Ensemble simulations with the Terrestrial Systems Modelling Platform (TSMP) covering northwestern Germany are evaluated for three summertime convective storms using polarimetric X-band radar measurements. Using a forward operator, the simulated microphysical processes have been evaluated in radar observation space. Observed differential reflectivity (Z_DR) columns, which are proxies for updrafts, and multi-variate fingerprints for size sorting and aggregation processes are captured by the model, but co-located specific differential phase (K_DP) columns in observations are not reproduced in the simulations. Also, the simulated Z_DR columns, generated by only small-sized supercooled drops, show smaller absolute Z_DR values and a reduced width compared to their observational counterparts, which points to deficiencies in the cloud microphysics scheme as well as the forward operator, which does not have explicit information of water content of ice hydrometeors. Above the melting layer, the simulated polarimetric variables also show weak variability, which can be at least partly explained by the reduced particle diversity in the model and the inability of the T-matrix method to reproduce the polarimetric signatures of snow and graupel; i.e. current forward operators need to be further developed to fully exploit radar data for model evaluation and improvement. Below the melting level, the model captures the observed increase in reflectivity, Z_DR and specific differential phase (K_DP) towards the ground. The contoured frequency altitude diagrams (CFADs) of the synthetic and observed polarimetric variables were also used to evaluate the model microphysical processes statistically. In general, CFADs of the cross-correlation coefficient (ρ_hv) were poorly simulated. CFADs of Z_DR and K_DP were similar but the model exhibits a relatively narrow distribution above the melting layer for both, and a bimodal distribution for Z_DR below the melting layer, indicating either differences in the mechanism of precipitation formation or errors in forward operator which uses a functional form of drop size distribution. In general, the model was found to underestimate the convective area fraction, high reflectivities, and the width/magnitude of Z_DR columns, all leading to an underestimation of the frequency distribution for high precipitation values.

During the AMMA campaign, the Doppler and polarimetric radar Ronsard was deployed in Kopargo, Benin. In the present paper, three squall lines sampled during that period are described. It was the first time this type of system had been observed with a polarimetric radar in the West African region. The strongest case occurred on 28 July 2006, and the others on 12 September and 30 June. The polarimetric characteristics of these systems are described and a hydrometeor classification algorithm is applied in order to identify the microphysics of the systems. Finally, the 3D wind field is retrieved for the 28 July event. The convective part of the squall lines is generally characterized by maxima of reflectivity, differential reflectivity (Z_DR), and specific differential phase (K_DP). The results of the hydrometeor classification show that the convective region is composed of strong (>30 mm h⁻¹) to moderate rain (5–30 mm h⁻¹) below the melting level, and graupel above. This zone seems to be characterized by relatively strong updraughts (5–10 m s⁻¹), responsible both for the formation of graupel by riming and for the growth of drops due to coalescence. To the rear the systems are characterized by light rain (<5 mm h⁻¹) below and snow or ice crystals above. These regions present downward flow below the melting layer, which prevents drop growth by condensation, consistent with the light rain observed. The stratiform part of these systems can be very non‐uniform, due to the presence of old cells dissipating, associated with moderate rain and thus stronger reflectivities. Copyright © 2010 Royal Meteorological Society

This study presents a first analysis of precipitation and related microphysical processes observed by three polarimetric X-band Doppler radars (BoXPol, JuXPol and KiXPol) in conjunction with a ground-based network of disdrometers, rain gauges and vertically pointing micro rain radars (MRRs) during the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)²) Observational Prototype Experiment (HOPE) during April and May 2013 in Germany. While JuXPol and KiXPol were continuously observing the central HOPE area near Forschungszentrum Jülich at a close distance, BoXPol observed the area from a distance of about 48.5 km. MRRs were deployed in the central HOPE area and one MRR close to BoXPol in Bonn, Germany. Seven disdrometers and three rain gauges providing point precipitation observations were deployed at five locations within a 5 km × 5 km region, while three other disdrometers were collocated with the MRR in Bonn. The daily rainfall accumulation at each rain gauge/disdrometer location estimated from the three X-band polarimetric radar observations showed very good agreement. Accompanying microphysical processes during the evolution of precipitation systems were well captured by the polarimetric X-band radars and corroborated by independent observations from the other ground-based instruments.

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 (Z_DR) columns indicative of updrafts, all leading to an underestimation of the frequency distribution for high precipitation values. The statistical distributions of Z_DR and specific differential phase (K_DP) were however similar, while the cross-correlation coefficient (p_hv) was poorly simulated, probably due to little variability of assumed hydrometeor shapes and orientations in the forward operator. The observed model bias in the Z_DR 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.

