The Impact of Data Assimilation on Severe Weather Forecast

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (3 July 2023) | Viewed by 9176

Special Issue Editors


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Guest Editor
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Via del Fosso del Cavaliere 100, Rome, Italy
Interests: numerical weather prediction; data assimilation; precipitation; satellite products
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Via del Fosso del Cavaliere 100, Rome, Italy
Interests: numerical weather prediction; data assimilation; lightning forecast; precipitation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Zona Industriale ex SIR, 88046 Lamezia Terme, Italy
Interests: mesoscale meteorological modeling; severe weather; numerical weather prediction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue in Atmosphere entitled “The impact of data assimilation on severe weather forecast”. For this Special Issue, we are inviting the submission of papers concerning different techniques, new or well-established, for data assimilation and their impact on forecasting meteorological parameters, especially precipitation.

Forecast time ranges can span from nowcasting to the sub-seasonal time scale or longer. This Special Issue will focus in particular on deterministic forecasts, ensemble forecasting, and ensemble data assimilation systems.

Papers considering sensitivity tests and hindcast studies using data assimilation are welcome, as well as specific case studies addressing the impact of data assimilation on weather forecasting or assessing its long-term performance; in the latter case, analysis is not limited to severe weather.

The main focus of this Special Issue is numerical weather prediction models with data assimilation; however, other modeling systems may be considered. The impact of data assimilation on different observations (atmospheric/surface/soil) can also be explored.

Dr. Rosa Claudia Torcasio
Dr. Stefano Federico
Dr. Elenio Avolio
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • numerical weather prediction models;
  • data assimilation;
  • precipitation forecast;
  • nowcasting of severe-weather events;
  • atmospheric observations of severe-weather events

Published Papers (6 papers)

