The ensemble Kalman filter (EnKF) has been extensively applied in sequential soil moisture data assimilation to improve the land surface model performance and in turn weather forecast capability. Usually, the ensemble size of EnKF is determined with limited sensitivity experiments. Thus, the optimal ensemble size may have never been reached. In this work, based on a series of mathematical derivations, we demonstrate that the maximum efficiency of the EnKF for assimilating observations into the models could be reached when the ensemble size is set to 12. Simulation experiments are designed in this study under ensemble size cases 2, 5, 12, 30, 50, 100, and 300 to support the mathematical derivations. All the simulations are conducted from 1 June to 30 September 2012 over southeast USA (from −90°W, 30°N to −80°W, 40°N) at 25 km resolution. We found that the simulations are perfectly consistent with the mathematical derivation. This optical ensemble size may have theoretical implications on the implementation of EnKF in other sequential data assimilation problems.

Winter wheat is a grass species widely planted in northern and central China, where the increase of aerosols, air pollutants and population density are causing significant reduction in solar irradiance. In order to investigate the adaptation of winter wheat (Triticum aestivum L., cv. Yangmai 13) to low irradiance conditions occurring in the downstream plain of the Yangtze River (China), plants were subjected to four solar irradiance treatments (100%, 60%, 40%, and 20% of environmental incident solar irradiance). Significant increases in chlorophyll (Chl) and xanthophyll (Xan) pigments, and decreases in Chl a/b and Xan/Chl ratios were observed in plants under low light. Light-response curves showed higher net photosynthetic rates (P _N) in fully irradiated plants, that also showed a higher light-compensation point. Shaded plants maintained high values of minimal fluorescence of dark-adapted state (F_o) and maximum quantum efficiency of PSII photochemistry (F_v/F_m) that assess a lower degree of photoinhibition under low light. Reduced irradiance caused decreases in effective quantum yield of PSII photochemistry (Φ_PSII), electron transport rate (ETR), and nonphotochemical quenching coefficient (q_N), and the promotion of excitation pressure of PSII (1 − q_P). The activities of the antioxidant enzymes superoxide dismutase and peroxidase were high under reduced light whereas no light-dependent changes in catalase activity were observed. Thiobarbituric acid reactive species content and electrolyte leakage decreased under shaded plants that showed a lower photooxidative damage. The results suggest that winter wheat cv. Yangmai 13 is able to maintain a high photosynthetic efficiency under reduced solar irradiance and acclimates well to shading tolerance. The photosynthetic and antioxidant responses of winter wheat to low light levels could be important for winter wheat cultivation and productivity.

A global Soil Moisture Operational Product System (SMOPS) has been developed to process satellite soil moisture observational data at the NOAA National Environmental Satellite, Data, and Information Service for improving numerical weather prediction (NWP) models at the NOAA National Weather Service (NWS). A few studies have shown the benefits of assimilating satellite soil moisture data in land surface models (LSMs), which are the components of most NWP models. In this study, synthetic experiments are conducted to determine how soil moisture data quality control may impact the benefit of their assimilation into LSMs. It is found that using green vegetation fraction to quality control the SMOPS soil moisture product may significantly increase the benefit of assimilating it into Noah LSM in terms of increasing the agreement of Noah LSM surface and root zone soil moisture simulations with the corresponding in situ measurements. The quality control procedures and parameters are suggested for the assimilation of SMOPS data into NWS NWP models.

This study analyzed the spatiotemporal variations in PM_2.5 and O₃, and explored their interaction in the summer and winter seasons in Beijing. To this aim, hourly PM_2.5 and O₃ data for 35 air quality monitoring sites were analyzed during the summer and winter of 2016. Results suggested that the highest PM_2.5 concentration and the lowest O₃ concentration were observed at traffic monitoring sites during the two seasons. A statistically significant (p < 0.05) different diurnal variation of PM_2.5 was observed between the summer and winter seasons, with higher concentrations during daytime summer and nighttime winter. Diurnal variations of O₃ concentrations during the two seasons showed a single peak, occurring at 16:00 and 15:00 in summer and winter, respectively. PM_2.5 presented a spatial pattern with higher concentrations in southern Beijing than in northern areas, particularly evident during wintertime. On the contrary, O₃ concentrations presented a decreasing spatial trend from the north to the south, particularly evident during summer. In addition, we found that PM_2.5 concentrations were positively correlated (p < 0.01, r = 0.57) with O₃ concentrations in summer, but negatively correlated (p < 0.01, r = −0.72) with O₃ concentrations in winter.

Ground-level ozone pollution in Beijing has been causing concern among the public due to the risks posed to human health. This study analyzed the temporal and spatial distribution of, and investigated population exposure to, ground-level ozone. We analyzed hourly ground-level ozone data from 35 ambient air quality monitoring sites, including urban, suburban, background, and traffic monitoring sites, during the summer in Beijing from 2014 to 2017. The results showed that the four-year mean ozone concentrations for urban, suburban, background, and traffic monitoring sites were 95.1, 99.8, 95.9, and 74.2 μg/m³, respectively. A total of 44, 43, 45, and 43 days exceeded the Chinese National Ambient Air Quality Standards (NAAQS) threshold for ground-level ozone in 2014, 2015, 2016, and 2017, respectively. The mean ozone concentration was higher in suburban sites than in urban sites, and the traffic monitoring sites had the lowest concentration. The diurnal variation in ground-level ozone concentration at the four types of monitoring sites displayed a single-peak curve. The peak and valley values occurred at 3:00–4:00 p.m. and 7:00 a.m., respectively. Spatially, ground-level ozone concentrations decreased in gradient from the north to the south. Population exposure levels were calculated based on ground-level ozone concentrations and population data. Approximately 50.38%, 44.85%, and 48.49% of the total population of Beijing were exposed to ground-level ozone concentrations exceeding the Chinese NAAQS threshold in 2014, 2015, and 2016, respectively.