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  • View in gallery

    Topography and station distribution over SWC (21°–34.5°N, 96°–109°E). The yellow, red, and cyan dots in the right figure represent the stations located in LER (<1000 m), MER (1000–2500 m), and HER (>2500 m), respectively.

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    Distributions of the annual and seasonal mean precipitation in the observations and HRPPs over SWC during 1998–2016. The data period of IMERG and GSMaP-Gauge is 2001–16, APHRODITE is 1998–2015, and that of the other products is 1998–2016.

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    Spatially averaged MREs of the annual and seasonal precipitation between the HRPPs and observations during 1998–2016 over (a) SWC, (b) LER, (c) MER, and (d) HER.

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    Taylor diagrams for the annual and seasonal precipitation between the HRPPs and observations during 1998–2016 over (a) SWC, (b) LER, (c) MER, and (d) HER.

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    Annual variations in the monthly mean precipitation based on the HRPPs and observations during 1998–2016 over (a) SWC, (b) LER, (c) MER, and (d) HER.

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    (a),(b) Taylor diagrams and (c),(d) spatially averaged NRMSEs for the monthly and daily precipitation between the HRPPs and observations during 1998–2016 over SWC and three subregions.

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    Spatially averaged PDFs of the daily precipitation based on the observations and HRPPs during 1998–2016 in the whole year, winter (DJF) and summer (JJA) over (a)–(c) SWC, (d)–(f) LER, (g)–(i) MER, and (j)–(l) HER.

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    Boxplots of BS and Sscore for the PDFs in the (a),(d) whole year; (b),(e) DJF, and (c),(f) JJA over three subregions.

  • View in gallery

    CSI values for the daily precipitation of the HRPPs during 1998–2016 in the whole year, DJF, and JJA over (a)–(c) SWC, (d)–(f) LER, (g)–(i) MER, and (j)–(l) HER at five thresholds.

  • View in gallery

    As in Fig. 9, but for BIAS.

  • View in gallery

    Spatial distributions of the mean R95p, PRCPTOT, Rx1d, and Rx5d values during 1998–2016 based on the observations and the HRPPs over SWC.

  • View in gallery

    As in Fig. 11, but for CDD, CWD, R10, and SDII.

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Evaluation of High-Resolution Precipitation Products over Southwest China

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  • 1 Nansen-Zhu International Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • | 2 University of Chinese Academy of Sciences, Beijing, China
  • | 3 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China
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Abstract

The evaluation of gridded high-resolution precipitation products (HRPPs) is important in areas with complex topography, because rain gauges that are unevenly and sparsely distributed over an area cannot effectively reflect the spatial variabilities of the precipitation and related extremes in detail. In this study, the applicability of six satellite-based precipitation products (TMPA 3B42V7, IMERG, GSMaP-Gauge, CMORPH-CRT, PERSIANN-CDR, and GPCP) and five gauge-based precipitation products (APHRODITE, CN05.1, GPCC-D, GPCC-M, and CRU) over southwest China from 1998 to 2016 is evaluated by performing a comparison with meteorological station observations. The results show that GPCC-M exhibits the best performances for annual, seasonal, and monthly precipitation, which is supported by the lowest root-mean-square errors (RMSEs) for annual and seasonal precipitation and the lowest normalized root-mean-square error (NRMSE) for monthly precipitation. According to the NRMSE and critical success index (CSI), CN05.1 outperforms the other HRPPs at detecting daily precipitation; however, CN05.1 tends to overestimate the frequencies of light precipitation and underestimate the frequencies of heavy precipitation, which is reflected by the probability density function (PDF) for daily precipitation. The bias ratio (BIAS) and extreme precipitation indices show that IMERG shows numerous advantages over the other HRPPs in detecting extreme precipitation and estimating the precipitation intensity. Such results are helpful for future research on precipitation/extremes and related hydrometeorological disasters that occur throughout southwest China.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0045.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jianqi Sun, sunjq@mail.iap.ac.cn

Abstract

The evaluation of gridded high-resolution precipitation products (HRPPs) is important in areas with complex topography, because rain gauges that are unevenly and sparsely distributed over an area cannot effectively reflect the spatial variabilities of the precipitation and related extremes in detail. In this study, the applicability of six satellite-based precipitation products (TMPA 3B42V7, IMERG, GSMaP-Gauge, CMORPH-CRT, PERSIANN-CDR, and GPCP) and five gauge-based precipitation products (APHRODITE, CN05.1, GPCC-D, GPCC-M, and CRU) over southwest China from 1998 to 2016 is evaluated by performing a comparison with meteorological station observations. The results show that GPCC-M exhibits the best performances for annual, seasonal, and monthly precipitation, which is supported by the lowest root-mean-square errors (RMSEs) for annual and seasonal precipitation and the lowest normalized root-mean-square error (NRMSE) for monthly precipitation. According to the NRMSE and critical success index (CSI), CN05.1 outperforms the other HRPPs at detecting daily precipitation; however, CN05.1 tends to overestimate the frequencies of light precipitation and underestimate the frequencies of heavy precipitation, which is reflected by the probability density function (PDF) for daily precipitation. The bias ratio (BIAS) and extreme precipitation indices show that IMERG shows numerous advantages over the other HRPPs in detecting extreme precipitation and estimating the precipitation intensity. Such results are helpful for future research on precipitation/extremes and related hydrometeorological disasters that occur throughout southwest China.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0045.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jianqi Sun, sunjq@mail.iap.ac.cn

1. Introduction

Located on the east side of the Tibetan Plateau (TP), southwest China (SWC) is a region with complex topography, drastically changing elevation, and fragile geological conditions. Geological hazards caused by precipitation such as landslides and debris flows occur frequently in SWC. Therefore, obtaining high-accuracy measurements of precipitation over SWC is of vital importance for the prevention of natural disasters and the conservation of water resources.

The temporal and spatial variabilities of precipitation in SWC are very complicated and are determined by multiple factors (Duan et al. 2000; Ma et al. 2006; He et al. 2007; Li et al. 2010; Jiang and Li 2011; Liu et al. 2011; Zhang et al. 2014). First, complex terrain plays a key role in the precipitation in SWC (Ma et al. 2006; Li et al. 2013). In addition, SWC is close to the Indian Ocean and South China Sea, so the precipitation in the region is also influenced by the South and East Asian monsoons (Qi et al. 2012; Li et al. 2013, 2014; Zhou and Xiao 2015; Zhang et al. 2017). Several studies in recent years have revealed that the precipitation over SWC could be influenced by the sea surface temperatures (SSTs) of the Pacific and Indian Oceans (Jiang and Li 2011; Huang et al. 2012; Cao et al. 2014; Dong et al. 2018; L. Wang et al. 2018; Wei et al. 2018; Dong et al. 2019), the SST of the North Atlantic (Li et al. 2018), the snow cover over the Tibetan Plateau (Li et al. 2011), and atmospheric teleconnections, such as the Arctic Oscillation (AO) (Jiang and Li 2011; Huang et al. 2012; Yang et al. 2012; Zhang et al. 2014), North Atlantic Oscillation (NAO) (Xu et al. 2012; Feng et al. 2014; Song et al. 2014; Zhang et al. 2014), and Silk Road pattern (Dong et al. 2018, 2019).

