Submitted as: development and technical paper
08 Apr 2024
Submitted as: development and technical paper |  | 08 Apr 2024
Status: this preprint is currently under review for the journal GMD.

Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system

Máté Mile, Stephanie Guedj, and Roger Randriamampianina

Abstract. The microwave radiances are key observations especially over data sparse regions for operational data assimilation in numerical weather prediction (NWP). An often applied simplification is that these observations are used as point measurements, however, the satellite field-of-view may cover many grid points of high-resolution models. Therefore, we examine a solution in high-resolution data assimilation to better account for the spatial representation of the radiance observations. This solution is based on a footprint operator implemented and tested in the variational assimilation scheme of the AROME-Arctic (Application of Research to Operations at Mesoscale – Arctic) limited-area model. In this paper, the design and technical challenges of the microwave radiance footprint operator are presented. In particular, implementation strategies, the representation of satellite field-of-view ellipses, and the emissivity retrieval inside the footprint area are discussed. Furthermore, the simulated brightness temperatures and the sub-footprint variability are analysed in a case study indicating particular areas where the use of the footprint operator is expected to provide significant added value. For radiances measured by the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) sensors, the standard deviation of the observation minus background (OmB) departures are computed on a short period in order to compare the statistics of the default and the implemented footprint observation operator. For all operationally used AMSU-A and MHS channels, it is shown that the standard deviation of OmB departures is reduced when the footprint operator is applied.

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Máté Mile, Stephanie Guedj, and Roger Randriamampianina

Status: open (until 03 Jun 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-195', Anonymous Referee #1, 15 Apr 2024 reply
  • RC2: 'Comment on gmd-2023-195', David Duncan, 03 May 2024 reply
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Máté Mile, Stephanie Guedj, and Roger Randriamampianina


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Short summary
The satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator highlighting its added value through a case study and diagnostics.