Experimental Dynamical Seasonal Forecasts of Tropical Cyclone Activity at IRI

Abstract

The International Research Institute for Climate and Society (IRI) has been issuing experimental seasonal tropical cyclone activity forecasts for several ocean basins since early 2003. In this paper the method used to obtain these forecasts is described and the forecast performance is evaluated. The forecasts are based on tropical cyclone-like features detected and tracked in a low-resolution climate model, namely ECHAM4.5. The simulation skill of the model using historical observed sea surface temperatures (SSTs) over several decades, as well as with SST anomalies persisted from the previous month's observations, is discussed. These simulation skills are compared with skills of purely statistically based hindcasts using as predictors recently observed SSTs. For the recent 6-yr period during which real-time forecasts have been made, the skill of the raw model output is compared with that of the subjectively modified probabilistic forecasts actually issued.Despite variations from one basin to another, the levels of hindcast skill for the dynamical and statistical forecast approaches are found, overall, to be approximately equivalent at fairly modest but statistically significant levels. The dynamical forecasts require statistical postprossessing (calibration) to be competitive with, and in some circumstances superior to, the statistical models. Skill levels decrease only slowly with increasing lead time up to 2-3 months. During the recent period of real-time forecasts, the issued forecasts have had higher probabilistic skill than the raw model output, due to the forecasters' subjective elimination of the ''overconfidence'' bias in the model's forecasts. Prospects for the future improvement of dynamical tropical cyclone prediction are considered.

Changes in intense tropical cyclone activity for the western North Pacific during the last decades derived from a regional climate model simulation

“…However, the coarse spatial resolution of reanalyses limits the possibility of deriving realistic features of meso-scale phenomena like TCs. General circulation models (GCMs) or atmospheric general circulation models (AGCMs) with relatively high horizontal resolution (up to 20 km horizontal resolution) were used recently for future climate projections (Knutson et al 1998;Bengtsson et al 2007;Camargo and Barnston 2009;Oouchi et al 2006;Vitart et al 1997;Kamahori et al 2011;Murakami et al 2012). However, these studies are still not providing univocal results.…”

Retrospective Forecasts of the Hurricane Season Using a Global Atmospheric Model Assuming Persistence of SST Anomalies

“…These models can be used to predict indices such as vertical shear, low-level vorticity, midtropospheric relative humidity, or potential intensity, which can then be related statistically to storm genesis (e.g., ). Or, more ambitiously, the storms developed by the atmospheric models can be used directly (e.g., Vitart 2006;Vitart et al 2007;Camargo and Barnston 2009;LaRow et al 2010). The latter approach obviously becomes more appealing as these dynamical models move to finer horizontal resolution and their simulations of storm statistics become more credible.…”

Predictability of the Mean Location of Typhoon Formation in a Seasonal Prediction Experiment with a Coupled General Circulation Model

“…Seasonal predictions of TCs in the WNP region motivated by these meteorological backgrounds have been performed with statistical models (Chan et al 1998) and dynamical models (Vitart and Stockdale 2001;Camargo and Barnston 2009). In most cases the predictions usually include only the number of TCs in the region.…”