Gale Warning
As of today, there is no Gale Warning Issued.




Weather Advisory
Issued at: 11:00 AM 22 September 2017
Weather Advisory in PDF file



Dams Water Level Update
As of 6AM, 23 September 2017

General Flood Advisories - Regional
As of  7AM, 23 September 2017 



Daily Basin Hydrological Forecast
Issued 22 September 2017




Monthly Climate Assessment and Outlook

Issued: 03 September 2017

Monthly Rainfall Forecast
RAINFALL FORECAST  (October 2017 - March 2018) 
UPDATED: 20 September 2017 (next update October 2017)


Regional Rainfall Forecast
Issued: 20 September 2017
Valid for: October 2017 - March 2018
Farm Weather Forecast and Advisories
ISSUED              : 8AM,FRIDAY, SEPTEMBER 22, 2017
VALID UNTIL      :  8AM, SATURDAY, SEPTEMBER 23, 2017
FWFA:  N0. 17-266

Ten-Day Regional Agri-Weather Information
DEKAD NO. 27 SEPTEMBER 21-30, 2017

PHILIPPINE AGRI-WEATHER FORECAST
The weather systems that will affect the whole country are southwest monsoon, intertropical convergence zone (ITCZ) and low pressure area (LPA).

Seasonal Climate Outlook
Issued:  06 July 2017
FOR July - December 2017
PDF 




Astronomical Diary
Issue for September 2017
Autumnal equinox will occur on September 23 at 4:02 a.m...




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CLIMATOLOGY and AGROMETEOROLOGY

DRY SPELL/DROUGHT OUTLOOK ( February - July 2016 )
Issued: 09 February, 2016




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DRY SPELL / DROUGHT ASSESSMENT
Issued: 02 September 2015





DRY SPELL/DROUGHT ASSESSMENT 
Issued: July 2, 2015
 




DROUGHT ASSESSMENT

as of 06 May 2015







El Niño/Southern Oscillation (ENSO)
El Niño Phenomenon


What is El Niño?

El Niño is a large scale oceanographic/meteorological phenomenon that develops in the Pacific Ocean, and is associated with extreme climatic variability i.e., devastating rains, winds, drought, etc. It is the migration from time to time of warm surface waters from the western equatorial Pacific Basin to the eastern equatorial Pacific region, along the coasts of Peru and Ecuador. This condition can prevail for more than a year thus adversely affecting the economy in both local and global scale.

El Niño translates from Spanish as the “Boy Child” or the “Little One”. It used to be considered a local event along the coast of Peru and Ecuador. The term was traditionally used by the Peruvian anchovy fishermen to describe the appearance of warm ocean current flowing the South American coast around Christmas time.

In normal condition, the prevailing southeasterly trade winds produce a surface current flowing toward the equator along the western South American coast. The waters leaving the coast are replaced by colder waters from below (upwelling), which is rich in phytoplankton, the food source of anchovy.

The warm current (El Niño) temporarily displaces nutrient-rich upwelling cold water resulting to heavy harvest of anchovies. The abundant catch, however, lasted for only a short period of time. What followed later was a sharp decline in the fish population resulting in lesser catch. At times, warming is exceptionally strong and ruins the anchovy harvest.

Characteristics of El Niño

•It occurs in the Pacific basin every 2 to 9 years;
•It usually starts during the Northern winter (December to February);
•Once stablished, it lasts until the first half of the following year, although at time , it stays longer (ex: 1939-1941 and 1989-1992 episodes);
•It exhibits phase-locking at annual cycles (El Niño and rainfall fluctuations with it tend to recur at the same time of the year); and
•It usually has a biennial cycle (El Niño events will often be preceded and/ or followed by La Niña).

What are the climatic indicators of El Niño phenomenon in the Philippines?

•Delayed onset of the rainy season
•Early termination of the rainy season
•Weak monsoon activity isolated heavy downpour with short duration
•Far tropical cyclone track
•Less number of tropical cyclones entering the PAR

What are the effects of ENSO in the Philippines?

