PAGASA’s SEASONAL LONG-RANGE FORECASTS, PRODUCTS AND CLIMATE SERVICES

Presented by: ANALIZA S. SOLIS SENIOR WEATHER SPECIALIST Climatology and Agrometeorology Division Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) PAGASA and Philippine climate in a nutshell

Sub-seasonal to Seasonal forecast products

Communicating Climate Information/ Provisioning climate services to user groups

Challenges, Summary

DOST-PAGASA The Weather and Climate Authority PAGASA in a nutshell HILIPPINE as the National TMOSPHERIC, Meteorological and Hydrological EOPHYSICAL & Services (NMHS) of the Philippines is the“authoritative” voice in STRONOMICAL providing the warning for public ERVICES safety. DMINISTRATION

2016 TC Forecast Track Error Year 2016 OFFICIAL FORECAST ERROR (KM) FORECAST ERROR (KM) TC Name INTENSITY TC Name 24 hr 48 hr 72 hr 24 hr 48 hr 72 hr 1 AMBO Tropical Depression AMBO 2 BUTCHOY 114.67 192.34 357.85 Typhoon BUTCHOY 114.67 192.34 357.85 3 CARINA 132.78 243.08 243.44 Severe Tropical Storm CARINA 76.20 PROTECTING LIVES, LIVELIHOODS AND 4 DINDO 86.15 146.43 154.21 Typhoon DINDO 86.15 146.43 154.21 5 ENTENG Severe Tropical Storm ENTENG 6 FERDIE 66.50 75.98 106.80 Typhoon FERDIE 66.50 75.98 106.80 7 GENER 83.63 180.90 314.44 Typhoon GENER 83.63 180.90 314.44 PROPERTIES THROUGH TIMELY, 8 HELEN 68.86 148.17 142.12 Typhoon HELEN 68.86 148.17 142.12 9 IGME 68.37 Typhoon IGME 68.37 10 JULIAN 53.48 Tropical Storm JULIAN 11 KAREN 84.49 170.31 360.29 Typhoon KAREN 74.81 186.46 ACCURATE AND RELIABLE WEATHER- 12 LAWIN 52.20 123.48 Super Typhoon LAWIN 52.20 123.48 13 MARCE 222.46 312.79 390.60 Tropical Storm MARCE 216.36 388.91 568.15 14 NINA 89.44 114.91 142.96 Typhoon NINA 89.44 114.91 142.96 Attained Average 93.59 170.84 245.86 Attained Average 90.65 173.06 255.22 RELATED INFORMATION AND Target 100 200 300 Target 100 200 300 DOST-PAGASA SERVICES The Weather and Climate Authority The country is susceptible and vulnerable to the impacts of weather and climate-related hazards…

• 20 Tropical Cyclones in a year • Hazards associated with Tropical Cyclone - Strong Winds - Excessive Rainfall & Thunderstorms - Floods - Landslides/Mudflows - Storm Surges - Tornado • ITCZ • Monsoons • Low Pressure Area (LPA)

• ENSO (droughts/floods)

Tracks of tropical cyclones that formed in the Western DOST-PAGASA North Pacific (WNW) during the period 1948-2010 (data The Weather and Climate Authority used : JMA Data set) Cinco, et. al.,2011) Philippine Climatology

SEASONAL MARCH OF RAINFALL ON THE WEST AND EAST COASTS OF THE COUNTRY

Southwest Monsoon Northeast Monsoon

http://www.meted.ucar.educ http://www.meted.ucar.educ STATE OF PHILIPPINE CLIMATE Annual number of TCs that entered PAR 1951-2015 TROPICAL CYCLONE INTENSITY (1951-2015) - Slightly decreasing trend in TC frequency - The number of extreme tropical cyclones (>150kph) continue to show a slightly increasing trend.

