Testing alternative ground-based cloud liquid water content measurement
methods for estimating cloud water interception in tropical montane
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.