A Sequential Estimation Approach to Cloud‐Clearing For Satellite Temperature Sounding

Abstract Temperature sounding of the troposphere using satellite‐borne infrared radiometers is complicated by the effects of clouds on the measured radiances. If accurate products are to be obtained these effects must first be detected and, if possible, corrections must be made for them in the retrieval procedures. This is normally done by converting the measured radiances to the ‘clear‐column’ values which would be observed from the same atmospheric profile in the absence of cloud—a process known as ‘cloud‐clearing’. In this paper a review of cloud‐clearing methods is presented. Then a new approach to cloud‐clearing, based on the principles of optimal estimation, is developed and applied to data from HIRS (the High‐resolution Infrared Radiation Sounder) on the TIROS‐N/NOAA satellite series. Preliminary estimates of clear‐column radiances and their expected errors are obtained at each HIRS spot by one of a number of methods, depending on the cloud characteristics. the properties of horizontal consistency expected in the clear‐column radiance field are then employed to improve the initial estimates using a sequential estimation procedure. This scheme is intended for implementation in the Local Area Sounding System of the Meteorological Office, which provides satellite soundings of high horizontal resolution for use in operational weather forecasting. Details of the new scheme, and of the old scheme which it replaces, are given. the improvements in clear‐column radiances are demonstrated by comparing the products of the old and new schemes with clear‐column radiances derived from coincident data of AVHRR (the Advanced Very High Resolution Radiometer).