Chonghua Yin
Senior Climate Scientist
New Zealand
Summary
👤
Senior🎓
Top SchoolChonghua Yin is a Senior Climate Scientist and Head of Data Science based in New Zealand with over a decade applying machine learning and software engineering to climate risk, extreme event diagnostics, and seasonal forecasting. He designs and leads development of climate analytics platforms, probabilistic forecasting tools, and GIS-based risk systems that operationalize satellite and model data for decision support. His background spans hands-on software development (C/C++, Delphi, SQL) and instrument design, enabling rigorous data governance and production-ready pipelines for downscaling, bias correction, and hydrological/vegetation monitoring. At ClimSystems and the International Global Change Institute he drives AI-led natural hazard modeling (wildfires, floods, droughts) and real-time early warning systems that bridge research and applied resilience planning. He holds a PhD in Environmental Science and combines deep domain expertise with practical system-building to turn large-scale meteorological data into actionable insights.
10 years of coding experience
6 years of employment as a software developer
Master of Meteorology, Global Change, Master of Meteorology, Global Change at Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), ChinaDoctor of Philosophy (PhD), Environmental Science, Doctor of Philosophy (PhD), Environmental Science at University of Waikato
English, Chinese
Github Skills (69)
geoviews10
wavelet10
statistic10
mat10
gtk10
geodesy10
eof10
standard-deviation10
deviation10
matlab10
csv10
holoviews10
decomposition10
spectral10
grids10
Programming languages (6)
Github contributions (5)
royalosyin/Practice-SQL-with-SQLite-and-Jupyter-Notebook
Oct 2018 - Apr 2021
Contributions:44 commits, 40 pushes, 1 branch in 2 years 7 months
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies
Sep 2017 - Oct 2019
This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python.
Contributions:41 commits, 1 PR, 41 pushes in 2 years 1 month
csvsignalclimateparticularpython
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