Columns of differential reflectivity, the difference between the horizontal and vertical reflectivity,hereafter Zdr columns, are vertical columns of enhanced Zdr that extend above the environmental 0°Clevel. These are easily identified when observed by polarimetric radars. Physically, these columns consistof rain dominated by large drops that are being lofted above the freezing level and have been recognizedas a proxy for the location of updrafts. Their potential for nowcasting severe weather has been shown inseveral past studies. We have developed an algorithm that identifies and tracks Zdr columns fromvolumetric radar data along with 3D wind fields from MultiDoppler analysis to spatially correlate Zdrcolumns with updrafts. Since Zdr columns are a manifestation of an updraft, different Zdr columnsproperties for example Zdr column maximum height, volume, and area are expected to be related toupdraft intensity levels. In turn intensification of updrafts, as indicated by changes in the Zdr columnsproperties, should be translated in intensification of observed precipitation at the surface. For theestimation of rain rates, we used a radar-based polarimetric approach, which will allow us to monitor thetemporal evolution for a number of identified convective rain cells. These cells will be identified fromsummer events observed by the C-band polarimetric German network. For each cell, the properties of Zdrcolumns are correlated with rain rate values. Similarly, correlations are also calculated for updraftvolumes, updraft intensity, and other updraft properties. For the nowcasting of observed rain rates, anextrapolation algorithm based on spatial and temporal properties of rain was used. Preliminary resultshave shown that higher precipitation rates are generally associated with Zdr columns, and cells without aZdr column produce lower precipitation rates, as expected. Zdr column height and volume show apositive correlation with precipitation intensity at the surface. The time lag between the intensification ofthe Zdr column and associated increase in precipitation at the surface varies significantly between cells,but it is generally short compared to previous studies, varying mostly between 5 to 15 minutes. An earlyidentification of cells associated with ZDR columns could benefit the skill of the nowcasting of localizedrain cells, which often are smoothed during extrapolation.

The RY precipitation product of the German Weather Service (DWD) is severely affected by the presence of the low melting layer and frequently shows circular features of enhanced precipitation around the radar sites during the winter time. The radars tend to be installed at relatively high terrain and to scan at elevations at a minimum of 0.5° in order to avoid beam blockage and ground clutter. In doing so two problems arise:1) the difference between the ground and the radar beams becomes a problem especially at large distances from the radar, and consequently precipitation processes in the lowest layers are not observed.2) the radar beam often reaches the melting layer and may even cross it where it is sampling the snow above.As a result problems arise when deriving surface QPE from the radar: regions of enhanced QPE in ring shapes around the radar sites, and underestimation of the precipitation beyond the melting layer.A new methodology (PVPR - Polarimetric Vertical Profile of Reflectivity) developed by Ryzhkov et al. 2022 is tested here for which the radar reflectivity (ZH) is reconstructed to correct for the effect of the melting layer and snow beyond. In this methodology the melting layer is detected independently for each azimuth based on the values of ZH and ρHV (cross-correlation coefficient between horizontal and vertically polarized radar waves). In particular the range bin at which the melting layer was reached is recorded (mlb_r). The strength of the melting layer (ML_S) is defined based on how much the value of ρHV dropped within the melting layer. The values of ML_r and ML_S at a specific elevation are considered sufficient to characterize the melting layer, and are then compared with lookuptables which were generated by simulations of the melting layer effect on the radar beam. A correction factor is then applied based on the lookuptables to the ZH profile within and beyond the melting layer. Visually the result shows a smoother field of reflectivity without the obvious bright band and decreased values associated with snow at farther ranges.In this study the PVPR methodology was used to correct ZH which in turn was used to calculate rain rates and rain accumulations in a few winter events in Germany. The results show a strong improvement in the quality of the QPE when compared to rain gauges. The quality of the resulting QPE depends on the event and on the location of the radar. More specifically, the quality decreases when the melting layer is very low, at heights comparable to the radar height, and when the difference between the beam and the surface increases. These problems will be analyzed and potential solutions will be tested in order to improve the quality of the rainfall product.Ryzhkov, Alexander, Pengfei Zhang, Petar Bukovčić, Jian Zhang, and Stephen Cocks. 2022. "Polarimetric Radar Quantitative Precipitation Estimation" Remote Sensing 14, no. 7: 1695. https://doi.org/10.3390/rs14071695