The DFG Priority Program 1803 "EarthShape - Earth Surface Shaping by Biota” (www.earthshape.net, short description of the project below) installed a meteorological station network consisting of four stations between ~26 °S to ~38 °S in the Coastal Cordillera of Chile, South America. The stations are intended to provide baseline meteorological data along the climate and ecological gradient investigated in the EarthShape program. The stations are located in the EarthShape study areas, encompassing desert, semi-desert, mediterranean, and temperate climate zones. Each station is configured to include sensors that record precipitation at ground level, radiation at 2.8 m height, wind at 3 m height, 25 cm depth soil temperature, soil water content and bulk electrical conductivity, 2 m air temperature and relative humidity, and barometric pressure at 30-minute intervals. The data recording started in March/April 2016. The EarthShape project runs until December 2021. Data collection will continue until that date, and potentially longer depending on available funds.
This publication provides two sets of data: raw data and processed data. The raw data contains 2 file types per meteorological station: (1) all measured parameters of the whole dataset measured in 30 minutes intervals as downloaded from the station. Furthermore, we provide (2) one table per station of high-resolution precipitation events, measured in 5 min. intervals that were triggered during rain events at each station. The processed data consists of a continuous timeseries of observations since the activation of each station. The processing consists of the exclusion of erroneous data, caused by maintenance of the weather-stations and sporadic malfunction of sensors detected during data screening. The excluded data is communicated in a logfile (excel table), comments from data screening, solar eclipse and others are summarized in history files (ASCII ). the full description of the data and methods is provided in the data description file (Data description file).
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Version history:
16 January 2023 (Version 1.1): Alexander Beer included as additional author, addition of new data from 2020-04-14 bis 2022-10-10. All files of the first version are moved to the "previous-versions" folder.
09 October 2023 (Version 1.2): Addition of new time series data to 2023-07-31. Detailed changelog information can be found in the “History” files in the respective subfolders for each site.
With the growing use of airborne platforms in Earth observation, accurate tropospheric delay corrections across various altitudes have become essential. Most existing tropospheric delay models are referenced to the Earth’s surface and rely on analytical closed-form vertical adjustments to approximate delays at user heights. However, these analytical models often fail to capture the complex vertical variations in atmospheric conditions.
To address this limitation, we developed a novel approach leveraging deep neural networks (DNN) to reconstruct global three-dimensional zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD) derived from numerical weather models (NWM). Using the ERA5 pressure-level product (Hersbach et al., 2020) for model training, the DNN refines predictions by correcting the residuals of an analytical third-order exponential model (EXP3). This hybrid method takes advantage of the non-linear fitting capabilities of DNN, significantly enhancing the accuracy of vertical tropospheric delay corrections up to 14 km above the Earth’s surface. The model achieves an average precision of 0.4 mm for ZHD and 0.8 mm for ZWD, reducing root-mean-square (RMS) errors by 63% and 36%, respectively, compared to EXP3.
This dataset includes the EXP3 model, structured on a 1° × 1° global grid at four synoptic times daily (00:00, 06:00, 12:00, and 18:00 UTC) for the period 2019–2022. Additionally, it provides the corresponding DNN model to correct errors in the EXP3 predictions. It is important to note that the model is designed for altitudes ranging from the Earth’s surface up to 14 km.