# The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China

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## Abstract

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## 1. Introduction

^{1.6}[23], studies have mainly focused on reconstructing the local Z–R relationship according to different climate regions, different seasons, and different rainfall types. This has demonstrated that the accuracy of radar QPE can be improved to some extent by applying a more appropriate local Z–R relationship [24,25,26,27]. In recent decades, many studies have also focused on merging AWS and radar rainfall data using various methods [22,28,29,30,31,32]. These methods were used to reduce precipitation estimation errors and include bias correction [33,34,35], the Kalman filter [36,37,38], optimum interpolation [39,40], the variation method [41,42,43,44], kriging, cokriging, kriging with external drift [45,46,47], conditional merging [48], frequency matching [49], and the multi-step combination of different methods based on average weight, the optimal integration of artificial intelligence, and the statistical weight matrix [50,51,52]. Recently, Song et al. [53] proposed a climatological correction algorithm to improve the accuracy of the rainfall amount estimations from the Beijing Auto NowCasting (BJ-ANC) system [24] using a long time series of radar QPE and AWS precipitation data for the North China region. However, whether the scaling method can be adequately applied for other systems or other regions is still unknown. Therefore, it is necessary to investigate the applicability and possible limitation of the climatological scaling method for improving QPE accuracy in the South China region.

## 2. Data and Methods

^{5}km

^{2}. There are 6083 automatic meteorological stations in the South China region (Figure 1). This area is relatively flat and has a station altitude that is generally less than 600 m. However, the topography is relatively complex and is characterized by mountainous and hilly areas. Low topography is mostly found along the coastline and in the Pearl River Delta region (Figure 1), which is one of the most economically developed regions in China. The area is mainly influenced by East Asian summer monsoons and South China Sea summer monsoons and receives the bulk of its annual precipitation in the summer season [60,61,62].

_{t,s}samples ≥ 0.1 mm/h).

_{i}and $\overline{Y}$ is a generic notation for QPE and the mean of the original QPE products from the SWAN system or from the QPE after correction. O

_{i}and $\overline{O}$ are the observational AWS rainfall and mean.

