# Topography and Data Mining Based Methods for Improving Satellite Precipitation in Mountainous Areas of China

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. The Gauge-Elevation-Consistency (GEC) Rule for Assessment

_{k}(k = 1, …, M − l + 1). For each G

_{k}, there are a lowest and highest elevation (${E}_{k}^{low},{E}_{k}^{high}$), as well as a minimum and maximum gauge rainfall ($P{G}_{k}^{low},P{G}_{k}^{high}$). The rectangle space of (${E}_{k}^{low},P{G}_{k}^{low};{E}_{k}^{high},P{G}_{k}^{high}$) forms a closer region, D

_{k}. All the closer regions consist of the whole rainfall-elevation mask D. The mask physically denotes the possible or reasonable rainfall range for each elevation. Figure 1 shows an example of deriving the rainfall-elevation mask (l = 3, M = 5).

**Figure 1.**The derivation of rainfall-elevation mask (l = 3, M = 5). The red solid lines are the up and low limit of rainfall and the black dashed lines are the up and low limit of elevation of each sub-mask. The whole pink region is the final rainfall-elevation mask (REM).

#### 2.2. The Location-Elevation-TMPA (LET) Correlation for Improvement

^{2}is the coefficient of correlation between gauge measurements (PG) and modeled actual rainfall (PA). CV(RMSD) is coefficient of variation of the RMSD, which is calculated by normalizing RMSD by the mean value of the measurements. The target of the GP problem is to minimize the R

^{2}and CV(RMSD) between actual rainfall from Equation (9) and gauge measurements.

## 3. Case Study and Results

#### 3.1. Data

**Figure 2.**The location of the studied mountainous areas of China. The red lines are the boundaries of the mountainous areas, and the blue dots are the gauge stations.

Region | Area (10^{3} km^{2}) | Mean Elevation ^{a}(m) | Peak Elevation (m) | Gauges Information | ||
---|---|---|---|---|---|---|

Gauges Numbers | Gauges Altitudes (m) | Mean Annual Rainfall (2001–2012) (mm) | ||||

Himalaya | 1054.7 | 4592 | 8848 | 33 | 2328–4900 | 467 |

Kunlun | 786.7 | 2897 | 7576 | 15 | 887–3504 | 102 |

Tianshan | 392.2 | 1712 | 7125 | 19 | 35–2458 | 180 |

Qilian | 337.6 | 2954 | 5820 | 23 | 1139–3367 | 230 |

Qinling | 129.5 | 921 | 3747 | 13 | 249–2065 | 770 |

Taihang | 223.2 | 1012 | 3059 | 22 | 63–2208 | 498 |

Changbai | 631.9 | 334 | 2667 | 48 | 4–775 | 663 |

Wuyi | 366.2 | 386 | 2154 | 43 | 3–1654 | 1589 |

^{a}The elevation is from the DEM of the Shuttle Radar Topography Mission with a spatial resolution of 90 m.

#### 3.2. Assessment the Uncertainty of Satellite Precipitation

#### 3.2.1. Grid Cells with Gauges

**Table 2.**Comparison of mean annual rainfall (2001–2012) from gauge measurements and the original 3B43.

Region | Altitude of Gauges (m) | Gauge (mm) | 3B43 (mm) | Bias (%) | RMSD (mm) | |
---|---|---|---|---|---|---|

Higher Mountains | Himalaya | 2328–4900 | 453 | 667 | 47.2 | 272 |

Kunlun | 887–3504 | 106 | 131 | 23.6 | 76 | |

Tianshan | 35–2458 | 175 | 200 | 14.3 | 54 | |

Qilian | 1139–3367 | 233 | 264 | 13.3 | 65 | |

Lower Mountains | Qinling | 249–2065 | 776 | 791 | 1.9 | 46 |

Taihang | 63–2208 | 502 | 542 | 8.0 | 49 | |

Changbai | 4–775 | 674 | 757 | 12.3 | 103 | |

Wuyi | 3–1654 | 1560 | 1654 | 6.0 | 161 | |

Average | -- | 560 | 626 | 15.8 | 103 |

**Figure 3.**Validation of the mean annual rainfall (2001–2012) from gauge measurements and the original 3B43.

#### 3.2.2. Grid Cells without Gauges

**Figure 4.**The mean annual (2001–2012) rainfall-elevation mask and rainfall filter (the pink space, l = 3). The green/black forks stand for TMPA rainfall grid cells with gauges located in/out of the masks. The grey circles are the cells without gauges. Kunlun has only one mask because of lacking of enough gauges on another hillside. (

