Performance Assessment of Gsmap and GPM IMERG Products During Typhoon Mangkhut

atmosphere

Article Performance Assessment of GSMaP and GPM IMERG Products during Typhoon Mangkhut

Xiaoyu Li 1,2, Sheng Chen 3,4,5,*, Zhenqing Liang 1,2, Chaoying Huang 3,4,5, Zhi Li 1,2 and Baoqing Hu 1,2

1 Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China; [email protected] (X.L.); [email protected] (Z.L.); [email protected] (Z.L.); [email protected] (B.H.) 2 School of Geographic Sciences and Planning, Nanning Normal University, Nanning 530001, China 3 Key Laboratory of Tropical Atmosphere-Ocean System, School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai 519000, China; [email protected] 4 Southern Laboratory of Ocean Science and Engineering, Zhuhai 519000, China 5 Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-Sen University, Zhuhai 519000, China * Correspondence: [email protected]; Tel.: +86-0756-3668330

Abstract: This paper evaluated the latest version 6.0 Global Satellite Mapping of Precipitation (GSMaP) and version 6.0 Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) products during 2018 Typhoon Mangkhut in China. The reference data is the rain gauge datasets from Gauge-Calibrated Climate Prediction Centre (CPC) Morphing Technique (CMOR- PHGC). The products for comparison include the GSMaP near-real-time, Microwave-IR merged, and gauge-calibrated (GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge) and the IMERG Early, Fi- nal, and Final gauge-calibrated (IMERG_ERUncal, IMERG_FRUncal, and IMERG_FRCal) products. The results show that (1) both GSMaP_Gauge and IMERG_FRCal considerably reduced the bias of their satellite-only products. GSMaP_Gauge outperforms IMERG_FRCal with higher Correlation Coefficient (CC) values of about 0.85, 0.78, and 0.50; lower Fractional Standard Error (FSE) values Citation: Li, X.; Chen, S.; Liang, Z.; of about 18.00, 18.85, and 29.30; and Root-Mean-Squared Error (RMSE) values of about 12.12, 33.35, Huang, C.; Li, Z.; Hu, B. Performance and 32.99 mm in the rainfall centers over mainland China, southern China, and eastern China, re- Assessment of GSMaP and GPM spectively. (2) GSMaP products perform better than IMERG products, with higher Probability of IMERG Products during Typhoon Detection (POD) and Critical Success Index (CSI) and lower False Alarm Ratio (FAR) in detecting Mangkhut. Atmosphere 2021, 12, 134. rainfall occurrence, especially for high rainfall rates. (3) For area-mean rainfall, IMERG performs https://doi.org/10.3390/atmos 12020134 worse than GSMaP in the rainfall centers over mainland China and southern China but shows better performance in the rainfall center over eastern China. GSMaP_Gauge and IMERG_FRCal perform Received: 24 December 2020 well in the three regions with a high CC (0.79 vs. 0.94, 0.81 vs. 0.96, and 0.95 vs. 0.97) and a low Accepted: 14 January 2021 RMSE (0.04 vs. 0.06, 0.40 vs. 0.59, and 0.19 vs. 0.34 mm). These useful findings will help algorithm Published: 21 January 2021 developers and data users to better understand the performance of GSMaP and IMERG products during typhoon precipitation events. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Keywords: rainfall; typhoon Mangkhut; GSMaP; IMERG published maps and institutional affil- iations.

1. Introduction With a maximum wind speed of about 270 km/h, typhoon Mangkhut became the most Copyright: © 2021 by the authors. destructive typhoon in Asia in 2018. It formed on 7 September 2018 as a tropical depression, Licensee MDPI, Basel, Switzerland. gradually intensiﬁed as a super typhoon on 15 September 2018, and landed in Cagayan, This article is an open access article Philippines. After its span in Philippines, the South China Sea helped the weakened distributed under the terms and typhoon Mangkhut gather more energy and caused its second landfall in Jiangmen, China conditions of the Creative Commons on 16 September 2018. Compared to common tropical cyclones (TCs), typhoon Mangkhut Attribution (CC BY) license (https:// not only had a stronger storm intensity with larger forward speed but also had a larger size creativecommons.org/licenses/by/ with a diameter of more than 1000 km. As a result, the storm surge was higher, the affected 4.0/).

