Retrieval of cloud and rainwater from ground-based passive microwave observations with the multi-frequency dual-polarized radiometer ADMIRARI

Abstract

A ground-breaking new-concept multi-wavelength dual-polarized passive microwave radiometer ADMIRARI (ADvanced MIcrowave RAdiometer for Rain Identification) has been developed to perform observations of atmospheric precipitating clouds. The radiometer is suited to be operated in rainy conditions, fully steerable in elevation and azimuth angle and comprises of six independent channels measuring brightness temperatures in horizontal and vertical polarization at 10.7, 21.0 and 36.5 GHz. The passive sensor has been enriched with two ancillary active sensors, i.e. a micro rain radar (MRR) and a cloud-Lidar, thus range resolving capability is also achieved.<br /> An inversion algorithm has been implemented to retrieve simultaneously cloud and rain liquid water paths and total water vapour from ADMIRARI's measurements. A long-term data set of linearly polarized brightness temperatures has been collected from field experiments. The slant path integrated values are retrieved via a Bayesian inversion approach based on many atmospheric states by a numerical weather prediction model which build up a-priori probability density function of rainfall profiles. Detailed three-dimensional radiative transfer calculations, which account for the presence of non-spherical particles in preferential orientation, simulate the down-welling brightness temperatures and establish the similarity of radiative signatures and thus the probability that a given profile is actually observed. Long-term measurements demonstrate that the observed brightness temperatures and polarization differences can be well interpreted and reproduced by the simulations.<br /> The quality of the inversion algorithm is evaluated by a simulation-based sensitivity study applied to rainy cases. In a pure radiometric retrieval approach the study indicates an absolute errors characterized by RMSE of 235.3 and 129.1 g/m² for cloud and rain liquid water path respectively, and 1.89 kg/m² for water vapour. Biases are found to be -19.3 g/m² for cloud, 43.3 for rain, and 0.17 kg/m² for water vapour. The retrievals are improved when extra information from the MRR is used.<br /> The inversion algorithm has been applied to long-term measurements from precipitating clouds, with results showing -for the zenith-normalized retrievals- an average statistical error of 1.54 kg/m², 144 g/m² and 52 g/m² for water vapour, cloud and rain liquid water path respectively. Based on these results, long-term estimated distributions of cloud/rain water partitioning for Mid-latitude precipitating clouds are presented for the first time as obtained by a ground-based radiometer. Finally for a case study the rain water path retrieval has been validated by a distinct instrument using an independent method. A systematic error of 68 g/m² (overestimation by ADMIRARI) is found, with a statistical error of 192 g/m²;.