**a**,

**b**) Himalaya, (

**c**) Kunlun, (

**d**,

**e**) Tianshan, (

**f**,

**g**) Qilian, (

**h**,

**i**) Qinling, (

**j**,

**k**) Taihang, (

**l**,

**m**) Changbai, (

**n**,

**o**) Wuyi.

Region | CR in the Whole Region | CR in the Hillside Face to Vapor Transportation | CR in the Hillside Back to Vapor Transportation | ||||
---|---|---|---|---|---|---|---|

Gauged Grids | Ungauged Grids | Gauged Grids | Ungauged Grids | Gauged Grids | Ungauged Grids | ||

Higher Mountains | Himalaya | 51.5 | 57.4 | 100.0 | 84.7 | 42.9 | 47.7 |

Kunlun | 57.1 | 60.0 | 57.1 | 60.0 | -- | -- | |

Tianshan | 57.9 | 63.5 | 55.6 | 74.4 | 60.0 | 53.4 | |

Qilian | 65.2 | 58.7 | 50.0 | 51.4 | 88.9 | 69.5 | |

Lower Mountains | Qinling | 92.3 | 75.0 | 100.0 | 73.1 | 87.5 | 77.6 |

Taihang | 63.6 | 64.6 | 42.9 | 55.0 | 73.3 | 68.2 | |

Changbai | 60.4 | 67.0 | 58.1 | 67.0 | 64.7 | 67.5 | |

Wuyi | 51.2 | 33.8 | 50.0 | 36.4 | 51.9 | 32.5 | |

Average | 62.4 | 60.4 | 64.2 | 62.8 | 67.0 | 59.5 |

#### 3.3. Improvement the Robust of Satellite Precipitation

#### 3.3.1. Testing Calibration and Cross Validation

#### 3.3.2. Final Calibration and Correction of TMPA

**Figure 5.**Cross-Validation of the Location-Elevation-TMPA (LET) method using mean annual rainfall (2001–2012) from gauge measurements and corrected 3B43.

Region | Mean of Gauges (mm) | Mean of gauged grids (mm) | Bias (%) | RMSD (mm) | CR of Gauged Grids (%) | CR of Ungauged Grids (%) |
---|---|---|---|---|---|---|

Himalaya | 453 | 422 | −6.8 | 92 | 84.8 | 78.2 |

Kunlun | 106 | 76 | −28.2 | 49 | 64.3 | 63.7 |

Tianshan | 175 | 171 | −2.2 | 31 | 84.2 | 66.7 |

Qilian | 233 | 235 | 1.0 | 26 | 82.6 | 60.4 |

Qinling | 776 | 777 | 0.2 | 27 | 76.9 | 71.6 |

Taihang | 502 | 503 | 0.3 | 19 | 72.7 | 77.0 |

Changbai | 674 | 674 | 0.0 | 44 | 75.0 | 55.1 |

Wuyi | 1560 | 1561 | 0.0 | 89 | 72.1 | 41.1 |

Average | 560 | 552 | −4.5 | 47 | 76.6 | 64.2 |

**Figure 6.**The original (black squares), corrected (black circles) 3B43 and gauged (red triangles) mean annual rainfalls (2001–2012) versus elevation in study areas. The error bars denote the lower 5% and upper 95% rainfall value within each elevation range. (

