气候变化领域集成服务门户(CCPortal): Understanding the model representation of clouds based on visible and infrared satellite observations

CCPortal

DOI	10.5194/acp-21-12273-2021
	Understanding the model representation of clouds based on visible and infrared satellite observations
	Geiss S.; Scheck L.; De Lozar A.; Weissmann M.
发表日期	2021
ISSN	1680-7316
起始页码	12273
结束页码	12290
卷号	21 期号:16
英文摘要	There is a rising interest in improving the representation of clouds in numerical weather prediction models. This will directly lead to improved radiation forecasts and, thus, to better predictions of the increasingly important production of photovoltaic power. Moreover, a more accurate representation of clouds is crucial for assimilating cloud-affected observations, in particular high-resolution observations from instruments on geostationary satellites. These observations can also be used to diagnose systematic errors in the model clouds, which are influenced by multiple parameterisations with many, often not well-constrained, parameters. In this study, the benefits of using both visible and infrared satellite channels for this purpose are demonstrated. We focus on visible and infrared Meteosat SEVIRI (Spinning Enhanced Visible InfraRed Imager) images and their model equivalents computed from the output of the ICON-D2 (ICOsahedral Non-hydrostatic, development version based on version 2.6.1; ) convection-permitting, limited area numerical weather prediction model using efficient forward operators. We analyse systematic deviations between observed and synthetic satellite images derived from semi-free hindcast simulations for a 30 d summer period with strong convection. Both visible and infrared satellite observations reveal significant deviations between the observations and model equivalents. The combination of infrared brightness temperature and visible reflectance facilitates the attribution of individual deviations to specific model shortcomings. Furthermore, we investigate the sensitivity of model-derived visible and infrared observation equivalents to modified model and visible forward operator settings to identify dominant error sources. Estimates of the uncertainty of the visible forward operator turned out to be sufficiently low; thus, it can be used to assess the impact of model modifications. Results obtained for various changes in the model settings reveal that model assumptions on subgrid-scale water clouds are the primary source of systematic deviations in the visible satellite images. Visible observations are, therefore, well-suited to constrain subgrid cloud settings. In contrast, infrared channels are much less sensitive to the subgrid clouds, but they can provide information on errors in the cloud-top height. © 2021 Stefan Geiss et al.
语种	英语
scopus关键词	climate modeling; cloud cover; cloud microphysics; computer simulation; error analysis; geostationary satellite; infrared imagery; Meteosat; numerical model; parameterization; satellite imagery; SEVIRI; weather forecasting; Satellites
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/246646
作者单位	Hans Ertel Centre for Weather Research, Ludwig-Maximilians-Universität, Munich, Germany; Deutscher Wetterdienst, Offenbach, Germany; Institut für Meteorologie und Geophysik, Universität Wien, Vienna, Austria
推荐引用方式 GB/T 7714	Geiss S.,Scheck L.,De Lozar A.,et al. Understanding the model representation of clouds based on visible and infrared satellite observations[J],2021,21(16).
APA	Geiss S.,Scheck L.,De Lozar A.,&Weissmann M..(2021).Understanding the model representation of clouds based on visible and infrared satellite observations.ATMOSPHERIC CHEMISTRY AND PHYSICS,21(16).
MLA	Geiss S.,et al."Understanding the model representation of clouds based on visible and infrared satellite observations".ATMOSPHERIC CHEMISTRY AND PHYSICS 21.16(2021).