Atmosphere 2021, 12, 134. https://doi.org/10.3390/atmos12020134 https://www.mdpi.com/journal/atmosphere Atmosphere 2021, 12, 134 2 of 13

inland area was larger, and the flooding duration also lasted longer. Typhoon Mangkhut also caused excessive rainfall in southern China and even brought heavy rainstorms to the far east of Jiangsu, Zhejiang, and Shanghai, China [1–3]. Therefore, it is too difficult to observe such a strong TC weather system in detail with conventional gauges, ground weather radars, and raindrop spectra. As remote sensing technology develops rapidly, these satellite-based Quantitative Precipitation Estimates (QPE) products become significant rainfall products and they can provide continuously high-resolution observations of precipitation over regional and global scales with their dramatically increased spatial and temporal resolution in recent years. Operating in the sky above the Earth, these satellites avoid the harmful effects of severe weather and complex terrain and were propitious to full land-ocean coverage and all- weather observation, which attracts widespread attention [4]. At present, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PER- SIANN) [5] and the later PERSIANN-Cloud Classification System (PERSIANN-CCS) [6], the Climate Prediction Center (CPC) morphing method (CMOPRH) [7], the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) [8], and the Global Satellite Mapping of Precipitation (GSMaP) [9] are widely used satellite-based QPE products. Based on the successful launch of Tropical Rainfall Measuring Mission (TRMM) in 1997, these satellite products have been popularized for precipitation-related research applications in hydrology, meteorology, climate change, hazards monitoring, and agriculture [10–15]. The satellite quantitative precipitation entered the era of the Global Precipitation Measurement program (GPM) since the TRMM officially retired in 2015. The National Aeronautics and Space Administration (NASA) and the Japan Aerospace Explo- ration Agency (JAXA) developed the GPM together to further improve the accuracy of the QPE, aiming for greater area coverage than the TRMM [16]. The GPM includes the Dual- frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI). The DPR is the first space-borne dual-frequency phased array precipitation radar to observe the internal structure of storms within and under the clouds, while the GMI has the conical-scanning multi-channel to physically measure the intensity, type, and size of the precipitation [17]. The new generation of satellite precipitation products of IMERG make use of the merits of PERSIANN-CSS, CMOPRH, and TRMM Multi-satellite Precipitation Analysis (TMPA) [18]. Both IMERG and GSMaP are multi-satellite rainfall products with a combination of infrared (IR) algorithms and passive microwave (PMW) algorithms. This merged PMW–IR information enhances the respective advantages of separate PMW or IR satellite-based rainfall estimates. Additionally, the IR satellite estimates can be adjusted with the improved accuracy of PMW data, while the PMW satellite estimates can be interpolated based on cloud motions obtained from the high IR data sampling rate [19–22]. Although conventional rain gauges were selected for ground reference in many previ- ous verification studies, various errors of rain gauges such as wind effect bias, evaporation, and other sources of deficiencies cannot be ignored. Traditional rain gauge networks can provide highly accurate rainfall measurements at specific locations, but in many cases, these instruments are too scarce to accurately identify the spatiotemporal variability of precipitation systems [23,24]. The object of this study is to evaluate and compare the performance of GSMaP and IMERG during the high-impact typhoon Mangkhut. The reference data are rain gauge data from the gauge-satellite merged product CMORPHGC without limitations in spatial coverage over China. The paper is organized as follows: Section2 describes the study area and available rainfall products to be evaluated for typhoon Mangkhut. Section3 provides an analysis of the spatial and temporal characteristics and error quantification for the rainfall brought by typhoon Mangkhut to mainland China from 01:00 AM on 15 September 2018 to 00:00 AM on 18 September 2018 at local standard time. Section4 gives a summary of the analysis results. Atmosphere 2021, 12, x FOR PEER REVIEW 3 of 14 Atmosphere 2021, 12, 134 3 of 13