The solid blue line indicates the annual total number of TCs in PAR (blue Annual number of extreme TCs (>150kph) that entered PAR from dashed line shows linear trend), solid dark gray line shows number of TCs 1951–2015. Blue bars represent La Niña years, red bars represent El that made landfall and crossed, and dashed light gray line shows number Niño years, and gray bars represent neutral years. of non-landfalling TCs. [Source: PAGASA, published in State of Phil. Climate,2016, OML] [ Source: PAGASA, published in State of Phil. Climate,2016, OML] Climate Monitoring, Sub-seasonal/Seasonal products and services CLIMATE MONITORING CHECKLISTS: https://web.pagasa.dost.gov.ph/index.php/climate/climate-monitoring/daily-rainfall. • Day-to-day rainfall monitoring and during Tropical cyclone passage; • Onset of rainy season; • Dry/wet spell; • monitoring NE/SW monsoon onset and cessation; • Temperature anomalies (SST, local) • ENSO monitoring (El Nino/ La Nina possible development) ENSO MONITORING, OUTLOOK AND GUIDANCE GLOBAL ADVISORIES INTERNATIONAL PREDICTION SUMMARY CPC/IRI Probabilistic ENSO Outlook CENTERS Updated: 18 May 2017 CPC/ International • ENSO-neutral and El Niño are nearly equally favored Research Institute (IRI), during the Northern Hemisphere summer and fall ENSO-neutral is favored through spring (MAM) 2017, with nearly equal chances (~45%) USA 2017. • ENSO Alert System Status: Not Active of El Niño and ENSO-neutral through the remainder of 2017. BureauAs of : 11of MayMeteorology 2017 • Tropical Pacific remains warmer than average (BOM)-Australia • The Bureau's ENSO Outlook remains at El Niño WATCH, As of : 23 May 2017 meaning there is around a 50% chance—double the normal likelihood—of El Niño developing in 2017.

Tokyo Climate • While the above-normal NINO.3 sea surface Center/JMA -Japan temperature (SST) persisted recently, clear signs of El As of: 12 May 2017 Niño development were not observed in April. • The probability is 50% that El Niño event will emerge by the beginning of boreal autumn. (based from SSTA averaged over NINO.3 (5°N-5°S, 150°W-90°W) APEC Climate Center, • indicates persistent positive temperature anomaly Busan, S. Korea across the tropical Pacific with the positive El Niño- As of : 25 May 2017 Southern Oscillation (ENSO) phase. WMO • Most climate models surveyed indicate that basin-wide as of: 28 April 2017 ENSO-neutral conditions will persist through April-June 2017, followed by a 50-60% chance of El Niño Based from model output + Based from model output only development in the subsequent months Human judgment PAGASA: Nearly equal probability for ENSO-neutral and weak El Niño are likely in 2017.

While uncertainty is still high in forecast made at this time of the year, all but one model falls within a 1.5ºC range, compared to a nearly 2.5ºC spread of model predictions last month.…

Models continue to diverge from MJJ As of mid-May, 24% of combined through end of the year ranging from a dynamical and statistical models predict moderate El Niño to a weak La Niña. ENSO- neutral conditions, while 76% predicts weak El Niño conditions for the May-June-July 2017 season. Figure provided by the International Research Institute (IRI) for Climate and Society (updated 18 May 2017). ENSO MONITORING, OUTLOOK AND GUIDANCE SST Outlook: NCEP CFS.v2 Forecast (PDF corrected) ENSO MONITORING, OUTLOOK AND GUIDANCE

CPC/NOAA NMME Pacific Niño 3.4 SST Model Outlook

NMME averages indicate ENSO- neutral conditions are favored to continue through MJJ 2017

These are the participating models being used in PAGASA’s Consensus Forecast. ONE-MONTH PROBABILISTIC GUIDANCE SYSTEM (RUNNING 10-DAY PROBABILISTIC FORECAST) from GEPS/JMA/TCC

https://pubfiles.pagasa.dost.gov.ph/climps/extended/map_prec.html

could provide advance notice of potential hazards related to climate, • Initial runs made 2013; weather and • produced and updated every hydrological events Thursday by PAGASA; across the country • from JMA/TCC GEPS: http://ds.data.jma.go. jp/tcc/tcc/products/model/o utline/index.html 10-DAY WEATHER OUTLOOK FOR FARM OPERATIONS utilizing NOAA GFS 10-DAY WEATHER OUTLOOK FOR FARM OPERATIONS

