Ground-based multi-channel microwave radiometers (MWRs) can continuously detect atmospheric profiles in the tropospheric atmosphere. This makes MWR an ideal tool to supplement radiosonde and satellite observations in monitoring the thermodynamic evolution of the atmosphere and improving numerical weather prediction (NWP) through data assimilation. The analysis of product characteristics of MWR is the basis for applying its data to real-time monitoring and assimilation. In this paper, observations from the latest generation of ground-based multi-channel MWR RPG-HATPRO-G5 installed in Shanghai, China, are compared with the radiosonde observations (RAOB) observed in the same location. The detection performance, characteristics of various channels, and the accuracy of the retrieval profile products of the MWR RPG are comprehensively evaluated during various weather conditions. The results show that the brightness temperatures (BTs) observed by the ground-based MWR RPG during precipitation conditions were high, which affected its detection performance. The bias and the standard deviation (SD) between the BT observed by MWR RPG and the simulated BT during clear and cloudy sky conditions were slight and large, respectively, and the coefficient of determination (R2) was high and low, respectively. However, when the cloud liquid water (CLW) information was added when simulating BT, the bias and the SD of the observed BT and the simulated BT during cloudy days were reduced and the R2 value improved, which indicated that CLW information should be taken into account when simulating BT during cloudy conditions. The temperature profiles of the MWR retrieval had the same accuracy of RMSEs (root-mean-square error) with heights during both clear-sky and cloudy sky conditions, where the RMSEs were below 2 K when the heights were below 4 km. In addition, the MWR RPG has the potential ability to retrieve the temperature inversion in the boundary layer, which has important application value for fog and air pollution monitoring.

Abstract. To recover the actual responsivity for Ultraviolet Multi-Filter Rotating Shadowband Radiometer (UV-MFRSR), the complex (e.g. unstable, noisy, and with gaps) time series of its in-situ calibration factors (Vo) need to be smoothed. Many smoothing techniques require accurate input uncertainty of the time series. A new method is proposed to estimate the dynamic input uncertainty by examining overall variation and subgroup means within a moving time window. Using this calculated dynamic input uncertainty within Gaussian Process regression (GP) provides the mean and uncertainty functions of the time series. This proposed GP solution was first applied on a synthetic signal and showed significant smaller RMSEs than a Gaussian Process regression performed with constant values of input uncertainty and the mean function. GP was then applied to three UV-MFRSR Vo time series at three ground sites; The method appropriately accounted for variation in slopes, noises, and gaps at all sites. The validation results demonstrated that the agreement between aerosol optical depths (AODs) calculated using Vo determined by the GP mean function and AERONET AODs were consistently better than those calculated using Vo from standard techniques (e.g. moving average). The improved accuracy of in-situ UVMRP Vo values suggests the GP solution is a robust technique for accurate analysis of complex time series and may be applicable to other fields.