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13 results for “precipitation methods”
EPTGODD-WHU: Ensemble Precipitation and Temperature from CMIP6 GCMs optimized by OLS-DT-DNN methods integration (1850-2100)
<p>This monthly global climate dataset EPTGODD-WHU (precipitation and mean temperature variables with grid size of 0.5°×0.5°) was ensembled from 16 selected CMIP6 GCMs. The published dataset was optimized by OLS (Ordinary Linear Square)-DT (Decision Tree)-DNN (Deep Neural Network) methods integration. The CF (Climate and Forecast) v1.6 was employed as the guideline for NetCDF4 format. The periods of temperature files can be divided into historical (1850-1900) and future (2015-2100) periods. For precipitation, this product provides future (2015-2100) period. Three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) were selected for both variables. The units of this dataset are degrees Celsius and mm/month for temperature and precipitation, respectively. Each NetCDF4 file in this dataset includes three dimensions (time, latitude (-89.75°N to 89.75°N) and longitude (-179.75°E to 179.75°E)).</p>
Daily precipitation and temperature for 2021–2050 over China: multiple RCMs and emission scenarios corrected by a trend-preserving method
<p>The datasets with spatial resolution of 0.5˚×0.5˚ were corrected from CORDEX-EA regional climate models based on a trend–preserving bias correction method, including daily daily maximum and minimum temperature, and precipitation (Tmax, Tmin and Pre). The datasets cover the main land area of China and two periods, the historical period (from 1980 to 2005 ) and future period (from 2021 to 2050). The observations used in the correction were obtained from China Meteorological Administration (http://cdc.cma.gov.cn), and were derived from 2472 weather stations over China. The evaluation indicated that the corrected datasets are reliable for the investigations related to climate change across China.</p>
Dataset for "A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods"
<p>Dataset for "A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods".</p> <p>Added file "Obs_metrics_all.RData" containing p-value for significance test of the clustering metric S_cl.</p> <p>Accepted version (August 2021).</p>
UKCP18 RCM precipitation and temperature bias corrected using non-parametric quantile mapping method
<p>The UKCP18 RCM PPE (Met Office Hadley Centre, 2018) projections of precipitation and daily average temperature were bias adjusted using a non-parametric quantile mapping method based on empirical quantiles (Boe et al, 2007, Gudmundsson et al, 2012). The datasets cover the period from December 1980 to November 2080 and are intended for use in climate change impact assessments, where the bias correction helps reduce biases in multiple statistics while <span>maintaining projected climatic changes</span>.</p> <p>-------------------------------------------------</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. CEDA, <em>8 March 2022</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Boe, J.; Terray, L.; Habets, F. & Martin, E. Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. International Journal of Climatology, 2007, 27, 1643-1655, doi: 10.1002/joc.1602.</p> <p>Gudmundsson, L.; Bremnes, J. B.; Haugen, J. E. & Engen-Skaugen, T. Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrology and Earth System Sciences, 2012, 16, 3383-3390, doi:10.5194/hess-16-3383-2012.</p> <p><strong>Paper Citation:</strong><br>We kindly ask users of this dataset to cite the paper that describes the dataset. The paper is published and can be accessed via the following link: <a href="https://doi.org/10.5194/essd-17-2113-2025" target="_new" rel="noopener">https://doi.org/10.5194/essd-17-2113-2025</a>.</p> <p><br>Please reference the paper as:<br>Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., and He, Y.: Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain, Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, 2025.</p>
Methods for precipitating plasma proteins for stable isotope analysis of elasmobranch blood
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Data and Software for Developing a General Comprehensive Evaluation Method for Cross-Scale Precipitation Forecasts
<div> </div> <div>Readme for GCEM Data and Code</div> <div> </div> <div>1 The data and code of two typical cases in Section 4.1</div> <div>1.1 Data</div> <div>1.1.1 Observed Precipitation Data</div> <div>1.1.1.1 /1_Two_Typical_Processes_data/1_1Observed_precipitation_data/2019071612</div> <div>(in the 1_Two_Typical_Processes_data.rar file)</div> <div>Hourly precipitation from 00:00 to 12:00 UTC on July 16, 2019 for Case 1</div> <div>surfr01h.nc</div> <div> surfr02h.nc</div> <div> surfr03h.nc</div> <div> surfr04h.nc</div> <div> surfr05h.nc</div> <div> surfr06h.nc</div> <div> surfr07h.nc</div> <div> surfr08h.nc</div> <div> surfr09h.nc</div> <div> surfr10h.nc</div> <div> surfr11h.nc</div> <div>surfr12h.nc</div> <div>1.1.1.2 /1_Two_Typical_Processes_data/1_1Observed_precipitation_data/2020061312</div> <div>(in