Precipitation data fusion or precipitation merging methods or blending
Over the last two decades, several studies have evaluated the quality of satellite precipitation data in different regions of the world and have also recommended several bias correction procedures to improve the data.
Satellite quantitative precipitation estimation (QPE) or radar QPE
\citet*{Hossain_2008}Gauge-satellite merging methods
Neither rain gauges nor satellite has demonstrated the ability to provide an accurate depiction of the rainfall field. Rain gauges provide accurate point rainfall estimates, but
their spatial resolution is limited by the low-density of a gauge network and the errors associated with interpolation schemes to fill in missing data. Satellite, on the other hand, provides accurate spatial and temporal resolution of the rainfall field at significant heights above the surface of the earth, but numerous measurement errors result in inaccuracies in rainfall depths at the ground. The problems associated with each measurement technique have led to numerous attempts to merge rainfall estimates from the two instruments.
Gauge-radar merging methods can generally be divided into two categories (Wang et al. 2013):
1) bias reduction techniques; and
2) error variance minimization techniques.
Bias reduction techniques
Each category follows a similar set of assumptions. In the following sub-sections, the merging methods will be discussed according to these two categories.
Gauge-radar merging methods categorized as bias reduction techniques attempt to correct
the bias present in satellite accumulations using rain gauge accumulations as the real rainfall
value. The radar field represents a background guess which is subsequently adjusted by
the known (rain gauge) information. According to Koistinen and Puhakka (1981), the
assumptions for bias correction schemes include:
(1) gauge measurements are accurate for each gauge’s respective location;
(2) radar accurately measures relative spatial and temporal variability of precipitation;
(3) gauge and radar measurements are valid for the same location in time and space;
and
(4) the relationships based on comparisons between gauges and radar(s) are valid for
other locations in time and space.
It is important to note that these assumptions, although necessary for the adjustment of
radar using rain gauges, are false and often lead to erroneous correction factors. Four
gauge-radar merging methods categorized as bias reduction techniques will be discussed
separately below
\citet*{McKee_2015}
\citet{Verdin_2016} Kriging and local Polynomial (LP)
\citet{Manz_2016}
\citet{Nerini_2015} assessed the performance of two classes of satellite gauge rainfall merging methods, namely the non-parametric methods,mean bias correction (MBC) and double-kernel smoothing (DS) with two geostatistical methods such as ordinary kriging (OK), kriging with external drift (KED) and Bayesian combination in terms of cross- validation and catchment water balance analysis and hydrological modeling They found that DS delivered the most consistent improvement over the original satellite product in both the cross-validation and hydrological evaluation and the success of the method depends on the gauge density.
\citet{Chao_2018} applied Geographic Weighted Regression (GWR) and its variant to merge gauge data with CMORPH satellite rainfall and spatially downscale the product from 8 kilometers to 1 kilometer for daily and hourly data. In addition the geographical variables (elevation, slope, aspect, surface roughness, and distance to the coastline) and a meteorological variable (wind speed) were also used for merging precipitation. They found that after the data merging the bias has reduced from 1.685 mm/day to 0.627 mm/day. Further for hourly data they found that the bias reduced from 0.021 mm/hr to 0.012 mm/hr.
The philosopy in data merging or data fusion or blending or combining the satellite and gauge rainfall is