**a**) Himalaya, (

**b**) Kunlun, (

**c**) Tianshan, (

**d**) Qilian, (

**e**) Qinling, (

**f**) Taihang, (

**g**) Changbai, (

**h**) Wuyi.

## 4. Discussion

#### 4.1. The Sensitive of CR to l of Rainfall-Elevation Mask

Region | l = 2 | l = 3 | l = 4 | l = 5 | |
---|---|---|---|---|---|

Higher Mountains | Himalaya | 42.0 | 57.4 | 61.4 | 62.8 |

Kunlun | 31.9 | 60.0 | 69.4 | 75.9 | |

Tianshan | 29.8 | 63.5 | 75.1 | 83.0 | |

Qilian | 36.9 | 58.7 | 71.0 | 77.8 | |

Lower Mountains | Qinling | 40.5 | 75.0 | 89.7 | 94.0 |

Taihang | 44.0 | 64.6 | 82.5 | 85.9 | |

Changbai | 38.2 | 67.0 | 79.8 | 84.0 | |

Wuyi | 12.9 | 33.8 | 44.0 | 53.6 | |

Average | 34.5 | 60.0 | 71.6 | 77.1 |

#### 4.2. The Suitability of LET for Monthly Precipitation of TMPA 3B43 (V7)

^{2}and CV(RMSD) before and after correction were listed in Table 6. Comparison between satellite rainfall and measurements were shown in Figure 7. For both every July and every month cases, the corrected rainfall has higher R

^{2}and lower CV(RMSD) than originals, indicating the valuable effectiveness of LET method on monthly scale.

Time Scale | Original | Corrected | ||
---|---|---|---|---|

R^{2} | CV(RMSD) (%) | R^{2} | CV(RMSD) (%) | |

Every July | 0.55 | 72.9 | 0.73 | 57.1 |

Every month | 0.53 | 124.9 | 0.61 | 109.2 |

**Figure 7.**Comparison between 3B43 rainfall (original and corrected) and gauge rainfall on every July (

**a**) and every month (

**b**) in Kunlun during 2001–2012.

#### 4.3. The Effectivity for the of TMPA 3B42RT (V7)

#### 4.3.1. Effective for Assessment

**Table 7.**Comparison of mean annual rainfall (2001–2012) from gauge measurements and original 3B42RT.

Region | Mean of Gauge (mm) | Mean of Gauged Grids (mm) | Bias (%) | RMSD (mm) | CR of Gauged Grids (%) | CR of Ungauged Grids (%) |
---|---|---|---|---|---|---|

Himalaya | 453 | 1457 | 221.6 | 1050 | 0.0 | 1.9 |

Kunlun | 106 | 360 | 239.6 | 332 | 14.3 | 44.0 |

Tianshan | 175 | 771 | 340.6 | 660 | 0.0 | 2.3 |

Qilian | 233 | 454 | 94.8 | 276 | 30.4 | 47.8 |

Qinling | 776 | 835 | 7.6 | 81 | 76.9 | 58.6 |

Taihang | 502 | 653 | 30.1 | 162 | 22.7 | 16.5 |

Changbai | 674 | 676 | 0.3 | 104 | 77.1 | 67.3 |

Wuyi | 1560 | 1562 | 0.1 | 313 | 34.9 | 22.5 |

Average | 560 | 846 | 116.8 | 372 | 32.0 | 32.6 |

**Figure 8.**Validation of the mean annual rainfall (2001–2012) from gauge measurements and original 3B42RT.

#### 4.3.2. Effective for Correction

**Table 8.**Comparison of mean annual rainfall (2001–2012) from gauge measurements and corrected 3B42RT.

Region | Mean of Gauges (mm) | Mean of Gauged Grids (mm) | Bias (%) | RMSD (mm) | CR of Gauged Grids (%) | CR of Ungauged Grids (%) |
---|---|---|---|---|---|---|

Himalaya | 453 | 454 | 0.2 | 95 | 87.9 | 76.1 |

Kunlun | 106 | 117 | 10.8 | 35 | 64.3 | 78.8 |

Tianshan | 175 | 290 | 65.2 | 130 | 42.1 | 46.8 |

Qilian | 233 | 231 | −0.6 | 38 | 78.3 | 61.1 |

Qinling | 776 | 770 | −0.8 | 41 | 100.0 | 62.1 |

Taihang | 502 | 496 | −1.2 | 30 | 86.4 | 71.8 |

Changbai | 674 | 656 | −2.7 | 60 | 83.3 | 50.5 |

Wuyi | 1560 | 1544 | −1.0 | 143 | 74.4 | 54.3 |

Average | 560 | 570 | 8.7 | 72 | 77.1 | 62.7 |

**Figure 9.**Validation of the mean annual rainfall (2001–2012) from gauge measurements and corrected 3B42RT.

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix

**Table A1.**Relationship between actual rainfall and related factors (P

_{T}: TMPA rainfall; E: elevation; X: longitude; Y: latitude).

