IceCloudNet is a novel method based on machine learning able to obtain high quality vertically resolved predictions for ice water content and ice crystal number concentration of clouds containing ice. The predictions come at the spatio-temporal coverage and resolution of Meteosat SEVIRI and the vertical resolution of DARDAR. IceCloudNet consists of a ConvNeXt-based U-Net and a 3D PatchGAN discriminator model and is trained by predicting DARDAR profiles from co-located SEVIRI images. Despite the sparse availability of DARDAR data due to its narrow overpass, IceCloudNet is able to predict cloud occurrence, macrophysical shape, and microphysical properties with high precision. We release 5 years of vertically resolved ice water content (IWC) and ice crystal number concentration (Nice) of clouds containing ice with a 3 km×3 km×240 m×15 minute resolution on a spatial domain of 30°W to 30°E and 30°S to 30°N. The resulting data set increases the availability of vertical cloud profiles for the period when DARDAR is available by more than six orders of magnitude and moreover, is able to provide vertical cloud profiles beyond the lifetime of the recently ended satellite missions underlying DARDAR.

These data originate from the stations of the DWD and legally as well as qualitatively equal partner network stations. Extensive station metadata, such as station relocations, instrument changes, reference time changes, algorithm changes or operator information are included. The dataset is divided into a versioned part with completed quality check, in the directory ./historical/. And a part for which the quality check has not yet been completed, in the directory ./recent/. The folder ./timeseries_overview/ contains information about long time series.

These data originate from the stations of the DWD and legally as well as qualitatively equal partner network stations. Extensive station metadata, such as station relocations, instrument changes, reference time changes, algorithm changes or operator information are included. The dataset is divided into a versioned part with completed quality check, in the directory ./historical/. And a part for which the quality check has not yet been completed, in the directory ./recent/. The folder ./timeseries_overview/ contains information about long time series.

Sentinel-3 OLCI images processed with the Atmospheric Correction for Optical Water Types, A4O [Hieronymi et al. in prep & 2023], and the water algorithm OLCI Neural Network Swarm, ONNS [Hieronymi et al., 2017]. ONNS derives inherent optical properties (IOPs) from which the concentrations of water constituents are estimated. In addition, the results of an Optical Water Type (OWT) classification based on A4O reflectances are provided [Bi and Hieronymi, 2024]. All available satellite data of a day for the region of interest are merged in a common grid at approximately original resolution. Information about the variables are given in the attached Additional Info. Version 2 of the data has the license and some metadata corrected. Please use and refer only to Version 2 (see link below).

Satellite remote sensing enables global monitoring of water quality in freshwater and marine ecosystems. However, consistent data quality is a challenge due to variations in the performance of used algorithms for different waters. In this exemplary dataset, we use a novel approach for atmospheric correction and retrieval for water quality characteristics in inland waters, coastal areas, and the open sea. Copernicus Sentinel-3 OLCI satellite images are processed with the Atmospheric Correction for Optical Water Types, A4O [Hieronymi et al. in prep & 2023], and the water algorithm OLCI Neural Network Swarm, ONNS [Hieronymi et al., 2017]. ONNS derives inherent optical properties (IOPs) from which the concentrations of water constituents are estimated. In addition, the results of an Optical Water Type (OWT) classification based on A4O reflectances are provided [Bi and Hieronymi, 2024]. All available satellite data of a day for the region of interest are merged in a common grid at approximately original resolution. An overview of the variables in the dataset can be found in the Additional Information; a detailed description of the contents and background, as well as an optical analysis of the waters, can be found in Hieronymi et al. [2025]. Version 2 of the dataset has the license and some metadata corrected. Data itself remains unchanged.