the 1_Two_Typical_Processes_data.rar file)</div> <div>Hourly precipitation from 00:00 to 12:00 UTC on June 13,2020 for Case 2</div> <div>surfr01h.nc</div> <div> surfr02h.nc</div> <div> surfr03h.nc</div> <div> surfr04h.nc</div> <div> surfr05h.nc</div> <div> surfr06h.nc</div> <div> surfr07h.nc</div> <div> surfr08h.nc</div> <div> surfr09h.nc</div> <div> surfr10h.nc</div> <div> surfr11h.nc</div> <div>surfr12h.nc</div> <div>1.1.2 Forecasted Precipitation Data</div> <div>1.1.2.1 /1_Two_Typical_Processes_data/1_2Forecasted_precipitation_data/2019071612</div> <div>(in the 1_Two_Typical_Processes_data.rar file)</div> <div> Data for Case 1 during 00:00-12:00 UTC on July 16, 2019</div> <div>WRF3.2019071600000.nc (initial field at 12:00 UTC on July 16, 2019)</div> <div>WRF3.2019071600012.nc (12-hour accumulated precipitation during 00:00–12:00 UTC on July 16, 2019)</div> <div>1.1.2.2 /1_Two_Typical_Processes_data/1_2Forecasted_precipitation_data/2020061312</div> <div>(in the 1_Two_Typical_Processes_data.rar file)</div> <div> Data for Case 2 during 00:00-12:00 UTC on June 13,2020</div> <div>WRF3.2020061300000.nc (initial field at 12:00 UTC on June 13,2020)</div> <div>WRF3.2020061300012.nc (12-hour accumulated precipitation during 00:00–12:00 UTC on June 13,2020)</div> <div> </div> <div>1.2 Code and Configuration Files</div> <div>1.2.1 GCEM of Software and Configuration</div> <div> (/Code/1_Two_Typical_Processes/1Software_Configuration_of_GCEM in the Code.rar file)</div> <div>pastonc6hd2.f90 Main program, reads observed and forecasted precipitation data, performs GCEM verification, and outputs result files.</div> <div> module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file</div> <div> module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div> mod_uxpasid2.f90 Subprogram, module for used to perform GCEM verification on forecasted data</div> <div> module_outnc.f90 Subprogram, module for outputting the verification results in netCDF file format</div> <div> compilePAS10mmd2.sh Used to compile source files to generate executable file under Linux</div> <div> r12hfile.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div> pastonc6hd2.exe Executable file</div> <div>1.2.2 TS of Software and Configuration</div> <div> (/Code/1_Two_Typical_Processes/2Software_Configuration_of_TS-Score in the Code.rar file)</div> <div>tsmain01.f90 Main program, reads observed and forecasted precipitation data, performs TS verification, and outputs result files.</div> <div> module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div> module_uxtsiTure.f90 Subprogram, module for used to perform TS verification on forecasted data</div> <div> compileTS.sh Used to compile source files to generate executable file under Linux</div> <div> r12hfile.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div> tsmain01.exe Executable file</div> <div> </div> <div>1.3 Output files</div> <div>1.3.1 GCEM verification results</div> <div> (/1_Two_Typical_Processes_data/1_3Results/1_3_1Results_GCEM in the 1_Two_Typical_Processes_data.rar file)</div> <div> rainverd2019071612012.nc Result file in netCDF format</div> <div> rainverd2019071612012.nc.txt Result explanation file in netCDF format</div> <div> outnc12hd22019071612.txt GCEM result file in text format</div> <div> rainverd2020061312012.nc Result file in netCDF format </div> <div> rainverd2020061312012.nc.txt Result explanation file in netCDF format</div> <div> outnc12hd22020061312.txt GCEM result file in text format</div> <div> </div> <div>1.3.2 TS verification results</div> <div> (/1_Two_Typical_Processes_data/1_3Results/1_3_2Results_TS in the 1_Two_Typical_Processes_data.rar file)</div> <div> ts12h2019071612.txt TS result file in text format</div> <div> ts12h2020061312.txt TS result file in text format</div> <div> </div> <div>1.4 Compiling Environment</div> <div> The verification program runs in a UNIX environment and requires the intel compiler (v2017) and the netCDF (v4.6.1) support library</div> <div> UNIX Environment Settings</div> <div># .bashrc</div> <div>module load intel/intel-compiler-2017.5.239</div> <div>module load intelmpi/2019.6.154</div> <div>export F90=ifort</div> <div>export NETCDF=/public/software/mathlib/netcdf/4.6.1_intel-2017_mpi-2017_hdf5-1.8.20-intel2017</div> <div>export NETCDF_LIB=$NETCDF/lib</div> <div>export NETCDF_INC=$NETCDF/include</div> <div>export PATH=$NETCDF/bin:$PATH</div> <div>export LD_LIBRARY_PATH=$NETCDF/lib:$LD_LIBRARY_PATH</div> <div> </div> <div>1.5 Compiling and Running Steps</div> <div>1.5.1 The steps for case 1 during 00:00–12:00 UTC on July 16, 2019</div> <div> 1. Creating an installation and running sub-directory</div> <div> mkdir p2019</div> <div> 2. Copying data sources, code files and configuration files to this directory</div> <div> 3. Running in this directory</div> <div> ./compilePAS10mmd2.sh Compile to generate executable file (pastonc6hd2.exe)</div> <div>./compileTS.sh Compile to generate executable file (tsmain01.exe)</div> <div> 4. Modifying the configuration file (r12hfile.txt)</div> <div> 