6-Mar ftp://ftp.ncep.noaa.gov/pub/data/nccf/com/gfs/prod/ Project Areas Ave. Temperature (°C) Rainfall(mm/day) Total Cloud Cover Rel. Humidity (%) Wind Min Max Mean Total Description Description Mean Mean Speed (mps) Mean Direction Tayum (Abra) 20 33 27 0 NO RAIN SUNNY 88 1 E Bucay (Abra) 19 33 26 0 NO RAIN SUNNY 85 1 E Pilar (Abra) 21 33 27 0 NO RAIN SUNNY 92 1 E • zyGrib - GRIB File Viewer Weather data Tineg (Abra) 17 32 25 < 60 LIGHT RAINS MOSTLY SUNNY 94 1 ESE Langiden (Abra) 22 33 28 0 NO RAIN SUNNY 92 1 ENE Daguioman (Abra) 16 29 22 < 60 LIGHT RAINS PARTLY CLOUDY 96 1 ESE visualization Malibcong (Abra) 17 29 23 < 60 LIGHT RAINS PARTLY CLOUDY 98 1 ESE Peñarrubia (Abra) 20 33 27 0 NO RAIN SUNNY 88 1 E Villaviciosa (Abra) 20 33 27 0 NO RAIN SUNNY 89 1 E Dolores (Abra) 19 33 26 0 NO RAIN SUNNY 87 1 E • Being updated every Tuesday and Friday Bangued (Abra) 21 34 27 0 NO RAIN SUNNY 90 1 ENE San Quintin (Abra) 22 34 28 0 NO RAIN SUNNY 93 1 ENE Pidigan (Abra) 22 34 28 0 NO RAIN SUNNY 92 1 ENE San Juan (Abra) 19 32 26 0 NO RAIN SUNNY 88 1 E Tubo (Abra) 16 30 23 < 60 LIGHT RAINS MOSTLY SUNNY 91 1 ESE Manabo (Abra) 19 33 26 0 NO RAIN SUNNY 86 1 E Lacub (Abra) 17 31 24 < 60 LIGHT RAINS MOSTLY SUNNY 97 1 ESE San Isidro (Abra) 21 34 27 0 NO RAIN SUNNY 90 1 E Boliney (Abra) 16 31 24 < 60 LIGHT RAINS PARTLY CLOUDY 91 1 ESE Danglas (Abra) 20 33 27 0 NO RAIN SUNNY 89 1 ENE La Paz (Abra) 20 33 27 0 NO RAIN SUNNY 89 1 ENE Bucloc (Abra) 17 31 24 0 NO RAIN MOSTLY SUNNY 89 1 ESE Lagayan (Abra) 19 32 26 0 NO RAIN SUNNY 88 1 E Luba (Abra) 18 32 25 0 NO RAIN SUNNY 88 1 ESE Lagangilang (Abra) 19 33 26 0 NO RAIN SUNNY 88 1 E Licuan-Baay (Abra) 17 32 25 0 NO RAIN MOSTLY SUNNY 92 1 ESE Tabaco City (Albay) 22 30 26 0 NO RAIN CLOUDY 89 2 NE Guinobatan (Albay) 21 31 26 0 NO RAIN CLOUDY 94 2 NE Legazpi City (Albay) 22 31 26 0 NO RAIN CLOUDY 90 2 NE Malinao (Albay) 22 30 26 0 NO RAIN CLOUDY 88 2 NE Tiwi (Albay) 22 30 26 0 NO RAIN CLOUDY 88 2 NE Libon (Albay) 21 31 26 0 NO RAIN CLOUDY 93 2 NNE Oas (Albay) 21 31 26 0 NO RAIN CLOUDY 92 2 NNE Bato Lake (Albay) 22 31 26 0 NO RAIN CLOUDY 94 2 NNE Ligao City (Albay) 21 31 26 0 NO RAIN CLOUDY 93 2 NNE Bacacay (Albay) 23 29 26 0 NO RAIN CLOUDY 84 3 NNE Pio Duran (Albay) 22 31 26 0 NO RAIN CLOUDY 91 2 NNE Jovellar (Albay) 22 31 26 0 NO RAIN CLOUDY 91 2 NNE Daraga (Albay) 22 31 26 0 NO RAIN CLOUDY 91 2 NNE Manito (Albay) 23 29 26 0 NO RAIN CLOUDY 86 3 NNE Camalig (Albay) 21 31 26 0 NO RAIN CLOUDY 92 2 NE Santo Domingo (Albay) 22 29 26 0 NO RAIN CLOUDY 89 2 NE Polangui (Albay) 21 31 26 0 NO RAIN CLOUDY 96 2 NE Rapu-Rapu (Albay) 25 27 26 0 NO RAIN CLOUDY 79 5 NNE Malilipot (Albay) 22 29 26 0 NO RAIN CLOUDY 88 2 NE Flora (Apayao) 19 32 25 < 60 LIGHT RAINS PARTLY CLOUDY 98 1 SSW Kabugao (Apayao) 17 31 24 < 60 LIGHT RAINS PARTLY CLOUDY 99 1 S Santa Marcela (Apayao) 21 32 26 < 60 LIGHT RAINS PARTLY CLOUDY 95 2 SSW Calanasan (Apayao) 17 30 24 < 60 LIGHT RAINS MOSTLY CLOUDY 97 1 SSE Luna (Apayao) 18 31 24 < 60 LIGHT RAINS PARTLY CLOUDY 96 1 SSW Conner (Apayao) 17 31 24 < 60 LIGHT RAINS PARTLY CLOUDY 99 1 S Pudtol (Apayao) 17 32 25 < 60 LIGHT RAINS PARTLY CLOUDY 99 1 SSW Baler (Aurora) 19 29 24 0 NO RAIN SUNNY 91 1 ENE Dipaculao (Aurora) 17 29 23 < 60 LIGHT RAINS SUNNY 92 1 ESE Dingalan (Aurora) 19 29 24 0 NO RAIN SUNNY 91 2 E Dinalungan (Aurora) 18 27 22 < 60 LIGHT RAINS SUNNY 91 1 ESE Dilasag (Aurora) 17 28 22 < 60 LIGHT RAINS SUNNY 90 1 ESE San Luis (Aurora) 20 31 25 0 NO RAIN SUNNY 92 1 ENE Casiguran (Aurora) 17 27 22 < 60 LIGHT RAINS SUNNY 91 1 ESE Maria Aurora (Aurora) 18 31 25 0 NO RAIN SUNNY 97 1 ENE Balanga City (Bataan) 23 31 27 0 NO RAIN SUNNY 86 2 ENE Morong (Bataan) 24 30 27 0 NO RAIN SUNNY 82 3 E Hermosa (Bataan) 23 31 27 0 NO RAIN SUNNY 87 2 ENE Limay (Bataan) 23 30 27 0 NO RAIN SUNNY 84 2 E Pilar (Bataan) 23 31 27 0 NO RAIN SUNNY 86 2 ENE SEASONAL OUTLOOK (6-MONTH FORECAST)

