The Role of PAGASA in Disaster Mitigation

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1 The Role of PAGASA in Disaster Mitigation Christopher F. Perez 2017 ACTS Workshop on Extreme Weather Forecast and Water Resource Management Hanoi, Vietnam September 26-27, 2017

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3 Tropical Storm AMANG(Mekkhala), Jan st Tropical Cyclone in 2015, during Papal Visit in Tacloban Critical decision point (8PM Jan. 16 4AM Jan 17): Critical Analyses made Forecast 6hrly Actual hrly 12MN to 8AM 17 Jan closely observing/ monitoring the track of TS Amang 4-5 AM 17 Jan started to recurve Starting 8PM, Jan 16, TS Amang unexpectedly shifted directions from northwest to southwest raising the possibility of directly hitting Tacloban. At 3-4AM Jan17,we observed the slowing down of movement of Amang. At 4-5 am TS Amang started to recurve westward a significant development indicating it would follow a northwesterly track as earlier predicted by PAGASA.

4 Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) Mission: Protecting lives, properties and livelihoods through timely, accurate and reliable weatherrelated information and services. as the National Meteorological and Hydrological Services (NMHS) of the Philippines shall be the authoritative voice in providing the warning against weather hazards for public safety.

5 PAGASA ORGANIZATIONAL CHART OFFICE OF THE ADMINISTRATOR 3 Deputy Administrators Administration & Engineering Services Operations & Services Research & Development Administrative Division Financial, Planning and Management Division Engineering and Technical Services Division Weather Division Climatology and Agrometeorology Division Hydro-Meteorology Division Research & Development and Training Division Synoptic Observation Network Flood Forecasting Warning Centers Agromet Observation Network Radar Network Upper-Air Observation Network AWS Network Rainfall Station Network PAGASA Regional Services Divisions (5) Field Stations National Capital Region Northern Luzon Southern Luzon Visayas Mindanao

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7 An average of tropical cyclones enter /develop inside the Philippine Area of Responsibility (PAR) every year, about 8 9 make landfall.

8 Monthly Average Tropical Cyclone Occurrences within the PAR JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

9 Consolidated tropical cyclone track within the PAR for the period

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11 Tropical Cyclone Warning System Tropical Cyclone Warning Signals (TCWS) TCWS LEAD TIME * (hrs) WINDS (KPH) IMPACTS OF THE WIND # No damage to low risk structure to very light damage # Light to moderate damage # Moderate to heavy damage # Heavy to very heavy damage #5 12 more than 220 Very heavy to widespread damage

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13 Mode of Dissemination PAGASA.DOST.GOV.PH

14 FOR ANY EARLY WARNING SYSTEM TO SUCCEED, SEVERAL COMPONENTS ARE NECESSARY: Technology to detect and monitor the hazard; Communication systems to alert the public; Local leaders trained to make the right decisions; A public that is educated to react appropriately to warnings; and Response protocols such as evacuation plans prepared and rehearsed well in advance of the threat.

15 All these elements must work well, both individually and in harmony. Failure in any one of these elements can mean failure of the whole early warning system.

16 MERANTI Strongest typhoon at landfall over any part of the Philippines in 2016 A REVISIT

17 TRACK OF TYPHOON MERANTI (1718) 11/0000 UTC TO 14/0600 UTC Left PAR Last bulletin issued by PAGASA 14/0600 UTC Landfall over Itbayat island 13/1700 UTC Reached Peak Intensity 13/1200 UTC First bulletin issued by PAGASA 11/0000 UTC Entered PAR Became Typhoon 11/0600 UTC

18 LANDFALL Meranti underwent rapid intensification over the Philippine Sea before reaching Luzon strait RI T+12H ( +20 kt) RI T+24H ( +30 kt) RI T+36H ( +40 kt) RI T+48H ( +50 kt) RI definition based on Kaplan et al. (2010)

19 ITBAYAT Itbayat BASCO TAIDONG Doppler Weather Radar Loop Central Weather Bureau, Taiwan HENGCHUN CALAYAN APARRI 98232

20 ITBAYAT HENGCHUN CALAYAN APARRI BASCO PAGASA-DOST Aparri Doppler Weather Radar 13 September 2016, 1500 UTC to 1900 UTC

21 MICROBAROGRAM Itbayat Data rendered unusable beyond 13/1630 UTC Last usable data 13/ 1630 UTC: STN: hpa MSL: hpa 12 AM 6 AM 12 PM 6 PM

22 METEOGRAM BASCO Highest observed wind NNE, 40 m/s (1600 UTC) Lowest pressure: hpa (1630 UTC)

23 NOTE: Microbarograph data from Itbayat (98132) alone could have been used to determine actual (near-peak or peak) intensity of Typhoon Meranti in terms of mean sea level pressure since the eye passed over the island. However, last usable data was 30 minutes before landfall or when the center fix was around 10 km from the station. Inferring that the central pressure was around 920 hpa given the last usable data doesn t make sense since the barograph tracing suggests that the pressure was still rapidly dropping. Basco (98134) was able to capture the v-like feature on the microbarograph before it ceased transmitting data (the stations was able to observe pressure data before, during and after the passage of the core). However, the center fix was still at least 25 km from the station during the entire passage (to put into perspective, radius of maximum winds (RMW) was 22 km based on JTWC best track data).

