BiasCast: learning and adjusting real time biases from meteorological forecasts to enhance runoff predictions

Physics-informed LSTMs improve streamflow prediction when storage-discharge dynamics are temporally resolved

Deep learning techniques for extreme rainfall prediction: a review of state-of-the-art

Extreme rainfall events (ERES) are among the most challenging hydro-meteorological phenomena to forecast because the complex, nonlinear atmospheric processes involved span multiple spatial and temporal scales and are further intensified by rising climate variability. While Numerical Weather Prediction (NWP) models offer physically consistent representations of atmospheric dynamics, they still struggle to resolve localized convection, rapidly evolving storm systems, and rare, high-intensity precipitation events. This study systematically reviews recent advances in Artificial Intelligence (AI) for extreme rainfall prediction published between 2020 and 2026, using a transparent literature search strategy, predefined screening criteria, quality assessment, and a structured literature review matrix. The review synthesizes evidence from a final evidence base of 139 studies across several dimensions, including deep learning (DL) methodologies, multimodal data fusion, forecasting horizons, operational deployment, uncertainty quantification, and emerging intelligent forecasting paradigms. The analysis reveals a transition from deterministic DI models to hybrid, physics-informed, uncertainty-aware, and operational forecasting systems that increasingly integrate AI with physical knowledge and heterogeneous environmental observations. Despite notable progress, persistent challenges remain, including data scarcity, class imbalance, model generalization, physical consistency, uncertainty estimation, transferability, and the lack of standardized evaluation frameworks. This review provides a unified synthesis of current methodologies. It identifies key research gaps, highlighting the need for evaluation frameworks that address physical plausibility, uncertainty quantification, reliability, transferability, and operational relevance. The findings indicate that future extreme rainfall prediction systems should advance beyond accuracy-focused evaluation toward integrated, trustworthy, uncertainty-aware, and physically consistent forecasting approaches.

Comparative evaluation of deep learning and statistical models for wind speed downscaling

Accurate wind speed downscaling is essential for meteorological applications and reliable station-scale wind estimation. This study presents a systematic comparison of recurrent deep learning architectures, including RNN, GRU, LSTM, Bidirectional LSTM, Sequence-to-Sequence LSTM, and Stacked LSTM, against conventional statistical models for wind speed downscaling using paired IMD station observations and NASA MERRA-2 reanalysis data for Pune, India. All models were evaluated under identical preprocessing and training protocols to ensure a fair comparison. Among the standard recurrent architectures, the Stacked LSTM achieved the best predictive performance. An Attention-Enhanced LSTM was subsequently evaluated as an extension to investigate the benefits of incorporating a soft-attention mechanism. It achieved the highest overall accuracy, with an RMSE of 0.5034 and an MAE of 0.3650, demonstrating additional performance gains over the standard recurrent architectures. The findings provide a robust benchmark for deep learning-based wind speed downscaling and future atmospheric prediction studies.

Literature Review of Hybrid Neural–Physics Flood-Relevant Streamflow Forecasting with Cross-Event and Cross-Basin Generalization

Reliable flood-relevant streamflow forecasting remains difficult at operational scale because physically based forecasting systems provide national coverage but cannot be directly verified at the overwhelming majority of ungauged river reaches. This study reconstructs and strengthens a hybrid neural–physics framework in which the National Water Model (NWM) supplies the physically constrained baseline forecast and an attribute-conditioned deep neural network learns forecast-error magnitude from gauged basins, then transfers that knowledge to ungauged basins. The emphasis is not on replacing process-based hydrology with a black-box predictor, but on improving forecast reliability under cross-event and cross-basin generalization. The empirical setting consists of 979 archived daily long-range forecasts, 30 forecast lead days, 16-member NWM ensembles, and 389 quality-controlled U.S. Geological Survey gauging stations across Alabama and Georgia, complemented by static watershed descriptors and gridded soil-moisture diagnostics. Exploratory analysis shows that forecast skill is strongly conditioned by basin properties: larger and forested watersheds are predicted more accurately than smaller and urban watersheds, while urban basins show severe positive bias, weaker anomaly correlation, and degraded hydrograph timing. These diagnostics motivate an error-learning postprocessor that maps NWM forecast magnitude and basin attributes to a non-negative error estimate and converts the deterministic physics forecast into a corrected predictive interval. Under a temporally disjoint evaluation intended to test cross-event transfer, observation coverage increases from 0.21±0.01 for the raw NWM ensemble range to 0.82±0.03 for the hybrid model; a repeated spatial holdout yields comparable performance (0.82±0.01). The gains are largest where the underlying physics model is weakest, especially in developed watersheds. The main limitation is that improved reliability is achieved through wider predictive intervals rather than sharper hydrographs. The study therefore supports a defensible conclusion of hybrid neural–physics postprocessing can materially improve flood-relevant streamflow reliability at ungauged basins, but interval sharpness and external cross-region validation remain open research needs.

Deep Learning Based Climate Forecasting Model Using LSTM (Long Short-Term Memory) of Kathmandu Valley

Reliable temperature and precipitation forecasting is more important than ever for communities and policymakers preparing for the challenges ahead for effective adaptation and mitigation strategies in the Kathmandu Valley, which is located within a landlocked mountain valley. A deep learning-based climate forecasting model for the area is proposed in this work with the intention of converting improved forecasts into more intelligent, timely decisions for resilience and adaptation. A Long Short-Term Memory (LSTM) network, an architecture ideal for identifying minute, distant patterns in climate data, lies at the heart of the method. Before the model was taught to identify intricate correlations between climatic variables throughout time, historical climate records were thoroughly cleaned and normalized. In order to evaluate real-world reliability, performance was then compared against ARIMA (AutoRegressive Integrated Moving Average) and two LSTM variants: Bidirectional LSTM and Stacked LSTM. It was then stress-tested against harsh weather conditions and unobserved data. The outcomes were evident: LSTM-based models regularly performed far better than ARIMA, with MAE, MSE, and RMSE of 0.0217, 0.0008, and 0.0285 as opposed to ARIMA’s 1.2809, 1.5290, and 1.2365. These results support deep learning’s significant potential as a scalable and dependable method for regional climate forecasting, giving it an advantage in the valley’s climate change adaptation policy-making process.

Interpretable Daily Inflow Forecasting Model via Cross-Attention Hybrid Neural Networks Integrated with SHAP Analysis

The case study for one-day-ahead forecasting indicates that the proposed framework achieves overall superior predictive performance relative to the benchmark methods according to six evaluation metrics and the Wilcoxon signed-rank test result, confirming the effectiveness of the proposed cross-attention fusion strategy and SHAP-guided input optimization.