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![Updated Tropical Cyclone Classifications [1]](publication/346368849/figure/tbl1/AS:1022191181254661@1620720777789/Updated-Tropical-Cyclone-Classifications-1.png)
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The Philippines is in the Western North Pacific region, where it is a recipient of several weather disturbances such as tropical cyclones. This study aims to determine trends and periodicities of typhoons (TY) within the Philippine Area of Responsibility (PAR), and the rainfall they brought in a 30-year period (1989–2018) for future forecast and di...
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... Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) developed the public storm warning system to alert the government and community to prepare for the possible impacts of an impending typhoon. Table 1 shows the updated tropical cyclone classifications from PAGASA. During the 1902 to 2005 (has a 32-year dominant periodicity) and years after 1945 (has a dominant periodicity of 10 yr to 22 yr), there are no trends found in their annual tropical cyclone landfall numbers (TLP). ...Citations
... This study characterized the MHW events within a combined region of the Philippine Area of Responsibility (PAR) and Philippine Exclusive Economic Zone (PEEZ) (Fig. 1). The Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) uses three domains in their analyses, monitoring, and forecasting whereby the one with boundaries closest to the Philippine islands is the PAR (Desquitado et al., 2020;PAGASA, 2022). Its dimensions are the area of the Western North Pacific bounded by imaginary lines connecting the following coordinates: 5 • N 115 • E, 15 • N 115 • E, 21 • N 120 • E, 25 • N 135 • E;and, 5 • N 135 • E (PAGASA, 2022). ...
... Located at the boundary of tectonic plates in the tropical western North Pacific, the Philippines is among the nations with highest risk of natural disasters and is under constant threat of volcanic eruptions, earthquakes, typhoons, tsunami, and other hazards such as sea level rise, e.g., [1][2][3][4]. As the most exposed country in the world to tropical cyclones (TCs) [5], the Philippines often experience floods, inundation, landslides, and storm surges in the category of meteorological hazards, e.g., [6][7][8][9][10], all mainly caused by the frequent landfall of TCs, or typhoons in the western North Pacific, e.g., [9][10][11][12][13][14][15]. Therefore, accurate quantitative precipitation forecasts (QPFs) for typhoons are urgently needed in order to help prevent and reduce the impacts of hazards from excess typhoon rainfall in the Philippines, especially under the current trend toward a warmer climate [12,[16][17][18][19][20]. ...
... Next, the categorical skill scores of the hindcasts are examined in Figure 5c for the two-day total rainfall from Mangkhut over [14][15] Figure 5c). Due to a longer accumulation period and higher total rainfall amounts, these TS values are in general higher than those for individual days seen in Figure 5a,b (at the same threshold amounts of accumulation). ...
... For the entire Philippine Archipelago, the SSS was less ideal between 0600 UTC 13 and 0000 UTC 14 October, but mostly at least around 0.7 afterwards (red curve), again with some run-to-run variations. It is noteworthy that the earliest run, with t 0 at 0000 UTC 13 October, also registered an SSS of 0.66 and comparable to some of the later runs executed from [14][15][16] October. This result is encouraging considering that the target period is in the range of 84-156 h. ...
In this study, the 2.5 km Cloud-Resolving Storm Simulator was applied to forecast the rainfall by three landfalling typhoons in the Philippines at high resolution: Mangkhut (2018), Koppu (2015), and Melor (2015), using a time-lagged strategy for ensemble. The three typhoons penetrated northern Luzon, central Luzon, and the middle of the Philippine Archipelago, respectively, and the present study verified the track and quantitative precipitation forecasts (QPFs) using categorical statistics against observations at 56 rain-gauge sites at seven thresholds up to 500 mm. The predictability of rainfall is the highest for Koppu, followed by Melor, and the lowest for Mangkhut, which had the highest peak rainfall amount. Targeted at the most-rainy 24 h of each case, the threat score (TS) within the short range (≤72 h) could reach 1.0 for Koppu at 350 mm in many runs (peak observation = 502 mm), and 1.0 for Mangkhut and 0.25 for Melor (peak observation = 407 mm) both at 200 mm in the best member, when the track errors were small enough. For rainfall from entire events (48 or 72 h), TS hitting 1.0 could also be achieved regularly at 500 mm for Koppu (peak observation = 695 mm), and 0.33 at 350 mm for Melor (407 mm) and 0.46 at 200 mm for Mangkhut (786 mm) in the best case. At lead times beyond the short range, one third of these earlier runs also produced good QPFs for both Koppu and Melor, but such runs were fewer for Mangkhut and the quality of QPFs was also not as high due to larger northward track biases. Overall, the QPF results are very encouraging, and comparable to the skill level for typhoon rainfall in Taiwan (with similar peak rainfall amounts). Thus, at high resolution, there is a fair chance to make decent QPFs even at lead times of 3–7 days before typhoon landfall in the Philippines, with useful information on rainfall scenarios for early preparation.
