A fast visible-wavelength 3D radiative transfer model for numerical weather prediction visualization and forward modeling

Abstract

Abstract. Solar radiation is the ultimate source of energy flowing through the atmosphere; it fuels all atmospheric motions. The visible-wavelength range of solar radiation represents a significant contribution to the earth's energy budget, and visible light is a vital indicator for the composition and thermodynamic processes of the atmosphere from the smallest weather scales to the largest climate scales. The accurate and fast description of light propagation in the atmosphere and its lower-boundary environment is therefore of critical importance for the simulation and prediction of weather and climate. Simulated Weather Imagery (SWIm) is a new, fast, and physically based visible-wavelength three-dimensional radiative transfer model. Given the location and intensity of the sources of light (natural or artificial) and the composition (e.g., clear or turbid air with aerosols, liquid or ice clouds, precipitating rain, snow, and ice hydrometeors) of the atmosphere, it describes the propagation of light and produces visually and physically realistic hemispheric or 360∘ spherical panoramic color images of the atmosphere and the underlying terrain from any specified vantage point either on or above the earth's surface. Applications of SWIm include the visualization of atmospheric and land surface conditions simulated or forecast by numerical weather or climate analysis and prediction systems for either scientific or lay audiences. Simulated SWIm imagery can also be generated for and compared with observed camera images to (i) assess the fidelity and (ii) improve the performance of numerical atmospheric and land surface models. Through the use of the latter in a data assimilation scheme, it can also (iii) improve the estimate of the state of atmospheric and land surface initial conditions for situational awareness and numerical weather prediction forecast initialization purposes.

A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case

“…Scheck et al (2016a) and Scheck (2021) developed a Look-up Table (LUT) and machine learningbased forward operator that is several orders faster than DOM-based methods. In addition, 3D radiative effects could be corrected for high-resolution modeling without adding expensive computation costs (Scheck et al, 2018;Albers et al, 2020;Zhou et al, 2021). These methods should be explored to improve forward operators that are suitable for assimilating the FY-4A visible radiance data.…”

Meta-Universe Enabled Digital Modeling for Urban 3D Reality Visualization

“…x y l L l  =   (14) A and B are the coefficient matrices, which can be obtained by solving the elements in the exterior orientation using Eqs. t and X are the corrections to the exterior orientation elements and point coordinates, respectively.…”

“…With a long extension chain, basically covering all core industries and technologies of the digital economy, it is the integrated fusion of digital infrastructure, digital technology, digital tools, and digital terminal equipment, involving multiple sectors and technologies such as 5G, chip, cloud computing, artificial intelligence, blockchain, Internet of Things, image rendering engine, professional tools, and software, holographic projection, intelligent terminals, etc. Moving the real world to the meta-universe space for three-dimensional presentation requires collecting, processing, and integrating the data of the real world and performing three-dimensional modeling [13][14][15].…”