Investigation of the Monsoonal Variation of Surface Microphysical Characteristics of Rainfall over Metro Manila Using A PARSIVEL2 Disdrometer

Date of Award


Document Type


Degree Name

Master of Science in Atmospheric Science



First Advisor

James Bernard B. Simpas, PhD


The variability of raindrop size distribution (RSD) and integral rainfall parameters (IRPs) over Metro Manila during the Northeast Monsoon (NEM) (October 2018 – February 2019), Pre-Southwest Monsoon (Pre-SWM) (March 2019 – May 2019), and Southwest Monsoon (SWM) (June 2019 – October 2019) seasons are studied by using one year of PARSIVEL2 Disdrometer data. Soundings, satellite, and reanalysis data sets are also used to determine the possible dynamic and microphysical processes that affect the RSD during the three different seasons. Results show that the NEM has the highest number concentration of small raindrops (D < 1 mm), while SWM season rainfall has more significant quantities of large raindrops (D ≥ 3 mm) among the three seasons. The SWM convective rainfall (RI ≥ 10 mm hr-1 ) has the highest values of rain intensity (RI), mean raindrop diameter (Dm), reflectivity factor (Z), and liquid water content (LWC) while the NEM convective rainfall has the highest value of normalized intercept parameter (Log10(Nw)). This result means that the high number concentration of (small) large raindrops during the (NEM) SWM season is due to the (low) high frequency of occurrence of convective rainfall. The SWM season has the highest values of Convective available potential energy (CAPE), which extends clouds further above the freezing layer (~ 5 km), resulting in cold-rain processes that promote aggregation and riming of ice particles and produces large raindrops at the surface. This work is the first rainfall microphysics study in the Philippines that is based on actual RSD measurements. The results obtained from this study will provide a physical basis for improving rainfall retrieval for weather radars and the parameterization of microphysics schemes in numerical weather prediction models.

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