Accurate identification of cloud phase in the Arctic is critical for evaluating surface energy budgets and reducing uncertainties in climate models, as clouds exert a complex, warming influence highly sensitive to phase partitioning amidst amplified warming. The submillimeter and infrared radiation exhibit distinct sensitivities toward hydrometeors in different cloud phases. In this study, the performance of synergistic observations of submillimeter and infrared spectrum for cloud identification and phase classification in the Arctic is explored, through sensitivity analysis and classification accuracy analysis. The spectral variances between the submillimeter and infrared bands under the scenarios of clear sky, ice cloud, liquid water cloud, and mixed-phase cloud are analyzed by quantifying the disparities in the observed spectra. The sensitivity analysis reveals that the synergistic metrics constructed by synergistic channels can distinguish clouds from clear skies or identify cloud thermodynamic phases. To quantitatively estimate the classification performance of the combined spectrum, a synergistic classification algorithm is constructed based on the Random Forest framework, and then trained and tested by the simulated synergistic observation datasets in the Arctic. Results from assessment metrics revealed that the overall accuracy of the classification model reaches 91.35%. Especially for clear skies and ice clouds, the classification accuracy is 99.43% and 92.23%, respectively. While for mixed-phase clouds, the overall classification accuracy reaches 87.34%. Specifically, ice-over-water clouds demonstrate 89.40% classification accuracy, while water-over-ice clouds achieve lower accuracy of 85.52%, reflecting fundamental differences in their thermodynamic stability and radiometric signatures. Results provide a robust statistical foundation for advanced cloud detection and phase classification algorithms, demonstrating notable improvements in classification accuracy by synergistic channels. With the upcoming spaceborne submillimeter and infrared passive sensors, the results demonstrate a pressing need and the potential of combining observations to better understand cloud phase and evolution in the future.

Atmospheric temperature and humidity profiles are the basic parameters used to describe the vertical distribution of atmospheric states. Continuous observations of accurate temperature and humidity profiles are essential for exploring boundary layer thermal and dynamic characteristics. To this end, an intelligent retrieval algorithm (IReA) based on a convolutional neural network (CNN) is proposed to retrieve atmospheric temperature and humidity profiles by combining observations from ground-based infrared hyperspectral radiometers and microwave radiometers (MWRs). The results show that the inclusion of microwave observations can effectively improve the retrieval accuracy of temperature and humidity profiles relative to the results from atmospheric emitted radiance interferometer (AERI) under clear-sky conditions, where the root mean square error (RMSE) of the temperature profile is 0.79 K and the RMSE of the humidity profile is 0.95 g/kg. The accuracies of different retrieval methods are also evaluated. In general, the RMSE derived from IReA is improved by at least 9% compared to the results from the physical retrieval method and BP neural network method. Given that clouds are semitransparent in the microwave region, the retrieval accuracy of the temperature and humidity profile of IReA are also improved under cloudy conditions when microwave observations are introduced.

Accurately measuring the phase of Arctic clouds is of great significance for understanding the process of Arctic climate change. A novel algorithm for classifying Arctic cloud phase is constructed based on ship-based remote sensing measurements during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Based on complementary information by Ka-Band ARM zenith radar (KAZR), high spectral resolution lidar (HSRL), microwave radiometer (MWR), and temperature soundings, cloud pixels in the vertical direction can be classified as clear sky, aerosol, ice, mixed-phase, liquid, drizzle, rain, and snow. The data set collected from September 2019 to September 2020 is analyzed. Results indicate that the proposed method can effectively distinguish Arctic mixed-phase clouds with low depolarization ratio from liquid clouds. Even if the mixed-phase clouds contain the lower concentration of ice crystals, the proposed algorithm can accurately identify them. Simultaneously, the cloud phase classification is less affected by environmental factors. It provides a reference for the subsequent cloud microphysical retrievals, reducing significant deviations in droplet microphysical retrievals caused by misidentifying mixed-phase cloud as liquid cloud. The algorithm proposed in this paper makes appropriate modifications to cloud classification boundaries and can also be applied to other sites.

The submillimeter and microwave radiometers exhibit distinct sensitivities toward hydrometeor in different cloud phases. This work aimed to investigate the application of the synergistic observations of spaceborne submillimeter and microwave radiometers in cloud detection and thermodynamic phase classification. The spectral variances between the submillimeter and microwave bands under the scenarios of clear sky, ice-phase cloud, liquid-phase cloud, and mixed-phase cloud were highlighted by quantifying the disparities in the observed spectra. Cloud detection and phase classification models based on random forests were trained and tested by the simulated synergistic observation datasets. Results from assessment metrics revealed that the overall accuracy of the classification model incorporating all features was 88.75%. Feature importance analysis provided the key metrics for cloud phase classifications, classification models trained with these simplified combinations of channels, and metrics achieved an accuracy of 86.25%. The results demonstrated the feasibility of combining submillimeter and microwave radiometer observations for detection and phase classification.