5. Run the executable files </div> <div> ./pastonc6hd2.exe > outnc12hd22019071612.txt</div> <div>Creating the GCEM result file (rainverd2019071612012.nc), procedure file (outnc12hd22019071612.txt)</div> <div>./tsmain01.exe >ts12h2019071612.txt</div> <div>Creating the TS result and procedure file (ts12h2019071612.txt)</div> <div> </div> <div>1.5.2 The steps for case 2 during 00:00–12:00 UTC on June 13,2020</div> <div> 1. Creating an installation and running sub-directory</div> <div> mkdir p2020</div> <div> 2. Copying data sources, code files and configuration files to this directory</div> <div> 3. Running in this directory</div> <div> ./compilePAS10mmd2.sh Compile to generate executable file (pastonc6hd2.exe)</div> <div>./compileTS.sh Compile to generate executable file (tsmain01.exe)</div> <div> 4. Modifying the configuration file (r12hfile.txt)</div> <div> 5. Run the executable files</div> <div> ./pastonc6hd2.exe > outnc12hd22020061312.txt</div> <div>Creating the GCEM result file (rainverd2020061312012.nc), procedure file (outnc12hd22020061312.txt)</div> <div>./tsmain01.exe >ts12h2020061312.txt</div> <div>Creating the TS result and procedure file (ts12h2019071612.txt)</div> <div> </div> <div>1.6 Module code main interface description</div> <div>1.6.1 skinput()</div> <div>subroutine skinput(skfile,skfilenum,rain,gridskx,gridsky,longitude,latitude)</div> <div> integer,intent(in) :: skfilenum</div> <div> character(len=200),dimension(skfilenum),intent(in) :: skfile</div> <div> real,dimension(:,:),allocatable,intent(out) :: rain</div> <div> integer,intent(out) :: gridskx,gridsky</div> <div> </div> <div>usage: Read a set of observed precipitation data files and output grid accumulated precipitation</div> <div>skfile, A set of filenames that are arrays of strings (input)</div> <div>skfilenum, Number of files (input)</div> <div>rain, Accumulated precipitation, rain(nx,ny) (output)</div> <div>gridskx, grid points, nx (output)</div> <div>gridsky, grid points, ny (output)</div> <div>gridlon, Longitude array, gridlon(nx) (output)</div> <div>gridlat, Latitude array, gridlat(ny) (output)</div> <div> </div> <div>1.6.2 ybinput()</div> <div>subroutine ybinput(ybfile,apcp,gridybx,gridyby,gridyblon,gridyblat)</div> <div> character(len=200),intent(in) :: ybfile</div> <div> real,dimension(:,:),allocatable,intent(out) :: apcp,gridyblat,gridyblon</div> <div> integer,intent(out) :: gridybx,gridyby</div> <div> </div> <div>usage: Read a set of forecasted precipitation data files and output forecast grid precipitation</div> <div>ybfile, Forecast file (input)</div> <div>apcp, forecasted precipitation array, apcp(nx, ny) (output)</div> <div>gridybx, Number of grid points for forecast data, nx (output)</div> <div>gridyby, Number of grid points for forecast data, ny (output)</div> <div>gridyblon, Longitude of forecast data, gridyblon(nx, ny) (output)</div> <div>gridyblat, Latitude of forecast data, gridyblat(nx, ny) (output)</div> <div> </div> <div>1.6.3 uxpasid2()</div> <div>subroutine uxpasid2(ui,xi,level,pas,iTure,iclass,ieps)</div> <div> real,intent(in) :: ui,xi,level</div> <div> real,intent(out) :: pas,ieps</div> <div> integer,intent(out) :: iTure,iclass</div> <div> </div> <div>usage: Read in the observed and forecasted precipitation, and output the PAS score result</div> <div>rainsk, Observed precipitation (input)</div> <div>rainyb, Forecasted precipitation (input)</div> <div>level, Specifing magnitude (input)</div> <div>ipas, Pas score value (0-1) or correct value of no precipitation forecast (1)</div> <div>iTure, 0 indicates that the rating is correct for a no precipitation forecast;</div> <div>1 indicates a PAS score of ≥ the specified magnitude;</div> <div>9 indicates that it is not in the no precipitation test, nor is it the verification the specified magnitude;</div> <div>-999 indicates default.</div> <div>iclass, 0 indicates the category (no precipitation forecast is correct)</div> <div>1 indicates the category of insufficient precipitation forecast (observation u<10mm)</div> <div>2 indicates the category of excessive(or equal) precipitation forecast(observation u<10mm)</div> <div>3 indicates the category of insufficient precipitation forecast (observation u≥10mm)</div> <div>4 indicates the category of excessive(or equal) precipitation forecast(observation u≥10mm)</div> <div>-999 indicates default.</div> <div>ieps, 0 indicates the forecasted and observed precipitation are equal</div> <div><0 indicates insufficient precipitation forecast</div> <div>>0 indicates excessive precipitation forecast</div> <div>-999 indicates default.