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Luftdaten Deutschland API | Air Data Germany API

Mehrmals täglich ermitteln Fachleute an Messstationen der Bundesländer und des Umweltbundesamtes die Qualität unserer Luft. Schon kurz nach der Messung können Sie sich über die Luftdaten-API die aktuellen Messwerte abrufen. Zurzeit sind Daten ab dem Jahr 2016 abrufbar. Bitte beachten Sie, dass es sich bei den Daten des laufenden Jahres um noch nicht endgültig geprüfte Daten handelt. Erst im Juni des Folgejahres werden die finalen Daten bereitgestellt. Die aktuellen Daten können Lücken aufgrund Übertragungsproblemen enthalten. Das UBA kann keine Vollständigkeit garantieren. Unterjährig erfolgen Updates mit vorläufig geprüften Daten.

Model Output Statistics for Saldenburg-Entschenreuth (P586)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Model Output Statistics for SULEJOW (12469)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Model Output Statistics for Mühldorf (10875)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

Forschungsgruppe (FOR) 2589: Zeitnahe Niederschlagsschätzung und -vorhersage; Near-Realtime Quantitative Precipitation Estimation and Prediction (RealPEP), sub project: Coordination Funds

High-quality near-real time Quantitative Precipitation Estimation (QPE) and its prediction for the next hours (Quantitative Precipitation Nowcasting, QPN) is of high importance for many applications in meteorology, hydrology, agriculture, construction, water and sewer system management. Especially for the prediction of floods in small to meso-scale catchments and of intense precipitation over cities timely, the value of high-resolution, and high-quality QPE/QPN cannot be overrated. Polarimetric weather radars provide the undisputed core information for QPE/QPN due to their area-covering and high-resolution observations, which allow estimating precipitation intensity, hydrometeor types, and wind. Despite extensive investments in such weather radars, QPE is still based primarily on rain gauge measurements since more than 100 years and no operational flood forecasting system actually dares to employ radar observations for QPE. RealPEP will advance QPE/QPN to a stage, that it verifiably outperforms rain gauge observations when employed for flood predictions in small to medium-sized catchments. To this goal state-of-the?art radar polarimetry will be sided with attenuation estimates from commercial microwave link networks for QPE improvement, and information on convection initiation and evolution from satellites and lightning counts from surface networks will be exploited to improve QPN. With increasing forecast horizons the predictive power of observation-based nowcasting quickly deteriorates and is outperformed by Numerical Weather Prediction (NWP) based on data assimilation, which fails, however, for the first hours due to the lead time required for model integration and spin-up. Thus, RealPEP will merge observation-based QPN with NWP towards seamless prediction in order to provide optimal forecasts from the time of observation to days ahead. Despite recent advances in simulating surface and sub-surface hydrology with distributed, physicsbased models, hydrologic components for operational flood prediction are still conceptual, need calibration, and are unable to objectively digest observational information on the state of the catchments. RealPEP will prove that in combination with advanced QPE/QPN physics-based hydrological models sided with assimilation of catchment state observations will outperform traditional flood forecasting in small to meso-scale catchments.

The forecast data for temperature at 850hPa [K] from GPC_Offenbach (DWD).

This resource contains the monthly mean temperature at 850hPa [K] for 6 months. The format of resource is GRIB2. It is provided through the web site of WMO Lead Centre for LRF MME (Long Range Forecast Multi-Model Ensemble) on about the 15th of each month. The web site requests a user account. The Grade A(GPCs) and Grade B(NMHSs, RCCs) users can download the data USAGE: Menu: Data and Plot > Data Exchange > Search/Download. This forecast data is made by GPC_Offenbach (DWD) using an operational seasonal prediction system. For more detailed information about the seasonal forecasts of GPC_Offenbach (DWD) visit the web site http://www.dwd.de/EN/ourservices/seasonals_forecasts/start.html.

The forecast data for sea surface temperature [K] from GPC_Offenbach (DWD).

This resource contains the monthly mean sea surface temperature [K] for 6 months. The format of resource is GRIB2. It is provided through the web site of WMO Lead Centre for LRF MME (Long Range Forecast Multi-Model Ensemble) on about the 15th of each month. The web site requests a user account. The Grade A(GPCs) and Grade B(NMHSs, RCCs) users can download the data USAGE: Menu: Data and Plot > Data Exchange > Search/Download. This forecast data is made by GPC_Offenbach (DWD) using an operational seasonal prediction system. For more detailed information about the seasonal forecasts of GPC_Offenbach (DWD) visit the web site http://www.dwd.de/EN/ourservices/seasonals_forecasts/start.html.

The forecast data for mean-sea level pressure [Pa] from GPC_Offenbach (DWD).

This resource contains the monthly mean mean-sea level pressure [Pa] for 6 months. The format of resource is GRIB2. It is provided through the web site of WMO Lead Centre for LRF MME (Long Range Forecast Multi-Model Ensemble) on about the 15th of each month. The web site requests a user account. The Grade A(GPCs) and Grade B(NMHSs, RCCs) users can download the data USAGE: Menu: Data and Plot > Data Exchange > Search/Download. This forecast data is made by GPC_Offenbach (DWD) using an operational seasonal prediction system. For more detailed information about the seasonal forecasts of GPC_Offenbach (DWD) visit the web site http://www.dwd.de/EN/ourservices/seasonals_forecasts/start.html.

GTS Bulletin: FCGL45 BGGH - Forecast (details are described in the abstract)

The FCGL45 TTAAii Data Designators decode as: T1 (F): Forecast T1T2 (FC): Aerodrome (VT < 12 hours) A1A2 (GL): Greenland (Remarks from Volume-C: NilReason)

Model Output Statistics for Schönteichen-Cunnersdorf (O268)

DWD’s fully automatic MOSMIX product optimizes and interprets the forecast calculations of the NWP models ICON (DWD) and IFS (ECMWF), combines these and calculates statistically optimized weather forecasts in terms of point forecasts (PFCs). Thus, statistically corrected, updated forecasts for the next ten days are calculated for about 5400 locations around the world. Most forecasting locations are spread over Germany and Europe. MOSMIX forecasts (PFCs) include nearly all common meteorological parameters measured by weather stations. For further information please refer to: [in German: https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/met_verfahren_mosmix.html ] [in English: https://www.dwd.de/EN/ourservices/met_application_mosmix/met_application_mosmix.html ]

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