Search Results (366)

Accurate short-term rainfall forecasting, an essential component of the broader framework of nowcasting, is crucial for managing extreme weather events. Traditional forecasting approaches, whether radar-based or satellite-based, often struggle with limited spatial coverage or temporal accuracy, reducing their effectiveness. This study tackles these challenges by implementing the Local Analysis and Prediction System (LAPS) enhanced with a forward advection nowcasting module, integrating multiple remote sensing rainfall datasets. Specifically, we combine weather radar data with three different satellite-derived rainfall products (H-SAF, GPM, and TRMM) to assess their impact on nowcasting performance for a rainfall event in Attica, Greece (29–30 September 2018). The results demonstrate that combined high-resolution radar data with the broader coverage and high temporal frequency of satellite retrievals, particularly H-SAF, leads to more accurate predictions with lower uncertainty. The assimilation of H-SAF with radar rainfall retrievals (HX experiment) substantially improved forecast skill, reducing the unbiased Root Mean Square Error by almost 60% compared to the control experiment for the 60 min rainfall nowcast and 55% for the 90 min rainfall nowcast. This work validates the effectiveness of the specific LAPS/advection configuration and underscores the importance of multi-source data assimilation for weather prediction. Full article

Accurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally efficient nowcasting method based on a Lagrangian advection scheme, estimating both the translation and rotation of radar-observed precipitation fields without relying on machine learning or resource-intensive computation. The method was tested on a two-year dataset (2022–2023) over Tuscany, using data collected from the Italian Civil Protection Department’s radar network. Forecast performance was evaluated using the Critical Success Index (CSI) and Mean Absolute Error (MAE) across varying spatial domains (1° × 1° to 2° × 2°) and precipitation regimes. The results show that, for high-intensity events (average rate > 1 mm/h), the method achieved CSI scores exceeding 0.5 for lead times up to 2 h. In the case of low-intensity rainfall (average rate < 0.3 mm/h), its forecasting skill dropped after 20–30 min. Forecast accuracy was shown to be highly sensitive to the temporal stability of precipitation intensity. The method performed well under quasi-stationary stratiform conditions, whereas its skill declined during rapidly evolving convective events. The method has low computational requirements, with forecasts generated in under one minute on standard hardware, and it is well suited for real-time application in regional meteorological centres. Overall, the findings highlight the method’s effective balance between simplicity and performance, making it a practical and scalable option for operational nowcasting in settings with limited computational capacity. Its deployment is currently being planned at the LaMMA Consortium, the official meteorological service of Tuscany. Full article

Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. Typically, radar echo intensity values ranging from −5 to 70 dBZ with a resolution of 5 dBZ are converted into 0–255 grayscale images from pseudo-color representations, which inevitably results in the loss of important echo details. Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. These variables are encoded jointly with high-resolution (0.5 dB) radar mosaic data to form multiple radar cells as input. A multi-channel radar echo extrapolation network architecture (MR-DCGAN) is then designed based on the DCGAN framework; (3) Since radar echo decay becomes more prominent over longer extrapolation horizons, this study departs from previous approaches that use a single model to extrapolate 120 min. Instead, it customizes time-specific loss functions for spatiotemporal attenuation correction and independently trains 20 separate models to achieve the full 120 min extrapolation. The dataset consists of radar composite reflectivity mosaics over North China within the range of 116.10–117.50°E and 37.77–38.77°N, collected from June to September during 2018–2022. A total of 39,000 data samples were matched with the initial zero-hour fields from RMAPS-NOW, with 80% (31,200 samples) used for training and 20% (7800 samples) for testing. Based on the ConvLSTM and the proposed MR-DCGAN architecture, 20 extrapolation models were trained using four different input encoding strategies. The models were evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Compared to the baseline ConvLSTM-based extrapolation model without physical variables, the models trained with the MR-DCGAN architecture achieved, on average, 18.59%, 8.76%, and 11.28% higher CSI values, 19.46%, 19.21%, and 19.18% higher POD values, and 19.85%, 11.48%, and 9.88% lower FAR values under the 20 dBZ, 30 dBZ, and 35 dBZ reflectivity thresholds, respectively. Among all tested configurations, the model that incorporated three physical variables—relative humidity (rh), u-wind, and v-wind—demonstrated the best overall performance across various thresholds, with CSI and POD values improving by an average of 16.75% and 24.75%, respectively, and FAR reduced by 15.36%. Moreover, the SSIM of the MR-DCGAN models demonstrates a more gradual decline and maintains higher overall values, indicating superior capability in preserving echo structural features. Meanwhile, the comparative experiments demonstrate that the MR-DCGAN (u, v + rh) model outperforms the MR-ConvLSTM (u, v + rh) model in terms of evaluation metrics. In summary, the model trained with the MR-DCGAN architecture effectively enhances the accuracy of radar echo extrapolation. Full article

