A seamless column of the precipitation process from mixed-phase clouds employing data from a polarimetric C-band radar, a microrain radar and disdrometers (HydroColumn)

Project by
Deutscher Wetterdienst - Observatorium Hohenpeißenberg; collaborations with PROM partners still at an early stage

Deutscher Wetterdienst - Observatorium Hohenpeißenberg: Mathias Gergely (Scientist), Michael Frech (PI)

The idea behind HydroColumn is to characterize precipitation processes inside a vertical atmospheric column by combining polarimetric Doppler weather radar observations with microrain radar (MRR) and in situ measurements. The C-band weather radar furnishes high-resolution polarimetric and spectral signatures of the precipitation aloft, while MRR and colocated in situ measurements allow tracking the precipitation down to the ground. The focus is the identification of ice- and mixed-phase precipitation processes above the melting layer which can then be related to atmospheric dynamics observed in wind and temperature profiles or modeled by numerical weather prediction models.

Figure 1: Schematic of measurement setup and 1st usable weather radar bin (~ 650 m above radar). Adopted from Frech et al. (2017).

Current status
The work in the first year aims at implementing a measurement setup suitable for combined radar and in situ observations of precipitation at the Observatorium Hohenpeißenberg, with emphasis on identifying useful weather radar scan strategies for the polarimetric and spectral analysis of precipitation and on evaluating the information contained in the radar Doppler spectra.

While the signal processor of the weather radar applies a clutter filter that is useful for estimating polarimetric variables, such as Zh, ZDR, or RHOhv, for example, a detailed analysis of the full Doppler spectra requires a more refined isolation of the meteorological radar signal. The clutter characteristics also seem to be more variable than what is observed in some cloud radars (e.g., Williams et al. 2018), which poses an additional challenge. Figure 2a shows Doppler spectra of precipitating clouds observed with the vertically pointing weather radar at horizontal polarization. Here, a strong wind effect is observed (resulting in smearing of the spectrum across a wide range of Doppler velocities).

Doppler spectra near the ground (up to about 0.65 km above the radar) exhibit strong ground clutter; a strong clutter peak around 0 m/s is observed across most elevations; and some background noise can be noted. To isolate the meteorological signal in Fig. 2b, a threshold filter was applied per velocity and height bin based on the difference between the Doppler spectra recorded at horizontal and at vertical polarization (DFTh - DFTh) combined with the small-scale variability of this difference (std(DFTh - DFTh)).

The threshold was selected based on results from a flexible, yet computation-intensive compared to simple thresholding, density-based hierarchical clustering algorithm (HDBSCAN; Campello et al. 2013) which does not require the prescription of a specific number of clusters or other characteristic features of the data clusters a priori (Fig. 2c). The filter performance is not very sensitive to the exact threshold value, so the threshold value determined for one radar observation, as shown in Fig. 2, can generally be applied to other radar observations during the same precipitation event and to different precipitation events. Changing the radar settings, however, can lead to a stark decrease in the performance of the threshold filter if the threshold value is not updated.

Figure 2: (a) Doppler spectra of precipitating clouds recorded with vertically pointing C-band polarimetrric Doppler weather radar at horizontal polarization. (b) The crucial step in isolating the meteorological radar signal is the filtering of Doppler spectra based on the difference in the recorded signals at horizontal and vertical polarization and its small-scale variability. © The filter threshold was derived based on results from applying the HDBSCAN density-based hierarchical clustering algorithm to the observations. Here, cluster 1 is interpreted as the meteorological signal, based on the assumed rotational symmetry in the horizontal plane (on average), and thus small differences between DFTh and DFTv, for a population of many scatterers within a radar bin; cluster 2 comprises near-field clutter, static clutter around 0 m/s, and background noise that all lead to either large differences between DFTh and DFTv (e.g. strong static clutter at 0 m/s) or highly variable values (e.g. background noise and transition regions from clutter to meteorological signal).

After exploiting the polarimetric characteristics of the Doppler spectra data to isolate the meteorological radar signal, typical features in terms of the shape and evolution of the Doppler spectra, as plotted in Fig. 2b, can be identified and spectral moments can be calculated, similar to Williams et al. (2018), for example, which will form the next steps of the Doppler spectra analysis. Figure 3a shows examples of recently recorded Doppler spectra for a precipitation event where multiple spectral modes are clearly visible near a height of about 1 km above the radar, indicating the simultaneous occurrence of multiple microphysical processes during the formation of precipitation. Colocated in situ disdrometer observations at the ground indicated both frozen and liquid precipitation occurring simultaneously around that time which formed a one-hour transition period from (liquid) rain to (frozen) snow.

Figure 3: Doppler spectra recorded during two distinct phases of a precipitation event where the melting layer was located around 1500 m, just below the observed spectra. (a) Transition period from rain to snow, where both liquid and frozen precipitation was recorded at the ground. (b) Period of only (frozen) snowfall at the ground. Black ellipses mark potentially similar processes during the formation of precipitation; light gray ellipsis marks the fingerprint of a microphysical process only observed during the transition period, probably injecting liquid into the precipitation which persists down to the ground. Overall, Doppler spectra in (a) also show a stronger impact of wind (broadening of the Doppler spectra) compared to (b).

In addition to a more quantitative analysis of the Doppler spectra and their relation to the overall atmospheric dynamics at the time of precipitation, the next steps for the HydroColumn project also include an evaluation of the polarimetrc variables recorded in quasi-vertical radar profiles at 25° radar elevation and utilizing the radar data to (i) test quantitative microphysical retrievals in collaboration with TROPOS Leipzig and (ii) support airborne in situ observations of DLR measurement campaigns, for example.

Campello, R. J. G. B., Moulavi, D., and Sander, J.: Density-Based Clustering Based on Hierarchical Density Estimates. In: Pei J., Tseng V.S., Cao L., Motoda H., Xu G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol 7819. Springer, Berlin, Heidelberg, doi: 10.1007/978-3-642-37456-2_14, 2013.

Frech, M., Hagen, M., and Mammen, T.: Monitoring the absolute calibration of a polarimetric weather radar, J. Atmos. Oceanic Technol. 34, doi: 10.1175/JTECH-D-16-0076.1, 2017.

Williams, C. R., Maahn, M., Hardin, J. C., and de Boer, G.: Clutter mitigation, multiple peaks, and high-order spectral moments in 35 GHz vertically pointing radar velocity spectra, Atmos. Meas. Tech. 11, doi: 10.5194/amt-11-4963-2018, 2018