Region | Relationships for 3B42RT | Relationships for 3B43 |
---|---|---|

Himalaya | ${P}_{\text{a}}={\left\{\mathrm{exp}\left[\mathrm{exp}\left(\frac{1}{4}{X}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}\right)-2E-{X}^{\raisebox{1ex}{$3$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}\right]\right\}}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$4$}\right.}{\left({P}_{\text{T}}-{E}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}+\frac{5.67{P}_{\text{T}}-9.35X+E}{{X}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}-\frac{{X}^{2}}{Y}$ | ${P}_{\text{a}}=\frac{{\left({P}_{\text{T}}-2Y-X\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$4$}\right.}\mathrm{exp}\left(0.25{\left(X+Y\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}\right)Y}{{\left(E-{X}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}{E}^{-\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}-2{P}_{\text{T}}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$8$}\right.}}-4X-Y$ |

Kunlun | ${P}_{\text{a}}=E\mathrm{exp}\left(3{Y}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}\right){X}^{-4}{\left[1+\frac{{E}^{4}}{{Y}^{3}\mathrm{exp}\left(3{Y}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}\right)}+\frac{XY}{{P}_{T}+{Y}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}}+Y\right]}^{-1}$ | ${P}_{\text{a}}=Y+{X}^{-\raisebox{1ex}{$3$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}\mathrm{exp}\left[\frac{6.13E}{\left(T+3X\right)X}+{Y}^{\raisebox{1ex}{$3$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}{\left({X}^{2}-1.35E\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$16$}\right.}{P}_{\text{T}}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$8$}\right.}{X}^{-1}\right]$ |

Tianshan | ${P}_{\text{a}}=\mathrm{exp}\left\{{\left[\frac{\mathrm{exp}\left(\raisebox{1ex}{${P}_{\text{T}}$}\!\left/ \!\raisebox{-1ex}{$X$}\right.\right)}{E+{P}_{\text{T}}}+\frac{\left(E-9.88\right){E}^{2}{P}_{\text{T}}^{3}}{\mathrm{exp}\left(\raisebox{1ex}{$E$}\!\left/ \!\raisebox{-1ex}{$Y$}\right.\right)E+{P}_{\text{T}}^{3}\mathrm{exp}\left(\raisebox{1ex}{${P}_{\text{T}}$}\!\left/ \!\raisebox{-1ex}{$X$}\right.\right)+X{P}_{\text{T}}^{3}E}+E\right]}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$16$}\right.}{Y}^{\raisebox{1ex}{$3$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}{X}^{-1}\right\}$ | ${P}_{\text{a}}={P}_{\text{T}}\left(X-Y\right){\left[\frac{\left({P}_{\text{T}}+3X\right)\left(Y-X\right)}{\mathrm{exp}\left(\frac{E}{T-X}\right)+1.69{P}_{\text{T}}-Y\left(Y-X\right)}+Y\right]}^{-1}-\frac{{P}_{\text{T}}}{\mathrm{exp}\left(\raisebox{1ex}{$E$}\!\left/ \!\raisebox{-1ex}{${P}_{\text{T}}$}\right.\right)}-Y+{P}_{\text{T}}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}$ |

Qilian | ${P}_{\text{a}}={\left(\frac{{P}_{\text{T}}}{X}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}{\left(4{P}_{\text{T}}+E\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$4$}\right.}\left(X-2Y\right)-\frac{E}{Y}-1228.5{\left(\frac{{P}_{\text{T}}}{E}\right)}^{2}-Y$ | ${P}_{\text{a}}={E}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}{P}_{\text{T}}{\{\left(3Y-X\right)\left[\frac{{P}_{\text{T}}^{3}X}{0.005{E}^{2}\left(E-X\right)}+Y\right]+0.62E\}}^{-\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}$ |

Qinling | ${P}_{\text{a}}=1+\frac{\mathrm{exp}\left[0.5\mathrm{exp}\left(0.5{P}_{\text{T}}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$4$}\right.}\right)-0.5{X}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}\right]{P}_{\text{T}}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}{E}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}}{X}+2X+T+{T}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}-\left(\frac{1}{{Y}^{2}}-\frac{1}{X}\right)\mathrm{exp}\left({X}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}\right)$ | ${P}_{\text{a}}=\frac{\left(E+2X\right)X}{{\left({P}_{\text{T}}-E\right)}^{2}}+\frac{\left(2X+{P}_{\text{T}}\right)E}{\left(2{P}_{\text{T}}-E\right)\left({P}_{\text{T}}-E\right)}+{P}_{\text{T}}-Y$ |