</div> <div> </div> <div>1.6.4 outpasnc()</div> <div>subroutine outpasnc(title,vtime,vhour,gridncx,gridncy,gridnclon,gridnclat,rainncsk,rainncyb,&</div> <div> pasc,pas01,pas10,pas25,pas50, pas2p5,pas5,pas15, pascnc,pasnc01,pasnc10,&</div> <div> pasnc25,pasnc50,pasnc2p5,pasnc5,pasnc15,ipsnc,epsnc,iepsnc,ips,eps,ieps)</div> <div> character(len=10),intent(in) :: vtime,title</div> <div> integer,intent(in) :: vhour,gridncx,gridncy</div> <div> character(len=10) :: chour</div> <div> real,dimension(gridncx),intent(in) :: gridnclon</div> <div> real,dimension(gridncy),intent(in) :: gridnclat</div> <div> real,dimension(gridncx,gridncy),intent(in) :: rainncsk,rainncyb,pascnc,pasnc01</div> <div> real,dimension(gridncx,gridncy),intent(in) :: pasnc10,pasnc25,pasnc50,ipsnc,epsnc,iepsnc</div> <div> real,dimension(gridncx,gridncy),intent(in) ::pasnc2p5,pasnc5,pasnc15</div> <div> real,intent(in) :: pasc,pas01,pas10,pas25,pas50,ips,eps,ieps,pas2p5,pas5,pas15</div> <div> </div> <div>usage: Output data to a netCDF format file</div> <div>title, File name tag (d)</div> <div>vtime, time string (yyyymmdddhh, eg. 2019071612)</div> <div>vhour, Accumulated precipitation duration (12)</div> <div>gridncx, x grid points (240)</div> <div>gridncy, y grid points (200)</div> <div>gridnclon, x grid points longitude array </div> <div>gridnclat, y grid points latitude array</div> <div>rainncsk, Observed precipitation</div> <div>rainncyb, Forecasted precipitation</div> <div>pasc, PASC</div> <div>pas01, ≥0.1mm PAS</div> <div>pas10, ≥10mm PAS</div> <div>pas25, ≥25mm PAS</div> <div>pas50, ≥50mm PAS</div> <div>pas2p5, ≥2.5mm PAS</div> <div>pas5, ≥5mm PAS</div> <div>pas15, ≥15mm PAS</div> <div>pascnc, PASC array</div> <div>pasnc01, ≥0.1mm PAS array</div> <div>pasnc10, ≥10mm PAS array</div> <div>pasnc25, ≥25mm PAS array</div> <div>pasnc50, ≥50mm PAS array</div> <div>pasnc2p5, ≥2.5mm PAS array</div> <div>pasnc5, ≥5mm PAS array</div> <div>pasnc15, ≥15mm PAS array</div> <div>ipsnc, IPS array</div> <div>epsnc, EPS array</div> <div>iepsnc, IEPS array</div> <div>ips, IPS</div> <div>eps, EPS</div> <div>ieps IEPS</div> <div> </div> <div>2 The data and code of extreme rainfall event in Section 4.2</div> <div>2.1 Data</div> <div>2.1.1 Observed Precipitation Data</div> <div> (/2_1Observed_precipitation_data in the 2_1Observed_precipitation_data.rar file)</div> <div>CMPAday-2021072100UTC.nc</div> <div>CMPAday-2021072200UTC.nc</div> <div>2.1.2 Forecasted Precipitation Data</div> <div>2.1.2.1 PWAFS data</div> <div> (in the wrfout_2021071812012.rar, wrfout_2021071812036.rar, wrfout_2021071912012.rar, wrfout_2021071912036.rar, wrfout_2021072012012.rar and wrfout_2021072012036.rar files)</div> <div>wrfout_2021071812012.nc (initial field at 12:00 UTC on 18 July 2021, with forecast time of 12 hours)</div> <div>wrfout_2021071812036.nc (initial field at 12:00 UTC on 18 July 2021, with forecast time of 36 hours)</div> <div>wrfout_2021071912012.nc (initial field at 12:00 UTC on 19 July 2021, with forecast time of 12 hours)</div> <div>wrfout_2021071912036.nc (initial field at 12:00 UTC on 19 July 2021, with forecast time of 36 hours)</div> <div>wrfout_2021072012012.nc (initial field at 12:00 UTC on 20 July 2021, with forecast time of 12 hours)</div> <div>wrfout_2021072012036.nc (initial field at 12:00 UTC on 20 July 2021, with forecast time of 36 hours)</div> <div>2.1.2.2 GRAPES data</div> <div> (/2_2_2Forecasted_02_data in the 2_2_2Forecasted_02_data.rar file)</div> <div>GRAPES2021071812012.nc (initial field at 12:00 UTC on 18 July 2021, with forecast time of 12 hours)</div> <div>GRAPES2021071812036.nc (initial field at 12:00 UTC on 18 July 2021, with forecast time of 36 hours)</div> <div>GRAPES2021071912012.nc (initial field at 12:00 UTC on 19 July 2021, with forecast time of 12 hours)</div> <div>GRAPES2021071912036.nc (initial field at 12:00 UTC on 19 July 2021, with forecast time of 36 hours)</div> <div>GRAPES2021072012012.nc (initial field at 12:00 UTC on 20 July 2021, with forecast time of 12 hours)</div> <div>GRAPES2021072012036.nc (initial field at 12:00 UTC on 20 July 2021, with forecast time of 36 hours)</div> <div> </div> <div>2.2 Code and Configuration Files</div> <div>2.2.1 PAS of Software and Configuration</div> <div> (/Code/2_extreme_rainfall_event/1Software_Configuration_of_PAS in the Code.rar file)</div> <div>pasmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs GCEM verification, and outputs result files.</div> <div>pasgmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs GCEM verification, and outputs result files.</div> <div> module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file</div> <div> module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div> module_ybinputGrapes.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div> mod_uxpasid2.f90 Subprogram, module for used to perform GCEM verification on forecasted data</div> <div> module_outnc.f90 Subprogram, module for outputting the verification results in netCDF file format</div> <div> compilePAS250mm.sh Used to compile source files to generate executable file under Linux</div> <div> compileGrapesPAS250mm.sh Used to compile source files to generate executable file under Linux</div> <div>r12hfilePWAFS2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>pasmain250mm.exe Executable file</div> <div>pasgmain250mm.exe Executable file</div> <div>2.2.2 TS of Software and Configuration</div> <div> (/Code/2_extreme_rainfall_event/2Software_Configuration_of_TS in the Code.rar file)</div> <div>tsmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs TS verification, and outputs result files.