Generally, rain gauges provide the most reliable measurements to observe precipitation, but this may not be the case in SWC because the spatial representativeness of rain gauges in the region is greatly affected by the irregular distributions of mountains and rivers (Li and Li 2017). The rain gauges in SWC are distributed so unevenly and sparsely that they fail to effectively represent the spatial variability of precipitation that is significant in the transition regions between mountains and basins (New et al. 2001; Villarini et al. 2008; Li and Li 2017). Therefore, precipitation records from meteorological stations cannot meet the needs of research and prevent disasters (Li and Li 2017).

High-resolution precipitation products (HRPPs) are received as ideal alternative precipitation datasets. To date, many HRPPs have been developed with different origins and time spans. Some of them, such as those based on the Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) (Huffman et al. 2007; Huffman and Bolvin 2015), the Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) (Hou et al. 2014), the Global Precipitation Climatology Project (GPCP) (Huffman et al. 2001), the Climate Prediction Center (CPC) morphing technique (CMORPH) (Joyce et al. 2004), the Global Satellite Mapping of Precipitation (GSMaP) (Kubota et al. 2007), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) (Sorooshian et al. 2000), are based mainly on satellite remote sensing. However, these products are available only after 1979. Some other HRPPs, such as the Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) data (Yatagai et al. 2012), the National Climate Center of China Meteorological Administration precipitation dataset (CN05.1) (Wu and Gao 2013), the Global Precipitation Climatology Centre (GPCC) (Becker et al. 2013), and the Climatic Research Unit (CRU) (Harris et al. 2014), stem mainly from rain gauges and are created by different interpolation techniques. Most gauge-based products cover longer periods than satellite-based products. Satellite-based HRPPs provide high-resolution observations in regions with scarce ground observations of precipitation, such as desserts, oceans, and mountainous regions, which give these products an edge over gauge-based HRPPs. However, in contrast to gauge-based products, which are derived from directly measured precipitation, the accuracy of remotely sensed HRPPs is affected by some sources of uncertainties, such as the inversion of cloud properties (Ebert et al. 2007). Given that these precipitation products have different traits and advantages, it is necessary to examine which product is the best for a specific research target.

Numerous studies have assessed the performance of precipitation products in a variety of areas (Maggioni et al. 2016; Sun et al. 2018). Satellite-based products perform better over wet regions and during warm seasons in China (Shen et al. 2010; Gao and Liu 2013). TMPA 3B42 boasts better performance than other satellite precipitation products in many regions and is also desirable for extreme events (Ghajarnia et al. 2015; Tan et al. 2015; Maggioni et al. 2016; Yang et al. 2017; Harrison et al. 2019). In addition, CMORPH performs best compared with other satellite products in terms of the spatial pattern and temporal variations of precipitation over China (Shen et al. 2010). Precipitation measurement has entered GPM era since GPM mission was launched in 2014. It has been reported by many studies that GPM/IMERG outperforms TMPA products (Tang et al. 2016; Xu et al. 2017; Gebregiorgis et al. 2018). The gauge-calibrated IMERG products demonstrate their potential for capturing extreme precipitation events in the Sichuan region (Zeng and Yong 2019). Besides, gauge-based HRPPs are also widely evaluated. APHRODITE outperforms other products in many studies but tends to underestimate heavy precipitation while overestimating light and moderate precipitation (Han and Zhou 2012; Ghajarnia et al. 2015; Tan et al. 2015; Yang et al. 2017). According to the quality of long-term precipitation data over the past century, GPCC data are generally better than CRU data in China (Wang and Wang 2017).

Complex terrain regions have poor rain gauge network, therefore the utility of HRPPs over the complex terrain regions was also investigated by previous studies. It is found that TMPA 3B42 near-real-time version (3B42RT) tends to overestimate precipitation over western United States and TMPA, CMORPH, and GSMaP products tend to overestimate precipitation over Central Asia, both of which are mountainous regions (Gottschalck et al. 2005; Guo et al. 2015). The PERSIANN products tend to overestimate summertime precipitation over central United States and low-elevation region of Mexico (Gottschalck et al. 2005; Hong et al. 2007) but underestimate precipitation over high-elevation regions of Ethiopia and central Asia (Hirpa et al. 2010; Guo et al. 2015). The biases of TMPA 3B42 and CMORPH products show a weak dependence on the topography in the TP, but TMPA 3B42RT and PERSIANN products are dependent on topography (Gao and Liu 2013). TMPA 3B43 exhibits large deviations from observations in the northwest and northeast of the Hengduan Mountains (Zhu et al. 2017). Generally, complex terrain regions have poor rain measurements and magnitude-dependent mean errors (Maggioni et al. 2016). Given the complexity of HRPP applicability over complex terrain regions, further estimations over other regions like SWC are needed.

Previous studies have indicated that the performances of HRPP are different over different regions and on different time scales. As mentioned above, SWC is a special region with complex orography that frequently experiences hydrometeorological disasters. Due to their uneven and sparse distribution, existing rain gauges cannot meet the needs of detailed scientific research and hydrometeorological disaster prevention in the region. HRPPs can provide spatially continuous precipitation data that could effectively overcome the disadvantages caused by the rain gauge observations over the region. However, few studies have been conducted to systematically evaluate the performance of HRPPs on the SWC precipitation variation on different time scale. Therefore, in this study, the performance of widely used HRPPs is evaluated to investigate which product performs best for annual, seasonal, daily and extreme precipitation in SWC. Based on the best-performed HRPPs, more features of the SWC precipitation and extreme precipitation are presented in this study, in particular over the areas with sparse rain gauge observations. The results in this study are conducive to promoting our understanding on the characteristics of SWC precipitation and also beneficial to predicting and preventing hydrometeorological disasters throughout the region.

The remainder of this paper is organized as follows. Section 2 presents the study area and describes the HRPPs, observations and methods used in this study. The results are shown in section 3, including the assessments of the HRPPs on annual, monthly, daily and extreme precipitation. Finally, section 4 presents the main conclusions and discussion.

