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Reference: Taylor, S, Stier, P, White, BA et al., (2016). Evaluating the diurnal cycle in cloud top temperature from SEVIRI. Atmospheric Chemistry and Physics Discussions.Citable link to this page:

 

Evaluating the diurnal cycle in cloud top temperature from SEVIRI

Abstract: The variability of convective cloud spans a wide range of temporal and spatial scales and is of fundamental importance for global weather and climate systems. Datasets from geostationary satellite instruments such as SEVIRI provide high time resolution observations across a large area. In this study we use data from SEVIRI to quantify the diurnal cycle of cloud top temperature within the instrument’s field of view and discuss these results in relation to retrieval biases.We evaluate SEVIRI cloud top temperatures from the new CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) dataset against CALIOP data. Results show a mean bias of + 0.44 K with a standard deviation of 11.7 K, which is in agreement with previous validation studies. Analysis of the spatiotemporal distribution of these errors shows that absolute retrieval biases vary from less than 5 K over the southeast Atlantic Ocean up to 30 K over central Africa at night. Night and daytime retrieval biases can also differ by up to 30 K in some areas, potentially contributing to biases in the estimated amplitude of the diurnal cycle. This illustrates the importance of considering spatial and diurnal variations in retrieval errors when using the CLAAS-2 dataset.Keeping these biases in mind, we quantify the seasonal, diurnal and spatial variation of cloud top temperature across SEVIRI’s field of view using the CLAAS-2 dataset. By comparing the mean diurnal cycle of cloud top temperature with the retrieval bias we find that diurnal variations in the retrieval bias can be small, but are often of the same order of magnitude as the amplitude of the observed diurnal cycle, indicating that in some regions the diurnal cycle apparent in the observations may be significantly impacted by diurnal variability in the accuracy of the retrieval.We show that the CLAAS-2 dataset can measure the diurnal cycle of cloud tops accurately in regions of stratiform cloud such as the southeast Atlantic Ocean and Europe, where cloud top temperature retrieval biases are small and exhibit limited spatial and temporal variability. Quantifying the diurnal cycle over the tropics and regions of desert is more difficult, as retrieval biases are larger and display significant diurnal variability. CLAAS-2 cloud top temperature data are found to be of limited skill in measuring the diurnal cycle accurately over desert regions. In tropical regions such as Central Africa, the diurnal cycle can be described by the CLAAS-2 data to some extent, although retrieval biases appear to reduce the amplitude of the real diurnal cycle of cloud top temperatures.This is the first study to relate the diurnal variations in SEVIRI retrieval bias to observed diurnal cycles in cloud top temperature. Our results may be of interest to those in the observation and modelling communities when using cloud top properties data from SEVIRI, particularly for studies considering the diurnal cycle of convection.

Publication status:PublishedPeer Review status:Peer reviewedVersion:Accepted manuscriptDate of acceptance:2016-10-28 Funder: Seventh Framework Programme   Notes:© Author(s) 2016. CC-BY 3.0 License.

Bibliographic Details

Publisher: European Geosciences Union

Publisher Website: http://www.egu.eu/

Journal: Atmospheric Chemistry and Physics Discussionssee more from them

Publication Website: http://www.atmospheric-chemistry-and-physics.net/index.html

Issue Date: 2016-10Identifiers

Doi: https://doi.org/10.5194/acp-2016-878

Issn: 1680-7375

Uuid: uuid:dc3c0c3e-33c0-441f-8430-a951c2e3668d

Urn: uri:dc3c0c3e-33c0-441f-8430-a951c2e3668d

Pubs-id: pubs:656116 Item Description

Type: journal-article;

Version: Accepted manuscript

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Autor: Taylor, S - Oxford, MPLS, Physics, Atmos Ocean and Planet Physics - - - Stier, P - Oxford, MPLS, Physics, Atmos Ocean and Planet

Fuente: https://ora.ox.ac.uk/objects/uuid:dc3c0c3e-33c0-441f-8430-a951c2e3668d



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