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Helen
Greatrex
Assistant Professor
Helen Greatrex

Biography

Helen Greatrex is an Assistant Professor of Remote Sensing and Geospatial Analysis at Penn State, split between the departments of Geography and Statistics. She is also a co-hire of the Institute of Computational Data Science

Greatrex received her Ph.D. in Meteorology from the University of Reading (UK) in 2012. She received a post-graduate diploma in Atmosphere and Ocean Science in 2007 from Reading and an M.Phys. in Physics with Astrophysics from the University of Manchester (UK) in 2006.

Greatrex's research interests include geo-statistics and end-user driven weather statistics within the field of weather risk and international development. Her focus is on how we can better use products such as satellite rainfall to make better decisions, for example improving validation metrics, mechanistic crop/soil/health/insurance modelling and historical burn analysis. Current projects include assessing the impact of rainfall on the disease hydrocephalus, designing livelihood based weather risk metrics in Somalia and assessing the impact of flash floods. See more here https://www.helengreatrex.com/research-agenda.

Greatrex joined Penn State in 2019 after working in climate adaptation consultancy and as an associate research scientist at the International Research Institute for Climate and Society (Columbia U.). She has also contributed the University of Reading's TAMSAT rainfall research group as a scientist since 2007. She works closely with the American Meteorological Society (AMS), recently chairing a Presidential Forum on the gulf between meteorologists and the humanitarian sector. Greatrex is a member of AMS, the Royal Meteorological Society and the Royal Anthropological Society.

 

Honors and Awards

AMS Editor's award for reviewing, for the journal Weather Climate and Society.

 

Publications

  • H. Greatrex, J. Hansen, S. Garvin, R. Diro, S. Blakeley, M. Le Guen, K. Rao, D. Osgood, (2015), “Scaling up index insurance for smallholder farmers: Recent evidence and insights”. CCAFS Report No. 14. Copenhagen, Denmark: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) – https://tinyurl.com/tvsvuaf
     
  • H. Greatrex, D.I.F. Grimes, and T.R. Wheeler, (2014), “Advances in the Stochastic Modelling of Satellite-Derived Rainfall Estimates Using a Sparse Calibration Dataset”. Journal of Hydrometeorology, 15, 1810–1831
     
  • E. Fisher, J. Hellin, H. Greatrex, N. Jenson, 2018 , “Index-based agricultural insurance: Addressing equity and differential socio-economic impact”, Development Policy Review https://doi.org/10.1111/dpr.12387
     
  • R. Maidment, D.I.F.Grimes, E. Black, E. Tarnavsky, H. Greatrex, R. Allan, T. Stein, E. Nkonde, S. Senkunda, E.U. Alecantara, 2017 “A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa”, Nature Scientific Data, 4
     
  • E. Black, E. Tarnavsky, R.I. Maidment, H. Greatrex, A. Mookerjee, T. Quaife, M. Brown (2016), “The use of remotely sensed rainfall for managing drought risk: a case study of weather index insurance in Zambia”, Remote Sensing 8 (4), 342
     
  • Black, E., Greatrex, H., Young, M., & Maidment, R. (2016). “Incorporating Satellite Data Into Weather Index Insurance”. Bulletin of the American Meteorological Society, 97(10)
     
  • G. Rose, T. Osborne, H. Greatrex, T.R. Wheeler (2016), “Impact of progressive global warming on the global-scale yield of maize and soybean”, Climatic Change 134 (3), 417-428
     
  • C.J. Skinner, T. Bellerby, H. Greatrex, D.I.F. Grimes (2015), “Hydrological Modelling using Ensemble Satellite Rainfall Estimates in a Sparsely Gauged River Basin: The Need for Whole-Ensemble Calibration”, Journal of Hydrology, 522, 110-122
     
  • R.I. Maidment, D.I.F. Grimes, R.P. Allan, H. Greatrex, O. Rojas, and O. Leo, O. (2013), “Evaluation of satellite-based & model re-analysis rainfall estimates for Uganda”. Meteorological Applications, 20 (3), 308–317

 

Teaching

STAT 462 - Applied Regression Analysis: Spring 2020

GEOG 364 - Spatial Analysis: Fall 2019/Fall 2020