In the Philippines, drought/dry spell events are associated with the occurrence of El Niño.

What provinces were already affected by drought/dry spell in the Philippines during the May to August 2015 rainfall assessment?

See maps and figures below.dryspell drought assess july31drought dryspell assessment May31 editedsize

 

The Climate of the Philippines is tropical and maritime. It is characterized by relatively high temperature, high humidity and abundant rainfall. It is similar in many respects to the climate of the countries of Central America. Temperature, humidity, and rainfall, which are discussed hereunder, are the most important elements of the country's weather and climate.

 

Temperature

Based on the average of all weather stations in the Philippines, excluding Baguio, the mean annual temperature is 26.6o C. The coolest months fall in January with a mean temperature of 25.5oC while the warmest month occurs in May with a mean temperature of 28.3oC. Latitude is an insignificant factor in the variation of temperature while altitude shows greater contrast in temperature. Thus, the mean annual temperature of Baguio with an elevation of 1,500 meters is 18.3oC. This makes the temperature of Baguio comparable with those in the temperate climate and because of this, it is known as the summer capital of the Philippines.

The difference between the mean annual temperature of the southernmost station in Zamboanga and that of the northermost station in Laoag is insignificant. In other words, there is essentially no difference in the mean annual temperature of places in Luzon, Visayas or Mindanao measured at or near sea level.

 

Humidity

Humidity refers to the moisture content of the atmosphere. Due to high temperature and the surrounding bodies of water, the Philippines has a high relative humidity. The average monthly relative humidty varies between 71 percent in March and 85 percent in September. The combination of warm temperature and high relative and absolute humidities give rise to high sensible temperature throughout the archipelago. It is especially uncomfortable during March to May, when temperature and humidity attain their maximum levels.

 

Rainfall

Rainfall is the most important climatic element in the Philippines. Rainfall distribution throughout the country varies from one region to another, depending upon the direction of the moisture-bearing winds and the location of the mountain systems.

The mean annual rainfall of the Philippines varies from 965 to 4,064 millimeters annually. Baguio City, eastern Samar, and eastern Surigao receive the greatest amount of rainfall while the southern portion of Cotabato receives the least amount of rain. At General Santos City in Cotabato, the average annual rainfall is only 978 millimeters.

 

The Seasons

Using temperature and rainfall as bases, the climate of the country can be divided into two major seasons: (1) the rainy season, from June to November; and (2) the dry season, from December to May. The dry season may be subdivided further into (a) the cool dry season, from December to February; and (b) the hot dry season, from March to May.

 

Climate Types

Based on the distribution of rainfall, four climate types are recognized, which are described as follows:

 

 

Typhoons have a great influence on the climate and weather conditions of the Philippines. A great portion of the rainfall, humidity and cloudiness are due to the influence of typhoons. They generally originate in the region of the Marianas and Caroline Islands of the Pacific Ocean which have the same latitudinal location as Mindanao. Their movements follow a northwesterly direction, sparing Mindanao from being directly hit by majorty of the typhoons that cross the country. This makes the southern Philippines very desirable for agriculture and industrial development.

 


dlyrr.jpg
aug2015.jpg

Note: Observed Rainfall of the previous month
Click here: http://www.pagasa.dost.gov.ph/index.php/climate/climate-advisories

 

Overview

Based on Intergovernmental Panel on Climate Change (IPCC, 2007b), changes in climate patterns are projected to have a number of impacts including possible water shortages, decreased agricultural production, and food insecurity. With these considerations, a joint project undertaking was forged between the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), the FAO-AMICAF (Food and Agriculture Organization of the United Nations), and University of Cantabria in Spain. The project aims to assess vulnerability of households to food insecurity through the use of a tool called MOSAICC (Modelling System for Agricultural Impacts of Climate Change). Ultimately, climate information generated from the project can be used to provide relevant and updated climate information for national socioeconomic policy making.

