Abstract

AbstractThis study applies and compares three different ways of assimilating MIPAS data in a global numerical weather prediction system. The three methods are: direct assimilation of emitted infrared limb radiances with a 1‐dimensional radiative transfer model that assumes local horizontal homogeneity; direct assimilation of radiances with a 2‐dimensional radiative transfer model that takes into account horizontal gradients in the atmosphere; and assimilation of retrieved profiles of temperature, humidity and ozone, derived under the assumption of horizontal homogeneity. The resulting analyses are compared against each other and against independent retrievals.The use of a 2‐dimensional radiative transfer model in the radiance assimilation leads to relatively small differences in the analyses compared to using a 1‐dimensional observation operator in our experiments. Nevertheless, the results show that the 2‐dimensional operator correctly takes into account the effect of tangent point drift, is capable of extracting a limited amount of horizontal structure from a single MIPAS scan, and leads to smaller first‐guess departures for lower tangent altitudes and more strongly absorbing channels. Consequently, humidity and ozone increments from a 2‐dimensional operator are smaller in the lower stratosphere and upper troposphere in areas where considerable horizontal gradients prevail, and forecasts of humidity and ozone are improved in these regions.In these first trials with assimilation of limb radiances, both the radiance and the retrieval assimilation appear capable of incorporating useful information from MIPAS in the analyses. Both methods introduce broadly similar changes to the mean analyses, with little indication of which method should be favoured. Nevertheless, results from the retrieval assimilation compare better to independent ozone data in the Tropics and over Antarctica. For the radiance assimilation, there are many areas with scope for improvement; these are discussed at the end of the paper. Copyright © 2007 Royal Meteorological Society

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