Impact of Radar Data Assimilation on the Numerical Simulation of a Severe Storm in Croatia

Stanešić, Antonio; Brewster, Keith

doi:10.1127/metz/2015/0574

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…More specifically, the assimilation of ground radar reflectivity and radial velocity with the three-dimensional variational (3D-Var) method provides good results in terms of a quantitative precipitation forecast (QPF) for several case studies in the United States and Korea (Xiao and Sun, 2007;Lee et al, 2010;Ha et al, 2011). In addition, the assimilation of radar data with 3D-Var confirms positive results in Europe using the Advanced Regional Prediction System (ARPS) and Application of Research to Operations at Mesoscale (AROME) models for two heavy rainfall cases in Croatia (Stanešić and Brewster, 2016) and France (Caumont et al, 2009) as well as using the Weather Research and Forecasting (WRF) model for the simulation of a convective event (Schwitalla and Wulfmeyer, 2014) during the Convective and Orographically induced Precipitation Study (COPS).…”

Section: Introductionmentioning

confidence: 81%

Investigating 3D and 4D variational rapid-update-cycling assimilation of weather radar reflectivity for a heavy rain event in central Italy

Mazzarella

Ferretti

Picciotti

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. Forecasting precipitation over the Mediterranean basin is still a challenge because of the complex orographic region that amplifies the need for local observation to correctly initialize the forecast. In this context, data assimilation techniques play a key role in improving the initial conditions and consequently the timing and position of the precipitation forecast. For the first time, the ability of a cycling 4D-Var to reproduce a heavy rain event in central Italy, as well as to provide a comparison with the largely used cycling 3D-Var, is evaluated in this study. The radar reflectivity measured by the Italian ground radar network is assimilated in the Weather Research and Forecasting (WRF) model to simulate an event that occurred on 3 May 2018 in central Italy. In order to evaluate the impact of data assimilation, several simulations are objectively compared by means of a fraction skill score (FSS), which is calculated for several threshold values, and a receiver operating characteristic (ROC) curve. The results suggest that both assimilation methods in the cycling mode improve the 1-, 3- and 6-hourly quantitative precipitation estimation. More specifically, the cycling 4D-Var with a warm start initialization shows the highest FSS values in the first hours of the simulation both with light and heavy precipitation. Finally, the ROC curve confirms the benefit of 4D-Var: the area under the curve is 0.91 compared to 0.88 for the control experiment without data assimilation.

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Section: Introductionmentioning

confidence: 81%

Investigating 3D and 4D variational rapid-update-cycling assimilation of weather radar reflectivity for a heavy rain event in central Italy

Mazzarella

Ferretti

Picciotti

et al. 2021

Nat. Hazards Earth Syst. Sci.

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“…They demonstrated that the 3D‐Var system improved the analysis field and the performance in terms of SQPF. Stanešić and Brewster () assimilated SYNOP data and radial velocity and reflectivity data from the Bilogora radar in Croatia using the 3D‐Var method assimilated into the Advanced Regional Prediction System (ARPS) high‐resolution model for the simulation of a storm in northwest Croatia. They showed that the assimilation of radar data and conventional observations improved the localization and estimation of precipitation of the thunderstorm.…”

Section: Introductionmentioning

confidence: 99%

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

Mazzarella

Maiello²,

Ferretti

et al. 2019

Quart J Royal Meteoro Soc

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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.

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“…where N D and N V are respectively dry and water vapor terms of gaseous characteristics combined in Equation (2), N L refers to suspended liquid water droplets and N R is rain approximation. The contribution of water droplets to the refractivity is modeled from WRF model variables.…”

Section: Appendix B Modeling Of Cloud and Rain Effect On Tropospherimentioning

confidence: 99%

“…Weather monitoring and particularly nowcasting is currently based on two major data sources: rapid update weather forecast [1,2] and weather radars [3,4], providing information about current weather and short term forecasts. However, weather radars are very expensive and observations may be affected by several errors such as echo lines, topography, canopy and building shadowing effects as well as rain bands reduction [5].…”

Section: Introductionmentioning

confidence: 99%

Residuals of Tropospheric Delays from GNSS Data and Ray-Tracing as a Potential Indicator of Rain and Clouds

et al. 2018

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The Global Navigation Satellite System (GNSS) is commonly recognized by its all-weather capability. However, observations depend on atmospheric conditions which requires the induced tropospheric delay to be estimated as an unknown parameter. In the following study, we investigate the impact of intense weather events on GNSS estimates. GNSS slant total delays (STD) in Precise Point Positioning technique (PPP) strategy were calculated for stations in southwest Poland in a 56 days period covering several heavy precipitation cases. The corresponding delays retrieved from Weather Research and Forecasting (WRF) model by a ray-tracing technique considered only gaseous parts of the atmosphere. The discrepancies are correlated with rain rates and cloud type products from remote sensing platforms. Positive correlation is found as well as GNSS estimates tend to be systematically larger than modeled delays. Mean differences mapped to the zenith direction are showed to vary between 10 mm and 30 mm. The magnitude of discrepancies follows the intensity of phenomena, especially for severe weather events. Results suggest that effects induced by commonly neglected liquid and solid water terms in the troposphere modeling should be considered in precise GNSS applications for the atmosphere monitoring. The state-of-art functional model applied in GNSS processing strategies shows certain deficits. Estimated tropospheric delays with gradients and post-fit residuals could be replaced by a loosely constrained solution without loss of quality.

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Impact of Radar Data Assimilation on the Numerical Simulation of a Severe Storm in Croatia

Cited by 6 publications

References 24 publications

Investigating 3D and 4D variational rapid-update-cycling assimilation of weather radar reflectivity for a heavy rain event in central Italy

Investigating 3D and 4D variational rapid-update-cycling assimilation of weather radar reflectivity for a heavy rain event in central Italy

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

Residuals of Tropospheric Delays from GNSS Data and Ray-Tracing as a Potential Indicator of Rain and Clouds

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