</div> <div>tsgmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs TS verification, and outputs result files.</div> <div>module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file</div> <div>module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div>module_ybinputGrapes.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div>module_uxtsiTure.f90 Subprogram, module for used to perform TS verification on forecasted data</div> <div>compileTS250mm.sh Used to compile source files to generate executable file under Linux</div> <div>compileGrapesTS250mm.sh Used to compile source files to generate executable file under Linux</div> <div>r12hfilePWAFS2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>tsmain250mm.exe Executable file</div> <div>tsgmain250mm.exe Executable file</div> <div>2.2.3 FSS of Software and Configuration</div> <div> (/Code/2_extreme_rainfall_event/3Software_Configuration_of_FSS in the Code.rar file)</div> <div>fssmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs TS verification, and outputs result files.</div> <div>fssgmain250mm.f90 Main program, reads observed and forecasted precipitation data, performs TS verification, and outputs result files.</div> <div>module_skinput.f90 Subprogram, module for reading one or more observed precipitation grid file</div> <div>module_ybinput.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div>module_ybinputGrapes.f90 Subprogram, module for reading the start (or end) forecasted precipitation grid file</div> <div>module_FSSom.f90 Subprogram, module for used to perform FSS verification on forecasted data</div> <div>compilefss250mm.sh Used to compile source files to generate executable file under Linux</div> <div>compileGrapesfss250mm.sh Used to compile source files to generate executable file under Linux</div> <div>r12hfilePWAFS2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfilePWAFS2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071812036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021071912036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>r12hfileGRAPES2021072012036-12.txt Configuration file, used to specify the latitude and longitude range for data source and verification</div> <div>fssmain250mm.exe Executable file</div> <div>fssgmain250mm.exe Executable file</div> <div> </div> <div>2.3 Output files</div> <div>2.3.1 GCEM verification results</div> <div> (/2_3Results/2_3_1Results_PAS in the 2_3Results.rar file)</div> <div> rainverd2021072000001.nc Result file in netCDF format</div> <div> rainverd2021072000001.nc.txt Result explanation file in netCDF format</div> <div> rainverd2021072100001.nc Result file in netCDF format</div> <div> rainverd2021072100001.nc.txt Result explanation file in netCDF format</div> <div> rainverd2021072200001.nc Result file in netCDF format</div> <div> rainverd2021072200001.nc.txt Result explanation file in netCDF format</div> <div> outnc22021072000_250mm.txt GCEM result file in text format</div> <div> outnc22021072100_250mm.txt GCEM result file in text format</div> <div> outnc22021072200_250mm.txt GCEM result file in text format</div> <div> rainverg2021072000001.nc Result file in netCDF format</div> <div> rainverg2021072000001.nc.txt Result explanation file in netCDF format</div> <div> rainverg2021072100001.nc Result file in netCDF format</div> <div> rainverg2021072100001.nc.txt Result explanation file in netCDF format</div> <div> rainverg2021072200001.nc Result file in netCDF format</div> <div> rainverg2021072200001.nc.txt Result explanation file in netCDF format</div> <div> outgnc22021072000_250mm.txt GCEM result file in text format</div> <div> outgnc22021072100_250mm.txt GCEM result file in text format</div> <div> outgnc22021072200_250mm.txt GCEM result file in text format</div> <div>2.3.2 TS verification results</div> <div> (/2_3Results/2_3_2Results_TS in the 2_3Results.rar file)</div> <div> ts2021072000_24h250mm.txt TS result file in text format</div> <div> ts2021072100_24h250mm.txt TS result file in text format</div> <div> ts2021072200_24h250mm.txt TS result file in text format</div> <div> tsg2021072000_24h250mm.txt TS result file in text format</div> <div> tsg2021072100_24h250mm.txt TS result file in text format</div> <div> tsg2021072200_24h250mm.txt TS result file in text format</div> <div>2.3.3 FSS verification results</div> <div> (/2_3Results/2_3_3Results_FSS in the 2_3Results.rar file)</div> <div> fss2021072000_24h250mm.txt TS result file in text format</div> <div> fss2021072100_24h250mm.txt TS result file in text format</div> <div> fss2021072200_24h250mm.txt TS result file in text format</div> <div> fssg2021072000_24h250mm.txt TS result file in text format</div> <div> fssg2021072100_24h250mm.txt TS result file in text format</div> <div> fssg2021072200_24h250mm.txt TS result file in text format</div> <div> </div> <div>2.4 Compiling Environment</div> <div> The verification program runs in a UNIX environment and requires the intel compiler (v2017) and the netCDF (v4.6.1) support