2. Data and methods

a. Study area

In this study, we refer to SWC as the domain bounded by 21°–34.5°N, 96°–109°E (Fig. 1), which encompasses the Hengduan Mountains (HDM), the Yunnan–Guizhou Plateau (YGP), and the Sichuan Basin (SCB); this delineation is generally consistent with Chinese administrative divisions. Precipitation in SWC causes frequent natural disasters that result in severe casualties and tremendous economic costs. For example, a severe landslide event occurred in Shuicheng, Guizhou Province, on 23 July 2019; the event affected nearly 1600 people, killing 43, and resulted in direct economic losses amounting to 190 million CNY (https://www.mem.gov.cn/xw/bndt/202001/t20200112_343410.shtml). Such serious hazards in the region are closely related to precipitation and the frequency of heavy rain in the rainy season (Tao et al. 2009). Furthermore, because SWC is close to the birthplace of the Three Rivers, precipitation in the region is essential for water resources in downstream areas throughout East Asia and Southeast Asia.

Fig. 1.
Fig. 1.

Topography and station distribution over SWC (21°–34.5°N, 96°–109°E). The yellow, red, and cyan dots in the right figure represent the stations located in LER (<1000 m), MER (1000–2500 m), and HER (>2500 m), respectively.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

Previous studies have indicated that elevation-dependent biases exist in HRPPs over the regions with complex topography (Hong et al. 2007; Gao and Liu 2013; Xu et al. 2017). Therefore, in this study, to evaluate the HRPP data in more detail, areas with elevations lower than 1000 m, between 1000 and 2500 m, and higher than 2500 m are defined as low-, middle-, and high-elevation regions (LER, MER, and HER), respectively. In this way, we can obtain more information about the influence of elevation on the performance of HRPPs.

b. High-resolution precipitation products

In this study, the performance of 11 widely used HRPPs, namely, six satellite-based products and five gauge-based products, is evaluated.

  1. TMPA 3B42V7 products are obtained from the TRMM Multisatellite Precipitation Analysis (TMPA) with a spatial resolution of 0.25° × 0.25° and a 3-hourly temporal resolution (Huffman et al. 2007). TMPA combines passive microwave (PMW) and infrared (IR) data. They are collected by multiple satellite sensors on low-Earth-orbit (LEO) satellites and geosynchronous-Earth-orbit (GEO) satellites, respectively. The GPCC monthly precipitation (based on gauge data) is also used to correct the bias of precipitation (Huffman and Bolvin 2015). The detailed algorithms employed for TMPA estimates are described by Huffman et al. (2007) (https://disc.gsfc.nasa.gov/datasets/TRMM_3B42_Daily_7/summary/).

  2. IMERG is a new generation of precipitation product of GPM, which is the successor of TRMM. IMERG is derived from multiple measurements of GPM, including IR, PMW, and radar data. The Dual-frequency Precipitation Radar (DPR) and the multichannel GPM Microwave Imager (GMI) help GPM improve the measurement of precipitation (Hou et al. 2014). Compared to TMPA products, IMERG is characterized by higher spatial (0.1° × 0.1°) and temporal (up to half-hourly) resolution and has global coverage. IMERG provide three runs, including early, late and final runs. The final run is calibrated by monthly gauge analysis. The Level 3 IMERG final run daily products V06 is used in this study. The dataset is available since June 2000, and the data from 2001 to 2016 are evaluated in our study (https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_06/summary/).

  3. GSMaP is developed by Japan Science and Technology Agency (JST) and Japan Aerospace Exploration Agency (JAXA) (Kubota et al. 2007). GSMaP products have been provided as JAXA GPM products since GPM era and have experienced major improvements in algorithms (Kubota et al. 2017). PMW precipitation retrieval algorithm, PMW–IR combined algorithm, and gauge-adjustment algorithm are core algorithms of GSMaP products. Orographic precipitation is also taken into consideration. GSMaP provides several precipitation products, including the real-time version, the near-real-time version, the microwave–IR combined product, and its gauge-calibrated version (GSMaP-Gauge). In this study, we used the reanalysis version of GSMaP-Gauge V6 (hereafter GSMaP-Gauge), which is available since March 2000. Only the data from 2001 to 2016 are evaluated here. GSMaP products have 0.1° × 0.1° spatial resolution and hourly temporal resolution, covering the region from 60°S to 60°N. Daily and monthly products are also available and daily products are used in this study (ftp://rainmap:Niskur+1404@hokusai.eorc.jaxa.jp/).

  4. CMORPH Version 1.0 bias-corrected products are provided by the National Oceanic and Atmospheric Administration (NOAA). CMORPH is a half-hourly global precipitation product that employs the morphing technique (Joyce et al. 2004). Time-weighted linear interpolation is used to morph the shape and intensity of precipitation features during the intervals between microwave sensor scans. Additionally, the CMORPH estimates are adjusted against daily gauge analyses over land and GPCP products over the oceans (Xie et al. 2017). The spatial resolution of CMORPH is 8 km (at the equator) and then gridded to a common 0.25° latitude/longitude grid. CMORPH V1.0 includes three products, including raw, bias-corrected, and gauge–satellite blended precipitation products. In this study, we choose the bias-corrected product (CMORPH-CRT) (ftp://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V1.0/CRT/0.25 deg-DLY_00Z/).

  5. The NOAA Climate Data Record (CDR) of PERSIANN (PERSIANN-CDR) Version 1 Revision 1 product is developed by the Center for Hydrometeorology and Remote Sensing (CHRS), University of California, Irvine. PERSIANN-CDR is a daily 0.25° precipitation product and covers the region ranging from 60°S to 60°N latitude and from 0° to 360° longitude over the period from 1982 to the present. Gridded satellite IR data are processed using an artificial neural network (ANN) model to produce 3-hourly estimates. The estimates are then adjusted by 2.5° GPCP monthly products (Ashouri et al. 2015). After that, the 3-hourly estimates are accumulated at a daily scale to generate the PERSIANN-CDR product (https://www.ncei.noaa.gov/data/precipitation-persiann/access/).

  6. GPCP One-Degree Daily (GPCP-1DD) Version 1.3 product is produced by the one-degree daily (1DD) precipitation estimation technique (Huffman et al. 2001). The technique combines GPCP satellite–gauge monthly estimates and infrared, microwave, and sounder data observed by satellites and precipitation gauges. The GPCP-1DD V1.3 (hereafter GPCP) product covers the period from 1996 to the present (https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-daily/access/).