2. Study Area and Data 2. Study Area and Data 2.1.2.1. StudyStudy AreaArea MainlandMainland ChinaChina coverscovers aa totaltotal landland areaarea ofof about about 9.63 9.63 million million km km22,, withwith anan elevationelevation rangingranging from from− −152152 to to 8752 8752 m, m, bounded bounded by by 18 ◦18N–55° N–◦55°N latitudeN latitude and and 73◦ E–13673° E–◦136°E longitude. E longi- Figuretude. Figure1a shows 1a shows the terrain the terrain of mainland of mainland China China and the and tracks the tracks of typhoon of typhoon Mangkhut. Mangkhut. The terrainThe terrain is characterized is characterized by a by westward a westward high high elevation elevation and anand eastward an eastward low slopelow slope to the to ocean,the ocean, which which is conducive is conducive to moist to moist air currents air currents over over theocean the ocean that that penetrate penetrate deep deep into into the inlandthe inland region region and and form form precipitation. precipitation. Eastern Eastern China China is a monsoonis a monsoon climate climate zone, zone, while while the northwest,the northwest, far from far from thesea, the issea, anon-monsoon is a non-monsoon zone zone with with a temperate a temperate continental continental climate. cli- Overmate. southeastern Over southeastern and southern and southern China, China, subtropical subtropical monsoons monsoon coupleds coupled with orographic with oro- effectsgraphic from effects complex from complex terrain usually terrain causeusually substantial cause substantial precipitation precipitation [25]. Moreover, [25]. Moreo- the monsoonver, the monsoon which moves which back moves and back forth and between forth thebetween Siberia the Mongolian Siberia Mongolian Plateau and Plateau the Pacificand the Ocean Pacific has Ocean a primary has a primary influence influence on the climate on the ofclimate southeastern of southeastern and southern and southern China, andChina, its and seasonal its seasonal propagation propagation contributes contributes to the to formation the formation of typhoons of typhoons in the in the western west- Pacificern Pacific Ocean Ocean and and their their landfall landfall in southeastern in southeastern and and southern southern China China [26]. [26] Furthermore,. Furthermore the, annualthe annual distribution distribution of precipitation of precipitation in southern in southern China China is uneven, is uneven, with with July July to September to Septem- dominatedber dominated by TC-generated by TC-generated precipitation precipitation [27]. [27] TCs. TCs not onlynot only lead le toa economicd to economic losses losses but alsobut also cause cause natural natural disasters disaste suchrs such as heavy as heavy rainfall, rainfall, urban urban flooding, flooding and, landslides.and landslides.

Figure 1. Terrain distributionFigure over Mainland 1. Terrain China distribution and the over tracks Mainland of typhoon China Mangkhut and the ( atracks) and theof typhoon rain gauge Mangkhut distribution (a) and over Mainland China (b). the rain gauge distribution over Mainland China (b).

