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

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.

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

2021 ◽  
Vol 21 (9) ◽  
pp. 2849-2865
Author(s):  
Vincenzo Mazzarella ◽  
Rossella Ferretti ◽  
Errico Picciotti ◽  
Frank Silvio Marzano
Keyword(s):  
Data Assimilation ◽  
Roc Curve ◽  
Initial Conditions ◽  
Central Italy ◽  
Heavy Rain ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Rain Event ◽  
Heavy Rain Event ◽  
The Impact

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.

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

2017 ◽  
Vol 21 (11) ◽  
pp. 5459-5476 ◽  
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.

A comparison between 4D-VAR and cycling 3D-VAR methods for the simulation of a severe weather event in Central Italy. Preliminary results.

2020 ◽  
Author(s):  
Vincenzo Mazzarella ◽  
Rossella Ferretti
Keyword(s):  
Positive Impact ◽  
Central Italy ◽  
Wrf Model ◽  
Severe Weather ◽  
Precipitation Forecast ◽  
Mountain Range ◽  
Weather Event ◽  
Maximum Rainfall ◽  
The Impact ◽  
Severe Weather Event

<p>Nowadays, the use of 4D-VAR assimilation technique has been investigated in several scientific papers with the aim of improving the localization and timing of precipitation in complex orography regions. The results show the positive impact in rainfall forecast but, the need to resolve the tangent linear and adjoint model makes the 4D-VAR computationally too expensive. Hence, it is used in operationally only in large forecast centres. To the aim of exploring a more reasonable method, a comparison between a cycling 3D-VAR, that needs less computational resources, and 4D-VAR techniques is performed for a severe weather event occurred in Central Italy. A cut-off low (992 hPa), located in western side of Sicily region, was associated with a strong south-easterly flow over Central Adriatic region, which supplied a large amount of warm and moist air. This mesoscale configuration, coupled with the Apennines mountain range that further increased the air column instability, produced heavy rainfall in Abruzzo region (Central Italy).</p><p>The numerical simulations are carried out using the Weather Research and Forecasting (WRF) model. In-situ surface and upper-air observations are assimilated in combination with radar reflectivity and radial velocity data over a high-resolution domain. Several experiments have been performed in order to evaluate the impact of 4D-VAR and cycling 3D-VAR in the precipitation forecast. In addition, a statistical analysis has been carried out to objectively compare the simulations. Two different verification approaches are used: Receiver Operating Characteristic (ROC) curve and Fraction Skill Score (FSS). Both statistical scores are calculated for different threshold values in the study area and in the sub-regions where the maximum rainfall occurred.</p>

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

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.

scholarly journals Impact of Assimilating China Precipitation Analysis Data Merging with Remote Sensing Products Using the 4DVar Method on the Prediction of Heavy Rainfall

2019 ◽  
Vol 11 (8) ◽  
pp. 973 ◽  
Author(s):  
Yuanbing Wang ◽  
Yaodeng Chen ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Eastern China ◽  
Wrf Model ◽  
Analysis Data ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Precipitation Analysis ◽  
The Impact

In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfall thinning distance, and the rainfall accumulation time within assimilation window are investigated in this study. Then, the impact of 4DVar data assimilation with and without CHMPA rainfall data is evaluated to show how the assimilation of CHMPA impacts the precipitation simulations. It is found that assimilation of the CHMPA data helps to produce a better short-range precipitation forecast in this study. The rainfall fields after assimilation of CHMPA is closer to observations in terms of quantity and pattern. However, the leading time of improved forecast is limited to about 18 hours. It is also found that CHMPA data assimilation produces stronger realistic moisture divergence, precipitabale water field and the vertical wind field in the forecasting fields, which eventually contributes to the improved forecast of heavy rainfall. This study can provide references for the assimilation of CHMPA data into the WRF model using 4DVar, which is valuable for limited-area numerical weather prediction and hydrological applications.

scholarly journals Data assimilation of radar reflectivity volumes in a LETKF scheme

2018 ◽  
Vol 25 (4) ◽  
pp. 747-764 ◽  
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.

scholarly journals A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

2020 ◽  
Vol 12 (8) ◽  
pp. 1243 ◽  
Author(s):  
Xuanli Li ◽  
John R. Mecikalski ◽  
Timothy J. Lang
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Data Assimilation ◽  
Positive Impact ◽  
Specular Reflection ◽  
Surface Wind ◽  
Wrf Model ◽  
Precipitation Forecast ◽  
Wind Speed Data ◽  
The Impact

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a ±35° latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated.

