Free access and download to of a growing selection of DWD’s climate data. Via CDC Search you will find data for direct download and interactive access to station data. The interactive mode gives graphical and tabular previews of the German station data. In addition, all data sets remain accessible from our ftp server for direct download
Exposure to ultrafine aerosol particles (UFPs) can cause adverse effects on human health, local environment and climate. Air traffic is associated with the emission of high numbers of UFPs, which results in increased UFP number concentrations close to airports. So far, the spatial distribution and variability of UFPs is poorly understood in the atmospheric boundary layer. The uncrewed aerial system (UAS) ALADINA (Application of Lightweight Aircraft for Detecting In-situ Aerosols, e.g. Altstädter et al., 2015) was operated close to the largest airport in Germany at Frankfurt airport (FRA) between 11 and 19 October 2024. The dataset provides airborne in-situ observations of the spatial distribution of aerosol particle number concentration with different sizes and meteorological parameters of temperature, humidity, wind, surface temperature and short-wave irradiance, as well as accurate position and orientation of ALADINA. Data are available from 26 measurement flights, comprising a number of 122 vertical profiles between ground and a maximum altitude of 750 m above mean sea level (ASL) and about 70 horizontal legs at different but constant altitude, e.g. in 100 m altitude intervals. Details about the ALADINA measurements will be provided in a publication (Harm-Altstädter et al., in prep.) soon.
As part of PhytOakmeter (www.phytoakmeter.de), time-domain transmission, soil moisture and -temperature sensors with custom-made logger systems were used to measure time series of soil state variables. The aim of these investigations was to provide data on environmental properties used in a cross-disciplinary approach. The measurement device consisted of two sensors at three different depths. The dataset contains the values of time (UTC), relative permittivity, soil moisture (in % vol) derived from permittivity and soil temperature (in °C). Determination of soil moisture was done using the formula of Topp et al. (1980). As sensors, the SMT100 soil moisture sensors with integrated temperature measurement were used. All sensors were installed within the upper 50cm below ground. The exact depths for each sensor are listed in the dataset and parameter comment.
Exposure to ultrafine aerosol particles (UFPs) can cause adverse effects on human health, local environment and climate. Air traffic is associated with the emission of high numbers of UFPs, which results in increased UFP number concentrations close to airports. So far, the spatial distribution and variability of UFPs is poorly understood in the atmospheric boundary layer. The uncrewed aerial system (UAS) ALADINA (Application of Lightweight Aircraft for Detecting In-situ Aerosols, e.g. Altstädter et al., 2015) was operated close to the largest airport in Germany at Frankfurt airport (FRA) between 11 and 19 October 2024. The dataset provides airborne in-situ observations of the spatial distribution of aerosol particle number concentration with different sizes and meteorological parameters of temperature, humidity, wind, surface temperature and short-wave irradiance, as well as accurate position and orientation of ALADINA. Data are available from 26 measurement flights, comprising a number of 122 vertical profiles between ground and a maximum altitude of 750 m above mean sea level (ASL) and about 70 horizontal legs at different but constant altitude, e.g. in 100 m altitude intervals. Details about the ALADINA measurements will be provided in a publication (Harm-Altstädter et al., in prep.) soon.
As part of the hydro-meteorological measurement campaign SwabianMOSES 2023 time-domain transmission soil moisture sensors and temperature sensors with custom-made logger systems were used to measure time series of these soil state variables. The aim of these investigations was to provide data on physical soil properties used in a cross-disciplinary approach for a better understanding of hydro-meteorological extremes (such as high precipitation events and droughts). Each measurement site consisted of sensors at three depths with two sensors each. Logger systems were installed at six different observation sites which were distributed across the whole campaign target area in the vicinity of the Swabian Jura in Germany. Decisions on the specific installation depths were made during the installation at the respective sites based on the constitution of the local soil profiles. Installation protocols with a brief soil profile description and photos are part of this dataset. The dataset contains the values of location and time (UTC), soil temperature (in °C), relative permittivity and soil moisture (in % vol) derived from permittivity. Determination of soil moisture was done using the formula of Topp et al. (1980). As sensors, the SMT100 soil moisture sensor with integrated temperature measurement were used. All sensors were installed within the upper 50cm below ground. The exact depths for each sensor are listed in the comments.
As part of the hydro-meteorological measurement campaign SwabianMOSES 2021 time-domain transmission soil moisture sensors and temperature sensors with custom-made logger systems were used to measure time series of these soil state variables. The aim of these investigations was to provide data on physical soil properties used in a cross-disciplinary approach for a better understanding of hydro-meteorological extremes (such as high precipitation events and droughts). Each measurement site consisted of sensors at three depths with two sensors each. Logger systems were installed at six different observation sites which were distributed across the whole campaign target area in the vicinity of the Swabian Jura in Germany. Decisions on the specific installation depths were made during the installation at the respective sites based on the constitution of the local soil profiles. Installation protocols with a brief soil profile description and photos are part of this dataset. The dataset contains the values of location and time (UTC), soil temperature (in °C), relative permittivity and soil moisture (in % vol) derived from permittivity. Determination of soil moisture was done using the formula of Topp et al. (1980). As sensors, the SMT100 soil moisture sensor with integrated temperature measurement were used. All sensors were installed within the upper 50cm below ground. The exact depths for each sensor are listed in the comments.