2.2. Rainfall Datasets 2.2. Rainfall Datasets 2.2.1.2.2.1. GaugeGauge PrecipitationPrecipitation ObservationObservation InIn thisthis paper,paper, wewe usedused CMORPHGCCMORPHGC precipitationprecipitation productsproducts [[28]28] initiatedinitiated byby thethe Na-Na- tionaltional MeteorologicalMeteorological InformationInformation CenterCenter ofof ChinaChina asas aa referencereference toto evaluateevaluate thetheGSMaP GSMaP andand IMERG IMERG products. products CMORPHGC. CMORPHGC is comprised is comprised of historical of historical combined combined precipitation precipitation data atdata 0.1 ◦at× 0.1°0.1 ×◦ resolution0.1° resolution per hourper hour in China, in China, employing employing the improvedthe improved probability probability density den- function–optimalsity function–optimal interpolation interpolation (Impr_PDF-OI) (Impr_PDF algorithm-OI) algorithm to ﬁrst to correct first correct the 8 km/30the 8 km/30 min CMORPHmin CMORPH and then and tothen merge to merge the bias-corrected the bias-correc CMORPHted CMORPH with hourly with hourly precipitation precipitation obser- vationsobservations from over from 30,000 over 30,000 automatic automatic weather weather stations stations (AWS) (AWS) in China. in China The cross-validation. The cross-val- resultsidation indicate results indicate that CMORPHGC that CMORPHGC has a tiny has random a tiny random error with error a lowwith mean a low root-mean-mean root- squaredmean-squared Error (0.594 Error mm/h), (0.594 mm/h), a small a systematic small systemat bias (−ic0.002 bias ( mm/h),−0.002 mm/h) and a high, and correlationa high cor- coefﬁcientrelation coefficient (0.8) between (0.8) between itself and itself the and gauge the analysis.gauge analysis. This merged This merged dataset dataset effectively effec- synthesizestively synthesizes the AWS the observations AWS observations and satellited and satellited products product of CMORPHs of CMORPH to provide to provide more reasonablemore reasonable reference reference precipitation precipitation information, information, which iswhich best onis best the precisionon the precision and spatial and andspatial temporal and temporal resolution resolution in China in China at present at present and canand capture can capture some some varying varying features features of hourlyof hourly precipitation precipitation in heavy in heavy weather weather events events very very well. well More. More details detail cansbe can found be found in [28 i].n In[28] particular,. In particular Chen, et Chen al. [29 et] investigatedal. [29] investigated the precipitation the precipitation spectra analysis spectra over analysis China with over CMORPHGC,China with CMORPHGC, documenting documenting well the geographical well the geographical and seasonal variationsand seasonal in precipitationvariations in overprecipitation seven subregions. over seven Different subregions. from Different the rain gaugefrom the datasets rain gauge used indatasets that study used [30 in– 32that], thisstudy paper [30– only32], selectsthis paper grid only datasets selects that grid contain datasets at least that one contain rain gauge at least from one CMORPHGC rain gauge

Atmosphere 2021, 12, 134 4 of 13

for calculation and quantitative comparison of the statistical indexes so that all the data used include observation data of AWS. Approximately 29,000 rain gauge observations are selected for this study (Figure1b).

2.2.2. Satellite Precipitation The main characteristics of the satellite-based algorithms used in this paper are shown in Table1. GSMaP and IMERG are the precipitation products with the highest spatiotemporal resolution (0.1◦/60 min and 0.1◦/30 min respectively) among the current satellite precipitation products. The latest version 6.0 GSMaP (V6) available since September 2014 was used in this study. The GSMaP project was sponsored by the Japan Science and Tech- nology Agency (JST) between 2002 and 2007 and later by the JAXA Precipitation Measuring Mission (PMM) Science Team since 2008. The GSMaP algorithm includes the GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge algorithms. GSMaP_NRT is available four hours after observation, while about 3 days after observation, GSMaP_MVK and GSMaP_Gauge are available. GSMaP_NRT applies to morph and Kalman ﬁlters only by the forward process. At the same time, GSMaP_MVK is obtained by cloud-top motion derived from two successive IR images and a Kalman ﬁlter by both the forward and backward propagation processes. GSMaP_Gauge adjusts the GSMaP_MVK estimate with a global daily gauge dataset supplied by the NOAA Climate Prediction Center (CPC). In addition, DPR was not directly used for developing the database or precipitation physical models while GMI was used as the inputs of GSMaP [22,33,34].

Table 1. Summary of the GSMaP and IMERG algorithms.

