SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations

Abstract

Abstract. Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent “bottom-up” approach that exploits satellite soil moisture observations for estimating rainfall through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data record 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 of Meteorological Satellites (EUMETSAT) Polar System programme. The continuity of the scatterometer sensor is ensured until the mid-2040s through the MetOp Second Generation Programme. Therefore, by applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a 12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record is assessed on a regional scale through comparison with high-quality ground networks in Europe, the United States, India, and Australia. Moreover, an assessment 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 Retrievals for Global Precipitation Measurement (IMERG), and the gauge-based Global Precipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relatively well at both a regional and global scale, mainly in terms of root mean square error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data record provides performance better than IMERG and GPCC in data-scarce regions of the world, such as Africa and South America. In these areas, we expect larger benefits in using SM2RAIN–ASCAT for hydrological and agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist of the underestimation of peak rainfall events and the presence of spurious rainfall events due to high-frequency soil moisture fluctuations that might be corrected in the future with more advanced bias correction techniques. The SM2RAIN–ASCAT data record is freely available at https://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end of August 2019).

An assessment of global satellite‐based precipitation datasets in capturing precipitation extremes: A comparison with observed precipitation dataset in India

“…However, SM2RAIN‐ASCAT and other three datasets have shown significant variations over Himalayan regions, Jammu & Kashmir and North‐East India regions, which could be attributed to limited density of precipitation gauges in these regions. Therefore, the satellite‐based precipitation such as TRMM, CHIRPS and soil moisture based generated precipitation datasets such as SM2RAIN‐ASCAT (Brocca et al ., ) could be the useful sources in the absence of gauge data to fulfil these gaps. The quantitative and qualitative comparisons of all datasets focusing on extreme events have been done in the next sections.…”

“…A new global precipitation dataset, obtained from satellite‐based soil moisture data through the SM2RAIN algorithm, has been produced at 12.5 km × 12.5 km scale /daily spatio‐temporal resolution by Brocca et al . (). This dataset is available from January 2007 to December 2018 and downloaded from the web source https://zenodo.org/record/2591215#.XX550CgzZPY (Brocca et al ., ).…”

“…(). This dataset is available from January 2007 to December 2018 and downloaded from the web source https://zenodo.org/record/2591215#.XX550CgzZPY (Brocca et al ., ). The datasets were downloaded at 12.5 km × 12.5 km resolution (about 0.125° × 0.125° scale); therefore, we applied the nearest neighbour interpolation method (Linsay, ; Singh and Xiaosheng, , ) and brought the data at 0.25° × 0.25° scale to compare it with the IMD precipitation dataset.…”

“…The evaluation of SM2RAIN‐ASCAT precipitation dataset has been performed at global scale by Brocca et al . (), but their evaluation in India (at smaller scales) has been explored in this study.…”

“…Different global datasets are available based on satellite measurements such as Tropical Rainfall Measuring Mission (TRMM) (Chen et al ., ), Global Precipitation Measurement (GPM) (Xu et al ., ), Climate Hazards Group InfraRed Precipitation (CHIRPS) (Funk et al ., ; Prakash, ), Soil Moisture to Rain (SM2RAIN‐ASCAT) (Brocca et al ., ), outputs from various climate models (Katiraie‐Boroujerdy et al ., ) and gauge observations (Singh et al ., ). However, each dataset has its own advantages and limitations.…”