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Advances in Statistical Climatology, Meteorology and Oceanography is a peer-reviewed journal published by Copernicus Publications, covering Climate variability and models, Meteorological Phenomena and Simulations, Hydrology and Drought Analysis. Over its lifetime it has published 111 papers, which have received 1,284 citations.
| Publisher | Copernicus Publications |
|---|---|
| Reference style | Author–year (Copernicus) Author–year — (Smith, 2023) in the text Smith, A., Jones, B., and Lee, C.: A representative article title, Advances in Statistical Climatology, Meteorology and Oceanography, 12, 45–58, 2023.
Formats any DOI in Advances in Statistical Climatology, Meteorology and Oceanography style. No sign-up. |
| Publishes research in | Climate variability and models Meteorological Phenomena and Simulations Hydrology and Drought Analysis Atmospheric and Environmental Gas Dynamics Plant Water Relations and Carbon Dynamics |
| ISSN | 2364-3579 |
| Citation impact (2-yr) | 2.4 |
| h-index | 16 |
| i10-index | 27 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | advances-statistical-climatology-meteorology-oceanography.net |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract. Over the last few years, methods have been developed to answer questions on the effect of global warming on recent extreme events. Many “event attribution” studies have now been performed, a sizeable fraction even within a few weeks of the event, to increase the usefulness of the results. In doing these analyses, it has…
Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model…
Abstract. The goal of the attribution of individual events is to estimate whether and to what extent the probability of an extreme climate event evolves when external conditions (e.g., due to anthropogenic forcings) change. Many types of climate extremes are linked to the variability of the large-scale atmospheric circulation. It is hence essential to decipher…
Abstract. Deep learning (DL) methods were used to develop an algorithm to automatically detect weather fronts in fields of atmospheric surface variables. An algorithm (DL-FRONT) for the automatic detection of fronts was developed by training a two-dimensional convolutional neural network (2-D CNN) with 5 years (2003–2007) of manually analyzed fronts and surface fields of five…
Abstract. Daily meteorological data such as temperature or precipitation from climate models are needed for many climate impact studies, e.g., in hydrology or agriculture, but direct model output can contain large systematic errors. A large variety of methods exist to adjust the bias of climate model outputs. Here we review existing statistical bias-adjustment methods and…
