Characterization of Precipitating Clouds by Ground-Based Measurements with the Triple-Frequency Polarized Microwave Radiometer ADMIRARI

Abstract

A groundbreaking new-concept multiwavelength dual-polarized Advanced Microwave Radiometer for Rain Identification (ADMIRARI) has been built and continuously operated in two field campaigns: the Convective and Orographically Induced Precipitation Study (COPS) and the European Integrated Project on Aerosol Cloud Climate Air Quality Interactions (EUCAARI). The radiometer has 6 channels working in horizontal and vertical polarization at 10.65, 21.0, and 36.5 GHz, and it is completely steerable both in azimuth and in elevation. The instrument is suited to be operated in rainy conditions and is intended for retrieving simultaneously water vapor, rain, and cloud liquid water paths. To this goal the authors implemented a Bayesian retrieval scheme based on many state realizations simulated by the Goddard Cumulus Ensemble model that build up a prior probability density function of rainfall profiles. Detailed three-dimensional radiative transfer calculations, which account for the presence of nonspherical particles in preferential orientation, simulate the downwelling brightness temperatures and establish the similarity of radiative signatures and thus the probability that a given profile is actually observed. Particular attention is devoted to the sensitivity of the ADMIRARI signal to 3D effects, raindrop size distribution, and axial ratio parameterizations. The polarization and multifrequency signals represent key information to separate the effects introduced by non-Rayleigh scatterers and to separate rainwater (r-LWP) from the cloud water component (c-LWP). Longterm observations demonstrate that observed brightness temperatures and polarization differences can be well interpreted and reproduced by the simulated ones for all three channels simultaneously. Rough estimates of r-LWP derived from collocated observations with a micro rain radar confirm the rain/no rain separation and the variability trend of r-LWP provided by the radiometer-based retrieval algorithm. With this work the authors demonstrate the potential of ADMIRARI to retrieve information about the rain/cloud partitioning for midlatitude precipitation systems; future studies with this instrument will provide crucial information on rain efficiency of clouds for cloud modelers that might lead toward a better characterization of rain processes.

Statistical characteristics of cloud variability. Part 1: Retrieved cloud liquid water path at three ARM sites

“…For cloud fields, Taylor's hypothesis seems to apply in most cases [ Sun and Thorne , ]. Recent advances in scanning sensors, e.g., scanning radiometers, and volume scanning radars enable direct evaluation of the Taylor's hypothesis [ Huang et al , ; Kneifel et al , ; Battaglia et al , ; Schween et al , ], and this is out of scope of this study. We assume the validity of Taylor's hypothesis and interpret the observed temporal cloud variability as cloud spatial variability accordingly.…”

Partitioning of cloud water and rainwater content by ground‐based observations with the Advanced Microwave Radiometer for Rain Identification (ADMIRARI) in synergy with a micro rain radar

“…It is important, however, to note that poor retrieval performance (large QI) like the period 04:09 to 04:15 UTC in Figure 7 could also be explained by a ∼−7° antenna misalignment of the 10.7 GHz channels relative to the others, a shortcoming already noted by Battaglia et al [2009a]. The current retrieval scheme takes this misalignment into account to some degree, by including in the database simulations of this proper misalignment [ Battaglia et al, 2009b, section 5].…”

“…In the following sections these two definitions of the measurement vector are denoted as RAD when and RADMRR when , with i counting for the three ADMIRARI frequencies and r indicating the MRR range. As stated by Battaglia et al [2009b, equation (6)], a quality index (QI) is adopted to assess the closeness between observations and simulations. QI is defined as QI ≡ min{ δ 2 } with the minimum determined by a search over the selected a priori subdatabase and δ 2 given by .…”

Ground-Based Microwave Radiometer Variational Analysis during No-Rain and Rain Conditions

“…The causes of the errors are classified into two effects: i) absorption/emission and scattering by raindrops and water film on the radome, and ii) absorption/ emission and scattering by large raindrops in the air (e.g., Battaglia et al 2010). To reduce the effect of raindrops on the radome, several techniques have been tested such as hydrophobic coatings on the radome and forced airflow over the radome surface from a blower (e.g., Marzano et al 2005).…”