The diurnal cycle of precipitation is a key feature in the Earth system and related to multiscale physical processes. The Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG) data are expected to improve its presentation of diurnal cycle, but how it really performs remains poorly known. This study compares the diurnal characteristics of the half‐hourly IMERG V05 Final Run product with the hourly rain gauge data collected at approximately 50,000 automatic weather stations in China during a 3‐year period (2014–2016). Our results show that IMERG performs well in terms of the diurnal cycle of precipitation amount but less well in terms of frequency and even worse in terms of intensity. Ground observations show that the frequency and intensity are rather stable for the diurnal cycle; however, IMERG shows strong fluctuations. Significant inverse correlation is found between the diurnal cycles of IMERG precipitation frequency and intensity; thus, the biases of these two variables offset and result in a better estimate of amount. A low probability of detection (POD≈50%) and a high false alarm ratio (FAR≈50%) are the major reasons for the inaccurate diurnal cycles of precipitation intensity and frequency. In particular, IMERG presents a false peak of frequency at approximately 23:00–01:00 (Beijing Time) because a large proportion of the data sources at this time contain the indirect estimates from thermal infrared observations. The significant underestimation of the frequency and overestimation of the intensity at approximately 09:00–11:00 and 20:00–22:00 is because the estimates primarily come from less accurate cross‐track microwave sensors.
Abstract. PM2.5 has been used as an important atmospheric environmental parameter primarily due to its impact on human health. PM2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Monitoring it does not only help understand the causes of air pollution but also our adaptation to it. Most existing PM2.5 products have been derived from polar-orbiting satellites. This study exploits the usage of the next-generation geostationary meteorological satellite Himawari-8/AHI in revealing its diurnal variations. Given the huge volume of the satellite data, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) learning approach, which is based on the idea of gradient boosting, is applied by involving the spatiotemporal characteristics of air pollution, named the space-time LightGBM (STLG) model. Hourly PM2.5 data set in China (i.e., ChinaHighPM2.5) at a 5 km spatial resolution is derived based on the Himawari-8/AHI aerosol products together with other variables. The hourly PM2.5 estimates (N = 1,415,188) are well correlated with ground measurements (R2 = 0.85) with a RMSE and MAE of 13.62 and 8.49 μg/m3 respectively in China. Our model can capture well the PM2.5 diurnal variations, where the pollution increases gradually in the morning, and reaches a peak at about 10:00 a.m. local time, then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine learning models with a much lower computation burden in terms of speed and memory, making it most suitable for routine pollution monitoring.
While gauge observations serve as a traditional way to measure precipitation, remote sensing technology has grown rapidly in recent decades and become another effective method for estimating precipitation (Kucera et al., 2013; Yang et al., 2013). By detecting the properties of precipitating clouds, satellites estimate snapshots of the precipitation rate from infrared (IR) sensors, relatively direct passive microwave (PMW) sensors, or Precipitation Radar (PR) (Kummerow et al., 2015; Sun et al., 2018; Yang et al., 2013). By further combining these multiple satellite sources, quasi-global gridded products have been developed (e.g., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), Climate Prediction Center (CPC) Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG)), which have been used in a wide range of applications (
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