Analysis of Landfalling Rapidly Weakening Tropical Cyclones in the Philippines
Joanne Mae B. Adelino1,2 and Gerry Bagtasa1*
1Institute of Environmental Science and Meteorology, University of the Philippines Diliman, Quezon City, Philippines
2Department of Science and Technology–Philippine Atmospheric, Geophysical, and Astronomical Services Administration (DOST-PAGASA), Quezon City, Philippines
Rapid weakening (RW) of tropical cyclones (TCs) is defined as the 90th percentile of all 24-h over-water weakening rates in the Western North Pacific (WNP) basin, corresponding to a decrease of at least 20 kt in the JMA dataset and 25 kt in the JTWC dataset. RW tends to occur along the 20 – 30° N latitude of the WNP, which makes the probability of RW TC landfall in the Philippines low. Over the study period from 1951–2020, a total of 468 and 563 WNP RW TCs were recorded, where only 17 and 19 of those made landfall in the country based on the JMA and JTWC datasets, respectively. Analysis of potential wind threats of landfalling RW TCs shows significantly lower hazards than non-RW TCs, except for those that make landfall on northern Luzon. RW occurs more frequently outside of the southwest monsoon or Habagat season. Simulations of two recently landfalling RW TCs – Typhoon (TY) Maysak (2015) and TY Yutu (2018) – using the Weather Research and Forecasting (WRF) model show the decrease in the equivalent potential temperature (𝜽e), a measure of the amount of heat and moisture in the atmosphere, in the TC inner core region can be used to diagnose RW. Constantly decreasing 𝜽e
values below 400 𝑲 caused by cooler underlying sea surface temperature and/or dry air intrusion lead to TC RW. RW can also occur in low-shear environments. Environmental conditions that result in RW are typically observed from October–April of the following year, which explains the higher occurrence frequency of RW in the inactive TC season of the WNP. While the impacts of RW TCs are lower, over-forecasting a TC in one event can lead to a complacent populace for the next, as well as damage the reputation of forecasters, hence the importance of understanding RW.
Keywords: tropical cyclones, rapid weakening, warning fatigue, WRF model
*Corresponding author: [email protected]
INTRODUCTION
A tropical cyclone (TC) is an organized warm-core rotating system that forms over warm tropical waters and is characterized by its low-pressure center, strong winds, and intense precipitation that causes damage along its path (Islam 2015). The Western North Pacific (WNP) basin is considered the most active ocean basin in terms
of TC formation and landfalls (Maue 2011; Shen et al.
2017). The Philippines, located in the WNP, is highly exposed to the TCs forming in the WNP. An average of 19.4 TCs enter the Philippine Area of Responsibility (PAR), whereas approximately 9 TCs cross the country yearly (Cinco et al. 2016). The northeast Luzon region is the most frequented by TCs, followed by southeastern Luzon (Bicol region) and eastern Visayas [see Figure 3 in the article by Cinco et al. (2016)]. The normalized cost of ISSN 0031 - 7683
Date Received: 14 Jan 2023
damages due to TCs and their hazards in the Philippines has presented an increasing trend (Cinco et al. 2016).
A robust forecasting system is one way to mitigate the potential threats brought by TCs. However, while there have been many advances in forecasting TC tracks, there are still limitations in forecasting TC intensities (Elsberry et al. 2013; Elsberry 2014). Recently, typhoon (TY) Rai (locally named Odette) in December 2021 underwent a rapid change in intensity hours before it made landfall in the southeastern region of the Philippines where the 24-, 48-, and 72-h lead-time intensity forecasts were 50, 45, and 55 kt lower than the actual landfall intensity, respectively (Bagtasa 2022a). The same late intensification also happened in the case of Supertyphoon Noru (Karding), which traversed Central Luzon in September 2022. Their late and sudden rapid intensification (RI) likely resulted in an under-prepared population to deal with the ensuing disaster. RI in the Philippines is defined by Tierra and Bagtasa (2022) as the upper 95th percentile change in TC intensity in a 24-h period. RI is not uncommon for TCs that affect the country. In fact, 28% of all TCs that made landfall in the Philippines underwent RI. Once a TC undergoes RI, it reaches the TY category or higher 82% of the time, in contrast to just 12% of TY-category-TCs from non-RI TCs. RI typically occurs in the Philippine Sea, just to the east of the country, where it was found that large- scale environmental characteristics such as large ocean heat content (OHC), high humidity, and low vertical wind shear (VWS) make this region climatologically conducive to TC RI (Fudeyasu et al. 2018).
On the opposite end, over-forecasting or false alarms of TC intensity forecasts can also be detrimental to a populace. Even though weaker-than-forecast TCs pose less of a threat over affected areas, over-forecasting of TC intensity can lead to warning fatigue that can negatively affect public response to future disaster warnings. It can also result in lessening public vigilance and preparedness for future events, regardless of the actual magnitude of the threat (Mackie 2012). Disseminating false alarms about a TC that can unexpectedly undergo rapid weakening (RW) before reaching land can lead to costly and unnecessary disaster management efforts and may damage the reputation and reliability of the warning system or agency to the public (Colomb et al. 2019). Rapid changes in intensity, including RW and RI, are considered the major sources of intensity forecast errors in TC warnings (Elsberry et al. 2007).
There have been previous studies on TC RW in the WNP.
