Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks
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,...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-09-01
|
| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://www.atmos-meas-tech.net/10/3547/2017/amt-10-3547-2017.pdf |
| _version_ | 1818110912520781824 |
|---|---|
| author | J. Strandgren L. Bugliaro F. Sehnke L. Schröder |
| author_facet | J. Strandgren L. Bugliaro F. Sehnke L. Schröder |
| author_sort | J. Strandgren |
| collection | DOAJ |
| description | 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.
<br><br>
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<sup>−2</sup>. 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.
<br><br>
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. |
| first_indexed | 2024-12-11T02:54:41Z |
| format | Article |
| id | doaj.art-4271d4d0171947c9afcac83ce482fb4b |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-12-11T02:54:41Z |
| publishDate | 2017-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-4271d4d0171947c9afcac83ce482fb4b2022-12-22T01:23:12ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482017-09-01103547357310.5194/amt-10-3547-2017Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networksJ. Strandgren0L. Bugliaro1F. Sehnke2L. Schröder3Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, GermanyDeutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, GermanyZentrum für Sonnenenergie- und Wasserstoff-Forschung Baden Württemberg, Systemanalyse, Stuttgart, GermanyZentrum für Sonnenenergie- und Wasserstoff-Forschung Baden Württemberg, Systemanalyse, Stuttgart, GermanyCirrus 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. <br><br> 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<sup>−2</sup>. 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. <br><br> 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.https://www.atmos-meas-tech.net/10/3547/2017/amt-10-3547-2017.pdf |
| spellingShingle | J. Strandgren L. Bugliaro F. Sehnke L. Schröder Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks Atmospheric Measurement Techniques |
| title | Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks |
| title_full | Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks |
| title_fullStr | Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks |
| title_full_unstemmed | Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks |
| title_short | Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks |
| title_sort | cirrus cloud retrieval with msg seviri using artificial neural networks |
| url | https://www.atmos-meas-tech.net/10/3547/2017/amt-10-3547-2017.pdf |
| work_keys_str_mv | AT jstrandgren cirruscloudretrievalwithmsgseviriusingartificialneuralnetworks AT lbugliaro cirruscloudretrievalwithmsgseviriusingartificialneuralnetworks AT fsehnke cirruscloudretrievalwithmsgseviriusingartificialneuralnetworks AT lschroder cirruscloudretrievalwithmsgseviriusingartificialneuralnetworks |