Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
doi:10.5194/acp-2017-99
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
15 Feb 2017
Review status
This discussion paper is under review for the journal Atmospheric Chemistry and Physics (ACP).
A ubiquitous ice size bias in simulations of tropical deep convection
McKenna W. Stanford1, Adam Varble1, Ed Zipser1, J. Walter Strapp2, Delphine Leroy3, Alfons Schwarzenboeck3, and Rodney Potts4 1Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah, 84112, USA
2Met Analytics, Inc., Aurora, Ontario, Canada
3Université Clermont Auvergne/CNRS, Laboratoire de Météorologie Physique, Clermont-Ferrand, France
4Center for Australian Weather and Climate Research, Melbourne, Victoria, Australia
Abstract. The High Altitude Ice Crystals – High Ice Water Content (HAIC-HIWC) joint field campaign produced aircraft retrievals of total condensed water content (TWC), hydrometeor particle size distributions (PSDs), and vertical velocity (w) in high ice water content regions of mature and decaying tropical mesoscale convective systems (MCSs). The resulting dataset is used here to explore causes of the commonly documented high bias in radar reflectivity within cloud-resolving simulations of deep convection. This bias has been linked to overly strong simulated convective updrafts lofting excessive condensate mass but is also modulated by parameterizations of hydrometeor size distributions, single particle properties, species separation, and microphysical processes. Observations are compared with three Weather Research and Forecasting model simulations of an observed MCS using differing microphysics while controlling for w, TWC, and temperature. Two bulk microphysics schemes (Thompson and Morrison) and one bin microphysics scheme (Fast Spectral Bin Microphysics) are compared. For temperatures between −10 °C and −40 °C and TWC > 1 g m−3 inside updrafts, all microphysics schemes produce median mass diameters (MMDs) that are generally larger than observed, and the precipitating ice species that controls this size bias varies by scheme, temperature, and w. Despite a much greater number of samples, all simulations fail to reproduce observed high TWC conditions (> 2 g m−3) between −20 °C and −40 °C in which only a small fraction of condensate mass is found in relatively large particle sizes greater than 1 mm in diameter. Although more mass is distributed to relatively large particle sizes relative to observed across all schemes when controlling for temperature, w, and TWC, differences with observations for a given particle size vary greatly between schemes. As a result, this bias is hypothesized to partly result from errors in parameterized hydrometeor PSD and single particle properties, but because it is present in all schemes, it may also partly result from errors in parameterized microphysical processes present in all schemes. Because of these ubiquitous ice size biases, microphysical parameterizations inherently produce a high bias in convective reflectivity for a wide range of temperatures, vertical velocities, and TWCs.

Citation: Stanford, M. W., Varble, A., Zipser, E., Strapp, J. W., Leroy, D., Schwarzenboeck, A., and Potts, R.: A ubiquitous ice size bias in simulations of tropical deep convection, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-99, in review, 2017.
McKenna W. Stanford et al.
McKenna W. Stanford et al.
McKenna W. Stanford et al.

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Short summary
Radar is a valuable observational tool used to guide numerical weather model improvement. Biases in simulated radar data help identify potential errors in physical process and property representation in models. This study uniquely compares simulated and observational data of tropical cloud systems to establish that a commonly documented high bias in radar reflectivity values at least partially results from the production of larger simulated ice particle sizes compared to observed sizes.
Radar is a valuable observational tool used to guide numerical weather model improvement. Biases...
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