RW is defined as the over-water weakening rates in the lower 5th percentile in a 24-h period. Over the WNP basin, this weakening corresponds to a maximum wind speed (MWS) decrease of at least 30 𝑘� or greater within 24 h (Ma et al. 2019). RW was shown to be affected by large-
scale environmental conditions such as VWS, sea surface temperature (SST), and relative humidity (RH) (Zhang et al. 2007; Qian and Zhang 2013, Bhattacharya et al. 2015;
Colomb et al. 2019; Ma et al. 2019). In the Philippines, there have been limited studies on how TC RW directly impacts the country. Thus, in the present study, we aim to characterize the spatiotemporal distribution of TCs affecting the Philippines that undergo RW. We also looked at the large-scale environmental conditions that directly influence TC RW in the context of the Philippines. Finally, we simulated two of the most recent RW TCs that affected the country – namely, TY Maysak (2015) and TY Yutu (2018) – using the Weather Research and Forecasting (WRF) model to determine RW TC dynamics to answer the question of what exactly happens inside a TC that leads to its weakening.
DATA AND METHODS
Identification of RW TCs
The 6-hourly TC data from 1951–2020 used were obtained from the Regional Specialized Meteorological Center (RSMC) Tokyo or the Japan Meteorological Agency (JMA) and from the Joint Typhoon Warning Center (JTWC) best-track datasets, which are both included in the IBTrACS (International Best-Track Archive for Climate Stewardship) version 4 dataset (Knapp and Kruk 2010;
Knapp et al. 2018a). The wind measurements in the JMA dataset only started in 1977. The missing MWS were calculated from the minimum central pressure values using the Koba table (Koba et al. 1991).
Before identifying the thresholds for RW, the time periods when TCs became extra-tropical cyclones were first removed. The data are then restricted only to 24-h weakening periods. The TC data was further restricted to weakening periods whose TC center is at a distance of
˃100 𝑘𝑚 from any landmass to exclude weakening due to the topographic effects of Luzon (Ma et al. 2019; Song et al. 2020). An analysis conducted on all landfalling TCs in the Philippines using the JTWC best-track data from 1979–2021 has shown that 77% of all landfalling TCs had their maximum intensities at a point right before landfall at a distance that ranges from 26–142 km with an average of 37 km. This means that even within a 100-km distance from land, TCs are able to maintain their intensities.
Therefore, the 100-km distance from land used by Ma et al. (2019) and Song et al. (2020) is deemed sufficient to state a no-landfall scenario.
After filtering the data based on the criteria above, the RW thresholds were identified. It is important to note that the JMA and JTWC best-track datasets use different wind-
averaging time periods – 10 min for JMA and 1 min for JTWC. Aside from that, JTWC has a higher rate of TC intensity increase corresponding to the current intensity or CI number (Knapp and Kruk 2010). Thus, separate thresholds were identified for each of the two best-track data. The threshold used to determine RW in this study is the lower 10�� percentile (or upper 90�� percentile weakening rate) of all 24-h over-water intensity-change rates in the WNP from the two best-track data. An RW TC was defined by having at least one RW event in its lifetime. RW events in a single TC with overlapping time intervals were counted as one RW event. A TC is identified as a landfalling TC if it crosses the Philippine coastline at least once. A buffer zone equal to 25.4 𝑘𝑚 on either side of the TC track was used for all landfalling TCs based on the results of Knapp et al. (2018b) that the mean eye radius of TCs in the WNP is 25.4 𝑘𝑚. Here, only RW TCs that have undergone RW at least once before making landfall in the Philippines will be considered landfalling RW TCs. For comparison, RI TCs in the WNP were also examined. RI TCs in this study were identified using the same method as that of Tierra and Bagtasa (2022) using a threshold of the upper 95th percentile of all 24-h over- water intensifying rates.
Analysis of the Impacts of RW TCs
The direct impacts of RW TCs in the Philippines were assessed by differentiating the wind-related potential threats of landfalling RW and non-RW TCs using their normalized power dissipation index (PDI). The PDI of a TC gives an overview of the degree of threat or risks associated with it and is calculated as the sum of the cube of the 6-hourly MWS (Emanuel 2005). Note that the PDI calculated here is associated with PDI over land and not the whole TC lifetime since most of the wind-related damages caused by TCs are only felt when the TC is near landfall and while crossing over land. In addition, it has been shown by Bagtasa (2021) that accumulated TC rain corresponds to on-land TC intensity, which means that on-land PDI may also imply rainfall hazard risk. The PDI of the TCs was then normalized according to the number of points on land. The normalized PDI is defined as:
where 𝑉��� is the maximum sustained wind speed near the surface or MWS, � represents time or the duration the TC stayed on land (Corral et al. 2010), and 𝑁 is equal to the total number of data points where the TC is on land. Then, to assess how the impacts of landfalling RW and non-RW TCs were significantly different from each other at the 95% confidence level, a two-tailed Mann-Whitney U-test was applied to the 𝑉³��� of the TCs instead of a Student’s t-test since the data were not normally distributed.
WRF Simulation
Two of the most recent landfalling RW TCs in the Philippines from the two best-track data – namely, TY Maysak (2015) and TY Yutu (2018) – were simulated using the WRF model version 4.2.1. Each of the TC simulations covered a 120-h period, from 31 Mar–05 Apr 2015 00 UTC for TY Maysak and 25–30 Oct 2018 00 UTC for TY Yutu.
