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31<br />

Analysis and research/survey results<br />

Assessing rainfall variability in <strong>Philippine</strong><br />

study sites: the Rainman application<br />

Because of its geographical location, the ranging from one season to over a year in advance. This<br />

<strong>Philippine</strong>s is prone to extreme weather and improvement means that impending extreme climate<br />

climate events. Floods and droughts have, <strong>for</strong> events can be predicted with greater accuracy.<br />

instance, been common occurrences in the country<br />

especially in the recent past resulting in massive<br />

destruction of property, loss of life, diseases, and food<br />

shortages. Sectors of the economy, including agriculture<br />

and water resources, have likewise been severely affected<br />

by these weather/climate events.<br />

The <strong>Philippine</strong> Atmospheric, Geophysical and<br />

Astronomical Services Administration (PAGASA) monitors<br />

weather and climate conditions from both local and global<br />

perspectives. It has a network of weather stations<br />

strategically located all over the country that monitor<br />

meteorological and weather elements. These parameters<br />

are then analyzed using various statistical techniques and<br />

procedures to come up with weather or climate <strong>for</strong>ecasts.<br />

Provision of these <strong>for</strong>ecasts and early warnings of<br />

potential crop failure due to drought, with a lead time of<br />

30-60 days be<strong>for</strong>e harvest, is important because it enables<br />

policy/decisionmakers to implement alternative courses<br />

of action to mitigate potential damages to the agricultural<br />

Predictability of the climate from season-to-season<br />

and year-to-year arises from the interaction of the ocean<br />

and the atmosphere. The best-known example is the<br />

ENSO phenomenon. The combination of the slowly<br />

changing temperature of the oceans and their<br />

interactions with the atmosphere provides a degree of<br />

predictability <strong>for</strong> seasonal climate in many regions of the<br />

world. Based on global studies, ENSO and other sea<br />

surface temperature anomalies are known to influence<br />

global climate, altering rainfall and other climate variables<br />

throughout much of the tropics and subtropics and in a<br />

few locations in mid-latitudes. Seasonal climate prediction<br />

is based on the expectation of the effects of these<br />

influences in the coming season. In this regard, climate<br />

<strong>for</strong>ecasters normally ask two basic questions: (1) what will<br />

the sea surface temperature anomalies be in the coming<br />

season and (2) how will they impact on global climate<br />

There are models available which can evaluate the<br />

effects of ENSO on seasonal climatic patterns and on the<br />

sector. Seasonal <strong>for</strong>ecasting is an<br />

attempt to provide in<strong>for</strong>mation on Statistical test results on <strong>for</strong>ecasts of rainfall in Southeast Asia<br />

(Analysis of historical data—1903 to 1995—using SST Phase <strong>for</strong>ecast in September <strong>for</strong> rainfall<br />

the likely conditions of the weather<br />

period: Oct to Dec, leadtime of 0 months)<br />

several months in advance.<br />

The Climate In<strong>for</strong>mation,<br />

Monitoring and Prediction Center<br />

(CLIMPC), one of the sections of the<br />

Climatology and Agrometeorology<br />

Branch (CAB) of PAGASA, is<br />

responsible <strong>for</strong> the issuance and<br />

dissemination of seasonal climate<br />

<strong>for</strong>ecasts and advisories. With the<br />

recent advancement in the<br />

understanding of the El Niño<br />

Southern Oscillation (ENSO)<br />

phenomenon and climate<br />

prediction, seasonal to interannual<br />

prediction has made it possible to<br />

predict climate with improved<br />

accuracy and with lead times

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