The significance of rainfall detection is generally acknowledged, and the linked opportunistic approach to giving it new momentum has also been extensively demonstrated. This study conducted a rainfall monitoring experiment using two stationary smartphones in a light indoor setting, receiving downlink signals from a long-term evolution (LTE) base station for two months. The analysis reveals that the received signals are not stable during dry periods, but the reference signal receiving power (RSRP) and reference signal strength indicator (RSSI) parameters can produce a certain degree of degradation during rainfall. To address the concern about frequent switching of connection to the base station resulting in different fluctuation levels, the standardized standard deviations of the four signaling parameters for different time windows were extracted as features to build a dry-rainy classification model. Furthermore, a rain rate class identification model is also established based on the extraction of the specific rain-induced attenuation and standardized standard deviation. The experiment mentioned has shown promising results in rainfall monitoring based on widely available opportunistic signal sources from wireless terminals.

In this paper, a novel adaptive rain-induced attenuation-rain rate (A-R) relationship model is proposed, which is built based on a clustering algorithm to improve the accuracy of rainfall inversion based on commercial microwave link (CML). The performance of this adaptiveA-Rrelationship is systematically evaluated at eight common CML operating frequencies with five years of raindrop size distribution data from a PARSIVEL disdrometer. The results show that the overall single-frequency adaptiveA-Rrelationship has a better performance than the ITU-R and local fittingA-Rrelationships (lower root mean square error, relative bias (an improvement of around 10% at several of the frequencies), and significant unbiasedness). In addition, the effect of cluster number on theA-Rrelationship and the performance of dual-frequency or dual-polarization adaptiveA-Rrelationships are also discussed. Finally, the relative errors of theseA-Rrelationships on the inversion of cumulative rainfall are depicted to visualize the performance, which demonstrate that certain single/dual-frequency adaptiveA-Rmodels have good performance in most cases.

Improving the accuracy of measuring ice cloud properties is crucial for the study of atmospheric circulation and climate models, and for understanding the radiative forcing effects of ice clouds. In this study, a novel approach to retrieve ice cloud microphysics involving synergistically analyzing the spectra of submillimeter (sub-mm) and infrared (IR) is proposed, combining the complementary information regarding ice cloud properties from each spectrum. The sensitivity of the synergistic channel pairs to ice water paths (IWPs) and mean mass diameters is thoroughly investigated by the synthetic lookup tables and sensitivity parameter analysis. A synergistic retrieval algorithm based on Quantile Regression Neural Networks is constructed toward a better evaluation of the retrieval biases and uncertainties quantitatively. The simulated retrieval results reveal that the synergistic retrievals outperform the results from individual spectra across the full range of IWP from 1 to 1000 g/m2 and mean mass diameter from 1 to$500~\mu $m. Specifically, the mean root-mean-square-error of the synergistic retrievals for IWP is 68% (95%) lower than that of the sub-mm-only (IR-only) retrievals, and a 10% (24%) lower root-mean-square-error for mean mass diameter, respectively. In addition, the synergy can improve the correlation for IWP by 3.7% (5.6%) and yields a 12.5% (17.6%) higher correlation for mean mass diameter compared to the sub-mm-only (IR-only) retrievals. With the upcoming spaceborne sub-mm and IR passive sensors, the results demonstrate a pressing need and the potential of combining observations to better understand ice cloud properties in the future.

A retrieval methodology based on the Bayesian neural network (BNN) is presented that inverts the ice water path (IWP), mean mass-weighted diameter (Dme), and cloud height of ice clouds from sub-millimeter radiometer observations. The training dataset was created using collecting cloud profiles from the DARDAR (raDAR/liDAR) database and running simulations by the Atmospheric Radiative Transfer Simulator (ARTS) model. Since the effective radius (re) is the size descriptor of ice particles in the DARDAR database, a look-up table of ice water content (IWC), Dme, and rewas constructed to convert reprofiles into Dmeprofiles. In addition, random noises corresponding to the measurement uncertainties of the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) during the TC4 experiment were added to the simulated brightness temperatures before training the BNN. The proposed retrieval method was first applied to the simulated testing database, and then to the observations of CoSSIR. Moreover, the retrieved IWP and Dmewere compared to the retrievals of the Bayesian Monte Carlo Integration (BMCI) method. The retrieved cloud height was assessed by cloud height extracted from the reflectivity data of the Cloud Radar System (CPS) flow on the same aircraft with CoSSIR. The comparison showed that the correlation coefficients of the retrieved IWP and Dmefrom the two methods are above 0.8, and the retrieved cloud height also showed good agreement with that extracted from the CPS.

Microwave links as a novel method of rainfall monitoring have been extensively studied over the last two decades. Still, the applicability of$\gamma $(rain-induced attenuation rate)–$R$(rain rate) relationship in quantitative rainfall inversion with microwave link under tilted conditions has not been systematically investigated to date. With the establishment of rain-induced attenuation fields based on multiple sets of measured raindrop spectral profile data from micro-rain radar, microwave links under various tilted conditions are simulated and the performance of$\gamma - R$relationship in inversion of rain rate is theoretically analyzed. Afterward, the effects of link height, tilt angle, and rainfall reference point variations on the rainfall inversion performance are discussed in depth from the theoretical and data analysis perspectives. Furthermore, the connection between the study and the satellite-ground link-based rainfall measurement technique is mapped. Finally, the applicability analysis to different regions and the introduction of errors is also investigated.

Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7. Compared with the PBLHs derived from the ceilometer, it can be found that the new method is more stable and less affected by clouds.

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).