Dr. Ying Chen

Chen Ying

Scientist

Paul Scherrer Institute
Forschungsstrasse 111
5232 Villigen PSI
Switzerland

02.2022 - Present,  Scientist, Paul Scherrer Institut (PSI), Switzerland

2020.12 - 2022.01,   Research Fellow,  College of Engineering, Math & Physical Science, University of Exeter, UK

2017.07-2020.11, Senior Research Associate, Lancaster Environment Centre + Data Science Institute, Lancaster University, UK

2012.02-2013.07, Clean Energy Senior Engineer, China Electric Power Research Institute, China

2009.07-2012.02, Clean Energy Engineer,  State Grid Electric Power Research Institute, China

2013.08-2017.07    Leibniz Institute for Tropospheric Research (TROPOS)                                        Leipzig, Germany

                                   Max-Planck-Institute for Chemistry (MPIC, Joint-Training)                                   Mainz, Germany

                                             Ph.D. degree in Atmospheric Science. Advisor: Prof. Dr. Alfred Wiedensohler

2006.09-2009.07    School of Physics, PeKing University (PKU)                                                           Beijing, China

                                             Master degree in Atmospheric Science

2002.09-2006.07    Nanjing University of Information Science & Technology                                 Nanjing, China

                                            BSc degree in Atmospheric Science.

1) Air pollution, formation mechanism and mitigation strategies.

The overarching goal of this theme is to optimize mitigation strategies for air pollution, which is one of the five leading threaten to public health according WHO. To achieve this goal: i) I develop fundamental understanding and its representation in models to improve process-level modelling; ii) I develop and improve observation method to advance our ability in understanding of aerosol key parameters, and hence better constrain and improve modelling; iii) I combine model with machine-learning to develop emulator to mimic explicit models to enable "scan" of thousands of mitigation strategies to optimize "effective .vs. feasible .vs. economic" decision for policy makers. My contribution in this theme has been awarded "2nd Price for Nature Science Award" by Ministry of Education, China, and also contributed into mitigation strategies in MoES, India.  

At PSI, I am working with AURORA project, where we widely collaborate with a cross-disciplinary team including specialist from PSI, Swiss TPH, Swiss Data Science Centre, and Universities across Europe to develop deeper understanding in source specified health cost of air pollution. The new finding from this project will foster more effective and targeted mitigation strategies to benefit public health over Europe. 

 

2) Climate change, in particular, aerosol-cloud-climate interactions (ACI)

The overarching goal of this theme is to help climate models improve ACI representation and hence improve climate projection. ACI is a leading uncertainty since IPCC 2001 until the recent IPCC AR6 2021, one primary reason of this slow progress is because the ACI large-scale signal has never been directly observed (can only observe neither perturbed or unperturbed clouds at one time), therefore we are lacking of reliable constraints for models. To help community overcame this gap, I pioneering a machine-learning approach to disentangle ACI signal from confounders and derive robust constraints for models. This will open a new horizon for climate modelling, reduce the largest uncertainty in climate models and leading to more reliable climate sensitivity estimate and hence advancing climate projection and impacts estimation. 

 

3) Utilize clean energy to mitigate climate change and air pollution 

The overarching goal of theme is to realize the destinations of Theme 1&2 via facilitating clean energy utilization. One very large challenge for large-scale utilize of wind and solar power is their nature of fluctuation, which is in confliction with electric system's high requirement of stability. To address this problem, I pioneered coupling numerical weather prediction model (and machine-learning technique) with electric dispatching system to optimize the dispatching strategies, therefore enabling large-scale utilization of wind&solar power whlist keeping system safe. My contribution in this theme has enable China cutting CO2 emission for 50M ton/year, my patents help previous employer secure £5M contracts per year. I was awarded the "2nd Price of Science and Technology Achievement of Jiangsu Province, China"

  • 2nd Prize for Natural Science Award, Ministry of Education, China (2018)
  • Outstanding Reviewer of “Environmental Pollution”  (2018)
  • Outstanding Reviewer of “Atmospheric Environment”  (2017)
  • Best Poster Award of EAC 2016, Tours France, European Aerosol Conference (2016)
  • 2ndPrize for Science and Technology Achievement of Jiangsu Province (2012)
  • Excellent Software Award for “Solar Power Forecasting System”, CEPRI (2012)
  • 3rd Prize for Scientific and Technological Achievement, SGEPRI (2011)
  • 2nd class of the Excellent Publication Award, SGEPRI (2010, 2011)
  • Outstanding employee, SGEPRI (2010, 2011)
  • Excellent Master Student of Peking University, Peking University (2009)
  • Chen Y., et al., Patent No.: CN102182629B, A wind power curtail evaluation method based on the wind real-time measurement, 2013 (China)
  • Chen Z.B., Chen Y., et al., Patent No.: CN103530508B, Method for establishing wind speed-power conversion probability model, 2017 (China)
  • Chen Z.B., Chen Y., et al., Patent No.: CN102938562B, Method for forecasting regional wind power, 2015 (China)
  • Cheng X., Chen Y., et al., Patent No.: CN102722760B, Method of regional scale solar power forecast, 2015 (CN)
  • Ding Y., Ding J., Zhou H., Cheng X., Chen Y., et al., Patent No.: CN103544679B, Ground cloud image distortion correction method for full sky imager, 2016 (China)
  • Wang N., Cheng X., Liu G., Zhou H., Ma Y., Cui F., Chen Y., et al., Patent No.: CN103245979B, Radiation resource monitoring system for large-scale solar power of million kilowatts, 2016 (China)

Please find my publication in my Google Scholar: 

‪Ying Chen‬ - ‪GooglScholar