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Research

18 pages, 9302 KiB  
Article
Assimilating Aeolus Satellite Wind Data on a Regional Level: Application in a Mediterranean Cyclone Using the WRF Model
by Christos Stathopoulos, Ioannis Chaniotis and Platon Patlakas
Atmosphere 2023, 14(12), 1811; https://doi.org/10.3390/atmos14121811 - 11 Dec 2023
Viewed by 933
Abstract
This study uses a limited area model to improve the understanding of assimilating Aeolus Level 2B wind profiles on a regional level under severe weather conditions. Aeolus wind profile measurements have offered new insights into weather analysis and applications. The assimilation of Aeolus [...] Read more.
This study uses a limited area model to improve the understanding of assimilating Aeolus Level 2B wind profiles on a regional level under severe weather conditions. Aeolus wind profile measurements have offered new insights into weather analysis and applications. The assimilation of Aeolus Level 2B winds has enhanced the observed state of the atmosphere spatially and temporally in global modeling systems. This work is focused on the development and evolution of a Mediterranean tropical-like cyclone that occurred between 27–30 September 2018. Aeolus coverage had a good spatial and temporal alignment with the broader area and time periods during which the cyclone originated and developed, affording the opportunity to explore the direct influence of Aeolus satellite retrievals in model initialization processes. Using the WRF 3DVar modeling system, model results showcase the effects stemming from Aeolus data ingestion, with the main differences presenting after the first 24 h of simulation. Smaller or larger deviations in the runs with and without the Aeolus wind data assimilation are evident in most cyclonic characteristics, extending vertically up to the mid-troposphere. The absence of a consistent trend in cyclone intensification or weakening underlines the unique impact of the Aeolus dataset in each case. Full article
(This article belongs to the Special Issue The Impact of Data Assimilation on Severe Weather Forecast)
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21 pages, 13800 KiB  
Article
Assessing the Accuracy of 3D-VAR in Supercell Thunderstorm Forecasting: A Regional Background Error Covariance Study
by Ioannis Samos, Petroula Louka and Helena Flocas
Atmosphere 2023, 14(11), 1611; https://doi.org/10.3390/atmos14111611 - 27 Oct 2023
Viewed by 831
Abstract
Data assimilation (DA) integrates observational data with numerical weather predictions to enhance weather forecast accuracy. This study evaluates three regional background error (BE) covariance statistics for numerical weather prediction (NWP) via a variational data assimilation (VAR) scheme. The best practices in DA are [...] Read more.
Data assimilation (DA) integrates observational data with numerical weather predictions to enhance weather forecast accuracy. This study evaluates three regional background error (BE) covariance statistics for numerical weather prediction (NWP) via a variational data assimilation (VAR) scheme. The best practices in DA are highlighted, as well as the impact of BE covariance calculation in DA procedures by employing the Weather Research and Forecasting (WRF) model. Forecasts initialized at different intervals were used to compute distinct regional background error statistics utilizing three control variable (CV) methodologies over a span of 20 days. These statistics are used by the three-dimensional VAR DA process of WRF DA software, producing analysis fields that lead to forecasts for a distinct convective supercell event during the summer of 2019 over northern Greece. This high-impact convective event underscores the importance of selecting appropriate BE over complex terrain areas. The results emphasize the significance of BE usage in DA, proposing the optimal DA approach for simulations of convective systems. Full article
(This article belongs to the Special Issue The Impact of Data Assimilation on Severe Weather Forecast)
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23 pages, 12765 KiB  
Article
Study of the Intense Meteorological Event Occurred in September 2022 over the Marche Region with WRF Model: Impact of Lightning Data Assimilation on Rainfall and Lightning Prediction
by Rosa Claudia Torcasio, Mario Papa, Fabio Del Frate, Stefano Dietrich, Felix Enyimah Toffah and Stefano Federico
Atmosphere 2023, 14(7), 1152; https://doi.org/10.3390/atmos14071152 - 15 Jul 2023
Cited by 3 | Viewed by 918
Abstract
A destructive V-shaped thunderstorm occurred over the Marche Region, in Central Italy, on 15 September 2022. Twelve people died during the event, and damage to properties was extensive because the small Misa River flooded the area. The synoptic-scale conditions that caused this disastrous [...] Read more.
A destructive V-shaped thunderstorm occurred over the Marche Region, in Central Italy, on 15 September 2022. Twelve people died during the event, and damage to properties was extensive because the small Misa River flooded the area. The synoptic-scale conditions that caused this disastrous event are analysed and go back to the presence of tropical cyclone Danielle in the eastern Atlantic. The performance of the weather research and forecasting (WRF) model using lightning data assimilation (LDA) is studied in this case by comparing the forecast with the control forecast without lightning data assimilation. The forecast performance is evaluated for precipitation and lightning. The case was characterised by four intense 3-h (3 h) periods. The forecasts of these four 3-h phases are analysed in a very short-term forecast (VSF) approach, in which a 3 h data assimilation phase is followed by a 3 h forecast. A homemade 3D-Var is used for lightning data assimilation with two different configurations: ANL, in which the lightning is assimilated until the start of the forecasting period, and ANL-1H, which assimilates lightning until 1 h before the 3 h forecasting period. A sensitivity test for the number of analyses used is also discussed. Results show that LDA has a significant and positive impact on the precipitation and lightning forecast for this case. Full article
