Preprints
https://doi.org/10.5194/amt-2021-50
https://doi.org/10.5194/amt-2021-50

  11 Mar 2021

11 Mar 2021

Review status: this preprint is currently under review for the journal AMT.

Physical characteristics of frozen hydrometeors inferred with parameter estimation

Alan J. Geer Alan J. Geer
  • ECMWF, Shinfield Park, Reading, RG2 9AX, UK

Abstract. Frozen hydrometeors are found in a huge range of shapes and sizes, with variability on much smaller scales than those of typical models or satellite observations. Neither models nor in-situ measurements can fully describe this variability, so assumptions have to be made in applications including atmospheric modelling and radiative transfer. Here parameter estimation has been used to optimise six different assumptions relevant to frozen hydrometeors in passive microwave radiative transfer. This covers cloud overlap, convective water content and particle size distribution (PSD), the shapes of large-scale snow and convective snow and an initial exploration of the ice cloud representation (particle shape and PSD combined). These parameters were simultaneously adjusted to find the best fit between simulations from the European Centre for Medium-range Weather Forecasts (ECMWF) assimilation system, and near-global microwave observations covering the frequency range 19 GHz to 190 GHz. The choices for the cloud overlap and the convective particle shape were particularly well constrained (or identifiable) and there was even constraint on the cloud ice PSD. The practical output is a set of improved assumptions to be used in version 13 of the Radiative Transfer for TOVS microwave scattering package (RTTOV-SCATT), taking into account newly available particle shapes such as aggregates and hail, and additional PSD options. The parameter estimation explored the full parameter space using an efficient assumption of linearly additive perturbations. This helped illustrate issues such as multiple minima in the cost function, and non-Gaussian errors, that would make it hard to implement the same approach in a standard data assimilation system for weather forecasting. Nevertheless, as modelling systems grow more complex, parameter estimation is likely to be a necessary part of the development process.

Alan J. Geer

Status: open (until 06 May 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-50', Anonymous Referee #1, 08 Apr 2021 reply
  • RC2: 'Comment on amt-2021-50', Anonymous Referee #2, 09 Apr 2021 reply
  • RC3: 'Comment on amt-2021-50', Anonymous Referee #3, 13 Apr 2021 reply

Alan J. Geer

Alan J. Geer

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Short summary
Satellite observations sensitive to cloud and precipitation help improve the quality of weather forecasts. However, they are sensitive to things that models do not forecast, such as the shapes and sizes of snow and ice particles. These details can be estimated from the observations themselves, and then incorporated in the satellite simulators used in weather forecasting. This approach, known as parameter estimation, will be increasingly useful to build models of poorly known physical processes.