Air quality (AQ) modeling is at the forefront of estimating pollution levels in areas where the spatial representativity is low. Large metropolitan areas in Asia such as Beijing face significant pollution issues due to rapid industrialization and urbanization. AQ nowcasting, especially in dense urban centers like Beijing, is crucial for public health and safety. One of the most popular and accurate modeling methodologies relies on black-box models that fail to explain the phenomena in an interpretable way. This study investigates the performance and interpretability of Explainable AI (XAI) applied with the eXtreme Gradient Boosting (XGBoost) algorithm employing the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model-Agnostic Explanations (LIME) for PM_2.5 nowcasting. Using a SHAP-based technique for dimensionality reduction, we identified the features responsible for 95% of the target variance, allowing us to perform an effective feature selection with minimal impact on accuracy. In addition, the findings show that SHAP and LIME supported orthogonal insights: SHAP provided a view of the model performance at a high level, identifying interaction effects that are often overlooked using gain-based metrics such as feature importance; while LIME presented an enhanced overlook by justifying its local explanation, providing low-bias estimates of the environmental data values that affect predictions. Our evaluation set included 12 monitoring stations using temporal split methods with or without lagged-feature engineering approaches. Moreover, the evaluation showed that models retained a substantial degree of predictive power (R² > 0.93) even in a reduced complexity size. The findings provide evidence for deploying interpretable and performant AQ modeling tools where policy interventions cannot solely depend on predictive analytics tools. Overall, the findings demonstrate the large potential of directly incorporating explainability methods during model development for equal and more transparent modeling processes. Full article

This paper predicts offered wage rates and job requirements in the logistics industry by utilizing data from online job postings collected through two methods: search tools and web scraping. We apply conventional estimation techniques, such as ordinary least squares and kernel density estimation, to analyze the collected data. Additionally, for the first time, we employ nowcasting methods (linear regression, decision tree, and K-nearest neighbor methods) in this context to generate robust results. Our main findings are as follows: First, the average real wage derived from online job postings aligns with officially published GDP per capita data for the studied countries and regions. Second, we identify significantly positive causal effects of work experience on real wages in the logistics industry. Third, skill requirements exhibit year-over-year variations. Finally, the decision tree method generates the closest nowcasted results in line with the actual web scraped data. The proposed methodologies and their findings establish a reliable approach using search tools and web scraping to define and predict labor demand for stakeholders in this sector as well as others. Full article

With an increase in the intensity and frequency of extreme rainfall events, there is a pressing need for accurate rainfall nowcasting applications. In recent years, precipitable water vapor (PWV) data obtained from GNSS observations have been widely used in rainfall prediction. Unlike previous studies mainly focusing on rainfall occurrences, this study proposes a transformer-based model for hourly rainfall prediction, integrating the GNSS PWV and ERA5 meteorological data. The proposed model employs the ProbSparse self-attention to efficiently capture long-range dependencies in time series data, crucial for correlating historical PWV variations with rainfall events. Additionally, the adoption of the DILATE loss function better captures the structural and timing aspects of rainfall prediction. Furthermore, traditional rainfall prediction models are typically trained on datasets specific to one region, which limits their generalization ability due to regional meteorological differences and the scarcity of data in certain areas. Therefore, we adopt a pre-training and fine-tuning strategy using global datasets to mitigate data scarcity in newly deployed GNSS stations, enhancing model adaptability to local conditions. The evaluation results demonstrate satisfactory performance over other methods, with the fine-tuned model achieving an MSE = 3.954, DTW = 0.232, and TDI = 0.101. This approach shows great potential for real-time rainfall nowcasting in a local area, especially with limited data. Full article

