Severe hydrometeorological hazards such as floods, droughts, and thunderstorms are expected to increase in the future due to climate change. Due to the significant impacts of these phenomena, it is essential to develop new and advanced early warning systems for advance preparation of the population and local authorities (civil protection, government agencies, etc.). Therefore, reliable forecasts of extreme events, with high spatial and temporal resolution and a very short time horizon are needed, due to the very fast development and localized nature of these events. In very short time-periods (up to 6 h), small-scale phenomena can be described accurately by adopting a “nowcasting” approach, providing reliable short-term forecasts and warnings. To this end, a novel nowcasting system was developed and presented in this study, combining a data assimilation system (LAPS), a large amount of observed data, including XPOL radar precipitation measurements, the Chemical Hydrological Atmospheric Ocean wave System (CHAOS), and the WRF-Hydro model. The system was evaluated on the catastrophic flash flood event that occurred in the sub-urban area of Mandra in Western Attica, Greece, on 15 November 2017. The event was one of the most catastrophic flash floods with human fatalities (24 people died) and extensive infrastructure damage. The update of the simulations with assimilated radar data improved the initial precipitation description and led to an improved simulation of the evolution of the phenomenon. Statistical evaluation and comparison with flood data from the FloodHub showed that the nowcasting system could have provided reliable early warning of the flood event 1, 2, and even to 3 h in advance, giving vital time to the local authorities to mobilize and even prevent fatalities and injuries to the local population.

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 combining 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.

A forward advection scheme is incorporated in an advanced data assimilation model to provide very short-term predictions. The Local Analysis and Prediction System (LAPS) is implemented in the nowcasting mode in a case study of extreme precipitation event over Attica, Greece. The LAPS assimilated remote sensing data from satellite retrievals and XPOL radar precipitation measurements to produce objective analyses alongside their nowcasts in a forecast window up to 3 h. The results indicate that the assimilation of remote sensing data can increase the short-term precipitation predictability, with varying performance depending on the type and the combination of the assimilated remote sensing data.

Accurate precipitation nowcasting is essential for short-term forecasting, but it remains challenging due to the dynamic nature of rainfall mechanisms. This study implements and evaluates two schemes for improving precipitation nowcasting: (1) an advection–diffusion scheme and (2) an advection–diffusion scheme integrated with the linear theory of orographic precipitation. These schemes are implemented into the Local Analysis and Prediction System (LAPS) to produce short-term precipitation forecasts and applied to a case study involving a rainfall event over the Athens metropolitan area in Greece. These schemes are compared against the default LAPS nowcasting module based on a first-order advection scheme (control). The first-order advection scheme, while computationally efficient, lacks the ability to simulate rainfall field evolution due to its exclusion of diffusion processes and orographic effects, leading to inaccurate nowcasts. To address these limitations, the advection–diffusion scheme is introduced to capture the precipitation evolution, and the third scheme integrates the linear theory of orographic precipitation to account for the influence of topography. Preliminary results show improvements in the spatiotemporal distribution of the nowcasted precipitation. These findings suggest that incorporating diffusion and orographic effects can enhance the accuracy of short-term precipitation forecasts, though further evaluation across diverse meteorological events is needed to confirm general applicability.

Accurate short-term precipitation nowcasting remains challenging in complex terrain regions, where storm displacement, evolution, and orographic enhancement strongly affect precipitation distribution. This study evaluates three precipitation nowcasting schemes initialized from LAPS analysis fields: first-order advection (Control), advection–diffusion (AD), and advection–diffusion coupled with the linear theory of orographic precipitation (ADLOP). The schemes are tested over the Attica region of Greece using three high-impact precipitation events representing different synoptic weather regimes and verified against high-resolution weather radar observations. Forecast performance is assessed using continuous, categorical, and neighborhood-based spatial verification metrics. Results show that the Control performs competitively for light precipitation and at larger neighborhood sizes in localized events. The inclusion of diffusion in the AD scheme generally reduces random errors. In this limited three-case sample, aggregated results show that ADLOP reduces systematic bias, with reductions reaching approximately 33% at longer lead times and showing higher detection scores. However, its added value is strongly dependent on the terrain-influenced precipitation regime and may be accompanied by increased error at longer lead times. Overall, the benefits of incorporating diffusion and simplified linear orographic forcing depend on precipitation regime, lead time, and verification metric; therefore, the results should be interpreted as diagnostic case-study evidence rather than as a general assessment of ADLOP performance.