The work plan was implemented through a series of workflow wherein PAGASA undertook the first step of the work plan which is the climate scenario downscaling. Global climate models (GCMs) were statistically downscaled at station level under the Coupled Model Intercomparison Project Phase 3 (CMIP3). These GCMs are BCM2, CNCM3, and MPEH5.

Results of climate projections are provided in two time period: historical climate (1971-2000) and future climate (2011-2040) using two Special Report on Emission Scenarios (SRES): A1B (medium-range) and A2 (high-range). SRES are based on projected greenhouse gases emissions in future years.

There are three seasonal variables available for download : precipitation, minimum temperature, and maximum temperature. Season is defined as an average of three-month values: DJF (December-January-February), MAM (March, April, May), JJA (June, July, August), and SON (September-October-November).

A technical note to you understand our products is also available for download via this link .

NOTE: Kindly refer to this article for citation of methodology.

References

(2014). Assessments of Climate Change Impacts and Mapping of Vulnerability to Food Insecurity under Climate Change to Strengthen Household Food Security with Livelihoods’ Adaptation Approaches (AMICAF): Project Terminal Report. Submitted to Food and Agriculture Organization of the United Nations Country Office in the Philippines. Quezon City: DOST-PAGASA.

Basconcillo, J., A. Lucero, A. Solis, R. Sandoval, Jr., E. Bautista, T. Koizumi, and H. Kanamaru, 2016: Statistically downscaled projected changes in Seasonal Mean Temperature and Rainfall in Cagayan Valley, Philippines. J. Meteor. Soc. Japan94A, 151-164.

IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Lucero, A., Basconcillo, J., Solis, A., Kanamaru, H., Bautista, E., Sandoval, R., Hilario, S., Juanillo, E., (2014). Recent Projected Changes (2011-2040) in Seasonal Mean Temperature and Rainfall in the Philippines. Paper presented at the 3rd National Climate Conference. Manila, Philippines.

Manzanas, R., Brands, S., San-Martin, D., Lucero, A., Limbo, C., Gutierrez, J. (2015) Statistical Downscaling in the Tropics is Sensitive to Reanalysis Choice. Journal of Climate., Vol. 28, 4171-4184



Overview

Based on Intergovernmental Panel on Climate Change (IPCC, 2007b), changes in climate patterns are projected to have a number of impacts including possible water shortages, decreased agricultural production, and food insecurity. With these considerations, a joint project undertaking was forged between the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), the FAO-AMICAF (Food and Agriculture Organization of the United Nations), and University of Cantabria in Spain. The project aims to assess vulnerability of households to food insecurity through the use of a tool called MOSAICC (Modelling System for Agricultural Impacts of Climate Change). Ultimately, climate information generated from the project can be used to provide relevant and updated climate information for national socioeconomic policy making.

The work plan was implemented through a series of workflow wherein PAGASA undertook the first step of the work plan which is the climate scenario downscaling. Global climate models (GCMs) were statistically downscaled at station level under the Coupled Model Intercomparison Project Phase 3 (CMIP3). These GCMs are BCM2, CNCM3, and MPEH5.

Results of climate projections are provided in two time period: historical climate (1971-2000) and future climate (2011-2040) using two Special Report on Emission Scenarios (SRES): A1B (medium-range) and A2 (high-range). SRES are based on projected greenhouse gases emissions in future years.

There are three seasonal variables available for download : precipitation, minimum temperature, and maximum temperature. Season is defined as an average of three-month values: DJF (December-January-February), MAM (March, April, May), JJA (June, July, August), and SON (September-October-November).

A technical note to you understand our products is also available for download via this link .

NOTE: Kindly refer to this article for citation of methodology.

