Blowing snow is a common weather phenomenon in Antarctica and plays an important role in the water vapor cycle and ice sheet mass balance. Although it has a significant impact on the climate of Antarctica, people do not know much about this process. Fog events are difficult to distinguish from blowing snow events using existing detection algorithms by a ceilometer. In this study, based on ceilometer, the meteorological parameters observed by surface meteorology systems are further combined to detect blowing snow and fog using the AdaBoost algorithm. The weather phenomena recorded by human observers are ‘true’. The dataset is collected from 1 January 2016 to 31 December 2016 at the AWARE site. Among them, three-quarters of the data are used as the training set and the rest of the data as the testing set. The classification accuracy of the proposed algorithm for the testing set is about 94%. Compared with the Loeb method, the proposed algorithm can detect 89.12% of blowing snow events and 76.10% of fog events, while the Loeb method can only identify 64.29% of blowing snow events and 31.87% of fog events.

A new cloud-base height (CBH) inversion algorithm based on infrared hyperspectral radiation using a machine learning algorithm is proposed in this paper. We use the LBLRTM and DISORT model for forward research. The minimal-redundancy-maximal-relevance (mRMR) algorithm is used to extract the sensitive channels of CBH as the feature vectors. The CBHs measured by Vaisala CL31 ceilometer (VCEIL) are taken as the reference values. The artificial neural network (ANN) method with two hidden layers of 50 and 10 respective is applied to construct the mapping relationship between AERI radiation and CBH (ANN-CBH algorithm). The dataset is collected during the period from January 2012 to December 2017 at the ARM SGP site and NSA site. Among them, the data from 2012 to 2014 are used as the training set, while the data of 2015, 2016, 2017 of each site are respectively used as the testing set. Compared with the traditional physical algorithm, the ANN-CBH algorithm has higher accuracy. The correlation coefficients (CCs) between the inversion results of CBH from the ANN-CBH algorithm and the measurement results of the VCEIL are about 0.9 at SGP site and 0.85 at NSA site, while the CC of the CBH inversion results between CO2 slicing algorithm and VCEIL is only about 0.7 and 0.65, respectively. In addition, the experimental results indicate that the ANN-CBH algorithm is less affected by precipitable water vapor (PWV).

The cloud phase is one of the most important parameters of clouds. In this paper, we propose a method for cloud phase classification that synergistically utilizes the far- and thermal-infrared bands based on the Atmospheric Emitted Radiance Interferometer (AERI) at the Atmospheric Radiation Measurement West Antarctic Radiation Experiment (AWARE) observatory in 2016. The possible features in the far- and thermal-infrared bands are analyzed based on the differences in the simulated cloud brightness temperature (BT) spectra with different cloud phases. Using the support vector machine (SVM) algorithm, four features are determined to identify the cloud phase, which include the BT at 900 cm⁻¹, the slope of the fitted function of BT in the 900–1000 cm⁻¹ interval, the BT difference (BTD) between 512 cm⁻¹ and 726 cm⁻¹, and the BTD between 550 cm⁻¹ and 726 cm⁻¹. Here, the performance of the proposed method is evaluated with Shupe’s and Turner’s method. The monthly average accuracy of the proposed method, the method without the two far-infrared features, and Turner’s method are about 76%, 36%, and 49%, respectively, which infer the good performance of the proposed method and also indicate that the far-infrared band features can effectively enhance cloud phase classification. It is notable that, compared to Shupe’s method, the accuracy for the proposed method is only 61% during the Antarctic summer, which results from the definitions of cloud phase and radiative effect. In addition, the accuracy is only 44% for Turner’s method in seasons with a low frequency of mixed clouds due to the significant effect of water vapor.