  7. APHRODITE Version 1901 (Yatagai et al. 2012) is a daily precipitation product developed by the Research Institute for Humanity and Nature (RIHN), Japan, and the Meteorological Research Institute of the Japan Meteorological Agency (MRI/JMA). The APHRODITE product is created from dense rain gauge data by interpolating the ratio of the station value to the climatology. The effect of orography is also considered in the algorithm. We use the latest version, V1901, for Monsoon Asia at a spatial resolution of 0.25° (only data from 1998 to 2015 are available) (http://aphrodite.st.hirosaki-u.ac.jp/products.html). Compared with previous versions, APHRODITE V1901 improves the dataset in three aspects: 1) CMORPH and TMPA 3B42 products are used for quality control to retain the real extremes; 2) the different end of the day in different countries is unified to 0000–2400 UTC using the CMORPH product; and 3) the interpolation algorithm is modified for extreme values and local precipitation.

  8. CN05.1 is a daily dataset on a 0.25° latitude/longitude grid. The product covers China from 1961 to the near present. Four variables (daily mean, minimum and maximum temperature, and daily precipitation) are included in the dataset. CN05.1 is constructed by the “anomaly approach” during the interpolation but with a relatively high number of station observations (more than 2400) in China (Wu and Gao 2013). In the anomaly approach, a gridded climatology is first calculated by thin-plate smoothing splines, and then a gridded daily anomaly is added to the climatology to obtain the final dataset.

  9. GPCC precipitation products are based on rain gauge data and data collected from the CRU and some other international and regional projects. These data are processed by a modified SPHEREMAP interpolation technique (Becker et al. 2013). Both the GPCC Full Data Monthly product and the GPCC Daily Product V2018 (hereafter GPCC-M and GPCC-D, respectively) are used in this study (Schneider et al. 2018; Ziese et al. 2018). GPCC-M is based on nearly 80 000 stations across the world that have records spanning 10 years or longer. Spatial resolutions of 0.25°, 0.5°, 1.0°, and 2.5° are all available; a resolution of 0.25° is chosen in our study. GPCC-D, with a spatial resolution of 1.0°, is based on near- and non-real-time data. Daily precipitation anomalies are interpolated and added to the monthly precipitation. GPCC-M and GPCC-D cover the periods from 1891 to 2016 and from 1982 to 2016, respectively (https://opendata.dwd.de/climate_environment/GPCC/html/download_gate.html).

  10. CRU Time series (TS) v4.03 (hereafter CRU) dataset is provided by the Climatic Research Unit, University of East Anglia, covering the period 1901–2018. The CRU dataset is derived from monthly observations at meteorological stations worldwide and includes six climate variables. Angular distance weighting interpolation is used to grid the monthly anomalies relative to the climatology onto a 0.5° grid across the land surface (Harris et al. 2014) (https://catalogue.ceda.ac.uk/uuid/10 d3e3640f004c578403419aac167d82). The climatology is computed as the averaged mean over the period of 1961–90 for each month and for each station.

c. Observed precipitation

The observed gauge precipitation data used in this study are obtained from the National Meteorological Information Center of the China Meteorological Administration (CMA). The gauge data have been strictly quality controlled by the CMA before releasing, so they could be considered as error-free ground truth. There are 154 stations within the SWC area. After eliminating the stations missing more than 5% of the precipitation values from 1998 to 2016, 152 stations remain (Fig. 1). We then fill the gaps in the records with the climatological values during this period.

d. Evaluation method

Several statistics, including the Pearson correlation coefficient (r), mean relative error (MRE), root-mean-square error (RMSE), and normalized root-mean-square error (NRMSE), are calculated to quantitatively assess the similarity between the HRPPs and meteorological station observations over SWC. An r value quantifies the linear correlation between HRPP products and gauge observations. MRE reflects overall deviation of HRPP products to gauge observations and indicate whether the HRPP products underestimate or overestimate precipitation. RMSE quantifies the absolute differences between HRPP products and gauge observations. The NRMSEs facilitates the comparison between the HRPP products and gauge observations on different scales. In addition, Student’s t test is used to test the significance of r at the 95% confidence level. The null hypothesis is that the correlation between HRPP data and gauge observations are not significant. The equations for these statistics are as follows:
r=i=1n(SiS¯)(OiO¯)i=1n(SiS¯)2i=1n(OiO¯)2,
MRE=i=1n(SiOi)i=1nOi×100%,
RMSE=1ni=1n(SiOi)2,
NRMSE=1ni=1n(SiOi)21ni=1nOi.

In these equations, n is the number of samples, Si and Oi denote the HRPP estimates and observed values, respectively, and S¯ and O¯ denote the mean values of the HRPP estimates and observed values, respectively.

The values of BS and Sscore (Perkins et al. 2007) are used to measure the differences between the estimated and observed PDFs. BS is the mean value of the square of errors between the observed and estimated PDFs, while Sscore represents the cumulative minimum value of two distributions in each bin. If an HRPP estimates the observed precipitation perfectly, the BS and Sscore will be equal to zero and one, respectively. The equations are as follows:
BS=1mi=1m(ZsiZoi)2,
Sscore=i=1mmin(Zsi,Zoi),
where m denotes the number of bins and Zsi and Zoi are the estimated and observed frequencies, respectively, in each bin.
To evaluate the ability of an HRPP to detect the occurrence of precipitation events characterized by different amounts, we calculate four indices, namely, the critical success index (CSI), bias ratio (BIAS), probability of detection (POD), and false alarm ratio (FAR), according to a 2 × 2 contingency table (Wilks 2011) (Table 1). In this contingency table, a, b, c, and d represent the numbers of hits, false alarms, misses, and correct rejections, respectively. The indices mentioned above are given by the following equations:
CSI=aa+b+c,
BIAS=a+ba+c,
POD=aa+c,
FAR=ba+b.
Table 1.

The 2 × 2 contingency table for the counts of observed and estimated precipitation and nonprecipitation events.

Table 1.

CSI denotes the proportion of correctly detected precipitation events to the total number of observed and estimated precipitation events. BIAS is the fraction of precipitation events detected by both the products and the observations. POD is the ratio of correctly estimated precipitation events to the frequencies at which the events occurred. FAR denotes the fraction of falsely estimated precipitation events. The perfect scores are 1 for CSI, BIAS, and POD and 0 for FAR. These metrics are calculated with thresholds of 0.1, 10, 25, 50, and 100 mm day−1 for summer and annual precipitation events and 0.1, 2.5, 5, 10, and 20 mm day−1 for winter precipitation events. The summer and annual thresholds are standards for light, moderate, large, heavy, and very heavy rainfalls defined by CMA. Considering precipitation in winter is much smaller than other seasons over SWC, another series of thresholds are used.

