Validation of automated cloud top phase algorithms: Distinguishing between cirrus clouds and snow in a priori analyses of AVH RR imagery

被引:6
作者
Hutchison, KD
Etherton, BJ
Topping, PC
机构
[1] Lockheed Martin Missiles and Space, Ctr. Remote Environ. Sensing T., Sunnyvale
[2] University of Utah, Air Force Institute of Technology, U.S. Air Force
[3] Lockheed Martin, Austin, TX
[4] Advanced Technology Center, Palo Alto, CA
[5] Ctr. Remote Environ. Sensing T., Lockheed Martin Missiles and Space
[6] Penn State University, College Park, PA
[7] University of California, Los Angeles, CA
[8] Lockheed Martin Missiles and Space, Sunnyvale, CA
关键词
automated cloud analysis; cloud top phase; automated algorithms; snow-cloud discrimination; validation; AVHRR;
D O I
10.1117/1.601366
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Quantitative assessments on the performance of automated cloud analysis algorithms require the creation of highly accurate, manual cloud, no cloud (CNC) images from multispectral meteorological satellite data. In general, the methodology to create these a priori analyses for the evaluation of cloud detection algorithms is relatively straightforward, although the task becomes more complicated when little spectral signature is evident between a cloud and its background, as appears to be the case in advanced very high resolution radiometer (AVHRR) imagery when thin cirrus is present over snow-covered surfaces. In addition, complex procedures are needed to help the analyst distinguish between water and ice cloud tops to construct the manual cloud tap phase analyses and to ensure that inaccuracies in automated cloud detection are not propagated into the results of the cloud classification algorithm. Procedures are described that enhance the researcher's ability to (1) distinguish between thin cirrus clouds and snow-covered surfaces in daytime AVHRR imagery, (2) construct accurate a priori cloud top phase manual analyses, and (3) quantitatively validate the performance of both automated cloud detection and cloud top phase classification algorithms. The methodology uses all AVHRR spectral bands, including a band derived from the daytime 3.7-mu m channel, which has proven most valuable for discriminating between thin cirrus clouds and snow. It is concluded that while the 1.6-mu m band is needed to distinguish between snow and water clouds in daytime data, the 3.7-mu m channel remains essential during the daytime to differentiate between thin ice clouds and snow. Unfortunately this capability that may be lost if the 3.7-mu m data switches to a nighttime-only transmission with the launch of future National Oceanographic and Atmospheric Administration (NOAA) satellites. (C) 1997 Society of Photo-Optical Instrumentation Engineers.
引用
收藏
页码:1727 / 1737
页数:11
相关论文
共 13 条
[1]  
DENTREMONT RP, 1987, P SOC PHOTO-OPT INS, V846, P96
[2]  
DENTREMONT RP, 1990, P 5 C SAT MET OC LON, P4
[3]  
HUGHES NA, 1984, J CLIM APPL METEOROL, V23, P724, DOI 10.1175/1520-0450(1984)023<0724:GCCAHR>2.0.CO
[4]  
2
[5]   Application of 1 center dot 38 mu m imagery for thin cirrus detection in daytime imagery collected over land surfaces [J].
Hutchison, KD ;
Choe, NJ .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 1996, 17 (17) :3325-3342
[6]  
HUTCHISON KD, 1995, J APPL METEOROL, V34, P1161, DOI 10.1175/1520-0450(1995)034<1161:IDOOTC>2.0.CO
[7]  
2
[8]  
HUTCHISON KD, IN PRESS INT J REMOT
[10]  
STOWE LL, UNPUB GLOBAL CLOUD 1