The Global Distribution of Cirrus Clouds Reflectance Based on MODIS Level-3 Data

Zhao, Fengmei; Tang, Chaoli; Dai, Congming; Wu, Xing; Wei, Heli

doi:10.3390/atmos11020219

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…Cluster 1's population of Ci and Cb pixels across all seasons is, on average, around 83% and 6%, respectively. In agreement with the existing literature [96] the percentages of Ci pixels reached a maximum (minimum) in winter (summer). These results are in accordance with a boreal lower temperature in the troposphere that favored both the formation and the maintenance of ice crystals, which are the constituents of this type of clouds [97].…”

Section: Identification Of Cirrus and Cumulonimbus In Clustersupporting

confidence: 92%

A Cluster Approach to Cloud Cover Classification over South America and Adjacent Oceans Using a k-means/k-means++ Unsupervised Algorithm on GOES IR Imagery

Yuchechen

Lakkis

Caferri³

et al. 2020

Remote Sensing

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An unsupervised k-means/k-means++ clustering algorithm was implemented on daily images of standardized anomalies of brightness temperature (Tb) derived from the Geostationary Operational Environmental Satellite (GOES)-13 infrared data for the period 1 December 2010 to 30 November 2016. The goal was to decompose each individual Tb image into four clusters that captures the characteristics of different cloud regimes. The extracted clusters were ordered by their mean value in an ascending fashion so that the lower the cluster order, the higher the clouds they represent. A linear regression between temperature and height with temperature used as the predictor was conducted to estimate cloud top heights (CTHs) from the Tb values. The analysis of the results was performed in two different ways: sample dates and seasonal features. Cluster 1 is the less dominant one, representing clouds with the highest tops and variabilities. Cluster 4 is the most dominant one and represents a cloud regime that spans the lowest 2 km of the troposphere. Clusters 2 and 3 are entangled in the sense that both have their CTHs spanning the middle troposphere. Correlations between the monthly time series of the number of pixels in each cluster and of the entropy with several circulation indices are also introduced. Additionally, a fractal-related analysis was carried out on cluster 1 in order to resolve cirrus and cumulonimbus.

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Section: Identification Of Cirrus and Cumulonimbus In Clustersupporting

confidence: 92%

A Cluster Approach to Cloud Cover Classification over South America and Adjacent Oceans Using a k-means/k-means++ Unsupervised Algorithm on GOES IR Imagery

Yuchechen

Lakkis

Caferri³

et al. 2020

Remote Sensing

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“…The decrease of POP at lower TB 10.8 values is mainly related to the presence of cirrus clouds, which are no-precipitating clouds with very low cloud top temperature. The occurrence of cirrus clouds reaches a maximum (minimum) in winter (summer) [86]. This aspect is related to the lower temperature in the troposphere during winter that favors both the formation and the maintenance of ice crystals, which are the constituents of this type of clouds [87].…”

Section: Rainbow Algorithmmentioning

confidence: 99%

RAINBOW: An Operational Oriented Combined IR-Algorithm

et al. 2020

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In this paper, precipitation estimates derived from the Italian ground radar network (IT GR) are used in conjunction with Spinning Enhanced Visible and InfraRed Imager (SEVIRI) measurements to develop an operational oriented algorithm (RAdar INfrared Blending algorithm for Operational Weather monitoring (RAINBOW)) able to provide precipitation pattern and intensity. The algorithm evaluates surface precipitation over five geographical boxes (in which the study area is divided). It is composed of two main modules that exploit a second-degree polynomial relationship between the SEVIRI brightness temperature at 10.8 µm TB10.8 and the precipitation rate estimates from IT GR. These relationships are applied to each acquisition of SEVIRI in order to provide a surface precipitation map. The results, based on a number of case studies, show good performance of RAINBOW when it is compared with ground reference (precipitation rate map from interpolated rain gauge measurements), with high Probability of Detection (POD) and low False Alarm Ratio (FAR) values, especially for light to moderate precipitation range. At the same time, the mean error (ME) values are about 0 mmh−1, while root mean square error (RMSE) is about 2 mmh−1, highlighting a limited variability of the RAINBOW estimations. The precipitation retrievals from RAINBOW have been also compared with the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) official microwave (MW)/infrared (IR) combined product (P-IN-SEVIRI). RAINBOW shows better performances than P-IN-SEVIRI, in terms of both detection and estimates of precipitation fields when they are compared to the ground reference. RAINBOW has been designed as an operational product, to provide complementary information to that of the national radar network where the IT GR coverage is absent, or the quality (expressed in terms of Quality Index (QI)) of the RAINBOW estimates is low. The aim of RAINBOW is to complement the radar and rain gauge network supporting the operational precipitation monitoring.

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“…7 was also found by Sourdeval et al (2018) higher N is associated with more frequent incidences of hom). Additional evidence for this seasonality can be found in the cirrus cloud reflectance measurements of Zhao et al (2020), where reflectance from cirrus clouds is highest during winter and lowest during summer at mid-to-high latitudes.…”

mentioning

confidence: 91%

An Estimate of Global, Regional and Seasonal Cirrus Cloud Radiative Effects Contributed by Homogeneous Ice Nucleation

Mitchell

Mejía

Tomii

et al. 2020

Preprint

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Abstract. There are two fundamental mechanisms through which cirrus clouds form; homo- and heterogeneous ice nucleation (henceforth hom and het). The relative contribution of each mechanism to ice crystal production often determines the microphysical and radiative properties of a cirrus cloud. This study attempts to estimate the radiative contribution of hom relative to het by constraining the cloud microphysics in a climate model to conform with satellite retrievals of cirrus cloud effective diameter De, where the sampled cirrus cloud base had a temperature T

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The Global Distribution of Cirrus Clouds Reflectance Based on MODIS Level-3 Data

Cited by 7 publications

References 33 publications

A Cluster Approach to Cloud Cover Classification over South America and Adjacent Oceans Using a k-means/k-means++ Unsupervised Algorithm on GOES IR Imagery

A Cluster Approach to Cloud Cover Classification over South America and Adjacent Oceans Using a k-means/k-means++ Unsupervised Algorithm on GOES IR Imagery

RAINBOW: An Operational Oriented Combined IR-Algorithm

An Estimate of Global, Regional and Seasonal Cirrus Cloud Radiative Effects Contributed by Homogeneous Ice Nucleation

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