# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 1 Introduction  
Precipitation is a key variable in the water cycle, and its estimation is critical for many applications such as water resource planning and management, agricultural assessment, and emergency response planning [1; 2]. While long-term precipitation patterns are required for climate research and drought monitoring [3; 4; 5], near-real-time precipitation data are essential to better understand vector and water-borne diseases [6; 7] and mitigate the impact of natural disasters such as floods [8; 9; 10; 11] and landslides [12; 13]. Rain gauges provide the most direct measurement of point precipitation and are generally considered to be the most accurate and reliable method of measuring precipitation [14]. However, a single or several rain gauges are not sufficient to represent rainfall distributions on large scales [15; 16], especially in areas where rainfall gauges are sparse, such as Africa and western China [17]. To address the problem of insufficient rainfall information due to limited rainfall gauges, ground-based radar data are often used as supplementary information. However, weather radar data also suffer from several problems including, for example, the calibration of the empirical relationship linking reflectivity to rainfall rate, atmospheric attenuation, frozen hydrometeors, and beam blockage, all of which affect the data reliability. Radar data is especially problematic over mountainous regions where rain gauges are also sparse, and are even completely lacking in some regions of the world [18; 19]. Satellite-based remote sensing has the potential to provide global coverage for precipitation measurement, and satellite precipitation products (SPPs) have undergone significant progress over the last decade [20]. The latest example is the Global Precipitation Measurement (GPM) Core Observatory launched in 2014 [2]. A 'core' satellite in the GPM mission measures rainfall from space, and the data is used to consolidate rainfall measurements from the international satellite network [21]. The newly-released Integrated Multi-Satellite Retrievals for GPM (IMERG) products provide better spatial resolution and quasi-global coverage than most other existing satellite products [22]. The retrieval of rainfall is obtained by the inversion of atmospheric signals scattered or emitted by hydrological surveyors [15]. However, some studies have shown that the satellite products (not just IMERG) derived from this method are biased compared with the reference data obtained on the ground [23; 24; 25; 26]. One of the effective ways to solve these problems is to correct real-time SPP data through ground observations. However, the adjusted products would not be available until several days or even several months later due to the availability of rain gauge observations [27]. Therefore, accurate and timely correction of the real-time SPP data without relying on contemporaneous ground observations has become an urgent issue to address. Near-real-time satellite soil moisture (SM) data is considered a promising tool for improving precipitation estimation [28; 29]. Soil moisture can reflect recent rainfall, as surface soil moisture increases upon the occurrence of rain events [30] and steadily decrease due to evaporation and drainage after the rain ends. Based on this principle, soil moisture observations can be used to estimate or correct rainfall from satellite products, and various algorithms have been developed. [PERSON] and [PERSON] [31] demonstrated how to estimate rainfall errors in global precipitation products by assimilating soil moisture from the Advanced Microwave Scanning Radiometer (AMSR-E) into the Antecedent Precipitation Index (API) model. [PERSON] et al. [32] developed a Kalman filtering-based tool that utilizes soil moisture retrievals from the AMSR-E to enhance short-term (2- to 10-day) satellite-based rainfall accumulation products. As a follow-up, [PERSON] et al. [33] modified their approach to develop the Soil Moisture Analysis Rainfall Tool (SMART) by incorporating more complex data assimilation method.