# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 1 Introduction
[PERSON] et al. [28] used in situ and satellite soil moisture observations to estimate rainfall accumulations with a water balance equation and showed satisfactory performance of the model in reproducing daily rainfall over Italy, Spain and France. Recent effort has increasingly sought to improve satellite precipitation product through comparing different satellite products, models, or methods. [PERSON] et al. [34] used satellite SM sensed from the Advanced SCATterometer (ASCAT), the AMSR-E and the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) to infer preceding rainfall amounts and showed that the global median of correlation coefficient are 0.54, 0.28 and 0.31 for three SM-derived products respectively. [PERSON] et al. [35] corrected the real-time Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis Real Time product (TMPA-RT) with the Variable Infiltration Capacity (VIC) model and the satellite-derived SM from AMSR-E, ASCAT, the Soil Moisture Ocean Salinity (SMOS) and land surface temperature (LST) from AMSR-E. Their results indicated that assimilation of SM or LST observations alone reduced the uncertainties, whereas the combined assimilation of both SM and LST did not significantly reduce the uncertainties comparing with the original TMPA-RT. [PERSON] et al. [36; 37] modified the API model to build a simple framework in correcting precipitation with soil moisture. [PERSON] et al. [1] compared three different methods:SM2 RAIN, SMART, and API Modification in improving TMPA-based rainfall estimates by inverting the SMOS soil moisture. The results showed that SM2 RAIN+3B42 RT performed best with a median R value 0.65 for daily rainfall over Australia. To demonstrate the effect of sophistication in hydrological model on improvement in satellite-based precipitation, [PERSON] et al. [38] compared the simple API method and a more sophisticated state-of-the-art land-surface model in correcting the real-time precipitation estimates within the framework of a particle filter assimilation. Their results showed that more sophisticated land surface models may not necessarily produce more effective bias correction. Another type of approach to correcting real-time satellite precipitation products is the fitting correction based on the relationship between historical satellite products and gauge observations. [PERSON] et al. [39] quantified the error characteristics of the Global Satellite Mapping of Precipitation (GSMaP) estimates over the Hanjiang River basin of China and applied a nonlinear fitting method to correct the GSMaP product. Their results showed the fitting correction increased the coefficient of determination from 0.538 to 0.775 over the basin. [PERSON] et al. [40] used the cumulative distribution functions (CDF) relationship between TMPA-RT and gauge-based data to support a regional drought monitoring system. [PERSON] and [PERSON] [41] and [PERSON] and [PERSON] [42] adopted probability density functions (PDFs) matching method in correcting the CMORPH-based precipitation and showed the substantial reduction of precipitation biases. [PERSON] and [PERSON] [43] used the CDF matching to combine TRMM-3B42 RT satellite precipitation and long-term gauge-based data for hydrological applications. Their results showed that the adjusted 3B42 RT agrees well with retrospective rainfall product, and the VIC model forced by corrected data performs better than the model forced by unadjusted data in reproducing the hydrographs and high/low flows. The aforementioned studies corrected satellite-based precipitation estimates using either the SM information alone or historical precipitation information. Since SM observations and historical precipitation records are two independent sources of information, combining the two could potentially lead to further improvement.