The cloud analysis tool APOLLO: Improvements and validations

Kriebel, K.T.; Gesell, Gerhard; Kästner, M.; Mannstein, Hermann

doi:10.1080/01431160210163065

Cited by 138 publications

(82 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, schemes which are using comparable tests (Kriebel et al 2003;Dozier & Painter 2004) do not report any angular dependence. However, this must be checked in future, especially if the scheme is adopted for satellites in geostationary orbit (e.g.…”

Section: Removal Of Misclassified Pixelsmentioning

confidence: 99%

“…The second threshold is well documented for most cloud classification schemes as a ratio test to exclude vegetation (also dormant in winter) which causes higher reflectances in the NIR wavelengths (e.g. APOLLO; Saunders & Kriebel 1988;Kriebel et al 2003). The current threshold has been determined by visible inspection of a set of MODIS images.…”

Section: Removal Of Misclassified Pixelsmentioning

confidence: 99%

See 1 more Smart Citation

A feasibility study of daytime fog and low stratus detection with TERRA/AQUA‐MODIS over land

Bendix

Thies

Nauß

et al. 2006

Meteorological Applications

View full text Add to dashboard Cite

show abstract

Section: Removal Of Misclassified Pixelsmentioning

confidence: 99%

Section: Removal Of Misclassified Pixelsmentioning

confidence: 99%

A feasibility study of daytime fog and low stratus detection with TERRA/AQUA‐MODIS over land

Bendix

Thies

Nauß

et al. 2006

Meteorological Applications

View full text Add to dashboard Cite

show abstract

“…As one of the basic products for VIRR, it is released in hierarchical data format version 5 (HDF5), and can be obtained from the website of the National Satellite Meteorological Center (online at: fy3.satellite.cma.gov.cn). MICAPS is an interactive computer system that integrates all meteorological, satellite, and radar data into one computer workstation (Li et al, 2010). Data used for validations include the total amount of cloud cover from MICAPS ground-based observations in the research region, which can be attained from Peking University (email: xyl@pku.edu.cn).…”

Section: Datamentioning

confidence: 99%

“…The International Satellite Cloud Climatology Project (ISCCP) presented statistical-threshold tests for visible and infrared radiance to differentiate cloudy and clear scenes over various climate regions (Rossow, 1989;Rossow and Garder, 1993). 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.…”

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.

View full text Add to dashboard Cite

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

show abstract

“…It discretizes all pixels into four different groups called cloud-free, fully cloudy, partially cloudy (i.e. neither cloud-free nor fully cloudy) and snow/ice-contaminated, before deriving physical properties ( [11][12][13][14]). Each fully cloudy pixel is checked to see whether it is thick or thin cloud, depending on its 11 μm and 12 μm brightness temperatures and, during daytime, its channel 0.6 μm and 0.8 μm reflectances.…”

Section: Annual Irradiation and Further Meteorological Parametersmentioning

confidence: 99%

The enerMENA meteorological network – Solar radiation measurements in the MENA region

Schüler¹,

Wilbert²,

Geuder

et al. 2016

AIP Conference Proceedings

View full text Add to dashboard Cite

Abstract. For solar resource assessment of solar power plants and adjustment of satellite data, high accuracy measurement data of irradiance and ancillary meteorological data is needed. For the MENA region (Middle East and Northern Africa), which is of high importance for concentrating solar power applications, so far merely 2 publicly available ground measurement stations existed (BSRN network). This gap has been filled by ten stations in Morocco, Algeria, Tunisia, Egypt and Jordan. In this publication the data quality is analyzed by evaluating data completeness and the cleanliness of irradiance sensors in comparison for all of the stations. The pyrheliometers have an average cleanliness of 99.2 % for week-daily cleaning. This is a 5 times higher effort than for Rotating Shadowband Irradiometer (RSI) stations which even have a slightly higher average cleanliness of 99.3 % for weekly cleaning. Furthermore, RSI stations show a data completeness of 99.4 % compared to 93.6 % at the stations equipped with thermal sensors. The results of this analysis are used to derive conclusions concerning instrument choice and are hence also applicable to other solar radiation measurements outside the enerMENA network. It turns out that RSIs are the more reliable and robust choice in cases of high soiling, rare station visits for cleaning and maintenance, as usual in desert sites. Furthermore, annual direct normal and global horizontal irradiation as well as average meteorological parameters are calculated for all of the stations.

show abstract

The cloud analysis tool APOLLO: Improvements and validations

Cited by 138 publications

References 0 publications

A feasibility study of daytime fog and low stratus detection with TERRA/AQUA‐MODIS over land

A feasibility study of daytime fog and low stratus detection with TERRA/AQUA‐MODIS over land

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

The enerMENA meteorological network – Solar radiation measurements in the MENA region

Contact Info

Product

Resources

About