Abstract
Ensemble simulations were conducted for three summertime convective storms over a temperate region in northwestern Germany using the Terrestrial Systems Modeling Platform (TSMP). The simulated microphysical processes were evaluated with polarimetric observations from two X-band radars, with the help of a forward operator applied to the model data. TSMP was found to generally underestimate the convective area fraction, high reflectivities, and the width/magnitude of so-called differential reflectivity (ZDR) columns indicative of updrafts, all leading to an underestimation of the frequency distribution for high precipitation values. The statistical distributions of ZDR and specific differential phase (KDP) were however similar, while the cross-correlation coefficient (phv) was poorly simulated, probably due to little variability of assumed hydrometeor shapes and orientations in the forward operator. The observed model bias in the ZDR columns could be associated with small size of supercooled raindrops and poorly resolved three dimensional flow at km-scale simulations, besides the treatment of freezing process in the model, which warrants further research.
Highlights
Clouds and precipitation are the major source of uncertainty in numerical predictions of weather and climate
Polarimetric radar observations provide besides ZH, estimates of differential reflectivity (ZDR[dB]), specific differential phase (KDP [degkm−1]), and cross-correlation coefficient, which depend on hydrometeor shape, orientation, density and phase composition, and enable a more detailed evaluation of the modeled microphysical and macrophysical processes (Andricet al., 2013; Snyder et al, 2017a; Putnam et al, 2017)
For the second case (Figure 2b), all ensemble members underestimate the average accumulated precipitation compared to RADOLAN; its frequency distribution for high precipitation is weaker compared to the first case
Summary
Clouds and precipitation are the major source of uncertainty in numerical predictions of weather and climate. The parameterization of cloud microphysical processes and its interaction with the resolved dynamics need to be well tuned in order to provide dependable predictions (Igel et al, 2015; Brown et al, 2016; Morrison et al, 2020). 15 the cloud microphysics is parameterized either using the so-called spectral (bin) approach or single/multi-moment bulk formulations, with the latter most common in numerical weather prediction (NWP) models due to computational efficiency (Khain et al, 2000). These parameterizations are often constrained using in-situ and/or radar reflectivity (ZH [dBZ]) observations. Polarimetric radar observations provide besides ZH , estimates of differential reflectivity (ZDR[dB]), specific differential phase (KDP [degkm−1]), and cross-correlation coefficient (ρhv[−]), which depend on hydrometeor shape, orientation, density and phase composition, and enable a more detailed evaluation of the modeled microphysical and macrophysical processes (Andricet al., 2013; Snyder et al, 2017a; Putnam et al, 2017)
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