The CPC/NOAA’s participating models in their North American Multimodel Ensemble (NMME) are being utilized by PAGASA available via ftp (http://ftp.cpc.ncep.noaa.gov/International/nmme/seasonal_nmme_forecast_in_cpt_format/)

Kirtman et al. 2014 the NMME project and data dissemination is supported by NOAA, NSF, NASA and DOE. Please also acknowledge the help of NCEP, IRI and NCAR personnel in creating, updating and maintaining the NMME archive. SEASONAL OUTLOOK (6-MONTH FORECAST) GUIDANCE

Spatial distribution of rainfall correlation against the SST of Nino3.4 region For OND season only SEASONAL OUTLOOK (6-MONTH FORECAST) At present, A Consensus Forecast Approach is being used to produce the official monthly rainfall (other variables) forecast. A consensus forecast involves:

• Statistical downscaling method (CCA); • getting the best models with higher forecast skill for particular season or forecast period; • taking into consideration the current trend of forecast and indicators and match the behavior of that indicators in the past (Analogue to some extent); • combination of knowledge in climatology and expert judgment; and • Climate Forecasters will ‘vote’ for their selected sets of forecasts based from

above measures/conditions and the consensus final forecast will be decided. SEASONAL OUTLOOK (6-MONTH FORECAST) PAGASA CLIMATE FORECAST SYSTEM USING CONSENSUS APPROACH • Using regression-based statistical downscaling technique that best explain the variability within and between sets of variables;

• Uses output from GCMs (aka MOS); Getting candidate predictor variables from GCM hindcast in cross validation manner; Build statistical model and make forecast;

• Forecast can be expressed as anomalies, absolute value, standardized anomalies or as probabilities;

• Can be best demonstrated using CPT tool strong cold signal in parts of the Philippines including South Asia and India

ENSO SIGNAL

strong warm signal in parts of the W. Pacific (N and S)including South Asia and India

GLOBAL WARMING

weights for the X and Y components of the CCA modes together SEASONAL OUTLOOK (6-MONTH FORECAST)

SKILL MAPS, MAY_ic, JJA 2017

SKILL MAPS, MAY_ic, ASO 2017

DOST-PAGASA The Weather and Climate Authority SEASONAL OUTLOOK (6-MONTH FORECAST)

Percent Normal

Probabilistic

SEPT. 2017 0 Province/s to be affected 15 Province/s to be affected 62 Province/s to be affected 3 Province/s to be affected SEASONAL OUTLOOK (6-MONTH FORECAST)

Outlook / Advisories Issuances SEASONAL CLIMATE OUTLOOK MONTHLY CLIMATE ASSESSMENT AND OUTLOOK

NARRATIVE INFOGRAPHICS

- ISSUED twice a year NARRATIVE INFOGRAPHICS Dec (Jan-June) - ISSUED once a year; June ( July-Dec) - ISSUED monthly - annual report that provides a Jan-Dec summary of observations of the country’s climate and climate-related disasters Outlook / Advisories Issuances DROUGHT/DRY SPELL ADVISORIES