The model domain is shown in Figure 1 with thirty-eight vertical levels and a model top at 50 �𝑃𝑎. The domain has 255 × 195 grid points with 10 𝑘𝑚 grid spacing. The model used the Ferrier (Eta) microphysics and the Kain- Fritsch cumulus parameterization schemes. The WRF model was driven by the European Centre for Medium- Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) (Hersbach et al.
2020) downloaded from https://cds.climate.copernicus.
eu/. The WRF model outputs were compared with the JTWC values instead of JMA because the WRF outputs are instantaneous and, therefore, should be closer to the values of the JTWC intensity data than JMA. The reanalysis data used for RH and SST were also from ERA5. The skin temperature parameter from the reanalysis data was used in depicting the SST to consider only the temperature at the surface of the water.
Figure 1. WRF model domain with 255 x 195 grid points with 10-km grid spacing.
RESULTS AND DISCUSSION
RW TCs in the WNP and the Philippines
From the criteria presented in the previous section, a total of 9,667 and 10,606 24-h weakening events out of 57,659 and 60,059 overlapping 24-h periods were identified from the JMA and JTWC best-track datasets, respectively, over WNP from 1951–2020. From Figure 2,
an intensity decrease of at least 20 kt within 24 h closely approximates the 90th percentile of all the 24-h weakening rates in the JMA dataset, whereas a decrease of at least 25 kt approximates the 90th percentile in the JTWC dataset.
Using this definition, a total of 468 RW TCs from JMA and 563 RW TCs from JTWC over WNP were identified,
where only 17 (3.6%)(JMA) and 19 (3.4%) (JTWC) of those made landfall in the Philippines.
The spatial distribution of the occurrences and onsets of RW TCs over the WNP basin is presented in Figure 3. RW events tend to occur on slightly higher latitudes compared
Figure 2. Count and percentage of 24-h weakening rates over WNP from 1951–2020 based on JMA and JTWC datasets.
Figure 3. Distribution of the RW occurrence (shading) and onset (black dots) of all RW TC over WNP from JMA and JTWC best-track data. The Philippine Area of Responsibility (PAR) is delineated in black.
to RI events [see Figure 6 of Tierra and Bagtasa (2022)].
This region is greatly influenced by the decreasing SST away from the equator, which makes higher latitudes less conducive for TC intensification. Most RI TCs also occur inside the PAR, whereas numerous RW events are located to the northeast of the Philippines. This suggests that most RW TCs are unlikely to make landfall in the country during or after its weakening process. This is in contrast to RI TCs, which often make landfall at their peak RI phase (Tierra and Bagtasa 2022).
Figure 4 shows the monthly frequencies of RW TCs in the WNP in relation to all WNP TCs, as well as all landfalling TCs and RW TCs in the Philippines. There is apparently less RW TC relative frequency during the southwest monsoon or Habagat season in the WNP region when WNP TC activity is at its peak (Bagtasa 2017).
The least active TC months from December–May of the following year have less conducive environments for TC formation and intensification, the same environment likely drives TC RW in this inactive TC season, resulting in the high percentage occurrence frequency of RW. In terms of TCs that made landfall in the Philippines, the months of October–December have the greatest number of RW TCs but are still at a low percentage occurrence
of just 4–9%, peaking in December for both the JMA and JTWC datasets.
Figure 5 shows the trends in the frequency of all TC and RW TCs, and the ratio of RW TCs to the number of all WNP TCs in the WNP region from 1951–2020 for both the JMA and JTWC datasets. We distinguish between two trends here, a trend covering the period of 1951–2020 and 1978–2020 to separate TC data covered by the "satellite era" starting from 1978. The two best-track data show varying TC and RW TC frequency and ratio trends, as well as their statistical significance. These differences in their trends likely stem from the different conversion tables used in estimating the TC MWS. The frequency of TC formation from 1951–2020 using the JTWC data showed a significant increasing trend (𝑝 = .001) with an average rate of 0.111 𝑦𝑟⁻¹, whereas the frequency of RW TC formation exhibited a significant decreasing trend (𝑝
= .050) of −0.035 𝑦𝑟⁻¹. In the more recent period from 1978–2020, the TC and RW TC formation frequencies showed significant decreasing trends (𝑝 = .052and 𝑝 = .002) using the JMA data with an average rate of −0.104 and −0.100 𝑦𝑟⁻¹, respectively. The ratio of RW TCs to all WNP TCs also decreased significantly (𝑝 = .013) by
−0.286 𝑦𝑟⁻¹ from 1978–2020 using the JMA data, and
Figure 4. Monthly frequencies of all WNP TCs (black) and RW TCs (red) for [a] JMA and [b] JTWC, and landfalling TCs in the Philippines for [c] JMA and [d] JTWC. The percentage of RW TCs to the total number of TCs is shown per month.
by −0.278 𝑦𝑟⁻¹ from 1951-2020 using the JTWC data (𝑝 < .001). The inconsistent results of the trend analysis in this study indicate that the determination of recent RW trends, or TC trends in general, is still inconclusive and is likely due to the inhomogeneity of TC best-track data over the recent decades (Moon et al. 2019).
Forecastability of RW
In this section, we assessed the archived intensity forecasts of both JMA and JTWC at different forecast lead times to determine how often RW, as well as RI, are correctly forecasted. A summary of the number of hits, misses, correct negatives, and false alarms at the 24-, 48-, and 72-h lead times of the intensity forecasts of the 135 and 146 PAR-passing TCs from 2013–2021 based on the JMA and JTWC forecast archives, respectively, are shown in Table 1. The JMA RW forecast at the 24-h lead time recorded 31 hits, 62 misses, 40 correct negatives, and 3 false alarms.
