Estimation of Spatially Continuous Near-Surface Relative Humidity Over Japan and South Korea

Near-surface relative humidity (RH
_ns
) is an essential meteorological parameter for water, carbon, and climate studies. However, spatially continuous RH
_ns
estimation is difficult due to the spatial discontinuity of in situ observations and the cloud contamination of satellite-based data. This article proposed machine learning-based models to estimate spatially continuous daily RH
_ns
at 1 km resolution over Japan and South Korea under all sky conditions and examined the spatiotemporal patterns of RH
_ns
. All sky estimation of RH
_ns
using machine learning has been rarely conducted, and it can be an alternative to the currently available RH
_ns
data mostly from numerical models, which have relatively low spatial resolution. We combined two schemes for clear sky conditions (scheme A, which uses satellite and reanalysis data) and cloudy sky conditions (scheme B, which uses reanalysis data solely). The relatively small numbers of data in extremely low and high RH
_ns
conditions (i.e., <; 30% or >70%, respectively) were augmented by applying an oversampling method to avoid biased training. The machine learning models based on random forest (RF) and XGBoost were trained and validated using 94 in situ observation sites from meteorological administrations of both countries from 2012 to 2017. The results showed that XGBoost produced slightly better performance than RF, and the spatially continuous RH
_ns
model combined based on XGBoost yielded the coefficient of determination of 0.72 and a root-mean-square error of 10.61%. Spatiotemporal patterns of the estimated RH
_ns
agreed with in situ observations, reflecting the effect of topography on RH
_ns
. We expect that the proposed RH
_ns
model could be used in various environmental studies that require RH
_ns
under all sky conditions as input data.