Regional Models¶
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MetMamba: Regional Weather Forecasting with Spatial-Temporal Mamba Model¶
Deep Learning based Weather Prediction (DLWP) models have been improving rapidly over the last few years, surpassing state of the art numerical weather forecasts by significant margins. While much of...Deep Learning based Weather Prediction (DLWP) models have been improving rapidly over the last few years, surpassing state of the art numerical weather forecasts by significant margins. While much of the optimization effort is focused on training curriculum to extend forecast range in the global context, two aspects remains less explored: limited area modeling and better backbones for weather forecasting. We show in this paper that MetMamba, a DLWP model built on a state-of-the-art state-space model, Mamba, offers notable performance gains and unique advantages over other popular backbones using traditional attention mechanisms and neural operators. We also demonstrate the feasibility of deep learning based limited area modeling via coupled training with a global host model. more
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Machine learning emulation of precipitation from km-scale UK regional climate simulations using a diffusion model¶
High-resolution climate simulations are valuable for understanding climate change impacts. This has motivated use of regional convection-permitting climate models (CPMs), but these are very...High-resolution climate simulations are valuable for understanding climate change impacts. This has motivated use of regional convection-permitting climate models (CPMs), but these are very computationally expensive. We present a convection-permitting model generative emulator (CPMGEM), to skilfully emulate precipitation simulations by a 2.2km-resolution regional CPM at much lower cost. This utilises a generative machine learning approach, a diffusion model. It takes inputs at the 60km resolution of the driving global climate model and downscales these to 8.8km, with daily-mean time resolution, capturing the effect of convective processes represented in the CPM at these scales. The emulator is trained on simulations over England and Wales from the United Kingdom Climate Projections Local product, covering years between 1980 and 2080 following a high emissions scenario. The output precipitation has a similar spatial structure and intensity distribution as in the CPM simulations. The emulator is stochastic, which improves the realism of samples. We include some evidence about the emulator's skill for extreme events with return times up to ~100 years. We demonstrate successful transfer from a "perfect model" training setting to application using GCM variable inputs. It captures the main features of the simulated 21st century climate change, but exhibits some error in the magnitude. We also show that the method can be useful in situations with limited amounts of high-resolution data. Potential applications include producing high-resolution precipitation predictions for large-ensemble climate simulations and producing output based on different GCMs and climate change scenarios to better sample uncertainty. more
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A Deep Learning Approach for Modeling and Hindcasting Lake Michigan Ice Cover¶
In large lakes, ice cover plays an important role in shipping and navigation, coastal erosion, regional weather and climate, and aquatic ecosystem function. In this study, a novel deep learning model...In large lakes, ice cover plays an important role in shipping and navigation, coastal erosion, regional weather and climate, and aquatic ecosystem function. In this study, a novel deep learning model for ice cover concentration prediction in Lake Michigan is introduced. The model uses hindcasted meteorological variables, water depth, and shoreline proximity as inputs, and NOAA ice charts for training, validation, and testing. The proposed framework leverages Convolution Long Short-Term Memory (ConvLSTM) and Convolution Neural Network (CNN) to capture both spatial and temporal dependencies between model input and output to simulate daily ice cover at 0.1° resolution. The model performance was assessed through lake-wide average metrics and local error metrics, with detailed evaluations conducted at six distinct locations in Lake Michigan. The results demonstrated a high degree of agreement between the model's predictions and ice charts, with an average RMSE of 0.029 for the daily lake-wide average ice concentration. Local daily prediction errors were greater, with an average RMSE of 0.102. Lake-wide and local errors for weekly and monthly averaged ice concentrations were reduced by almost 50% from daily values. The accuracy of the proposed model surpasses currently available physics-based models in the lake-wide ice concentration prediction, offering a promising avenue for enhancing ice prediction and hindcasting in large lakes. more