The JTWC forecast archive, on the other hand, has 35 hits, 70 misses, 42 correct negatives, and 3 false alarms. The RI forecast of JMA recorded 17 hits, 55 misses, 60 correct negatives, and 1 false alarm, whereas the JTWC has 20 hits, 63 misses, 69 correct negatives, and 1 false alarm. The results immediately show that there are a greater number
of misses than hits. Also, despite the almost equal number of TC data, the RI forecasts of both agencies have a lower ratio of hits to misses than the RW forecasts.
In Table 1, most of the forecasts have the highest accuracy at the 24-h lead time. The RW forecast accuracy of JMA and JTWC at the 24-h lead time was 52 and 51%, respectively. The accuracy of the RI forecast at the 24-h lead time was 58% for both of the two agencies. The forecast accuracies of RW and RI forecasts at all three lead times were all below 60%. In terms of the bias score and probability of detection (POD), the RW forecasts of the two agencies have higher bias scores and POD than the RI forecast. Aside from that, the 24-h lead time of the RI forecast obtained a higher bias score and POD than the other two lead times. These indicate that the forecast errors decrease at shorter lead times. To summarize, the results here mainly show that the misses have a considerably higher proportion compared to the hits, which leads to the conclusion that the accuracy of the intensity forecasts of the two agencies is still low. Nevertheless, it can be concluded that RW forecasts perform better than RI forecasts based on their higher accuracy scores and a larger number of hits.
Figure 5. Trends in total (black) and RW (red) TC frequencies and the ratio of RW TCs to the total number of TCs from 1951–2020 from the JMA and JTWC best-track data. The trend from 1978 onward is also shown (blue).
Impacts of RW TCs in the Philippines
The direct impacts of landfalling RW TCs here were measured based on winds only and does not include the amount of rainfall. Although, to a certain extent, on-land PDI can imply TC rainfall intensity (Bagtasa 2021). While a large part of the destructive hazard brought by TCs is due to heavy precipitation and the consequent flooding, the amount of rainfall brought by landfalling RW TCs was not explicitly included in the analysis due to the high variability of TC rainfall impacts (Bagtasa 2017; Bagtasa 2022b) as other factors such as duration of the rainfall and total land area affected influence TC rain (Bagtasa 2022b).
Hence, the potential impacts of RW TCs discussed in this study are only wind-related.
Landfalling RW TCs in the Philippines were initially divided into two main regions based on their landfall location – northern Luzon (NL) and the rest of the country (Luzon-Visayas-Mindanao or LVM). NL was separated since TCs traversing this region cross over the rugged Sierra Madre and the Cordillera Mountain Ranges, which would influence TC intensity changes (Racoma et al. 2022). In addition, more RW TCs have made landfall in NL, whereas the landfall location of the remaining RW TCs was spread out across the other parts of the Philippines, thus the separation of regions. Table 2 presents the PDI, normalized PDI, and number of NL and LVM RW and non-RW TCs. The PDI and normalized PDI of the landfalling TCs were calculated from the
JMA best-track data only, as it has a similar wind- averaging time period with the PAGASA (Philippine Atmospheric, Geophysical, and Astronomical Services Administration).
Due to the higher proportion of landfalling non-RW TCs to landfalling RW TCs, the normalized PDI was used to properly evaluate the difference between RW and non- RW TCs in terms of their potential threats or risks. The normalized PDI of the NL and LVM RW TCs were 0.20 × 10⁶ 𝑘�³and 0.042 × 10⁶ 𝑘�³, which are smaller than the normalized PDI of non-RW TCs with the values of 0.23
× 10⁶ 𝑘�³ and 0.15 × 10⁶ 𝑘�³. The total normalized PDI of RW TCs was 0.12 × 10⁶ 𝑘�³, which is smaller than the total normalized PDI of non-RW TCs equal to 0.17 × 10⁶ 𝑘�³. However, the 𝑉³��� of the NL RW TCs were not significantly different (𝑝 = .459) to the 𝑉³��� of the NL non-RW TCs. On the other hand, LVM RW and non-RW TCs were significantly different ( 𝑝 = 1.44 × 10⁻⁵) from each other. This means that RW TCs landfalling on LVM tend to have lower wind-related impacts and hazards on land than the LVM TCs that did not undergo RW before landfall. Landfalling TCs in NL, on other hand, tend to have similar intensities and impacts whether they have or have not undergone RW before reaching land. Comparing the 𝑉³ of all RW and non-RW TCs, it was determined that the overall wind-related potential impacts of landfalling RW and non-RW TCs in the Philippines were significantly different (𝑝 = .04).
Table 2. Power dissipation index (PDI), normalized PDI, and number of landfalling northern Luzon (NL) and Luzon-Visayas-Mindanao (LVM) RW and non-RW TCs based on JMA.
Total TCs PDI (kt3) Normalized PDI (kt3)
RW TCs
NL 7 4.89 × 106 0.20 × 106
LVM 10 1.01 × 106 0.042 × 106
Total 17 5.90 × 106 0.12 × 106
Non-RW TCs
NL 153 73.99 × 106 0.23 × 106
LVM 276 137.8 × 106 0.15 × 106
Total 429 211.8 × 106 0.17 × 106
Table 1. JMA (and JTWC, in parenthesis) forecast contingency table values and skill scores where POD stands for probability of detection.
