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Meteorological Forecasting and Modeling in Climatology
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Meteorological Forecasting and Modeling in Climatology

A special issue of Climate (ISSN 2225-1154). This special issue belongs to the section "Climate Dynamics and Modelling".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 2931

Special Issue Editor

Dr. Xiaoyun Liang

E-Mail Website
Guest Editor
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China
Interests: climate variability; climate modeling; hydrology; meteorology; atmospheric sciences

Special Issue Information

Dear Colleagues,

The evolution of meteorological disasters (such as tropical cyclones, droughts, floods, heat waves, etc.) is driven by multiple factors, including short-term weather processes, climate variability (such as El Niño, Pacific Decadal Oscillation, etc.), and long-term climate change. Therefore, it is very important and necessary to understand the interaction of these different-scale processes and analyze the impact of climate change on the frequency and intensity changes in meteorological disaster events.

This Special Issue aims to collect cutting-edge methods, challenges, and applications in meteorological forecasting, climate modeling, and prediction, promoting the cross-integration of multi-scale research and providing scientific support for disaster prevention and mitigation, as well as climate policies. Potential topics include but are not limited to:

  • The impact of multiple climate indicators on the change in meteorological events;
  • Forecasting and numerical simulation of extreme meteorological events (rainstorms, drought, heat waves, cold waves, typhoons, etc.);
  • High-resolution numerical weather forecasting and climate prediction;
  • Aerosol-cloud-precipitation interaction;
  • Analysis of severe weather mechanisms such as convective system organization;
  • Cloud microphysics, boundary layer processes, and parameterization schemes;
  • Observation and simulation of atmospheric physical processes;
  • Simulation of complex systems in coupled model (ocean–atmosphere–land).

Dr. Xiaoyun Liang
Guest Editor

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. Climate 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 1800 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

  • meteorological events
  • climate variability
  • extreme weather events
  • numerical weather
  • forecasts and modeling
  • atmospheric physics
  • complex coupling model

