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Issued at: 5:00 PM TODAY , 02 July 2022
Gale Warning # 5 (Final)
For: moderate to strong winds associated with the SOUTHWEST MONSOON

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Weather Advisory
Issued at: 11:00 AM 2022 August 08
Weather Advisory in PDF file

General Flood Advisories - Regional
 General Flood Advisory issued as of 7 PM, 20 August 2018


Monthly Climate Assessment and Outlook (July-August 2018)

Issued: 06 August 2018

Monthly Rainfall Forecast
RAINFALL FORECAST  (September 2018 - February 2019) 
UPDATED: 29 August 2018 (next update September 26, 2018)

Regional Rainfall Forecast
Issued: 29 August 2018
Valid for: September 2018 - February 2019
Farm Weather Forecast and Advisories
ISSUED: 8 AM,  FRIDAY,  MAY 24, 2019
, 2019
FWFA:  N0. 19-144

Ten-Day Regional Agri-Weather Information
DEKAD NO. 15   MAY 21 - 31, 2019
The weather systems that will affect the whole country are the frontal system, easterlies, ridge of high pressure area, intertropical convergence zone (ITCZ) and low pressure area (LPA).

Seasonal Climate Outlook
Issued:  13 July 2018
FOR July - December 2018

Astronomical Diary
Issue for October 2018
The October Orionids meteor shower will be active from October 17-25, 2017.

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Downscaling of Seasonal Rainfall Over the Philippines: Dynamical vs. Statistical Approaches 

(Published in Monthly Weather Review, American Meteorological Society, April 2012)

Andrew W. Robertson1, Jian-Hua Qian1+, Michael K. Tippett 1 , Vincent Moron 1,2, and Anthony Lucero3

  • International Research Institute for Climate and Society (IRI), The Earth Institute at Columbia University, Palisades, NY
  •  Aix-Marseille University and CEREGE, Aix-en-Provence, France 
  • Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), Manila, Philippines

+Current A_liation: Department of Environmental, Earth & Atmospheric Sciences University of Massachusetts, Lowell, MA 


The additional value derived from a regional climate model (RCM) nested within general circulation model (GCM) seasonal simulations, over and above statistical methods of downscaling is compared over the Philippines for the April{June monsoon transition season. Spatial interpolation of RCM and GCM grid box values to station locations is compared with model-output statistics (MOS) correction.

The anomaly correlation coe_cient (ACC) skill at the station scale of seasonal total rainfall is somewhat higher in the RCM compared to the GCM when using spatial interpolation. However, the ACC skills obtained using MOS of the GCM or RCM wind fields are shown to be generally|and rather equally|superior. The ranked probability skill scores (RPSS) are also generally much higher when using MOS, with slightly higher scores in the GCM case.

Very high skills were found for MOS correction of daily rainfall frequency as a function of GCM and RCM seasonal-average low-level wind fields, but with no apparent advantage from the RCM. MOS-corrected monsoon onset dates often showed skill values similar to those of seasonal rainfall total, with good skill over the central Philippines. Finally, it is shown that the MOS skills decrease markedly and become inferior to those of spatial interpolation when the length of the 28-year training set is halved. The results may be region dependent, and the excellent station data coverage and strong impact of ENSO on the Philippines may be factors contributing to the good MOS performance when using the full-length dataset over the Philippines.



 Prediction of Rice Production in the Philippines Using Seasonal Climate Forecasts

(Published in the Journal of Applied Climatology, September 2012)

Andrew Robertson1, Naohisa Koide1, Amor Ines1, Jian-Hua Qian1, Anthony Joseph Lucero2

  1. Columbia University, Palisades, United States of America
  2. Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), Manila, Philippines

Predictive skills of retrospective seasonal climate forecasts tailored to Philippine Rice production data of national, regional and provincial levels are investigated using precipitation hindcasts from one uncoupled general circulation model (GCM) and two coupled GCMs, as well as using antecedent observations of tropical Pacific sea surface temperatures, warm water volume and zonal winds (WWW and ZW).  Contrasting cross-validated predictive skills are found between the “dry” January-June and “rainy” July-December crop-production seasons.  For the dry season, both irrigated and rainfed rice production are shown to depend strongly on rainfall in the previous October to December.  Furthermore, rice-crop hindcasts bases on the two coupled GCMS, or on the observed WWW and ZW, are each able to account for more than half the total variance of the dry-season national detrended rice production with about a six-month lead time prior to the beginning of the harvest season.  At regional and provincial level, predictive skills are generally low.