Space/ Spatial Name Input Data Latency Time Scales Domain GSMaP_NRT IR, PMW, DPR/GMI 0.1◦/hourly 60◦ N-S 4 h GSMaP_MVK IR, PMW, DPR/GMI 0.1◦/hourly 60◦ N-S 3 days IR, PMW, DPR/GMI, GSMaP_Gauge 0.1◦/hourly 60◦ N-S 3 days Daily gauges (CPC) IMERG_ERUnCal IR, PMW, DPR/GMI 0.1◦/0.5 h 90◦ N-S 4 h 3.5 IMERG_FRUnCal IR, PMW, DPR/GMI 0.1◦/0.5 h 90◦ N-S months IR, PMW, DPR/GMI, 3.5 IMERG_FRCal 0.1◦/0.5 h 90◦ N-S Monthly gauges (GPCC) months

IMERG is the level-3 GPM precipitation estimation product that includes Early Run, Late Run and Final Run products. The Early Run and Late Run data are considered near- real-time products with a latency of about 4 h and 14 h, respectively. The Final Run is the post-real-time research product at about 3.5 months after observation time. Moreover, the Early Run provides relatively quick results for ﬂood analysis and other short-fuse applications by only employing forward morphing. Both the forward and backward morphing schemes are used in the Late and Final Runs. The Late and Final Runs are expected to better describe changes in the intensity and shape of rainfall features. The Late Run is appropriate for daily and longer applications, such as crop forecasting. The Final Run is considered the research-grade product, providing multi-satellite precipitation estimates without gauge calibration (IMERG_FRUncal) as well as the calibrated estimates (IMERG_FRCal). For bias correction, climatological gauge data are used for the Early and Late Runs, while the IMERG_FRCal ingests monthly Global Precipitation Climatology Centre (GPCC) gauge analyses to calibrate the IMERG_FRUncal. The GPCC used two gauged analysis products in IMERG_FRCal such as the full data reanalysis (Version 2018) and GPCC monitoring product (Version 6). Firstly, the original half-hourly satellited-only estimates are summed for the calendar month. Secondly, the GPCC monthly precipitation gauge analysis is undercatch-corrected and is combined directly with the monthly gauge- adjusted satellited-only estimate by inverse error variance weighting. Thirdly, the half hourly multi-satellite estimates in the month are multiplied by the ratio between the Atmosphere 2021, 12, 134 5 of 13

monthly multi-satellite-only ﬁelds and satellite-gauge ﬁelds. Moreover, the calibrations are based on DPR and GMI and are applied in the IMERG processing [35,36].

2.3. Statistical Metrics Seven commonly used skill scores like Root-Mean-Squared Error (RMSE), Fractional Standard Error (FSE), Relative Bias (RB), Correlation Coefﬁcient (CC), Probability of De- tection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) are employed to assess the performance of the satellite-based precipitation products quantitatively. These metrics are deﬁned in Table2, while the number of hits (A), the false alarms (B), and the misses (C) are computed in Table3[ 27,37]. Taylor diagrams allow visualization and comparison of the overall performance between the QPE products and the gauge on CC, RMSE, and standard deviation. More detailed information on the Taylor diagram can be found in [38].

Table 2. Statistical evaluation metrics.

Metrics Formula Perfect Value RB (%) ≡ ∑(QPE−Gauge) 0 RB ∑ Gauge Cov(QPE,Gauge) 1 CC CC ≡ σQPEσGauge q 2 ∑(QPE−Gauge) 0 RMSE (mm) RMSE ≡ N r 1 ( − )2 N ∑ QPE Gauge 0 FSE FSE ≡ 1 N ∑ Gauge ≡ A POD POD A+C 1 ≡ B FAR FAR A+B 0 A CSI CSI ≡ A+B+C 1 N is the number of the samples. Table 3. Contingency table of gauge and Quantitative Precipitation Estimates (QPE) products.

