Machine Learning Models for the Seasonal Forecast of Winter Surface Air Temperature in North America

Abstract

In this study, two machine learning (ML) models (support vector regression (SVR) and extreme gradient boosting (XGBoost)) are developed to perform seasonal forecasts of the surface air temperature (SAT) in winter (December‐January‐February, DJF) in North America (NA). The seasonal forecast skills of the two ML models are evaluated via cross validation. The forecast results from one linear regression (LR) model, and two dynamic climate models are used for comparison. In the take‐one‐out hindcast experiment, the two ML models and the LR model show reasonable seasonal forecast skills for winter SAT in NA. Compared to the two dynamic models, the two ML models and the LR model have better forecast skill for the winter SAT over central NA, which is mainly derived from a skillful forecast of the second empirical orthogonal function (EOF) mode of winter SAT over NA. In general, the SVR model and XGBoost model hindcasts show better forecast performances than the LR model. However, the LR model shows less dependence on the size of the training data set than the SVR and XGBoost models. In the real forecast experiments during the period of 2011–2017, the two ML models exhibit better forecasting skills for the winter SAT over northern and central NA than do the two dynamic models. The results of this study suggest that the ML models may provide improved forecasting skill for seasonal forecasts of the winter climate in NA.

Seasonal forecasts of Eurasian summer heat wave frequency

“…In the present work, the HWF of a grid point over the Eurasian continent (15°W-175°E, 5°S-80°N), namely, the HWF_EC, for summer is calculated as the sum of the total number of days of the selected heat wave events from June through August. The method used to perform the seasonal forecast follows (Qian et al 2020(Qian et al , 2021. According to an empirical orthogonal function (EOF) analysis, the HWF_EC can be decomposed into EOF (spatial patterns) modes and their corresponding principal components (PCs, time series) as follows:…”

“…With the development of computer science, machine learning (ML) models have played an increasingly important role in climate forecasting (Badr et al 2014, Chi and Kim, 2017. Some previous works have demonstrated that ML models perform comparably well or even better in seasonal forecasting than complex dynamic climate models over some regions (Ham et al 2019, Qian et al 2020. However, how well ML models can forecast some characteristics of extreme climate events on a seasonal time scale remains unclear.…”

Machine Learning to Characterize Biogenic Isoprene Emissions and Atmospheric Formaldehyde with Their Environmental Drivers in the Marine Boundary Layer

“…With the development of artificial intelligence technology, machine learning methods within statistical prediction algorithms are increasingly gaining attention. The greatest advantage of machine learning lies in its capability to represent any type of nonlinear relationship between variables from different data sources [41], especially crucial for characterizing air pollutants, given the complex interactions among these variables [42]. Moreover, in the field of Earth system science, some newly developed machine learning models have outperformed traditional numerical models [41,43].…”

“…The greatest advantage of machine learning lies in its capability to represent any type of nonlinear relationship between variables from different data sources [41], especially crucial for characterizing air pollutants, given the complex interactions among these variables [42]. Moreover, in the field of Earth system science, some newly developed machine learning models have outperformed traditional numerical models [41,43]. Random Forests (RF), Decision Tree Regression (DTR), and the eXtreme Gradient Boosting (XGBoost) algorithm, among others, have been widely utilized for simulating atmospheric components such as VOCs, NOX, O3, PM2.5, and more [36,42,[44][45][46][47].…”

Short‐Term Forecasting of Wind Gusts at Airports Across CONUS Using Machine Learning

“…Advanced machine learning tools are increasingly being applied in the geosciences (Dill et al., 2021; Qian et al., 2020). While skill enhancement from artificial neural networks (ANNs) over regression is well established for some contexts (Pryor et al., 2017; Reichstein et al., 2019), relatively little previous work has considered applications of ANNs to wind gust forecasting.…”