Wet‐bulb temperature (TW), which combines dry‐bulb temperature and relative humidity (RH), is a key variable for human health and heat stress because in hot environments evaporation is the main process by which bodies cool down. For this study, we use two independent RH data sets: a new homogenized network of daily ground observations in China and the recent ERA5 reanalysis, for which we highlight the need to apply humidity correction. Based on these data sets, we show that Chinese wet‐bulb temperatures since 1980 have increased largely due to a rise in dry‐bulb temperatures while RH has remained approximately constant. We also find that TW change has been previously underestimated due to humidity bias in reanalysis and nonhomogenized observation where RH was decreasing sharply after 2000s, greatly limiting changes in TW.
Using 141 CME-interplanetary shock (CME-IPS) events and foF2 from eight ionosonde stations from January 2000 to September 2005, from the statistical results we find that there is a "same side -opposite side effect" in ionospheric negative storms, i.e., a large portion of ionospheric negative disturbances are induced by the same-side events (referring to the CMEs whose source located on the same side of the heliospheric current sheet (HCS) as the Earth), while only a small portion is associated with the opposite-side events (the CMEs source located on the opposite side of the HCS as the Earth); the ratio is 128 vs. 46, and it reaches 41 vs. 14 for the intense ionospheric negative storms. In addition, the ionospheric negative storms associated with the same-side events are often more intense. A comparison of the same-side event (4 April 2000) and the opposite-side event (2 April 2001) shows that the intensity of the ionospheric negative storm caused by the same-side event is higher than that by the opposite-side event, although their initial conditions are quite similar. Our preliminary results show that the HCS has an "impeding" effect to CME-IPS, which results in a shortage of energy injection in the auroral zone and restraining the development of ionospheric negative perturbations.
Global hydrological stations are unevenly distributed, with sparse hydrometeorological observation networks in developing countries and dense ones in developed countries (Ma et al., 2021). Moreover, observations over some regions are not publicly available for policy reasons (Feng et al., 2021). Thus, PUB is challenging for the hydrological sciences (Tsai et al., 2021). World Bank statistics show that river monitoring networks cannot meet current needs in 78% of low-and middle-income countries and 86% of least-developed countries. Precise streamflow PUB is essential for both water management and policy decision-makers (Cho & Kim, 2022;Merz et al., 2021;Tellman et al., 2021).Traditional PUB primarily based on catchment hydrological similarity, whereby parameters are migrated from adjacent or similar catchments a regression function is constructed between physical descriptors of the catchment and model parameters for streamflow PUB (Guo et al., 2021). Bao et al. (2012) found that a similarity-based approach outperformed a regression-based approach in 55 catchments in China. Gou et al. (2021) successfully constructed a high-quality natural runoff dataset in China using a variable infiltration capacity (VIC) macroscale hydrologic model and multiscale parameter regionalization techniques. In recent years, the geophysical community has shown great interest in deep learning, with relevant research exploding exponentially in the society of exploration geophysics and the American Geophysical Union (X X Zhu et al., 2017). Long Short-Term Memory (LSTM) neural networks are widely used in hydrology, because of their excellent simulation performance and suitability for streamflow forecasting (
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