References

(2014). Assessments of Climate Change Impacts and Mapping of Vulnerability to Food Insecurity under Climate Change to Strengthen Household Food Security with Livelihoods’ Adaptation Approaches (AMICAF): Project Terminal Report. Submitted to Food and Agriculture Organization of the United Nations Country Office in the Philippines. Quezon City: DOST-PAGASA.

Basconcillo, J., A. Lucero, A. Solis, R. Sandoval, Jr., E. Bautista, T. Koizumi, and H. Kanamaru, 2016: Statistically downscaled projected changes in Seasonal Mean Temperature and Rainfall in Cagayan Valley, Philippines. J. Meteor. Soc. Japan94A, 151-164.

IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Lucero, A., Basconcillo, J., Solis, A., Kanamaru, H., Bautista, E., Sandoval, R., Hilario, S., Juanillo, E., (2014). Recent Projected Changes (2011-2040) in Seasonal Mean Temperature and Rainfall in the Philippines. Paper presented at the 3rd National Climate Conference. Manila, Philippines.

Manzanas, R., Brands, S., San-Martin, D., Lucero, A., Limbo, C., Gutierrez, J. (2015) Statistical Downscaling in the Tropics is Sensitive to Reanalysis Choice. Journal of Climate., Vol. 28, 4171-4184



Overview

Based on Intergovernmental Panel on Climate Change (IPCC, 2007b), changes in climate patterns are projected to have a number of impacts including possible water shortages, decreased agricultural production, and food insecurity. With these considerations, a joint project undertaking was forged between the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), the FAO-AMICAF (Food and Agriculture Organization of the United Nations), and University of Cantabria in Spain. The project aims to assess vulnerability of households to food insecurity through the use of a tool called MOSAICC (Modelling System for Agricultural Impacts of Climate Change). Ultimately, climate information generated from the project can be used to provide relevant and updated climate information for national socioeconomic policy making.

Overview

Based on Intergovernmental Panel on Climate Change (IPCC, 2007b), changes in climate patterns are projected to have a number of impacts including possible water shortages, decreased agricultural production, and food insecurity. With these considerations, a joint project undertaking was forged between the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), the FAO-AMICAF (Food and Agriculture Organization of the United Nations), and University of Cantabria in Spain. The project aims to assess vulnerability of households to food insecurity through the use of a tool called MOSAICC (Modelling System for Agricultural Impacts of Climate Change). Ultimately, climate information generated from the project can be used to provide relevant and updated climate information for national socioeconomic policy making.

The work plan was implemented through a series of workflow wherein PAGASA undertook the first step of the work plan which is the climate scenario downscaling. Global climate models (GCMs) were statistically downscaled at station level under the Coupled Model Intercomparison Project Phase 3 (CMIP3). These GCMs are BCM2, CNCM3, and MPEH5.

Results of climate projections are provided in two time period: historical climate (1971-2000) and future climate (2011-2040) using two Special Report on Emission Scenarios (SRES): A1B (medium-range) and A2 (high-range). SRES are based on projected greenhouse gases emissions in future years.

There are three seasonal variables available for download : precipitation, minimum temperature, and maximum temperature. Season is defined as an average of three-month values: DJF (December-January-February), MAM (March, April, May), JJA (June, July, August), and SON (September-October-November).

A technical note to you understand our products is also available for download via this link .

NOTE: Kindly refer to this article for citation of methodology.

References

(2014). Assessments of Climate Change Impacts and Mapping of Vulnerability to Food Insecurity under Climate Change to Strengthen Household Food Security with Livelihoods’ Adaptation Approaches (AMICAF): Project Terminal Report. Submitted to Food and Agriculture Organization of the United Nations Country Office in the Philippines. Quezon City: DOST-PAGASA.

Basconcillo, J., A. Lucero, A. Solis, R. Sandoval, Jr., E. Bautista, T. Koizumi, and H. Kanamaru, 2016: Statistically downscaled projected changes in Seasonal Mean Temperature and Rainfall in Cagayan Valley, Philippines. J. Meteor. Soc. Japan94A, 151-164.

IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Lucero, A., Basconcillo, J., Solis, A., Kanamaru, H., Bautista, E., Sandoval, R., Hilario, S., Juanillo, E., (2014). Recent Projected Changes (2011-2040) in Seasonal Mean Temperature and Rainfall in the Philippines. Paper presented at the 3rd National Climate Conference. Manila, Philippines.

Manzanas, R., Brands, S., San-Martin, D., Lucero, A., Limbo, C., Gutierrez, J. (2015) Statistical Downscaling in the Tropics is Sensitive to Reanalysis Choice. Journal of Climate., Vol. 28, 4171-4184



Overview

Based on Intergovernmental Panel on Climate Change (IPCC, 2007b), changes in climate patterns are projected to have a number of impacts including possible water shortages, decreased agricultural production, and food insecurity. With these considerations, a joint project undertaking was forged between the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), the FAO-AMICAF (Food and Agriculture Organization of the United Nations), and University of Cantabria in Spain. The project aims to assess vulnerability of households to food insecurity through the use of a tool called MOSAICC (Modelling System for Agricultural Impacts of Climate Change). Ultimately, climate information generated from the project can be used to provide relevant and updated climate information for national socioeconomic policy making.

The work plan was implemented through a series of workflow wherein PAGASA undertook the first step of the work plan which is the climate scenario downscaling. Global climate models (GCMs) were statistically downscaled at station level under the Coupled Model Intercomparison Project Phase 3 (CMIP3). These GCMs are BCM2, CNCM3, and MPEH5.

Results of climate projections are provided in two time period: historical climate (1971-2000) and future climate (2011-2040) using two Special Report on Emission Scenarios (SRES): A1B (medium-range) and A2 (high-range). SRES are based on projected greenhouse gases emissions in future years.

There are three seasonal variables available for download : precipitation, minimum temperature, and maximum temperature. Season is defined as an average of three-month values: DJF (December-January-February), MAM (March, April, May), JJA (June, July, August), and SON (September-October-November).

A technical note to you understand our products is also available for download via this link .

NOTE: Kindly refer to this article for citation of methodology.


References

(2014). Assessments of Climate Change Impacts and Mapping of Vulnerability to Food Insecurity under Climate Change to Strengthen Household Food Security with Livelihoods’ Adaptation Approaches (AMICAF): Project Terminal Report. Submitted to Food and Agriculture Organization of the United Nations Country Office in the Philippines. Quezon City: DOST-PAGASA.

Basconcillo, J., A. Lucero, A. Solis, R. Sandoval, Jr., E. Bautista, T. Koizumi, and H. Kanamaru, 2016: Statistically downscaled projected changes in Seasonal Mean Temperature and Rainfall in Cagayan Valley, Philippines. J. Meteor. Soc. Japan94A, 151-164.

IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.


Lucero, A., Basconcillo, J., Solis, A., Kanamaru, H., Bautista, E., Sandoval, R., Hilario, S., Juanillo, E., (2014). Recent Projected Changes (2011-2040) in Seasonal Mean Temperature and Rainfall in the Philippines. Paper presented at the 3rd National Climate Conference. Manila, Philippines.

Manzanas, R., Brands, S., San-Martin, D., Lucero, A., Limbo, C., Gutierrez, J. (2015) Statistical Downscaling in the Tropics is Sensitive to Reanalysis Choice. Journal of Climate., Vol. 28, 4171-4184






                     One-Month (running 
10-day Probabilistic Forecast) 

                    
  • This website was developed by PAGASA in collaboration with the Japan Meteorological Agency/Tokyo Climate Centre (JMA/TCC), the World Meteorological Organization (WMO) Regional Climate Centre in RA II (Asia). This one-month (running 10-day) probabilistic forecast product is being produced and updated every Thursday by PAGASA;
  • This sub-seasonal forecast (10-day forecast, but usually weekly or pentad) could provide advance notice of potential hazards related to climate, weather and hydrological events across the country that will eventually support various economic sectors (agriculture, water resource management and others).     

