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The Challenge of Weather and Climate Prediction (2nd Edition)

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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2033

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Special Issue Editors

Dr. Kreso Pandzic

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Guest Editor

Independent Researcher, Zagreb, Croatia
Interests: data assimilation methods for numerical weather prediction; ensemble forecasts; seasonal and climate forecasting; verification of weather and climate forecasting and outlooks
Special Issues, Collections and Topics in MDPI journals

Dr. Tanja Likso

E-Mail Website
Guest Editor

Croatian Meteorological and Hydrological Service, Ravnice 48, 10000 Zagreb, Croatia
Interests: boundary-layer meteorology (application of Monin–Obukhov similarity theory for the wind speed estimation in the lower part of the atmospheric surface layer)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues，

This Special Issue is the second volume of the Special Issue titled "The Challenge of Weather and Climate Prediction”, which was published in Atmosphere in 2025: https://www.mdpi.com/journal/atmosphere/special_issues/VI821FU9JO.

Weather and climate prediction (forecasting) are among the greatest challenges faced in theoretical and applied atmospheric science. Both require comprehensive theoretical knowledge and extensive operational systems. The first successful numerical weather forecast for one day in advance was achieved in the 1950s, when computers began to operate and so-called filtered atmospheric models were utilized. During the 1990s, a novel but basic equation atmospheric model was employed on a global scale; this was operational after the application of a specific data assimilation procedure. Very soon after that, the application of coupled atmosphere and ocean models resulted in the prolongation of the forecasting period of up to five or six days in advance. Unfortunately, due to the presence of deterministic chaos in the atmosphere–ocean dynamic system, long-range forecasting is limited. Ensemble forecasting is a useful probabilistic component of the forecasting system as it is able to inform us of the reliability of medium-range weather forecasting. As time goes on, the reliability decreases; thus, after 10 days, medium-range weather forecasts are considered non-reliable. However, if atmosphere–ocean global models are considered boundary condition problems instead of initial condition problems, then useful seasonal and even centennial outlooks can be achieved, but with “condensed” products. Determining the limitations of weather and climate forecasting can be achieved by comparing forecasts with real observations, i.e., via forecast verification.

Dr. Kreso Pandzic
Dr. Tanja Likso
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 250 words) can be sent to the Editorial Office for assessment.

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

data assimilation for numerical weather and climate forecasting
application of atmospheric and oceanic models
ensemble forecasting
weather and climate forecasting verification

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Published Papers (2 papers)

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Research

21 pages, 1254 KB

Open AccessEditor’s ChoiceArticle

Solar and Anthropogenic Climate Drivers: An Updated Regression Model and Refined Forecast

by Frank Stefani

Atmosphere 2026, 17(3), 252; https://doi.org/10.3390/atmos17030252 - 28 Feb 2026

Viewed by 1205

Abstract

Recently, an attempt was made to quantify the respective solar and anthropogenic influences on the terrestrial climate, and to cautiously predict the global mean temperature over the next 130 years. In a double regression analysis, both the binary logarithm of carbon dioxide concentration and the geomagnetic aa index were used as predictors of the sea surface temperature (SST) since the mid-19th century. The regression results turned out to be sensitive to end effects, leading to a disconcertingly broad range of the climate sensitivity between 0.6 K and 1.6 K per doubling of CO₂ when varying the final year of the data used. The aim of this paper is to significantly narrow down this range. To this end, the correlations between the two predictors and the dependent variable (SST) are analysed in detail. It is demonstrated that the SST can be predicted until around 2000 almost perfectly using only the aa index, whereas for later periods the role of CO₂ increases significantly. Therefore, the weight of the aa index is fixed to its very robust outcome (around 0.04 K/nT) from the single and double regressions up to 1990. The SST data, reduced by the aa contribution thus specified, are then used in a single regression with CO₂ as the only remaining predictor. This results in a significant reduction in the range of CO₂ sensitivity, narrowing it to 1.1–1.4 K. Given the exceptionally high temperatures in recent years, these values are considered a kind of upper limit that could still be subject to downward corrections when future data are incorporated. Based on this estimate, a prediction of the temperature up to the year 2100 is ventured, assuming various constant emission scenarios combined with a linear sink model for atmospheric CO₂ content. The most risky factor in this prediction is the future of the aa index. For its forecast, the results of a recently developed synchronization model of the solar dynamo are tentatively employed. Full article

(This article belongs to the Special Issue The Challenge of Weather and Climate Prediction (2nd Edition))

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23 pages, 6865 KB

Open AccessArticle

A Comprehensive Evaluation of Evapotranspiration in Mainland Portugal Based on Climate Reanalysis Data

by João Pedro Pegas, João Filipe Santos and Maria Manuela Portela

Atmosphere 2026, 17(2), 215; https://doi.org/10.3390/atmos17020215 - 18 Feb 2026

Viewed by 461

Abstract

Gridded meteorological data sources, such as reanalysis datasets, are increasingly used to estimate evapotranspiration, a key variable for surface water-budget analyses at regional and national scales and for assessing plant water requirements for irrigation. This study, conducted over mainland Portugal for the 44-year [...] Read more.

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) and reference (

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) evapotranspiration at a 0.1° spatial resolution using daily data. Estimates derived from two high-resolution datasets (GLEAM and ERA5-Land) are compared with those obtained from the Thornthwaite, Hargreaves–Samani, and Penman–Monteith models. Secondly, trend analyses of

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magnitudes on a monthly and annual basis in a gridded format were conducted. The resulting spatial distributions of

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and

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show higher values in milder and flatter southern Portugal and lower values in the cooler and more mountainous northern regions, in agreement with existing knowledge. The Penman–Monteith model exhibited the highest reliability, while the Thornthwaite model generally underestimated evapotranspiration across the country, and the Hargreaves–Samani model showed underestimation in coastal areas. Trend analysis of

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indicates an overall increase in atmospheric evaporative demand over the full study period, with a more pronounced rise during the recent 22-year period (2002–2023) compared with the earlier period (1980–2001). These increases are statistically significant in August and October and may reflect a climate shift towards a progressively longer dry season. Understanding how changes in evapotranspiration affect hydrological processes—including surface water availability, river discharge, reservoir performance, and crop requirement—is critical. This study aims to contribute to addressing these emerging challenges. Full article