On the Sun’s distance from the center and the shape of the inner halo in the Galaxy: Gaia EDR3, HST and literature globular clusters

A statistical method is used to derive both the Sun’s distance $r_0$ from the Galactic Center (GC) and the 3D geometry of the inner ($<$ 25 kpc) halo. The spatial distribution of the 138 Gaia EDR3 globular clusters (GCs) with distances established on a combination of HST and literature data of Baumgardt and Vasiliev (2021) is explored. An estimate by using these ancient objects of the pressure-supported subsystem of the Galaxy with newly derived distances leads to the mean $r_0=7.81\pm 0.14$ kpc. The distribution of GCs within 25 kpc is almost spherically symmetric, and has the shape of an ellipsoid with a major axis of its symmetry slightly elongated toward the Sun and two minor axes of almost the same length. The obtained scale-length ratio of the major axis to the minor axis in the plane and to the vertical axis of the ellipsoid is $\approx$ 1:0.8:0.7. Based on the papers of a series, for practical use we argue to employ the following Sun’s distances from the GC and the plane: $r_0=8.15\pm 0.15$ kpc and $z_0=15\pm 5$ pc.     

适用于沙坝上Bragg反射的二阶Boussinesq方程数学模型及其数值验证

在二阶 Boussinesq 方程基础上,通过引入含水深导数项对该方程进行了理论上的改进,使得该方程在应用于无限沙坝 Bragg反射问题时与理论解析解在更大范围内符合.基于该改进的高阶 Boussinesq 方程,在非交错网格下建立了混合 4 阶的Adams-Bashforth- Moulton 格式的数学模型.将数值模型应用到有限个连续沙坝上波浪传播变形问题的数值模拟中,通过两点法给出数值波浪反射系数,将这些反射系数与已有的实验数据进行对比,对比表明改进后的模型计算出的反射系数与实验结果吻合更好,这验证了本文理论改进的有效性.     

Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations

As most air quality monitoring sites are in urban areas worldwide, machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants. The bias stems from the issue of dataset shift, as the density distributions of predictor variables differ greatly between urban and rural areas. We propose a data-augmentation approach based on the multiple imputation by chained equations (MICE-DA) to remedy the dataset shift problem. Compared with the benchmark models, MICE-DA exhibits superior predictive performance in deriving the spatiotemporal distributions of hourly PM_2.5 in the megacity (Chengdu) at the foot of the Tibetan Plateau, especially for correcting the estimation bias, with the mean bias decreasing from –3.4 µg/m³ to –1.6 µg/m³. As a complement to the holdout validation, the semi-variance results show that MICE-DA decently preserves the spatial autocorrelation pattern of PM_2.5 over the study area. The essence of MICE-DA is strengthening the correlation between PM_2.5 and aerosol optical depth (AOD) during the data augmentation. Consequently, the importance of AOD is largely enhanced for predicting PM_2.5, and the summed relative importance value of the two satellite-retrieved AOD variables increases from 5.5% to 18.4%. This study resolved the puzzle that AOD exhibited relatively lower importance in local or regional studies. The results of this study can advance the utilization of satellite remote sensing in modeling air quality while drawing more attention to the common dataset shift problem in data-driven environmental research.