weather_italy

A dataset containing daily gridded weather variables for Italy that are obtained from the ERA5-Land dataset era5SpatialDownscaling.

datasets

The aim of the spatial downscaling is to increase the spatial resolution of the gridded geospatial input data. This package contains two deep learning based spatial downscaling methods, super-resolution deep residual network (SRDRN) (Wang et al., 2021 <doi:10.1029/2020WR029308>) and UNet (Ronneberger et al., 2015 <doi:10.1007/978-3-319-24574-4_28>), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 <doi:10.1023/B:CLIM.0000013685.99609.9e>). The SRDRN and UNet methods are implemented to optionally account for cyclical temporal patterns in case of spatio-temporal data. For more details of the methods, see Sipilä et al. (2025) <doi:10.48550/arXiv.2512.13753>.

Mika Sipil<e4>

SpatialDownscaling

Methods for Spatial Downscaling Using Deep Learning

Mika Sipilä

Claudia Cappello

Sandra De Iaco

Klaus Nordhausen

Sara Taskinen

weather_italy function

A 4-dimensional array with dimensions:<dl>
<dt>longitude</dt>
<dd>Longitude grid points (0.2 degree resolution)</dd><dt>latitude</dt>
<dd>Latitude grid points (0.2 degree resolution)</dd><dt>variable</dt>
<dd>Weather variables: relative humidity (%), temperature (°C), total precipitation (meters per day)</dd><dt>time</dt>
<dd>Daily measurements from 2023-11-01 to 2023-12-31</dd>
</dl>

Format

Daily Weather Data for Italy — weather_italy

A 4-dimensional array with dimensions:<dl>
<dt>longitude</dt>
<dd>Longitude grid points (0.2 degree resolution)</dd>

<dt>latitude</dt>
<dd>Latitude grid points (0.2 degree resolution)</dd>

<dt>variable</dt>
<dd>Weather variables: relative humidity (%), temperature (°C), total precipitation (meters per day)</dd>

<dt>time</dt>
<dd>Daily measurements from 2023-11-01 to 2023-12-31</dd>


</dl>

weather_italy: Daily Weather Data for Italy

Description

Usage

Arguments

Format