Abstract
Abstract Errors in tropical cyclone intensity forecasts are dominated by initial-condition errors out to at least a few days. Initialization errors are usually thought of in terms of position and intensity, but here it is shown that growth of intensity error is at least as sensitive to the specification of inner-core moisture as to that of the wind field. Implications of this finding for tropical cyclone observational strategies and for overall predictability of storm intensity are discussed.
Highlights
Prediction of tropical cyclone intensity remains a significant challenge, with little improvement in forecast skill over the past few decades (DeMaria et al 2014)
They showed that forecast intensity error out to a few days is dominated by errors in the initial wind field, after which errors in forecasting the large-scale environment begin to dominate through their effects on the track of and wind shear experienced by the storms
This result generalizes our findings from the single case study of Hurricane Joaquin: failure to properly initialize inner-core water vapor in the free troposphere can be a large source of tropical cyclone intensity error
Summary
Prediction of tropical cyclone intensity remains a significant challenge, with little improvement in forecast skill over the past few decades (DeMaria et al 2014). In a recently published paper (Emanuel and Zhang 2016), the authors attempted to quantify the intrinsic predictability of tropical cyclone intensity and to distinguish the various causes of loss of predictability, using a perfect model framework to isolate the intrinsic predictability They showed that forecast intensity error out to a few days is dominated by errors in the initial wind field, after which errors in forecasting the large-scale environment begin to dominate through their effects on the track of and wind shear experienced by the storms. Tang and Emanuel (2010) showed quantitatively how ventilation of the tropical cyclone core reduces the storm’s intensity and that too much ventilation will destroy it altogether This is consistent with Pauluis and Held’s (2002) observation that entropy production is dominated by mixing of dry and moist air, subtracting from that which could be used for kinetic energy dissipation. We introduce a new toy intensity model, consisting of a pair of ordinary differential equations, designed to mimic the behavior of the full CHIPS model, and use this to assess error growth in a simple forecast system
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