To make HRPPs comparable to the gauges, the HRPP data and gauge observations should have the same resolution. Considering the drastically changing elevation and representativeness of gauges, the sparse and uneven gauge network of SWC (especially HRE in the northwest of SWC) may be insufficient for an accurate estimation of a high-resolution precipitation field. Interpolating the rain gauges to the regular grids is of great difficulty and may cause errors over SWC. Thus, bilinear interpolation is used to interpolate HRPP data to the location of each station by combining the four closest grids, following the studies of Sapiano and Arkin (2009) and Gao and Liu (2013). The details of bilinear interpolation could be found in Press et al. (1992).

e. Definition of extreme precipitation indices

A series of extreme precipitation indices have been proposed on the basis of daily precipitation data by the Expert Team on Climate Change Detection and Indices (ETCCDI; http://www.clivar.org/organization/etccdi). Eight extreme precipitation indices are used to analyze the distribution features of extreme precipitation over SWC. We choose these indices based on some previous studies (Yang et al. 2017). The definitions of the indices are given in Table 2. More details are available at http://etccdi.pacificclimate.org/list_27_indices.shtml.

Table 2.

Definitions of the extreme precipitation indices.

Table 2.

3. Results

a. Annual and seasonal

The chosen HRPPs can basically reproduce the main characteristics of the spatial distributions of annual and seasonal precipitation over SWC (Fig. 2). Generally, annual precipitation increases from northwest to southeast, and there are relatively high values in the Sichuan Basin. The maximum and minimum values of annual precipitation are 2704.7 and 454.7 mm yr−1, respectively. The spatial distributions of precipitation in winter (DJF), spring (MAM), and summer (JJA) are similar to those of annual precipitation. However, the precipitation in autumn is distributed differently with relatively high values located in the southwest and northeast areas of SWC. Among all the HRPPs, PERSIANN-CDR and GPCP tend to overestimate the precipitation over the HDM; in summer, these two products also overestimate the precipitation over the HDM and underestimate that over the Sichuan Basin. The winter precipitation over the southeast area of SWC is underestimated by CMORPH-CRT and PERSIANN-CDR. Although the summer and autumn precipitation in most of the HRPPs over the southern side of the Himalayas is apparently higher than that in other areas, the precipitation in this area could not be analyzed in this study.

Fig. 2.
Fig. 2.

Distributions of the annual and seasonal mean precipitation in the observations and HRPPs over SWC during 1998–2016. The data period of IMERG and GSMaP-Gauge is 2001–16, APHRODITE is 1998–2015, and that of the other products is 1998–2016.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

By calculating the MREs of the annual and seasonal precipitation between the HRPPs and observational data, we find that the MREs exhibit pronounced spatial and temporal differences with larger values over HER and in winter (Fig. 3). Overall, the rain gauge-based HRPPs perform better than the satellite-based HRPPs, with GPCC-M performing the best in almost all seasons and over all subregions. In general, PERSIANN-CDR, GPCP, and CN05.1 tend to overestimate precipitation, while the opposite situation occurs to CMORPH-CRT and APHRODITE. GPCP and PERSIANN-CDR show the largest MREs over HER, which are higher than 20% in all the seasons. In addition, although the accuracy of CMORPH-CRT is acceptable in MAM, JJA, and SON (less than 10% in most cases), its MREs increase tremendously in winter and are several times larger than those in the other seasons. TMPA 3B42V7, IMERG, and GSMaP-Gauge also underestimate winter precipitation (GSMaP-Gauge underestimates less), which is similar to CMORPH-CRT. Such a performance of the satellite-based products in winter is also consistent with the findings of previous studies (Ebert et al. 2007; Stampoulis and Anagnostou 2012; Peña-Arancibia et al. 2013; Maggioni et al. 2016; Xie et al. 2017). The degraded performance of satellite-based HRPPs during winter might be due to that cold surfaces, snow cover, and icy hydrometeors can exert adverse effects on PMW retrievals, making it difficult for satellite-based HRPPs to estimate winter precipitation.

Fig. 3.
Fig. 3.

Spatially averaged MREs of the annual and seasonal precipitation between the HRPPs and observations during 1998–2016 over (a) SWC, (b) LER, (c) MER, and (d) HER.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

It should be noticed that the offsets between positive and negative values inevitably reduce the biases between HRPPs and gauge observations. Thus, more metrics should be applied to evaluate the performance of HRPPs. Taylor diagrams (Taylor 2001) of the annual and seasonal precipitation over SWC between the HRPPs and observations indicate that GPCC-M provides the most accurate estimates (Fig. 4). A Taylor diagram is a polar plot that consists of the spatial correlation coefficient (r) and the ratio of the standard deviations. The RMSE can be derived from these two variables and is represented by the distance between a point and the reference point. The closer the values of r and the ratio of the standard deviations are to 1 and the closer the RMSE is to 0, the better the precision of the HRPP. Nearly all HRPPs tend to underestimate the ratio of the standard deviations, indicating that most HRPPs reproduce less spatial variability of the precipitation in SWC than is observed. However, CMORPH-CRT tends to overestimate the spatial variability in winter and autumn, and the spatial variability in summer over HER is overestimated by most satellite-based products. In most situations, the r values (especially over LER) are larger than 0.6, and some gauge-based products, such as APHRODITE, GPCC-M, and GPCC-D, show even higher r values exceeding 0.9. In contrast, except for TMPA 3B42V7, the satellite-based HRPPs show relatively low r values that are more obvious over HER. Similar to the MRE results, the HRPPs perform better over LER, and the values of r and the ratio of the standard deviations vary greatly over HER. Moreover, the performances of the gauge-based products are much more stable and less affected by the season than the satellite-based products regardless of the correlation coefficient or the ratio of the standard deviations. In terms of the RMSE, GPCC-M exhibits the best estimates; the RMSEs of APHRODITE, CN05.1, and GPCC-D are also very low.

Fig. 4.
Fig. 4.

Taylor diagrams for the annual and seasonal precipitation between the HRPPs and observations during 1998–2016 over (a) SWC, (b) LER, (c) MER, and (d) HER.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

The pronounced analysis indicates that the measurement abilities of precipitation by satellite-based HRPPs are dependent on elevation. The accuracy of satellite-based HRPPs is lower over HER than other regions, in particular for GPCP and PERSIANN-CDR, which overestimates precipitation more in all season. Such a shortage of the satellite-based HRPPs might be related to the complicated variation of precipitation over the SWC high-elevation regions and to that less rain gauge observations can be used to correct the satellite-based HRPPs over the region.

b. Monthly and daily

The annual cycles of monthly precipitation are shown in Fig. 5, and the average RMSEs between the observations and HRPPs are calculated accordingly (Table 3). The seasonal variations of the observed precipitation are quite similar over different subregions, with the precipitation being concentrated mainly between May and September and reaching low points in winter. Somewhat differently, the precipitation reaches its peak in July over MER and HER but in June over LER. Moreover, the precipitation in the rainy season over HER is much lower than that over LER and MER. The biases vary among different seasons and subregions, with larger values appearing in the rainy season and over HER. The two GPCC products (particularly GPCC-M) perform better than the other products in terms of the RMSE and are closer to the observations. The precipitation amounts tend to be overestimated by CN05.1 and GPCP but underestimated by APHRODITE. The RMSEs of APHRODITE over LER and MER are the largest among the HRPPs, while the RMSEs of PERSIANN-CDR and GPCP are much higher than those of the other products over HER.

