A Tropical Cyclone Rapid Intensification Prediction Aid for the Joint Typhoon Warning Center’s Areas of Responsibility

Abstract

In late 2017, the Rapid Intensification Prediction Aid (RIPA) was transitioned to operations at the Joint Typhoon Warning Center (JTWC). RIPA probabilistically predicts seven rapid intensification (RI) thresholds over three separate time periods: 25-, 30-, 35-, and 40-kt (1 kt ≈ 0.51 m s−1) increases in 24 h (RI25, RI30, RI35, RI40); 45- and 55-kt increases in 36 h (RI45 and RI55); and 70-kt increases in 48 h (RI70). RIPA’s probabilistic forecasts are also used to produce deterministic forecasts when probabilities exceed 40%, and the latter are included as members of the operational intensity consensus forecast aid. RIPA, initially designed for the western North Pacific, performed remarkably well in all JTWC areas of responsibility (AOR) and is now incorporated into JTWC’s ever improving suite of intensity forecast guidance. Even so, making real-time operational RIPA forecasts exposed some methodological weaknesses such as overprediction of RI for weak/disorganized systems (i.e., systems with maximum winds less than 35 kt), prediction of RI during landfall, input data reliability, and statistical inconsistencies. Modifications to the deterministic forecasts that address these issues are presented, and newly derived and more statistically consistent models are developed using data from all of JTWC’s AORs. The updated RIPA is tested as it would be run in operations and verified using a 2-yr (2018–19) independent sample. The performance from this test indicates the new RIPA—when compared to its predecessor—has improved probabilistic verification statistics, and similar deterministic skill while using fewer predictors to make forecasts.

Ice Water Content as a Precursor to Tropical Cyclone Rapid Intensification

“…Rapidly intensifying TCs in our data set have the strongest mean TC intensity, indicating that the rapid intensification often occurs when TCs are structurally organized, consistent with previous research (Shapiro & Willoughby, 1982; Vigh et al, 2012). The distribution of TC intensity found here is consistent with a previous study which also demonstrated that rapidly intensifying TCs have the strongest TC intensity among all intensification rates (Hendricks et al, 2010; Knaff et al, 2020).…”

“…However, the predictive skill of intensification rates is limited due, in part, to a lack of observations to identify crucial physical processes that trigger TC rapid intensification (Elsberry et al, 2007;Gall et al, 2013). Observations of processes that regulate the evolution of TC intensity can provide key metrics that serve as predictors for future intensification, and almost all these statistical-dynamical techniques currently use information from geostationary satellites for inner core (Kaplan et al, 2010;Knaff et al, 2005Knaff et al, , 2020Shimada et al, 2018). To better understand such processes, it is important to examine observational data that are associated with the primary energy source for TCs, i.e., latent heat release.…”

Development of objective forecast guidance on tropical cyclone rapid intensity change

“…They also found that cloud top temperature (CTT) was more uniformly distributed in order to trigger RI. More recent studies including Knaff et al (2018Knaff et al ( , 2020 studied more RI characteristics from infrared (IR) imageries. From their analysis, the percentage of IR pixels colder than −50 C and −60 C were predictors positively correlated to RI.…”

“…An objective guidance for WNP TCs would provide more reliable RI forecasts and help decision making as the most intense TCs are commonly found in the region, affecting several countries such as the Philippines, China and Japan. During the development, Knaff et al (2018Knaff et al ( , 2020) used both linear discriminant analysis and logistic regression with improvements such as considering more thresholds for the same lead-time, evaluating TC size using IR predictors and comparing with climatology, and incorporating new predictors such as temperature advection in the lower troposphere. This has led to significant negative forecast bias reduction and was also proved to be skilful relative to climatology.…”

Enhanced Predictability of Eastern North Pacific Tropical Cyclone Activity Using the ENSO Longitude Index

“…In the Northern Hemisphere, the upper-ocean heat content has been shown to play a significantly larger role in the intensification of ENP TCs when compared to other basins (Balaguru et al, 2015;Kaplan et al, 2015;Knaff et al, 2018Knaff et al, , 2020. Hence, we next examine the climatological mean seasonal cycle of Tdy, a metric for the ocean heat content relevant for TCs (Figure 1d).…”