24 QUESTION What was the actual intensity (near-peak or peak) of Typhoon Meranti in terms of MSLP based on ground observations from these 2 stations and from other stations in the periphery of the tropical cyclone? SOLUTION Use the typhoon pressure profile model H80 that best fits observation data from 0300 to 1800 UTC 13 September from 6 stations in Southern Taiwan Extreme Northern Luzon area. P r = P c + Pe RMW r P = P env P c (Holland 1980, Holland 2008) P env = P OCI + 2 (Courtney and Knaff 2009) B ASSUMPTION B parameter limited from 1.0 to 2.5 (Holland 1980) at 0.5 intervals. Possible values of central pressure range from 850 to 925 hpa at 5 hpa intervals. Meranti is assumed to have symmetric, circular pressure field during the period used. Environmental Pressure (Penv) = 1009 hpa and RMW = 22 km (JTWC best track data). Pressure profile did not change significantly from 0300 to 1800 UTC 13 September Pressure data from all 6 stations are reliable Given that Meranti was undergoing an eyewall replacement cycle, RMW still co-located in the inner eyewall.

25 Taidong N, E Hourly Satellite and Radar Fixes of Typhoon Meranti UTC 13 September Hengchun N, E Basco N, E Itbayat N, E Calayan N, E Aparri N, E 2 stations within the core 4 stations in the periphery

26 74 observation data from 0300 UTC to 1800 UTC Philippines: Hourly Taiwan: 3-Hourly MSLP Observations from Synoptic Stations in Southern Taiwan and Extreme Northern Luzon UTC 13 September

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28 RMSE OF VARIOUS H80 PRESSURE MODELS (VARYING B PARAMETER AND CENTRAL PRESSURE) UTC 13 September

29 Possible H80 models: Model Equation P r = e 22 r P r = e 22 r P r = e 22 r P r = e 22 r P r = e 22 r P r = e 22 r RMSD (hpa)

30 Central Pressure: 895 to 900 hpa Environmental Pressure: 1009 hpa Radius of Maximum Winds: 22 km

31 FINDINGS Based on MSLP observation from 6 synoptic stations in Southern Taiwan and Extreme Northern Luzon, the H80 model that best describes the pressure profile of Typhoon Meranti near landfall (following aforementioned assumptions) suggests that the central pressure of Meranti falls between 895 and 900 hpa, possibly 899 hpa. This represents the near-peak or peak intensity of Meranti. To put the obtained value into perspective, JMA and JTWC puts the lowest central pressure of Meranti at 890 and 887 hpa, respectively However, it was also noted that H80 models using 895 to 900 hpa as central pressure and 1.0 as Holland B parameter yields lower MSLP over radial distances above 150 km when compared with observation data. Nevertheless, these models yielded the best pressure profile when compared against observation following the assumptions stated earlier.

32 SOME NOTES Mean wind and gust observations were not used in determining the landfall intensity of Typhoon Meranti due to doubts on the reliability of instrument readings under such wind exposure. Both Itbayat and Basco stations reported damaged wind sensors in the aftermath of the passage, which puts into question the reliability of observed wind while the stations were within the eyewall of the Typhoon. Perform similar analysis on other intense landfalling tropical cyclones during the 2016 season (i.e. Super Typhoon Haima / Lawin and Typhoon Nock-ten / Nina) in order to verify if Meranti was indeed the strongest tropical cyclone to make landfall in any part of the Philippines in Nevertheless, the findings on this report puts Meranti as one of the strongest to make landfall in 2016 and possibly since modern PAGASA records began in 1950s.

33 REFERENCES Courtney, J., and J.A. Knaff, 2009: Adapting the Knaff and Zehr Wind-Pressure Relationship for operational use in Tropical Cyclone Warning Centres. Australian Meteorological and Oceanographic Journal, 58:3, Holland, G.J., 1980: An Analytic Model of the Wind and Pressure Profiles in Hurricanes. Mon. Wea. Rev., 108, , Holland, G., 2008: A Revised Hurricane Pressure Wind Model. Mon. Wea. Rev., 136, , Kaplan, J., M. DeMaria, and J.A. Knaff, 2010: A Revised Tropical Cyclone Rapid Intensification Index for the Atlantic and Eastern North Pacific Basins. Wea. Forecasting, 25, , EXTERNAL DATASET Best Track Data for 2016 from Japan Meteorological Agency (JMA) and Joint Typhoon Warning Center (JTWC) Weather Radar Composite from Central Weather Bureau (CWB) Inset satellite images from Naval Research Laboratory (NRL)

34 Thank you for your attention.