Five priorities for weather and climate research

Hov, Øystein; Terblanche, Deon; Carmichael, Gregory R.; Jones, Sarah; Ruti, Paolo; Tarasova, Oksana

doi:10.1038/d41586-017-08463-3

Cited by 18 publications

(11 citation statements)

References 3 publications

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“…In current numerical weather-prediction and climate models, the landatmosphere continuum is separately treated and parameterized (Hov et al, 2017;WMO, 2015). As a result, global models lack dynamic and static heterogeneity on small spatiotemporal scales (i.e., seconds and meters), thereby missing essential interactions that play a vital role in the formation of cumuli.…”

Section: Introductionmentioning

confidence: 99%

Substantial Reductions in Cloud Cover and Moisture Transport by Dynamic Plant Responses

Sikma

Arellano

2019

Geophysical Research Letters

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Cumulus clouds make a significant contribution to the Earth's energy balance and hydrological cycle and are a major source of uncertainty in climate projections. Reducing uncertainty by expanding our understanding of the processes that drive cumulus convection is vital to the accurate identification of future global and regional climate impacts. Here we adopt an interdisciplinary approach that integrates interrelated scales from plant physiology to atmospheric turbulence. Our explicit simulations mimic the land‐atmosphere approach implemented in current numerical weather prediction, and global climate models enable us to conclude that neglecting local plant dynamic responses leads to misrepresentations in the cloud cover and midtropospheric moisture convection of up to 21% and 56%, respectively. Our approach offers insights into the key role played by the active vegetation on atmospheric convective mixing that has recently been identified as the source of half of the variance in global warming projections (i.e., equilibrium climate sensitivity).

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Section: Introductionmentioning

confidence: 99%

Substantial Reductions in Cloud Cover and Moisture Transport by Dynamic Plant Responses

Sikma

Arellano

2019

Geophysical Research Letters

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“…These include the sustainable use of our oceans, and security for our coastal populations, among others. As such there is increasing convergence on these common goals which can act as catalysts to assemble human talent, to explore business and technology opportunity, and build collaborations (Hov et al, 2017).…”

Section: Converging On Common Goalsmentioning

confidence: 99%

The Role of Stakeholders in Creating Societal Value From Coastal and Ocean Observations

et al. 2019

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The importance of stakeholder engagement in ocean observation and in particular the realization of economic and societal benefits is discussed, introducing a number of overarching principles such as the convergence on common goals, effective communication, co-production of information and knowledge and the need for innovation. A series of case studies examine the role of coordinating frameworks such as the United States' Interagency Ocean Observing System (IOOS R), and the European Ocean Observing System (EOOS), public-private partnerships such as Project Azul and the Coastal Data Information Program (CDIP) and finally the role of the "third" or voluntary sector. The paper explores the value that stakeholder engagement can bring as well as making recommendations for the future.

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“…Weather forecast [21], [22], [23] is not the only area that demands optimization to reach high resolution results. Biology [24], [25] and medicine [26] are examples of areas that require simulations of physical models in high resolution and could benefit from the findings provided in this paper.…”

Section: Related Workmentioning

confidence: 99%

DeepDownscale: A Deep Learning Strategy for High-Resolution Weather Forecast

Rodrigues¹,

Oliveira²,

Cunha³

et al. 2018

2018 IEEE 14th International Conference on E-Science (E-Science)

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Running high-resolution physical models is computationally expensive and essential for many disciplines. Agriculture, transportation, and energy are sectors that depend on highresolution weather models, which typically consume many hours of large High Performance Computing (HPC) systems to deliver timely results. Many users cannot afford to run the desired resolution and are forced to use low resolution output. One simple solution is to interpolate results for visualization. It is also possible to combine an ensemble of low resolution models to obtain a better prediction. However, these approaches fail to capture the redundant information and patterns in the lowresolution input that could help improve the quality of prediction. In this paper, we propose and evaluate a strategy based on a deep neural network to learn a high-resolution representation from low-resolution predictions using weather forecast as a practical use case. We take a supervised learning approach, since obtaining labeled data can be done automatically. Our results show significant improvement when compared with standard practices and the strategy is still lightweight enough to run on modest computer systems.Author pre-print. Paper accepted for publication at 14th IEEE eScience.

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Five priorities for weather and climate research

Abstract: Adapt to how data are made and used, urge Øystein Hov and colleagues.

Cited by 18 publications

References 3 publications

Substantial Reductions in Cloud Cover and Moisture Transport by Dynamic Plant Responses

Substantial Reductions in Cloud Cover and Moisture Transport by Dynamic Plant Responses

The Role of Stakeholders in Creating Societal Value From Coastal and Ocean Observations

DeepDownscale: A Deep Learning Strategy for High-Resolution Weather Forecast

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