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

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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Investigating 3D and 4D Variational Rapid-Update-Cycling Assimilation of Weather Radar Reflectivity for a Flash Flood Event in Central Italy

10.5194/nhess-2020-406 ◽

2021 ◽

Author(s):

Vincenzo Mazzarella ◽

Rossella Ferretti ◽

Errico Picciotti ◽

Frank S. Marzano

Keyword(s):

Data Assimilation ◽

Roc Curve ◽

Initial Conditions ◽

Flash Flood ◽

Central Italy ◽

Wrf Model ◽

Radar Reflectivity ◽

Precipitation Forecast ◽

Precipitation Pattern ◽

The Impact

Abstract. The precipitation forecast over the Mediterranean basin is still a challenge because of the complex orographic region which amplifies the need for local observation to correctly initialize the forecast. In this context the data assimilation techniques play a key role in improving the initial conditions and consequently the timing and position of precipitation pattern. For the first time, the ability of a cycling 4D-Var to reproduce a severe weather 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 WRF model to simulate an event occurred on May 3, 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 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 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 the 0.88 of control experiment without data assimilation.

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Statistics of the Z–R Relationship for Strong Convective Weather over the Yangtze–Huaihe River Basin and Its Application to Radar Reflectivity Data Assimilation for a Heavy Rain Event

Journal of Meteorological Research ◽

10.1007/s13351-018-7163-1 ◽

2018 ◽

Vol 32 (4) ◽

pp. 598-611 ◽

Cited By ~ 2

Author(s):

Xue Fang ◽

Aimei Shao ◽

Xinjian Yue ◽

Weicheng Liu

Keyword(s):

Data Assimilation ◽

River Basin ◽

Heavy Rain ◽

Radar Reflectivity ◽

Rain Event ◽

Huaihe River Basin ◽

Huaihe River ◽

Heavy Rain Event ◽

Reflectivity Data ◽

Convective Weather

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

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5459-2017 ◽

2017 ◽

Vol 21 (11) ◽

pp. 5459-5476 ◽

Cited By ~ 4

Author(s):

Ida Maiello ◽

Sabrina Gentile ◽

Rossella Ferretti ◽

Luca Baldini ◽

Nicoletta Roberto ◽

...

Keyword(s):

Hydrological Cycle ◽

Initial Conditions ◽

Flash Flood ◽

Central Italy ◽

Heavy Precipitation ◽

Radar Reflectivity ◽

Precipitation Event ◽

Italian Regions ◽

Reflectivity Data ◽

The Impact

Abstract. 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.

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The impact of heavy rain event on groundwater microbial communities in Xikuangshan, Hunan Province, P.R. China

Journal of Hydrology ◽

10.1016/j.jhydrol.2020.125674 ◽

2020 ◽

pp. 125674

Author(s):

Mengxiaojun Wu ◽

Hongmei Wang ◽

Weiqi Wang ◽

Yuyang Song ◽

Liyuan Ma ◽

...

Keyword(s):

Microbial Communities ◽

Heavy Rain ◽

Hunan Province ◽

Rain Event ◽

Heavy Rain Event ◽

The Impact

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Impact of radar data assimilation using WRF three-dimensional variational system, for the simulation of a heavy rainfall case in Central Italy

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-7315-2013 ◽

2013 ◽

Vol 6 (4) ◽

pp. 7315-7353

Author(s):

I. Maiello ◽

R. Ferretti ◽

S. Gentile ◽

M. Montopoli ◽

E. Picciotti ◽

...

Keyword(s):

Data Assimilation ◽

Radial Velocity ◽

Heavy Rainfall ◽

Initial Conditions ◽

Positive Impact ◽

Central Italy ◽

Three Dimensional ◽

Warm Start ◽

The Impact ◽

Dwr Data

Abstract. This work is a first assessment of the role of Doppler Weather radar (DWR) data in a mesoscale model for the prediction of a heavy rainfall. The study analyzes the event occurred during 19–22 May 2008 in the urban area of Rome. The impact of the radar reflectivity and radial velocity acquired from Monte Midia Doppler radar, on the assimilation into the Weather Research Forecasting (WRF) model version 3.2, is discussed. The goal is to improve the WRF high resolution initial condition by assimilating DWR data and using ECMWF analyses as First Guess thus improving the forecast of surface rainfall. Several experiments are performed using different set of Initial Conditions (ECMWF analyses and warm start or cycling) and a different assimilation strategy (3 h-data assimilation cycle). In addition, 3DVAR (three-dimensional variational) sensitivity tests to outer loops are performed for each of the previous experiment to include the non-linearity in the observation operators. In order to identify the best ICs, statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used. The results show that the assimilation of DWR data has a positive impact on the prediction of the heavy rainfall of this event, both assimilating reflectivity and radial velocity, together with conventional observations. Finally, warm start results in more accurate experiments as well as the outer loops strategy.

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Data assimilation of radar reflectivity volumes in a LETKF scheme

Nonlinear Processes in Geophysics ◽

10.5194/npg-25-747-2018 ◽

2018 ◽

Vol 25 (4) ◽

pp. 747-764 ◽

Cited By ~ 5

Author(s):

Thomas Gastaldo ◽

Virginia Poli ◽

Chiara Marsigli ◽

Pier Paolo Alberoni ◽

Tiziana Paccagnella

Keyword(s):

Data Assimilation ◽

Positive Impact ◽

Weather Prediction ◽

Forecast Accuracy ◽

Skill Score ◽

Radar Reflectivity ◽

Precipitation Forecast ◽

Small Scale ◽

Observational Error ◽

The Impact

Abstract. 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.

