161 Space Analog’s Searching to Improve Deterministic Forecasting Using Analog Ensemble Method Over Morocco Badreddine Alaoui, Driss Bari and Yamna Ghabbar Abstract 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 limita- tions 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 test- ing (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 weight- ing 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, perfor- mances remain geographically dependent. In some air- ports, where topography is heterogeneous, applying this new analog searching strategy might lead to some wors- ening since weather conditions can vary at a hectometric scale. Keywords Analog ensemble · Machine learning · Space neighborhood · AROME · Space analog search 1 Introduction Weather surface parameters play a key role in all weather and climate applications. For most world weather centers, predicting those parameters is based on determinism which raises several limitations and uncertainty sources (Eckel & Mass, 2005; Orrell et al., 2001; Paimazumder & Mölders, 2009). Ensemble forecasting provides a reliable alterna- tive since it covers all the potential prediction scenarios (Leith, 1974), but compared to determinism, the latter requires high computational capacities. Analog ensemble method is one of the most effective and low-cost ensemble methods. This method has shown high success in several applications (Alessandrini et al., 2018; Davò et al., 2016; Delle Monache et al. 2013). The fundamental idea behind is to look for all the similar weather situations to the cur- rent one in a set of past forecasts, analogs, provided by the same numerical weather prediction (NWP) model. For a given location, whenever the most analogous past situa- tions are defined, their associated observations construct our © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 H. Chenchouni et al. (eds.), Recent Advancements from Aquifers to Skies in Hydrogeology, Geoecology, and Atmospheric Sciences, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-031-47079-0_36 B. Alaoui (*) · D. Bari Direction Générale de la Météorologie, CNRM, Casablanca, Maroc e-mail: alaoui.badreddine.abe@gmail.com B. Alaoui · Y. Ghabbar Hassania School of Public Works, LaGeS/MoNum, Casablanca, Maroc