Gauge≥Threshold Gauge

3. Results 3.1. Spatial Analysis The spatial pattern of 72-h accumulated precipitation during typhoon Mangkhut is im- aged in Figure2. The accumulated precipitation over southern China (i.e., Guangdong and Guangxi province) is much higher than that over eastern China (i.e., Zhejiang and Jiangsu province) since the southern China rainfall center is triggered by typhoon Mangkhut while the eastern China rainfall center is triggered by an inverted typhoon trough. The inverted typhoon trough is mixed with cold air, and it is not like the typhoon itself, being full of warm and wet air. Therefore, the warm and humid air from the huge circulation of typhoon Mangkhut was transported to eastern China, where it gathered together with the cold air from the north, moving southward, causing heavy rain in a small area and for a short time in the region. More importantly, both the GSMaP and IMERG products can generally capture the spatial distribution of 72-h accumulated precipitation (Figure2b–g), and the satellite-only precipitation products are not signiﬁcantly different from their bias-adjusted products. Both GSMaP_Gauge and IMERG_FRCal better capture the spatial pattern of precipitation over mainland China, while the satellite-only products are not as good as the bias-corrected products. The satellite-only products slightly underestimated the precipitation in the rainfall center over southern China but showed overestimation in the rainfall center over eastern China. On top of that, the most impressive feature of GSMaP products is that the rainfall intensity closely resembles that of the gauge observations with much Atmosphere 2021, 12, x FOR PEER REVIEW 6 of 14

different from their bias-adjusted products. Both GSMaP_Gauge and IMERG_FRCal better capture the spatial pattern of precipitation over mainland China, while the satellite- Atmosphere 2021, 12, 134 only products are not as good as the bias-corrected products. The satellite-only products6 of 13 slightly underestimated the precipitation in the rainfall center over southern China but showed overestimation in the rainfall center over eastern China. On top of that, the most impressive feature of GSMaP products is that the rainfall intensity closely resembles that higher precipitation intensity than that of IMERG in the rainfall center over southern China. of the gauge observations with much higher precipitation intensity than that of IMERG in In comparison, the rainfall intensity is much lower than that of IMERG in the rainfall center the rainfall center over southern China. In comparison, the rainfall intensity is much lower overthan eastern that of China.IMERG This in the indicates rainfall that center GSMaP over performseastern Chin bettera. This than indicates IMERG. that On GSMaP the whole, theperforms gauge correctionbetter than algorithms IMERG. On applied the whole, in GSMaP_Gauge the gauge correction and IMERG_FRCal algorithms applied contribute in moreGSMaP_Gauge positive effects and IMERG_ in southernFRCal China. contribute more positive effects in southern China.

FigureFigure 2. Spatial2. Spatial distribution distribution of of 72 72 h h of of accumulated accumulated precipitationprecipitation during typhoon typhoon Mangkhut Mangkhut over over Mainland Mainland China China as as measuredmeasured by by (a )(a gauge) gauge observations, observations, ( b) GSMaP_NRT, GSMaP_NRT, (c ()c )IMERG_ERUncal, IMERG_ERUncal, (d) (GSMaP_MVK,d) GSMaP_MVK, (e) IMERG_FRUncal, (e) IMERG_FRUncal, (f) (f) GSMaP_Gauge,GSMaP_Gauge, and and ( (gg)) IMERG_FRCal: IMERG_FRCal: white white rectangles rectangles indicate indicate the the rainfall rainfall center. center.

