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Special Issue "The Impact of Data Assimilation on Severe Weather Forecast"

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

Deadline for manuscript submissions: 3 July 2023 | Viewed by 2967

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Special Issue Editors

Dr. Rosa Claudia Torcasio

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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
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Dr. Stefano Federico

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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; lightning forecast; precipitation
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Dr. Elenio Avolio

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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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (3 papers)

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Research

Open AccessArticle

Nowcasting of Wind in the Venice Lagoon Using WRF-FDDA

and

Mario Marcello Miglietta

Atmosphere 2023, 14(3), 502; https://doi.org/10.3390/atmos14030502 - 04 Mar 2023

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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 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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Open AccessArticle

Performance of the WRF Model for the Forecasting of the V-Shaped Storm Recorded on 11–12 November 2019 in the Eastern Sicily

and

Atmosphere 2023, 14(2), 390; https://doi.org/10.3390/atmos14020390 - 16 Feb 2023

Cited by 1 | Viewed by 656

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 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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Open AccessArticle

Data Assimilation of Doppler Wind Lidar for the Extreme Rainfall Event Prediction over Northern Taiwan: A Case Study

Chih-Ying Chen

Nan-Ching Yeh

and

Chuan-Yao Lin

Atmosphere 2022, 13(6), 987; https://doi.org/10.3390/atmos13060987 - 18 Jun 2022

Viewed by 1207

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 (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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