Taihang | ${P}_{\text{a}}=\frac{X-1.51}{Y}\left\{\frac{{\left(1.41{Y}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}{P}_{\text{T}}-E\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}{\left[X{Y}^{8}+{X}^{\raisebox{1ex}{$3$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}{\left(X-2.35\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}{Y}^{\raisebox{1ex}{$15$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}+{P}_{\text{T}}{E}^{4}\right]}^{\raisebox{1ex}{$3$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}}{1.19{Y}^{\raisebox{1ex}{$61$}\!\left/ \!\raisebox{-1ex}{$4$}\right.}}+X\right\}$ | ${P}_{\text{a}}=\left(X-Y-1.5\right)\left[{\left(\frac{XY}{E}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}+\frac{{X}^{\raisebox{1ex}{$33$}\!\left/ \!\raisebox{-1ex}{$16$}\right.}}{Y{E}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$16$}\right.}}-Y-X+{P}_{\text{T}}-1.2\right]{\left(X-\frac{EY}{XY-E}\right)}^{-1}$ |

Changbai | ${P}_{\text{a}}={\left(\frac{{P}_{\text{T}}-2Y+X}{{P}_{\text{T}}{E}^{-2}+{E}^{\raisebox{1ex}{$3$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}}+X\right)}^{2}{\left[Y-\frac{\left({P}_{\text{T}}-Y\right)\left({E}^{2}+XY{P}_{\text{T}}+EY\right)}{Y{E}^{2}+X{Y}^{2}{P}_{\text{T}}+E{Y}^{2}-Y{P}_{\text{T}}^{2}}\right]}^{-1}$ | ${P}_{\text{a}}={P}_{\text{T}}-Y-\frac{E}{{Y}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}}-\frac{\left(0.731-X-Y-E\right){P}_{\text{T}}-2{\left(Y-E\right)}^{2}}{\left(0.80{P}_{\text{T}}-E\right)\left(Y-E\right)}$ |

Wuyi | ${P}_{\text{a}}=X+Y+{\left[{P}_{\text{T}}+\frac{{P}_{\text{T}}^{9}}{{\left({P}_{\text{T}}{E}^{-1}+E\right)}^{3}{Y}^{9}{E}^{3}{\left({E}^{2}+Y+X\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}}\right]}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$16$}\right.}{P}_{\text{T}}^{\raisebox{1ex}{$5$}\!\left/ \!\raisebox{-1ex}{$16$}\right.}{E}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$16$}\right.}{X}^{\raisebox{1ex}{$7$}\!\left/ \!\raisebox{-1ex}{$8$}\right.}$ | ${P}_{\text{a}}={P}_{\text{T}}+{\left(3E+{P}_{\text{T}}+Y+{P}_{\text{T}}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}-X\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}-\left(2E+{P}_{\text{T}}+Y\right){\{\frac{XE}{{P}_{\text{T}}+E-X}+\mathrm{exp}\left[{\left(X-Y\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}-{E}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}\right]\}}^{-1}$ |

