Climate, weather, space weather: model development in an operational context
Doris Folini

TL;DR
This paper discusses the development and evaluation of models for climate, weather, and space weather forecasting, emphasizing the need for collaboration, technological advances, and balancing stability with innovation.
Contribution
It highlights common elements across modeling communities and advocates for joint development and collaboration to enhance operational forecast models.
Findings
Operational model development is driven by skill, physics, technology, and observational needs.
Interaction between research and operational teams improves model quality.
Large collaborations and supercomputing are essential for future progress.
Abstract
Aspects of operational modeling for climate, weather, and space weather forecasts are contrasted, with a particular focus on the somewhat conflicting demands of 'operational stability' versus 'dynamic development' of the involved models. Some common key elements are identified, indicating potential for fruitful exchange across communities. Operational model development is compelling, driven by factors that broadly fall into four categories: model skill, basic physics, advances in computer architecture, and new aspects to be covered, from costumer needs over physics to observational data. Evaluation of model skill as part of the operational chain goes beyond an automated skill score. Permanent interaction between 'pure research' and 'operational forecast' people is beneficial to both sides. This includes joint model development projects, although ultimate responsibility for the…
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