## Mesoscale Modeling & Dynamics Group

The **POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS)** is state-of-art multi polarimetric radar simulator and algorithm emulator,built upon the multi-instrumental simulator package, the Goddard Satellite Data Simulator Unit (G-SDSU) (Matsui 2013, Matsui et al. 2014). POLARRIS developmend will be under collaboration with Colorado State University Radar Group (http://www.radar.colostate.edu/). POLARRIS will provide a rigorous evaluation framework of cloud microphysics and kinetics in various ranges of CRMs in comparison with currently operating polarimetric radar observation.

POLARRIS will be composed of CRM IO module, T-Matrix module, Mueller-Matrix modules, and the CSU HID radar algorithm module. T-matrix computes the single scattering matrix of axis-symmetric oblate hydrometeors. The Mueller-Matrix uses the properties derived from the T-Matrix, and estimates radar observables assuming particle size distributions and radar scanning geometry (Vivekenandan et al. 1991). There are three steps to compute polarimetric radar observables.

**Scattering Matrix**: 3D CRM geophysical parameters (e.g., all hydrometeor particles) are converted into a single-scattering matrix through the T-matrix module. For fast computation, we will pre-compute the large sets of scattering matricies representing size, density, phase (liquid, solid, mixed-phase) variations at three precipitation radar wavelengths, X-, C- and S-band (3 cm, 5 cm and 10 cm respectively) corresponding to the available polarimetric radar observations. Effective dielectric properties are estimated using the Debye theory method for air-ice-liquid mixtures. Then, the actual scattering matrix will be computed by interpolating the pre-computed database. Note that CRM microphysics assumptions of phase, effective density, and size distributions are retained in this process. However, assumptions of orientation, shape and axis ratio will be derived from the CSU HID algorithm, since these properties are not explicitly considered in the bulk microphysics as well as the SBM schemes.

**Radar Scanning and Mueller-matrix**: Then, radar scanning information (bins of elevation angle, antenna rotation speed, antenna beamwidth and sampling rate) from the ground radar systems (both PPI and RHI scan modes) is directly used to estimate sampling volume and locations in spherical coordinates. In order to match the radar sampling volume (spherical coordinates) and scattering matrix (WRF coordinates), a matrix rotation method is used to convert them into Earth-Centered, Earth-Fixed (ECEF) Cartesian coordinates (Matsui 2013). From the derived sample volume, the particle size distribution and hydrometeor mixtures from the model will be used in conjunction with CSU HID assumptions about canting angle distributions in Mueller matrix. The Mueller-matrix calculates polarimetric radar observables (Zh, Zdr, Kdp, ρhv) for each sample volume. In addition, CRM wind fields (u, v, w components) are also averaged in the radar sampling volume with a radar reflectivity weighting scheme, and normalized toward the specific radar elevation and scanning angle in order to mimic “radial velocity”.

**CSU Radar Algorithm**: As the last step, CRM-derived synthetic radar observables (Zh, Zdr, Kdp, ρhv) are input into the CSU radar algorithm package (e.g. MWHID). This package will compute algorithm-consistent hydrometeor type, vertical and horizontal wind speed, and precipitation rate. The point here is that the derived geophysical parameters have identical (physics) assumptions in the single-scattering matrix as the hydrometeor identification physics used in the CSU algorithm, while it retains CRM specific information, such as hydrometeor-specific particle size distributions, density, and phase, and their 3D variability. In this way, we can analyze CRM outputs in the “algorithm view”, rather than comparing with direct CRM output performed in Dolan et al. (2014).