Earth system modelling is currently playing an increasing role in weather forecasting and understanding climate change, however, the operation, deployment and development of numerical Earth system models are extremely demanding in terms of computational resources and human effort. Merging synergies has become a natural process by which national meteorological services assess and contribute to the development of such systems. With the advent of joining synergies at the national level, the second edition of the workshop on Numerical Weather Prediction in Portugal was promoted by the Portuguese Institute for the Sea and Atmosphere, I.P. (IPMA), in cooperation with several Portuguese Universities. The event was hosted by the University of Évora, during the period of 11–12 of November 2021. It was dedicated to surface–atmosphere interactions and allowed the exchange of experiences between experts, students and newcomers. The workshop provided a refreshed overview of ongoing research and development topics in Portugal on surface–atmosphere interaction modelling and its applications and an opportunity to revisit some of the concepts associated with this area of atmospheric sciences. This article reports on the main aspects discussed and offers guidance on the many technical and scientific modelling platforms currently under study.

Water content is one of the most critical characteristics in plant physiological development. Therefore, this information is a crucial factor in determining the water stress conditions of vegetation, which is essential for assessing the wildfire risk and land management decision-making. Remote sensing can be vital for obtaining information over large, limited access areas with global coverage. This is important since conventional techniques for collecting vegetation water content are expensive, time-consuming, and spatially limited. This work aims to evaluate the vegetation live fuel moisture content (LFMC) seasonal variability using a multiscale remote sensing approach, particularly on rockroses, the Cistus ladanifer species, a Western Mediterranean basin native species with wide spatial distribution, over the Herdade da Mitra at the University of Évora, Portugal. This work used four dataset sources, collected monthly between June 2022 and July 2023: (i) Vegetation samples used to calculate the LFMC; (ii) Vegetation reflectance spectral signature using the portable spectroradiometer FieldSpec HandHeld-2 (HH2); (iii) Multispectral optical imagery obtained from the Multispectral Instrument (MSI) sensor onboard the Sentinel-2 satellite; and (iv) Multispectral optical imagery derived from a camera onboard an Unmanned Aerial Vehicle Phantom 4 Multispectral (P4M). Several temporal analyses were performed based on datasets from different sensors and on their intercomparison. Furthermore, the Random Forest (RF) classifier, a machine learning model, was used to estimate the LFMC considering each sensor approach. MSI sensor presented the best results (R² = 0.94) due to the presence of bands on the Short-Wave Infrared Imagery region. However, despite having information only in the Visible and Near Infrared spectral regions, the HH2 presents promising results (R² = 0.86). This suggests that by combining these spectral regions with a RF classifier, it is possible to effectively estimate the LFMC. This work shows how different spatial scales, from remote sensing observations, affect the LFMC estimation through machine learning techniques.

Wildfires are among the most powerful natural phenomena that have severe environmental and social impacts. Their increasing frequency and intensity, driven by climate change and unsustainable land-use practices, emphasise the importance of reliable fire risk assessment tools. Live fuel moisture content (LFMC) is a key variable for wildfire risk assessment and management, as it directly influences fire behaviour and propagation. However, retrieving this information is challenging owing to its high spatial and temporal variability, combined with time-consuming and expensive in situ sampling methods. This study introduces a novel approach to estimate the LFMC from a Land Surface model (LSM). This approach was achieved by integrating remote sensing, numerical modelling, and a machine learning (ML) framework. Land surface model simulations were conducted to generate surface and soil conditions, which served as predictors in an ML-based regressor model to estimate the LFMC. The proposed model demonstrated robust performance, with a coefficient of determination (r ² ) of 0.72 and an absolute root mean square error (RMSE) of 11.6%. This approach produces reliable LFMC estimates with high spatial resolution, which can be used in wildfire propagation models. Finally, this study highlights a novel model to produce LFMC information, which can significantly enhance wildfire management and support more informed decisions in fire prevention and action strategies. Graphical abstract

The operational ALADIN‐France 3D‐Var system is based on static background error covariances calculated off‐line during a few week past period. In this study, the impact of an online updated specification of background error covariances is evaluated in the ALADIN‐France system. This evaluation is done by comparing three experiments, respectively based on (i) covariances calculated from a monthly average over a past period, (ii) covariances calculated from a monthly average over the period of study, and (iii) covariances calculated from a sliding daily average over the period of study. First, it is shown through a comparison between experiments (i) and (ii) that updating the monthly average of error covariances has a positive impact on the short‐range forecast quality. This is related to the specification of covariances which are more representative of average weather regimes at play during the period of study. Second, a comparison between experiments (ii) and (iii) indicates that additional positive impacts of a daily update of error covariances are also visible, although they tend to be somewhat localized and modest during this period. These impacts are illustrated by case studies for humidity during an anticyclonic situation, and for wind during a cyclonic event. These results support the idea to consider an online updated specification of background error covariances.