Bias correction for spatially interpolated daily mean air temperature during winter in eastern Hokkaido using multimodal machine learning

Abstract

Interactions between boundary layer wind and topography form non-uniform air temperature distributions in cold and snow-covered regions. Because of this heterogeneity, spatially interpolated air temperatures sometimes deviate from observed values. To evaluate the quality of spatially interpolated daily mean temperatures T int provided by a 1 km gridded meteorological data service Ohno et al., 2016 , we collected observed temperatures T obs obtained at meteorological observation sites located near farmland in the Tokachi and Okhotsk regions in eastern Hokkaido, Japan in winter October-March and revisited the bias in the interpolated temperatures dT. The root-mean-square error RMSE of T int obtained at 88 sites was 1.16 C, and the absolute median dT values were greater than 1 C at 14 sites. The variance of dT was greater on cold and calm days, suggesting the involvement of radiative cooling and the accumulation of cold air parcels. To correct T int by estimating dT at a given site by considering the formation mechanisms of the temperature distributions, we attempted to develop a multimodal machine learning model that had four predictors: surface and boundary layer meteorological data and topographical and geographical features around each site. To analyze the influence of the spatial extent of the topography and geography around each site, we compared models having these predictors with various sizes of the region of interest ROI. By training the models and applying them to an independent test dataset, it has been shown that bias correction using models with a small topographical ROI 30 30 km or smaller reduced the RMSE. The RMSE of the test dataset decreased by ~0.1 C via the application of a nested model, suggesting the potential usefulness of the presented approach for locally confined meteorological events. However, the biases were increased at several sites by application of the models, thus implying that further improvement is essential for practical use.

Promotion to Farmers of Snow Compaction (Yuki-Fumi) on Winter Wheat to Control Volunteer Potatoes without Depending on Chemical Materials

“…In northern Japan, rainfall in December is expected to increase and snowfall is expected to decrease in the future (Inatsu et al 2016, Kawase et al 2021, which could lead to widespread implementation in regions previously unimplemented. Refinement of winter weather information (Fukushima et al 2019, Murakami et al 2020, Yazaki et al 2017 will also contribute to the advancement of snow compacting operations in the future. Previous studies have shown that soil frost can be useful for the efficient use of water resources and nitrogen in natural ecosystems (Fukuzawa et al 2021, Makoto et al 2014) and agricultural fields (Iwata et al 2018, Onodera et al 2022, Yanai et al 2011.…”