Effective analysis of central bank communications is critical for anticipating monetary policy changes and guiding market expectations. However, traditional natural language processing models face significant challenges in processing lengthy and nuanced policy documents, which often exceed tens of thousands of tokens. This study addresses these challenges by proposing a novel integrated deep learning framework based on Hyena Hierarchy architectures, which utilize sub-quadratic convolution mechanisms to efficiently process ultra-long sequences. The framework employs Delta-LoRA (low-rank adaptation) for parameter-efficient fine-tuning, updating less than 1% of the total parameters without additional inference overhead. To ensure robust performance across institutions and policy cycles, domain-adversarial neural networks are incorporated to learn domain-invariant representations, and a multi-task learning approach integrates auxiliary hawkish/dovish sentiment signals. Evaluations conducted on a comprehensive dataset comprising Federal Open Market Committee statements and European Central Bank speeches from 1977 to 2024 demonstrate state-of-the-art performance, achieving over 6% improvement in macro-F1 score compared to baseline models while significantly reducing inference latency by 65%. This work offers a powerful and efficient new paradigm for handling ultra-long financial policy texts and demonstrates the effectiveness of integrating advanced sequence modeling, efficient fine-tuning, and domain adaptation techniques for extracting timely economic signals, with the aim to open new avenues for quantitative policy analysis and financial market forecasting. Full article

Background: With COVID-19 having a significant impact on economic activity, it has become difficult for the existing dynamic factor models (nowcasting models) to forecast macroeconomics with high accuracy. The real-time monitoring of macroeconomics has become an important research problem faced by banks, governments, and corporations. Subjects and Methods: This paper proposes an adaptive evolutionary causal dynamic factor model (AcNowcasting) for macroeconomic forecasting. Unlike the classical nowcasting models, the AcNowcasting algorithm has the ability to perform feature selection. The criteria for feature selection are based on causality strength rather than being based on the quality of the prediction results. In addition, the factors in the AcNowcasting algorithm have the capacity for adaptive differential evolution, which can generate the best factors. These two abilities are not possessed by classical nowcasting models. Results: The experimental results show that the AcNowcasting algorithm can extract common factors that reflect macroeconomic fluctuations better, and the prediction accuracy of the AcNowcasting algorithm is more accurate than that of traditional nowcasting models. Contributions: The AcNowcasting algorithm provides a new prediction theory and a means for the real-time monitoring of macroeconomics, which has good theoretical and practical value. Full article

Weather forecasting is essential for agriculture, yet current methods often lack the localized accuracy required to manage extreme weather events and optimize irrigation. The MAGDA Horizon Europe/EUSPA project addresses this gap by developing a modular system that integrates novel European space-based, airborne, and ground-based technologies. Unlike conventional forecasting systems, MAGDA enables precise, field-level predictions through the integration of cutting-edge technologies: Meteodrones provide vertical atmospheric profiles where traditional data are sparse; GNSS-reflectometry offers real-time soil moisture insights; and all observations feed into convection-permitting models for accurate nowcasting of extreme events. By combining satellite data, GNSS, Meteodrones, and high-resolution meteorological models, MAGDA enhances agricultural and water management with precise, tailored forecasts. Climate change is intensifying extreme weather events such as heavy rainfall, hail, and droughts, threatening both crop yields and water resources. Improving forecast reliability requires better observational data to refine initial atmospheric conditions. Recent advancements in assimilating reflectivity and in situ observations into high-resolution NWMs show promise, particularly for convective weather. Experiments using Sentinel and GNSS-derived data have further improved severe weather prediction. MAGDA employs a high-resolution cloud-resolving model and integrates GNSS, radar, weather stations, and Meteodrones to provide comprehensive atmospheric insights. These enhanced forecasts support both irrigation management and extreme weather warnings, delivered through a Farm Management System to assist farmers. As climate change increases the frequency of floods and droughts, MAGDA’s integration of high-resolution, multi-source observational technologies, including GNSS-reflectometry and drone-based atmospheric profiling, is crucial for ensuring sustainable agriculture and efficient water resource management. Full article