## References

- Nespor, V.; Sevruk, B. Estimation of wind-induced error of rainfall gauge measurements using a numerical simulation. J. Atmos. Ocean. Technol.
**1999**, 16, 450–464. [Google Scholar] [CrossRef] - Rubel, F.; Hantel, M. Correction of daily rain gauge measurements in the Baltic Sea drainage basin. Nord. Hydrol.
**1999**, 30, 191–208. [Google Scholar] - AghaKouchak, A.; Nasrollahi, N.; Habib, E. Accounting for Uncertainties of the TRMM Satellite Estimates. Remote Sens.
**2009**, 1, 606–619. [Google Scholar] [CrossRef] - Huffman, G.J.; Adler, R.F.; Morrissey, M.M.; Bolvin, D.T.; Curtis, S.; Joyce, R.; McGavock, B.; Susskind, J. Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeorol.
**2001**, 2, 36–50. [Google Scholar] [CrossRef] - Sorooshian, S.; Hsu, K.L.; Gao, X.; Gupta, H.V.; Imam, B.; Braithwaite, D. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Am. Meteorol. Soc.
**2000**, 81, 2035–2046. [Google Scholar] [CrossRef] - Joyce, R.J.; Janowiak, J.E.; Arkin, P.A.; Xie, P. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeorol.
**2004**, 5, 487–503. [Google Scholar] [CrossRef] - Kubota, T.; Shige, S.; Hashizume, H.; Aonashi, K.; Takahashi, N.; Seto, S.; Hirose, M.; Takayabu, Y.N.; Ushio, T.; Nakagawa, K.; et al. Global precipitation map using satellite-borne microwave radiometers by the GSMaP Project: Production and validation. IEEE Trans. Geosci. Remote Sens.
**2007**, 45, 2259–2275. [Google Scholar] [CrossRef] - Huffman, G.J.; Bolvin, D.T.; Nelkin, E.J.; Wolff, D.B.; Adler, R.F.; Gu, G.; Nelkin, E.J.; Bowman, K.P.; Hong, Y.; Stocker, E.F.; et al. The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol.
**2007**, 8, 38–55. [Google Scholar] [CrossRef] - Huffman, G.J.; Adler, R.F.; Bolvin, D.T.; Nelkin, E.J. The TRMM Multi-satellite Precipitation Analysis (TMPA). Chapter 1. In Satellite Rainfall Applications for Surface Hydrology; ISBN 978-90-481-2914-0. Springer Netherlands: Berlin, Germany, 2010; pp. 3–22. [Google Scholar]
- Hossain, F.; Lettenmaier, D.P. Flood prediction in the future: Recognizing hydrologic issues in anticipation of the Global Precipitation Measurement mission. Water Resour. Res.
**2006**, 42, W11301. [Google Scholar] [CrossRef] - Hong, Y.; Adler, R.; Huffman, G. Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophys. Res. Lett.
**2006**, 33, L22402. [Google Scholar] [CrossRef] - Huffman, G.J.; Bolvin, D.T. TRMM and Other Data Precipitation Data Set Documentation; NASA Goddard Space Flight Center: Greenbelt, MD, USA, 2014; p. 42. [Google Scholar]
- Hong, Y.; Hsu, K.L.; Moradkhani, H.; Sorooshian, S. Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response. Water Resour. Res.
**2006**, 42, W08421. [Google Scholar] [CrossRef] - Khan, S.I.; Hong, Y.; Gourley, J.J.; Khattak, M.U.K.; Yong, B.; Vergara, H.J. Evaluation of three high-resolution satellite precipitation estimates: Potential for monsoon monitoring over Pakistan. Adv. Space Res.
**2014**, 54, 670–683. [Google Scholar] [CrossRef] - Hu, Q.; Yang, D.; Li, Z.; Mishra, A.K.; Wang, Y.; Yang, H. Multi-scale evaluation of six high-resolution satellite monthly rainfall estimates over a humid region in China with dense rain gauges. Int. J. Remote Sens.
**2014**, 35, 1272–1294. [Google Scholar] [CrossRef] - Tian, Y.; Peters-Lidard, C.D.; Choudhury, B.J.; Garcia, M. Multitemporal analysis of TRMM-based satellite precipitation products for land data assimilation applications. J. Hydrometeorol.
**2007**, 8, 1165–1183. [Google Scholar] [CrossRef] - Li, Z.; Yang, D.; Hong, Y. Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River. J. Hydrol.
**2013**, 500, 157–169. [Google Scholar] [CrossRef] - Cheema, M.J.M.; Bastiaanssen, W.G. Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin. Int. J. Remote Sens.
