Gazing at Cirrus Clouds for 25 Years through a Split Window. Part I: Methodology

Heidinger, Andrew K.; Pavolonis, Michael J.

doi:10.1175/2008jamc1882.1

Cited by 154 publications

(141 citation statements)

References 25 publications

Supporting

Mentioning

139

Contrasting

Order By: Relevance

“…Various active and passive satellite cloud climatologies exist; for example, the active Cloud Profiling Radar (CPR) (Stephens et al, 2008) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) sensors are able to provide height-resolved information on cloud properties (Winker et al, 2007), however, coverage is limited to the subsatellite track and the time-series are short. Of the passive satellite instruments the most widely known are the High resolution Infrared Sounder (HIRS, Wylie and Menzel, 1999), Moderate Resolution Imaging Spectroradiometer (MODIS, Platnick et al, 2003), Advanced Very High Resolution Radiometer (AVHRR, Jacobowitz et al, 2003;Heidinger and Pavolonis, 2009) and Multi-angle imaging SpectroRadiometer (MISR, Moroney et al, 2002) datasets. The passive sensors cannot represent the complex vertical structure, but have much better global coverage and longer time series than active instruments.…”

Section: A Poulsen Et Al: Cloud Retrieval Algorithmmentioning

confidence: 99%

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Poulsen

Siddans

Thomas³

et al. 2012

Atmos. Meas. Tech.

View full text Add to dashboard Cite

Abstract. Clouds play an important role in balancing the Earth's radiation budget. Hence, it is vital that cloud climatologies are produced that quantify cloud macro and micro physical parameters and the associated uncertainty. In this paper, we present an algorithm ORAC (Oxford-RAL retrieval of Aerosol and Cloud) which is based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared, thereby ensuring that the resulting cloud properties provide both a good representation of the short-wave and long-wave radiative effects of the observed cloud. The advantages of the optimal estimation method are that it enables rigorous error propagation and the inclusion of all measurements and any a priori information and associated errors in a rigorous mathematical framework. The algorithm provides a measure of the consistency between retrieval representation of cloud and satellite radiances. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase.The algorithm can be applied to most visible/infrared satellite instruments. In this paper, we demonstrate the applicability to the Along-Track Scanning Radiometers ATSR-2 and AATSR. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed, in particular the algorithm is evaluated for a number of simulated single-layer and multi-layer conditions. The algorithm was found to perform well for single-layer cloud except when the cloud was very thin; i.e., less than 1 optical depths. For the multi-layer cloud, the algorithm was robust except when the upper ice cloud layer is less than five optical depths. In these cases the retrieved cloud top pressure and cloud effective radius become a weighted average of the 2 layers. The sum of optical depth of multi-layer cloud is retrieved well until the cloud becomes thick, greater than 50 optical depths, where the cloud begins to saturate. The cost proved a good indicator of multi-layer scenarios. Both the retrieval cost and the error need to be considered together in order to evaluate the quality of the retrieval. This algorithm in the configuration described here has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation) project to produce a 14 yr consistent record for climate research.

show abstract

Section: A Poulsen Et Al: Cloud Retrieval Algorithmmentioning

confidence: 99%

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Poulsen

Siddans

Thomas³

et al. 2012

Atmos. Meas. Tech.

View full text Add to dashboard Cite

show abstract

“…Szejwach, 1982;Nieman et al, 1993;Schmetz et al, 1993) and optimal estimation (e.g. Heidinger and Pavolonis, 2009;Sayer et al, 2011;Watts et al, 2011). An intercomparison of different techniques currently used for SEVIRI is presented in Hamann et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

et al. 2017

View full text Add to dashboard Cite

Abstract. 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 SE-VIRI 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.

show abstract

“…This concerns especially those based on data from widerswath scanning sensors measuring in visible, infrared, and microwave spectral regions (i.e. data from passive imagers) as demonstrated by Holz et al (2008), Minnis et al (2008), Reuter et al (2009), Heidinger and Pavolonis (2009), and Karlsson and Dybbroe (2010).…”

Section: Introductionmentioning

confidence: 99%

On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset

Karlsson

Johansson

2013

Atmos. Meas. Tech.

View full text Add to dashboard Cite

A method for detailed evaluation of a new satellite-derived global 28 yr cloud and radiation climatology (Climate Monitoring SAF Clouds, Albedo and Radiation from AVHRR data, named CLARA-A1) from polar-orbiting NOAA and Metop satellites is presented. The method combines 1 km and 5 km resolution cloud datasets from the CALIPSO-CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation – Cloud-Aerosol Lidar with Orthogonal Polarization) cloud lidar for estimating cloud detection limitations and the accuracy of cloud top height estimations.

Cloud detection is shown to work efficiently for clouds with optical thicknesses above 0.30 except for at twilight conditions when this value increases to 0.45. Some misclassifications of cloud-free surfaces during daytime were revealed for semi-arid land areas in the sub-tropical and tropical regions leading to up to 20% overestimated cloud amounts. In addition, a substantial fraction (at least 20–30%) of all clouds remains undetected in the polar regions during the polar winter season due to the lack of or an inverted temperature contrast between Earth surfaces and clouds.

Subsequent cloud top height evaluation took into account the derived information about the cloud detection limits. It was shown that this has fundamental importance for the achieved results. An overall bias of −274 m was achieved compared to a bias of −2762 m when no measures were taken to compensate for cloud detection limitations. Despite this improvement it was concluded that high-level clouds still suffer from substantial height underestimations, while the opposite is true for low-level (boundary layer) clouds.

The validation method and the specifically collected satellite dataset with optimal matching in time and space are suggested for a wider use in the future for evaluation of other cloud retrieval methods based on passive satellite imagery

show abstract

Gazing at Cirrus Clouds for 25 Years through a Split Window. Part I: Methodology

Cited by 154 publications

References 25 publications

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset

Contact Info

Product

Resources

About