An objective regional cloud mask algorithm for GOES infrared imager radiance assimilation

Abstract

A local, regime-dependent cloud mask (CM) algorithm is developed for isolating cloud-free pixels from cloudy pixels for Geostationary Operational Environmental Satellite (GOES) imager radiance assimilation using mesoscale forecast models. In this CM algorithm, thresholds for six different CM tests are determined by a one-dimensional optimization approach based on probability distribution functions of the nearby cloudy and clear-sky pixels within a 10°× 10°box centered at a target pixel. It is shown that the optimizal thresholds over land are, in general, larger and display more spatial variations than over ocean. The performance of the proposed CM algorithm is compared with Moderate Resolution Imaging Spectroradiometer (MODIS) CM for a 1 week period from 19 to 23 May 2008. Based on MODIS CM results, the average Probability of Correct Typing reaches 92.94% and 91.50% over land and ocean, respectively.

Added Value of Assimilating Himawari‐8 AHI Water Vapor Radiances on Analyses and Forecasts for “7.19” Severe Storm Over North China

“…Cloud detection is crucial as part of quality control procedures to reject cloud‐affected radiances for clear‐sky radiance assimilation in this study (McNally & Watts, ; Zou & Da, ). The cloud detection algorithm generates a “cloud mask” product that is used in radiance assimilation systems to determine cloud conditions.…”

Development of a daytime cloud and haze detection algorithm for Himawari‐8 satellite measurements over central and eastern China

“…The leakage rate (LR), false alarm rate (FAR), and haze missing rate (HMR) are calculated to examine the performance of the detection results. The definitions of LR and FAR in this study are different from those used in the studies of Kopp et al [2014] and Zou and Da [2014] because there is one additional feature type, i.e., haze (in addition to cloudy and clear-sky conditions). Moreover, there are no "probably clear," "probably hazy," and "probably cloudy" estimates in the HCHM algorithm.…”

“…The design of the HCHM algorithm builds upon the work and valuable experience of MOD‐CLD‐MASK [ Ackerman et al , ], MOD‐CLD‐DT [ Levy et al , ], MOD‐CLD‐DB [ Hsu et al , ], VII‐CLD‐MASK [ Hutchison et al , ; Baker , ], CLD‐Claudia [ Ishida and Nakajima , ; Nakajima et al , ], and a regime‐dependent cloud mask algorithm [ Zou and Da , ]. Based on the available AHI band sets, the tests are divided into two groups to identify cloud and clear and haze pixels.…”

“…To represent ambiguous pixels (e.g., cloud boundaries) more reasonably in the cloud mask result, an unbiased cloud detection algorithm (CLD‐Claudia) has been proposed to provide a quantitative estimate of the level of ambiguity in the clear or cloudy conditions instead of identifying the ambiguous pixels as cloudy or clear [ Ishida and Nakajima , ]. Zou and Da [] developed an optimization approach by using best fitted tests to isolate cloud‐free pixels from cloud pixels for Geostationary Operational Environmental Satellite imager radiance assimilation. Therefore, a cloud and haze detection algorithm that is designed specifically for use in China and that is capable of simultaneously identifying cloudy and hazy areas is urgently needed.…”

Cloud Cover in the Australian Region: Development and Validation of a Cloud Masking, Classification and Optical Depth Retrieval Algorithm for the Advanced Himawari Imager

“…Given the importance of cloud masking it remains a very active research subject, and alternatives to the above spectralthresholding-only approach for cloud masking continue to be developed. Four recent examples are: (i) Lyapustin et al (2008) based their approach on the high spatial correlation between consecutive cloud-free surfaces of the same area; (ii) Zou and Da (2014) presented a method with dynamic thresholds determined by statistical relationship between nearby region and the target pixel; (iii) Koner et al (2016) presented a cloud detection algorithm combining traditional static spectral-thresholdingonly criteria and radiative transfer modeling to improve sea surface temperature retrieval; and (iv) Qin et al (2015) and Gomez-Chova et al (2017) both developed time-series-based approaches based on the contrasting temporal scale of variation exhibited by a clear surface compared to that introduced by the onset of clouds.…”