SUMMARYThe development of an assimilation system for radiance data from the Atmospheric InfraRed Sounder (AIRS) is described, in particular the identification of cloud contamination, bias correction and the characterization of errors in the measured radiances and radiative-transfer model. The results of assimilation experiments are presented. These show that a conservative use of AIRS radiance data (in a system already extensively observed with other satellite data) results in a small, but consistent, improvement in the quality of analyses and forecasts. Larger impacts of AIRS are found in hypothetical experiments that test the use of radiances from only a single sounding instrument. In these, the use of AIRS is found to outperform the use of data either from a single Advanced Microwave Sounding Unit-A (AMSU-A) or from a single High-resolution InfraRed Sounder (HIRS). In this hypothetical context the relative forecast performance of each sensor is found to correlate with the size and vertical scale of increments caused by the assimilation of the radiances.

SUMMARYA method for detecting cloud contamination in radiances measured by high-spectral-resolution infrared sounders is presented. It seeks to identify clear channels within a measured spectrum, rather than the locations of completely clear spectra. Applied to simulated cloudy spectra, the scheme is able to detect clear channels with residual cloud contamination better than (i.e. less than) 0.2 K in many channels, and is thus considered suf ciently stringent for numerical weather-prediction applications. The scheme has been applied to spectra measured by the Advanced InfraRed Sounder and, whilst a quantitative validation is more dif cult with real data (without true clear radiances), it is found to perform well compared with coincident imagery.

[1] An original method is presented in this paper for the joint retrieval of the mean daily total column aerosol optical depth and surface BRF from the daily accumulated Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) observations in the solar channels. The proposed algorithm is based on the optimal estimation (OE) theory, a one-dimensional variational retrieval scheme that seeks an optimal balance between information that can be derived from the observations, and the one that is derived from prior knowledge of the system. The forward radiative transfer model explicitly accounts for the surface anisotropy and its coupling with the atmosphere. The low rate of change in the surface reflectance is used to derive the prior information on the surface state variables. The reliable estimation of the measurement system error is one of the most critical aspects of the OE method as it strongly determines the likelihood of the solution. An important effort in the proposed method has thus been dedicated to this issue, where the actual radiometric performances of SEVIRI are dynamically taken into account.Citation: Govaerts, Y. M., S. Wagner, A. Lattanzio, and P. Watts (2010), Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 1. Theory,

Abstract. Clouds play an important role in balancing the Earth's radiation budget. Hence, it is vital that cloud climatologies are produced that quantify cloud macro and micro physical parameters and the associated uncertainty. In this paper, we present an algorithm ORAC (Oxford-RAL retrieval of Aerosol and Cloud) which is based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared, thereby ensuring that the resulting cloud properties provide both a good representation of the short-wave and long-wave radiative effects of the observed cloud. The advantages of the optimal estimation method are that it enables rigorous error propagation and the inclusion of all measurements and any a priori information and associated errors in a rigorous mathematical framework. The algorithm provides a measure of the consistency between retrieval representation of cloud and satellite radiances. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase.The algorithm can be applied to most visible/infrared satellite instruments. In this paper, we demonstrate the applicability to the Along-Track Scanning Radiometers ATSR-2 and AATSR. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed, in particular the algorithm is evaluated for a number of simulated single-layer and multi-layer conditions. The algorithm was found to perform well for single-layer cloud except when the cloud was very thin; i.e., less than 1 optical depths. For the multi-layer cloud, the algorithm was robust except when the upper ice cloud layer is less than five optical depths. In these cases the retrieved cloud top pressure and cloud effective radius become a weighted average of the 2 layers. The sum of optical depth of multi-layer cloud is retrieved well until the cloud becomes thick, greater than 50 optical depths, where the cloud begins to saturate. The cost proved a good indicator of multi-layer scenarios. Both the retrieval cost and the error need to be considered together in order to evaluate the quality of the retrieval. This algorithm in the configuration described here has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation) project to produce a 14 yr consistent record for climate research.

[1] A method to derive two-layer cloud properties from concurrent visible, near-infrared, and infrared observations is described. It is a modification of a single-layer scheme and is applied to Spinning Enhanced Visible Infrared Imager (SEVIRI) observations and validated against coincident A-Train data, principally to evaluate the accuracy and characterize cloud top pressure (CTP) estimates. CTP values obtained from the single-layer scheme applied to multilayer clouds are significant overestimates of the upper layer value. The effect is usually larger than that on coincident IR-only retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), and this characteristic can be traced to the use of visible wavelength observations. However, the solution cost from the optimal estimation method is found to be especially high in multilayer situations and is a strong indicator of CTP accuracy. Tighter thresholds on the solution cost select, with increasing stringency, scenes with single-layer or opaque upper layer cloud. High-cost (presumed multilayer) pixels are reprocessed with the scheme adapted to simulate a two-layer cloud and with only infrared measurements. The upper cloud is represented by the parameters of the original formulation; the additional lower cloud layer is gray and has a proxy height given by the surface temperature. Despite the simplicity of the cloud-atmosphere modeling under the upper layer, results obtained from the two-layer scheme are promising. Upper layer CTPs are of comparable accuracy to the single-layer cases, lower-layer CTPs show some useful accuracy, and upper layer optical depths correlate well with radar observations.