Due to the polarization effects of the Earth's surface reflection and atmospheric particles' scattering, high-precision retrieval of atmospheric parameters from near-infrared satellite data requires accurate vector atmospheric radiative transfer simulations. This paper presents a near-infrared vector radiative transfer model based on the doubling and adding method. This new model utilizes approximate calculations of the atmospheric transmittance, reflection, and solar scattering radiance for a finitely thin atmospheric layer. To verify its accuracy, the results for four typical scenarios(single molecular layer with Rayleigh scattering, single aerosol layer scattering, multi-layer Rayleigh scattering, and true atmospheric with multi-layer molecular absorption, Rayleigh and aerosol scattering) were compared with benchmarks from a well-known model. The comparison revealed an excellent agreement between the results and the reference data, with accuracy within a few thousandths. Besides, to fulfill the retrieval algorithm, a numerical differentiation-based Jacobian calculation method is developed for the atmospheric and surface parameters. This is coupled with the adding and doubling process for the radiative transfer calculation. The Jacobian matrix produced by the new algorithm is evaluated by comparison with that from the perturbation method. The relative Jacobian matrix deviations between the two methods are within a few thousandths for carbon dioxide and less than 1.0×10~(-3)% for aerosol optical depth. The two methods are consistent for surface albedo, with a deviation below 2.03×10~(-4)%. All validation results suggest that the accuracy of the proposed radiative transfer model is suitable for inversion applications. This model exhibits the potential for simulating near-infrared measurements of greenhouse gas monitoring instruments. 相似文献
Acta Geochimica - In order to obtain Pb content in soil quickly and efficiently, a multivariate linear regression (MLR) and a principal component regression (PCR) Pb content estimation model were... 相似文献
To achieve accurate evaluation of evapotranspiration of reference crops (ET0) in Jiangxi, China, in the absence of systematic climatological data, with reference to the FAO-56 Penman–Monteith (P-M) equation, the Priestley-Taylor (P–T) method, the Makkink method, the Hargreaves-Samani (H–S) method, the Irmak-Allen (I-A) method, the Penman1948 (48PM) method, the Penman-Van Bavel (PVB) method, the Baier-Robertson (B-R) method, the improved Baier-Robertson (M-B-R) method, the Schendel (Sch) method, the Turc method, the Jensen-Haise (J-H) method, and the Brutsaert-Stricker (B-S) method were used to evaluate the daily climatological data collected by 26 weather stations in Jiangxi, China, and 17 weather stations in adjacent provinces. The results were compared with each other and parameter rate determination was conducted. The results indicated that the Turc method exhibited optimized applicability before parameter rate determination and the average root mean square error (RMSE) and the average normalized root mean square error (NRMSE) by this method were 0.39 mm/d and 0.157 mm, respectively. However, parameter rate determination led to negligible improvement in accuracy for this method. The Turc method could be directly applied in Jiangxi (except Nanchang). For special distribution of error after parameter rate determination, all methods exhibited significant errors in Northern Jiangxi. Herein, the 48PM method and the B-S method showed good applicability after parameter rate determination and RMSE and NRMSE of data by these methods ranged in 0.06 ~ 0.34 mm/d and 0.08 ~ 0.27, 8 ~ 27%, respectively, and their d-indices were close to 1. The annual over-estimations in weather stations in Jiangxi were below 30 mm. In the absence of data about relative humidity and wind speed, the P–T method was an appropriate simplified method for Jiangxi. In this case, α was slightly lower than the default value (1.05 ~ 1.18), RMSE was within 0.21 ~ 0.66 mm/d, and NRMSE was within 0.08 ~ 0.308 ~ 30%. Accuracy of RMSE, d-index, and NRMSE of data by the P–T method, the I-A method, and the PVB method was consistent with all stations, while that by the Mak method was slightly lower, which could be attributed to severe over-estimation in July and August. RMSE of the H–S method, the B-R method, the M-B-R method, the J-H method, and the Sch method were above 0.75 mm/d and these methods were not suitable for accurate evaluation of ET0 in Jiangxi, China. The annual ET0 was calculated by various methods (except the 48PM method and the B-S method) exhibited significant variation around 2003. This may be attributed to significant changes in certain meteorological factors over recent years.
Climate change caused by carbon emissions continuously threatens sustainable development. Due to China’s immense territory, there are remarkable regional differences in carbon emissions. The construction industry, which has strong internal industrial differences, further leads to carbon emission disparity in China. Policymakers should consider spatial effects and attempt to eliminate carbon emission inequality to promote the sustainable development of the construction industry and realize emission reduction targets. Based on the classic Markov chain and spatial Markov chain, this paper investigates the club convergence and spatial distribution dynamics of China’s carbon intensity in the construction industry from 2005 to 2014. The results show that the provincial carbon intensity in the construction industry is characterized by “convergence clubs” during the research period, and very low-level and very high-level convergence clubs have strong stability. Moreover, the carbon intensity class transitions of provinces tend to be consistent with that of their neighbors. Furthermore, the transition of carbon intensity types is highly influenced by their regional backgrounds. The provinces with high carbon emissions have a negative influence on their neighbors, whereas the provinces with low carbon emissions have a positive influence. These analyses provide a spatial interpretation to the “club convergence” of carbon intensity. 相似文献
