AERA5-Asia: A Long-Term Asian Precipitation Dataset (0.1°, 1-hourly, 1951–2015, Asia) Anchoring the ERA5-Land under the Total Volume Control by APHRODITE

Abstract

Accurate long-term precipitation information is critical for understanding the mechanisms behind how precipitation couples with Earth’s water fluxes, energy balances, and biogeochemical cycles across space–time scales under the changing climate. This study proposes a novel approach (Daily Total Volume Controlled Merging and Disaggregation Algorithm, DTVCMDA) for generating a new long-term precipitation dataset, AERA5-Asia (0.1°, 1 hourly, 1981–2015, Asia; “AERA5” is a combination of the “A” from APHRODITE and the “ERA5” from ERA5-Land; for abbreviations, see the Appendix), by comprehensively considering the characteristics of the high spatiotemporal resolutions and continuity of the ERA5-Land dataset and the high quality of the APHRODITE dataset; the AERA5-Asia back extensions (0.1°, 1 hourly, 1951–1980, Asia) will soon be available. The main conclusions include, but are not limited to, the following: (1) AERA5-Asia provides time series of precipitation of sufficient resolutions, length, consistency, continuity, and quality over Asia.(2) AERA5-Asia substantially outperforms ERA5-Land and IMERG-Final in terms of both magnitudes and occurrences of precipitation events in Mainland China, especially the systematic biases. For instance, the Bias of ERA5-Land, IMERG-Final, and AERA5-Asia against ground gauge-based observations in Mainland China are ∼20%, ∼11%, and ∼5%, respectively. (3) AERA5-Asia performs notably better than ERA5-Land and IMERG-Final against ground gauge-based observations in regional extreme rainfall systems (e.g., two typhoon events, Trami and Usagi). (4) AERA5-Asia should prove to be a useful precipitation dataset for addressing various key climatological and hydrological research questions that require precipitation data with longer spans and finer resolutions (0.1°, 1 hourly).

Spatiotemporal characteristics of precipitation extremes based on reanalysis precipitation data during 1950–2020 over the Ganjiang River Basin and its surroundings, China

“…Importantly, the most outstanding advantage of ERA5 precipitation data is the long time series with high quality confirmed by comprehensive comparisons with satellite‐based precipitation datasets, e.g. IMERG and GSMaP (Ma et al, 2022; Xu et al, 2022). For instance, ERA5 precipitation data show highest performance over subregions of temperate monsoon climate and temperate continental climate over China mainland (Xu et al, 2022).…”

“…For instance, ERA5 precipitation data show highest performance over subregions of temperate monsoon climate and temperate continental climate over China mainland (Xu et al, 2022). Moreover, the performance of model‐based precipitation products significantly outperform satellite‐based products over high‐latitude regions in cold seasons (Gao et al, 2020; Ma et al, 2022; Sui et al, 2020; Zhu et al, 2021). Therefore, ERA5 could convincingly stand out among many state‐of‐the‐art global and regional precipitation products obtained through gauge‐based, satellite‐based, and atmospheric retrospective‐analysis models, as well as through the merging of their products (Hersbach et al, 2020; Chen et al, 2008; Harris et al, 2020; Huffman et al, 2007; Huffman et al, 2019; Kobayashi et al, 2015; Ma et al, 2020; Ma et al, 2022, shown in Table 1) in terms of long time series with high quality.…”

Ecological Response to Climate Change Across China From Combined Soil Temperature and Moisture Changes

“…The preceding evaluations were justified using ERA5‐Land as a reference data set in terms of the physical adequacy of water and energy processes. Nevertheless, the adverse influence of wet biases in ERA5 precipitation in China, especially before 2000, on the long‐term trends of SM and ST was considered (Jiao et al., 2021; Z. Q. Ma et al., 2022). Hereafter, we focused on the period 2000–2020 to examine soil temperature‐moisture change regimes.…”

“…Nevertheless, the adverse influence of wet biases in ERA5 precipitation in China, especially before 2000, on the long-term trends of SM and ST was considered (Jiao et al, 2021; Z. Q. Ma et al, 2022). Hereafter, we focused on the period 2000-2020 to examine soil temperature-moisture change regimes.…”

PCSSR-DNNWA: A Physical Constraints Based Surface Snowfall Rate Retrieval Algorithm Using Deep Neural Networks With Attention Module

“…Surface snowfall rate; Microwave; Deep learning; Attention module; Physical constraints 1 Introduction Precipitation is a critical component in water, energy and biogeochemical cycles (Kidd and Huffman, 2011;Ma et al, 2022a;Xu et al, 2022) and snowfall predominates over other types of precipitation in high-latitude regions (Skofronick-Jackson et al, 2015). Today, satellite remote sensing is the primary observational source for gathering data on worldwide rainfall and snowfall since meteorological stations, ocean buoys, and weather radars have limited coverage (Hong et al, 2007;Ma et al, 2022b;Zhu and Ma, 2022).…”