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5 results for “zonal statistics”
Zonal Statistics of Climate Indicators from ERA5-Land for Brazilian Municipalities, 2023
<p>Climate indicators are used in several statistical models for many research areas and are specially important for modelling Climate Sensitive Diseases (CSD) incidence. Those models usually adopts a lattice structure, where its data is aggregated at administrative boundaries (e.g. disease incidence), but climate indicators are usually presented in a continuous regular grid format.</p> <p>To make climate indicators compatible with lattice structures, zonal statistics may be adopted. Zonal statistics are descriptive statistics calculated using a set of cells that spatially intersects a given spatial boundary. For each boundary in a map, statistics like average, maximum value, minimum value, standard deviation, and sum are obtained to represent the cell's values that intersect the boundary.</p> <p>This dataset present zonal statistic of climate indicators computed from Copernicus ERA5-Land daily aggregates for the Brazilian municipalities, for the year of 2023.</p>
Zonal Statistics of Weather Indicators for Brazilian Municipalities from the BR-DWGD Project
<p>This dataset presents daily weather indicators for Brazilian municipalities computed with zonal statistics using the data from the <a href="https://sites.google.com/site/alexandrecandidoxavierufes/brazilian-daily-weather-gridded-data" target="_blank" rel="noopener">BR-DWGD project</a> (version 3.2.3), from 1961-01-01 to 2024-03-20.</p> <p> </p> <table> <tbody> <tr> <td>File</td> <td>Indicator</td> <td>Unit</td> </tr> <tr> <td>pr_3.2.3.parquet</td> <td>Precipitation</td> <td>mm</td> </tr> <tr> <td>ETo_3.2.3.parquet</td> <td>Evapotranspiration</td> <td>mm</td> </tr> <tr> <td>Tmax_3.2.3.parquet</td> <td>Maximum temperature</td> <td>°C</td> </tr> <tr> <td>Tmin_3.2.3.parquet</td> <td>Minimum temperature</td> <td>°C</td> </tr> <tr> <td>Rs_3.2.3.parquet</td> <td>Solar radiation</td> <td>MJm-2</td> </tr> <tr> <td>u2_3.2.3.parquet</td> <td>Wind speed at 2 m height</td> <td>m/s</td> </tr> <tr> <td>RH_3.2.3.parquet</td> <td>Relative humidity</td> <td>%</td> </tr> </tbody> </table> <p>The methodology to compute the zonal statistics follows <a href="https://doi.org/10.1017/eds.2024.3" target="_blank" rel="noopener">https://doi.org/10.1017/eds.2024.3</a> .</p>
Zonal Statistics of Weather Indicators for Brazilian Municipalities from the TerraClimate Project
<p>This dataset contains 14 parquet-format files with monthly data.</p> <table align="center"> <tbody> <tr> <td>File</td> <td>Indicator</td> <td>Unit</td> </tr> <tr> <td>aet.parquet</td> <td>Actual Evapotranspiration</td> <td>mm</td> </tr> <tr> <td>def.parquet</td> <td>Climate Water Deficit</td> <td>mm</td> </tr> <tr> <td>pdsi.parquet</td> <td>Palmer Drought Severity Index (PDSI)</td> <td>unitless</td> </tr> <tr> <td>pet.parquet</td> <td>Precipitation</td> <td>mm</td> </tr> <tr> <td>ppt.parquet</td> <td>Potential evapotranspiration</td> <td>mm</td> </tr> <tr> <td>q.parquet</td> <td>Runoff</td> <td>mm</td> </tr> <tr> <td>soil.parquet</td> <td>Soil Moisture</td> <td>mm</td> </tr> <tr> <td>srad.parquet</td> <td>Downward surface shortwave radiation</td> <td>W/m2</td> </tr> <tr> <td>swe.parquet</td> <td>Snow water equivalent</td> <td>mm</td> </tr> <tr> <td>tmax.parquet</td> <td>Maximun Temperature</td> <td>°C</td> </tr> <tr> <td>tmin.parquet</td> <td>Minimum Temperature</td> <td>°C</td> </tr> <tr> <td>vap.parquet</td> <td>Vapor pressure</td> <td>kPa</td> </tr> <tr> <td>vpd.parquet</td> <td>Vapor Pressure Deficit</td> <td>kpq</td> </tr> <tr> <td>ws.parquet</td> <td>Wind speed</td> <td>m/s</td> </tr> </tbody> </table> <p> </p>
Zonal Statistics of Climate Indicators from ERA5-Land for Brazilian Municipalities, 1950-2022
<p>Climate indicators are used in several statistical models for many research areas and are specially important for modelling Climate Sensitive Diseases (CSD) incidence. Those models usually adopts a lattice structure, where its data is aggregated at administrative boundaries (e.g. disease incidence), but climate indicators are usually presented in a continuous regular grid format.</p><p>To make climate indicators compatible with lattice structures, zonal statistics may be adopted. Zonal statistics are descriptive statistics calculated using a set of cells that spatially intersects a given spatial boundary. For each boundary in a map, statistics like average, maximum value, minimum value, standard deviation, and sum are obtained to represent the cell's values that intersect the boundary.</p><p>This dataset present zonal statistic of climate indicators computed from Copernicus ERA5-Land daily aggregates for the Brazilian municipalities, from 1950 to 2022.</p><p> </p><p> </p>
Zonal Statistics
<p>This data set is composed of two parts each having its proper origins, formats and rights. This data set was used to answer a challenge given by a high school to help students to learn the problem of population density and needs.</p>
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