Precipitable Water Vapor (PWV), the measure of the entire atmospheric water vapor carried in a vertical air column, can be used to represent measurements of water vapor (Mina in Mongabay 2021). The variations and correlation between PWV and rainfall over Puerto Princesa, Palawan, and Legazpi City, Albay, during the Philippine Southwest Monsoon (SWM) Season from 2013 to 2022 are presented through the use of radiosonde databases of both PWV and Rainfall, as extracted from Integrated Global Radiosonde Archives (IGRA) and Global Surface Summary of the Day (GSOD). It was interpreted by Microsoft Excel and MATLAB. A time series analysis with the Lomb-Scargle periodogram, Mann–Kendall test, and One-Way ANOVA graph was used to examine the temporal variations and analyze the correlations of the data extracted from respective sources. The results demonstrated an intriguing trend between the two stations and their locations. In the ten-year course of the study, Legazpi provided a higher peak of PWV and rainfall than Puerto Princesa, despite being the opposite course of SWM winds. PWV and Rainfall usually peak during the start of the SWM Season (June) and fall during the end of it (October). Lomb-Scargle presented apparent periodicities of 4.4 and 6.7 years for Puerto Princesa and Legazpi, while the Mann–Kendall test showed a downward trend for PWV and an upward trend for Rainfall. No significant difference exists between the variables presented, and the stations have a strong correlation, according to the One-Way ANOVA Graph. This study can be a reference for future studies of the Philippines’ shifting climate.
Coastal fishing communities depend on marine resources for their protein and livelihood needs, making them vulnerable to natural hazards. We assessed the exposure and adaptive capacity of six fishing villages located in three municipalities (Cortes, Lianga, Lanuza) in Surigao del Sur, Philippines by determining their resilience to various climatic hazards. We held six focus groups with 10–15 participants from each fishing community (N = 80 participants). We also conducted stakeholder meetings (N = 100 participants) to validate our findings. We used 12 indicators, divided into three components of resilience scored using a Likert scale: preparedness, coping, and adaptive capacity. We identified the important roles of good communication between community and municipality leaders, seminars and training on natural hazard awareness, livelihood alternatives, the presence of marine protected areas, and infrastructure to mitigate the impact of natural hazards (e.g., regular typhoons) such as sea walls.
Fishing communities depend on natural resources to fulfill their livelihood needs, making them more vulnerable during climatic events. However, despite the impacts brought by climate hazards, fishing communities have adaptation strategies and the capacity to be resilient. The study assessed fishing communities' exposure and capacity to adapt to various climatic events by determining their resilience to natural hazards. A focus group discussion (N = 80) and stakeholder meetings (N = 100) were conducted to assess the resilience of fishers in selected fishing villages in Surigao del Sur. A total of six fishing villages with 10–15 fisher participants attended the focus groups. Twelve behavioral indicators were used for the three components of resilience: preparedness, coping, and adaptive capacity. The finding shows that Habag and Nurcia were most exposed to climate hazards among fishing villages in Surigao del Sur, with an average of 3.14. However, Nurcia village had the highest average of 3.33 in preparedness capacity, such as conducting training and seminars on climate hazard awareness. In addition, Nurcia also had the highest average of 4.00 in coping capacity, such as having communication connectedness through an active organization. Overall, Nurcia was the most resilient to climate hazards. Despite their exposure to natural hazards and stressors, fishers in the communities have common connectedness that helps them recover easily and take necessary actions to mitigate the impacts of natural hazards. The local government units should be more active in providing relevant policies, regulations, and assistance to help affected areas during natural hazards.