Fig. 5.
Fig. 5.

Annual variations in the monthly mean precipitation based on the HRPPs and observations during 1998–2016 over (a) SWC, (b) LER, (c) MER, and (d) HER.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

Table 3.

RMSEs of the annual variation in precipitation between the HRPPs and observations (bold values represent the best performance among all the HRPPs).

Table 3.

By comparing the precipitation amounts between the observations and HRPPs on both monthly and daily scales, we find that the biases appear to increase from the monthly scale to the daily scale (Fig. 6). In Fig. 6a, the estimates of the HRPPs are highly concentrated, and nearly all the points are close to the reference point; this indicates that the HRPPs can generally reproduce the monthly precipitation over SWC. However, the NRMSEs of GPCP and PERSIANN-CDR over HER are much higher than those of the other HRPPs (Fig. 6c). On a daily scale, CN05.1 performs much better than the other HRPPs, followed by APHRODITE and GPCC-D, which can be easily seen from Figs. 6b and 6d. This means that gauge-based products have advantages over satellite-based products when reproducing daily precipitation. Additionally, GSMaP-Gauge shows the best estimation of daily precipitation among the satellite-based HRPPs.

Fig. 6.
Fig. 6.

(a),(b) Taylor diagrams and (c),(d) spatially averaged NRMSEs for the monthly and daily precipitation between the HRPPs and observations during 1998–2016 over SWC and three subregions.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

The observed and HRPP-estimated probability density functions (PDFs) for the daily precipitation data are shown in Fig. 7. Precipitation amounts below 0.1 mm day−1 are not shown here. For the frequencies of light precipitation (0.1–10 mm in summer and 0.1–1.5 mm in winter), the gauge-based products obviously overestimate the precipitation over the whole region. As expected, the frequencies of heavy precipitation are dramatically underestimated by the gauge-based HRPPs, particularly by CN05.1. These defects of the gauge-based products are consistent with previous studies (Han and Zhou 2012; Ghajarnia et al. 2015; Tan et al. 2015; Yang et al. 2017). The gauge-based products are constructed by interpolating the precipitation obtained by rain gauges, but these interpolation methods have certain inherent defects. Heavy precipitation usually occurs in localized regions, but interpolation could weaken locally large precipitation amounts and erroneously estimate light precipitation in regions that receive no rainfall but are surrounded by areas that do receive precipitation. Essentially, the precipitation patterns are spatially smoothed by interpolation. This defect is also why the satellite-based HRPPs show finer spatial structures than the gauge-based HRPPs.

Fig. 7.
Fig. 7.

Spatially averaged PDFs of the daily precipitation based on the observations and HRPPs during 1998–2016 in the whole year, winter (DJF) and summer (JJA) over (a)–(c) SWC, (d)–(f) LER, (g)–(i) MER, and (j)–(l) HER.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

In contrast to the gauge-based HRPPs, the frequencies of heavy precipitation estimated by the satellite-based HRPPs are closer to (or slightly larger than) the observed frequencies than those estimated by the gauge-based HRPPs. In addition, the biases of the precipitation frequencies are much larger over HER than over LER and MER. Nearly all the HRPPs overestimate the frequencies of light precipitation over HER.

To quantitatively compare the accuracy of the PDFs estimated by the seven daily HRPPs, two metrics, namely, BS and Sscore, are calculated for each station (Fig. 8). Both metrics range from 0 to 1, and a value of 0 (1) represents a perfect estimate for BS (Sscore). For all the HRPPs, both BS and Sscore are relatively close to their perfect values in summer and show large spatial variabilities in winter, which indicates that precipitation estimates are more difficult to obtain in winter. Generally, GSMaP-Gauge, GPCP, GPCC-D, and PERSIANN-CDR perform better over HER; in contrast, the performances of IMERG, CMORPH-CRT, and APHRODITE are worse over HER. In summer, the spatial difference for a certain HRPP is not obvious, but the performance of CN05.1 with low Sscore and high BS values is worse than that of the other HRPPs. In addition, the performances of CMORPH-CRT and CN05.1 are worse in winter. It is interesting that GPCP performs well in estimating the PDF, which is probably because this product overestimates or underestimates the precipitation for all amounts; combined with the results shown in Fig. 5, GPCP is more likely to overestimate precipitation.

Fig. 8.
Fig. 8.

Boxplots of BS and Sscore for the PDFs in the (a),(d) whole year; (b),(e) DJF, and (c),(f) JJA over three subregions.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

By calculating CSI with thresholds of 0.1, 10, 25, 50, and 100 mm in summer and thresholds of 0.1, 2.5, 5, 10, and 20 mm in winter, we find that CN05.1 performs the best among the HRPPs and GSMaP-Gauge outperforms the other satellite-based HRPPs (Fig. 9). In most cases, the higher the threshold is, the lower the value of CSI. Moreover, the CSI values are higher in summer and over LER and lower in winter and over HER. The values of the gauge-based HRPPs are slightly higher than those of the satellite-based HRPPs in summer and are much higher in winter, indicating that gauge-based HRPPs may have advantages over satellite-based HRPPs in the correct detection of daily precipitation over SWC. At a threshold of 0.1 mm, the CSI values of CN05.1 are slightly lower than those of the other two gauge-based products. However, CN05.1 shows dramatically higher values than the other products (including APHRODITE and GPCC-D) at higher thresholds, especially over LER and MER. Such results suggest that CN05.1 has a stronger ability to correctly detect medium and large amounts of precipitation. Similar findings are derived from the POD and FAR results (Figs. S1 and S2 in the online supplemental material).

Fig. 9.
Fig. 9.

