Satellite Application Facilities (SAFs) are centres for processing satellite data, and form an integral part of the EUMETSAT Application Ground Segment. MeteoSwiss has been a partner in the "Satellite Application Facility on Climate Monitoring" project (CM SAF) since 2004.
| Project start | 01.03.2022 |
|---|---|
| Project end | 28.02.2027 |
| Region | International |
| Status | Current projects |
The SAF on Climate Monitoring generates climate datasets from satellite data on cloud physical parameters, surface radiation and radiation budget at the edge of the atmosphere, as well as water vapour in the atmosphere.
In the CM SAF project MeteoSwiss is addressing the Essential Climate Variable (ECVs) Surface Earth Radiation Budget (SRB). MeteoSwiss is leading a CM SAF activity to generate a 40 years climate data record for the full radiation budget including surface incoming solar radiation, albedo, surface outgoing longwave radiation and surface downward longwave radiation. The CM SAF partner RMI (à www.meteo.be) models the latent and sensible heat fluxed based on the MeteoSwiss radiation components and external satellite-based information on the vegetation state and the soil moisture.
This Land Flux climate data record consists of a multi-decadal hourly resolved set of land radiation, heat and water flux ECV’s which will enable a surface to atmosphere radiation and water balance analysis for the entire Meteosat period over Africa and Europe. This will be mainly achieved by consolidating and unifying previously separated developments in CM SAF, LSA SAF and the EUMETSAT Secretariat and running them in a joint retrieval.
In the current project phase (2022 to 2027) MeteoSwiss produces a global data set on the entire Surface Energy Balance (SRB) in close cooperation with the German Weather Service.
MeteoSwiss produced a cloud climate data record back to the 80s. The cloud data are derived from First and Second Generation Meteosat satellites and cover large parts of Europe and Africa with a 5 km spatial resolution. It is planned to extend the data back to the 80s in this project phase.
The cloud data are a valuable source of climate information. They can expand and/or complement observations made with the naked eye (SYNOP) in regions where ground observations are sparsely distributed or where the cloud cover varies greatly within an area, e.g. in mountainous or coastal regions. Additionally, cloud information is often required as input data for deriving physical parameters from satellite data, such as land and sea surface characteristics, surface radiation flux or atmospheric variables like humidity and temperature.
The cloud mask algorithm is based on continuous cloud cover data, rather than using the traditional approach of the binary decision tree. The values are calculated for various channels as well as for different spatial and temporal resolutions. Each value represents the probability of cloud cover for the relevant pixel (cloudy: value > 0, clear: value < 0). The end result - i.e. cloud cover probability - is calculated by adding together all available values, taking into account the variations in these values during the day, at night and when snow is present.
Land Surface Temperature (LST) is a measure on how hot or cold the uppermost surface of the Earth is. From a climate perspective, the Earth skin temperature is essential to study land surface and land -atmosphere exchange. Complementary to the air temperature, it is an independent temperature measure to quantify climate change.
LST can be determined from Meteosat First and Second Generation satellite measurements in the infrared atmospheric windows. MeteoSwiss has released a long-term LST climate data record as contribution to the CM SAF project. This climate data record is available at hourly time steps and covers large parts of Europe and Africa with a 0.05° spatial resolution.
The LST is derived from satellite measurements using radiative transfer models. Satellite sensors measure the temperature at the top of the atmosphere. The atmospheric contribution to the radiance needs to be corrected to accurately measure LSTs. We use information from weather models such as e.g. the atmospheric water vapour to correct the atmospheric contribution.
Stöckli, R., Bojanowski, J.S., John, V.O., Duguay-Tetzlaff, A., Bourgeois, Q., Schulz, J., Hollmann, R. (2019) Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications. Remote Sens, 11, 1052.
Bojanowski, J., Stöckli, R., Tetzlaff, A., Finkensieper, S., Hollmann, R. (2018) Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations, Remote Sensing, 10.
Duguay-Tetzlaff, A., Bento, V.A., Göttsche, F.M., Stöckli, R., Martins, J.P.A., Trigo, I., Olesen, F.,Bojanowski, J.S., Da Camara, C., Kunz, H. (2015) Meteosat Land Surface Temperature Climate Data Record: Achievable Accuracy and Potential Uncertainties. Remote Sens., 7, 13139-13156.
Posselt, R., Mueller, R., Stöckli, R., Trentmann, J., Liniger, M.A. (2014) A surface radiation climatology across two Meteosat satellite generations, Remote Sensing of Environment, 142, 103-110; http://dx.doi.org/10.1016/j.rse.2013.11.007
Posselt, R., R. Müller, J. Trentmann, and R. Stöckli (2012). Remote sensing of solar surface radiation for climate monitoring -- the CM-SAF retrieval in international comparison, Remote Sensing of Environment, 118, 186-198; doi:10.1016/j.rse.2011.11.016
Posselt, R., R. Müller, R. Stöckli, and J. Trentmann (2011). Spatial and Temporal Homogeneity of Solar Surface Irradiance across Satellite Generations, Remote Sensing, 3, 1029-1046; doi:10.3390/rs3051029
Mueller R., J. Trentmann, C. Träger-Chatterjee, R. Posselt, R. Stöckli (2011). The Role of the Effective Cloud Albedo for Climate Monitoring and Analysis. Remote Sensing, 3, 2305-2320; doi:10.3390/rs3112305
Dürr, B.; Zelenka, A.; Müller, R. & Philipona, R. (2010). Verification of CM-SAF and MeteoSwiss satellite based retrievals of surface shortwave irradiance over the Alpine region International Journal of Remote Sensing, 2010, 31, 4179 - 4198
Dürr, B. & Zelenka, A. (2009). Deriving surface global irradiance over the Alpine region from METEOSAT Second Generation data by supplementing the HELIOSAT method, International Journal of Remote Sensing 30(22), 5821 - 5841.
Schulz, J.; Albert, P.; Behr, H. D.; Caprion, D.; Deneke, H.; Dewitte, S.; Dürr, B.; Fuchs, P.; Gratzki, A.; Hechler, P.; Hollmann, R.; Johnston, S.; Karlsson, K. G.; Manninen, T.; Mueller, R.; Reuter, M.; Riihela, A.; Roebeling, R.; Selbach, N.; Tetzlaff, A.; Thomas, W.; Werscheck, M.; Wolters, E. & Zelenka, A. (2009). Operational climate monitoring from space: the EUMETSAT Satellite Application Facility on Climate Monitoring (CM-SAF), Atmos Chem Phys 9(5), 1687 - 1709.