4/30/2023 0 Comments Apollo cloud manualThis gives the user of the cloud detection scheme an increased cloud masking flexibility compared to the traditional APOLLO scheme. Consequently, the APOLLO_NG cloud detection can be tuned from clear sky confident (i.e., having low clear sky misclassification rate) to cloud confident (low cloud misclassification rate). The minimum value of cloud probability for assigning an observation to the cloud mask can be set according to the user’s needs. After having obtained an estimate of the cloud probability, the cloud affected pixels (over land) undergo a snow detection test in the legacy of the Gesell et al. It propagates the probability in a clearly prescribed statistical way with equal weight for all cloud tests to the final cloud probability. Consequently, the probabilistic extension is more flexible (allowing all probabilities between 0 and 1) and is also stricter in the mathematical interpretation. Thus it assigns a cloud probability of either 0 or 1 to a specific cloud test without allowing for fractional probabilities. Binary threshold methods such as the traditional APOLLO scheme assume that, if an observed value is greater (lower) than a threshold, it is “definitely” cloudy or cloud free. Figure 1 showcases how the distance from the threshold can be interpreted as a cloud probability. A Bayesian probability update scheme then uses the cloud test probability (interpreted as confidence in observing a cloud with corresponding properties) for updating the overall cloud probability. But, instead of binary yes/no information gained from “simple” threshold tests, the distance from the respective threshold is used as an estimate of the likelihood of cloud presence in the observation. Consequently, only the classical “AVHRR heritage” channels are used in APOLLO_NG. It is designed to be applicable to AVHRR. The new APOLLO_NG cloud detection scheme is based on the same physical principles and thus channels and channel combinations. As modern sensors provide many more channels than the AVHRR family, the traditional APOLLO tests are nevertheless only a subset of cloud detection tests in modern schemes such as from MODIS. The classical APOLLO cloud detection tests are also in- corporated in more modern cloud detection schemes like the cloud masking for the Moderate Resolution Imaging Spec- troradiometer (MODIS) as reviewed by Frey et al. Any observed AVHRR pixel has been said to be cloud-contaminated or fully cloudy, if a sufficiently large number of bits are added in the second run (see Saunders and Kriebel, 1988 Kriebel et al., 2003). The succession of the respective tests fed into a bit- adding scheme. Two of these tests were run twice with updated information. Moreover water clouds can be identified by their reflectance ratio between two solar channels (being close to 1) as well as by differential absorption at two wavelengths in the infrared window. The physical ideas behind the five APOLLO cloud tests are that cloud tops are cold, bright or inhomogeneous or a combination thereof. The five tests include the infrared gross temperature test, the dynamical visible cloud test, the spatial coherence test, the reflectance ratio test and the brightness temperature difference test. The original APOLLO cloud detection is based on five consecutive threshold tests, for which the thresholds are determined dynamically from the analyzed scene. 5, a discussion of the algorithms and corresponding results in Sect. 4 describes the retrieval of physical cloud properties subsequent to the cloud detection. Section 3 deals with the detection of snow and its discrimination from clouds while Sect. Section 2 introduces the APOLLO_NG cloud detection algorithm. Re- sults from different sensors will thus be at least comparable, although they are not absolutely consistent. But at least it uses a similar mathematical framework for all sensors without introducing specific additional information from one channel or another which is not available from AVHRR. We are fully aware that the scheme will not provide fully consistent results for different sensors due to the varying sensor characteristics of the AVHRR family and the differences in sensor design for other instruments. The AVHRR channel terminology is used, i.e., channel numbering from 1 to 5 with channel 1 referring to a red channel centered at 0.6 μm and channel 5 referring to an IR channel centered at 12 μm (see Kriebel et al., 2003). Consequently, the scheme will be called APOLLO_NextGeneration (or APOLLO_NG throughout this article). Consequently, we feel it is justified to still call the method APOLLO although it is not exactly an update of the existing algorithm but rather a new approach using the same physical ideas. The scheme is specifically designed to be applied to the full AVHRR series, which lacks the information of additional channels.
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