## 3. Results

#### 3.1. QPE Errors

#### 3.2. Comparison of the Climatological Correction Scaling Algorithm and Q-matching Methods

## 4. Conclusions

## 5. Discussion

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Pellarin, T.G.; Delrieu, G.M.; Saulnier, H.A. Hydrologic visibility of weather radars operating in mountainous regions: Case study for the Ardeche catchment (France). J. Hydrometeorol.
**2002**, 3, 539–555. [Google Scholar] [CrossRef] - Maggioni, V.; Massari, C. On the performance of satellite precipitation products in riverine flood modeling: A review. J. Hydrol.
**2018**, 558, 214–224. [Google Scholar] [CrossRef] - Yin, Z.C.; Guo, W.L.; Li, N.J.; Xie, Y.Y. Numerical simulation of urban ponding in Beijing. Meteorol. Mon.
**2012**, 41, 1111–1118. (In Chinese) [Google Scholar] - Zhao, W.; Chen, S.; Chen, W.; Yao, S.; Nath, D.; Yu, B. Interannual variations of the rainy season withdrawal of the monsoon transitional zone in China. Clim. Dyn.
**2019**, 53, 2031–2046. [Google Scholar] [CrossRef] - Jonkman, S.N. Global Perspectives on Loss of Human Life Caused by Floods. Nat. Hazards
**2005**, 34, 151–175. [Google Scholar] [CrossRef] - Yu, X.D. Detection and Warnings of Severe Convection with Doppler Weather Radar. Adv. Meteorol. Sci. Technol.
**2011**, 1, 31–41. (In Chinese) [Google Scholar] - Wang, Y.; Han, L.; Wang, H.Q. Statistical characteristics of convective initiation in the Beijing-Tianjin region revealed by six-year radar data. J. Meteorol. Res.
**2014**, 28, 1127–1136. [Google Scholar] [CrossRef] - Gabella, M.; Speirs, P.; Hamann, U.; Germann, U.; Berne, A. Measurement of Precipitation in the Alps Using Dual-Polarization C-Band Ground-Based Radars, the GPM Spaceborne Ku-Band Radar, and Rain Gauges. Remote Sens.
**2017**, 9, 1147. [Google Scholar] [CrossRef] [Green Version] - Germann, U.; Galli, G.; Boscacci, M.; Bolliger, M. Radar precipitation measurement in a mountainous region. Q. J. R. Meteorol. Soc.
**2006**, 132, 1669–1692. [Google Scholar] [CrossRef] - Berne, A.; Krajewski, W.F. Radar for hydrology: Unfulfilled promise or unrecognized potential? Adv. Water Resour.
**2013**, 51, 357–366. [Google Scholar] [CrossRef] - Zhuang, W. Key Method Research on Radar Precipitation Estimation of Complex Terrain over the Tibet Plateau. Ph.D. Thesis, Chinese Academy of Meteorological Sciences, Beijing, China, 2013; p. 128. (In Chinese). [Google Scholar]
- Wang, H.Y.; Wang, G.L.; Liu, L.P.; Jiang, Y.; Wang, D.; Feng, L.I. Development of a-real time quality control method for automatic rainfall gauge data using radar quantitative precipitation estimation. Chin. J. Atmos. Sci.
**2015**, 39, 59–67. (In Chinese) [Google Scholar] - Steiner, M.; Smith, J.A. Use of Three-Dimensional reflectivity structure for automated detection and removal of nonpre-cipitating echoes in radar data. J. Atmos. Ocean. Technol.
**2002**, 19, 673–686. [Google Scholar] [CrossRef] - Gabella, M. Improving Operational Measurement of Precipitation Using Radar in Mountainous Terrain—Part II: Verification and Applications. IEEE Geosci. Remote Sens. Lett.
**2004**, 1, 84–89. [Google Scholar] [CrossRef] - Parker, M.D.; Knievel, J.C. Do Meteorologists Suppress Thunderstorms?: Radar-Derived Statistics and the Behavior of Moist Convection. Bull. Am. Meteorol. Soc.
**2005**, 86, 341–358. [Google Scholar] [CrossRef] [Green Version] - Prat, O.P.; Barros, A.P. Exploring the transient behavior of Z-R relationships: Implications for radar rainfall estimation. Am. Meteorol. Soc.
**2009**, 48, 2127–2143. [Google Scholar] [CrossRef] - Krajewski, W.F.; Villarini, G.; Smith, J.A. Radar-Rainfall Uncertainties: Where are we after thirty years of effort? Bull. Am. Meteorol. Soc.
**2010**, 91, 87–94. [Google Scholar] [CrossRef] - Ku, J.M.; Ro, Y.; Kim, K.; Yoo, C. Analysis on Characteristics of Orographic Effect about the Rainfall Using Radar Data: A Case Study on Chungju Dam Basin. J. Korea Water Resour. Assoc.
**2015**, 48, 393–407. [Google Scholar] [CrossRef] - Jacobi, S.; Heistermann, M. Benchmarking attenuation correction procedures for six years of single-polarized C-band weather radar observations in South-West Germany. Geomat. Nat. Hazards Risk
**2016**, 7, 1785–1799. [Google Scholar] [CrossRef] [Green Version] - Cong, F.; Liu, L. A comprehensive analysis of data from the CINRAD and the ground rainfall station. Meteorol. Monogr.
**2011**, 37, 532–539. [Google Scholar] - Lee, J.; Byun, H.; Kim, H.; Jun, H. Evaluation of a rain gauge network considering the spatial distribution characteristics and entropy: A case study of Imha dam basin. J. Korean Soc. Hazard. Mitig.
**2013**, 13, 217–226. [Google Scholar] [CrossRef] [Green Version] - Ochoa-Rodriguez, S.; Wang, L.