An AVHRR Multiple Cloud-Type Classification Package

Abstract.The recently increasing development of whole sky imagers enables temporal and spatial high-resolution sky observations. One application already performed in most cases is the estimation of fractional sky cover. A distinction between different cloud types, however, is still in progress. Here, an automatic cloud classification algorithm is presented, based on a set of mainly statistical features describing the color as well as the texture of an image. The k-nearestneighbour classifier is used due to its high performance in solving complex issues, simplicity of implementation and low computational complexity. Seven different sky conditions are distinguished: high thin clouds (cirrus and cirrostratus), high patched cumuliform clouds (cirrocumulus and altocumulus), stratocumulus clouds, low cumuliform clouds, thick clouds (cumulonimbus and nimbostratus), stratiform clouds and clear sky. Based on the Leave-One-Out CrossValidation the algorithm achieves an accuracy of about 97%. In addition, a test run of random images is presented, still outperforming previous algorithms by yielding a success rate of about 75%, or up to 88% if only "serious" errors with respect to radiation impact are considered. Reasons for the decrement in accuracy are discussed, and ideas to further improve the classification results, especially in problematic cases, are investigated.

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“…In cloud type recognition the LOOCV has been applied by e.g. Tag et al (2000) or Bankert and Wade (2007).…”

Section: Methodology Of Performance Estimationmentioning

confidence: 99%

Automatic cloud classification of whole sky images

2010

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“…The NRL Cloud Classification (CC) algorithm (Tag et al, 2000) is another product that is used by the OW PDT to assist in the accurate detection of deep convective clouds. The CC product classifies satellite imagery into several cloud types or layers and has been optimized to provide coverage over the western Atlantic Ocean, continental United States and eastern Pacific Ocean.…”

Section: Cloud Classification (Cc) Productmentioning

confidence: 99%

The Identification and Verification of Hazardous Convective Cells over Oceans Using Visible and Infrared Satellite Observations

Donovan

Williams

Kessinger

et al. 2008

Journal of Applied Meteorology and Climatology

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Three algorithms based on geostationary visible and infrared (IR) observations are used to identify convective cells that do (or may) present a hazard to aviation over the oceans. The performance of these algorithms in detecting potentially hazardous cells is determined through verification with Tropical Rainfall Measuring Mission (TRMM) satellite observations of lightning and radar reflectivity, which provide internal information about the convective cells. The probability of detection of hazardous cells using the satellite algorithms can exceed 90% when lightning is used as a criterion for hazard, but the false-alarm ratio with all three algorithms is consistently large (∼40%), thereby exaggerating the presence of hazardous conditions. This shortcoming results in part from the algorithms’ dependence upon visible and IR observations, and can be traced to the widespread prevalence of deep cumulonimbi with weak updrafts but without lightning over tropical oceans, whose origin is attributed to significant entrainment during ascent.

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“…In the late 1990s, NCAR began to incorporate satellite information into the 0-1-h convective storm nowcasting system (the Auto-Nowcaster). Satellite feature detection algorithms (e.g., Bankert 1994;Roberts et al 1999;Tag et al 2000, andRutledge 2003) were used to classify cloud types, identify early cumulus growth above surface convergence boundaries, and monitor the growth of these clouds by observing the changes in their IR cloud-top temperatures. The ASAP team is currently working with the FAA convective weather PDT to improve methodologies for satellite-based techniques for detecting, tracking, and monitoring the early development of small cumulus in order to improve thunderstorm initiation forecasts over the CONUS.…”

Section: Convective Initiationmentioning

confidence: 99%