Simplified version

EL NINO/LA NINA ADVISORIES Outlook / Advisories Issuances CLIMATE MONITORING AND PREDICTION SECTION (CLIMPS) - Day-to-day rainfall and during TC passage Climate Monitoring - Onset of rainy season • Continuous monitoring and analyses of the climate affecting - Dry spell /wet spell the Philippines - NE/SW monsoon monitoring - Temperature anomalies (SST, local) - Monthly Rainfall and Temperature Assessment Climate Prediction [Sub-seasonal / Seasonal Climate Forecast (SCF)] - Monthly /Seasonal / 6-month rainfall forecast • Collection, application and interpretation of various global - 10-day probabilistic forecast - Temperature Forecast indicators influencing local condition - Tropical cyclone frequency forecast • Development/validation of indices and methodologies for - Dry day/Wet day forecast seasonal prediction - Drought Outlook

- Seasonal Outlook (3 to 6 months) Climate Outlook & Advisories - Monthly Weather Situation and Outlook • Provision of Advisories, updates and outlooks especially in relation to El Nino/La Nina and its impacts - Dry Spell/Wet Spell Situation and Outlook • Press Statement - El Nino/La Nina Advisory - Dry Spell / Drought Advisory - Press Statement

Tailored- Products and Services - Statistically downscaled Climate Projections (AR4/AR5) • Climate Change-related products and services - Watershed/Dam/River Basin Rainfall Forecast • Sector-specific forecasts - 10-day forecast for Farm Advisories • Weather-within climate /sub-seasonal forecast - Climate briefing with NGAs and TWG meetings • Probabilistic forecast - IEC • IEC OTHER INITIATIVES Application of Regional Climate Models [RegCM, CWRF, etc.] for Seasonal Forecasting

RegCM Simulation runs:

• Multi-year runs (15-yr) using PBC • domain and resolution testing • Several Convective parameterizations • TC and Heavy rainfall events • Land use change (Bats vs. CLM) • Real-time fcst. using CFS seasonal fcst • Hydrostatic vs. non-hydrostatic runs OTHER INITIATIVES

DROUGHT AND CROP ASSESSMENT AND FORECASTING (DCAF)

•To understand drought events using the combination of in situ and satellite data; and Develop advanced warning system to forecast probable drought events using combined statistical and dynamical models

Development of Drought Vulnerability Map

Crop Classification Map for Central Luzon: (Phenology based crop classification map for Central Luzon in 2003) OTHER INITIATIVES

STATISTICALLY/DYNAMICALLY DOWNSCALED CLIMATE PROJECTIONS

SIMPLIFIED ENSO MATERIALS COMMUNICATING CLIMATE INFORMATION FOR USER GROUPS Conduct of climate outlook forum (COF) every month

COF - platform to disseminate climate information, ENSO update and outlook in the coming seasons provides opportunity for regular dialogue between PAGASA and its stakeholder institutions to promote: (a) enhanced understanding of forecast products and services, including their limitations and uncertainties, and (b) better appreciation by PAGASA of user’s information requirements. • f Handbook of Weather and Climate Hazards for Agriculture/ Aquaculture Technicians • ormulate policies that will support climate-resilient aagriculture/quaculture Climate Field School (CFS) • involved different states of capacity building and customization: Training of Trainers, Training of Extension Workers, and Training of Farmers which are conducted by the (Left) Identifying Climate Risk and Management by local personnel trained by PAGASA; PAGASA expert personnel. (middle) Orientation/Briefing of participants by trained Local Agricultural Technicians; (right) The CFS 3rd Batch Graduates Climate Resiliency Field School (CrFS) Municipal Climate Information and Monitoring Center – Local Weather Observation, Localization and Dissemination of Farm Weather Advisories and Weather and Impacts benchmarking; -Community agrometeorologist - LGUs became more capacitated on (DRR-CCA); - Farmers get access to climate information

towards sustainable and resilient livelihoods (top) installation and demonstration of utilization of weather monitoring instruments; (below left) disseminations of strategies; (below right) weather boards PAGASA PAGASA expert personnelPayong as a resource The Weather and Climate Authority person for “climate PAGASAresiliency field schools” Impacts of Climate Change on Agriculture

PAGASA Payong The Weather and Climate Authority PAGASA Livelihood Adaptation to Climate Change - identification, validation, field-testing and evaluation of good adaptation practices at local context through participatory processes and capacity development.