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

20 pages, 9389 KB  
Article
Let Us Change the Aerodynamic Roughness Length as a Function of Snow Depth
by Jessica E. Sanow and Steven R. Fassnacht
Climate 2025, 13(11), 226; https://doi.org/10.3390/cli13110226 - 31 Oct 2025
Viewed by 146
Abstract
A shallow, seasonal snowpack is rarely homogeneous in depth, layer characteristics, or surface structure throughout an entire winter. Aerodynamic roughness length (z0) is typically considered a static parameter within hydrologic and atmospheric models. Here, we present observations showing z0 [...] Read more.
A shallow, seasonal snowpack is rarely homogeneous in depth, layer characteristics, or surface structure throughout an entire winter. Aerodynamic roughness length (z0) is typically considered a static parameter within hydrologic and atmospheric models. Here, we present observations showing z0 as a dynamic variable that is a function of snow depth (ds). This has a significant impact on sublimation modeling, especially for shallow snowpacks. Terrestrial LiDAR data were collected at nine different study sites in northwest Colorado from the 2019 to 2020 winter season to measure the spatial and temporal variability of the snowpack surface. These data were used to estimate the geometric z0 from 91 site visits. Values of z0 decrease during initial snow accumulation, as the snow conforms to the underlying terrain. Once the snowpack is sufficiently deep, which depends on the height of the ground surface roughness features, the surface becomes more uniform. As melt begins, z0 increases, when the snow surface becomes more irregular. The correlation value of z0 was altered by human disturbance at several of the sites. The z0 versus ds correlation was almost constant, regardless of the initial roughness conditions that only affected the initial z0. Full article
(This article belongs to the Special Issue Meteorological Forecasting and Modeling in Climatology)
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23 pages, 7551 KB  
Article
Development of Automatic Labels for Cold Front Detection in South America: A 2009 Case Study for Deep Learning Applications
by Dejanira Ferreira Braz, Luana Albertani Pampuch, Michelle Simões Reboita, Tercio Ambrizzi and Tristan Pryer
Climate 2025, 13(10), 211; https://doi.org/10.3390/cli13100211 - 8 Oct 2025
Viewed by 449
Abstract
Deep learning models for atmospheric pattern recognition require spatially consistent training labels that align precisely with input meteorological fields. This study introduces an automatic cold front detection method using the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) at [...] Read more.
Deep learning models for atmospheric pattern recognition require spatially consistent training labels that align precisely with input meteorological fields. This study introduces an automatic cold front detection method using the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) at 850 hPa, specifically designed to generate physically consistent labels for machine learning applications. The approach combines the Thermal Front Parameter (TFP) with temperature advection (AdvT), applying optimized thresholds (TFP < 5 × 10−11 K m−2; AdvT < −1 × 10−4 K s−1), morphological filtering, and polynomial smoothing. Comparison against 1426 manual charts from 2009 revealed systematic spatial displacement, with mean offsets of ~502 km. Although pixel-level overlap was low, with Intersection over Union (IoU) = 0.013 and Dice coefficient (Dice) = 0.034, spatial concordance exceeded 99%, confirming both methods identify the same synoptic systems. The automatic method detects 58% more fronts over the South Atlantic and 44% fewer over the Andes compared to manual charts. Seasonal variability shows maximum activity in austral winter (31.3%) and minimum in summer (20.1%). This is the first automatic front detection system calibrated for South America that maintains direct correspondence between training labels and reanalysis input fields, addressing the spatial misalignment problem that limits deep learning applications in atmospheric sciences. Full article
(This article belongs to the Special Issue Meteorological Forecasting and Modeling in Climatology)
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19 pages, 6756 KB  
Article
Future Meteorological Impact on Air Quality in the Po Valley
by Loris Colombo, Alessandro Marongiu, Giulia Malvestiti and Guido Giuseppe Lanzani
Climate 2025, 13(9), 183; https://doi.org/10.3390/cli13090183 - 5 Sep 2025
Viewed by 1235
Abstract
Air quality in the Po Valley (Northern Italy), one of Europe’s most polluted regions, remains a major concern due to its unfavorable orographic setting and intense anthropogenic emissions. Climate change may further hinder progress by modifying meteorological conditions that regulate pollutant dispersion and [...] Read more.
Air quality in the Po Valley (Northern Italy), one of Europe’s most polluted regions, remains a major concern due to its unfavorable orographic setting and intense anthropogenic emissions. Climate change may further hinder progress by modifying meteorological conditions that regulate pollutant dispersion and chemistry. This study applies a modeling framework combining regional climate simulations and chemical transport models to assess the climate penalty, i.e., the adverse impact of climate-driven meteorology on air quality independent of emissions. Three scenarios were analyzed: Baseline Reference Scenario (SRB) (2011–2015), Near-Future Medium Scenario (NF) (2028–2032), and Mid-Future Medium Scenario (2048–2052), with emissions held constant. A mitigation scenario (SC_MF_2050) under the Current Legislation was also tested to accomplish the new EU Ambient Air Quality Directive. Results show that PM10 and NO2 increase under future climates, mainly due to reduced wind speed and precipitation, enhancing pollutant accumulation. Multivariate analyses confirm the strong association between stagnant conditions and higher concentrations. Even with projected emission reductions, compliance with stricter EU targets may not be achieved everywhere. Climate penalty zones, especially in lowland and transport corridors, underscore the need to integrate climate resilience into air quality planning and adopt adaptive strategies for long-term effectiveness. Full article
(This article belongs to the Special Issue Meteorological Forecasting and Modeling in Climatology)
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30 pages, 68660 KB  
Article
Optimizing WRF Configurations for Improved Precipitation Forecasting in West Africa: Sensitivity to Cumulus and PBL Schemes in a Senegal Case Study
by Abdou Aziz Coly, Emmanuel Dazangwende Poan, Youssouph Sane, Habib Senghor, Semou Diouf, Ousmane Ndiaye, Abdoulaye Deme and Dame Gueye
Climate 2025, 13(9), 181; https://doi.org/10.3390/cli13090181 - 29 Aug 2025
Viewed by 857
Abstract
Despite significant progress, precipitation forecasting in West Africa remains challenging due to the complexity of atmospheric processes and the region’s climatic variability. This study aims to identify optimal configurations of the WRF model to improve precipitation forecasting. To evaluate the sensitivity of the [...] Read more.
Despite significant progress, precipitation forecasting in West Africa remains challenging due to the complexity of atmospheric processes and the region’s climatic variability. This study aims to identify optimal configurations of the WRF model to improve precipitation forecasting. To evaluate the sensitivity of the model’s physical parameterizations, 15 configurations were tested by combining various cumulus parameterization schemes (CPSs) and planetary boundary layer (PBL) schemes. The analysis examines two contrasting rainfall events in Senegal: one characterized by widespread intense precipitation and another featuring localized moderate rainfall. Simulated rainfall, temperature, and humidity were validated against rain gauges, satellite products (ENACTS, ARC2, CHIRPS, and IMERG), and ERA5 reanalysis data. The results show that the WRF configurations achieve correlation coefficients (r) ranging from 0.27 to 0.62 against ENACTS and from 0.15 to 0.41 against rain gauges. The sensitivity analysis reveals that PBL schemes primarily influence temperature and humidity, while CPSs significantly affect precipitation. For the heavy rainfall event, several configurations accurately captured the observed patterns, particularly those using Tiedtke or Grell–Devenyi CPSs coupled with the Mellor–Yamada–Janjic (MYJ) PBL. However, the model showed limited skill in simulating localized convection during the moderate rainfall event. These findings highlight the importance of selecting appropriate parameterizations to enhance WRF-based precipitation forecasting, especially for extreme weather events in West Africa. Full article
(This article belongs to the Special Issue Meteorological Forecasting and Modeling in Climatology)
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