Deep Learning for Climate Model Output Statistics
This paper discusses how deep learning techniques, specifically convolutional neural networks, can improve the accuracy of climate models, particularly in predicting precipitation.
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- 1 The grids of the model and observational data are interpolated bilinearly to the same 0.11°grid .
- 2 To evaluate ConvMOS we apply it to the data described in Section 2.
- 3 To reduce the dimensionality of the predictors, we apply a supervised PCA , which is explained in Appendix D .
- 4 This indicates that our approach is able to estimate precipitation more accurately than all considered comparison methods.
Introduction
An important source of information for the prospective effects of climate change are numerical climate models such as general circulation models (GCMs) and regional climate models (RCMs). However, these climate models often exhibit systematic errors and deficiencies in representations of climate processes which limit the quality of the resulting projections.
MOS approaches correct the modeled precipitation to correspond more closely to observational data.
This allows us to study future climate conditions and the effects of climate change more accurately especially at a local scale .
Methodology
A very simple approach is local scaling where an individual Linear Regression is fitted per location of interest, which has shown to work reasonably well . This can lead to a large number of predictors for the MOS, which is why dimensionality reduction techniques, e. g. principal component analysis (PCA), are often applied .
Study Design
Non-local MOS has been done with a range of machine learning models namely Linear Regression , Random Forests (RFs) , Support Vector Machines (SVMs) , and Multilayer Perceptrons (MLPs) .
We also apply three other commonly used MOS approaches, a local Linear Regression, a non-local Principal Component Regression approach and a non-local RF method, for comparison.
Results & Findings
Event-wise MOS directly links the simulated and observed time series through statistical models, which generally performs better than distribution-wise MOS . In this work, we examine the feasibility and potential of convolutional deep learning models as MOS.
- Event-wise MOS directly links the simulated and observed time series through statistical models, which generally performs better than distribution-wise MOS .
- In this work, we examine the feasibility and potential of convolutional deep learning models as MOS.
- Our results show that ConvMOS can reduce errors considerably, providing mostly better performance than three commonly used MOS approaches.
- Model Data For our study we use daily data of the hydrostatic version of the RCM REMO (version REMO2015) for the period 2000 to 2015.
- More specifics about the climate model can be found in Appendix A.
Note that there are some cells in REMO raw with considerably higher RMSE than 10 mm but we limited the colorbar’s extent for better visibility of the general performance.
The grids of the model and observational data are interpolated bilinearly to the same 0.11°grid .
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Dataset
The dataset used includes daily data from the REMO climate model for the period 2000 to 2015, covering a specific German region. It details the predictors used from the model and the observational data sourced from the E-OBS dataset.
Deep Learning for Climate Model Output Statistics
This section introduces the ConvMOS architecture, designed to reduce errors in climate models by combining local and global network modules. It explains how these modules work to correct specific local errors and systematic errors in precipitation.
Experiment
The experiment evaluates the ConvMOS architecture against three other commonly used MOS approaches: local Linear Regression, non-local Principal Component Regression, and non-local Random Forest. It describes the experimental setup, including data splitting and evaluation metrics.
Figures Explained
The paper’s visual material highlights the workflow and the main system components.
- Figure 1 :: Figure 1: Architecture of ConvMOS.
Frequently Asked Questions
There are two general approaches to MOS -distribution-wise MOS and event-wise MOS. A number of approaches to event-wise MOS have been used in previous work.
A very simple approach is local scaling where an individual Linear Regression is fitted per location of interest, which has shown to work reasonably well . This can lead to a large number of predictors for the MOS, which is why dimensionality.
The grids of the model and observational data are interpolated bilinearly to the same 0.11°grid . To evaluate ConvMOS we apply it to the data described in Section 2.
It is therefore common to apply model output statistics (MOS), which are statistical post-processing techniques to reduce these errors. It is therefore promising to assess how this can be used for MOS and whether there is potential for performance improvements.
Note that there are some cells in REMO raw with considerably higher RMSE than 10 mm but we limited the colorbar’s extent for better visibility of the general performance.
This paper discusses how deep learning techniques, specifically convolutional neural networks, can improve the accuracy of climate models, particularly in predicting precipitation.