Running high-resolution physical models is computationally expensive and essential for many disciplines. Agriculture, transportation, and energy are sectors that depend on high-resolution weather models, which typically consume many hours of large High Performance Computing (HPC) systems to deliver timely results. Many users cannot afford to run the desired resolution and are forced to use low resolution output. One simple solution is to interpolate results for visualization. It is also possible to combine an ensemble of low resolution models to obtain a better prediction. However, these approaches fail to capture the redundant information and patterns in the low-resolution input that could help improve the quality of prediction. In this paper, we propose and evaluate a strategy based on a deep neural network to learn a high-resolution representation from low-resolution predictions using weather forecast as a practical use case. We take a supervised learning approach, since obtaining labeled data can be done automatically. Our results show significant improvement when compared with standard practices and the strategy is still lightweight enough to run on modest computer systems.
In weather and climate, the efforts for dynamical downscaling data normally make use of higher resolution regional models nested within coarser global models. Boundary conditions are provided by the global models while the physics in the regional models are tasked with the simulation of the sub-grid processes that are not represented in the coarser model. Yang @cite_4 performed simulations with the LMDZ4 model at a 0.6 @math @math 0.6 @math resolution nested in 3 models with resolutions varying from 1.125 @math @math 1.125 @math to 2.8125 @math @math 2.8125 @math , successfully reproducing precipitation and temperature patterns over the domains analyzed. Even the model with the lowest resolution showed significant improvement after dynamical downscaling.
@cite_4
2277077742
A dynamical downscaling is performed to improve the regional climate simulation in China. It consists of using a variable resolution model LMDZ4 nested into three global climate models (GCMs): BCC-csm1-1-m, FGOALS-g2 and IPSL-CM5A-MR, respectively. The regional climate from different simulations is assessed in terms of surface air temperature and rainfalls through a comparison to observations (both station data and gridded data). The comparison includes climatic trends during the last 40 years, statistical distribution of sub-regional climate, and the seasonal cycle. For surface air temperature, a significant part of the improvement provided by LMDZ4 is related to the effect of surface elevation which is more realistic in high-resolution simulations; the rest is related to changes in regional or local atmospheric general circulation. All GCMs and the downscaling model LMDZ4 are, more or less, able to describe the spatial distribution of surface air temperature and precipitation in China. LMDZ4 does show its superiority, compared to GCMs, in depicting a good regional terrain including the Tibetan Plateau, the Sichuan Basin and the Qilian Mountains.