library</div> <div> UNIX Environment Settings</div> <div># .bashrc</div> <div>module load intel/intel-compiler-2017.5.239</div> <div>module load intelmpi/2019.6.154</div> <div>export F90=ifort</div> <div>export NETCDF=/public/software/mathlib/netcdf/4.6.1_intel-2017_mpi-2017_hdf5-1.8.20-intel2017</div> <div>export NETCDF_LIB=$NETCDF/lib</div> <div>export NETCDF_INC=$NETCDF/include</div> <div>export PATH=$NETCDF/bin:$PATH</div> <div>export LD_LIBRARY_PATH=$NETCDF/lib:$LD_LIBRARY_PATH</div> <div> </div> <div>2.5 Compiling and Running Steps</div> <div>2.5.1 Creating an installation and running sub-directory</div> <div> mkdir zzby</div> <div>2.5.2 Copying data sources, code files and configuration files to this directory</div> <div>2.5.3 Running in this directory</div> <div>./compilePAS250mm.sh Compile to generate executable file (pasmain250mm.exe)</div> <div>./compileGrapesPAS250mm.sh Compile to generate executable file (pasgmain250mm.exe)</div> <div>./compileTS250mm.sh Compile to generate executable file (tsmain250mm.exe)</div> <div>./compileGrapesTS250mm.sh Compile to generate executable file (tsgmain250mm.exe)</div> <div>./compilefss250mm.sh Compile to generate executable file (fssmain250mm.exe)</div> <div>./compileGrapesfss250mm.sh Compile to generate executable file (fssgmain250mm.exe)</div> <div>2.5.4 Modifying the configuration file (r12hfile.txt)</div> <div>Link Configuration file to r12hfile.txt</div> <div>Mainly modifying the data sources</div> <div>2.5.5 Run the executable files</div> <div>Link different configuration files, and run executable files, then output different results.</div> <div>Creating the GCEM result and procedure files</div> <div>./pasmain250mm.exe > outnc22021072000_250mm.txt</div> <div>./pasmain250mm.exe > outnc22021072100_250mm.txt</div> <div>./pasmain250mm.exe > outnc22021072200_250mm.txt</div> <div>./pasgmain250mm.exe > outgnc22021072000_250mm.txt</div> <div>./pasgmain250mm.exe > outgnc22021072100_250mm.txt</div> <div>./pasgmain250mm.exe > outgnc22021072200_250mm.txt</div> <div>Creating the TS result and procedure files</div> <div>./tsmain250mm.exe > ts2021072000_24h250mm.txt</div> <div>./tsmain250mm.exe > ts2021072100_24h250mm.txt</div> <div>./tsmain250mm.exe > ts2021072200_24h250mm.txt</div> <div>./tsgmain250mm.exe > tsg2021072000_24h250mm.txt</div> <div>./tsgmain250mm.exe > tsg2021072100_24h250mm.txt</div> <div>./tsgmain250mm.exe > tsg2021072200_24h250mm.txt</div> <div>Creating the FSS result and procedure files</div> <div>./fssmain250mm.exe >fss2021072000_24h250mm.txt</div> <div>./fssmain250mm.exe >fss2021072100_24h250mm.txt</div> <div>./fssmain250mm.exe >fss2021072200_24h250mm.txt</div> <div>./fssgmain250mm.exe >fssg2021072000_24h250mm.txt</div> <div>./fssgmain250mm.exe >fssg2021072100_24h250mm.txt</div> <div>./fssgmain250mm.exe >fssg2021072200_24h250mm.txt</div> <div> </div> <div>3 PAS mini-program</div> <div>3.1 Code for PAS mini-program</div> <div>(/Code/3_Software_of_PAS in the Code.rar file)</div> <div> pas10ux.f90 Main program, used to perform PAS verification on single point precipitation forecast</div> <div> mod_uxpasid2.f90 Subprogram, Module for PAS of single point forecast</div> <div> compilePAS10ux.sh Used to compile source files to generate executable file under Linux</div> <div> pas10ux.exe Executable file</div> <div> </div> <div>3.2 The steps for PAS mini-program</div> <div> 1. Creating an installation and running sub-directory</div> <div> mkdir pas</div> <div> 2. Copying code files and configuration files to this directory</div> <div> 3. Running in this directory</div> <div> ./compilePAS10ux.sh Compile to generate executable file (pastonc6hd2.exe)</div> <div> 4. linking the executable file as pas</div> <div> ln -sf pas10ux.exe pas</div> <div> 5. Running the PAS mini-program</div> <div> </div> <div>for example: ./pas 15 20</div> <div>Parameter 1: 15 represents observed precipitation</div> <div>Parameter 2: 20 represents forecasted precipitation</div> <div>Output: 0.895 1</div> <div> </div> <div> The following instructions for specific usage:</div> <div>pas rainsk rainyb [level]</div> <div> Input parameters</div> <div> Parameter 1 (rainsk): observed precipitation (mm)</div> <div> Parameter 2 (rainyb): forecasted precipitation (mm)</div> <div> Parameter 3 (level): Specifing magnitude (Optional, default to ≥0.1 mm)</div> <div> Onput parameters</div> <div>Parameter 1 (ipas): Pas score value (0-1) or correct value of no precipitation forecast (1);</div> <div>-999.000 represents default.</div> <div>Parameter 2 (iTure):</div> <div>0 indicates that the rating is correct for a no precipitation forecast;</div> <div>1 indicates a PAS score of ≥ the specified magnitude;</div> <div>9 indicates that it is not in the no precipitation test, nor is it the verification the specified magnitude;</div> <div>-999 indicates default.</div> <div> </div> <div> </div>
The HEC method processing code and output for constrained precipitation extremes reveal unequal future socioeconomic exposure
Open the record for dataset details and reuse information.
