[1] The Sub-Millimetre Radiometer (SMR) on board the Odin satellite, launched on 20 February 2001, observes key species with respect to stratospheric chemistry and dynamics such as O 3 , ClO, N 2 O, and HNO 3 using two bands centered at 501.8 and 544.6 GHz. We present the adopted methodology for level 2 processing and the achieved in-orbit measurement capabilities of the SMR radiometer for these species in terms of altitude range, altitude resolution, and measurement precision. The characteristics of the relevant level 2 data versions, namely version 1.2 of the operational processor as well as versions 222 and 223 of the reference code, are discussed and differences are evaluated. An analysis of systematic retrieval errors, resulting from spectroscopic and instrumental uncertainties, is also presented.

Abstract.More accurate global measurements of the amount of ice in thicker clouds are needed to validate atmospheric models and sub-mm radiometry can be an important component in this respect. A cloud ice retrieval scheme for the first such instrument in space, Odin-SMR, is presented here. Several advantages of sub-mm observations are shown, such as low influence of particle shape and orientation, and a high dynamic range of the retrievals. In the case of Odin-SMR, only cloud ice above ≈12.5 km can be measured. The present retrieval scheme gives a detection threshold of about 4 g/m 2 above 12.5 km and does not saturate even for thickest observed clouds (>500 g/m 2 ). The main retrieval uncertainties are the assumed particle size distribution and cloud inhomogeneity effects. The overall retrieval accuracy is estimated to be ∼75%. The retrieval error is judged to have large random components and to be significantly lower than this value for averaged results, but high fixed errors can not be excluded. However, a firm lower value can always be provided. Initial results are found to be consistent with similar Aura MLS retrievals, but show important differences to corresponding data from atmospheric models. This first retrieval algorithm is limited to lowermost Odin-SMR tangent altitudes, and further development should improve the detection threshold and the vertical resolution. It should also be possible to decrease the retrieval uncertainty associated with cloud inhomogeneities by detailed analysis of other data sets.

Abstract. Odin-SMR is a limb-sounder operating in the 500 GHz region with the capability of performing measurements down to altitudes of about 10 km with relatively low influence of ice clouds. Until now spectra from tropospheric tangent altitudes have been disregarded due to inadequate handling of scattering. A first method to extract upper tropospheric quantities has now been developed, yielding the humidity in two layers around 200 and 130 hPa and information on cloud ice content above 200 hPa. First results are compared with in situ MOZAIC measurements and presented to give a global view of the horizontal distribution. The seasonal structures are in agreement with other satellite measurements.The main concern for these retrievals is the calibration performance. A careful analysis indicates a systematic calibration error of about 1 K, but also a random component that differs between the two bands. The random calibration uncertainty results in retrieval errors of 10-60% depending on humidity and band. Presently this prohibits use of single retrievals, but averages can be presented with good accuracy. The fixed calibration error can largely be removed, leaving the spectroscopic uncertainties to dominate the humidity retrieval accuracy, with a worst case estimate of 30%. However, the comparison of MOZAIC data and the measurements for the 200 hPa layer shows a systematic difference of <10%. This indicates that the actual systematic error is low and gives further confidence in the capability of Odin-SMR to measure humidity in the upper tropical troposphere.

Abstract. Emerging microwave satellite techniques are expected to provide improved global measurements of cloud ice mass. CloudSat, Aura MLS and Odin-SMR fall into this category and early cloud ice retrievals from these instruments are compared. The comparison follows the SMR retrieval product and is made for partial ice water columns above 12 km. None of the retrievals shows a significant degree of false cloud detections, the ratio between local mean values from the instruments is fairly constant and a consistent view of the geographical distribution of cloud ice is obtained. However, important differences on the absolute levels exist, where the overall mean is 9.6, 4.2 and 3.7 g m −2 for CloudSat, SMR and MLS, respectively. Assumptions about the particle size distribution (PSD) are a consideration for all three instruments and constitute the dominating retrieval uncertainty for CloudSat. The mean for CloudSat when applying the same PSD as for MLS and SMR was estimated to 6.3 g m −2 . A second main consideration for MLS and SMR are the effects caused by the poorer spatial resolution: a possible vertical misplacement of retrieved values and an impact of cloud inhomogeneities. The latter effect was found to be the dominating retrieval uncertainty for SMR, giving a possible mean value range of 2.3-8.9 g m −2 . The comparison indicates a common retrieval accuracy in the order of 70%. Already this number should suffice for improved validations of cloud ice parametrisation schemes in atmospheric models, but a substantially better consistency between the datasets should be attainable through an increased understanding of main retrieval error sources.

The polarization, frequency and spatial responses of the sensor can be considered by calculating the Stokes vector of monochromatic pencil beam radiances for a set of frequencies and viewing directions, and weight these radiances with the instrument responses. This paper presents a highly efficient solution for this calculation procedure. The basic idea is to pre-calculate a matrix that represents the mapping from polarisation, frequency and spatial values to measured data. Sensor impacts can then be included by a simple matrix multiplication. The full sensor matrix can be obtained by determining the response matrix for the sensor parts individually. Data reduction methods can also be incorporated. A simple method for optimizing the calculation grids is further presented. The described approach for sensor modeling has been implemented in two public available softwares for atmospheric radiative transfer simulations.