Physical Basis

Distinguishing clouds according to their optical thickness and cloud top temperature

In cloudy areas the NIR0.87 channel values depend mainly on cloud thickness. NIR0.87 reflectivity values are high for opaque and lower for semi-transparent clouds.

The IR10.8 channel distinguishes thick clouds by their cloud top temperatures. IR10.8 is an atmospheric window channel (where the absorption of gas molecules is low). For opaque clouds the measured signal depends mainly on cloud top temperature. For semi-transparent clouds the interpretation of the measured radiation is more complicated: besides the cloud's temperature, it also depends on its transparency and the temperature of the underlying surface.

Distinguishing ice and water clouds and clouds with small and large cloud top particles

NIR1.61 channel data contains information on cloud top microphysics: the phase and the effective particle size of the cloud top elements. (Effective particle size is the size of an 'averaged' particle, calculated by dividing total particle volume by total particle surface area.)

The NIR1.61 channel helps to distinguish water clouds from ice clouds. Ice crystals absorb radiation more strongly than water particles in the NIR1.6 channel. Fig. 1 presents the imaginary refraction index of water and ice. This index characterizes the absorption of the material, not the absorption of a cloud layer. A cloud layer's absorption also depends on many other things (like the number of droplets/particles etc.), but the absorption coefficient of the material has a dominant role. Due to ice's higher absorption in the NIR1.61 channel their reflection is lower than that of water clouds. (Note that the difference in absorption between ice and water is not small: it is shown on a logarithmic scale.) For the NIR0.87 channel the index is much lower and about the same for ice and water particles.

Figure 1: Absorption spectra of water (blue curve) and ice (red curve)

Fig. 2a shows 1.6 micrometer simulated thick cloud reflectivity as a function of effective cloud particle size for water (red curve) and ice (blue curve) elements. The ellipses represent the typical range of water droplet and ice particle sizes.

The figure shows that

  • water clouds usually have higher reflectivity than ice clouds
  • the reflectivity also depends on effective particle size:

    • Water clouds with small particles have higher reflectivity than water clouds with large particles.
    • Ice clouds with small particles have higher reflectivity than ice clouds with large particles.
    • Ice clouds with small ice crystals can have similar reflectivity as water clouds.

Figure 2a: 1.6 micrometer simulated thick cloud reflectivity as a function of effective cloud particle size for water (red curve) and ice (blue curve) elements. The ellipses represent the typical range of water droplet and ice particle sizes.
(Courtesy of Ralf Bennartz, University of Wisconsin)

Although the typical range of ice particle sizes is 'large', there are some special cases when small ice crystals are present at the cloud top.

  • High-level lee clouds usually consist of very small ice crystals.
  • Severe convective clouds with very strong updrafts often contain small ice crystals at their tops.
  • Highly polluted ice clouds usually consist of small ice crystals because of the high number of condensation nuclei.
  • Convective clouds with cold cloud bases have small ice crystals at the top.

Fig. 2b shows that ice clouds with small ice crystals can possess a reflectivity similar to water clouds. It shows a scatterplot of satellite-measured VIS0.6 and NIR1.6 reflectivity value pairs from water and ice clouds (indicated by orange and blue symbols). While the two groups are generally quite distinct, there is a region with overlap.

Figure 2b: Scatterplot of satellite-measured VIS0.6 and NIR1.6: reflectivity value pairs of water and ice clouds. (VIIRS: Visible Infrared Imaging Radiometer Suite) (Courtesy to HL and KG, Meteo-France)

Note that 1.6 micrometer reflectivity is less sensitive to effective particle size than 3.9 micrometer reflectivity, which is used in the SEVIRI Day Microphysics RGB. (For more on the topic, see the section 'Comparison with SEVIRI Day Microphysics RGB type'.)

Distinguishing snow and fog or water clouds

The NIR1.61 channel helps to discriminate between snow-covered land and fog or low water clouds. Fig. 3 presents the typical shortwave reflectivity spectra of some surface types including snow. The snow's reflectivity is much higher in NIR0.87 than NIR1.61 (see the brown vertical lines in Fig. 3). Because water clouds reflect much of the radiation in both channels (not shown in Fig. 3), they can be used in combination to distinguish snow and water clouds.

Figure 3: Typical shortwave reflectivity spectra of different surface types. The brown vertical lines show the reflectivity at 0.87 and 1.61 micrometers
(Courtesy of Steve Ackerman, CIMSS)