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Aims and Means of Supermodeling by Cross-Pollination in Time
Published on Apr 24, 20141781 Views
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Chapter list
Map of a weather system00:00
Aims & Means of Cross-Pollination in Time: or "Probably not a Probability Forecast"00:55
Model Imperfections01:46
Overview - 105:02
Overview - 205:22
Overview - 305:44
Overview - 405:59
Overview - 506:17
Overview - 606:25
Overview - 706:31
Overview - 806:34
When would Cross Pollination of the models do better than any one of them?06:36
Take Home Points09:07
Challenges in Meteorological Forecasting14:09
Raw (uninformed) CPT (for ONR in the late 90’s)16:42
Different Models Excel at Different Things - 117:57
Different Models Excel at Different Things - 219:09
Different Models Excel at Different Things - 320:48
Different Models Excel at Different Things - 420:57
Different Models Excel at Different Things - 521:04
Different Models Excel at Different Things - 624:30
Different Models Excel at Different Things - 726:33
Different Models Excel at Different Things - 826:43
Laplace's Demon (1814)27:31
Original CPT(2000)29:49
CPT (2000) - 129:50
CPT (2000) - 229:50
Original Ikeda Example - or - Circuit Story29:51
Lorenz ‘95 Two Level System29:52
Given Four Models, each gets one region very well30:43
Three Challenges of CPT31:21
Parameters Estimation via Forecasting P(x) H Du34:48
Parameter Estimation: Correct Model Structure36:08
Parameter Estimation: Imperfect Model Structure37:56
Parameter Estimation: IGN in the Logistic Map Model38:36
The target40:05
Parameter Estimation: IGN in the HenonMap42:04
Outside PMS: Target Parameter depends on Noise Level and Lead Time42:05
What does a good IC ensemble give us?42:07
Lorenz 96 One Layer Model Worked Example - 143:35
Lorenz 96 One Layer Model Worked Example - 243:42
CPT2Lorenz 96 M=40 One Layer Model44:11
Lorenz 96 M=40 Worked Example - 145:55
Lorenz 96 M=40 Worked Example - 248:05
Lorenz 96 M=4 Worked Example53:40
Probability Forecasts: Chaos53:44
Fitzroy, 186256:22
Model-based probability forecasts57:26
Publications01:00:51