Detection of Clouds in Antarctica from Infrared Multispectral Data of AVHRR

Abstract

A method to detect cloud cover in the Antarctic using only the infrared channels of AVHRR is discussed. From the data of NOAA-7 received at Syowa Station, the difference in the brightness temperature of each channel appeared to be useful for the identification of clouds. The brightness temperature of the channels 3 (3.7*m) and 4 (11*m) shows the positive difference when the thickness of clouds are in some particular range, and then tends to show negative difference for the thick cloud. Thin clouds have the difference in the brightness temperature between channels 4 and 5 (12 * m). These tendencies are explained by the radiative properties of model clouds theoretically calculated. On the graph of these temperature difference against the channel 4 brightness temperature, pixels of the same cloud distribute on the particular arch starting from the clear pixel. From the arch, clouds can be distinguished from the ground surface. The particle size, temperature and thickness of the cloud can also be inferred. At the low temperature over the inland snow surface, many troubles arise. The channel 3 brightness temperature accompanies poor resolution and large noise at the low temperature. The brightness temperature difference between channels 4 and 5 shows strong dependence on temperature and viewing angle at the low temperature due to the nonlinearity error and variation of snow surface emissivity. An empirical correction is applied to the low temperature data for the automatic cloud detection.

Volcanic ash detection and retrievals using MODIS data by means of neural networks

“…The main BTD advantage is its very simple and fast application, while the main drawbacks are the false alarms ("false positive" when a particular pixel is recognized as ash affected but that in fact doesn't contain ash and vice-versa for "false negative") obtained in specific and well documented cases (Simpson et al, 2000;, as over clear land surfaces at night (Platt and Prata, 1993), over soils with a high quartz content (e.g. deserts) (Barton and Takashima, 1986), over very cold surfaces (Potts and Ebert, 1996), over ice-covered surfaces (Yamanouchi et al, 1987) and in presence of high water vapour content Yu et al, 2002;Corradini et al, 2008a). This latter effect, that tends to attenuate and in some cases can completely cancel-out the BTD signal, has been corrected by applying a procedure developed by Corradini et al (2008a).…”

Modeling angular-dependent spectral emissivity of snow and ice in the thermal infrared atmospheric window

“…Directional emissivity spectra of snow cover at thermal infrared (TIR) wavelengths are essential properties for remote sensing of the earth's thermal properties, such as snow surface temperature [1][2][3][4][5], cloud discrimination from space [6,7], and for estimating Earth's long-wave radiation budget on the Greenland and Antarctic ice sheets by broadband radiometer such as the clouds and the Earth's radiant energy system (CeRES) [8]. The spectra may also be potentially utilized for sensing remote planets in astronomical applications [9].…”

Analysis of polar stratospheric cloud measurements from AVHRR

“…Inoue [1985,1987] found that cirrus clouds can be identified over tropical oceans using the difference between AVHRR channels 4 and 5 brightness temperatures. BTDs were then used by Yamanouchi et al [1987] …”