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SUMMARY:Machine learning seminar series - Probabilistic downscaling to det
 ect regional present and future climate hazards
DTSTART:20200428T103000Z
DTEND:20200428T160000Z
DTSTAMP:20260315T174400Z
UID:indico-event-189@events.ecmwf.int
CONTACT:events@ecmwf.int
DESCRIPTION:\n\nHost\n\nPeter Dueben (ECMWF)\n\nSpeakers\n\nSherman Lo hol
 ds a PhD in statistics and is currently working\, at the University of War
 wick\, on precipitation forecasting of the UK using climate models. He has
  obtained degrees in Physics and Computational Statistics & Machine Learni
 ng at University College London. He is interested in applied and computati
 onal statistics and has previously worked on x-ray imaging for 3D printing
 \, fusion energy and chromosome imaging.\n\nRitabrata Dutta is an Assistan
 t Professor in the Department of Statistics\, University of Warwick\, broa
 dly working on the applications of statistical methodology in the domain o
 f meteorology\, population genetics and complex bio-medical processes. He 
 has developed methods for Approximate Bayesian computation (ABC)\, collabo
 rated with Swiss National Supercomputing Centre (CSCS) to develop a Python
  package for ABC utilizing HPC infrastructure optimally and applied ABC me
 thodology to learn expensive mechanistic models explaining epidemic proces
 ses on network\, molecular dynamics of water\, arterial flow of blood\, vo
 lcanic eruption and passenger dynamics in airports. Presently Dr Dutta is 
 supervising 6 PhD students and 1 Postdoc\, working broadly on methodologic
 al developments of statistics and machine learning\, applications in the d
 omain sciences. He is currently PI of a project on Quantifying effects of 
 climate change on extreme weather events via distributional downscaling fu
 nded by the Alan Turing Institute (ATI) as well as a special project of EC
 MWF on Data-driven calibration of stochastic parametrization of IFS using 
 ABC.\n\nAbstract\n\nWe present a statistical methodology to do forecasting
  of precipitation at 0.1° resolution using model fields from computer sim
 ulations (air temperature\, geopotential\, specific humidity\, total colum
 n water vapour\, wind velocity) at a lower resolution (5/9° and 5/6° lon
 /lat). Observed precipitation at the 0.1° resolution for the past 4 decad
 es was used to fit our model onto it in order to do forecasting with quant
 ifiable uncertainty.\n\nOur model is the compound Poisson distribution (Re
 vfeim\, K.J.A.\, 1984\, Dunn\, P.K.\, 2004) which can model both occurrenc
 e and quantity of precipitation as a random variable. We impose time autoc
 orrelation by introducing auto-regressive and moving average terms into th
 e model. Spatial dependencies were introduced by putting a Gaussian proces
 s prior on the parameters.\n\nThe model fields were interpolated\, with un
 certainty bars\, from low resolution to high resolution using a Gaussian p
 rocess. The entire Bayesian inference (or model fitting) was done using a 
 Gibbs sampling scheme consisting of Metropolis-Hastings\, slice sampling a
 nd elliptical slice sampling.\n\nhttps://events.ecmwf.int/event/189/
IMAGE;VALUE=URI:https://events.ecmwf.int/event/189/logo-528950275.png
LOCATION:11:30 BST
URL:https://events.ecmwf.int/event/189/
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