In this paper, we propose a novel forecast method which addresses the difficulty in short-term solar irradiance forecasting that arises due to rapidly evolving environmental factors over short time periods. This involves the forecasting of Global Horizontal Irradiance (GHI) that combines prediction sky images with a Radiative Transfer Model (RTM). The prediction images (up to 10 min ahead) are produced by a non-local optical flow method, which is used to calculate the cloud motion for each pixel, with consecutive sky images at 1 min intervals. The Direct Normal Irradiance (DNI) and the diffuse radiation intensity field under clear sky and overcast conditions obtained from the RTM are then mapped to the sky images. Through combining the cloud locations on the prediction image with the corresponding instance of image-based DNI and diffuse radiation intensity fields, the GHI can be quantitatively forecasted for time horizons of 1–10 min ahead. The solar forecasts are evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE) in relation to in-situ measurements and compared to the performance of the persistence model. The results of our experiment show that GHI forecasts using the proposed method perform better than the persistence model.

A one‐dimensional variational (1DVAR) algorithm was developed, which combines measurements taken by the ground‐based microwave radiometer profiler (MWRP) with the Rapid Refresh (RAP) model to retrieve a more accurate temperature and humidity profiles under clear sky. The passive and active microwave‐vector radiative transfer model was used to simulate brightness temperatures and calculate weighting functions for 22 channels of the MWRP. As MWRP measurements are mainly weighted in the lower atmosphere, a measurement adjustment is performed to reduce the contribution from above 10 km. The results of the 1DVAR algorithm have been compared with the MWRP built‐in neural network (NN) retrievals and radiosonde observations, which show that the 1DVAR method outperforms the NN retrieval both in temperature and water vapor. Our statistical study further shows that the water vapor profiles from RAP are biased higher than the radiosonde observations, while the 1DVAR‐retrieved humidity values show significant improvements throughout the entire 10‐km range. The improvements in temperature profiles occur mainly within the lowest 4‐km atmosphere, while upper level retrievals are mostly influenced by initial values. This is consistent with the characteristics of the Jacobian matrix and the background error covariance matrix. The outcomes of 44 clear‐sky cases show that the maximum mean retrieval error of the 1DVAR algorithm is less than 0.2 K for temperature below 4 km and less than 0.15 g/m³ in water vapor density. These results are shown to be significantly better than the NN retrieval (3.0 K and 1.25 g/m³) and also superior to the RAP reanalysis (0.3 K and 0.5 g/m³).

Cloud liquid water (CLW) and total precipitable water (TPW) are two important parameters for weather and climate applications. Typically, microwave temperature sounding instruments onboard satellites are designed with two low-frequency channels at 23.8 and 31.4 GHz and can be used for retrieving CLW and TPW over global oceans. Since MWTS-III polarization at above two frequencies is uncertain, we must first determine their polarization involved in retrieval algorithms. Through radiative transfer simulation, we found that uses of the quasi-horizontal polarization for MWTS-III can produce smaller biases between observations and simulations and the scan-angle dependence of the biases is also in a general frown pattern, which is similar to ATMS pitch-maneuver observations. After the characterization of MWTS-III polarization, CLW and TPW are derived from Microwave Temperature Sounder (MWTS-III) and are compared with those from ATMS. It is found that CLW and TPW derived from two instruments exhibit a high consistency in terms of their spatial distributions and magnitudes.

This study proposes a fast radiative transfer model, the Advanced Radiative Transfer Modeling System – ground-based (ARMS-gb), designed to simulate brightness temperatures observed by ground-based microwave radiometers. ARMS-gb employs a clear-sky radiative transfer solver to account for atmospheric thermal emissions, while gaseous absorption is estimated using a statistical regression scheme. To enhance simulation accuracy, particularly in moist environments, seven humid profiles from the University of Maryland, Baltimore County 48-profile dataset are added to the European Centre for Medium-Range Weather Forecasts 83-profile dataset to train the gaseous absorption scheme. Additionally, an advanced water vapor vertical interpolation method is incorporated, offering improved accuracy compared to the interpolation method used in Radiative Transfer for TOVS (RTTOV)-gb. The standard deviation is reduced by 0.15 K in channels with strong water vapor absorption. The Jacobians calculated by these two interpolation modes are also different. To further validate ARMS-gb's performance, simulations using both ARMS-gb and RTTOV-gb are compared against real observations from two ground-based microwave radiometers. The observation minus background analyses demonstrates that ARMS-gb aligns well with RTTOV-gb and achieves smaller standard deviations under high-humidity conditions. Furthermore, the capability of ARMS-gb to monitor the observational quality of ground-based microwave radiometers is demonstrated.

The physical and optical properties of aerosols and the change of planetary boundary layer height (PBLH) are of great scientific significance for the study of atmospheric environment and air quality. This paper intends to present integrated observations of aerosol physical‐optical characteristics and PBLH properties under different air pollution levels in Shouxian (National Climatology Observatory (32°26’ N, 116°47’ E)), China. The combined measurements of ground‐based Lidar and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are analyzed from December 2016 to November 2017 in this study. Results show that the extinction coefficients decrease significantly with height below 2 km. The extinction coefficients below 1 km, the aerosol optical depth (AOD), and the PBLH increase with the aggravation of pollution. The daily average aerosol extinction coefficient near the surface is about 0.78 km⁻¹, the AOD is 0.87 ± 0.33 m (mean ± std, the same below), and the PBLH is 827 ± 132 m in polluted conditions (PM_2.5 > 115 μg m⁻³). With the increase in pollution and the decrease in PBLH, more aerosols are accumulated at a lower height. The daily average ratios of AOD below PBLH (AOD_PBLH) to total AOD (AOD_tot) are about 64% in polluted conditions. According to the CALIPSO‐observed aerosol vertical profile, aerosols are identified for about 20.54% of the vertical space below 6 km altitude under polluted conditions. The spherical fine particles with lower particulate depolarization ratio (PDR, 79% of the PDR is <0.2), lower color ratio (CR, 64% of the CR is <0.75), and stronger scattering ability are dominant in 0∼2 km.