AN OBJECTIVE AND OPTIMAL ESTIMATION APPROACH TO CLOUD-CLEARING FOR INFRARED SOUNDER MEASUREMENTS

In order to produce temperature or water vapour profiles from infrared radiance measurements the detection or possible cloud contamination within the field-of-view is required. In most retrieval schemes a correction phase follows so that the inversion algorithm operates on clear column infrared radiances. We describe a new sequential objective and optimal filtering scheme aiming at processing radiances, for each infrared measuring channel, to produce a field of cloud-cleared values with sufficiently well-defined statistical properties and error structures. Basically the method uses clear measurements only and treats cloudy data as unmeasured or missing data. Simulated values of clear column radiances for HIRS/2 channel 7 are used as a test field. The results presented are retrieval of clear radiance fields from cloudy data sets, each consisting of the test field with instrumental noise added and a cloud mask defining whether each individual field of view is clear or not. Radiances defined as cloudy are consistently treated as missing data. The cloud masks used for the present exercise are obtained from the processing of real data with a very high cloud content in order to understand the behaviour and quality of the algorithm in situations close as possible to worst real cases.