2. 22/11/2005T. NIEDZIELSKI & W. KOSEK; Coastal Governance, Planning, Design and GI, 21st - 26th November 2005, Nice, France 2

3. 22/11/2005T. NIEDZIELSKI & W. KOSEK; Coastal Governance, Planning, Design and GI, 21st - 26th November 2005, Nice, France 3 Forecasting sea level anomalies (SLA) »Differences between the best estimate of the see surface height and the mean sea surface Application of the sea surface temperature (SST) as an explanatory variable for SLA predictions Data A.SLA – TOPEX/Poseidon satellite altimetry, monthly (gridded) B.SST – NOAA OI.v2 SST monthly fields (gridded) Averaged

4. 22/11/2005T. NIEDZIELSKI & W. KOSEK; Coastal Governance, Planning, Design and GI, 21st - 26th November 2005, Nice, France 4 Multivariate time series techniques –The multivariate time series corresponds to the multivariate stochastic process –Transformation of the data to obtain residuals –Modelling residuals using multivariate autoregressive models (MAR) –Forecasting a MAR process –Forecasting the „real” data

5. 22/11/2005T. NIEDZIELSKI & W. KOSEK; Coastal Governance, Planning, Design and GI, 21st - 26th November 2005, Nice, France 5 MAR(2) is fitted to the residuals by the Bayes-Schwartz Criterion (SBC) MAR(11) is fitted to the residuals by Akaike Information Criterion (AIC) The forecasts based upon MAR(11) are more accurate than the predictions based on MAR(2) The precision of the bivariate (SLA&SST) MAR-based forecast is better than for the forecast based on univariate autoregressive models of the same order MAR MAR(2)MAR(11)

6. 22/11/2005T. NIEDZIELSKI & W. KOSEK; Coastal Governance, Planning, Design and GI, 21st - 26th November 2005, Nice, France 6 To perform the similar procedure for each of the available grids 10-variate time series As a result we yield a set of maps (forecasts) (1-month, 2-month,…) Problems: A.Automatic selection of an order of a MAR process at each location B.Can we extrapolate the global results and utilize AIC Source: http://www.cbk.waw.pl/~kosek

7. 22/11/2005T. NIEDZIELSKI & W. KOSEK; Coastal Governance, Planning, Design and GI, 21st - 26th November 2005, Nice, France 7 The project will lead to the answers to the following questions: –How does the SST influence the SLA forecasts at the dissimilar locations? –What is the difference between the accuracies of predictions at the dissimilar location? –How does the vicinity of the land influence the precision of forecasts? –Is it possible to forecast El Nino extreme events? The possible outputs for users –An automatic computer algorithm which generates and updates the SLA forecast for dissimilar locations in the World –Queries seeking locations at which the SLA predictions fulfill the previously assumed conditions

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