Super-resolution technology is a new computing method. It was used to enhance older meteorological model data. By using this, scientists can better assess Earth’s global climate history. Upscaling digital photos and videos super-resolution calculations are an important analysis tool. This tool calculates historical high-resolution model assimilation data. This is according to Dr. Chunxiang Shi, Chief Scientist at the National Meteorological Information Center of China Meteorological Administration.
Dr. Shi and her team from the National Meteorological Information Center of China Meteorological Administration are known for CMA’s Land Data Assimilation System (CLDAS). They are also known for China’s 40-year global atmospheric/land surface reanalysis dataset (CRA-40). They published their super-resolution downscaling research based on CLDAS data in Advances in Atmospheric Sciences.
They built a deep learning downscaling model CLDASSD (CLDAS Statistical Downscaling). They used 2m temperature model data within the Beijing-Tianjin-Hebei region. Scientists performed their downscaling test. They made large-scale (low resolution) model output available to enhance local scale forecasts (high resolution).
Their method successfully reconstructed fine textures in complex mountain areas. There human observation can be impossible. The root mean square error of CLDASSD is smaller than the general interpolation-based downscaling methods, through comparison with observational data. The general interpolation-based downscaling methods used with different daily times, seasons and terrain.