CSI values for the daily precipitation of the HRPPs during 1998–2016 in the whole year, DJF, and JJA over (a)–(c) SWC, (d)–(f) LER, (g)–(i) MER, and (j)–(l) HER at five thresholds.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

Nevertheless, the BIAS results show that TMPA 3B42V7, IMERG, and CMORPH-CRT show the best measurements for high precipitation amounts (Fig. 10). In general, the BIAS values of TMPA 3B42V7, IMERG, and CMORPH-CRT are close to 1 (perfect value). In summer, over LER and HER, the BIAS values of GSMaP-Gauge, GPCP, and PERSIANN-CDR are also close to 1 at the 0.1–25 mm thresholds but sharply decline at a threshold of 50 mm. The BIAS values of the gauge-based products are higher than 1 at small thresholds and lower than 1 at large thresholds, indicating that these products tend to overestimate the frequencies of light precipitation and overestimate those of heavy precipitation (more obvious for CN05.1), which is consistent with the results obtained from the PDFs. Most HRPPs show BIAS values that are close to 1 at thresholds of 0.1 and 10 mm in summer over HER, but as the threshold increases, the BIAS values of the satellite-based HRPPs and gauge-based HRPPs drastically increase and decrease, respectively. The BIAS values of TMPA 3B42V7, IMERG, CMORPH-CRT, and GPCP increase with the threshold in winter over LER and MER. Furthermore, the BIAS values of the gauge-based HRPPs are higher than 1 at the 0.1 mm threshold and slightly lower than 1 as the threshold increases. In contrast, the BIAS values of PERSIANN-CDR always remain steadily close to 1. However, none of the HRPPs perform well in winter over HER. It should be noticed that because of the lack of samples for some cases (such as the precipitation at the largest threshold over HER), there is even no records for some HRPPs (especially the gauge-based ones). Therefore, the evaluation for such cases may be less meaningful.

Fig. 10.
Fig. 10.

As in Fig. 9, but for BIAS.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

c. Extreme precipitation

The spatial distributions of the indices related to the precipitation amount, namely, R95p, PRCPTOT, Rx1d, and Rx5d, show similar features (Fig. 11). In general, the distributions of these four indices are similar to those of the annual precipitation, increasing from northwest to southeast. Large values are distributed in the SCB and west of Guizhou and Guangxi Provinces. For R95p, Rx1d, and Rx5d, the index values of TMPA 3B42V7, IMERG, and CMORPH-CRT are close to those of the observations, while the other HRPPs tend to underestimate the indices over MER and HER. The indices calculated from APHRODITE and CN05.1 are much lower than those of the observations. In addition, the values of R95p and Rx5d are overestimated by PERSIANN-CDR and GPCP, and all the HRPPs can accurately reproduce the spatial distribution of PRCPTOT. Although we do not have observational data from outside of China, we can clearly see differences in the indices over the southern Himalayas among the HRPPs.

Fig. 11.
Fig. 11.

Spatial distributions of the mean R95p, PRCPTOT, Rx1d, and Rx5d values during 1998–2016 based on the observations and the HRPPs over SWC.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

The spatial patterns of the extreme precipitation indices (CDD, CWD, and R10) and precipitation intensity (SDII) are shown in Fig. 12. The distribution features of the different indices estimated by the HRPPs vary significantly among each other. The CDD patterns exhibit distributions opposite to those of the annual precipitation, increasing from southeast to northwest. The maximum CDD is located in the HDM. The gauge-based HRPPs perform better than the satellite-based HRPPs. TMPA 3B42V7 overestimates CDD over MER and LER and underestimates CDD over HER. In contrast, IMERG and CMORPH-CRT overestimates CDD over HER. Additionally, the CDD values detected by GPCP and PERSIANN-CDR are low over HDM. TMPA 3B42V7, IMERG and GPCP estimate CWD well, while CN05.1 dramatically overestimates CWD, especially over HER, which may be associated with its overestimation of light precipitation. Although the spatial pattern of R10 is not effectively reproduced by GPCP and PERSIANN-CDR, the other HRPPs are able to estimate the distribution features of R10. The satellite-based products retrieve SDII values that are relatively close to those of the observations, while the SDII values estimated by APHRODITE and CN05.1 are low over LER, which may also be attributable to their overestimation of light precipitation.

Fig. 12.
Fig. 12.

As in Fig. 11, but for CDD, CWD, R10, and SDII.

Citation: Journal of Hydrometeorology 21, 11; 10.1175/JHM-D-20-0045.1

According to the metrics for the eight extreme precipitation indices, IMERG generally shows the best index estimation performance, followed by TMPA 3B42V7 (Table 4). Such an improvement from TMPA 3B42V7 to IMERG may benefit from the advances in spatial resolution (from 0.25° to 0.1°) and the more advanced sensors and algorithms. The r values of the gauge-based HRPPs are generally better than those of the satellite-based HRPPs, and APHRODITE displays the best performance for six of the eight indices. It indicates that the gauge-based HRPPs outperform the satellite-based HRPPs in terms of the spatial distribution of extreme precipitation. However, the RMSE results are very different from the r results. IMERG shows the lowest RMSEs for most of the indices (except for CDD and R10). The RMSEs of TMPA 3B42V7 and CMORPH-CRT are also small. Although APHRODITE V1901 has an improved interpolation algorithm to better represent localized precipitation and extreme values, its results are still not very satisfactory. Thus, IMERG has numerous advantages over the gauge-based HRPPs in detecting the amounts and intensity of extreme precipitation, even though its reproduced spatial distribution is less accurate than that of APHRODITE and some other gauge-based HRPPs. The upgraded instruments, improved algorithm and higher resolution may give IMERG an edge on detecting extreme precipitation. In addition, although the spatial resolution of GPCC-D is 1°, which is lower than that of CN05.1 and APHRODITE (0.25°), GPCC-D shows lower RMSEs than the other two gauge-based HRPPs and the best performance for both the r and RMSE of CDD. Therefore, GPCC-D may be a good alternative product in analyzing precipitation extremes and drought events. CN05.1 and APHRODITE may be unsuitable for drought study. Finally, the MREs of the extreme indices do not distinguish a best HRPP, but the results still indicate that GSMaP-Gauge, CMORPH-CRT, PERSIANN-CDR, and three gauge-based HRPPs tend to underestimate the indices related to the precipitation amount and intensity and overestimate CWD.

Table 4.

Pearson correlation coefficient r, RMSE, and MRE for the extreme precipitation indices between the HRPPs and observations (bold values represent the best statistical performances among the products). All the r values are statistically significant at the 95% confidence level except the value marked by an asterisk.

Table 4.

4. Conclusions and discussion

The retrieval of precipitation over SWC, which is host to many mountains, is a difficult task because of the sparsity of surface gauge observations. Therefore, evaluating the quality of HRPPs over this region is crucial. In this study, we compare 11 widely used HRPPs, including six satellite-based products (TMPA 3B42V7, IMERG, GSMaP-Gauge, CMORPH-CRT, PERSIANN-CDR, and GPCP) and five gauge-based products (APHRODITE, CN05.1, GPCC-D, GPCC-M, and CRU), with surface rain gauge observations over southwest China in terms of annual, seasonal, monthly, daily, and extreme precipitation from 1998 to 2016. The main results are as follows:

  1. GPCC-M provides the best estimates for annual, seasonal, and monthly precipitation, followed by APHRODITE and GPCC-D. GPCC-M shows the lowest MREs for annual and seasonal precipitation, the lowest RMSEs for annual and seasonal precipitation and annual cycle of precipitation and the lowest NRMSEs for the monthly precipitation over SWC and three subregions.