JMA(JTWC) Hits Misses Correct
negatives
False
alarms Accuracy (%) Bias score % POD %
RW
24 h 31 (35) 62 (70) 40 (42) 3 (3) 52 (51) 37 (36) 33 (33)
48 h 37 (42) 49 (61) 22 (37) 23 (2) 45 (56) 70 (43) 43 (41)
72 h 27 (33) 50 (67) 27 (29) 11 (1) 47 (48) 50 (34) 35 (33)
RI
24 h 17 (20) 55 (63) 60 (69) 1 (1) 58 (58) 25 (25) 24 (24)
48 h 3 (8) 41 (61) 36 (36) 0 (1) 49 (42) 7 (13) 7 (12)
72 h 0 (4) 23 (44) 17 (22) 0 (1) 43 (37) 0 (10) 0 (8)
TCs that make landfall weakens as it interacts with land.
Typically, the intensity of a TC decreases as it makes landfall due to surface friction and abrupt loss of ocean heat and moisture fluxes. The effect of land also varies depending on the surface topography. So just how much difference does weakening due to the influence of terrain and the identified over-ocean TC RW is? To answer this question, we calculated the mean intensity at landfall and the mean drop in intensity after landfall of all TCs based on both the JMA and JTWC best-track data, which are summarized in Table 3. Previous studies have shown that higher terrain has a greater impact on changes in TC intensity and rainfall (Racoma et al. 2016; Wu and Choy 2015). In the Philippines, various mountain ranges from the north to the south of the country vary in height.
Therefore, we further subdivided the landfall regions into TCs that passed the Cordillera-Sierra Madre Mountain Ranges (COR-SM), the Sierra Madre Mountains around the east of Central Luzon (SM), Southern Luzon and the Bicol region (BIC), Visayas (VIS), and Mindanao (MIN).
In Table 3, the mean intensity drops over Luzon (COR- SM, SM, and BIC) are all less than 10 𝑘�, which are consistent with the results of Wu and Choy (2015). The intensity drops of TCs landfalling in VIS and MIN, in both datasets, were lower than the intensity drops of COR-SM and SM TCs. A summary of the mean drops in MWS of the WNP and landfalling RW TCs identified by the thresholds in the previous section is also shown in Table 4. It is apparent that the mean intensity drops for both best-track datasets, whether in absolute or relative values, are significantly higher for the RW TCs than for TC landfall, which indicates that landfall or interaction with the terrain is not a driver (or at least not the main driver) of TC RW. The environmental conditions that result in RW have more impact on TC intensity than the rugged terrain
of the Philippines. This is why it is important to properly identify the RW process of TCs that are likely to affect the country since RW can lead to over-forecasting TC intensity, which can eventually lead to warning fatigue.
WRF Simulation of RW TC Events
From the statistical analysis in the previous section, we have established that there are environmental drivers other than terrain-induced weakening for RW TCs. In this section, we present the outputs of the WRF simulation of the two RW TCs that are then compared to the best-track data of JTWC instead of JMA because WRF outputs are instantaneous and, therefore, should be closer to the values of JTWC than JMA.
TY Maysak (2015). TY Maysak (2015), also known as TY Chedeng in the Philippines, started as a tropical depression (TD) on 26 Mar 2015 at 12 UTC. It is also considered one of the strongest TCs in the WNP that formed before the month of Apr which reached a minimum pressure of 910 hPa. The PDI of 0.015 × 10⁶ 𝑘�³ (JMA) and 0.08
× 10⁶ 𝑘�³ (JTWC) for TY Maysak over the Philippines is relatively low since its intensity decreased even before reaching land. The partial track of TY Maysak during its RW process from 01 Apr 2015 at 06 UTC to 03 Apr 2015 at 18 UTC at 12-hourly intervals is shown in Figure 6a.
TY Maysak started to weaken on 02 Apr 2015 at 06 UTC from an MWS of 115 kt to 90 kt within 24 h.
TC Radius-pressure height of the zonal cross sections from the WRF output of the equivalent potential temperature (𝜃�) and wind speed is shown in Figure 7. The RW of TY Maysak was simulated by the WRF model reasonably well, as shown by the decrease of MWS from 131.7 𝑘� at 𝑅𝑊 − 24 � to 85.26 𝑘� at 𝑅𝑊 in Figures 6b and 7. The zonal VWS peaked with a value of 8.53 m/s during 𝑅𝑊
Table 3. Mean intensity and percentage drop during landfall of Philippine TCs based on JMA (JTWC; in parenthesis) best-track archive.
Number of TCs Mean landfall intensity (kt3) Mean intensity drop (kt) Mean percentage drop (%)
COR-SM 113/(107) 57.69/(66.96) 3.97/(9.44) 5.95/(11.14)
SM 85/(85) 49.44/(56.29) 4.93/(9.82) 7.82/(11.97)
BIC 75/(71) 50.68/(66.13) 2.73/(4.51) 5.51/(5.17)
VIS 98/(115) 46.06/(49.61) 2.76/(3.04) 4.99/(4.48)
MIN 49/(55) 36.64/(39.09) 2.28/(3.73) 4.85/(6.38)
Table 4. Mean intensity and percentage drop of WNP and landfalling RW TCs in the Philippines from JMA and JTWC datasets.
