Several important improvements have been made in recent Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol retrievals, but few regional validations are conducted. We compared Deep Blue (DB) and Dark Target (DT) retrievals over China and evaluated their performance with ground observations. Sampling frequency of DB was much higher than that of DT, which was mainly caused by unavailable retrieval of DT in bright surface and heavy pollution conditions. It is found that merged aerosol optical depth (AOD) will miss some cases if it was determined only by vegetation density. Collocated comparison shows that DT AOD was substantially higher than DB with seasonal difference exceeding 0.3–0.4 over eastern China. However, when all available retrievals were considered, DT AOD was obviously lower than DB in all the seasons except spring due to their large difference in spatial coverage in high‐AOD conditions. DB can well reveal spatial extent of the widespread haze pollution while there were few values in DT. More than one half of the situations with AOD > 1.0 was missed by DT, which largely underestimated the common regional haze pollution in eastern China. Ground validations show that DT tended to overestimate the aerosol loading by underestimation of the surface contribution and single scattering albedo of aerosols. DB performed generally well with higher accuracy in northern China but exhibited obvious underestimation in northwestern and southern China. Despite of slight decrease in accuracy, DB retrievals with all quality enable a large increase in spatial coverage, especially when dense haze clouds existed.

In the past decades, continuous efforts have been made at a national level to reduce Nitrogen Dioxide (NO₂) emissions in the atmosphere over China. However, public concern and related research mostly deal with tropospheric NO₂ columns rather than ground-level NO₂ concentrations, but actually ground-level NO₂ concentrations are more closely related to anthropogenic emissions, and directly affect human health. This paper presents one method to derive the ground-level NO₂ concentrations using the total column of NO₂ observed from the Ozone Monitoring Instrument (OMI) and the simulations from the Community Multi-scale Air Quality (CMAQ) model in China. One year’s worth of data from 2014 was processed and the results compared with ground-based NO₂ measurements from a network of China’s National Environmental Monitoring Centre (CNEMC). The standard deviation between ground-level NO₂ concentrations over China, the CMAQ simulated measurements and in-situ measurements by CNEMC for January was 21.79 μg/m³, which was improved to a standard deviation of 18.90 μg/m³ between our method and CNEMC data. Correlation coefficients between the CMAQ simulation and in-situ measurements were 0.75 for January and July, and they were improved to 0.80 and 0.78, respectively. Our results revealed that the method presented in this paper can be used to better measure ground-level NO₂ concentrations over China.

Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] 3BV42, PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks] CDR, GSMaP [Global Satellite Mapping of Precipitation] RENALYSIS, CMORPH [Climate Prediction Center’s morphing technique] BLD and CMORPH_RAW) were compared with in situ measurements over China for the period of 2005 to 2013. To completely evaluate these precipitation products, the annual, seasonal and monthly precipitation averages were calculated. Overall, the Huaihe River and Qinlin mountains are shown to have heavy precipitation to the southeast and lighter precipitation to the northwest. The comparison results indicate that Gauge correction (CMORPH_BLD) improves the quality of the original satellite products (CMORPH_RAW), resulting in the higher correlation coefficient (CC), the low relative bias (BIAS) and root mean square error (RMSE). Over China, the GSMaP_RENALYSIS outperforms other products and shows the highest CC (0.91) and lowest RMSE (0.85 mm/day) and all products except for PERSIANN_CDR exhibit underestimation. GSMaP_RENALYSIS gives the highest of probability of detection (81%), critical success index (63%) and lowest false alarm ratio (36%) while TRMM3BV42 gives the highest of frequency bias index (1.00). Over Tibetan Plateau, CMORPH_RAW demonstrates the poorest performance with the biggest BIAS (4.2 mm/month) and lowest CC (0.22) in December 2013. GSMaP_RENALYSIS displays quite consistent with in situ measurements in summer. However, GSMaP_RENALYSIS and CMORPH_RAW underestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer.

Onboard the Chinese GaoFen-5 (GF5) satellite, the Environmental trace gases Monitoring Instrument (EMI) is a nadir-viewing wide-field spectrometer that was launched on May 9, 2018. EMI measures the back-scattered earthshine solar radiance in the ultraviolet and visible spectral range. By using the differential optical absorption spectrometry (DOAS) method and the EMI measurements in the VIS1 band (405–465 nm), we performed retrievals of NO₂. Some first retrieval results of NO₂ from EMI and a comparison with OMI and TROPOMI products are presented in this paper. The monthly mean total vertical column densities (VCD) of NO₂ show similar spatial distributions to OMI and TROPOMI (r > 0.88) and their difference is less than 27%. A comparison of the daily total VCD shows that EMI could detect the NO₂ patterns in good agreement with OMI (r = 0.93) and TROPOMI (r = 0.95). However, the slant column density (SCD) uncertainty (0.79 × 10¹⁵ molec cm⁻²) of the current EMI algorithm is relatively larger than OMI. The daily variation pattern of NO₂ from EMI in Beijing in January 2019 is consistent with TROPOMI (r = 0.96). The spatial distribution correlation of the tropospheric NO₂ VCD of EMI with OMI and TROPOMI is 0.88 and 0.89, respectively, but shows an overestimate compared to OMI (15%) and TROPOMI (23%), respectively. This study demonstrates the capability of using EMI for global NO₂ monitoring.