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Jonathan R. Moskaitis

Abstract

Deterministic predictions of tropical cyclone (TC) intensity from operational forecast systems traditionally have been verified with a summary accuracy measure (e.g., mean absolute error). Since the forecast system development process is coupled to the verification procedure, it follows that TC intensity forecast systems have been developed with the goal of producing predictions that optimize the chosen summary accuracy measure. Here, the consequences of this development process for the quality of the resultant forecasts are diagnosed through a distributions-oriented (DO) verification of operational TC intensity forecasts. DO verification techniques examine the full relationship between a set of forecasts and the corresponding set of observations (i.e., forecast quality), rather than just the accuracy attribute of that relationship.

The DO verification results reveal similar first-order characteristics in the quality of predictions from four TC intensity forecast systems. These characteristics are shown to be consistent with the theoretical response of a forecast system to the imposed goal of summary accuracy measure optimization: production of forecasts that asymptote with lead time to the central tendency of the observed distribution. While such forecasts perform well with respect to the accuracy, unconditional bias, and type I conditional bias attributes of forecast quality, they perform poorly with respect to type II conditional bias. Thus, it is clear that optimization of forecast accuracy is not equivalent to optimization of forecast quality. Ultimately, developers of deterministic forecast systems must take care to employ a verification procedure that promotes good performance with respect to the most desired attributes of forecast quality.

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William A. Komaromi, Patrick A. Reinecke, James D. Doyle, and Jonathan R. Moskaitis

Abstract

The 11-member Coupled Ocean–Atmosphere Mesoscale Prediction System-Tropical Cyclones (COAMPS-TC) ensemble has been developed by the Naval Research Laboratory (NRL) to produce probabilistic forecasts of tropical cyclone (TC) track, intensity and structure. All members run with a storm-following inner grid at convection-permitting 4-km horizontal resolution. The COAMPS-TC ensemble is constructed via a combination of perturbations to initial and boundary conditions, the initial vortex, and model physics to account for a variety of different sources of uncertainty that affect track and intensity forecasts. Unlike global model ensembles, which do a reasonable job capturing track uncertainty but not intensity, mesoscale ensembles such as the COAMPS-TC ensemble are necessary to provide a realistic intensity forecast spectrum. The initial and boundary condition perturbations are responsible for generating the majority of track spread at all lead times, as well as the intensity spread from 36 to 120 h. The vortex and physics perturbations are necessary to produce meaningful spread in the intensity prediction from 0 to 36 h. In a large sample of forecasts from 2014 to 2017, the ensemble-mean track and intensity forecast is superior to the unperturbed control forecast at all lead times, demonstrating a clear advantage to running an ensemble versus a deterministic forecast. The spread–skill relationship of the ensemble is also examined, and is found to be very well calibrated for track, but is underdispersive for intensity. Using a mixture of lateral boundary conditions derived from different global models is found to improve upon the spread–skill score for intensity, but it is hypothesized that additional physics perturbations will be necessary to achieve realistic ensemble spread.

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Eric A. Hendricks, Yi Jin, Jonathan R. Moskaitis, James D. Doyle, Melinda S. Peng, Chun-Chieh Wu, and Hung-Chi Kuo

Abstract

High-impact Typhoon Morakot (2009) was investigated using a multiply nested regional tropical cyclone prediction model. In the numerical simulations, the horizontal grid spacing, cumulus parameterizations, and microphysical parameterizations were varied, and the sensitivity of the track, intensity, and quantitative precipitation forecasts (QPFs) was examined. With regard to horizontal grid spacing, it is found that convective-permitting (5 km) resolution is necessary for a reasonably accurate QPF, while little benefit is gained through the use of a fourth domain at 1.67-km horizontal resolution. Significant sensitivity of the track forecast was found to the cumulus parameterization, which impacted the model QPFs. In particular, the simplified Arakawa–Schubert parameterization tended to erroneously regenerate the remnants of Tropical Storm Goni to the southwest of Morakot, affecting the large-scale steering flow and the track of Morakot. Strong sensitivity of the QPFs to the microphysical parameterization was found, with the track and intensity showing little sensitivity. It is also found that Morakot’s accumulated precipitation was reasonably predictable, with the control simulation producing an equitable threat score of 0.56 for the 3-day accumulated precipitation using a threshold of 500 mm. This high predictability of precipitation is due in part to more predictable large-scale and topographic forcing.

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