Understanding the intricate relationship between air quality and human health necessitates a comprehensive assessment of the spatial distribution of pollutants at high resolutions. Here, we employ deep learning methodologies for downscaling atmospheric concentrations of pollutants demonstrated to adversely affect human health (NO2and PM2.5). We train a model using data fusing multi-source information from the Copernicus Atmosphere Monitoring Service (CAMS) and in-situ observations from ground-based stations in North Italy. Our model demonstrates robust generalisation capabilities by effectively improving bias compared to ground-truth station observations when applied to the state of California, at an order of magnitude different downscaling resolution, despite not previously being trained on or exposed to this region. In addition to the demonstrated transferability, we developed and applied an occlusion-based method over the fused data sources for interpretability. Our results suggest that our model adeptly leverages auxiliary informed data encompassing past, present and future insights for inference. Thus, we are able to quantify the influence of the input variables on both predictability and on the uncertainty associated with the physical deterministic model.

We present a series of optimizations to alleviate stack memory overflow issues and improve overall performance of GPU computational kernels in atmospheric chemical kinetics model simulations. We use heap memory in numerical solvers for stiff ODEs, move chemical reaction constants and tracer concentration arrays from stack to global memory, use direct pointer indexing for array memory access, and use CUDA streams to overlap computation with memory transfer to the device. Overall, an order of magnitude reduction in GPU memory requirements is achieved, allowing for simultaneous offloading from multiple MPI processes per node and/or increasing the chemical mechanism complexity.

The study of atmospheric chemistry-climate interactions is one of today's great computational challenges. Advances in the architecture of Graphics Processing Units (GPUs) in both raw computational power and memory bandwidth sparked the interest for General-Purpose computing on graphics accelerators in scientific applications. However, the introduction of GPUs in the High Performance Computing (HPC) landscape increased the complexity of software development, due to the inherent heterogeneity requirements of programming models and design approaches, creating a gap in uptake and attainable performance in the presently available scientific community codes. This paper provides an overview of the challenges encountered when using GPU accelerators to achieve optimal performance to calculate the kinetics of chemical tracers in climate models, the techniques used to address them and the insights gained from the process. The paper presents the development of a chemical kinetics code-to-code parser to automatically generate chemical kinetics calculations on three different generations of GPU accelerators (M2070, K80, and P100). The accelerated portion of the application achieves a speedup of up to 22×, equivalent to performance gains of +19 percent up to +90 percent compared with the processor-only version, when using a cluster of 8 Nodes with dual Intel E5-2680 v3 processor and a Kepler architecture (K80), allowing faster completion of the simulations. The paper also provides practical insights and relevant considerations for the development and acceleration of complex applications.