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25 results for “Climate grids”

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zenodo28/100

BioClim Austria: Gridded climate indicators for 1961-1990 and 1991-2020 at 250m resolution

<h2>Overview</h2> This gridded meteorological data set consists of climatologies (climate indicators) on a 30-year average basis for Austria and covers two historical periods with a high spatial resolution of 250m. The two 30-year periods provided for the observations allow the analysis of the climate change that has already occurred. The selection of climate indicators is optimized for the needs of ecological models.  Resolution: 250x250m Projection: EPSG 31287 Extent: Austria Periods: 1961-1990 and 1991-2020 Format: GeoTIFF Data sources: Station data and derived products List of climatologies (climate indicators)  <table> <tbody> <tr> <td> # </td> <td> Short name </td> <td> Name </td> <td> Description </td> <td> Unit </td> <td> Yearly (Y),  monthly (M) or growing season (GS) </td> </tr> <tr> <td> 1 </td> <td> tasmin </td> <td> Average daily minimum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 2 </td> <td> tasmax </td> <td> Average daily maximum temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 3 </td> <td> tas </td> <td> Average temperature </td> <td> Arithmetic mean </td> <td> °C </td> <td> Y, M </td> </tr> <tr> <td> 4 </td> <td> tas_warmest_month </td> <td> Average temperature mean in the warmest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 5 </td> <td> tas_coldest_month </td> <td> Average temperature mean in the coldest month </td> <td> Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 6 </td> <td> tasmin_coldest_month </td> <td> Average temperature minimum in the coldest month </td> <td> Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 7 </td> <td> tasmax_warmest_month </td> <td> Average temperature maximum in the warmest month </td> <td> Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 8 </td> <td> thermal_continentality </td> <td> Average annual amplitude of monthly mean temperature (Thermal continentality) </td> <td> Climatological monthly mean temperature in the warmest month minus climatological monthly mean temperature in the coldest month (04_tas* minus 05_tas*).  </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 9 </td> <td> FD </td> <td> Average number of frost days per year </td> <td> Defined by 0°C daily minimum temperature at 2m height. </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 10 </td> <td> GSL </td> <td> Average length of the growing season </td> <td> The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 11 </td> <td> tas </td> <td> Average temperature in the growing season </td> <td> Average temperature in the growing season defined as in climate indicator 10 (GSL). If a year does not have a growing season, this value is not defined.  </td> <td> °C </td> <td> GS </td> </tr> <tr> <td> 12 </td> <td> GDD </td> <td> Average Growing Degree Days per year above 5°C </td> <td> Σ(Tmean – 5°C) per year.  </td> <td> °C </td> <td> Y </td> </tr> <tr> <td> 13 </td> <td> FD_first </td> <td> Average date of the first frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 14 </td> <td> FD_last </td> <td> Average date of the last frost occurrence </td> <td> Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate </td> <td> day of year </td> <td> Y </td> </tr> <tr> <td> 15 </td> <td> HD35 </td> <td> Average number of extremely hot days above 35°C per year </td> <td> Defined by 35°C daily maximum temperature at 2m height. </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 16 </td> <td> FD_10 </td> <td> Average number of days with hard frost below -10°C per year </td> <td> Defined by -10°C daily minimum temperature at 2m height. </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 20 </td> <td> GLO_hori </td> <td> Average sum of global radiation on horizontal surface </td> <td> Also called irradiance or shortwave incoming radiation, taking cloud cover into account. </td> <td> kWh </td> <td> Y, M </td> </tr> <tr> <td> 21 </td> <td> GLO_real </td> <td> Average sum of global radiation on the real surface </td> <td> Also called irradiance or shortwave incoming radiation, taking cloud cover into account. </td> <td> kWh </td> <td> Y, M </td> </tr> <tr> <td> 31 </td> <td> vpd </td> <td> Average water vapor pressure deficit </td> <td> Calculated from daily dew point temperature and temperature.  </td> <td> hPa </td> <td> Y, M </td> </tr> <tr> <td> 31 </td> <td> hurs </td> <td> Average relative humidity </td> <td> Calculated from daily dew point temperature and temperature.  </td> <td> % </td> <td> Y, M </td> </tr> <tr> <td> 32 </td> <td> vpd </td> <td> Average water vapor pressure deficit </td> <td> Calculated from daily dew point temperature and temperature.  </td> <td> hPa </td> <td> GS </td> </tr> <tr> <td> 32 </td> <td> hurs </td> <td> Average relative humidity </td> <td> Calculated from daily dew point temperature and temperature.  </td> <td> % </td> <td> GS </td> </tr> <tr> <td> 33 </td> <td> pr </td> <td> Average precipitation sum </td> <td>   </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 34 </td> <td> hygric_continentality </td> <td> Average hygric continentality according to Gams </td> <td> Defined as arctan of (elevation/annual_precip). Gams, H. (1931). Die klimatische Begrenzung von Pflanzenarealen und die Verteilung der hygrischen Kontinentalität in den Alpen.  </td> <td> ° </td> <td> Y </td> </tr> <tr> <td> 35 </td> <td> pr1mm </td> <td> Average number of days with precipitation  </td> <td> Daily precipitation of at least 1mm.  </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 36 </td> <td> pr1mm </td> <td> Average number of days with precipitation in the growing season </td> <td> Daily precipitation of at least 1mm.  </td> <td> days </td> <td> GS </td> </tr> <tr> <td> 37 </td> <td> DP_3days, DP_5days, DP_7days </td> <td> Average number of days in dry periods in summer half-year </td> <td> Number of days in periods of at least 3, 5, or 7 days with a daily precipitation total of less than 1mm. Summer half-year: April to September </td> <td> days </td> <td> Y </td> </tr> <tr> <td> 39 </td> <td> ET0 </td> <td> Average annual potential evapotranspiration </td> <td> Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 40 </td> <td> WBAL </td> <td> Average climatic water balance </td> <td> Precipitation minus potential evapotranspiration </td> <td> mm </td> <td> Y, M </td> </tr> <tr> <td> 41 </td> <td> WBAL </td> <td> Average climatic water balance in the growing season </td> <td> Precipitation minus potential evapotranspiration in the growing season </td> <td> mm </td> <td> GS </td> </tr> </tbody> </table>   <h2>Data sources</h2> The climate indicators were calculated using daily data from different sources: <ul> <li>Temperature: SPARTACUS v2.1 (Gridded data set, 1x1km, https://doi.org/10.1007/s00704-015-1411-4 )</li> <li>Precipitation: SPARTACUS v2.1 (Gridded data set, 1x1km, https://doi.org/10.1007/s00704-017-2093-x )</li> <li>Radiation: APOLIS SHORT(Gridded data set, 100x100m, 2006-2020, https://adsabs.harvard.edu/abs/2012EGUGA..14.9705O) APOLIS LONG (Gridded data set, 100x100m, 1981-2016) SPARTACUS v2.1 (Gridded data set, 1x1km, daily sunshine duration, 1961-2020).</li> <li>Wind: Daily station data (https://doi.org/10.60669/gs6w-jd70)</li> <li>Humidity: Daily station data  (https://doi.org/10.60669/gs6w-jd70)</li> <li>Digital elevation model: © Kooperation Länder, Bund (BEV, BML), 2022</li> </ul>