-P.; Willems, P.; Onof, C. A Review of Radar-Rain Gauge Data Merging Methods and Their Potential for Urban Hydrological Applications. Water Resour. Res.
**2019**, 55, 6356–6391. [Google Scholar] [CrossRef] - Marshall, J.S.; Palmer, W.M.K. The distribution of raindrops with size. J. Meteorol.
**1948**, 5, 165–166. [Google Scholar] [CrossRef] - Chen, M.X.; Gao, F.; Kong, R.; Wang, Y.; Ding, Q. Introduction of auto-nowcasting system for convective storm and its performance in Beijing Olympics meteorological service. J. Appl. Meteorol. Sci.
**2010**, 21, 395–404. (In Chinese) [Google Scholar] - Wang, G.L.; Liu, L.P.; Ding, Y.Y. Improvement of radar quantitative precipitation estimation based on real-time adjustments to Z-R relationships and inverse distance weighting correction schemes. Adv. Atmos. Sci.
**2012**, 29, 575–584. [Google Scholar] [CrossRef] - Gou, Y.B.; Liu, L.; Wang, D.; Zhong, L.; Chen, C. Evaluation and analysis of the Z-R storm-grouping relationships fitting scheme based on storm identification. Torrential Rain Disasters
**2015**, 34, 1–8. [Google Scholar] - Zhang, J.; Howard, K.W.; Langston, C.; Kaney, B.; Qi, Y.; Tang, L.; Grams, H.; Wang, Y.; Cocks, S.; Martinaitis, S.M.; et al. Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities. Bull. Am. Meteorol. Soc.
**2016**, 97, 621–638. [Google Scholar] [CrossRef] - Goudenhoofdt, E.; Delobbe, L. Evaluation of radar-gauge merging methods for quantitative precipitation estimates. Hydrol. Earth Syst. Sci.
**2009**, 13, 195–203. [Google Scholar] [CrossRef] [Green Version] - Wang, L.P.; Ochoa-Rodriguez, S.; Simões, N.; Onof, C.; Maksimovic, Č. Radar-rain gauge data combination techniques: A revision and analysis of their suitability for urban hydrology. Water Sci. Technol.
**2013**, 68, 737–747. [Google Scholar] [CrossRef] - Li, J.T.; Li, B.; Yang, H.P.; Liu, X.Y.; Zhang, L.; Guo, L. A study of regional rainfall estimation by using radar and rain gauge: Proposal of model integration method. Meteorol. Sci. Technol.
**2014**, 42, 556–562. [Google Scholar] - Berndt, C.; Rabiei, E.; Haberlandt, U. Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios. J. Hydrol.
**2014**, 508, 88–101. [Google Scholar] [CrossRef] - Hasan, M.M.; Sharma, A.; Mariethoz, G.; Johnson, F.; Seed, A. Improving radar rainfall estimation by merging point rainfall measurements within a model combination framework. Adv. Water Resour.
**2016**, 97, 205–218. [Google Scholar] [CrossRef] - Seo, D.J.; Breidenbach, J.P.; Johnson, E.R. Real-time estimation of mean field bias in radar rainfall data. J. Hydrol.
**1999**, 223, 131–147. [Google Scholar] [CrossRef] - Rabiei, E.; Haberlandt, U. Applying bias correction for merging rain gauge and radar data. J. Hydrol.
**2015**, 522, 544–557. [Google Scholar] [CrossRef] - Ringard, J.; Seyler, F.; Linguet, L. A Quantile Mapping Bias Correction Method Based on Hydroclimatic Classification of the Guiana Shield. Sensors
**2017**, 17, 1413. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Chumchean, S.; Seed, A.; Sharma, A. Correcting of real-time radar rainfall bias using a Kalman filtering approach. J. Hydrol.
**2006**, 317, 123–137. [Google Scholar] [CrossRef] - Kim, J.; Yoo, C. Using Extended Kalman Filter for Real-time Decision of Parameters of Z-R Relationship. J. Korea Water Resour. Assoc.
**2014**, 47, 119–133. [Google Scholar] [CrossRef] [Green Version] - Kim, J.; Yoo, C. Use of a dual Kalman filter for real-time correction of mean field bias of radar rain rate. J. Hydrol.
**2014**, 519, 2785–2796. [Google Scholar] [CrossRef] - Álvarez, V.H.; Aznar, M. An efficient approach to optimal interpolation of experimental data. J. Taiwan Inst. Chem. Eng.
**2010**, 41, 184–189. [Google Scholar] [CrossRef] - Gao, X.R.; Liang, J.Y.; Li, C.H. Radar quantitative precipitation estimation techniques and effect evaluation. J. Trop. Meteorol.
**2012**, 28, 77–88. (In Chinese) [Google Scholar] - Fu, D.S.; Dai, T.P. Application of the variational method in the calibration of regional rainfall measurement by weather radar. J. Nanjing Inst. Meteorol.
**1990**, 13, 598–603. (In Chinese) [Google Scholar] - Zhang, P.C.; Dai, T.P.; Fu, D.S.; Wu, Z.F. Principle and accuracy of adjusting the area precipitation from digital weather radar through variational method. Chin. J. Atmos. Sci.
**1992**, 16, 248–256. [Google Scholar] - Deng, X.J.; Huang, H.H.; Wu, D. Application of variational method for the calibration radar quantitative estimates of precip-itation. Q. J. Appl. Meteorol.
**2000**, 11, 255–256. (In Chinese) [Google Scholar] - Bianchi, B.; van Leeuwen, P.J.; Hogan, R.; Berne, A. A Variational Approach to Retrieve Rain Rate by Combining Information from Rain Gauges, Radars, and Microwave Links. J. Hydrometeorol.
