# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 6 Validation
### Uncertainties and Recommendations for SPPs
\begin{table}
**Methodology** & **Advantages** & **Limitations** & **Usage in SPEs** \\ \hline Cloud motion & Assumption: no relation between IR Tb and underlying rainfall & Assumption: precipitation linearly evolves during the time between PMW images; ground rainfall and cloud tops move at different speeds. & IMERG, MORPH, GSMaP \\ \hline Probability matching & Latency of PMW data less critical; a reasonable measure of cloud movement; Computationally fast & Indirectness of the IR to sense rainfall itself; subjective rain-no-rain threshold & TMPA, AGPI, PERSIANN-CCS, CMORPH-CDR \\ \hline \hline \end{tabular} \
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{Probability matching} & \multirow{2}{*}{Lategory of PMW data less critical; a reasonable measure of cloud movement; Computationally fast} & \multirow{2}{*}{Indirectness of the IR to sense rainfall itself; subjective rain-no-rain threshold} & \multirow{2}{*}{
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline MPPA, AGPI, PERSIANN-CCS, CMORPH-CDR \\ \end{tabular} } \\ \cline{1-1} \cline{3-3}  & & & \\ \cline{1-1} \cline{3-3}  & & & \\ \cline{1-1} \cline{3-3}  & & & \\ \cline{1-1} \cline{3-3}  & & & \\ \cline{1-1} \cline{3-3}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advantages and limitations of combining methods. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**SPP** & **Region** & **Scale** & **Main Results** & & & & & **Reference** & **Remarks** \\ \cline{3-8}  & & & & Bias & U.S. & East Asia & Europe & Australia & \\ \cline{3-8}
3B42 V7 & Global & Daily & 3842 & 0.27 & \(-6.96\) & \(-6.76\) & \(-16.89\) & [189] & All seasons \\ \cline{3-8}
3B42 RT V7 & & RT & \(-12.57\) & \(-38.63\) & \(-28.09\) & \(-41.28\) & & \\ \hline \multirow{3}{*}{3B42 RT V6} & Australia & Daily & & POD & FAR & RMSE/mm & & [155] & validation for 5 Jan 2005 \\ \cline{3-8}  & & & & CORPH & 0.55 & 0.28 & 7.9 & & \\ \cline{3-8}
3B42 RT V6 & Northwestern & & & POD & FAR & RMSE/mm & & \\ \cline{3-8}  & Europe & & 3842 RT & 0.57 & 0.17 & 4.4 & & \\ \cline{3-8}  & & & Bias/\(\%\) & GSMap & CMORPHH & 3B42 & PERSIANN & Winter \\ \cline{3-8}
5 GMap-MVK V4.84 & & & West & \(-50\) & \(-75\) & \(-41\) & \(-27\) & \\ \cline{3-8}
5 GMap IV0.x & Continental U.S. & Daily & & East & \(-32\) & \(-48\) & \(-8\) & \(-23\) & \\ \cline{3-8}
5B42 V6 & (CONUS) & & West & \(77\) & \(88\) & \(-8\) & \(72\) & \\ \cline{3-8}
5 PERSIANN & & & & East & \(25\) & \(32\) & \(-13\) & \(28\) & & \\ \hline \hline \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } & & & & 3B42 RT & CMORPH & 3B42 & PERSIANN &
\begin{tabular}{} \end{tabular} \\ \cline{3-8}  & & & & POD & 0.41 & 0.74 & 0.42 & 0.55 & \\ \cline{3-8}
3B42 V6 & central U.S. & hourly & & FAR & 0.80 & 0.62 & 0.83 & 0.58 & \\ \cline{3-8}
3B42 RT V6 & & & POD & 0.28 & 0.47 & 0.22 & 0.47 & \\ \cline{3-8}  & & & FAR & 0.63 & 0.46 & 0.46 & 0.14 & & \\ \cline{3-8}  & & & Bias/\(\%\) & 56 & 50 & 2 & 43 & All data \\ \hline \hline \multicolumn{8}{c}{Mountainous areas} & \multicolumn{3}{c}{CONUS} \\ \cline{3-8}
3B42 V7 & \multirow{3}{*}{CONUS} & \multirow{3}{*}{Daily} & & \multicolumn{1}{c}{bias/\(\%\)} & \multicolumn{1}{c}{RMSE/mm} & \multicolumn{1}{c}{bias/\(\%\)} & \multicolumn{1}{c}{RMSE/mm} \\ \cline{3-8}
3B42 RT V7 & & & 3842 & \(-25.88\) & 0.74 & \(-2.37\) & 0.92 & \\ \cline{3-8} \cline{3-8}  & & & 3842 RT & \(-27.97\) & 1.1 & 0.22 & 0.75 & \\ \hline \end{tabular}
\end{table} 
Table 4: Examples of SPP evaluation studies.