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Abstract. Precipitation estimates with fine quality and spatio-temporal resolutions play significant roles in understanding the global and regional cycles of water, carbon, and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at fine resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation products are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. In this study, focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in the Tropical Rainfall Measuring Mission (TRMM) era (finished in July 2019), which were only calibrated at a monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0°/monthly), we aim to propose a new calibration algorithm for IMERG at a daily scale and to provide a new AIMERG precipitation dataset (0.1°/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 0.25°/daily) at the daily scale for the Asian applications. The main conclusions include but are not limited to the following: (1) the proposed daily calibration algorithm (Daily Spatio-Temporal Disaggregation Calibration Algorithm, DSTDCA) is effective in considering the advantages from both satellite-based precipitation estimates and the ground observations; (2) AIMERG performs better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over mainland China; and (3) APHRODITE demonstrates significant advantages compared to GPCC in calibrating IMERG, especially over mountainous regions with complex terrain, e.g. the Tibetan Plateau. Additionally, results of this study suggest that it is a promising and applicable daily calibration algorithm for GPM in generating the future IMERG in either an operational scheme or a retrospective manner.The AIMERG data are freely accessible at https://doi.org/10.5281/zenodo.3609352 (for the period from 2000 to 2008) (Ma et al., 2020a) and https://doi.org/10.5281/zenodo.3609507 (for the period from 2009 to 2015) (Ma et al., 2020b). Highlights. A new effective daily calibration approach, DSTDCA, for improving the GPM-era IMERG is provided.New AIMERG precipitation data (0.1°/half-hourly, 2000–2015, Asia) are provided.Bias of AIMERG is significantly improved compared with that of IMERG.APHRODITE is more suitable than GPCC in anchoring IMERG over Asia.

Accurate precipitation data is crucial in many applications such as hydrology, meteorology, and ecology. Compared with ground observations, satellite-based precipitation estimates can provide much more spatial information to characterize precipitation. In this study, the satellite-based precipitation products of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) were firstly evaluated over the Tibetan Plateau (TP) in 2015 against ground observations at both annual and monthly scales. Secondly, random forest algorithm was used to obtain the annual downscaled results (~1 km) based on IMERG and TMPA data and the downscaled results were examined against rain gauge data. Thirdly, a disaggregation algorithm was used to obtain the monthly downscaled results based on those at annual scale. The results indicated that (1) IMERG performed better than TMPA at both annual and monthly scales; (2) IMERG had few anomalies while TMPA displayed significant numbers of outliers in central and western parts of the TP; (3) random forest was a promising algorithm in acquiring high resolution precipitation data with improved accuracy; (4) the downscaled results based on IMERG had better performances than those based on TMPA.

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).

Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of~0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields.Collecting precipitation information from ground rain gauge stations is the traditional and common method of measurement. However, the limitations are obvious due to the uneven spatial distribution of the stations. The measurements of ground stations are usually very sparse over some regions of the earth (e.g., the Tibetan plateau), which are meteorologically important [6,7]. As for ground-based weather radars, they have certain superiorities when observing precipitation in local areas. Nevertheless, due to the limitations of the scope of observation and the huge cost of equipment acquisition and maintenance, ground-based weather radars are not the first choice for large-scale precipitation observations. However, precipitation information obtained from satellites does not meet such limitations. Satellite-based precipitation datasets can depict the spatial and temporal variability of precipitation with a considerable accuracy over regions that have few ground stations [5,8]. Over the last four decades, the progress of meteorological satellites has made it possible for scientists to acquire reliable and cost-effective precipitation datasets through a variety of sensors and inversion algorithms [9][10][11][12][13]. Therefore, obtaining high-resolution and accurate precipitation estimates derived from sensors on satellites at a regional or global scale has become a highly-efficient research method at present [4,14,15].Satellite-based precipitation products provided by several institutions and organizations from all over the world are different in terms o...

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