**2013**, 14, 1897–1909. [Google Scholar] [CrossRef] - Krajewski, W.F. Cokriging radar-rainfall and rain gage data. J. Geophys. Res. Space Phys.
**1987**, 92, 9571–9580. [Google Scholar] [CrossRef] - Sideris, I.V.; Gabella, M.; Erdin, R.; Germann, U. Real-time radar–rain gauge merging using spatio-temporal co-kriging with external drift in the alpine terrain of Switzerland. Q. J. R. Meteorol. Soc.
**2014**, 140, 1097–1111. [Google Scholar] [CrossRef] - Cantet, P. Mapping the mean monthly precipitation of a small island using kriging with external drifts. Theor. Appl. Clim.
**2015**, 127, 31–44. [Google Scholar] [CrossRef] [Green Version] - Sinclair, S.; Pegram, G. Combining radar and rain gauge rainfall estimates using conditional merging. Atmos. Sci. Lett.
**2005**, 6, 19–22. [Google Scholar] [CrossRef] - Zhu, Y.; Luo, Y. Precipitation Calibration Based on the Frequency-Matching Method. Weather. Forecast.
**2015**, 30, 1109–1124. [Google Scholar] [CrossRef] - Wu, M.C.; Lin, G.F.; Hwang, L.R.; Chen, D.Y.C.; Chiang, C.C.; Wang, Y.C. Optimal Integration of the Ensemble Forecasts from an Ensemble Quantitative Precipitation Forecast Experiment. Procedia Eng.
**2016**, 154, 1291–1297. [Google Scholar] [CrossRef] [Green Version] - Chao, L.; Zhang, K.; Li, Z.; Zhu, Y.; Wang, J.; Yu, Z. Geographically weighted regression based methods for merging satellite and gauge precipitation. J. Hydrol.
**2018**, 558, 275–289. [Google Scholar] [CrossRef] - Shao, Y.; Fu, A.; Zhao, J.; Xu, J.; Wu, J. Improving quantitative precipitation estimates by radar-rain gauge merging and an inte-gration algorithm in the Yishu River catchment, China. Theor. Appl. Climatol.
**2021**, 144, 611–623. [Google Scholar] [CrossRef] - Song, L.Y.; Chen, M.X.; Cheng, C.L.; Gao, F.; Chen, M. Characteristics of summer QPE error and a climatological correction method over Beijing-Tianjin-Hebei region. Acta Meteorol. Sin.
**2019**, 77, 497–515. (In Chinese) [Google Scholar] - Panofsky, H.A.; Brier, G.W. Some Applications of Statistics to Meteorology; Penn State University Press: University Park, PA, USA, 1958; p. 234. [Google Scholar]
- Haddad, Z.S.; Rosenfeld, D. Optimality of empirical Z-R relations. Meteorol. Soc.
**1997**, 123, 1283–1293. [Google Scholar] [CrossRef] - Maraun, D. Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue. J. Clim.
**2013**, 26, 2137–2143. [Google Scholar] [CrossRef] [Green Version] - Vrac, M.; Ayar, P.V. Influence of Bias Correcting Predictors on Statistical Downscaling Models. J. Appl. Meteorol. Clim.
**2017**, 56, 5–26. [Google Scholar] [CrossRef] - Matthias, J.; Themeßl, M.; Gobiet, A.; Leuprecht, A. Empirical–statistical downscaling and error correction of daily precip-itation from regional climate models. Int. J. Climatol.
**2010**, 31, 1530–1544. [Google Scholar] - Chen, J.; Brissette, F.P.; Chaumont, D.; Braun, M. Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resour. Res.
**2013**, 49, 4187–4205. [Google Scholar] [CrossRef] - Song, L.Y.; Duan, W.S.; Li, Y.; Mao, J.Y. A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall. Adv. Atmos. Sci.
**2016**, 33, 1071–1084. [Google Scholar] [CrossRef] - Hu, P.; Chen, W.; Chen, S.; Huang, R. Interannual variability and triggers of the South China Sea summer monsoon withdrawal. Clim. Dyn.
**2019**, 53, 4355–4372. [Google Scholar] [CrossRef] - Hu, P.; Chen, W.; Chen, S.; Liu, Y.; Huang, R. Extremely Early Summer Monsoon Onset in the South China Sea in 2019 Following an El Niño Event. Mon. Weather. Rev.
**2020**, 148, 1877–1890. [Google Scholar] [CrossRef] - Han, F.; Wo, W.F. Design and Implementation of SWAN2.0 Platform. J. Appl. Meteorol. Sci.
**2018**, 29, 25–34. (In Chinese) [Google Scholar] - Wen, H.; Liu, L.P.; Zhang, C.A.; Yin, C.; Zhang, Y.; Cheng, S. Operational evaluation of radar data quality control for ground clutter and electromagnetic interference. J. Meteorol. Sci.
**2016**, 36, 789–799. [Google Scholar] - Haiden, T.; Kann, A.; Pistotnik, G.; Stadlbacher, K.; Wittmann, C. Integrated Nowcasting through Comprehensive Analysis (INCA)—System description. ZAMG Rep.
**2010**, 59. Available online: http://www.zamg.ac.at/fix/INCA_system.pdf (accessed on 20 June 2021). - Michelangeli, P.-A.; Vrac, M.; Loukos, H. Probabilistic Downscaling Approaches: Application to Wind Cumulative Dis-tribution Functions. Geophys. Res. Lett.
**2009**, 36. [Google Scholar] [CrossRef] - Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol.
**1970**, 10, 282–290. [Google Scholar] [CrossRef] - Knoben, W.J.M.; Freer, J.E.; Woods, R.A. Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores. Hydrol. Earth Syst. Sci.
**2019**, 23, 4323–4331. [Google Scholar] [CrossRef] [Green Version]

