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The New Zealand Modelling Consortium WWW.ENVLIB.ORG

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posted on 04.03.2022, 01:06 by eRNZ AdmineRNZ Admin, Marwan KaturjiMarwan Katurji, Jiawei ZhangJiawei Zhang, Mike Kittridge, Peyman Zawar-Reza

Atmospheric and environmental research and applications often rely on regional climate and numerical weather model simulations, which all consume considerable computational energy and time from high performance computational clusters. The lack of easy access to these numerical model outputs is unfortunately supported by a closed data source policy or community unawareness of available open source data. Open source weather, climate and environmental data can further help bring New Zealand on-par with the international science community where such initiatives have increased creativity, nurtured development and built stronger collaborative networks.

We have built an open environmental digital library, which we aim to form the scientific and data hub of New Zealand’s open environmental data. We are supporting this initiative with the establishment of the New Zealand Modelling Consortium, a collection of Universities, Research Institutes and private organisations that supports the development of model simulation outputs for a diverse range of scientific disciplines (climate science, air pollution and health, weather hazards, ecology and biodiversity, and urban design and development). We have demonstrated an example of a successful partnership with a professional end-user who used our open source data and supported the availability of this newly developed dataset for the community.

Data access is facilitated by a web API and python library ( which utilizes high performance computational resources for efficient data access. We are also building interactive data access/analysis toolkits using Jupyter Notebooks to reduce the development time of environmental research tools and provide a virtual community hub for education and research.

We have received positive feedback from research organisations, universities and consultancy companies. Our partner list is expanding with more organisations and companies contributing to the Open Environmental Digital Library. Recently, we have also teamed up with MetService to host 25-years of the Weather Research and Forecasting (WRF) data to accompany University of Canterbury’s existing WRF database. Please see for other examples.

Moving forward, we hope to collaborate with NESI and build data analysis, machine learning and computational toolkits within the Open Environmental Digital Library. This will help researchers, educators, and students to better utilize the open environmental digital library and the national computational resource.


Dr Marwan Katurji is a senior lecturer in the School of Earth and Environment, University of Canterbury. He specialises in surface-atmosphere interactions and the dynamics of the atmospheric boundary layer. His research interest is in modelling, simulating, measuring and analysing atmospheric phenomena, using advanced in-situ, aerial and remote sensing measurements and advanced numerical modelling techniques.

Dr. Jiawei Zhang is a senior scientist in the School of Earth and Environment, University of Canterbury. He is an atmospheric scientist with a special interest in boundary layer meteorology and wildfire. His expertise includes large eddy simulations of boundary layer dynamics.

Dr. Mike Kittridge is a data scientist/hydrologist working on a variety of applied research projects across New Zealand. This includes projects with regional councils, research projects with universities (including the University of Canterbury), and research projects with other government and non-government organisations in New Zealand. All of these projects have a hydrologic/atmospheric science component and a data science component.

Peyman Zawar-Reza is a professor in the School of Earth and Environment with Research interests in air pollution climatology/meteorology, boundary layer meteorology, and mountain meteorology, mesoscale numerical modelling and computational physics, and regional climate modelling (dynamical downscaling).