Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks
| Autor: | Luca Bugliaro, Johan Strandgren, Leon Schröder, Frank Sehnke |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Opacity Meteorology Backscatter Reference data (financial markets) 0211 other engineering and technologies 02 engineering and technology 01 natural sciences cirrus clouds remote sensing lcsh:TA170-171 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Pixel lcsh:TA715-787 Fernerkundung der Atmosphäre Cloud top lcsh:Earthwork. Foundations SEVIRI neural networks lcsh:Environmental engineering Geostationary orbit Environmental science Satellite Cirrus |
| Zdroj: | Atmospheric Measurement Techniques, Vol 10, Pp 3547-3573 (2017) |
| ISSN: | 1867-8548 |
| Popis: | Cirrus clouds play an important role in climate as they tend to warm the Earth–atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SEVIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data. CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m−2. Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness. By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed. |
| Databáze: | OpenAIRE |
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