opencc-by-4.0Apr 2024View details →

nasa28/100

LBA Regional Climate Data, 0.5-Degree Grid, 1960-1990 (Willmott and Webber)

This data set is a subset of a 0.5-degree gridded temperature and precipitation data set for South America (Willmott and Webber 1998). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), defined as 10° N to 25° S, 30° to 85° W. The data are in ASCII GRID format. The data consist of the following: Monthly mean air temperature time series (1960-1990), in degrees C: monthly mean air temperatures for 1960-1990 cross validation errors associated with time series monthly mean air temperatures for 1960-1990, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation time series Monthly mean air temperature climatology, in degrees C: climatic means of monthly and annual air temperatures cross validation errors associated with climatic means climatic means of monthly and annual mean air temperatures, DEM assisted interpolation cross validation errors associated with DEM assisted interpolation climatic means Monthly total precipitation time series (1960-1990), in millimeters: monthly precipitation totals for 1960-1990 cross validation errors associated with time series monthly precipitation totals for 1960-1990, climatologically aided interpolation cross validation errors associated with climatologically aided interpolation time series Monthly total precipitation climatology, in millimeters: climatic means of monthly and annual precipitation totals cross validation errors associated with climatic means More information about the full data set can be found at "Willmott, Matsuura, and Collaborators' Global Climate Resource Pages" (http://climate.geog.udel.edu/~climate) at the University of Delaware. To obtain the original documentation and data, follow the link for "Available Climate Data," register or sign in, and follow the link for "South American Climate Data." Information on the LBA subset can be found at ftp://daac.ornl.gov/data/lba/physical_climate/willmott/comp/willmott_readme.pdf.