  2. For daily precipitation, the gauge-based HRPPs have advantages over the satellite-based HRPPs, and the best product is CN05.1. The RMSE and NRMSE for daily precipitation of CN05.1 are much lower than those of the other HRPPs. In addition, CN05.1 shows the best CSI values, and the performance of CN05.1 is similar for FAR and POD. A possible reason for this excellence may be that CN05.1 is constructed directly by the observational data used in this study. Nevertheless, we must note that CN05.1 tends to overestimate the frequency of light precipitation and underestimate the intensity of heavy precipitation (Fig. 7).

  3. IMERG displays the best performance at estimating extreme precipitation. Overall, the BIAS values of IMERG are closer to 1 (perfect value) than those of the other HRPPs except TMPA 3B42V7 and CMORPH-CRT at high thresholds. In addition, IMERG shows the lowest RMSEs for six of the eight extreme precipitation indices over SWC, including four indices for the precipitation amount, despite APHRODITE shows the best estimation of the spatial distribution of extreme precipitation indices.

In summary, our study suggests that most HRPPs can be used to estimate the precipitation over SWC. GPCC-M, CN05.1, and IMERG perform best for annual to monthly, daily, and extreme precipitation over SWC. These conclusions may be helpful for research on precipitation/extremes and hydrometeorological disasters that occur throughout southwest China. On the other hand, the accuracy of satellite-based HRPPs depends on topography, climate, the sensors and the precipitation retrieval techniques, and the type and intensity of precipitation (Jin et al. 2016; Xu et al. 2017; S. Wang et al. 2018; Zeng and Yong 2019). Located on the east slope of Tibetan Plateau, the SWC precipitation is affected by South Asian monsoon, East Asian monsoon, complex terrain, and local thermal convection. Considering the specificity of the climate and topography in SWC, our results may not hold in other regions, but these results can still serve as a reference for similar work, especially over other regions with complex topography.

Another limitation of this study is that the fairness of the evaluations may be impaired by the incorporation of gauge data used as reference by the HRPPs. The HRPPs incorporate gauge observations to different degrees: gauge-based HRPPs are directly derived from gauge observations and most of satellite-based HRPPs evaluated in this study also corrected by rain gauge data (e.g., TMPA 3B42V7 product has incorporated GPCC monthly products). However, it is difficult to verify that, to what extent, the 152 stations used in this study are incorporated in all the products. As far as we know, the gauge observations used as reference are part of sources of CN05.1, which may give it an edge on measuring daily precipitation. Other HRPPs may also incorporate a part of the gauge reference data in various degrees, but we did not obtain the detail information on these rain gauge observations, which makes it difficult to exclude the incorporated data and make an independent evaluation for the HRPPs.

In this study, the bilinear interpolation method is used to evaluate the performance of the HRPP products. To eliminate the impact of the method, the nearest neighbor method is also used. In this method, the precipitation time series for each gauge is compared with that of the nearest grid point from the HRPP products. This method has also been commonly used in the data evaluation (Cavazos 2000; McEvoy et al. 2014; Yang et al. 2017). The results from the nearest neighbor method are consistent with that from the bilinear interpolation method (Table S1, Figs. S3 and S4), indicating the results in this study is robust.

In addition, in this study, the HRPP products are interpolated to the location of the 152 rain gauge stations for evaluation. In other studies, the observed rain gauge data are interpolated to the HRPP grids and then used to evaluate the HRPP data (Han and Zhou 2012; Kirstetter et al. 2013; Ghajarnia et al. 2015). To compare the results using these two methods, we further rescale the rain gauge data to the resolutions of the HRPP precipitation products and reevaluate the performance of the HRPP products over SWC. To do so, a method of objective analysis, the successive correction method (SCM) (Cressman 1959; Barnes 1964), is adopted to interpolate gauge observations to HRPP resolutions and the assessments are repeated. The radii of influence are set to 2°, 1°, and 0.5°. The results using the SCM method on annual, seasonal, monthly, and daily precipitation are quite similar with that using the bilinear interpolation. GPCC-M and CN05.1 show the best estimates for annual-to-monthly and daily precipitation over SWC (figure not shown). However, the results on extreme precipitation using SCM method are nearly opposite to that using the bilinear interpolation. The RMSEs of extreme indices of satellite-based HRPPs are greatly higher than gauge-based HRPPs, and GPCC-D shows the best performance in terms of r and RMSE (Table S2). The BIAS values of satellite-based HRPPs are much higher than 1 at high thresholds. The gauge-based HRPPs seem closer to observational fields (Fig. S5). These pronounced results are reasonable. The precipitation fields obtained by the SCM method should have similar defects as gauge-based HRPPs because the SCM method can inevitably smooth the real precipitation fields and weaken the extremes. Therefore, the real variability of precipitation is underestimated by the SCM-related fields. The high RMSEs and high-threshold BIASs of satellite-based HRPPs can also be explained by the underestimated extremes in the SCM-related precipitation fields. These results imply that the upscale interpolation methods (at least SCM) may be unsuitable for extreme precipitation analysis, at least in regions where gauges are sparsely distributed. It is important to keep the authenticity of gauge observations in terms of extreme precipitation evaluation.

All the evaluations are conducted during the whole time period in this study, while there could be a possibility that the performance of HRPPs might be different during different time. For example, TMPA 3B42V7 may not be consistent over the comparison period because the TRMM satellite was operating its reentry in 2014–15 and its performance may be affected by the missing data. To examine whether the different precipitation climatology and selecting different period could affect our results or not, an early 5-yr period (2001–05) and a late 5-yr period (2011–15) are selected for further comparison. The results from both the 5-yr periods are consistent with that of the whole period (figure not shown), suggesting the results in this study are robust.

Precipitation over SWC has significant diurnal cycle (Tang et al. 2011). An intensive convective precipitation usually occurs on an hourly temporal scale. Therefore, an accuracy measurement of diurnal precipitation cycle over SWC is of great importance. Much deep and detailed work is needed to systematically evaluate the accuracy of HRPPs on a subdaily scale. Note that some satellite-based HRPPs are available on 3-hourly or even finer temporal resolution, the evaluation of SWC precipitation in diurnal cycle is achievable and will be considered in our future work.

Acknowledgments

This research was jointly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA23090102) and National Natural Science Foundation of China (Grant 41825010).

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