**Figure 1.**(

**a**) The topography distribution in the study area of South China. (

**b**) Schematic diagram of station distribution (indicated by dots) and radar locations (denoted by + and circles) in the South China region. The colors at each dot represent the topography height (unit: meter) at the corresponding station points. The 17 radars include Guangzhou (SA), Heyuan (SA), Meizhou (SA), Shantou (SA), Shanwei (SA), Shaoguan (SA), Shenzhen (SA), Yangjiang (SA), Zhanjiang (SA), Zhaoqing (SA), Liuzhou (SB), Nanning (SA), Wuzhou (SB), Fangchenggang (SA), Yulin (SA), Chenzhou (SA), and Ganzhou (SC).

**Figure 2.**The number (unit: hours) of valid samples and invalid samples in the summer (June, July, and August) during 2019–2020. Valid samples are defined as data that are present in both the AWS and radar QPE data for each hour. Otherwise, the sample was determined to be invalid otherwise and could not be used in the analysis.

**Figure 3.**The box plot of the hourly rain for the AWS and radar QPE (unit: mm/h) based on (

**a**) all samples, (

**b**) samples of rainfall less than 20 mm/h, and (

**c**) samples of rainfall larger than (including equal to) 20 mm/h. In each box, the top (bottom) of the box indicates the 75% (25%) quartile, and the middle of the box provides the median value. In (