RW TCs Number of TCS Mean intensity drop (kt) Mean percentage drop (%)
JMA WNP 468 23.36 34.20
landfalling 17 23.82 33.88
JTWC WNP 563 30.71 31.62
landfalling 19 30.83 34.61
Figure 6. Partial tracks of [a] TY Maysak from 01 Apr 2015 06:00 UTC to 03 Apr 2015 18:00 UTC and [b] TY Yutu from 27 Oct 2018 06:00 UTC to 29 Oct 2018 18:00 UTC at 12-hourly intervals. The intensity changes of [c]
TY Maysak and [d] TY Yutu during their RW period at 6-hourly intervals based on JTWC (black) and WRF output (red) are also shown.
when the MWS is at the lowest, which can be considered as a moderate VWS value (Molinari and Vollaro 2010).
The warm core 𝜃� at 𝑅𝑊 − 48 � and 𝑅𝑊 − 36 � cooled on the succeeding time frames and MWS decreased consequently. 𝜃� reached its peak value of 389 𝐾 during 𝑅𝑊 − 36 �, and decreased to 367 𝐾 at the end of the 𝑅𝑊 process. Since 𝜃� can both represent the amount of heat and moisture available in the atmosphere, the changes in 𝜃� can be associated with changes in the values of heat and moisture fluxes in the underlying region or around the vicinity of the TC by the intrusion of dry air. Comparing the maximum values of 𝜃� in Figure 7 and the SST values around TY Maysak center in Figure 8a, near-surface 𝜃� subsequently follows the variations of SST values within the vicinity of the TC, which are below ~ 28 °𝐶. The SST radial profile from 𝑅𝑊 − 48 � to 𝑅𝑊 + 12 � was gradually decreasing throughout the 𝑅𝑊 process, as shown in Figure 8a, which likely decreased the heat fluxes toward the TC center and, thus, contributed to the drop in the value of 𝜃�. These suggest that the TC moving to relatively cooler waters reduced the 𝜃� of the TC core.
In 𝑅𝑊 − 12 � in Figure 7, a region of low 𝜃� to the west (left) of the TC center from 900 to 700 �𝑃𝑎 height is apparent. Figure 9 shows the 800-850 �𝑃𝑎environmental
RH during the 𝑅𝑊 . It can be observed that there was a large region of low RH to the northwest quadrant of the TC starting from 𝑅𝑊 − 48 �, and eventually, this region of dry air advected inward at 𝑅𝑊 − 12 � near the TC core (inside the box figure). This dry air would have contributed to the decrease in the TC core 𝜃�,which led to its RW.
TY Yutu (2018). TY Yutu (2018), also known as TY Rosita locally, started as a TD on 20 Oct 2018 at 18 UTC. It was also one of the strongest TCs in 2018 together with TY Kong-Rey, with a minimum central pressure reaching 900 hPa. The PDI of TY Yutu over land, 0.51 × 10⁶ 𝑘�³ for JMA and 1.15 × 10⁶ 𝑘�³ for JTWC, is higher than the PDI of TY Maysak since even if it has undergone RW, it remained a strong TC. The cost of damages to agriculture brought by TY Yutu in the regions of Ilocos, Cagayan Valley, Central Luzon, and Cordillera Administrative Region was estimated to be around PHP 2,904,840,308.18 (NDRRMC 2022). The partial track and intensity of TY Yutu during RW from 27 Oct 2018 at 06 UTC to 29 Oct 2018 at 18 UTC at 12-hourly intervals are shown in Figures 6c and d, respectively. The simulated RW onset in the WRF output, however, was late compared to the observation. Also, the values of the simulated MWS shown in Figure 6d have a bias of about 10 kt compared to the best-track data, nevertheless, the rate of weakening
Figure 7. Radius-pressure cross-sections of equivalent potential temperature (shaded, 𝐾) and wind magnitude (contoured, 𝑘�) before, during, and after RW of TY Maysak. The thick black contour represents the 100 𝑘� wind magnitude value. The green line represents the TC center. The red arrows at the left side of the plots are the magnitude and direction of averaged zonal winds at 800–700 �𝑃𝑎 and 300–200
�𝑃𝑎. The MWS values from JTWC and WRF, maximum 𝜃�, and zonal VWS are also included in the plots. The black box in 𝑅𝑊 − 12 � enclosed the layer where dry air is present.
Figure 8. Radial profile of the SST around the TC centers of [a] TY Maysak and [b] TY Yutu at the time frames: 𝑅𝑊 − 48 � in blue, 𝑅𝑊
− 36 � in orange, 𝑅𝑊 − 24 � in green, RW – 12 h in magenta, 𝑅𝑊 in purple, and RW + 12 h in brown.
Figure 9. Longitude-latitude plot of 800 �𝑃𝑎RH from time RW – 48 h to 𝑅𝑊 + 12 h of TY Maysak at 12-h intervals. The
× mark represents the location of the TC center.
as indicated by the decrease of MWS from 142.52 𝑘� at 𝑅𝑊 − 24 � to 117.56 𝑘� was still captured by the WRF model. The radius-pressure cross-section of the 𝜃� and the wind speed of TY Yutu is presented in Figure 10. Similar to the case of TY Maysak, there is an observed decrease in the TC inner core 𝜃� during its RW. This cooling is followed by a decrease in the TC intensity.
