21
<p>From July 13th to 16th 2021, a long period of sustained and heavy rainfall affected Central Europe producing extreme rainfall amounts in western Germany, eastern Belgium, Luxembourg and The Netherlands. In Belgium, this unusual event induced massive flooding on a large part of the country and was responsible for 39 fatalities and strong damages to buildings and infrastructures.</p><p>Such extremely rare event needs to be documented as much as possible and data must be made available for further studies in hydrology, in urban planning and, more generally, in all multi-disciplinary studies aiming at identifying and understanding all factors leading to such disaster.</p><p>The observational rainfall data available for Belgium during the period from July 13th to July 16th 2021 are here shared with the scientific community. These data are twofold and provided in 2 parts:</p><p><br><strong>Part 1. </strong><a href="https://doi.org/10.5281/zenodo.7739983"><strong>Observations from high-quality rain gauges</strong></a></p><p>The dataset includes daily precipitation accumulation recorded by 323 weighing and manual rain gauges in Belgium as well as 5-min precipitation data recorded by 168 weighing rain gauges. These data were checked for possible errors and inconsistencies.</p><p>The rain gauges observations are provided in csv format in 2 files:</p><ul><li>RainGaugesData_FLOOD21_daily.csv</li><li>RainGaugesData_FLOOD21_5min.csv</li></ul><p><br><strong>Part 2. </strong><a href="https://doi.org/10.5281/zenodo.7740059"><strong>Radar-based quantitative precipitation estimation (RADFLOOD21)</strong></a></p><p>This product provides a quantitative precipitation estimation of the event at high spatial (i.e., 1 km) and temporal (i.e., 5 min and hourly) resolutions. It is obtained after a careful processing of the weather radar measurements and a merging with rain gauge measurements. The data is provided in hdf5 format. In addition, an animation of the 5-min RADFLOOD21 data is also made available.</p><p> </p><p>These data are exposed and discussed in <a href="https://hess.copernicus.org/articles/27/3169/2023/">https://hess.copernicus.org/articles/27/3169/2023/</a>. In particular, several analyses of these data are performed to describe the spatial and temporal distribution of rainfall during the event and to illustrate its exceptional character.</p><p> </p>
Observed Rainfall And CMIP6 Data
<p>The gridded rainfall datasets used in this analysis were sourced from the Indian Meteorological Department (IMD). These datasets were collected on a daily basis from 1951 to 2021 and were obtained from the National Climate Centre (NCC) of IMD from the following URL: https://www.imdpune.gov.in/lrfindex.php. The dataset covers the Indian River Basins (IRBs) and has been generated at high resolution by assimilating data from 6955 rain gauge stations spread across the IRBs.</p> <p>For the simulated daily precipitation spanning the years 2021 to 2100, four different Shared Socioeconomic Pathways (SSPs) were considered: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The simulated precipitation outputs were derived from the Coupled Model Intercomparison Project-6 (CMIP6) and were obtained from the following URL: https://esgf-node.llnl.gov/search/cmip6/. All 12 models of CMIP6 were downloaded, specifically using the variant label r1i1p1f1 as an initial condition.</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>
A microwave link simulation dataset for rainfall observation applications
<p>A microwave link simulation dataset is developed for theoretical research of rainfall observation applications. The dataset is built based on electromagnetic propagation theory attenuation effects with measured raindrop size distribution data from a PARSIVEL disdrometer and air temperature, air pressure and humidity data from nearby weather stations. Frequencies from 6 to 180 GHz, horizontal or vertical polarization, link lengths from 0.1 km to 20 km, and quantization resolutions from 0 to 1 dB are all fully considered for various existing and future complications. The dataset can be utilised to validate the performance of the microwave link-based rainfall inversion process algorithm in various equipment configurations and to assess the potential of microwave link-based rainfall monitoring technology in future high-frequency communication infrastructure conditions on a theoretical level with a very low cost.</p>
The Observed Disdrometer Heavy Rainfall Period Datasets in East China during the Meiyu Season
<p>The attached file includes all the 1757 Parsivel disdrometer heavy rainfall periods (HRPs) in East China during the 2019-2022 Meiyu season. The files are the XXX.mat format and can be directly opened with MATLAB.</p>
Data from: Observation definitions and their implications in machine learning-based predictions of excessive rainfall
Open the record for dataset details and reuse information.
Observed trends in the South Asian monsoon low-pressure systems and rainfall extremes since the late1970s
<p>LPS tracks for the manuscript "Observed trends in the South Asian monsoon low-pressure systems and rainfall extremes since the late1970s"</p>
The observed and simulated datasets of "21·7" Henan extremely heavy rainfall event
<p>The uploaded files are the observed and simulated datasets of “21·7” Henan extremely heavy rainfall event, including OTT disdrometers, Radar, national and regional gauges, and WRFOUT files. </p>
pywaterinfo dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>This forcings dataset is the output of the pywaterinfo (https://fluves.github.io/pywaterinfo/) read in of forcing data (rain and potential evapotranspiration).</p> <p>Code related to this dataset can be found here: https://github.com/olivierbonte/master_thesis</p>
OpenEO dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>This dataset is the output of the <a href="https://openeo.org/">OpenEO</a> processing of satellite data (SAR backscatter and LAI). </p> <p>Code related to this dataset can be found <a href="https://github.com/olivierbonte/master_thesis">here</a></p>
Minimal dataset for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>The minimal dataset needed of data which can not be retrieved from the internet by APIs in the preprocessing. Consists of shape, land use and rivers for the Zwalm catchment. </p> <p>Code related to this dataset can be found here: https://github.com/olivierbonte/master_thesis </p>
Preprocessing output for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>Outputs of the local preprocessing of the OpenEO data (see <a href="https://doi.org/10.5281/zenodo.7691342">here</a>) and pywaterinfo data (see <a href="https://doi.org/10.5281/zenodo.7689200">here</a>).</p> <p>Code related to this dataset can be found <a href="http://github.com/olivierbonte/master_thesis">here</a></p>
Inverse observation operator parameters/models for master's dissertation: Updating a conceptual rainfall-runoff model based on radar observation and machine learning
<p>Both saved models and results of hyperparameter tuning are given. </p> <p>Code related to this dataset can be found <a href="http://github.com/olivierbonte/master_thesis">here</a></p> <p> </p>
A new dataset of rain cell generated from observations of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner and microwave imager
<p>This new dataset (M.TRMM-1B01-1B11-2A25-PMD-Rain) contains orbit-level data with 5 km spatial resolution and 0.25 km vertical resolution. It is produced by merging TRMM PR, VIRS and TMI measurements at PR pixel resolution combined with rain cell identification. The near-surface rain rate, profiles of rain rate and precipitation reflectivity factor, visible and infrared signals and microwave signals can be obtained in the dataset. The dataset provides new important data for in-depth research on the structural characteristics of rain cells and supports the study of precipitation mechanisms.</p>
Datasets of GEV distribution values of observed surface pressure, rainfall and westward wind speed
<p>These are the generalized extreme value (GEV) distribution results obtained from Figure 3.5B, Figure 3.6B, Figure 3.7B and Figure C.4d in Peirson et al. (2011) and Figure 4, Figure 5, Figure 6 in Peirson et al. (2014). These datasets had been used to evaluate the climate model characterization of extreme storms. The evaluation results have been summarized in a paper which submitted to Journal of Climate: Wenjun Zhu et al., 2023: "An Assessment of Model Projections of Climate-change Induced Extreme Storms on the South-eastern Coast of Australia" (submitted).</p>
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