Joint project between
University of Bonn and Deutscher Wetterdienst
University of Bonn: Velibor Pejcic (PhD student); Silke Trömel (PI) and Clemens Simmer (PI)
Deutscher Wetterdienst: Jana Mendrok (Scientist); Urlich Blahak (PI)
Operation Hydrometeors provides an efficient polarimetric forward operator required for the fusion of radar polarimetry and atmospheric modelling. The non-polarimetric forward operator (EMVORADO), used in Germans weather service models COSMO and ICON-LAM, will be extended to polarimetry assuming spheroids at precipitation radar wavelengths. This enables to generate synthetic volume scans of polarimetric observables based on the simulated model state and use it for evaluate and improve the hydrometeor (HM) types and size representation.
The evaluation with the state-of-the-art hydrometeor classification (HMC) schemes with modelled hydrometeors is challenging due to different hydrometeor type definitions and numbers and difficulties to identify dominant types in hydrometeor mixtures. Therefor, a dual strategy to evaluate the hydrometeor type representation in COSMO/ICON-LAM is suggested, based on advanced radar-based hydrometeor typing and a direct comparison of multivariate observed and synthetic polarimetric signal distributions.
The work in the first year addresses the selection of convective and stratiform case studies (WP 1) as the useability of common hydrometeor classification schemes for model evaluation (WP 2) and the coding of clustering routine (WP 3). At the same time spheroids will be consolidated in EMVORADO based on existing code (WP 4) and an automatic generation mechanism of lookup tables for efficiency will be introduced (WP 5).
Contribution of University Bonn
A first goal of the University Bonn contribution to Operation Hydrometeors was the selection of case study days (convective and stratiform) and to apply hydrometeor classification schemes (HMC) for model evaluation to the selected events to build a reference (in preparation of WP 3) for the advanced radar-based hydrometeor typing (Grazioli et al. 2015, Besic et al. 2016). Conventional HMC suffer from: Uncertain of hydrometeor (HM) class properties defined by theoretical scattering simulations especially in solid phase (Tyynetla et al 2011), impact of radar observation accuracy on HM typing (Park et al. 2009), less represented HM class identified as dominant in HM mixtures due to disproportional impact on polarimetric variables. Furthermore, HMC schemes have different a-priori class definitions and numbers. Even the same classes of different HMC can be defined by different polarimetric properties, making comparability with modelled HM even more difficult. A direct comparison between modelled and measured variables cannot be made due to temporal and spatial shifts. Therefore, distributions of hydrometeors of several hours/days or of whole system evolutions are compared statistically. It also has to be investigated whether HMCs should be applied to gridded polarimetric moments or gridding of HMC output achieves more reliable results.
An in-depth evaluation of these problems has not been done so far and we present and discuss the first results of these. The three selected radar-based classifications are those according to Dolan and Rutledge (2009) [HMCDR], Zrnic et al. (2001) adapted by Evaristo et al (2013) [HMCZE] and Thomson et al 2014 [HMCT]. The first is more for warm season and convective cases, HMCZE is for general cases and the third one is applicable to more stratiform winter precipitation. For example Figure 1 shows the differences of the HM typing in class definition and numbers, observed by the X-band radar in Bonn at 2013.07.01 at 15:40 UTC, which make a comparison with the six HM types (cloud water, rain, cloud ice, snow, graupel and hail) in COSMO/ICO-LAM challenging. Note also that each HMC also differs slightly in its methodology. For example, the HM can be classified with simple (Dolan and Rutledge 2009) or bivariate (Zrnic et a.l 2001) membership function. Thompson et al. 2014 first detects the melting layer that divides the precipitates into liquid, solid or mixed and afterwards classifies these regimes independently of each other.
Figure 1: Two-dimensional projection of horizontal reflectivity against differential reflectivity for the comparison of three HMC typing (left: HMCZE, middle: HMCDR, right: HMCT) applied on the data set of one RHI scan (2013.07.01, 15:40 UTC) of the X-band radar in Bonn (BoXPol). The colors indicates the specific HM types of the three different schemes
Regarding the impact of the accuracy of radar measurements on HM typing in 100 realizations the polarimetric measurements of an event (2016.06.20 15:30 UTC) are perturbated with an error. For this purpose, we calculate the standard deviation (Ryzhkov and Zrnic 2019) of the error for each polarimetric variable, add a random perturbation from the associated Gaussian distribution to the measurements and determine the HM type with the disturbed measurements. The figure 2 shows the distributions of the HM types resulting from measurement disturbance for a given HM type from undisturbed measurement. Basically, measurement inaccuracies can result in misclassification of any HM type, but the probability that a certain type occurs depends on the HM class and HMC scheme.
Mainly measurements classified by HMCDR with No Rain are very often misclassified as Drizzel/Light Rain. In HMCZE No Rain is often misclassified with Light Rain and Light rain with Dry/Wet Snow and Crystals. Measurement inaccuracies at HMCZE lead to the fact that the misclassification takes place particularly strongly within the ice phase. With HMCDR, liquid HM are very often classified as wet snow.
