Cloud water interception (CWI) is not captured by conventional rain gauges and not well characterized, but could have ecohydrological significance in systems such as tropical montane cloud forests. Quantifying CWI is necessary to assess the impacts of climate and land cover changes in places such as Hawai‘i. CWI can be estimated from wind speed, cloud liquid water content (LWC), and vegetation characteristics with an empirical model. Cloud microphysics sensors measure LWC accurately, but are expensive and often designed only for use on aircraft. LWC can be estimated by fog gauges, but poorly constrained catch efficiency and spurious rain catch can cause large errors. Visibility is related to LWC, but the relationship is non-linear and depends on the (usually unknown) drop size distribution. This study is part of a project aimed at mapping CWI across the Hawaiian Islands. Earlier analyses found disagreement between LWC estimated from fog gauge and visibility observations at the project field sites. In this study, we experimented with a novel in situ observation platform and cross disciplinary collaboration to compare cloud microphysics observations with those commonly used in cloud forest studies. Field missions took place from April to July 2018 at the summit of Mt. Ka‘ala (1,200 m) on O‘ahu Island. We built a pickup truck-mounted mobile weather station that can be assembled in the field, with weather-sensitive processing modules inside the cab. A total of 10 instruments were deployed: Phase Doppler Interferometer, Cloud Droplet Probe, fog gauge, visibility sensor, rain gauge, wind monitor, camera, water isotope sampler, UAV atmospheric sensor, and Aerosol Spectrometer. A nearby long-term station provides climate and canopy water balance data. Analyses found a strong relationship between visibility and LWC in dense fog. The fog gauge showed weak correlations due to coarse resolution and false rain catch, but had a reasonable catch efficiency. The start of fog catch lagged compared to the nearby station possibly due to screen surface wetting. Concurrent with other analyses, one goal is to calibrate the fog gauge and visibility sensor for long-term LWC monitoring. The mobile platform was effective for short-term deployment of airborne sensors. We hope to repeat the experiment in the future on O‘ahu and other islands.