BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20260925T065456EDT-8207MXaHdR@132.216.98.100 DTSTAMP:20260925T105456Z DESCRIPTION:Please join us as we welcome Dr. S. Lovejoy and Dr.  L. Del Rio Amador from the department of Physics at McGill University for their semi nar titled 'Harnessing butterflies for improved monthly\, seasonal\, and i nterannual forecasts'. Coffee will be served.\n\nAbstract\n\nOver the past ten years\, a key advance in our understanding of atmospheric variability is the discovery that between the weather and climate regime lies an inte rmediate “macroweather” regime\, spanning the range of scales from ≈ 10 da ys to ≈30 years (in the anthropocene\; it is longer in the pre-anthropocen e).   Macroweather statistics are characterized by two fundamental symmetr ies: scaling and the factorization of the joint space-time statistics.  In the time domain\, the scaling has low intermittency with the additional p roperty that successive fluctuations tend to cancel.  In space\, on the co ntrary the scaling has high intermittency corresponding to the existence o f different climate zones.  \n\nThese properties are fundamental for macro weather forecasting.  For example:\n\n\n The temporal scaling implies that the system has a long range - indeed elephantine - memory that can be expl oited for forecasting.\n The low temporal intermittency implies that mathem atically well-established (Gaussian) forecasting techniques can be used.\n The statistical factorization property implies that although spatial corre lations are large\, that they are not useful in making forecasts.\n\n\nThe se properties can be directly exploited by the Stochastic Seasonal and Int erannual Prediction System (StocSIPS).  StocSIPS is a straightforward\, hi ghly efficient forecasting system that makes global\, monthly\, seasonal a nd interannual forecasts.  Using hindcasts\, we compare StocSIPS with Envi ronment Canada’s CanSIPS model\, finding that most of the earth\, for hori zons beyond about one month\, that StocSIPS is significantly more accurate . \n\nStocSIPS’ advantages include:\n\n\n Convergence to the real – not mod el - climate: The key to StocSIPS skill is the forecasting module that use s past data – and the huge memory in the system - to ensure that the forec ast converges to the real world climate.\n Speed: In order to get good stat istics\, conventional seasonal to annual forecasts typically re-forecast o ver ten to twenty realizations\, each time using slightly different initia l data often taking the equivalent of hundreds of thousands of CPU hours o n the world’s fastest computers.  In comparison\, StocSIPS uses only a few minutes of CPU time to directly calculate the statistics of an infinite n umber of realizations.\n No data assimilation: StocSIPS can directly foreca st either gridded or individual station data\, there is no need to transfo rm the input data to make it digestible by the numerical model\; StocSIPS avoids complex data “assimilation” techniques.  \n No ad hoc post processin g: The raw temperatures and precipitation rates forecast by conventional m odels have unrealistic variability.  This is usually “corrected” using com plex ad hoc post processing algorithms that use hindcasts to incorporate p ast information in order to make the forecasts more realistic.  StocSIPS u ses only past information with a theoretically justified forecast procedur e.\n No need for downscaling: Conventional models have pixels of 100\,000 k m2 or more in size and must be “downscaled” to adapt them to local conditi ons.   Whenever long station temperature series are available\, StocSIPS c an forecast them directly.\n\n DTSTART:20160411T193000Z DTEND:20160411T203000Z LOCATION:Room 934\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue Sherbrooke Ouest SUMMARY:Seminar: Dr. S. Lovejoy and Dr. L. Del Rio Amador URL:https://www.mcgill.ca/meteo/channels/event/seminar-dr-s-lovejoy-and-dr- l-del-rio-amador-260066 END:VEVENT END:VCALENDAR