Die Charakterisierung der Land-Atmosphäre (L-A)-Rückkopplung erfordert detaillierte Untersuchungen des Bodens, der Landbedeckung und der Atmosphäre bis hin zur unteren freien Troposphäre. Fast alle aktuellen Observatorien und Beobachtungsnetzwerke konzentrieren sich nur auf Teilkomponenten des L-A-Systems. Ziel des Projekts P1 der Forschungsgruppe Land-Atmosphäre Feedback Initiative (LAFI) ist es, am Land-Atmosphäre Feedback Observatorium (LAFO) der Universität Hohenheim ein umfassendes Feldprogramm auf heterogenem Agrarland zu realisieren, das diese Beobachtungslücken schließt. Dieses wird durch die Messungen von Profilen der Schlüsselvariablen in allen drei Kompartimenten und die Charakterisierung ihrer horizontalen Strukturen in einer heterogenen Landschaft ermöglicht. Darüber hinaus werden wir einen 20-Jahres-Datensatz analysieren, der am MOL-RAO des DWD gesammelt wurde und die wichtigsten L-A-Systemvariablen für die Ableitung statistischer Metriken, Oberflächenflüsse und die Schließung des Energiehaushalts mit besonderem Schwerpunkt auf Hitzewellen und Dürren abdeckt. Am LAFO werden wir über mindestens eine Vegetationsperiode (Frühling-Herbst 2025) eine General Operations Period (GOP) in Kombination mit fünf Intensiven Observations Periods (IOPS) durchführen, um L-A-Feedbackprozesse zu charakterisieren. Neben etablierten Messtechniken wie Flusskammern und Eddy-Kovarianz-Systemen sowie Doppler-Lidar-Systemen werden wir neue Techniken einsetzen. Dazu gehören Isotopensensoren zur Unterscheidung zwischen Verdunstung und Transpiration, faseroptische Sensoren im Boden, in der Vegetation und in der Prandtl-Schicht für Temperatur- und Windmessungen, die Messung von Profilen der Strahlung, der Temperatur und Luftfeuchtigkeit in der Vegetation sowie unbemannte Luftfahrzeuge zur Kartierung von Landoberflächeneigenschaften und hochauflösende scannende Raman- und Differentialabsorptions-Lidarsysteme zusammen mit Doppler-Lidar-Systemen zur Ableitung dreidimensionaler turbulenter Eigenschaften der Oberflächenschicht und der atmosphärischen Grenzschicht mit besonderem Schwerpunkt auf Entrainment. P1 ist für alle anderen Projekte von unmittelbarer Bedeutung und dient als Schwerpunkt von LAFI. P2-P6 und P11 werden zur Sensorsynergie beitragen und die Daten unter verschiedenen wissenschaftlichen Aspekten analysieren. P7-P9 modellieren die gesamte GOP und verwenden die P1-Daten für Vergleiche von Feedback-Metriken und Prozessdarstellungen sowie für die Modellevaluation. P10 wird die P1-Daten mithilfe von Deep-Learning-Methoden analysieren. Die in P1 durchgeführten Messungen liefern die wesentliche Grundlage, um alle sechs Forschungsziele zu erreichen und alle Hypothesen von LAFI zu überprüfen. Ebenso stützen sich alle bereichsübergreifenden Arbeitsgruppen auf die Beobachtungen von P1. Durch das Design und die Durchführung der LAFI GOP und IOPs erwarten wir neue Erkenntnisse über L-A-Rückkopplungen und -Prozesse in Agrarlandschaften.
Mit dem ICON-D2 verfügt der Deutsche Wetterdienst (DWD) über ein Modell für Vorhersagen im Kürzestfristbereich bis +27 Stunden (bzw. +45 Stunden aus dem 03 UTC-Lauf). Mit seiner feinen Gitterpunktauflösung erzeugt das ICON-D2 insbesondere bei gefährlichen Wetterlagen verbesserte Vorhersagen, z. B. bei Wetterlagen mit hochreichender Feuchtkonvektion (Super- und Multizellengewitter, Böenwalzen, mesoskalige konvektive Komplexe) und bei Wetterereignissen, die durch Wechselwirkungen mit der feinskaligen Topographie entstehen (Bodennebel, Föhnstürme, heftige Hangabwinde, Sturzfluten). Das Modellgebiet des ICON-D2 deckt ganz Deutschland, Dänemark, die Benelux-Staaten, die Schweiz, Österreich und Teile der übrigen Nachbarstaaten ab. Die horizontale Auflösung des ICON-D2 im nativen Dreiecksgitter beträgt 2,1 km. Die einzelnen Parameter des Modells sind in den Keywords aufgeführt.
As part of PhytOakmeter (www.phytoakmeter.de), time-domain transmission, soil moisture and -temperature sensors with custom-made logger systems were used to measure time series of soil state variables. The aim of these investigations was to provide data on environmental properties used in a cross-disciplinary approach. The measurement device consisted of two sensors at three different depths. The dataset contains the values of time (UTC), relative permittivity, soil moisture (in % vol) derived from permittivity and soil temperature (in °C). Determination of soil moisture was done using the formula of Topp et al. (1980). As sensors, the SMT100 soil moisture sensors with integrated temperature measurement were used. All sensors were installed within the upper 50cm below ground. The exact depths for each sensor are listed in the dataset and parameter comment.
Soil temperature at 15cm depth and air temperature at 60cm height were collected using HOBO Pro V2 loggers, model U23-004. Two loggers were used. After data visualization, unrealistic values were removed manually for each logger, and mean temperature values were calculated at 30-minute intervals.
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