English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Based on the high-quality homogenized precipitation data from all 2,419 national weather stations in China, the climatology and anomaly percentage fields are derived, and then the digital elevation model (DEM) is employed to reduce the influence of elevation on the spatial interpolation accuracy of precipitation due to the unique topography in China. Then, the gradient plus inverse distance squared (GIDS) method and the inverse distance squared (IDS) method are used to grid the climatology field and the anomaly percentage field, respectively, and the 0.5 × 0.5° gridded datasets during 1961–2018 in China are obtained by combining them together. The evaluation shows that the mean absolute error (MAE) between the analysis value and the observation is 15.8 mm/month. The MAE in South China is generally higher than that in North China, and the MAE is obviously larger in summer than in other seasons. Specifically, 94.6, 54.4, 4.6, and 53.8% of the MAE are below 10 mm/month in winter (DJF), spring (MAM), summer (JJA), and autumn (SON), respectively, and 99.5, 79.9, 22.8, and 82.1% of them are less than 20 mm/month. The MAE over China in four seasons is 3.8, 13.2, 33.5, and 12.7 mm/month, respectively. This dataset has the potential of broad application prospects in the evaluations of weather and climate models and satellite products.

Development and Assessment of the Monthly Grid Precipitation Datasets in China