Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data

Stowe, Larry L.; McClain, E. Paul; Carey, Robert M.; Pellegrino, Paul; Gutman, Graciela E.; Davis, Paul A.; Long, C.; Hart, Sarah J.

doi:10.1016/0273-1177(91)90402-6

Cited by 191 publications

(88 citation statements)

References 6 publications

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“…Many studies have been conducted on snow cover area monitoring (Gutzler and Rosen, 1992;Hall et al, 2001;Liang et al, 2008b;Wang and Xie, 2009;Gao et al, 2010;Paudel et al, 2011), snow depth (SD) simulation (Stowe et al, 1991;Chang et al, 1996;Frei et al, 2012;Yu et al, 2012), snow disaster risk assessment (Romanov et al, 2002), loss evaluation post-disaster (i.e., the research for the pastoral areas after snow disasters have occurred and have resulted in losses of livestock) (Nakamura and Shindo, 2001), snow disaster and avalanche mapping, as well as their relations to climate change (Jones and Jamieson, 2001;Hendrikx et al, 2005;Bocchiola et al, 2008;Delparte et al, 2008;Hirashima et al, 2008;Lato et al, 2012). In 2006, the Chinese government issued a national standard for grading snow disasters in pastoral area (GB/T 20482-2006) (General Administration of Quality Supervision of China, 2006).…”

Section: Introductionmentioning

confidence: 99%

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Wang

Liang

Huang

et al. 2013

Nat. Hazards Earth Syst. Sci.

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This study develops a model for early warning of snow-caused livestock disasters on a county basis and proposes a method of qualitative risk assessment of snow disasters at 500 m resolution for pastoral areas on the Tibetan Plateau (TP). Data used for the model development include remote sensing data, statistical data of weather, livestock, and social economy, and 45 typical snow disaster cases from 2000 to 2010. The principal component analysis (PCA) approach is used to choose 7 crucial factors that contribute over 85% of information for early warning snow disasters on the TP. They are mean annual probability of snow disaster, number of snow-covered days, livestock stocking rate, continual days of mean daily temperature below −10 °C, grassland burial index, rate of snow-covered grassland, and per livestock gross domestic product. The chosen 411 cases from 2008 to 2010 are used to validate the prediction results from the developed early warning model, with an overall accuracy of 85.64% in predicting snow disasters and no disasters. This suggests that the early warning approach developed in the study has operational potential for predicting snow disasters on the TP

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Section: Introductionmentioning

confidence: 99%

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Wang

Liang

Huang

et al. 2013

Nat. Hazards Earth Syst. Sci.

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“…A multispectral cloud detection scheme, clouds from AVHRR (CLAVR) [Stowe et al, 1991], is used at the front end of the PATMOS processing. We used intermediate level-I daily PATMOS orbital data, consisting of reflectances produced at the original GAC resolution.…”

Section: Datamentioning

confidence: 99%

“…Only 1 month of data (for September 1989) was available for this pilot study. We used only daytime observations over land, identified by CLAVR as "clear" as well as those restored to "clear" after initially satisfying a cloud test [see Stowe et al, 1991].…”

Section: Datamentioning

confidence: 99%

“…The GVI composites were screened for clouds with thermal thresholds [Gutman et al, 1995]. In the case of PATMOS data, no compositing was made, and only observations flagged as "pure clear" and "restored clear" by CLAVR were used [see Stowe et al, 1991]. It is important to understand to what extent these differences affect the weekly and monthly albedo products.…”

Section: Intercomparison Of Surface Albedo Products From Gvi and Patmosmentioning

confidence: 99%

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Mapping global land surface albedo from NOAA AVHRR

Csiszar¹,

Gutman²

1999

J. Geophys. Res.

143

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Abstract. A set of algorithms is combined for a simple derivation of land surface albedo from measurements of reflected visible and near-infrared radiation made by the advanced very high resolution radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites. The system consists of a narrowband-to-broadband conversion and bidirectional correction at the top of the atmosphere and an atmospheric correction. We demonstrate the results with 1 month worth of data from the NOAA National Environmental Satellite, Data, and Information Service (NESDIS) global vegetation index (GVI) weekly data set and the NOAA/NASA Pathfinder Atmosphere (PATMOS) project daily data. Error analysis of the methodology indicates that the surface albedo can be retrieved with 10-15% relative accuracy. Monthly albedo maps derived from September 1989 GVI and PATMOS data agree well except for small discrepancies attributed mainly to different preprocessing and residual atmospheric effects. A 5-year mean September map derived from the GVI multiannual time series is consistent with that derived from low-resolution Earth Radiation Budget Experiment data as well as with a September map compiled from ground observations and used in many numerical weather and climate models. Instantaneous GVI-derived albedos were found to be consistent with surface albedo measurements over various surface types. The discrepancies found can be attributed to differences in areal coverage and representativeness of the satellite and ground data. The present pilot study is a prototype for a routine real-time production of high-resolution global surface albedo maps from NOAA AVHRR Global Area Coverage (GAC) data.

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“…The Advanced Very High Resolution Radiometer (AVHRR) Processing scheme Over cLouds, Land, and Ocean (APOLLO) used two visible and three infrared bands to screen clouds (Saunders and Kriebel, 1988;Gesell, 1989;Kriebel et al, 2003). Clouds from AVHRR (CLAVR) used sequential decision-threshold tree tests to identify the pixels, which were classified into 2 × 2 global area-coverage pixel arrays over cloud-free, mixed, and cloudy regions (Stowe et al, 1991(Stowe et al, , 1994. The dynamic threshold cloud masking (DTCM) algorithm used dynamic thresholds to screen clouds over land for AVHRR data and correctly identified a comparable or higher number…”

Section: Introductionmentioning

confidence: 99%

Improved cloud mask algorithm for FY-3A/VIRR data over the northwest region of China

Wang

Zhu

et al. 2013

Atmos. Meas. Tech.

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The existence of various land surfaces always leads to more difficulties in cloud detection based on satellite observations, especially over bright surfaces such as snow and deserts. To improve the cloud mask result over complex terrain, an unbiased, daytime cloud detection algorithm for the Visible and InfRared Radiometer (VIRR) on board the Chinese FengYun-3A polar-orbiting meteorological satellite is applied over the northwest region of China. The algorithm refers to the concept of the clear confidence level from Moderate Resolution Imaging Spectroradiometer (MODIS) and the unbiased structure of the CLoud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA). Six main channels of VIRR centered at the wavelengths of 0.455, 0.63, 0.865, 1.595, 1.36, and 10.8 μm are designed to estimate the degree of a pixel's cloud contamination judged by the clear confidence level. Based on the statistical data set during four months (January, April, July, and October) in 2010, seasonal thresholds are applied to improve the accuracy of the cloud detection results. Flags depicting snow and water are also generated by the specific threshold tests for special surfaces. As shown in image inspections, the cloud detection results over snow and deserts, adopting the proposed scheme, exhibit better correlations with true-color images than the VIRR official cloud mask results do. The performance of the proposed algorithm has been evaluated in detail for four seasons in 2011, using cloud mask products from MODIS and the ground-based observations. The evaluation is based on, overall, 47 scenes collocated with MODIS and 96 individual matchups between VIRR and the ground-based observations from two weather stations located in the research region. The quantitative validations suggest that the estimations of clear-sky regions have been greatly improved by the proposed algorithm, while a poor identification of the cirrus clouds occurs over deserts

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Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data

Cited by 191 publications

References 6 publications

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Mapping global land surface albedo from NOAA AVHRR

Improved cloud mask algorithm for FY-3A/VIRR data over the northwest region of China

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