restrictednotspecifiedApr 2025View details →

nasa28/100

Daymet: Monthly Climate Summaries on a 1-km Grid for North America, Version 4 R1

This dataset provides Daymet Version 4 R1 monthly climate summaries derived from Daymet Version 4 R1 daily data at a 1 km x 1 km spatial resolution for five Daymet variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Monthly averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and monthly totals are provided for the precipitation variable. Each data file is yearly by variable with 12 monthly time steps and covers the same period of record as the Daymet V4 R1 daily data. The monthly climatology files are derived from the larger datasets of daily weather parameters produced on a 1 km x 1 km grid for North America, Hawaii, and Puerto Rico. Separate monthly files are provided for the land areas of continental North America (Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Data are distributed in standardized Climate and Forecast (CF)-compliant netCDF (*.nc) and Cloud-Optimized GeoTIFF (*.tif) formats. In Version 4 R1 (ver 4.1), all 2020 and 2021 files (60 total) were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.

restrictednotspecifiedApr 2025View details →

nasa28/100

Daymet: Annual Climate Summaries on a 1-km Grid for North America, Version 4 R1

This dataset provides annual climate summaries derived from Daymet Version 4 R1 daily data at a 1 km x 1 km spatial resolution for five Daymet variables: minimum and maximum temperature, precipitation, vapor pressure, and snow water equivalent. Annual averages are provided for minimum and maximum temperature, vapor pressure, and snow water equivalent, and annual totals are provided for the precipitation variable. Each data file is provided as a single year by variable and covers the same period of record as the Daymet V4 R1 daily data. The annual climatology files are derived from the larger datasets of daily weather parameters produced on a 1 km x 1 km grid for North America (including Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Separate annual files are provided for the land areas of continental North America, Hawaii, and Puerto Rico. Data are distributed in standardized Climate and Forecast (CF)-compliant netCDF (*.nc) and Cloud Optimized GeoTIFF (*.tif) file formats. In Version 4 R1, all 2020 and 2021 files (60 total) were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.

restrictednotspecifiedApr 2025View details →

zenodo24/100

Platform for Economic Analysis of Climate Hazards (PEACH): Gridded Hazards from 2000-2019

A (quasi) global gridded hazard and socioeconomic exposure database, spanning 2000 to 2019, covering 14 climate impact drivers (CIDs) and 3 geophysical hazards. The Platform for Economic Analysis of Climate Hazards (PEACH) provides monthly data at an approximate 10 × 10 km resolution, with full coverage between 2004 and 2015. Unlike event-based datasets, PEACH avoids rigid inclusion thresholds whenever possible, enabling flexible definition of “extreme events” and analysis across the full hazard intensity spectrum. Six complementary socioeconomic and environmental variables are included: population, nighttime light, relative wealth, critical infrastructure, vegetation indices, and land use. If you use PEACH in your work, please cite the paper: R. Reinhardt (2025) The Platform for Economic Analysis of Climate Hazards (PEACH): Gridded Hazards from 2000-2019,  CES Working Paper, Université Paris 1 Panthéon-Sorbonne <a href="https://shs.hal.science/halshs-05314666v1" target="_blank" rel="noopener">⟨halshs-05314666⟩</a> Data Description The main dataset is provided in Apache Arrow Feather format, partitioned into world subregions based on the UN M49 classification. Each file contains monthly hazard exposure and socioeconomic variables per 10 km grid cell. Variable definitions, formats, and usage notes are detailed in the accompanying README. For ease of use, see also the ConversionScript.zip files, which include exemplary code for loading the data in R and Python. Overview <ul> <li>Temporal resolution: Monthly (2000–2019; full coverage 2004–2015)</li> <li>Spatial resolution: 10 × 10 km grid cells (global coverage, rainfall quasi-global: 50°N–50°S)</li> <li>Projection: EPSG:4326 (WGS 84, standard Mercator)</li> <li>File format: Apache Arrow Feather (.feather), partitioned by UN M49 subregions</li> <li>Hazards included:</li> <ul> <li>Climate impact drivers (14): mean air temperature, cold spells, extreme heat, mean precipitation, heavy precipitation, floods (river/pluvial), landslides, drought, fire weather & wildfires, severe windstorms, tropical cyclones, sand and dust environments (proxy indicator, not direct dust storm records), hail, air pollution (PM2.5/PM10).</li> <li>Geophysical hazards (3): earthquakes, volcanic eruptions, tsunamis</li> </ul> </ul>   Access information: Access to the PEACH dataset is granted upon request for non-commercial academic and policy research. Requests will be processed as timely as possible, typically within a few business days.

restrictedcc-by-nc-sa-4.0Nov 2024View details →

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