The dataset contains an urban weather record from the hydro-meteorological monitoring station of the Institute of Geographical Sciences at the Freie Universitaet Berlin (working group Applied Geography, Environmental Hydrology and Resource Management; Geo Campus Lankwitz, Malteserstraße 74-100, 12249 Berlin). The station is located at an elevation of 45 m a.s.l. and consists of a 7.5 x 7.5 m wide fenced measuring field covered by short grass which is cut in weekly intervals (spring to fall) to ensure reference evaporation conditions.The field is equipped with a range of redundant devices that record weather information. In this summary we focus on a description of the devices from which data were included in the published dataset. A actual list of all devices is available at the Website of the Hydrometeorological monitoring station "Berlin-Lankwitz, FU Geo Campus" (https://www.geo.fu-berlin.de/en/geog/fachrichtungen/angeog/Messfeld-auf-dem-Campus/index.html).The dataset contains rainfall, air temperature, humidity, dew point temperature, air pressure, solar radiation as well as wind speed and direction, each measured in intervals of 15 min. It starts in January 2017 and is updated annually. Rainfall is collected with a Davis VantagePro tipping bucket which is part of the ISS (Integrated Sensor Suite, DAV-6323EU, manufactured before 2007) and mounted 2 m above ground. The collector diameter is 16.3 cm resulting in a collecting area of 210 cm². The measuring resolution of the tipping bucket is 0.25 mm (0.01 inch). During winter the DAVIS rain gauge is heated using the DAV-7720EU heating system. The begin of the heating period in each year is determined by the air temperature and starts before the daily minimum drops below 0°C. In addition a stainless steel Hellmann gauge with standard diameter of 16 cm (area: 200 m²) is installed on the monitoring field 1 m above ground. Rain water is collected in a steel can, which is emptied manually every morning from Monday to Friday using a DIN58667 measuring glass. Between December and February accumulated snow and ice is thawed. Paired data from the Hellman and DAVIS collector to assess accuracy are published separately (Reinhardt-Imjela et al. 2018). Temperature (°C), humidity (%) and air pressure (hPa) are measured 2 m above ground with the DAVIS ISS and the dew point is generated automatically from the data. Temperature includes mean, minimum and maximum of each 15 minute interval. Wind speed and direction are recorded by a Vaisale Weather Transmitter WXT520 2 m above ground. For solar radiation (W/m²) a Kipp & Zonen CMP3 Pyranometer is mounted also 2 m above ground.The data are provided as a tab-separated ASCII file with column names in the first line. The first column contains the date and time (date format: DD/MM/YYYY hh:mm). In the following columns all measured parameters are listed (units are included in the column name). Measuring errors or missing values are marked with “N/A”. Empty fields for the wind direction indicate intervals without measurable wind speed.
The data set contains hydrological, meteorological and gravity time series collected at Argentine-German Geodetic Observatory (AGGO) in La Plata, Argentina. The hydrological series include soil moisture, temperature, electric conductivity, soil parameters, and groundwater variation. The meteorological time series comprise air temperature, humidity, pressure, wind speed, solar short- and long-waver radiation, and precipitation. The observed hydrometeorological parameters are extended by modelled value of evapotranspiration and water content variation in the zone between deepest soil moisture sensor and the groundwater level. Gravity products include large-scale hydrological, oceanic as well as atmospheric effects. These gravity effects are furthermore extended by local hydrological effects and gravity residuals suitable for comparison and evaluation of the model performance. Provided are directly observed values denoted as Level 1 product along with pre-processed series corrected for known issues (Level 2). Level 3 products are model outputs acquired using Level 2 data. The maximal temporal coverage of the data set ranges from May 2016 up to November 2018 with some exceptions for sensors and models set up in May 2017. The data set is organized in a database structure suitable for implementation in a relational database management system. All definitions and data tables are provided in separate text files allowing for traditional use without database installation.Software related to the data acquisition, processing, and modelling can be found in a separate publication describing scripts applied to the data set presented here. The software publication is available at https://doi.org/10.5880/GFZ.5.4.2018.002 (Mikolaj, 2018)
This software publication describes the data acquisition, processing and modelling of hydrological, meteorological and gravity time series prepared for the Argentine-German Geodetic Observatory (AGGO) in La Plata, Argentina. The corresponding output data set is available at http://doi.org/10.5880/GFZ.5.4.2018.001 (Mikolaj et al., 2018).Processed hydrological series include soil moisture, temperature, electric conductivity, and groundwater variation. The processed meteorological time series comprise air temperature, humidity, pressure, wind speed, solar short- and long-waver radiation, and precipitation. Modelling scripts include evapotranspiration, combined precipitation, and water content variation in the zone between deepest soil moisture sensor and groundwater. In addition, large-scale hydrological, oceanic as well as atmospheric effect are modelled along with the local hydrological effects. To allow for a comparison of the model outputs to observations, processing script of gravity residuals is provided as well.
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.