Reflectivity and velocity radar data assimilation for two flash flood events in central Italy: A comparison between 3D and 4D variational methods

Abstract

The aim of this study is to provide an evaluation of the impact of two largely used data assimilation techniques, namely three‐ and four‐dimensional variational data assimilation systems (3D‐Var and 4D‐Var), on the forecasting of heavy precipitation events using the Weather Research and Forecasting (WRF) model. For this purpose, two flash flood events in central Italy are analysed. The first occurred on September 14, 2012 during an Intensive Observation Period of the Hydrological cycle in the Mediterranean experiment (HyMeX) campaign, while the other occurred on May 3, 2018. Radial velocity and reflectivity acquired by C‐band weather radars at Mt. Midia (central Italy) and San Pietro Capofiume (northern Italy), as well as conventional observations (SYNOP and TEMP), are assimilated into the WRF model to simulate these damaging flash flood events. In order to evaluate the impact of the 3D‐Var and 4D‐Var assimilation systems on the estimation of short‐term quantitative precipitation forecasts, several experiments are carried out using conventional observations with and without radar data. Rainfall evaluation is performed by means of point‐by‐point and filtering methodologies. The results point to a positive impact of the 4D‐Var technique compared to results without assimilation and with 3D‐Var experiments. More specifically, the 4D‐Var system produces an increase of up to 22% in terms of the Fractions Skill Score compared to 3D‐Var for the first flash flood event, while an increase of about 5% is achieved for the second event. The use of a warm start initialization results in a considerable reduction in the spin‐up time and a significant improvement in the rainfall forecast, suggesting that the initial precipitation spin‐up problem still occurs when using 4D‐Var.

Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy

“…The quality of the background error covariance matrix is crucial in a 3D-Var assimilation step [40], because its statistics determine how the observations spread in the model space and how each model grid point contains dynamically balanced increments. We claim that this represents a consistent improvement with respect to previous studies, which computed the background error covariance matrix for a shorter time period (less than three months [39,40,45,49,50]) or used the default matrix provided by the software developers [42]. Finally, to evaluate the results obtained when assimilating observations from weather stations and radars and compare such results with those obtained without assimilation, we developed an object-oriented verification method, aimed at evaluating both the intensity and position errors of rainfall predictions.…”

“…This is a remarkable improvement with respect to previous similar studies; to mention a few works, Refs. [39,43] employed a 1-week and a 1-month period, respectively, to compute the B matrix, whereas [45,50] applied the NMC method during the Hydrological cycle in the Mediterranean Experiment-First Special Observing Period (HyMeX-SOP1, which lasted for about two months in 2012, see [20]). It is also worth mentioning [42] who used the default matrix provided by the WRFDA system, which is produced with global data and its use for regional cases is sometimes discouraged [65].…”

Impact of Lightning Data Assimilation on the Short-Term Precipitation Forecast over the Central Mediterranean Sea

“…Lightning data have been widely used in the past two decades and have some advantages compared to other sources of data [17,18]. For example, an important source of data to improve effectively the precipitation forecast at short range is given by radars (reflectivity and radial winds) [19][20][21][22]. However, compared to lightning data assimilation (LDA), the assimilation of radar data is achieved through more sophisticated and computationally expensive techniques, such as three-dimensional variational assimilation (3DVAR) [23,24] and the ensemble Kalman filter [20,25,26].…”

Assimilating X- and S-band Radar Data for a Heavy Precipitation Event in Italy

“…This is a remarkable improvement with respect to previous similar studies; to mention a few works [34] and [36] employed a 1-week and a 1-month period, respectively, to compute the B matrix, [39] and [87] applied the NMC method during the Hydrological cycle in the Mediterranean Experiment -First Special Observing Period (HyMeX-SOP1, which lasted for about two months in 2012, see [20] ),…”