Timeseries dataset for modeling the multi-scale temporal variability of fog harvesting potential in the coastal Atacama region

This dataset contains (1) ERA5 data at hourly resolution on pressure levels and (2) downscaled ERA5 data processed using artificial neural networks. The resulting downscaled ERA5 dataset was used as an input to run a model that estimates fog water collection at different height levels (Lobos-Roco et al., 2025). The resulting dataset from the model (3) was used to analize the long-term (~70 years) variability of fog water collection at Alto Patache, a research station located within the coastal Atacama Desert. Contact: [e-mail] / [e-mail] Files description AMARU_output_z2_timeseries.csv - columns description: - datetime[utc]: Timestamps, hourly resolution- P[hPa]: Air pressure- Temp[K]: Air temperature- RH[%] Relative humidity- q[g/kg]: Specific humidity- rl[g/kg]: Liquid water content- U[m/s]: Wind speed- WD[deg]: Wind direction- CB[m]: Cloud base height- CT[m]: Cloud top height- Wh[l/m2]: Fog water harvesting- Fin[l/m2]: Fog influx Z1_data.csv and Z2_data.csv - columns description:- Data at hourly resolution- Columns ending with _ERA5, data from RAW ERA5- Columns ending with _ANN, downscaled ERA5 data from ANN adjustment For more information regarding AMARU model, used for estimating fog water harvesting at height levels, please see: Lobos-Roco, F., Vilà-Guerau de Arellano, J., & del Río, C. (2025). Observation-driven model for calculating water-harvesting potential from advective fog in (semi-) arid coastal regions. Hydrology and Earth System Sciences, 29(1), 109-125. https://doi.org/10.5194/hess-29-109-2025 An additional repository containing the python script to use AMARU model can be found in: Lobos-Roco, Felipe (2024), “Advective fog Model for (semi-)Arid Regions Under climate change (AMARU)”, Mendeley Data, V1, doi: 10.17632/jyk8v2mrhd.1