The relationships are found to be more complex for rainy season  rice production.  Area harvested correlates positively with rainfall during the preceding dry season, whereas the yield has positive and negative correlations with rainfall in June-September and in October-December of the harvested year respectively; tropical cyclone activity is shown to be contributing factor in the latter three0month season.  Retrospective forecasts based on the WWV and ZW are able to account for almost half of the variance of detrended rice production data in Luzon with a few months lead time prior to the beginning of the rainy season.

Multimodel output statistical downscaling prediction of precipitation in the Philippines and Thailand

Hongwen Kang,1,2 Kyong-Hee An,1 Chung-Kyu Park,1,3 Ana Liza S. Solis,1,4 and Kornrawee Stitthichivapak 1,5

Received 18 May 2007; revised 29 June 2007; accepted 10 July 2007; published 14 August 2007.


Six dynamical seasonal model outputs, which are currently used in the APEC Climate Center Multimodel Ensemble (MME) prediction system, are employed for statistical downscaling prediction of station-scale precipitation in the Philippines and Thailand. Correlation analysis and Singular Value Decomposition Analysis are used to reveal atmosphere dynamic linkage based on the observed data other than model data. The observed linkage provides a robust basis for the choice of predictor and its range in predicted fields. In order to avoid spatial shift of predicted field away from observed climate, a movable window is set to select the most sensible area within the range of predictor for downscaling. The downscaled MME prediction is verified against observed station precipitation in a cross-validation manner, and the prediction skill is apparently improved compared with the simple composite of raw model predictions for most of the stations. 

Citation: Kang, H., K.-H. An, C.-K. Park, A. L. S. Solis, and K. Stitthichivapak (2007), Multimodel

output statistical downscaling prediction of precipitation in the Philippines and Thailand, Geophys. Res. Lett., 34, L15710, doi:10.1029/2007GL030730.



Climate Change Projections in some Asian Countries

Kitoh A., Kusunoki S., Sato, Y., Ferdousi, N., Rahman, M., Makmur, E., Solis, A., Chaowiwat, W., and Trong, T


The super-high-resolution (20 km) AGCM has made possible the simulation of the present and future (at the end of the 21st century) climate over the Philippines with characterization of complex land-sea contrast and mountain ranges. However, finer resolution does not necessarily assure accuracy. Thus, firstly, the super-high-resolution present-day simulation has been assessed based on high-resolution daily gridded, observed rainfall data. The reliability of the model in representing the present-day climatological features of the Philippine monsoon is crucial for building confidence in future projections of the country’s climate. Rainfall is realistically simulated by the 20-km MRI model, especially the detailed orographic rainfall in all seasons but with slight overestimation during the southwest monsoon (JAS) season. The model shows some weak points in the representation of interannual extremes which either relate to tropical cyclone occurrence and their tracks during the peak typhoon months or may have some fragile relation with the cumulus parameterization scheme. For the future climate change scenario for the JAS season, the predicted climate from the model is estimated using the observed climate as a basis. In the future, a significant increase in rainfall can be found mostly in coastal areas during JFM. Moreover, a slight increase in rainfall is projected in most areas brought about by convective rainfall. Further investigation of projections for JAS is challenging because of the overestimation of JAS present-day rainfall. During OND, a reduction in orographic rainfall over Luzon and mountainous area of Mindanao is projected.

In all seasons, mean temperature (not shown in the text) will generally increase, but the largest increase is during the hot summer season of MAM. 

It is important to thoroughly understand changes in tropical cyclones especially in Western North Pacific region in order to recognize the Philippines’ increased risk potential from natural disasters and to take effective countermeasures. 

Resolutions of climate models are now becoming finer. With these improvements in the available climate model output for the region, projections of the future climate using a super high resolution (20-km mesh) AGCM could lead to a substantially improved assessment of the country’s vulnerability and adaptation to climate change. This could be followed by concrete adaptation actions, integration of adaptation into sector development and adaptation policy formulation and implementation.


Change in seasonal mean precipitation from present simulation for 1979-2003 to future simulation for 2075-2099. Change ratio (Future – Present) / Present are shown in %. (a) January to March. (b) April to June. (c) July to September. (d) October to December


Citation: Kitoh A., Kusunoki S., Sato, Y., Ferdousi, N., Rahman, M., Makmur, E., Solis, A., Chaowiwat, W., and Trong, T., 2011. Climate Change Projections in some Asian Countries, in Climate Change Adaptation and International Development, pp.19-62.