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

2017 ◽  
Vol 14 ◽  
pp. 271-278 ◽  
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.

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

2021 ◽  
Vol 13 (4) ◽  
pp. 682
Author(s):  
Rosa Claudia Torcasio ◽  
Stefano Federico ◽  
Albert Comellas Prat ◽  
Giulia Panegrossi ◽  
Leo Pio D'Adderio ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Mediterranean Sea ◽  
Positive Impact ◽  
Weather Forecast ◽  
Precipitation Forecast ◽  
Time Interval ◽  
Precipitation Pattern ◽  
Central Mediterranean ◽  
Central Mediterranean Sea ◽  
The Impact

Lightning data assimilation (LDA) is a powerful tool to improve the weather forecast of convective events and has been widely applied with this purpose in the past two decades. Most of these applications refer to events hitting coastal and land areas, where people live. However, a weather forecast over the sea has many important practical applications, and this paper focuses on the impact of LDA on the precipitation forecast over the central Mediterranean Sea around Italy. The 3 h rapid update cycle (RUC) configuration of the weather research and forecasting (WRF) model) has been used to simulate the whole month of November 2019. Two sets of forecasts have been considered: CTRL, without lightning data assimilation, and LIGHT, which assimilates data from the LIghtning detection NETwork (LINET). The 3 h precipitation forecast has been compared with observations of the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) (IMERG) dataset and with rain gauge observations recorded in six small Italian islands. The comparison of CTRL and LIGHT precipitation forecasts with the IMERG dataset shows a positive impact of LDA. The correlation between predicted and observed precipitation improves over wide areas of the Ionian and Adriatic Seas when LDA is applied. Specifically, the correlation coefficient for the whole domain increases from 0.59 to 0.67, and the anomaly correlation (AC) improves by 5% over land and by 8% over the sea when lightning is assimilated. The impact of LDA on the 3 h precipitation forecast over six small islands is also positive. LDA improves the forecast by both decreasing the false alarms and increasing the hits of the precipitation forecast, although with variability among the islands. The case study of 12 November 2019 (time interval 00–03 UTC) has been used to show how important the impact of LDA can be in practice. In particular, the shifting of the main precipitation pattern from land to the sea caused by LDA gives a much better representation of the precipitation field observed by the IMERG precipitation product.

scholarly journals Application of Physical Filter Initialization in 4DVar

2017 ◽  
Vol 145 (6) ◽  
pp. 2201-2216 ◽  
Author(s):  
Wei Peng ◽  
Xudong Liang ◽  
Xin Zhang ◽  
Xiangyu Huang ◽  
Bing Lu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
High Frequency ◽  
Initial Conditions ◽  
Wrf Model ◽  
Real Data ◽  
Precipitation Forecast ◽  
Frequency Noise ◽  
Single Observation ◽  
High Frequency Noise ◽  
Model Constraint

Abstract Generally, the results of data assimilation are not well balanced dynamically due to errors in background, observations, or the model itself. So, initialization methods have been introduced to remove spurious gravity waves from the analysis. One of the initialization methods is digital filter initialization (DFI), which has been used in operational forecast systems, though its physical meaning is not well understood. Other methods eliminate high-frequency noise in optimized initial conditions by introducing physical constraints, such as the model constraint scheme, which minimizes the time tendency of model variables. In this study, a physical filter initialization (PFI) scheme, based on the model constraint scheme, is implemented in the four-dimensional variational data assimilation (4DVar) system of the Weather Research and Forecasting (WRF) Model. The impacts of the PFI scheme are examined by both single-observation and real-data experiments. The results indicate that the PFI scheme can eliminate high-frequency noise effectively, obtain flow-dependent analysis increments, and shorten forecast spinup time. Consequently, the precipitation forecast is improved to a certain extent, especially during the first few hours thanks to the shorter spinup time.

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