How to enhance the inverse distance weighting method to detect the precipitation pattern in a large-scale watershed
<p>These datasets provide precipitation data over Central Plateau watershed of Iran. Four different variants of the Inverse Distance Weighting (IDW) method are utilized to create these datasets. Two out of four IDW variants are proposed and developed by the authors to enhance the performance of the available standard models.</p> <p> </p> <p>Data Format: xlsx</p> <p>Spatial Resolution: ~0.08˚ & 0.25˚</p> <p>Spatial Coverage: Central Plateau watershed, Iran (48˚07'E to 61˚25'E - 26˚33'N to 37˚27'N)</p> <p>Temporal Resolution: Monthly</p> <p>Temporal Coverage: 2005 - 2015 (Inclusively)</p> <p> </p> <p><strong>How to cite:</strong></p> <p><strong>Arash Ghomlaghi, Mohsen Nasseri & Bardia Bayat (2022): How to enhance the inverse distance weighting method to detect the precipitation pattern in a large-scale watershed, Hydrological Sciences Journal, DOI: 10.1080/02626667.2022.2124874</strong></p>
On the Sensitivity of Future Hydrology in the Colorado River to the Selection of the Precipitation Partitioning Method
<p>This dataset contains the simulation results of the sensitivity of future hydrology to the selection of different precipitation partitioning method in the Colorado River Basin. The simulation is conducted with the Variable Infiltration Capacity (VIC) model at 8-km, hourly resolution from 1976-2095 (water year) and aggregated to 30-yr average for historical period (1976-2005) and far future period (2066-2095) in this dataset. </p> <p>The 30-yr average results are compressed and organized into three folders: <strong>TA</strong>, <strong>TW</strong>, and <strong>TWC</strong>. The filename of modeling results contains the associated experiments, GCM, and simulation period as follows: "fluxes.CRB.$EXP.$GCM.$PER.nc", where <em>$EXP</em> is the VIC experiments (TA, TW, and TWC), <em>$GCM</em> is the GCM used (eight in total), $PER is the simulation period (his, RCP45, and RCP85)</p> <p>More details can be found on the associated paper (this record will be updated when the paper is published):</p> <p>Wang, Z., Vivoni, E.R., Whitney, K.M., Xiao, M., and Mascaro, G. 2024. On the Sensitivity of Future Hydrology in the Colorado River to the Precipitation Partitioning Method. <em>Water Resources Research</em>. (In Revision). </p>
Dataset: A Non-Stationary Bias Adjustment Method for improving the Inter-annual Variability and Persistence of Projected Precipitation
<p>This dataset includes: (1) unbiased data using a novel non-stationary bias adjustment methodology specifically tailored for environmental variables exhibiting sporadic events characterized by substantial intensity variability; (2) the parameters of the non-stationary probability distributions using marinetools.temporal. The methodology involves establishing a probability threshold to adapt the occurrence of precipitation events and employing a non-stationary theoretical and parametric quantile mapping to adjust associated biases.</p> <p>The dataset is part of the results obtained after the application of the methodology to daily precipitation projections from seven regional climatic models of the RCP 8.5 scenario spanning 2006-2100, alongside historical concurrent data from projections and observations spanning 1970-2005.</p> <p>Its efficacy is compared a widely used quantile mapping method, revealing notable differences in the performance of the methods concerning the distribution of events throughout the year and the behaviour of mean and extreme intensity values. The proposed method demonstrates promising potential in reducing uncertainty associated with systematic errors in inter-annual precipitation variability. This bears significance in evaluating hydrological responses and its associated impacts particularly in semi-arid mountainous basins.</p>
Data for exploring topography-based methods for downscaling subgrid precipitation for use in Earth System Models
<p>Topography exerts major control on land surface processes. To improve representation of topographic impacts on land surface processes, a new topography-based subgrid structure has been introduced to the Energy Exascale Earth System Model representing the subgrid heterogeneity of surface elevation. Four topography-based methods of downscaling grid precipitation to the subgrids have been explored. The data utilized for the study include precipitation, surface elevation, and height rise data derived from wind speed and Brunt Vaisala parameter and outputs of downscaled precipitation and statistical metrics calculated in this study. Results show that utilizing hypsometric elevation of the subgrid landscape within the model grid cell improves downscaling of precipitation in mountainous areas. Furthermore, accounting for blocking of airflow further improves precipitation downscaling slightly in mountainous regions consistently across multiple grid sizes.</p> <p>The data files include:</p> <ol> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">daily_prism_precip.zip</a>: high resolution precipitation data (4 km) obtained from PRISM [Daly et al. 1994, Daly et al. 2008].