projects:pomodori

Combining radar POlarimetry, weather forecast MOdel outputs and DOppler radar observations for RIming analysis (POMODORI)


Joint project between
Ludwig-Maximilians-University of Munich (LMU) and Deutscher Wetterdienst (DWD), Phase 2

LMU: Paul Ockenfuß (PhD), Stefan Kneifel (PI)
DWD: Mathias Gergely (Scientist), Michael Frech (PI)

Abstract

The idea behind PROM-POMODORI is to gain a better quantitative understanding of the riming process, which is one of the main growth mechanisms of frozen precipitation and yet is difficult to identify in remote sensing observations and to faithfully represent in atmospheric models. PROM-POMODORI aims to overcome current challenges in investigating riming by leveraging a combination of polarimetric measurements from the German C-band radar network, vertically pointing Doppler radar measurements from Ka-band cloud radars and C-band weather radars, and outputs from atmospheric models over Germany. The focus is estimating the degree of riming both in an atmospheric column above the radars locally and on assessing the spatiotemporal dimensions and the variability of riming events.


pomodori_prom_abstract.jpg

Figure 1: Correlate riming retrievals from vertically pointing cloud radars with measurements of DWD C-band radar polarimetric variables and atmospheric (thermo)dynamics from ICON-D2 model outputs. First, analyzing riming above radar sites and then throughout the radar scan volume.

Status Spring 2024

For the first year of PROM-POMODORI at LMU, we focused mainly on data from the vertically pointing Ka-band cloud radars located in Jülich and Lindenberg. The main objective was to create a database of riming events, serving as a training dataset and benchmark for future riming retrievals based on the C-band radars of the German Meteorological Service (DWD). To achieve this, we re-implemented the riming retrieval from Kneifel et al. (2020), which is based on the higher fall velocity of rimed particles compared to pristine crystals. The algorithm was improved and subsequently applied to 14 years of data (2010-2024). From the algorithm output, we created a database of continuous riming events. Four examples of riming events are shown in Figure 1.

Combining this database with thermodynamic profiles from atmospheric models, we were able to derive a high number of characteristics for each event, e.g. duration, height, vertical extent, temperature, etc. Using wind information from the NWP models, we investigated different approaches to estimate the horizontal extent of the detected events. This analysis has provided insight into the typical temporal and spatial scales and the associated variability of riming, which forms the groundwork to implement a polarimetric C-band retrieval, but is also interesting from a microphysical perspective.

For the Lindenberg site, we also investigated possible correlations between riming and radiosonde measurements. Particularly, we derived estimates of the liquid water content from the radiosonde and showed that riming events have a significantly higher probability to coincide with high liquid water content than similar ice clouds where no riming is observed.
Currently, we are moving toward C-band radar data and analyze the potential for identifying and estimating riming from these weather radar measurements at longer wavelengths.




Figure 1: Selected examples of riming events. The upper part of each panel shows Dopplervelocity, the lower part the retrieved rime mass fraction. Hatched areas are excluded due to a high convection index. Events are indicated by a green overlay. The number of samples per event is depicted by the green number.


For riming events identified by the LMU project partners based on Ka-band cloud radars, the DWD researchers have analyzed concurrent polarimetric measurements from the operational scanning C-band weather radars to see whether a typical riming signature can also be observed in the C-band polarimetric data recorded above the cloud radars. Figure 2 shows an example where such a riming signature is indeed evident in the C-band radar volume-scan data: elevated radar reflectivity, differential reflectivity close to 0, and a very high correlation coefficient approaching a value of 1. However, other riming events identified in Ka-band cloud radar data do not show any clear signature in the corresponding polarimetric C-band radar measurements.

To gain a better quantitative understanding of riming across Germany, DWD has provided long-term C-band radar data from the operational (vertically pointing) birdbath scans (Frech et al. 2017) of all 17 C-band radars from 2021 through 2023 to the project partners at LMU for transferring the riming retrieval algorithm which has been extensively tested for vertically pointing cloud radars to the C-band radar birdbath scan. Additionally, DWD continues to explore the new birdbath scan (developed as part of PROM phase 1 and implemented in DWD's operational radar scanning cycle; see Gergely et al. 2022) in the context of analyzing convective storms and, particularly, for estimating hail characteristics relevant to assessing the damage potential of these thunderstorms.




Figure 2: PPI of Essen C-band radar at 2.5° elevation in October 2021, recorded when a significant riming event was identified based on the vertically pointing Ka-band cloud radar at Jülich. Here, a characteristic riming signature can also be observed in the polarimetric radar data above the cloud radar site.


PROM-POMODORI partners have presented these results at the 2022 North American Hail Workshop, at the AMS Conference on Radar Meteorology 2023, and at the International Summer Snowfall Workshop 2023.

Looking ahead, the next tasks of the POMODORI project include publishing the results obtained in the first year of the project in peer-reviewed journals. Estimating the degree of riming from the long-term dataset of operational C-band radar birdbath scans will also provide a first estimate of the variability of riming across larger regions.

References

  • projects/pomodori.txt
  • Last modified: 2024/10/15 13:05
  • by ayush