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gfdl on-line bibliography > 2007 citations
Evaluation of GFDL and simple statistical model rainfall forecasts for U.S. landfalling tropical storms
| Tuleya, R., M. DeMaria, and R. J. Kuligowski, 2007: Evaluation of GFDL and simple statistical model rainfall forecasts for U.S. landfalling tropical storms. Weather and Forecasting, 22(1), 56-70. |
Abstract: To date, little objective
verification has been performed for rainfall predictions from numerical
forecasts of landfalling tropical cyclones. Until 2001, digital output from
the operational version of the Geophysical Fluid Dynamics Laboratory (GFDL)
hurricane forecast model was available only on a 1° grid. The GFDL model was
rerun or reanalyzed for 25 U.S. landfalling tropical cyclones from 1995 to
2002 to obtain higher resolution (1/3°) output. Several measures of forecast
quality were used to evaluate the predicted rainfall from these runs, using
daily rain gauge data as ground truth. The overall quality was measured by
the mean error and bias averaged over all the gauge sites. An estimate of
the quality of the forecasted pattern was obtained through the correlation
coefficient of the model versus gauge values. In addition, more traditional
precipitation verification scores were calculated including equitable threat
and bias scores. To evaluate the skill of the rainfall forecasts, a simple
rainfall climatology and persistence (R-CLIPER) model was developed, where a
climatological rainfall rate is accumulated along either the forecasted or
observed storm track. Results show that the R-CLIPER and GFDL forecasts had
comparable mean absolute errors of
 0.9 in. (23 mm) for the
25 cases. The GFDL model exhibited a higher pattern correlation with
observations than R-CLIPER, but still only explained
30% of the spatial
variance. The GFDL model also had higher equitable threat scores than R-CLIPER,
partially because of a low bias of R-CLIPER for rainfall amounts larger than
0.5 in. (13 mm). A large case-to-case variability was found that was
dependent on both synoptic conditions and track error. |