[1] The High Resolution Dynamics Limb Sounder (HIRDLS) experiment was designed to provide global temperature and composition data on the region from the upper troposphere to the mesopause with vertical and horizontal resolution not previously available. The science objectives are the study of small-scale dynamics and transports, including stratosphere-troposphere exchange, upper troposphere/lower stratosphere chemistry, aerosol, cirrus and PSC distributions, and gravity waves. The instrument features 21 channels, low noise levels, high vertical resolution, and a mechanical cooler for long life. During launch most of the optical aperture became obscured, so that only a potion of an optical beam width at a large azimuth from the orbital plane on the side away from the Sun can see the atmosphere. Irrecoverable loss of capabilities include limitation of coverage to the region 65°S-82°N and inability to obtain longitudinal resolution finer than an orbital spacing. While this optical blockage also impacted radiometric performance, extensive effort has gone into developing corrections for the several effects of the obstruction, so that radiances from some of the channels can be put into retrievals for temperature. Changes were also necessary for the retrieval algorithm. The validation of the resulting temperature retrievals is presented to demonstrate the effectiveness of these corrections. The random errors range from $0.5 K at 20 km to $1.0 at 60 km, close to those predicted. Comparisons with high-resolution radiosondes, lidars, ACE-FTS, and ECMWF analyses give a consistent picture of HIRDLS temperatures being 1-2 K warm from 200 to 10 hPa and within ±2 K of standards from 200 to 2 hPa (but warmer in the region of the tropical tropopause), above which HIRDLS appears to be cold. Comparisons show that both COSMIC and HIRDLS can see small vertical features down to about 2 km

Abstract. Use of data assimilation techniques such as optimal interpolation or the Kalman filter in global chemistry transport models (CTM) is becoming more common. However, owing to high computational requirements, it is often difficult to apply these techniques to multidimensional models containing extensive photochemical schemes. We present a sequential assimilation approach developed for use with general global chemistry transport models. It allows fast assimilation and mapping of satellite observations and provides estimates of analysis errors. The suggested data assimilation scheme evolved from the one described by Levelt et al.

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Abstract. We present a short overview of applications of estimation theory in atmospheric chemistry and discuss some common methods of gridding and mapping of irregular satellite observations of chemical constituents. It is shown that these methods are unable to produce truly synoptic maps of short-lived photochemically active species due to insufficient temporal and spatial density of satellite observations. The only way to overcome this limitation is to supplement observations with prior independent information given, for instance, by atmospheric numerical models and/or climatologies. Objective approaches to combining such prior information with observations are commonly referred to as data assimilation. Mathematical basis of data assimilation known as optimal estimation equations is presented following Lorenc [ 1986]. Two particular techniques of data assimilation, the variational method and the extended Kalman filter, are briefly described, and their applications to time-dependent numerical photochemical models are discussed. We investigate validity of the linear approximation which is utilized in both methods, present