Physical characteristics of frozen hydrometeors inferred with parameter estimation

Alan J Geer

doi:10.5194/amt-14-5369-2021

Abstract

Abstract. Frozen hydrometeors are found in a huge range of shapes and sizes, with variability on much smaller scales than those of typical model grid boxes or satellite fields of view. 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. In this work, 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 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 13.0 of the Radiative Transfer for TOVS microwave scattering package (RTTOV-SCATT), taking into account newly available particle shapes such as aggregates and hail, as well as 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.

Highlights

Clouds and precipitation are some of the most uncertain processes in the earth system, leading to systematic errors in models (e.g. Klein et al, 2009; Forbes et al, 2016) and big uncertainties in climate change predictions (e.g. Zelinka et al, 2020)
Cav overlap over land is clearly better than the Cmax overlap used in the control. Another big difference is that the choice of convective snow particle is well bounded at the extremes: at low-scattering the ARTS column aggregate has high cost and is clearly inappropriate; at the high-scattering end the ARTS graupel gives a high cost
This section discusses whether the parameter estimation has provided physical suggestions that may be generally applicable, or whether the results just reflect tuning to fit the biases of the forecast model used as the reference, and/or the limitations of the radiative transfer model

Summary

Introduction

Clouds and precipitation are some of the most uncertain processes in the earth system, leading to systematic errors in models (e.g. Klein et al, 2009; Forbes et al, 2016) and big uncertainties in climate change predictions (e.g. Zelinka et al, 2020). They rely on compressing reality into simple functional fits, such as particle fall-speed and size distributions Bechtold et al, 2014; Forbes et al, 2011), there remain many uncertainties and compensating errors, and increasing complexity can bring additional problems of parameter tuning, making new developments ever harder. This motivates a more objective, automated, and observation-driven approach to developing parametrisations, using machine learning (ML), data assimilation (DA), or a mixture of both The process of learning model parameters using data assimilation is known as parameter estimation, with cloud and precipitation parameters a major target

Results

Discussion

Conclusion