摘要：Abstract. Long-term gridded precipitation products are crucial for severalapplications in hydrology, agriculture and climate sciences. Currentlyavailable precipitation products suffer from space and time inconsistencydue to the non-uniform density of ground networks and the difficulties inmerging multiple satellite sensors. The recent “bottom-up” approach thatexploits satellite soil moisture observations for estimating rainfallthrough the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall datarecord as a single polar orbiting satellite sensor is used. Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp)satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation ofMeteorological Satellites (EUMETSAT) PolarSystem programme. The continuity of the scatterometer sensor is ensureduntil the mid-2040s through the MetOp Second Generation Programme. Therefore, byapplying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-termrainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. Thepaper describes the recent improvements in data pre-processing, SM2RAINalgorithm formulation, and data post-processing for obtaining theSM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data recordis assessed on a regional scale through comparison with high-qualityground networks in Europe, the United States, India, and Australia. Moreover, anassessment on a global scale is provided by using the triple-collocation (TC)technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis(ERA5), the Early Run version of the Integrated Multi-Satellite Retrievalsfor Global Precipitation Measurement (IMERG), and the gauge-based GlobalPrecipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relativelywell at both a regional and global scale, mainly in terms of root mean squareerror (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT datarecord provides performance better than IMERG and GPCC in data-scarceregions of the world, such as Africa and South America. In these areas, weexpect larger benefits in using SM2RAIN–ASCAT for hydrological andagricultural applications. The limitations of the SM2RAIN–ASCAT data record consistof the underestimation of peak rainfall events and the presence ofspurious rainfall events due to high-frequency soil moisture fluctuationsthat might be corrected in the future with more advanced bias correctiontechniques. The SM2RAIN–ASCAT data record is freely available athttps://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end ofAugust 2019).