         for Rainfall (Precipitation) and Temperature forecast at Selected PAGASA Stations,  Click here

 







 

 

Overview

Based on Intergovernmental Panel on Climate Change (IPCC, 2007b), changes in climate patterns are projected to have a number of impacts including possible water shortages, decreased agricultural production, and food insecurity. With these considerations, a joint project undertaking was forged between the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), the FAO-AMICAF (Food and Agriculture Organization of the United Nations), and University of Cantabria in Spain. The project aims to assess vulnerability of households to food insecurity through the use of a tool called MOSAICC (Modelling System for Agricultural Impacts of Climate Change). Ultimately, climate information generated from the project can be used to provide relevant and updated climate information for national socioeconomic policy making.

The work plan was implemented through a series of workflow wherein PAGASA undertook the first step of the work plan which is the climate scenario downscaling. Global climate models (GCMs) were statistically downscaled at station level under the Coupled Model Intercomparison Project Phase 3 (CMIP3). These GCMs are BCM2, CNCM3, and MPEH5.

Results of climate projections are provided in two time period: historical climate (1971-2000) and future climate (2011-2040) using two Special Report on Emission Scenarios (SRES): A1B (medium-range) and A2 (high-range). SRES are based on projected greenhouse gases emissions in future years.

There are three seasonal variables available for download : precipitation, minimum temperature, and maximum temperature. Season is defined as an average of three-month values: DJF (December-January-February), MAM (March, April, May), JJA (June, July, August), and SON (September-October-November).

A technical note to you understand our products is also available for download via this link .

NOTE: Kindly refer to this article for citation of methodology.

References

(2014). Assessments of Climate Change Impacts and Mapping of Vulnerability to Food Insecurity under Climate Change to Strengthen Household Food Security with Livelihoods’ Adaptation Approaches (AMICAF): Project Terminal Report. Submitted to Food and Agriculture Organization of the United Nations Country Office in the Philippines. Quezon City: DOST-PAGASA.

Basconcillo, J., A. Lucero, A. Solis, R. Sandoval, Jr., E. Bautista, T. Koizumi, and H. Kanamaru, 2016: Statistically downscaled projected changes in Seasonal Mean Temperature and Rainfall in Cagayan Valley, Philippines. J. Meteor. Soc. Japan94A, 151-164.

IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Lucero, A., Basconcillo, J., Solis, A., Kanamaru, H., Bautista, E., Sandoval, R., Hilario, S., Juanillo, E., (2014). Recent Projected Changes (2011-2040) in Seasonal Mean Temperature and Rainfall in the Philippines. Paper presented at the 3rd National Climate Conference. Manila, Philippines.

Manzanas, R., Brands, S., San-Martin, D., Lucero, A., Limbo, C., Gutierrez, J. (2015) Statistical Downscaling in the Tropics is Sensitive to Reanalysis Choice. Journal of Climate., Vol. 28, 4171-4184



Issued: 20 September 2017
Valid for: October 2017 - March 2018

This section covers long term climate information and prediction services for the benefit of various sectors. Drought bulletins, intra-seasonal climate predictions, weather-based crop calendars, agromet bulletins and advisories, and customized climate information are periodically prepared and issued for educational, engineering, commercial, industrial, agricultural and other purposes.



October 2017 - March 2018 (updated 20 September 2017)    

Issued: 03 September 2017

Monthly Rainfall Forecast
RAINFALL FORECAST  (October 2017 - March 2018) 
UPDATED: 20 September 2017 (next update October 2017)

Issued:  06 July 2017
FOR July - December 2017
PDF 

ISSUED              : 8AM,FRIDAY, SEPTEMBER 22, 2017
VALID UNTIL      :  8AM, SATURDAY, SEPTEMBER 23, 2017
FWFA:  N0. 17-266

DEKAD NO. 27 SEPTEMBER 21-30, 2017

PHILIPPINE AGRI-WEATHER FORECAST
The weather systems that will affect the whole country are southwest monsoon, intertropical convergence zone (ITCZ) and low pressure area (LPA).