PAG-ASA senior weather specialist Anthony Lucero (middle) explains how the AWS console transmits weather data from the AWS unit to the computer and to the weather website; PAGASA (right) varietal evaluation during Field Day Payong The Weather and Climate Authority PAGASA CHALLENGES AND FUTURE PLANS

PAGASA Payong The Weather and Climate Authority PAGASA Climate Products for Risk Management and Adaptation Planning Short to medium Medium-term Seasonal to inter- Decadal Climate term weather intra- seasonal annual climate climate trend change forecasts forecasts forecasts analysis scenarios Seasonal forecast Climate Climate Change Daily Weekly Sub-seasonal to Hazard/ normals/trends projections weather forecast seasonal (S2S) risk mapping Hazard/risk mapping Next hour to 2 weeks to Long term climate Season to year Decade 10 days two months change

Short-term Extended short- Medium-term Medium term to Long-term planning range operational Long-term strategic planning  Emergency  preparation for planning strategic planning New Preparedness extremes Risk Infrastructures tecnologies such assessment and planning, drought/flood management retrofitting resistant crops

Decision-making Timelines PAGASA Need further improvement @ Payong The Weather and Climate Authority this time scale PAGASA • LEAD Centre for Climate Monitoring and as Consortium Member for TCLRF with RCC Node for RAV SEA - July 2017 (Demonstration Phase) - Perform Climate diagnostics for SEA region utilizing GPC Products; - Issue Climate Watch during significant climate phenomena

• Utilization of newly acquired PAGASA IHPC for S2S Prediction System (R&D, operation) - January 2018 onwards

- PAGASA Modernization Program (2017 –

- Strong collaboration linkage /partnership/co- produce

PAGASA Payong The Weather and Climate Authority PAGASA SUMMARY

• The Philippines is very much at risk from the impacts of hydrometeorological hazards. • PAGASA is doing its best to provide necessary information to prepare the public for possible impacts from weather and climate-related hazards; • Strong political will. • Education and awareness raising (local authorities and the general public) • It is proposed to upgrade the PAGASA’s early warning system into impact- based warning system.

PAGASAPAGASA Payong The WeatherThe Weather and Climate and Climate Authority Authority PAGASA Building Resilient Community thru 4Gs

Good forecast… Good communication… Good decision… Go and TAKE ACTION!

PAGASA Payong The Weather and Climate Authority PAGASA THANK YOU! Website: www.pagasa.dost.gov.ph Facebook: www.facebook.com/ pagasa.dost.gov.ph Twitter: @dost_pagasa Typhoon Flood +632-9271541 +632-9266970 +632-9271335 +632-9204052 2016 TC Forecast Track Error Year 2016 OFFICIAL FORECAST ERROR (KM) FORECAST ERROR (KM) TC Name INTENSITY TC Name 24 hr 48 hr 72 hr 24 hr 48 hr 72 hr 1 AMBO Tropical Depression AMBO 2 BUTCHOY 114.67 192.34 357.85 Typhoon BUTCHOY 114.67 192.34 357.85 3 CARINA 132.78 243.08 243.44 Severe Tropical Storm CARINA 76.20 Climate IEC 4 DINDO 86.15 146.43 154.21 Typhoon DINDO 86.15 146.43 154.21 5 ENTENG Severe Tropical Storm ENTENG 6 FERDIE 66.50 75.98 106.80 Typhoon FERDIE 66.50 75.98 106.80 7 GENER 83.63 180.90 314.44 Typhoon GENER 83.63 180.90 314.44 +632-4351675 +632-4342696 8 HELEN 68.86 148.17 142.12 Typhoon HELEN 68.86 148.17 142.12 9 IGME 68.37 Typhoon IGME 68.37 10 JULIAN 53.48 Tropical Storm JULIAN 11 KAREN 84.49 170.31 360.29 Typhoon KAREN 74.81 186.46 +632-9279308 12 LAWIN 52.20 123.48 Super Typhoon LAWIN 52.20 123.48 +632-4340955 13 MARCE 222.46 312.79 390.60 Tropical Storm MARCE 216.36 388.91 568.15 14 NINA 89.44 114.91 142.96 Typhoon NINA 89.44 114.91 142.96 Attained Average 93.59 170.84 245.86 Attained Average 90.65 173.06 255.22 Target 100 200 300 Target 100 200 300

PAGASAPAGASA Payong The WeatherThe Weather and Climate and Climate Authority Authority PAGASA 3. DEVELOPMENT OF CLIMATE-SMART KNOWLEDGE DATABASE, FORECAST PRODUCTS; KNOWLEDGE –SHARING AND THE PROVISION OF OTHER RELATED SERVICES TO INCREASE CLIMATE –RESILIENCE OF TILAPIA FARMERS - UN-FAO, BFAR, BSWM, PCIC, PAGASA, Philippine Climate Change Commission, universities and research and development organizations

- Aims to help improve their capacities to formulate policies that will support climate-resilient aquaculture, adopt a better response and adaptation framework, integrate new scientific knowledge, and develop and disseminate climate-smart techniques and information and communications technology- based tools

