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Atmosphere
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The Impact of Data Assimilation on Severe Weather Forecast (2nd...
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The Impact of Data Assimilation on Severe Weather Forecast (2nd Edition)

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 (31 October 2024) | Viewed by 1206

Special Issue Editors

Dr. Rosa Claudia Torcasio

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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
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
Special Issues, Collections and Topics in MDPI journals
Dr. Elenio Avolio

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 of Atmosphere entitled “The Impact of Data Assimilation on Severe Weather Forecast” (2nd Edition). This Special Issue is a follow-up of the first Special Issue entitled “The Impact of Data Assimilation on Severe Weather Forecast” (https://www.mdpi.com/journal/atmosphere/special_issues/data_assimilation_severe_weather_forecast) published in Atmosphere.

For this Special Issue, we welcome the submission of papers concerning different data assimilation techniques, new or well-established, and their impact on the forecasting of 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 an assessment of its long-term performance; in the latter case, the 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 2400 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

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Published Papers (1 paper)

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Research

28 pages, 9272 KiB  
Article
CNN vs. LSTM: A Comparative Study of Hourly Precipitation Intensity Prediction as a Key Factor in Flood Forecasting Frameworks
by Isa Ebtehaj and Hossein Bonakdari
Atmosphere 2024, 15(9), 1082; https://doi.org/10.3390/atmos15091082 - 6 Sep 2024
Viewed by 857
Abstract
Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques [...] Read more.
Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques Cartier station near Québec City. The models predict precipitation levels from one to six hours ahead, which are categorized into slight, moderate, heavy, and very heavy precipitation intensities. Our methodology involved gathering hourly precipitation data, defining input combinations for multistep ahead forecasting, and employing CNN and LSTM models. The performances of these models were assessed through qualitative and quantitative evaluations. The key findings reveal that the LSTM model excelled in the short-term (1HA to 2HA) and long-term (3HA to 6HA) forecasting, with higher R2 (up to 0.999) and NSE values (up to 0.999), while the CNN model was more computationally efficient, with lower AICc values (e.g., −16,041.1 for 1HA). The error analysis shows that the CNN demonstrated higher precision in the heavy and very heavy categories, with a lower relative error, whereas the LSTM performed better for the slight and moderate categories. The LSTM outperformed the CNN in minor- and high-intensity events, but the CNN exhibited a better performance for significant precipitation events with shorter lead times. Overall, both models were adequate, with the LSTM providing better accuracy for extended forecasts and the CNN offering efficiency for immediate predictions, highlighting their complementary roles in enhancing early warning systems and flood management strategies. Full article
(This article belongs to the Special Issue The Impact of Data Assimilation on Severe Weather Forecast (2nd Edition))
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