**2012**, 33, 2603–2627. [Google Scholar] [CrossRef] - Shen, Y.; Zhao, P.; Pan, Y.; Yu, J. A high spatiotemporal gauge-satellite merged precipitation analysis over China. J. Geophys. Res. Atmos.
**2014**, 119, 3063–3075. [Google Scholar] [CrossRef] - Anders, A.M.; Roe, G.H.; Hallet, B.; Montgomery, D.R.; Finnegan, N.J.; Putkonen, J. Spatial patterns of precipitation and topography in the Himalaya. Geol. Soc. Am. Spec. Pap.
**2006**, 398, 39–53. [Google Scholar] - Gebregiorgis, A.S.; Hossain, F. Understanding the dependence of satellite rainfall uncertainty on topography and climate for hydrologic model simulation. IEEE Trans. Geosci. Remote Sens.
**2012**, 51, 704–718. [Google Scholar] [CrossRef] - Bookhagen, B.; Burbank, D.W. Topography, relief, and TRMM-derived rainfall variations along the Himalaya. Geophys. Res. Lett.
**2006**, 33, L08405. [Google Scholar] - Hirpa, F.A.; Gebremichael, M.; Hopson, T. Evaluation of high-resolution satellite precipitation products over very complex terrain in Ethiopia. J. Appl. Meteorol. Climatol.
**2010**, 49, 1044–1051. [Google Scholar] [CrossRef] - Ji, X.; Luo, Y. Quality Assessment of the TRMM Precipitation Data in Mid Tianshan Mountains. Arid Land Geogr.
**2013**, 36, 253–262. [Google Scholar] - Zhu, G.F.; Pu, T.; Zhang, T.; Liu, H.L.; Zhang, X.B.; Liang, F. The Accuracy of TRMM Precipitation Data in Hengduan Mountainous Region, China. Sci. Geogr. Sinca
**2013**, 33, 1125–1131. [Google Scholar] - Yin, Z.Y.; Zhang, X.; Liu, X.; Colella, M.; Chen, X. An assessment of the biases of satellite rainfall estimates over the Tibetan Plateau and correction methods based on topographic analysis. J. Hydrometeorol.
**2008**, 9, 301–326. [Google Scholar] [CrossRef] - Müller, M.F.; Thompson, S.E. Bias adjustment of satellite rainfall data through stochastic modeling: Methods development and application to Nepal. Adv. Water Resour.
**2013**, 60, 121–134. [Google Scholar] [CrossRef] - Brunsdon, C.; McClatchey, J.; Unwin, D.J. Spatial variations in the average rainfall–altitude relationship in Great Britain: An approach using geographically weighted regression. Int. J. Climatol.
**2001**, 21, 455–466. [Google Scholar] [CrossRef] - Derin, Y.; Yilmaz, K.K. Evaluation of multiple satellite-based precipitation products over complex topography. J. Hydrometeorol.
**2014**, 15, 1498–1516. [Google Scholar] [CrossRef] - Shen, Y.; Xiong, A.; Wang, Y.; Xie, P. Performance of high-resolution satellite precipitation products over China. J. Geophys. Res.
**2010**, 115, D02114. [Google Scholar] [CrossRef] - Scheel, M.L.M.; Rohrer, M.; Huggel, C.; Santos Villar, D.; Silvestre, E.; Huffman, G.J. Evaluation of TRMM multi-satellite precipitation analysis (TMPA) performance in the central Andes region and its dependency on spatial and temporal resolution. Hydrol. Earth Syst. Sci.
**2011**, 15, 2649–2663. [Google Scholar] [CrossRef][Green Version] - AghaKouchak, A.; Mehran, A.; Norouzi, H.; Behrangi, A. Systematic and random error components in satellite precipitation data sets. Geophys. Res. Lett.
**2012**, 39, L09406. [Google Scholar] [CrossRef] - Yong, B.; Liu, D.; Gourley, J.J.; Tian, Y.; Huffman, G.J.; Ren, L.; Hong, Y. Global view of real-time TRMM Multi-satellite Precipitation Analysis: Implication to its successor Global Precipitation Measurement mission. Bull. Am. Meteorol. Soc.
**2014**. [Google Scholar] [CrossRef] - Krakauer, N.Y.; Pradhanang, S.M.; Lakhankar, T.; Jha, A.K. Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal. Remote Sens.
**2013**, 5, 4107–4123. [Google Scholar] [CrossRef] - Zhang, W.Y.; Mischke, S.; Zhang, C.J.; Gao, D.; Fan, R. Ostracod distribution and habitat relationships in the Kunlun Mountains, northern Tibetan Plateau. Quat. Int.
