Uncertainty in hydrological model variables, if left unaccounted for, introduces systematic
tematic biases in parameter estimates. We introduce here a method to determine the true value of parameter-specific uncertainties in model inputs. This method, known as simulated extrapolation (SIM), is based on running empirical relationships between parameters and levels of input noise (or uncertainty). The method begins by generating a series of candidate models that are artificially varied by adding white noise to the Dutch multiples of the known error variance. The resulting parameter set allows us to formulate empirical relationships between latent values ??and noise levels. SGX is
based on the theory that this trend in alternating parameters can be extrapolated back to
a nominal error zone.
Illustrated the strength of our improved skill forecast model in the Singapore International Monetary Exchange, using
Uncertain Sea Surface Temperature Anomalies (SSTA) data over the Nino3 region as Forecast -
The Tor Southern Oscillation Index (SOI), a candidate measure of the strength of the El Ni?o Southern Oscillation. Our hypothesis is that the higher amplitude noise in the pre-1960 period data introduces a bias to model parameters such as sea surface temperature anomalies that are input variables.
The relative error is the constant Southern Oscillation Index (SOI), which is regressed over sea surface temperature anomalies and
calibrated using a subseries from 1900 to 1960. We validated the resulting pattern
with less erroneous data for the period 1960 to 2003. The overall application is found in SGX to reduce residual forecast errors during the validation period. 2007 Elsevier Inc. B. All rights reserved.
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