The maximum value of 𝜃� at the onset (𝑅𝑊 − 24 �) decreased from 401.7 𝐾 to 375.7 𝐾throughout the 𝑅𝑊 process, 24 h later. We looked at the SST radial profiles of Yutu in Figure 8b to see if the cooling of the initially warm inner-core region coincides with the cooling of the underlying SST. From the RW onset at 𝑅𝑊 − 24 � onward, most SST values along the TC have dropped to 28 °𝐶 and below. It can also be noticed that the SST profile is radially flat outward. A radially uniform SST profile can induce secondary updrafts outside the inner core region (or primary circulation) which can suppress convection around the inner core and, therefore, inhibit TC intensification (Kanada et al. 2017). This is supported by the secondary updrafts that are seen in the WRF simulation outside the primary circulation (not shown).
In addition, there is also an intrusion of dry air at around 850 hPa height from the northwest at the RW onset, 𝑅𝑊
− 24 �, to the west of the TC center. A wider view of 𝜃�
at the times when dry air layers are present during the RW of TY Yutu is shown in Figure 11. Just as in the case of TY Maysak (Figures 7 and 9), the northwest region of the WNP tends to be dry during the northeast monsoon season owing to the northerly monsoon flow. Although the cool dry air layer during TY Yutu was approximately 200 km away from the TC center, which was farther than the dry air layer found in TY Maysak, it has a wider spatial extent along the western side of the TC. The strong low-level inflow in lower latitudes assists in the advection of dry air towards the inner core of a TC before it gets completely moistened due to surface enthalpy fluxes, which can suppress further TC intensification (Shi et al. 2019). The presence of this cool dry air and the flat SST profile of ≤ 28 °𝐶 contributed to the decrease in the value of 𝜃�. The further decrease in SST values near the TC center from 𝑅𝑊 − 24 � and 𝑅𝑊 − 12 � likely caused a decrease in the supply of heat and vapor fluxes into the TC center,
Figure 10. Similar to Figure 7, but for the case of TY Yutu (2018). The black box in 𝑅𝑊 – 24 h enclosed the layer where dry air is present.
Figure 11. Same longitude-pressure cross-section of equivalent potential temperature (shaded, K) of TY Yutu at 𝑅𝑊 – 24 h as Figure 10c with wider longitudinal coverage. The black box enclosed the layers where dry air is present while the green line represents the TC center.
which played a part in the drop of 𝜃�. This explains why more RW occurs (percentage-wise) during the Amihan season, which is partly because of the [1] cooler SST in the region from around 20° 𝑁 latitude and [2] the dry air intrusion from the dry air region present to the northeast of the Philippines or northwest of the TCs in the WNP region. The zonal VWS peaked with a magnitude of 6.40 m/s during 𝑅𝑊 − 48 � but generally had low values of just 1 to 3 m/s during the RW process. Low VWS during TC RW suggests that VWS may not be a major contributor to RW, particularly for TY Yutu.
Drivers of TC RW
Table 5 summarizes the values of 𝜃�, 𝑉𝑊 𝑆�����, and 𝑆𝑆𝑇𝑇𝐶
𝑐𝑒𝑛𝑡𝑒𝑟, of TY Maysak and TY Yutu at their RW onset 𝑅𝑊 − 24 and 𝑅𝑊 to compare their corresponding environmental parameters. From the table, the environmental factors that influenced RW vary depending on the TC. In the case of TY Maysak, the intrusion of a large region of dry air, cooler underlying SST values, and a moderate zonal VWS equal to 8.53 m/s influenced the RW process. In the case of TY Yutu (2018), the zonal VWS was low, but there was also the presence of a large region of cool dry air within the 200 – 800 𝑘𝑚 radius of the TC, in addition, the decrease in SST was seen as the main contributors to the TC RW. We also simulated another TC, TY Parma in 2009, albeit the details of the results are not presented for brevity. TY Parma was also in a low VWS environment with consistently high SST (≥ 28.75
°𝐶) but still underwent RW briefly in the Philippine Sea.
TY Parma occurred during the Fall monsoon transition period which was in an environment without distinctive dry air patches around the vicinity of the TC. Its 𝜃� also decreased as the TC rapidly weakened likely due to the warmer SST around the periphery of TY Parma's primary circulation, inducing secondary updrafts that inhibited its overall intensification (Kanada et al. 2017).
In all the simulated TC cases, TCs undergo RW when the value of the 𝜃� in the TCs' primary circulation had a decreasing trend and is below around 400 K. Since the 𝜃� is a parameter that measures both the available heat and moisture in the atmosphere, we see that the reduction of these two parameters is what drives TC RW. Either reduction of surface heat fluxes due to cooler
Table 5. Summary of environmental factors of TY Maysak and TY Yutu at 𝑅𝑊 − 24 � and 𝑅𝑊 .
RW − 24 h RW
𝜽e, max (𝑲)
|VWSzonal| (m/s)
SSTTC center (°C)
𝜽e, max (𝑲)
|VWSzonal| (m/s)
SSTTC center (°C)
TY Maysak 376.2 5.00 27.55 367.0 8.53 27.30
TY Yutu 401.7 2.12 28.08 375.7 1.60 27.51
Table 6. Summary of the abbreviations used in the text.
