Radar-based characterisation of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model

Abstract

Abstract. Heavy precipitation events (HPEs) can lead to natural hazards (e.g. floods and debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological, and societal effects of HPEs. Thus, a correct characterisation and prediction of rainfall patterns is crucial for coping with these events. Information from rain gauges is generally limited due to the sparseness of the networks, especially in the presence of sharp climatic gradients. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. This paper characterises rainfall patterns during HPEs based on high-resolution weather radar data and evaluates the performance of a high-resolution, convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were characterised by the highest rain intensities; however, for short durations, the highest rain intensities were found for the inland desert. During the rainy season, the rain field's centre of mass progresses from the sea inland. Rainfall during HPEs is highly localised in both space (less than a 10 km decorrelation distance) and time (less than 5 min). WRF model simulations were accurate in generating the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited errors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipitation patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location errors.

Hydrometeorological analysis and forecasting of a 3-day flash-flood-triggering desert rainstorm

“…However, as already suggested, the forecasted rainfall might be erroneously placed in space and time (Ben Bouallègue and Theis, 2014;Collier, 2007). To account for this source of uncertainty, a simple and costeffective forecast-shifting approach was applied: shifting the last two available COSMO runs closest to the April 26 th flashflood occurrence (i.e., Apr 25, 21:30, and Apr 26, 09:30) within a reasonable spatial error range of 20 km (Armon et al, 2020;Khain et al, 2019;Fig. 9) in 1-km intervals.…”

“…Global weatherprediction models are routinely used by meteorological agencies worldwide, but their spatiotemporal scales are too coarse for flash-flood applications (Sene, 2013). In recent years, convection-permitting models with spatial resolution of ≤3 km have enabled explicit representation of the convective process, providing better representation of rainfall and better forecast skills on the flash-flood scale (Armon et al, 2020;Clark et al, 2016;Khain et al, 2019;Prein et al, 2015). However, the finer scale increases the sensitivity of these models to initial conditions, leading to spatial uncertainties in their output (Bartsotas et al, 2016;Ben Bouallègue and Theis, 2014;Collier, 2007;Sivakumar, 2017).…”

Data-driven distinction between convective, frontal and mixed extreme rainfall events in radar data

“…Another approach could be to sample extreme events from each grid cells in the case area, as shown in (Goudenhoofdt et al, 2017;10 Panziera et al, 2018), but these papers do not propose a spatial definition of independent events and merging of nonindependent events in order to analyse spatio-temporal characteristics of events as proposed in this article. Other articles have suggested different methods to sample extreme events from gridded data, with no methods being similar and with very different definition of extremes (Armon et al, 2020;Hamidi et al, 2017;Panziera et al, 2016;Thorndahl et al, 2014). Common for the proposed event sampling strategies in these articles is a difficulty in defining the beginning and end of events, and no 15 methodology to define a suitable number of events sampled or the extremity of the sampled events.…”

“…Goudenhoofdt et al, 2017;Panziera et al, 2016Panziera et al, , 2018. However, few studies have studied extreme event characteristics over the spatial extent of the events, and these are often limited to analysing area and intensity (Armon et al, 2020;Hamidi et al, 20 2017;Thorndahl et al, 2014). Here we apply a broad range of spatio-temporal characteristics in order to develop an automatic classification scheme of different event types and provide a better understanding of actual precipitation processes.…”

Response to reviewer 2

“…* Thank you for your suggestion, we will add that and make sure to clarify the ending of the sentence. P4,L20: Please note that some studies typify extremes from spatial measurements based on a large enough amount of pixel passing a threshold (Armon et al, 2020), or based on spatialIDF curves (Rinat et al, 2020).…”