Evaluation against CALIPSO lidar observations of the multi‐geostationary cloud cover and type dataset assembled in the framework of the Megha‐Tropiques mission

Abstract

International audienceTo support the MEGHA-Tropiques space mission, cloud mask and cloud type classification are needed at high spatial and time resolutions over the tropical belt for water vapour and precipitation analysis. For this purpose, visible and infrared radiance data from geostationary satellites (GEO) are used with a single algorithm initially developed by SAFNWC (Satellite-Application-Facility-for-Nowcasting) for Meteosat-Second-Generation. This algorithm has been adapted by SAFNWC to the spectral characteristics and field of view of each satellite. Retrieved cloud cover characteristics (cloud mask, classification and top pressure) are evaluated over four months in summer of 2009 against CALIOP lidar observations from the CALIPSO polar-orbiting satellite. To better identify atmospheric and instrumental issues, separate analyses are performed over land and ocean, for 0130 AM and 0130 PM CALIPSO overpasses and for each GEO. Both mean cloud cover occurrence and instantaneous cloud cover statistics are compared. We found that each classification has specific features, which depend on observed cloud regimes and instrument capabilities. Most important, a common behaviour of the GEOs against CALIOP depending on cloud types is observed. We found that GEO cloud occurrence is lower by about 10% than CALIOP, with the largest biases over land during daytime and the smallest over ocean during daytime. Further detailed analysis reveals specific discrepancies in the retrieved cloud types. As expected, high-level clouds are detected more frequently by the lidar. We show that, over ocean when the optical thickness of detected high-level clouds is limited to greater than 0.1 in the comparisons, multi-spectral radiometry performs very similarly. However, the most significant difference is attributed to non-detection of low-level clouds that are often broken, which causes a reduction of up to 20% in low-level cloud fraction and even 30% in some regions. Other significant differences are seen over land, where mid-level clouds are not detected or are misclassified

Deep convective influence on the UTLS composition in the Asian Monsoon Anticyclone region: 2017 StratoClim campaign results

“…More details on the retrieval algorithm can be found in Stengel et al (2014), Finkensieper et al (2016 and the ATBD Meteo-France (2016) document (http://www.nwcsaf.org). A comparison fo the SAF-NWC product with spaceborne active LiDAR measurements can be found in Sèze et al (2015). Here we use a specific version of the product where the ancillary data are taken from ERA-5 at hourly resolution.…”

Synergistic use of Lagrangian dispersion and radiative transfer modelling with satellite and surface remote sensing measurements for the investigation of volcanic plumes: the Mount Etna eruption of 25–27 October 2013

“…The SAFNWC algorithm requires, as ancillary inputs, surface height maps, land/sea mask, climatological maps of sea surface temperature, continental reflectance maps and temperature and humidity profiles. Recently, this algorithm was adapted to other geostationary data and verified using CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) data (Sèze et al, 2014). The first two steps of the SAFNWC algorithm, cloud detection and classification, rely on multi-spectral threshold tests applied at the pixel scale to a set of spectral and textural features.…”

The diurnal cycle of cloud profiles over land and ocean between 51° S and 51° N, seen by the CATS spaceborne lidar from the International Space Station

“…The maximum low cloud amount (CA) is often reached in the early afternoon. This sun-driven variation reaches a maximum over continents, where it depends on orography (Wilson and Barros, 2017;Shang et al, 2018), and in summer. It is more limited over ocean and during winter (Rozendaal et al, 1995;Soden, 2000).…”