Can liquid cloud microphysical processes be used for vertically pointing cloud radar calibration?

Abstract

Abstract. Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Thus far, no single robust method exists for assessing the calibration of past cloud radar data sets. Here, we investigate whether observations of microphysical processes in liquid clouds such as the transition of cloud droplets to drizzle drops can be used to calibrate cloud radars. Specifically, we study the relationships between the radar reflectivity factor and three variables not affected by absolute radar calibration: the skewness of the radar Doppler spectrum (γ), the radar mean Doppler velocity (W), and the liquid water path (LWP). For each relation, we evaluate the potential for radar calibration. For γ and W, we use box model simulations to determine typical radar reflectivity values for reference points. We apply the new methods to observations at the Atmospheric Radiation Measurement (ARM) sites North Slope of Alaska (NSA) and Oliktok Point (OLI) in 2016 using two 35 GHz Ka-band ARM Zenith Radars (KAZR). For periods with a sufficient number of liquid cloud observations, we find that liquid cloud processes are robust enough for cloud radar calibration, with the LWP-based method performing best. We estimate that, in 2016, the radar reflectivity at NSA was about 1±1 dB too low but stable. For OLI, we identify serious problems with maintaining an accurate calibration including a sudden decrease of 5 to 7 dB in June 2016.

PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere

“…All following simulations assume spheres (Mie) for the hydrometeor categories liquid water, rain, graupel, and hail. The SSRGA with the coefficients as in Mason et al (2019) are used for cloud ice and snow. Herein, using the SSRGA allows us to ensure maximum consistency regarding particle properties such as the mass-size relation assumed in the microphysical schemes.…”

Evaluation of the reflectivity calibration of W-band radars based on observations in rain

“…By combining these two approaches, the Levenberg-Marquardt algorithm, in general, converges faster. The problem of overfitting was avoided by using the low number of neurons in the hidden layer and applying the Bayesian regularization (MacKay, 1992), which restricts the magnitude of the weights. Further details and examples, with ready-to-use MATLAB codes of function approximations using neural networks, can be found in Demuth et al (2014).…”

“…Proper calibration and monitoring of reflectivity calibration are key, considering the growing number of meteorological radars worldwide. However, even radars operated within large observational networks have been shown to sometimes be prone to calibration errors (Protat et al, 2011;Ewald et al, 2019;Maahn et al, 2019;Kollias et al, 2019). Chandrasekar et al (2015) compiled a detailed review of the centimeter wavelength radar calibration techniques for an operational use.…”

PAMTRA 1.0: A Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere

“…Similarly, Lagrangian super-particle models (Brdar and Seifert, 2018), models with full-bin microphysics or box models (Hoffmann et al, 2017) require similar flexibility in the assumptions of hydrometeor properties from the RT. PAMTRA addresses those needs with a full-bin interface (Maahn et al, 2019). In order to demonstrate this feature, we simulate radar Doppler spectra based on airborne in situ observations of liquid clouds.…”

“…3.4) or the output of numerical models employing size-resolved (binned) microphysical schemes. This flexible interface can also be used to connect PAMTRA with atmospheric models that do not require predefined hydrometeor properties such as those involving the Particle Prediction Properties (P3; Morrison and Milbrandt, 2015) microphysical scheme, or even the semi-Lagrangian super-particle models used for snow(McSnow;Brdar and Seifert, 2018) or drizzle formation(Hoffmann et al, 2017;Maahn et al, 2019).…”