Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture
Abstract
:1. Introduction
2. Methodology
2.1. Machine Learning Framework
2.2. Case Study
3. Results
3.1. Development of Random Forest Models
3.2. GEM Simulations
3.2.1. Case 1: ’Normal’ GEM Simulation
3.2.2. Case 2: ‘Perturbed’ GEM Simulation
4. Discussion and Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
CLASS | Canadian Land Surface Scheme |
COSMO-CLM | Cosmo-Climate Lokalmodell(German regional climate model) |
CMIIP5 | Coupled Model Intercomparison Project Phase 5 |
CO2 | Carbon dioxide |
DT | Decision Tree |
ECMWF | European Centre for Medium-Range Weather Forecasts |
ERA5 | ECMWF Re-Analysis |
GCM | Global Climate Model |
GEM | Global Environmental Multiscale |
ML | Machine Learning |
MLS | Minimum Leaf Size |
MSWE | Maximum Snow Water Equivalent |
RCA4 | Rosby Centre Regional Atmospheric Model Version 4 |
RCM | Regional Climate Model |
RF | Random Forest |
RH | Relative Humidity |
RMSE | Root Mean Square Error |
RTM | Radiative Transfer Model |
SM | Soil Moisture |
SMLT | Snowmelt |
SSM | Surface Soil Moisture |
TT | 2 m Temperature |
WA | Water Availability |
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Ramírez Casas, F.A.; Sushama, L.; Teufel, B. Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture. Hydrology 2022, 9, 186. https://doi.org/10.3390/hydrology9100186
Ramírez Casas FA, Sushama L, Teufel B. Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture. Hydrology. 2022; 9(10):186. https://doi.org/10.3390/hydrology9100186
Chicago/Turabian StyleRamírez Casas, Francisco Andree, Laxmi Sushama, and Bernardo Teufel. 2022. "Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture" Hydrology 9, no. 10: 186. https://doi.org/10.3390/hydrology9100186
APA StyleRamírez Casas, F. A., Sushama, L., & Teufel, B. (2022). Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture. Hydrology, 9(10), 186. https://doi.org/10.3390/hydrology9100186