As the solar share in energy generation is expanding globally, solar nowcasting is becoming increasingly important for the efficient and economical management of the power grid. This study leveraged the spatial context provided by all-sky images (ASI) in addition to the meteorological records for improved nowcasting of global, direct, and diffuse irradiance components. The proposed methodology consists of two branches for processing the multimodal data of ASIs and meteorological data. Due to its capability of understanding the overall characteristics of the image through self-attention, a vision transformer is utilized for the image branch while normal dense layers process the tabular meteorological data. The proposed architecture is compared against the baselines of the Ineichen clear sky model, a feedforward neural network (FFNN) where cloud coverage is computed from the ASIs by a simple color-channel threshold algorithm, and a hybrid of FFNN and U-Net model, which replaces the color threshold algorithm with fully convolutional layers for cloud segmentation. The models are trained, validated, and tested using the quality-assured ground-truth data collected in Ulaanbaatar, Mongolia, from May to August 2024, under one-minute intervals with a random split of 70%, 15%, and 15%. Our approach exhibits superior performance to baselines with a significantly lower mean absolute error (MAE) of 15–33 W/m² and root mean square error (RMSE) of 26–72 W/m², thus potentially aiding grid operators’ decision-making in real-time. Full article

Precipitation nowcasting is pivotal in monitoring extreme weather events and issuing early warnings for meteorological disasters. However, the inherent complexity of precipitation systems, coupled with their nonlinear spatiotemporal evolution, poses significant challenges for traditional numerical weather prediction methods in capturing multi-scale details effectively. Existing deep learning models similarly struggle to simultaneously capture local multi-scale features and global long-term spatiotemporal dependencies. To tackle this challenge, we propose SwinNowcast, a deep learning model based on the Swin Transformer architecture. Through the novel design of a multi-scale feature balancing module (M-FBM), the model dynamically integrates local-scale features with global spatiotemporal dependencies. Specifically, the multi-scale convolutional block attention module (MSCBAM) captures local multi-scale features, while the gated attention feature fusion unit (GAFFU) adaptively regulates the fusion intensity, thereby enhancing spatial structure and temporal continuity in a synergistic manner. Experiments were performed on the precipitation dataset from the Royal Netherlands Meteorological Institute (KNMI) under thresholds of 0.5 mm, 5 mm, and 10 mm. The results indicate that SwinNowcast surpasses six state-of-the-art approaches regarding the critical success index (CSI) and the Heidke skill score (HSS), while markedly reducing the false alarm rate (FAR). The proposed model holds substantial practical value in applications such as short-term heavy rainfall monitoring and urban flood early warning, offering effective technological support for meteorological disaster mitigation. Full article

Feature detection is one of the hot topics in the weather radar research community. This study employed a convective–stratiform classification algorithm to detect features in polarimetric radar variables and Quantitative Precipitation Estimation (QPE) retrieval during a heavy precipitation event in Crossville, Tennessee, during warm-season convection. Analysis of polarimetric radar variables revealed that strong updrafts, mixed-phase precipitation, and large hailstones in the radar resolution volume during the event were driven by the existence of supercell thunderstorms. The results of feature detection highlight that the regions with convective–stratiform cores and strong–faint features in the reflectivity field are similar to those in the rainfall field, demonstrating how the algorithm more effectively detects features in both fields. The results of the estimates, accounting for uncertainty during feature detection, indicate that an offset of +2 dB overestimated convective features in the northeast in both the reflectivity and rainfall fields, while an offset of −2 dB underestimated convective features in the northwest part of both fields. The results highlight that convective cores cover a small area with high rainfall exceeding 50 mmh⁻¹, while stratiform cores cover a larger area with greater horizontal homogeneity and lower rainfall intensity. These findings are significant for nowcasting weather, numerical models, hydrological applications, and enhancing climatological computations. Full article

