John Kalogiros

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.

Assimilation of weather radar data, as well as additional data like lightning data, in high-resolution weather forecast models is a promising method to improve short-term forecasting (nowcasting) of flash-flood events. A data assimilation system based on the Weather Research and Forecasting model is used in this study, with radar reflectivity and radial velocity data collected with X-band Doppler polarimetric radar in the area of Athens, Greece, and lightning observations obtained from a lightning detection network covering Greece. Radar data are assimilated with the four-dimensional variational method, which includes a full-hydrometeor assimilation scheme, in a nested domain of the model with a resolution of 3 km. Humidity, vertical velocity and horizontal wind divergence profiles estimated from lightning data are assimilated with a three-dimensional variation method in the parent domain of the model with a resolution of 9 km. The results from a case study are presented to show the effect of assimilating each type of 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 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.

Mediterranean tropical-like cyclones, or Medicanes, present unique challenges for precipitation estimations due to their rapid development and localized impacts. This study evaluates the performance of satellite precipitation products in capturing the precipitation associated with Medicane Daniel that struck Greece in early September 2023. Utilizing a combination of ground-based observations, reanalysis, and satellite-derived precipitation data, we assess the accuracy and spatial distribution of the satellite precipitation products GPM IMERG, GSMaP, and CMOPRH during the cyclone event, which formed in the Eastern Mediterranean from 4 to 7 September 2023, hitting with unprecedented, enormous amounts of rainfall, especially in the region of Thessaly in central Greece. The results indicate that, while satellite precipitation products demonstrate overall skill in capturing the broad-scale precipitation patterns associated with Medicane Daniel, discrepancies exist in estimating localized intense rainfall rates, particularly in convective cells within the cyclone’s core. Indeed, most of the satellite precipitation products studied in this work showed a misplacement of the highest amounts of associated rainfall, a significant underestimation of the event, and large unbiased root mean square error in the areas of heavy precipitation. The total precipitation field from IMERG Late Run and CMORPH showed the smallest bias (but significant) and good temporal correlation against rain gauges and ERA5-Land reanalysis data as a reference, while IMERG Final Run and GSMaP showed the largest underestimation and overestimation, respectively. Further investigation is needed to improve the representation of extreme precipitation events associated with tropical-like cyclones in satellite precipitation products.

In our era, the rapid increase of parallel programming coupled with high‐performance computing (HPC) facilities allows for the use of two‐dimensional shallow water equation (2D‐SWE) algorithms for simulating floods at the “hydrological” catchment scale, rather than just at the “hydraulic” fluvial scale. This approach paves the way for the development of new operational systems focused on impact‐based flash‐floods nowcasting, wherein hydrodynamic simulations directly model the spatial and temporal variability of measured or predicted rainfall on impacts even at a street scale. Specifically, the main goal of this research is to make a step to move toward the implementation of an effective flash flood nowcasting system in which timely and accurate impact warnings are provided by including weather radar products in the HPC 2D‐SWEs modelling framework able to integrate watershed hydrology, flow hydrodynamics, and river urban flooding in just one model. The timing, location, and intensity of the street‐level evolution of some key elements at risk (people, vehicles, and infrastructures) are also discussed considering both calibration issues and the role played by the spatial and temporal rainfall resolution. All these issues are analyzed and discussed having as a starting point the flood event which hit the Mandra town (Athens, Greece) on the 15 November 2017, highlighting the feasibility and the accuracy of the overall approach and providing new insights for the research in this field.

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.