An Index to Better Estimate Tropical Cyclone Intensity Change in the Western North Pacific

Abstract

A revised predictor called the net energy gain rate (NGR) is suggested by considering wind‐dependent drag coefficient based on the existing maximum potential intensity theory. A series of wind speed‐dependent NGR, known as NGR‐w, is calculated based on pretropical cyclone (TC) averaged ocean temperatures from the surface down to 120 m (at 10‐m intervals) to include the TC‐induced vertical mixing for 13 years (2004–2016) in the western North Pacific. It turns out that NGR50‐w (NGR‐w based on temperature averaged over top 50 m) has the highest correlation with 24‐hr TC intensity change compared with the commonly used sea surface temperature‐based intensification potential (POT), depth‐averaged temperature‐based POT (POTDAT), and constant drag coefficient in the NGR. To demonstrate the effectiveness of NGR50‐w, we designed and conducted experiments for training (2004–2014) and testing (2015–2016). The model with NGR50‐w shows greater skill than does the model with POTDAT or POT by reducing prediction errors by about 16%.

Decision-Tree-Based Classification of Lifetime Maximum Intensity of Tropical Cyclones in the Tropical Western North Pacific

“…Lin et al [31] suggested DMPI using DAT instead of prestorm sea surface temperature to consider negative feedback by TC-induced sea surface cooling on existing SST-based MPI. DMPI has significantly reduced the overestimation of maximum intensity of the existing SST-based MPI and has frequently been used to predict TC intensity and RI [2,19,32,33]. The variables based on intensification potential (POT; MPI − initial intensity) were the essential factors in the CSTIPS-DAT model.…”

Advection by the North Equatorial Current of a Cold Wake due to Multiple Typhoons in the Western Pacific: Measurements From a Profiling Float Array

“…The qualitative differences between H 50 and H 75 are minor. H is often calculated to the depth of the 26°C, but TC intensity change in the deep ocean is better correlated with metrics including either a depth‐mean T , an integration of T over a fixed depth, or stratification (Lee et al, ; Price, ; Vincent et al, ).…”

Tropical Cyclone Intensity Change Prediction Based on Surrounding Environmental Conditions with Deep Learning

“…Numerous attempts have been made to predict 24 h TC intensity changes using empirical models as well as numerical modelling techniques [7]. In empirical prediction, much of the effort based on statistical-dynamical approaches has gone towards improving TC intensity forecasts via nonlinear approaches using new or optimized predictors [8,9].…”

“…In empirical prediction, much of the effort based on statistical-dynamical approaches has gone towards improving TC intensity forecasts via nonlinear approaches using new or optimized predictors [8,9]. Finding and utilizing new predictors that accurately represent TC intensity changes can help to improve the forecasting performance of statistical-dynamical models [7]. For numerical models, different parameterization schemes can be adopted to achieve obvious and unique results in terms of performance and characteristics [10].…”

“…These parameters also attempt to describe the real TC evolution process as much as possible. However, given the large scale and high complexity of the physical mechanisms of a TC in space, the depiction of TC evolution in terms of these parameters is usually incomplete and circumscribed, which probably accounts for the slow progress in TC intensity prediction [7,12]. In fact, the three-dimensional (3D) environmental field of a TC contains rich features, such as internal circulation, distribution, and interaction [8].…”