(This article belongs to the Special Issue The Impact of Data Assimilation on Severe Weather Forecast)
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19 pages, 8626 KiB  
Article
Nowcasting of Wind in the Venice Lagoon Using WRF-FDDA
by Dario Conte, Alessandro Tiesi, Will Cheng, Alvise Papa and Mario Marcello Miglietta
Atmosphere 2023, 14(3), 502; https://doi.org/10.3390/atmos14030502 - 04 Mar 2023
Viewed by 1468
Abstract
The Four-Dimensional Data Assimilation module (FDDA) is used in combination with the WRF model for the analysis of two case studies of high tide (on 4 April 2019 and on 12 November 2019) that affected the Venice Lagoon in the recent past. The [...] Read more.
The Four-Dimensional Data Assimilation module (FDDA) is used in combination with the WRF model for the analysis of two case studies of high tide (on 4 April 2019 and on 12 November 2019) that affected the Venice Lagoon in the recent past. The system is implemented in the perspective of an operational use for nowcasting of 10 m wind, which will be part of a numerical system aimed at the forecast of the sea level height in the Venice Lagoon. The procedure involves the assimilation of data from meteorological surface stations distributed within the Venice Lagoon and in the open northern Adriatic Sea in front of the lagoon, as well asthe radiosonde profiles available within the simulation domain. The two cases were selected considering that the real-time forecasts missed their evolution, and the sea level height was significantly underpredicted. The comparison of the simulated wind with the observations shows a fairly good agreement over short time scales (1–2 h) in both cases; hence, the WRF-FDDA system represents a promising tool and a possibly valuable support to the decision makers in case of high tide in the Venice Lagoon. Full article
(This article belongs to the Special Issue The Impact of Data Assimilation on Severe Weather Forecast)
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26 pages, 17840 KiB  
Article
Performance of the WRF Model for the Forecasting of the V-Shaped Storm Recorded on 11–12 November 2019 in the Eastern Sicily
by Giuseppe Castorina, Agostino Semprebello, Vincenzo Insinga, Francesco Italiano, Maria Teresa Caccamo, Salvatore Magazù, Mauro Morichetti and Umberto Rizza
Atmosphere 2023, 14(2), 390; https://doi.org/10.3390/atmos14020390 - 16 Feb 2023
Cited by 2 | Viewed by 1761
Abstract
During the autumn season, Sicily is often affected by severe weather events, such as self-healing storms called V-shaped storms. These phenomena cause significant total rainfall quantities in short time intervals in localized spatial areas. In this framework, this study analyzes the meteorological event [...] Read more.
During the autumn season, Sicily is often affected by severe weather events, such as self-healing storms called V-shaped storms. These phenomena cause significant total rainfall quantities in short time intervals in localized spatial areas. In this framework, this study analyzes the meteorological event recorded on 11–12 November 2019 in Sicily (southern Italy), using the Weather Research and Forecasting (WRF) model with a horizontal spatial grid resolution of 3 km. It is important to note that, in this event, the most significant rainfall accumulations were recorded in eastern Sicily. In particular, the weather station of Linguaglossa North Etna (Catania) recorded a total rainfall of 293.6 mm. The precipitation forecasting provided by the WRF model simulation has been compared with the data recorded by the meteorological stations located in Sicily. In addition, a further simulation was carried out using the Four-Dimensional Data Assimilation (FDDA) technique to improve the model capability in the event reproduction. In this regard, in order to evaluate which approach provides the best performance (with or without FDDA), the Root Mean Square Error (RMSE) and dichotomous indexes (Accuracy, Threat Score, BIAS, Probability of Detection, and False Alarm Rate) were calculated. Full article
(This article belongs to the Special Issue The Impact of Data Assimilation on Severe Weather Forecast)
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22 pages, 7097 KiB  
Article
Data Assimilation of Doppler Wind Lidar for the Extreme Rainfall Event Prediction over Northern Taiwan: A Case Study
by Chih-Ying Chen, Nan-Ching Yeh and Chuan-Yao Lin
Atmosphere 2022, 13(6), 987; https://doi.org/10.3390/atmos13060987 - 18 Jun 2022
Cited by 2 | Viewed by 2229
Abstract
On 4 June 2021, short-duration extreme precipitation occurred in Taipei. Within 2 h, over 200 mm of rainfall accumulated in the Xinyi district. In this study, advanced data assimilation technology (e.g., hybrid data and 3D variations) was incorporated to develop a high-resolution, small-scale [...] Read more.
On 4 June 2021, short-duration extreme precipitation occurred in Taipei. Within 2 h, over 200 mm of rainfall accumulated in the Xinyi district. In this study, advanced data assimilation technology (e.g., hybrid data and 3D variations) was incorporated to develop a high-resolution, small-scale (e.g., northern Taiwan) data assimilation forecast system, namely the weather research and forecast-grid statistical interpolation (WRF-GSI) model. The 3D wind field data recorded by the Doppler wind lidar system of Taipei Songshan Airport were assimilated for effective simulation of the extreme precipitation. The results revealed that the extreme rainfall was caused by the interaction between the northeast wind incurred by a front to the north of Taiwan, a humid southerly wind generated by Typhoon Choi-wan, and the regional sea–land breeze circulation. For the Xinyi district, the WRF-GSI_lidar model reported accumulated rainfall 30 mm higher than that in the non-assimilated experiment (WRF-GSI_noDA), indicating that the WRF-GSI model with lidar observation was improved 15% more than the nonassimilated run. Full article
(This article belongs to the Special Issue The Impact of Data Assimilation on Severe Weather Forecast)
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