Abstract

Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany, to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying two parameters (Dice and Tgr) responsible for the production of snow and graupel, respectively, was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias in simulated polarimetric moments at lower levels above the melting layer (−3 to −13 °C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g., fragmentation due to ice-ice collisions), and use of more reliable snow scattering models to draw valid conclusions.

Highlights

  • Polarimetric radar networks provide an unprecedented database to evaluate and improve cloud microphysical parameterisations in numerical weather prediction (NWP) models

  • This study focuses on uncertainties regarding the (1) partitioning of ice water content among different hydrometeor types in the cloud microphysics scheme and (2) assumptions of hydrometeor scattering properties in polarimetric radar forward operators using a hindcast numerical experiment setup for a widespread wintertime stratiform precipitation event over northwestern Germany

  • control run (CTRL) and EXP1 do not show the sharp gradient in qr near the melting layer as simulated for EXP2 and EXP3 (Fig. 4)

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Summary

Introduction

Polarimetric radar networks provide an unprecedented database to evaluate and improve cloud microphysical parameterisations in numerical weather prediction (NWP) models. With the increasing availability and use of such modern remote sensing observations ( satellites, radiometers, etc.) for NWP model validation, evaluation and data assimilation, there is an increasing demand for cloud microphysics parameterisation schemes to realistically approximate cloud microphysical processes and hydrometeor properties, such as size distributions, partitioning into different classes and types, bulk densities, and fall speeds. This is key for consistent forward simulations of cloud-related quantities across different measurement platforms and different parts of the electromagnetic spectrum because these radiative transfer calculations critically depend on the particle properties and their spatial distributions. Even with a single device like a polarimetric radar, there can be such inconsistencies because different polarimetric parameters are related to different moments of the particle size distributions of modelled hydrometeor species.

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