Abbreviation Meaning
RW Rapid weakening/ rapidly weakening RI Rapid intensification/ rapidly intensifying TC Tropical cyclone
WNP Western North Pacific
MWS Maximum wind speed
SST Sea surface temperature RH Relative humidity VWS Vertical wind shear
PDI Power dissipation index JMA Japan Meteorological Agency JTWC Joint Typhoon Warning Center
NL Northern Luzon
LVM Luzon-Visayas-Mindanao COR-SM Cordillera–Sierra Madre
SM Sierra Madre
BIC Bicol Region
VIS Visayas
MIN Mindanao
WRF Weather Research and Forecasting
underlying SST, the intrusion of dry air into the inner- core region, or the combination of both, are effective
“anti-fuel" to TC strength consistent with the findings of Riemer et al. (2010) and Colomb et al. (2019). We do note, however, that analysis of more TC events is needed to better understand the diagnostic thresholds for proper identification of RW precursors or forecasting of TC RW in the future.
CONCLUSION
In this study, the characteristics of RW TCs in the WNP basin and those that made landfall in the Philippines from 1951–2020 were examined using the best-track data from both the JMA and JTWC. We defined RW as the 90th percentile of all 24-h period over-water weakening rates.
A total of 468 and 563 RW TCs over the WNP basin were identified based on JMA and JTWC data, respectively.
However, only 17 for JMA and 19 for JTWC RW TCs made landfall in the Philippines in the same 70-yr period.
As most RW tends to occur along the region bounded by 20 – 30° 𝑁latitude in the WNP, a region located to the northeast of the Philippines, landfall of RW TCs in the Philippines has a low probability.
TC RW can occur in any month in the WNP region but with the least frequency during the southwest monsoon season or Habagat coincident with the peak of the WNP TC season. From October–April of the following year, 35–59% (JMA) or 24–42% (JTWC) of TCs in the WNP undergo RW, but only 6–9% (JMA) or 4–14% (JTWC) of TCs undergoing RW made landfall in the Philippines.
This study did not consider the influence of interannual climate drivers such as the El Niño Southern Oscillation or the Pacific Decadal Oscillation on RW occurrences.
Looking at the trends of TC RW, we got opposing results in relation to the statistical significance of the trends from the two TC best-track datasets, which indicates inconclusive trend values.
TC intensity decreases as it interacts with land terrain. We compared the amount of TC intensity drops from RW TCs and TCs making landfall in the country. The results show that the mean TC intensity drop of TCs making landfall in the Philippines varies from 4.85–7.82% for JMA and 4.48–11.97% for JTWC, depending on the location of landfall. While the mean intensity drops of RW TCs are approximately 34% for JMA and 33% for JTWC, significantly higher than landfall intensity drops. This leads us to conclude that TC weakening for RW is different from weakening due to TC landfall. We then investigated the potential threats of landfalling RW and non-RW TCs by computing their normalized PDI over land at 6-h intervals. The wind-associated threats of RW and non- RW TCs hitting NL are not statistically different at the 95% confidence interval. RW and non-RW TCs making landfall in southern Luzon, Visayas, and Mindanao do significantly differ with RW TCs generally having weaker intensities during landfall.
We further investigated the dynamics of RW by simulating two landfalling RW TCs – TY Maysak (2015) and TY Yutu (2018) – using the WRF model to determine the factors that can lead to RW. Generally, intense TCs tend to have a warm inner-core region characterized by a high equivalent potential temperature (𝜃�). Comparing the TC cases, when the 𝜃� around the TC center is constantly decreasing and is lower than 400 𝐾, RW onset can ensue. This decrease in 𝜃� means that the amount of moisture and/or heat present within the TC core has decreased due to external factors.
We found that the presence of a dry air intrusion and/
or decreasing SST (≤ 28 °𝐶) around the TC drives the
changes in the 𝜃� even with low VWS. VWS may or may not contribute to RW depending on the TC cases. These results explain the relatively high occurrence frequency of RW TC outside of the southwest monsoon season during which a dry air region to the northeast of the Philippines and cooler SST conditions are present.
Forecasting rapid changes in TC intensity – including both RW and RI – is important, especially for those TCs that are expected to make landfall. Focusing on RW, even though the impacts of RW TCs and the possibility of landfalling RW TCs are both low, this does not remove the possibility of over-forecasting a TC in one event, and people becoming complacent in the next. This can be referred to as "the boy who cried wolf" effect. We also showed that the predictability of RW (and RI) is low.
Hence, understanding the dynamics of RW is essential to avoid warning fatigue scenarios. We do note that this study looked only at three TC events and simulating more TCs can give us a better understanding of rapid intensity changes of TCs. It is also recommended that investigation of other environmental conditions such as OHC and upper- level outflow, and/or TC inner-core dynamics, can also be vital in understanding such weakening (or intensifying) processes.
ACKNOWLEDGMENTS
The authors would like to thank DOST-ASTHRDP (Department of Science and Technology–Accelerated Science and Technology Human Resource Development Program) for the master’s degree in meteorology scholarship grant for the first author. This study is also supported by DOST-PCIEERD (Department of Science and Technology– Philippine Council for Industry, Energy, and Emerging Technology Research and Development) under the project entitled "Analysis of tropical cyclone rapid intensification in the Philippines: its characteristics, impacts, and future projections" (1211131). We also thank Dr. Olive Cabrera, Dr. Prisco Nilo, Dr. Flaviana D.
Hilario, and the anonymous reviewers for their input in improving this study.
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