</li> <li> <a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">dem_4km4.nc</a>: 4 km surface elevation data derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">fr_number.zip</a>: Height rise of airflow calculated from wind speed and Brunt Vaisala parameter derived from the North American Regional Reanalysis data.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_128km.zip?versionId=5f64ec8c-4018-4d97-ae1d-eb6f15ccc564">output_from_dwnscaling_methods_at_128km.zip</a>: Output data of the downscaling methods at 128 km spatial resolution.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_96km.zip?versionId=b2c67f80-9794-41cb-9986-a4c7259ccf1c">output_from_dwnscaling_methods_at_96km.zip</a>: Output data of the downscaling methods at 96 km spatial resolution. </li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_64km.zip?versionId=b83230a0-308e-4f90-971b-6636a5add796">output_from_dwnscaling_methods_at_64km.zip</a>: Output data of the downscaling methods at 64 km spatial resolution.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/output_from_dwnscaling_methods_at_32km.zip?versionId=cc9021cc-c3b4-4c04-a558-752c151c49ba">output_from_dwnscaling_methods_at_32km.zip</a>: Output data of the downscaling methods at 32 km spatial resolution. </li> <li>ppt_spatial_downscaling_daily_data_flatten_withFr_test_filt0_v3rev_64.py: Python code used to calculate downscaled precipitation data from aggregated grid precipitation data.</li> <li><a href="https://zenodo.org/api/files/12bc9f1c-be98-4721-8c92-6e23845be441/stns_precip_2015.csv">stns_precip_2015.csv</a>: Precipitation data at rain gauge stations in the Conterminous US extracted from the Daymet station-level input datasets are used for evaluation of the downscaled results </li> </ol> <p>Other datasets used to calculate wind speed and Brunt Vaisala parameter were extracted from the North American Regional Reanalysis (NARR) including wind speed, temperature, surface pressure, specific humidity and relative humidity [Mesinger et al. 2006].</p> <p> </p> <p><strong>References:</strong></p> <p>Daly, C., et al. (1994). "A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain." Journal of Applied Meteorology <strong>33</strong>(2): 140-158. </p> <p>Daly, C., et al. (2008). "Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States." International Journal of Climatology <strong>28</strong>(15): 2031-2064.</p> <p>Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union <strong>89</strong>(10): 93-94.</p> <p>Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes <strong>27</strong>(15): 2171-2186.</p> <p>Mesinger, F., et al. (2006). "NORTH AMERICAN REGIONAL REANALYSIS." Bulletin of the American Meteorological Society <strong>87</strong>(3): 343-360.</p>
Determination of process parameters and precipitation methods for potential large-scale production of sugar beet leaf protein concentrate
<p>Abstract</p> <p><br>BACKGROUND</p> <p>Sugar beet is one of the most produced industrial plants in the world, and during manufacturing, it produces a large quantity of leaf waste. Because this waste is rich in protein, this study aimed to identify an efficient method for producing large-scale protein concentrate from sugar beet leaves.</p> <p><br>RESULTS</p> <p>Results showed that protein extraction from fresh leaves was more effective than dried leaves. Maximum protein extraction was achieved at pH 9, compared with pH 7 or 8. Blanching as a pretreatment reduced protein yield during isoelectric precipitation, with a yield of 2.31% compared to 20.20% without blanching. Consequently, blanching was excluded from the extraction process. After extraction, isoelectric precipitation, heat coagulation, and isoelectric–ammonium sulfate precipitation were compared. Although the latter resulted in the highest protein yield, Fourier transform infrared analysis revealed that excessive salt was not removed during dialysis, making it unsuitable for scale-up due to its additional cost and complexity. Therefore, isoelectric precipitation was selected as the appropriate method for protein precipitation from sugar beet leaves. To increase yield, extractions were assisted by ultrasound or enzyme addition. Ultrasound-assisted extraction resulted in an increased protein yield from 20.20% to 28.60%, while Pectinex Ultra SP-L-assisted extraction was the most effective, increasing protein yield from 20.20% to 38.09%.</p> <p><br>CONCLUSION</p> <p>Proteins were extracted from fresh sugar beet leaves using optimum conditions (50 °C, 30 min, pH 9) and precipitated at the isoelectric point, with enzymatic-assisted extraction yielding the maximum protein recovery.<br><br></p>
The code and data for "Deep-Learning Correction Methods for WRF Model Precipitation Forecasting from 2014 Through 2022 in Zhengzhou city, China"
<p>The code and data for “Deep-Learning Correction Methods for WRF Model Precipitation Forecasting from 2014 Through 2022 in Zhengzhou city, China”</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.