Radiosonde data collected from 83 stations in China from January to December 2012 were used to evaluate Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) and thermal infrared (IR) total precipitable water vapour (PWV) products. The results indicate that MODIS NIR PWV products shows better agreement with radiosonde data than with IR PWV products, with the correlation coefficients up to 0.95. The root mean square errors (RMSEs) of NIR PWV range from 2 to 8 mm with different stations, which shows significant regional differences over China. The mean RMSE is about 5.03 mm (~35%) with a positive deviation of 2.56 mm (~18%), indicating the occurrence of a slight overestimation. Moreover, MODIS IR PWV during night-time has a better agreement with radiosonde PWV than that during daytime. The mean RMSE of IR PWV during daytime was ~6.02 mm (~42%), with a positive deviation of 1.54 mm (~11%). The mean RMSE of IR PWV during night-time was ~5.81 mm (~40%), with a negative deviation of approximately −0.04 mm (~0.25%). Both the NIR and IR PWV products during daytime tend to be higher than radiosonde PWV.

In this unique processing study, the mixing state of ambient submicron aerosol particles in terms of hygroscopicity and volatility was investigated with a Hygroscopicity Tandem Differential Mobility Analyzer and a Volatility Tandem Differential Mobility Analyzer. The measurements were conducted at a regional atmospheric observational site in the North China Plain (NCP) from 8 July to 9 August, 2013. Multimodal patterns were observed in the probability density functions of the hygroscopicity parameter κ and the shrink factor, indicating that ambient particles are mostly an external mixture of particles with different hygroscopicity and volatility. Linear relationships were found between the number fraction of hydrophobic and non-volatile populations, reflecting the dominance of soot in hydrophobic and non-volatile particles. The number fraction of non-volatile particles is lower than that of hydrophobic particles in most cases, indicating that a certain fraction of hydrophobic particles is volatile. Distinct diurnal patterns were found for the number fraction of the hydrophobic and non-volatile particles, with a higher level at nighttime and a lower level during the daytime. The result of air mass classification shows that aerosol particles in air masses coming from north with high moving speed have a high number fraction of hydrophobic/non-volatile population, and are more externally mixed. Only minor differences can be found between the measured aerosol properties for the rest of the air masses. With abundant precursor in the NCP, no matter where the air mass originates, as far as it stays in the NCP for a certain time, aerosol particles may get aged and mixed with newly emitted particles in a short time

This study proposed an instantaneous summer air temperature (T_air) estimation model using the Himawari-8 Advanced Himawari Imager (AHI) brightness temperatures (BTs) in split-window channels and other auxiliary data. Correlation analysis and stepwise linear regression were used to select the predictors for T_air estimation. Nine predictors such as AHI BTs in channels 14 and 15, elevation, precipitable water vapor (PWV), and relative humidity (RH) were finally selected. Stepwise linear regression and neural network (NN) methods were applied to construct summer T_air estimation models over China, respectively. The estimated T_air by linear and NN models was evaluated using the observed T_air from 272 meteorological stations over China. The results showed that AHI BTs in channels 14 and 15, elevation, PWV, and RH were more important for T_air estimation than other predictors. The accuracy of the NN models was better than the linear models. The correlation coefficient (R), root mean square error (RMSE), and bias were 0.97, 1.72°C, and 0.04°C, respectively, for the NN model and were 0.89, 3.28°C, and 0.07°C, respectively, for the linear model. About 75.6% of the T_air differences by the NN model were within 2.0°C, and even 45.8% were within 1.0°C. The performance of the T_air estimation model for each site was also investigated, and the accuracy of T_air estimation in southeast China is better than northwest China.

New particle formation is one of the major sources of atmospheric aerosol particles. Beside daytime nucleation, nocturnal new particle formation was also found in different regions around the world. Compared with daytime nucleation events, the understanding of nocturnal ones is still sparse. The variety of aerosol particle physico-chemical properties, including particle number size distribution, volatility and hygroscopicity were measured in the North China Plain during July–August 2013. During the observation period, rapid increase in ultrafine particle number concentration was attributed to new particle formation. The nocturnal new particle formation rate was 45 cm^–3s^–1, which is 1.25 times higher than an observed daytime value. Condensation sink was found to be 0.055 s^–1.

Precipitable water vapour (PWV) plays an important role in weather prediction, hydrological cycles and climate change. In this study, a physics-based algorithm was proposed to retrieve PWV over land using satellite passive microwave observations near 165.50 GHz and 183.31 GHz, which have higher spatial resolution and lower sensitivity to land surface emissivity than low-frequency channels. The algorithm uses a perturbation formulation of simplified radiative transfer equation to derive PWV and at-surface brightness temperature (BT) in 165.50 GHz simultaneously. It directly derives the at-surface BT instead of land surface temperature, so the algorithm needs no prior information of surface emissivity. Sensitivity analysis and retrieval experiments from simulated data were carried out for Advanced Technology Microwave Sounder (ATMS) onboard the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite. The results showed that the algorithm using ATMS channels 17, 18 and 19 measurements can significantly reduce the PWV retrieval errors caused by surface emissivity uncertainty. The coefficient of determination (R ²), root-mean-square error (RMSE), and bias of the retrieved PWV from ATMS data by the proposed algorithm and SuomiNet Global Positioning System (GPS) PWV were 0.90, 0.43 cm and −0.02 cm, respectively. In addition, the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud products were used to evaluate the effects of cloud on the PWV retrievals. The results showed that the presence of cloud does not decrease the accuracy of the PWV retrievals.