Mapping the representativeness of precipitation measurements in Mainland China

Abstract

Meteorological observations provide essential data for weather forecasting and climate change studies. Whether the measured data can accurately support such applications closely relates to the representativeness of the data collected, which depends on both the scale of observation and the density of the measurement network. Precipitation presents in the form of events and is discontinuous both in time and space. Gauge observations of precipitation could provide fundamental data but have difficulty quantitatively assessing precipitation system scale. Therefore, assessments on the representativeness of precipitation at synoptic and climatological scales remain needed. Here, we show the first high-resolution map of the representativeness of precipitation over Mainland China based on the latest satellite data. Our results show that the daily precipitation spatial consistency is the highest in eastern China and lowest on the Tibetan Plateau. However, the pattern of the monthly spatial consistency is different and is the highest over Northeast China Plain, the Loess Plateau, and the Middle-Lower Yangtze Plain. Compared to the density of rain gauges, we find that the current national station network with 2400 stations still has difficulty supporting synoptic studies in western China. However, for climate change studies based on monthly data, the density of the national reference climatological station network is sufficient, except in the western Tibetan Plateau and deserts with no available stations. For climatological studies, the quality of precipitation gauge observations is more important than its spatial density. Our results could provide great practical significance for considering the layout of rain gauges.

A Machine Learning Method to Retrieve Global Rainfall and Snowfall Rates From the Passive Microwave Observations of FY‐3E

“…Ground‐based gauge and radar observations are considered the most accurate precipitation estimates; however, it is difficult to obtain ground‐based observations over ocean and remote areas (Kidd & Huffman, 2011; Skofronick‐Jackson, 2020). Satellite remote sensing is a good supplement, as it has global coverage and helps us better understand the structure of precipitation worldwide (Zhang & Wang, 2023a, 2023b). Therefore, satellite retrieval of precipitation is at the forefront of recent studies (Kidd & Levizzani, 2011; Phu et al., 2018).…”