This article presents the data-driven equation-free modeling of the dynamics of a hexafloat floating offshore wind turbine based on the application of dynamic mode decomposition (DMD). The DMD has here been used (i) to extract knowledge from the dynamic system through its modal analysis, (ii) for short-term forecasting (nowcasting) from the knowledge of the immediate past of the system state, and (iii) for system identification and reduced-order modeling. All the analyses are performed on experimental data collected from an operating prototype. The nowcasting method for motions, accelerations, and forces acting on the floating system applies Hankel-DMD, a methodological extension that includes time-delayed copies of the states in an augmented state vector. The system identification task is performed by using Hankel-DMD with a control (Hankel-DMDc), which models the system as externally forced. The influence of the main hyperparameters of the methods is investigated with a full factorial analysis using error metrics analyzing complementary aspects of the prediction. A Bayesian extension of the Hankel-DMD and Hankel-DMDc is introduced by considering the hyperparameters as stochastic variables, enriching the predictions with uncertainty quantification. The results show the capability of the approaches for data-lean nowcasting and system identification, with computational costs being compatible with real-time applications. Accurate predictions are obtained up to 4 wave encounters for nowcasting and 20 wave encounters for system identification, suggesting the potential of the methods for real-time continuous-learning digital twinning and surrogate data-driven reduced-order modeling. Full article

Nowcasting is a critical technology for disaster prevention and mitigation, and the accuracy of radar echo extrapolation directly impacts forecasting performance. In most deep learning-based models, accurately predicting heavy precipitation remains a challenging task. Focusing on the region of China, this study proposes an improved model based on residual and attention mechanisms—RA-UNet—for precipitation nowcasting with a lead time of 3 h. The model introduces the residual neural network (ResNet) and the convolutional block attention module (CBAM) to integrate multi-scale features into the U-Net encoder–decoder architecture, enhancing its ability to capture the spatiotemporal evolution of precipitation systems. Meanwhile, depthwise separable convolutions are employed to replace conventional convolutions, significantly improving computational efficiency while preserving model performance. To evaluate the model’s performance, experiments were conducted using 6 min resolution radar echo data from China in 2024, with comparisons made against the optical flow (OF) method and the U-Net model. The experimental results show that RA-UNet demonstrates significant advantages in 3 h forecasting: its mean absolute error (MAE) is reduced by approximately 7%, the false alarm rate (FAR) decreases by about 20%, and it outperforms the comparison models in metrics such as the critical success index (CSI) and structural similarity index (SSIM). Notably, RA-UNet effectively mitigates intensity degradation in long-term forecasts, successfully predicting the trend of >40 dBZ strong echo cores in two typical cases and significantly improving the premature dissipation problem of precipitation fields. This study provides a new approach to refined forecasting of complex precipitation systems, and future work will combine multi-source data fusion with physical constraint mechanisms to further enhance precipitation event prediction capabilities. Full article

In the field of weather forecasting, improving the accuracy of nowcasting is a highly researched topic, and radar echo extrapolation technology plays a crucial role in this process. Aiming to address the limitations of existing deep learning methods in radar echo extrapolation, this paper proposes a spatio-temporal long short-term memory (LSTM) network model that integrates an attention mechanism and the full-dimensional dynamic convolution technique. The multi-scale spatial and temporal features of radar images can be fully extracted by an efficient multi-scale attention module to enhance the model’s ability to perceive global and local information. The full-dimensional dynamic convolutional module introduces the dynamic attention mechanism in the spatial position and input and output channels of the convolutional kernel, adaptively adjusts the weight of the convolutional kernel, and improves the flexibility and efficiency of feature extraction. Combined with the network constructed by the above modules, the accuracy and time dependence of the model for predicting the strong echo region are significantly improved. Our experiments based on Jiangsu meteorological radar data show that the model achieved excellent results in terms of the Critical Success Index (CSI) and Heidke Skill Score (HSS), which show its efficiency and stability in predicting radar echo, especially under the condition of a high 35 dBZ threshold, and its prediction performance improved significantly. It provides an effective solution for fine short-term impending precipitation forecasting. Full article