**c**), the maximum and minimum are shown with the highest and lowest bar.

**Figure 4.**Distribution of summer rainfall totals (unit: mm) for the years 2019–2020. (

**a**) Interpolated AWS rainfall totals, (

**b**) radar QPE rainfall totals, (

**c**) and difference between radar QPE and AWS rainfall.

**Figure 5.**The results o F field correction calculated by means of the climatological scaling method.

**Figure 6.**The probability density function of the precipitation difference between QPE and AWS for (

**a**,

**b**) original radar QPE product, (

**c**,

**d**) after correction by scaling, (

**e**,

**f**) and after correction by Q-matching. (

**a**,

**c**,

**e**) are for hourly rainfall samples less than 20 mm/h, and (

**b**,

**d**,

**f**) are for heavy rainfall that are larger than and equal to 20 mm/h.

**Figure 7.**The spatial distribution of the correlation coefficients between the radar QPE product and AWS observational station rainfall (

**a**) after correction by scaling and (

**b**) after correction by Q-matching, respectively.

**Figure 8.**Precipitation case study on 07 UTC 20 August 2020. (

**a**) AWS observational rainfall. (

**b**) The original radar QPE products. (

**c**) QPE after correction by scaling. (

**d**) QPE after correction by Q-matching. Unit is mm/h.

**Figure 9.**Scatter plot of hourly rainfall on 07 UTC 20 August 2020 between AWS and radar QPE (

**a**) after correction by scaling, (

**b**) after correction by Q-matching, and (

**c**) before correction. The horizontal axis represents AWS rainfall, and the vertical axis represents radar QPE. The correlation coefficient is also shown.

**Table 1.**The results of MAE, RMSE error, and CC of hourly radar QPE products compared to AWS rain observations. The results and improvement percentages are also given after the scaling and Q-matching methods were implemented, respectively. The units for MAE and RMSE are mm/h.

Original QPE | QPE after Scaling | QPE after Q-Matching | Improvement by Scaling | Improvement by Q-Matching | |
---|---|---|---|---|---|

MAE | 2.237 | 1.947 | 1.257 | 12.96% | 43.81% |

RMSE | 4.948 | 4.323 | 3.011 | 14.46% | 39.15% |

CC | 0.629 | 0.650 | 0.893 | 3.34% | 41.97% |

**Table 2.**The results of POD scores for 1 mm/h, 5 mm/h, 10 mm/h, 15 mm/h, and 20 mm/h thresholds of hourly radar QPE products compared to AWS rain observations. The improvement percentages are also given after correction methods by means of scaling and Q-matching.

1 mm/h | 5 mm/h | 10 mm/h | 15 mm/h | 20 mm/h | |
---|---|---|---|---|---|

original QPE | 0.756 | 0.609 | 0.523 | 0.460 | 0.411 |

QPE after scaling | 0.702 | 0.512 | 0.393 | 0.314 | 0.257 |

QPE after Q-matching | 0.914 | 0.840 | 0.786 | 0.756 | 0.741 |

Improvement by scaling | −7.14% | −15.93% | −24.86% | −31.74% | −37.47% |

Improvement by Q-matching | 20.90% | 37.93% | 50.29% | 64.35% | 80.29% |

**Table 3.**Same as Table 2 but for the results of the FAR scores.

1 mm/h | 5 mm/h | 10 mm/h | 15 mm/h | 20 mm/h | |
---|---|---|---|---|---|

original QPE | 0.323 | 0.438 | 0.497 | 0.559 | 0.612 |

QPE after scaling | 0.262 | 0.354 | 0.398 | 0.448 | 0.495 |

QPE after Q-matching | 0.188 | 0.241 | 0.280 | 0.337 | 0.398 |

Improvement by scaling | 18.89% | 19.18% | 19.92% | 19.86% | 19.12% |

Improvement by Q-matching | 41.80% | 44.98% | 43.66% | 39.71% | 34.97% |

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**MDPI and ACS Style**

Song, L.; Chen, S.; Li, Y.; Qi, D.; Wu, J.; Chen, M.; Cao, W.
The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China. *Remote Sens.* **2021**, *13*, 4956.
https://doi.org/10.3390/rs13234956

**AMA Style**

Song L, Chen S, Li Y, Qi D, Wu J, Chen M, Cao W.
The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China. *Remote Sensing*. 2021; 13(23):4956.
https://doi.org/10.3390/rs13234956

**Chicago/Turabian Style**

Song, Linye, Shangfeng Chen, Yun Li, Duo Qi, Jiankun Wu, Mingxuan Chen, and Weihua Cao.
2021. "The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China" *Remote Sensing* 13, no. 23: 4956.
https://doi.org/10.3390/rs13234956