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Comparison between 3D-Var and 4D-Var data assimilation methods for the simulation of a heavy rainfall case in central Italy

Advances in Science and Research ◽

10.5194/asr-14-271-2017 ◽

2017 ◽

Vol 14 ◽

pp. 271-278 ◽

Cited By ~ 7

Author(s):

Vincenzo Mazzarella ◽

Ida Maiello ◽

Vincenzo Capozzi ◽

Giorgio Budillon ◽

Rossella Ferretti

Keyword(s):

Data Assimilation ◽

Heavy Rainfall ◽

Hydrological Cycle ◽

Central Italy ◽

Probability Of Detection ◽

Precipitation Forecast ◽

Heavy Rainfall Event ◽

Tyrrhenian Sea ◽

Pressure System ◽

The Impact

Abstract. This work aims to provide a comparison between three dimensional and four dimensional variational data assimilation methods (3D-Var and 4D-Var) for a heavy rainfall case in central Italy. To evaluate the impact of the assimilation of reflectivity and radial velocity acquired from Monte Midia Doppler radar into the Weather Research Forecasting (WRF) model, the quantitative precipitation forecast (QPF) is used.The two methods are compared for a heavy rainfall event that occurred in central Italy on 14 September 2012 during the first Special Observation Period (SOP1) of the HyMeX (HYdrological cycle in Mediterranean EXperiment) campaign. This event, characterized by a deep low pressure system over the Tyrrhenian Sea, produced flash floods over the Marche and Abruzzo regions, where rainfall maxima reached more than 150 mm 24 h−1.To identify the best QPF, nine experiments are performed using 3D-Var and 4D-Var data assimilation techniques. All simulations are compared in terms of rainfall forecast and precipitation measured by the gauges through three statistical indicators: probability of detection (POD), critical success index (CSI) and false alarm ratio (FAR). The assimilation of conventional observations with 4D-Var method improves the QPF compared to 3D-Var. In addition, the use of radar measurements in 4D-Var simulations enhances the performances of statistical scores for higher rainfall thresholds.

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Improvement of numerical weather prediction of heavy rain event using radar data assimilation using rapid update cycle method in Jabodetabek Region

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/399/1/012124 ◽

2019 ◽

Vol 399 ◽

pp. 012124

Author(s):

N V Santi ◽

J A I Paski ◽

A Mulsandi

Keyword(s):

Data Assimilation ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Heavy Rain ◽

Radar Data ◽

Rain Event ◽

Numerical Weather ◽

Heavy Rain Event ◽

Radar Data Assimilation

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Impact of Ensemble-Variational Data Assimilation in Heavy Rain Forecast over Brazilian Northeast

Atmosphere ◽

10.3390/atmos12091201 ◽

2021 ◽

Vol 12 (9) ◽

pp. 1201

Author(s):

João Pedro Gonçalves Nobre ◽

Éder Paulo Vendrasco ◽

Carlos Frederico Bastarz

Keyword(s):

Data Assimilation ◽

Covariance Matrix ◽

Initial Conditions ◽

Three Dimensional ◽

Heavy Rain ◽

Air Masses ◽

Brazilian Northeast ◽

The Impact ◽

Data Assimilation System ◽

Assimilation System

The Brazilian Northeast (BNE) is located in the tropical region of Brazil. It is bounded by the Atlantic Ocean, and its climate and vegetation are strongly affected by continental plateaus. The plateaus keep the humid air masses to the east and are responsible for the rain episodes, and at the west side the northeastern hinterland and dry air masses are observed. This work is a case study that aims to evaluate the impact of updating the model initial condition using the 3DEnVar (Three-Dimensional Ensemble Variational) system in heavy rain episodes associated with Mesoscale Convective Systems (MCS). The results were compared to 3DVar (Three-Dimensional Variational) and EnSRF (Ensemble Square Root Filter) systems and with no data assimilation. The study enclosed two MCS cases occurring on 14 and 24 January 2017. For that purpose, the RMS (Regional Modeling System) version 3.0.0, maintained by the Center for Weather Forecasting and Climate Studies (CPTEC), used two components: the Weather Research and Forecasting (WRF) mesoscale model and the GSI (Gridpoint Statistical Interpolation) data assimilation system. Currently, the RMS provides the WRF initial conditions by using 3DVar data assimilation methodology. The 3DVar uses a climatological covariance matrix to minimize model errors. In this work, the 3DEnVar updates the RMS climatological covariance matrix through the forecast members based on the errors of the day. This work evaluated the improvements in the detection and estimation of 24 h accumulated precipitation in MCS events. The statistic index RMSE (Root Mean Square Error) showed that the hybrid data assimilation system (3DEnVar) performed better in reproducing the precipitation in the MCS occurred on 14 January 2017. On 24 January 2017, the EnSRF was the best system for improving the WRF forecast. In general, the BIAS showed that the WRF initialized with different initial conditions overestimated the 24 h accumulated precipitation. Therefore, the viability of using a hybrid system may depend on the hybrid algorithm that can modify the weights attributed to the EnSRF and 3DVar matrix in the GSI over the assimilation cycles.

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