PAGASA Payong The Weather and Climate Authority PAGASA ACTIVITIES: 1. Project Inceptions Workshop 2. Training of Trainors 3. Climate Forum, AWS Installation and Mentoring at Three (3) Project Sites – Minalin, Pampanga; Iriga City, Albay ; Santiago City, Isabela 4. Provision of weather- and climate-based inputs for ‘Climate-Smart Tilapia Technology Guide’, Set of Aquaculture Farm Forecast and Agrometeorology Guide for Aqua culturists 5. Development of Handbook of Weather and Climate Hazards for Agriculture/ Aquaculture Technicians

PAGASA Payong The Weather and Climate Authority PAGASA PAGASA expert personnel conducted training about AWS set-up and utilization to LGUs representative from Minalin, Pampanga, Iriga and Santiago, Isabela with BFAR Region 2, 3 and 5; Installation of AWS by PAGASA personnel in selected sites

PAGASA Payong The Weather and Climate Authority PAGASA 5. WFP – Forecast-based Financing (FbF) and Emergency Preparedness for Climate Risks

PAGASA Payong The Weather and Climate Authority PAGASA Baseline Assessment: data gathering at the municipality of Pila, Province of Laguna

PAGASA Payong The Weather and Climate Authority PAGASA Climate Information Services (CIS) Provisioning in the Agriculture Sector

Climate Information Services (CIS) Provisioning in the Agriculture Sector

PAGASA Payong The Weather and Climate Authority PAGASA UN-FAO AMICAF

PAGASA Payong The Weather and Climate Authority PAGASA WIBI MINDANAO PROJECT

Project Title:“Scaling-up Risk Transfer Mechanisms for Climate Vulnerable Agriculture- based Communities in Mindanao or the “WIBI MINDANAO PROJECT”

Duration: 2015-2017 Funding Source: UNDP Budget: Php 3 Million Objective:

To determine the feasibility of developing Weather Index-Based Insurance for sugarcane, banana, coconut and cacao through analyzing the correlation between weather parameters and crop yield.

PAGASA Payong The Weather and Climate Authority PAGASA Communication of Early Warning Information

Accuracy Skill

disseminate Warning/ Information Users receive understand believe

Source: (WMO) Take action Enhancement of Early Warning Service Color-coded Warning System Hazards Signal/Warning Tropical Cyclone

TCWS 1 TCWS 2 TCWS 3 TCWS 4 TCWS 5 Heavy Rainfall Advisory Alert Emergency Thunderstorm Information Watch Advisory Floods Flood Advisory/Bulletin Severe Weather Bulletin/Storm Surge Storm Surge Bulletin Gale Gale Warning Enhancement of Early Warning Service Color-coded Warning System Enhancement of Early Warning Service Color-coded Warning System 24-Hr 48-Hr 72-Hr

number of number of number of number of municipalities number of barangays municipalities number of municipalities barangays affected affected affected barangays affected rainfall amount affected affected

196 3123 Moderate 150-300 mm 0 0 9 124

High >300-450 mm 0 0 0 0 66 859

Very High >450 mm 0 0 0 0 0 0 PAGASA Storm Surge Forecast

Tools: • PAGASA Forecasts: Severe Weather Bulletin, Tropical Cyclone Warning for Shipping

• Japan Meteorological Agency (JMA) Storm Surge Model

DOST-PAGASA The Weather and Climate Authority PAGASA New Initiatives

• JMA Storm Surge Graphical User Interface (GUI) PAGASA New Initiatives

• Storm Surge Bulletin with Inundation Map

DOST-PAGASA The Weather and Climate Authority IMPROVED LONG TERM CLIMATE INFORMATION FOR CRM and CCA Measures Sub-seasonal to Seasonal Climate Forecast

El Nino/La Nina /Drought Monitoring and Advisories

Rolling-out of Climate Change Projection Scenarios Information

Farm- Weather Forecast and Advisories From climate science … To climate services

- Ensuring that the best available climate science is effectively communicated with the agriculture sector and other socio- economic sectors - Easily accessible, timely and relevant scientific information to help society cope with climate variability - Effective climate services requires technical capacities and active communication and exchange between info producers, translators and user communities’

DOST-PAGASA The Weather and Climate Authority Delivery of Service

Weather on TV PAGASA Daily Weather Forecast Video Typhoon “Nina”(Nock-Ten) Update • 5PM, 25 December 2016

At 4PM, 25 Dec. 2016, Typhoon “NINA” was located at 65 km East Southeast of Virac, Catanduanes (13.4 °N, 124.8 °E). Maximum Wind/Gust: 185/255 Kph Forecast to move West @ 15 Kph