Figure 2: Two-dimensional distribution of the HM types determined with Dolan and Rutledge 2009 (left) and Evaristo et al. 2013 (right) with 100 realizations applied to a PPI scan from 2016.06.20 15.30 UTC with perturbated polarimetric variables
Contribution of Deutscher Wetterdienst
Work in the first year focused on extending DWD‘s operational radar forward operator (FO) EMVORADO, that can be online-coupled to NWP models COSMO and ICON, with the capability to calculate polarimetric parameters and to handle hydrometeors as spheroidal-shaped scatterers (WP-4). A prototype, based on third-party code (J. Snyder, NOAA), has been implemented. Spheroid shapes and orientations, which are not constrained by the NWP models, follow parametrizations given in Ryzhkov (2013), and scattering properties are derived using a T-Matrix approach. This implementation will serve as a basis for a refined, consistent implementation of EMVORADO-Pol flexible enough to handle arbitrary shaped and oriented particles for all hydrometeor classes (in preparation of WP-18).
Before further EMVORADO-Pol development, including the implementation of polarimetric lookup tables (WP-5), we are consolidating bulk microphysics and bulk scattering implementations in EMVORADO in order to handle 1- and 2-moment microphysics consistent in the FO (and avoid error-prone and hard-to-maintain duplicated code). Additionally, the University of Bonn has been supported by debugging and improving parts of their – in wait for EMVORADO-Pol - currently applied polarimetric forward operator BPFO. Furthermore, some sensitivity analyses (e.g. of the melting parametrization, see Figure) and comparison to a third polarimetric forward operator (J. Carlin, NOAA) have been performed with BPFO in the framework of evaluating melting layer and frozen hydrometeor microphysics in COSMO (early contributions to WP-14,15,19).
Figure 3: Histograms of simulated observable polarimetric radar parameters as function of height (model level) for two different melting parametrizations. Left: reflectivity in horizontal polarization ZH, middle: differential reflectivity ZDR, right: specific differential phase KDP. Bottom: Original BPFO scheme with many hardcoded parameters, latent heat flux induced, below-0C melting and dynamic estimate of the maximum temperature Tmax, where ice still exists. Top: BPFO with now user-contollable melting scheme parameters, set to Carlin-FO like values (no below-0C melting, fixed but hydrometeor type dependent Tmax). Grey lines indicate rough estimates of measurement uncertainty.
Besic, N., J. Figueras i Ventura, J. Grazioli, M. Gabella, U. Germann, and A. Berne, 2016: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach. Atmos. Meas. Tech., 9, 4425-4445, https://doi.org/10.5194/amt-9-4425-2016.
Dolan, B., and S. A. Rutledge, 2009: A theory-based hydrometeor identification algorithm for X-band polarimetric radars. J. Atmos. Oceanic Technol., 26, 2071–2088.
Evaristo, R. M., X. Xie, S. Trömel and C. Simmer, 2013: A holsitic view of precipitation systems freom macro- and microscopic perspective, 36th Conference on Radar Meteorology, https://ams.confex.com/ams/36Radar/webprogram/Paper229078.html
Grazioli, J., D. Tuia, and A. Bern, 2015: Hydrometeor classification from polarimetric radar measurements: a clustering approach. Atmos. Meas. Tech., 8, 149–170, doi:10.5194/amt-8-149-2015.
Park, H. S., A. V. Ryzhkov, D. S. Zrnic, and K.-E. Kim, 2009: The hydrometeor classification algorithm for the polarimetric WSR-88D: Description and application to an MCS. Wea. Forecasting, 24, 730–748, doi:10.1175/2008WAF2222205.1.
Ryzhkov, A.V., M.R. Kumjian, S.M. Ganson, and A.P. Khain, 2013: Polarimetric Radar Characteristics ofMelting Hail. Part I: Theoretical Simulations Using Spectral Microphysical Modeling. J. Appl. Meteor. Climatol., 52, 2849–2870, DOI: 10.1175/JAMC-D-13-073.1
Ryzhkov, A.V. and Zrnic, D.S. 2019: Radar Polarimetry for Weather Observations, Springer Atmospheric Science ,198, https://doi.org/10.1007/978-3-030-05093-1
Snyder, J. 2013: Observations and simulations of polarimetric weather radar signatures in supercells. PhD thesis. University of Oklahoma, 213 pp.
Straka, J.M., D.S. Zrnić, and A.V. Ryzhkov, 2000: Bulk Hydrometeor Classification and Quantification Using Polarimetric Radar Data: Synthesis of Relations. J. Appl. Meteor., 39, 1341–1372, https://doi.org/10.1175/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2
Thompson, E.J., S.A. Rutledge, B. Dolan, V. Chandrasekar, and B.L. Cheong, 2014: A Dual-Polarization Radar Hydrometeor Classification Algorithm for Winter Precipitation. J. Atmos. Oceanic Technol., 311457–1481, DOI: 10.1175/JTECH-D-13-00119.1
Tyynelä, J.,J. Leinonen, D. Moisseev, and T. Nousiainen, 2011: Radar Backscattering from Snowflakes: Comparison of Fractal, Aggregate, and Soft Spheroid Models. J. Atmos. Oceanic Technol., 28, 1365–1372, DOI: 10.1175/JTECH-D-11-00004.1
Zrnić, D.S., A. Ryzhkov, J. Straka, Y. Liu, and J. Vivekanandan, 2001: Testing a Procedure for Automatic Classification of Hydrometeor Types. J. Atmos. Oceanic Technol., 18, 892913, https://doi.org/10.1175/15200426(2001)018<0892:TAPFAC>2.0.CO;2