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Due to the severe threat of tropical cyclones to human life, recent years have witnessed an increase in the investigations on raindrop size distributions of tropical cyclones to improve their quantitative precipitation estimation algorithms and modeling simulations. So far, the raindrop size distributions of tropical cyclones using disdrometer measurements have been conducted at coastal and inland stations, but such studies are still missing for oceanic locations. To the authors’ knowledge, the current study examines—for the first time—the raindrop size distributions of fourteen tropical cyclones observed (during 2003–2007) at an oceanic station, Aimeliik, located in the Palau islands in the Western Pacific. The raindrop size distributions of Western Pacific tropical cyclones measured in the Palau islands showed unlike characteristics between stratiform and convective clusters, with a larger mass-weighted mean diameter and smaller normalized intercept parameter in the convective type. The contribution of the drop diameters to the total number concentration showed a gradual decrease with the increase in drop diameter size. Raindrop size distributions of Western Pacific tropical cyclones measured in the Palau islands differed slightly from Taiwan and Japan. The helpfulness of empirical relations in raindrop size distribution in rainfall estimation algorithms of ground-based (Z–R, μ–Λ, Dm–R, and Nw–R) and remote-sensing (σm–Dm, μo–Dm, Dm–Zku, and Dm–Zka) radars are evaluated. Furthermore, the present study also related the rainfall kinetic energy of fourteen tropical cyclones with rainfall rate and mass-weighted mean diameter (KEtime–R, KEmm–R, and KEmm–Dm). The raindrop size distribution empirical relations appraised in this study offer a chance to: (1) enhance the rain retrieval algorithms of ground-based, remote sensing radars; and (2) improve rainfall kinetic energy estimations using disdrometers and GPM DPR in rainfall erosivity studies.

Abstract The long-term variability of Philippine summer monsoon onset from 1903 to 2013 was investigated. The onset date is defined by daily rainfall data at eight stations in the northwestern Philippines. Summer monsoons tended to start earlier in May after the mid-1990s. Other early onset periods were found during the 1900s, 1920s, and 1930s, and an interdecadal variability of summer monsoon onset was identified. Independent surface wind data observed by ships in the South China Sea (SCS) revealed prevailing westerly wind in May during the early monsoon onset period. To identify atmospheric structures that trigger Philippine summer monsoon onset, we focused on the year 2013, conducting intensive upper-air observations. Tropical cyclone (TC) Yagi traveled northward in the Philippine Sea (PS) in 2013 and triggered the Philippine monsoon onset by intensifying moist low-level southwesterly wind in the southwestern Philippines and intensifying low-level southerly wind after the monsoon onset in the northwestern Philippines. The influence of TC was analyzed by the probability of the existence of TC in the PS and the SCS since 1951, which was found to be significantly correlated with the Philippine summer monsoon onset date. After the mid-1990s, early monsoon onset was influenced by active TC formation in the PS and the SCS. However, the role of TC activity decreased during the late summer monsoon periods. In general, it was found that TC activity in the PS and the SCS plays a key role in initiating Philippine summer monsoon onset.

This article describes humidity data correction based on an intercomparison between two manufacturers' radiosondes with the assessment using precipitable water vapor (PWV) derived from Global Navigation Satellite System (GNSS) signals. In addition, we propose a method to determine whether the same correction procedure can be applied if such intercomparison cannot be conducted. During the intensive observation called Years of the Maritime Continent (YMC) - Boreal Summer Monsoon (BSM) study in 2018 (YMC-BSM 2018), intercomparison of radiosonde between Lockheed Martin LMS6 and Vaisala RS41-SGP was conducted at Laoag, Ilocos Norte, Philippines from late July to early August 2018. While their mean difference of relative humidity (RH) was better than 5 %, dry bias was confirmed for LMS6 only during clear sky daytime soundings based on comparing PWV with that derived from GNSS signals. To use different radiosonde data with the same research-quality, we developed a correction table for LMS6 RH data. While a direct intercomparison between different radiosondes and independently developed observational tools such as a GNSS-receiver is ideal to evaluate data quality, it cannot always be performed. We obtained LMS6 radiosonde data at different site at Yap Island, Federated States of Micronesia from another field campaign, YMC-BSM 2020, where any intercomparison could not be conducted. To decide whether the same correction procedure obtained from YMC-BSM 2018 can be applied to those data, we assessed their similarity based on the relationship between specific humidity from surface meteorological station data that were obtained independently before launch and radiosonde specific humidity averaged over 300 m from the initial radiosonde measurement point. This method allowed us to confirm the same behavior between Laoag data in 2018 and Yap data in 2020; thus, we applied our correction method to RH data in YMC-BSM 2020. [ABSTRACT FROM AUTHOR]