• 5PM, 25 December 2016

Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA) 2016 TC Forecast Track Error Year 2016 OFFICIAL FORECAST ERROR (KM) FORECAST ERROR (KM) TC Name INTENSITY TC Name 24 hr 48 hr 72 hr 24 hr 48 hr 72 hr 1 AMBO Tropical Depression AMBO 2 BUTCHOY 114.67 192.34 357.85 Typhoon BUTCHOY 114.67 192.34 357.85 3 CARINA 132.78 243.08 243.44 Severe Tropical Storm CARINA 76.20 4 DINDO 86.15 146.43 154.21 Typhoon DINDO 86.15 146.43 154.21 5 ENTENG Severe Tropical Storm ENTENG 6 FERDIE 66.50 75.98 106.80 Typhoon FERDIE 66.50 75.98 106.80 7 GENER 83.63 180.90 314.44 Typhoon GENER 83.63 180.90 314.44 8 HELEN 68.86 148.17 142.12 Typhoon HELEN 68.86 148.17 142.12 9 IGME 68.37 Typhoon IGME 68.37 10 JULIAN 53.48 Tropical Storm JULIAN 11 KAREN 84.49 170.31 360.29 Typhoon KAREN 74.81 186.46 12 LAWIN 52.20 123.48 Super Typhoon LAWIN 52.20 123.48 13 MARCE 222.46 312.79 390.60 Tropical Storm MARCE 216.36 388.91 568.15 14 NINA 89.44 114.91 142.96 Typhoon NINA 89.44 114.91 142.96 Attained Average 93.59 170.84 245.86 Attained Average 90.65 173.06 255.22 Target 100 200 300 Target 100 200 300 PAGASA MODERNIZATION PLAN

• Integration of Weather Observational Data and Information Through Automated Data Transmission • Upgrading of Advanced Forecast Computing Facilities • Enhancement of Aviation Forecasting and Warning Capability by Setting up Lightning Detection Systems • Infrastructure Development Program a) Telemetering and Installation of Automatic Raingauge (ARG) and Water Level Sensor Gauges in two (2) River Basins b) PAGASA Meteorological-Hydrological Telecommunication Network The country is susceptible and vulnerable to the impacts of climate related hazards… Disastrous Tropical Cyclones during the last 6 years (2010-2015) Based on the number of casualties, people affected and total cost of damages TOTAL NUMBER OF TYPHOON NAME DATE CASUALTIES 6300 TY Haiyan (Yolanda) November 2013 1268 TS Washi (Sendong) December 2011 1067 TY Bopha (Pablo) December 2012 196 TY Conson (Basyang) July 2010 112 Habagat (SW Monsoon)* August 2012 110 Flooding (Bicol)* December 2010 103 TY Nesat (Pedring) September 2011 TOTAL NUMBER OF PEOPLE AFFECTED 16,078,181 TY Haiyan (Yolanda) November 2013 6,243,998 TY Bopha (Pablo) December 2012 4,653,716 TY Rammasun (Glenda) August 2012 4,451,725 Habagat (SW August 2013 Monsoon)* 4,149,484 TY Hagupit (Ruby) December 2014 3,096,422 TY Utor (Labuyo )/ SW August 2013 Monsoon* 3,030,846 TY Nesat (Pedring) September 2011 2,052,141 TS Mario October 2010 2,008,894 TY Juan December 2010 TOTAL COST OF DAMAGES (PhP) 89,598,068,634.88 TY Yolanda November 2013 38,616,610,414.00 TY Glenda July 2014 36,949,230,987.07 TY Pablo December 2012 14,964,489,302.72 TY Pedring September 2011 12,013,575,820.00 TY Juan October 2010 11,000,003,194.04 TY Lando October 2015 6,455,183,879.80 TY Nona December 2015 4,406,454,628.74 TY Ineng August 2015 Source: NDRRMC and with* http;//www.emdat.be/ 3,399,424,192.515 TS Mario September 2014 PHILIPPINE CLIMATE Climate Classification: Type I Two pronounced season dry from November to April and wet for the rest of the year. maximum rain period coincides with the peak of the southwest monsoon (July to September). Humid equatorial Type II or Tropical maritime climate No dry season with a very pronounced maximum rainfall from November to April and wet for the rest of the year. Type III SeasonNormal not Onset very ofpronounced rainy season relatively – dry November to April and wetnd for the rest of the year Mean annual rr=2824.6 Type IV 2 half of May – 1st half of June Infanta =4129.6 Hinatuan =4464.9 Rainfall more or less evenly distributed through out the year Based from 1981-2010