# Machine Learning Methods in Weather and Climate Applications: A Survey
## 2 Background  
In this section, the objective is to provide a thorough understanding of key meteorological principles, tailored to be accessible even to readers outside the meteorological domain. The section commences with an overview of Reanalysis Data, the cornerstone for data inputs in weather forecasting and climate projection models. Following this, the focus shifts to the vital aspect of model output validation. It is necessary to identify appropriate benchmarks and key performance indicators for assessing the model's predictive accuracy. Without well-defined standards, the evaluation of a model's effectiveness remains nebulous. Furthermore, three essential concepts--bias-correction, down-scaling, and emulation--are introduced. These become particularly relevant when discussing the role of machine learning in augmenting physical models. Finally, the text offers an in-depth explanation of predicting extreme events, clearly defining \"extreme event\" and differentiating them from routine occurrences. **Data source. ** Observed data undergoes a series of rigorous processing steps before it enters the predictive model (or what is known as the reanalysis data generation process). They are amassed from heterogeneous sources, such as ground-based networks like the Global Historical Climatology Network (GHCN), atmospheric tools like Next-Generation Radar (NEXRAD), and satellite systems like the Geostationary Operational Environmental Satellites (GOES). Oceanic measurements are captured through the specialized ARGO float network, focusing on key parameters like temperature and salinity. These raw datasets are further audited for quality control, spatial and temporal interpolation, and unit standardization. Despite meticulous preprocessing, observational data exhibit challenges such as spatial-temporal heterogeneity, inherent measurement errors, and discrepancies with numerical models. To mitigate these issues, data assimilation techniques are employed. These techniques synergize observations with model forecasts using mathematical and statistical algorithms like Kalman filtering, Three-Dimensional Variational Analysis (3D-Var), and Four-Dimensional Variational Analysis (4D-Var) [25]. Additionally, data assimilation can be utilized to enhance the initial model conditions and correct systemic model biases. The scope of data assimilation extends beyond singular meteorological models to complex Earth System Models that integrate dynamics from atmospheric, oceanic, and terrestrial subsystems. Post-assimilation, where the model state is updated, leads to the generation of \"reanalysis data\". Popular reanalysis datasets include ERA5 from the European Centre for Medium-Range Weather Forecasts (ECMWF), NCEP/NCAR Reanalysis from the National Centers for Environmental Prediction and the National Center for Atmospheric Research, JRA-55 from the Japan Meteorological Agency, and MERRA-2 from NASA. **Result evaluation. ** Result evaluation serves as a critical stage in the iterative process of predictive modeling. It involves comparing forecasted outcomes against observed data to gauge the model's reliability and accuracy. The temporal dimension is a critical factor in result evaluation. Short-term predictive models, like those used in weather forecasting, benefit from near-real-time feedback, which allows for frequent recalibration using machine learning algorithms like Ensemble Kalman Filters. On the other hand, long-term models, such as climate projections based on General Circulation Models (GCMs), are constrained by the absence of an immediate validation period. In weather forecasting, meteorologists employ a variety of numerical models, like the Weather Research and Forecasting (WRF) model, which are evaluated based on short-term observational data. Standard metrics for evaluation include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Skill Scores. The high-frequency availability of data from sources like weather radars and satellites facilitates rapid iterations and refinements.