Issued at : 5:00 AM 31 May 2017
Valid Beginning : 5:00 AM today until 5:00 AM tomorrow

February  2015

Overview:

There was no tropical cyclone (TC) that entered the Philippine Area of Responsibility (PAR) in this month. Because of that, the present report provides climatological TC information for the benefit of the readers. In addition, this report highlights some of the noted differences among the TC best track data provided by the Joint Typhoon Warning Center (JTWC); the Japan Meteorological Agency (JMA); and the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA).

The non-occurrence of TC during this month is in line with the slim chance of having a TC to affect the country in February. Climatologically, there were just 23 TCs that existed within the PAR during this month from 1951 to 2014. Among such TCs, eight have crossed the Philippine landmass. The last TC that existed within the PAR and crossed the country's landmass during the month of February was observed last year [Tropical Depression (TD) Basyang]. Basyang originated northeast of Palau as a low pressure area (LPA). It strengthened into a TD as it entered the PAR in the evening of January 30, 2014 (around 9:00 PM, Philippine Standard Time). It moved into west northwest direction traversing the northeastern sections of Mindanao and southwestern portions of the Visayas through the northern tip of Palawan. It weakened and dissipated as it approached the western boundary of the PAR in the evening of February 1, 2014.



Read the full report here

Link to the previous reports:
January



CLIMATE PREDICTION 

This section covers long term climate forecast and prediction services for the benefit of various sectors. These forecasts serve as basis for the preparation of Monthly Weather Situation and Outlook, Seasonal Outlook, El Nino/La Nina Advisories, drought bulletins, weather-based crop calendars, agromet bulletins, other advisories, and customized climate information that are periodically issued for educational, engineering, commercial, industrial, agricultural and other planning purposes.



PAGASA has been closely monitoring the oceanic and atmospheric conditions in the tropical Pacific that could lead to possible development of an El Niño. A majority of climate models indicate that El Niño may develop this year. El Niño is characterized by unusually warm ocean surface temperatures in the central and eastern equatorial Pacific (CEEP).

The sea surface temperature anomaly (SSTA) over the tropical Pacific remained to be El Niño Southern Oscillation (ENSO)- neutral during the past several months. The established threshold of SSTA for an El Niño phenomenon is 0.5°C or higher during a three-month period.
PAGASA has already noted significant increase in the SSTA from 0.2 to 0.4°C from April 21 to April 28, 2014. Because of this development and as climate models predict that this condition may persist for the next nine months, PAGASA is foreseeing the onset of El Niño in June which may peak during the last quarter of 2014 and may last up to the first quarter of 2015.

El Niño could affect the normal rainfall pattern in the country generally resulting in reduced rainfall. Different parts of the country may experience varying rainfall impacts. PAGASA will be furnishing monthly rainfall outlook for six months for the different parts of the country.

The country could still experience normal number of tropical cyclone this year. However, El Niño causes the behavior of tropical cyclones to become erratic, affecting its tracks and intensity. The tropical cyclone tracks are expected to shift northward and its intensity could become stronger.

PAGASA will continue to closely monitor the tropical Pacific and updates/advisories shall be issued as appropriate. Concerned agencies are advised to take precautionary measures to mitigate the potential impacts of this phenomenon.



Original Signed:

MARIO G. MONTEJO
Secretary, DOST

For more details please contact the Climatology and Agrometeorology Division (CAD)
at telephone numbers 434-9024 & 434-0955
TROPICAL CYCLONE WARNING FOR AGRICULTURE
TCWA NO. 2 – TROPICAL STORM “MARIO”
INILABAS ALAS 11: NU SETYEMBRE 19, 2014    MAY BISA HANGGANG ALAS 11:00NG MAMAYA