随着城市信息模型的发展,人们对于数据的需求也日益增加,然而,大规模的城市信息模型在传输、存储和处理等方面所面临的挑战也变得日益突出。本文旨在对城市信息模型减面技术进行综述,首先重点回顾了与CIM轻量化相关的关键技术和标准定义...随着城市信息模型的发展,人们对于数据的需求也日益增加,然而,大规模的城市信息模型在传输、存储和处理等方面所面临的挑战也变得日益突出。本文旨在对城市信息模型减面技术进行综述,首先重点回顾了与CIM轻量化相关的关键技术和标准定义,其次深入探讨了开发级别(Level of Development,LOD)在城市信息模型中的重要性和应用;最后回顾了CIM模型轻量化的具体技术方法,包括QEM(Quadric Error Metrics)和Structure-Aware等减面技术。本文旨在为城市信息模型轻量化研究提供一个全面的概述,为相关领域的研究者和从业者提供参考和启发。展开更多
Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural networ...Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 were inputted into the Mono RTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234 GHz to 58.8 GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model's effect by comparing its output with the real measured data and the microwave radiometer's own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1 K and 2.0 K; the water vapor density's RMS error is between 0.2 g/m^3 and 1.93 g/m3; and the relative humidity's RMS error is between 2.5% and 18.6%.展开更多
文摘随着城市信息模型的发展,人们对于数据的需求也日益增加,然而,大规模的城市信息模型在传输、存储和处理等方面所面临的挑战也变得日益突出。本文旨在对城市信息模型减面技术进行综述,首先重点回顾了与CIM轻量化相关的关键技术和标准定义,其次深入探讨了开发级别(Level of Development,LOD)在城市信息模型中的重要性和应用;最后回顾了CIM模型轻量化的具体技术方法,包括QEM(Quadric Error Metrics)和Structure-Aware等减面技术。本文旨在为城市信息模型轻量化研究提供一个全面的概述,为相关领域的研究者和从业者提供参考和启发。
基金National Key Research and Development Program of China(2017YFC1501704,2016YFA0600703)Projects of International Cooperation and Exchanges NSFC(NSFC-RCUK_STFC)(61661136005)+2 种基金Major State Basic Research Development Program of China(973 Program)(2013CB430101)Six Talent Peaks Project in Jiangsu Province(2015-JY-013)Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,National Satellite Meteorological Center,China Meteorological Administration
文摘Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 were inputted into the Mono RTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234 GHz to 58.8 GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model's effect by comparing its output with the real measured data and the microwave radiometer's own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1 K and 2.0 K; the water vapor density's RMS error is between 0.2 g/m^3 and 1.93 g/m3; and the relative humidity's RMS error is between 2.5% and 18.6%.
