Space Analog’s Searching to Improve Deterministic Forecasting Using Analog Ensemble Method Over Morocco

The forecasting of surface weather parameters is a key stone to every weather and climate application, and it is mainly conducted by deterministic approaches, where several uncertainty sources are faced. These limitations enhanced the reflection toward using ensemble forecasting methods, which provides helpful tools for decision-making. In this work, we aim to develop a low computational cost ensemble forecasting approach based on the analog ensemble method (AnEn), to predict six surface parameters (T2m, WS10m, WD10m, RH2m, SURFP, and MSLP) at 10 airports of Morocco for 24 forecast hours. For this goal, we use hourly observations and forecasts from the operational mesoscale numerical model AROME covering the 4-year period (2016–2019). The latter was split into training (2016–2018) and testing (2019) periods. In AnEn, the selection of analogs commonly considers only historical data for each grid point in the study domain closest to the observation site. Herein, we propose two novelties: firstly, a new weighting strategy for predictors where we use three machine learning algorithms (linear regression, XGBoost, and random forest) to assign predictors’ weights. Secondly, since AnEn requires larger training dataset to enhance chances to find best analogs, we extend the search space by integrating neighboring grid points. Thus, the analog detection is based here on 16 nearest grid points. As a result of the combination of these two techniques, it is found that the machine learning weighting strategies proved an improvement of performances in bias and root mean square error for different lead times and locations. Since the new space neighboring strategy maximizes the chances to find the best analogs, clear improvements were perceived for most airports. However, performances remain geographically dependent. In some airports, where topography is heterogeneous, applying this new analog searching strategy might lead to some worsening since weather conditions can vary at a hectometric scale.