**2013**, 313, 38–46. [Google Scholar] [CrossRef] - Wang, Y.Z.; Zhang, H.P.; Zheng, D.W.; Zheng, W.J.; Zhang, Z.Q.; Wang, W.T.; Yu, J.X. Controls on decadal erosion rates in Qilian Shan: Re-evaluation and new insights into landscape evolution in north-east Tibet. Geomorphology
**2014**, 223, 117–128. [Google Scholar] - Xu, G.B.; Liu, X.H.; Qin, D.H.; Chen, T.; Wang, W.Z.; Wu, G.J.; Sun, W.Z.; An, W.L.; Zeng, X.M. Tree-ring delta δ
^{18}O evidence for the drought history of eastern Tianshan Mountains, northwest China since 1700 AD. Int. J. Climatol.**2014**, 34, 3336–3347. [Google Scholar] [CrossRef] - Cai, Q.F.; Liu, Y. Climatic response of Chinese pine and PDSI variability in the middle Taihang Mountains, north China since 1873. Trees
**2013**, 27, 419–427. [Google Scholar] [CrossRef] - Cai, Y.J.; Tan, L.C.; Cheng, H.; An, Z.S.; Edwards, R.L.; Kelly, M.J.; Kong, X.G.; Wang, X.F. The variation of summer monsoon precipitation in central China since the last deglaciation. Earth Planet. Sci. Lett.
**2010**, 291, 21–31. [Google Scholar] [CrossRef] - Zheng, D.L.; Wallin1, D.O.; Hao, Z.Q. Rates and patterns of landscape change between 1972 and 1988 in the Changbai Mountain area of China and North Korea. Landsc. Ecol.
**1997**, 12, 241–254. [Google Scholar] [CrossRef] - Luo, P.; Peng, P.A.; Gleixner, G.; Zheng, Z.; Pang, Z.H.; Ding, Z.L. Empirical relationship between leaf wax n-alkane delta D and altitude in the Wuyi, Shennongjia and Tianshan Mountains, China: Implications for paleoaltimetry. Earth Planet. Sci. Lett.
**2011**, 301, 285–296. [Google Scholar] [CrossRef] - Long, D.; Shen, Y.J.; Sun, A.; Hong, Y.; Longuevergne, L.; Yang, Y.T.; Li, B.; Chen, L. Drought and flood monitoring for a large karst plateau in Southwest China using extended GRACE data. Remote Sens. Environ.
**2014**, 155, 145–160. [Google Scholar] [CrossRef] - Qin, Y.X.; Chen, Z.Q.; Shen, Y.; Zhang, S.P.; Shi, R.H. Evaluation of Satellite Rainfall Estimates over the Chinese Mainland. Remote Sens.
**2014**, 6, 11649–11672. [Google Scholar] [CrossRef] - Tong, K.; Su, F.; Yang, D.; Hao, Z. Evaluation ofsatellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau. J. Hydrol.
**2014**, 519, 423–437. [Google Scholar] [CrossRef] - Yong, B.; Chen, B.; Hong, Y.; Gourley, J.J.; Li, Z. Impact of Missing Passive Microwave Sensors on Multi-Satellite Precipitation Retrieval Algorithm. Remote Sens.
**2015**, 7, 668–683. [Google Scholar] [CrossRef] - Chen, S.; Hong, Y.; Cao, Q.; Gourley, J.J.; Kirstetter, P.E.; Yong, B.; Tian, Y.; Zhang, Z.; Shen, Y.; Hardy, J.; et al. Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China. J. Geophys. Res. Atmos.
**2013**, 118, 13060–13074. [Google Scholar] [CrossRef] - Yong, B.; Ren, L.L.; Hong, Y.; Wang, J.H.; Gourley, J.J.; Jiang, S.H.; Chen, X.; Wang, W. Hydrologic evaluation of Multisatellite Precipitation Analysis standard precipitation products in basins beyond its inclined latitude band: A case study in Laohahe basin, China. Water Resour. Res.
**2010**, 46, W07542. [Google Scholar] [CrossRef]

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**MDPI and ACS Style**

Xia, T.; Wang, Z.-J.; Zheng, H. Topography and Data Mining Based Methods for Improving Satellite Precipitation in Mountainous Areas of China. *Atmosphere* **2015**, *6*, 983-1005.
https://doi.org/10.3390/atmos6080983

**AMA Style**

Xia T, Wang Z-J, Zheng H. Topography and Data Mining Based Methods for Improving Satellite Precipitation in Mountainous Areas of China. *Atmosphere*. 2015; 6(8):983-1005.
https://doi.org/10.3390/atmos6080983

**Chicago/Turabian Style**

Xia, Ting, Zhong-Jing Wang, and Hang Zheng. 2015. "Topography and Data Mining Based Methods for Improving Satellite Precipitation in Mountainous Areas of China" *Atmosphere* 6, no. 8: 983-1005.
https://doi.org/10.3390/atmos6080983