Impact of multiple radar reflectivity data assimilation on the numerical simulation of a flash flood event during the HyMeX campaign

An analysis to evaluate the impact of multiple radar reflectivity data with a three-dimensional variational (3-D-Var) assimilation system on a heavy precipitation event is presented. The main goal is to build a regionally tuned numerical prediction model and a decision-support system for environmental civil protection services and demonstrate it in the central Italian regions, distinguishing which type of observations, conventional and not (or a combination of them), is more effective in improving the accuracy of the forecasted rainfall. In that respect, during the first special observation period (SOP1) of HyMeX (Hydrological cycle in the Mediterranean Experiment) campaign several intensive observing periods (IOPs) were launched and nine of which occurred in Italy. Among them, IOP4 is chosen for this study because of its low predictability regarding the exact location and amount of precipitation. This event hit central Italy on 14 September 2012 producing heavy precipitation and causing several cases of damage to buildings, infrastructure, and roads. Reflectivity data taken from three C-band Doppler radars running operationally during the event are assimilated using the 3-D-Var technique to improve high-resolution initial conditions. In order to evaluate the impact of the assimilation procedure at different horizontal resolutions and to assess the impact of assimilating reflectivity data from multiple radars, several experiments using the Weather Research and Forecasting (WRF) model are performed. Finally, traditional verification scores such as accuracy, equitable threat score, false alarm ratio, and frequency bias - interpreted by analysing their uncertainty through bootstrap confidence intervals (CIs) - are used to objectively compare the experiments, using rain gauge data as a benchmark

Using Weather Radar Data (Rainfall and Lightning Flashes) for the Analysis of Debris Flows Occurrence in Emilia-Romagna Apennines (Italy)

During the last years, the Emilia-Romagna Apennines have been severely affected by debris flows, a type of landslide that is relatively uncommon in this area. These phenomena occur as a result of intense rainfall. The two most significant events are the one that affected the Province of Parma in October 2014 and the one that affected the Province of Piacenza in September 2015, in the night between the 13th and 14th. The objective of this work is to identify relationships between rainfall and debris flows occurrence for the Piacenza 2015 event, through the analysis of the distribution of debris flows with respect to rainfall data from weather radar and rain gauges recorded by ARPAE. The analysis of the relationships between spatial occurrence of debris flows and rainfall peaks has been based on the definition of the % of debris flow triggering points that can be contoured inside isohyets and on the ROC curve method. Moreover, we analyzed possible correlations between rainfall intensity and density or number of lighting flashes. The rainfall intensity vs duration plot showed that the September 2015 event largely exceeded debris-flows triggering thresholds proposed in literature. Analysis of debris flows occurrence with respect to hourly precipitation peaks retrieved by weather radar data, evidenced that 100% of the debris flows points occurred above the 30 mm/h isohyet, 97%, above the 50 mm/h isohyet and 82.5% above the 60 mm/h isohyet. Using ROC curves, the spatial distribution of debris flows triggering points can be more precisely predicted by considering, rainfall peaks at 1 h and 30 min over the event or by considering hourly rainfall between 02:00 and 03:00 of 15/09/2015. Rainfall classes of the best cut-off points in these ROC curves, i.e. most significant classifiers of the location debris flows points, are 75–90 mm/1 h and 45–60 mm/30 min. The analysis of lightning data shows that rainfall intensity was directly correlated to the lightning density but, also, that in some sub-areas a better correlation is obtained by considering rainfall intensity versus the lightning density recorded in the previous 30 mi

Data assimilation of radar reflectivity volumes in a LETKF scheme

Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate the impact of the assimilation of radar reflectivity volumes employing a local ensemble transform Kalman filter (LETKF), implemented for the convection-permitting model of the COnsortium for Small-scale MOdelling (COSMO). A 4-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (S), amplitude (A) and location (L). Results obtained assimilating both conventional data and radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna Region (Arpae-SIMC), in which only conventional observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (roe). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of roe introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used

Sewer Flow Prediction at a Large Urban Scale: Influence of Radar Rainfall Spatial Resolution

There is a growing interest in using radar rainfall data to evaluate the performance of urban drainage systems in near real time. This paper describes a study based on a large (55 km2) urban catchment in northern Italy. Different spatial resolutions of radar data have been compared and used as input to a numerical hydrological-hydraulic model of the drainage system, constructed by means of Infoworks CS software. The results show that the spatial resolution of weather radar data plays a significant role when modelling the hydrological behaviour of the drainage system and using different resolutions may result in significant differences in peak flows and runoff volumes