TheThe scatter scatter plotsplots inin Figure 33aa–r–r provideprovide a quantitative comparison comparison of of the the satellite satellite-- basedbased precipitation precipitation products (GSMaP (GSMaP and and IMERG) IMERG) versus versus gauge gauge on accumulated on accumulated precip- pre- cipitationitation during during typhoon typhoon Mangk Mangkhut.hut. The Thetwo bias two-corrected bias-corrected products products GSMaP_Gauge GSMaP_Gauge and andIMERG_FRCal IMERG_FRCal perform perform much much better better than thanthe satellite the satellite-only-only estimates estimates GSMaP_NRT GSMaP_NRT and andIMERG_ERUncal IMERG_ERUncal in the in rainfall the rainfall centers centers over mainland over mainland China and China southern and southern China, with China, withincreased increased CCs CCsand andreduced reduced RMSEs, RMSEs, FSEs, FSEs, and RBs.and RBs. GSMaP_Gauge GSMaP_Gauge outperforms outperforms the theIMERG_FRCal IMERG_FRCal estimates estimates in each in each region region according according to CCs to (0.85 CCs vs. (0.85 0.74, vs. 0.78 0.74, vs. 0.78 0.64 vs., and 0.64, and0.50 0.50 vs. 0.34) vs. 0.34)and displays and displays lower FSEs lower (18.00 FSEs vs. (18.00 29.52, vs. 18.85 29.52, vs. 28.16 18.85, and vs. 29.30 28.16, vs. and 51.03) 29.30 and RMSEs (12.12 vs. 15.52 mm, 33.35 vs. 40.76 mm, and 32.99 vs. 43.54 mm). Among vs. 51.03) and RMSEs (12.12 vs. 15.52 mm, 33.35 vs. 40.76 mm, and 32.99 vs. 43.54 mm). satellite-only precipitation products, IMERG products have similar CCs to GSMaP prod- Among satellite-only precipitation products, IMERG products have similar CCs to GSMaP ucts and have lower RMSEs and FSEs in rainfall centers over mainland China and south- products and have lower RMSEs and FSEs in rainfall centers over mainland China and ern China. In contrast, GSMaP products perform better than IMERG products, with higher southern China. In contrast, GSMaP products perform better than IMERG products, with CC over the rainfall center in eastern China. These statistics are slightly different from the higher CC over the rainfall center in eastern China. These statistics are slightly different spatial distribution of accumulated precipitation represented in Figure 2. from the spatial distribution of accumulated precipitation represented in Figure2. In order to determine which product more accurately estimates precipitation in these three regions, Figure4 depicts the Taylor diagrams created by Taylor [ 38] to visually compare the statistics summary between individual satellite-based products and gauge data on CC, normalized standard deviation (SD), and RMSE. The position of each point that appears on the Taylor diagrams quantiﬁes how well the satellite-based precipitation product matches the reference data, which is marked by a black star, and closer points denote better products. The gauge-corrected products have the distinct advantage of showing much closer distances than their satellite-only counterparts over the rainfall centers in mainland China and southern China. For the satellite-only products, the points of IMERG products are much closer to the gauge against GSMaP products over the rainfall centers in mainland China and southern China while the GSMaP products perform better over the rainfall centers in eastern China. These ﬁndings are consistent with the analysis of the scatter plots in Figure3. GSMaP_MVK shows the most inferior performance in all regions, especially the rainfall center in southern China, with the highest SD and RMSE and relatively small CC. It is also found that IMERG_FRUncal outperforms other satellite- only products in the rainfall centers over mainland China and southern China. Over the rainfall center in eastern China, GSMaP_NRT performs best, with slightly better agreement than other satellite-based precipitation products. In addition, IMERG products have Atmosphere 2021, 12, 134 7 of 13

Atmosphere 2021, 12, x FOR PEER REVIEW 7 of 14 similar Taylor diagrams, with their close CCs and RMSEs, indicating that gauge-corrected IMERG_FRCal does not signiﬁcantly improve the accuracy of uncalibrated data.

Figure 3. Scatter plots of 72 h of accumulated precipitation between satellite products (the satellite-only GSMaP and IMERG products for (a–l) and their gauge-corrected products for (m–r)) and gauge observation over different areas during typhoon Mangkhut for rainfall centers over mainland China (the 1st column), southern China (the 2nd column), and eastern China (the 3rd column). Atmosphere 2021, 12, x FOR PEER REVIEW 8 of 14

Figure 3. Scatter plots of 72 h of accumulated precipitation between satellite products (the satellite-only GSMaP and IMERG products for (a–l) and their gauge-corrected products for (m–r) and gauge observation over different areas during typhoon Mangkhut for rainfall centers over mainland China (the 1st column), southern China (the 2nd column), and eastern China (the 3rd column).

In order to determine which product more accurately estimates precipitation in these three regions, Figure 4 depicts the Taylor diagrams created by Taylor [38] to visually compare the statistics summary between individual satellite-based products and gauge data on CC, normalized standard deviation (SD), and RMSE. The position of each point that appears on the Taylor diagrams quantifies how well the satellite-based precipitation product matches the reference data, which is marked by a black star, and closer points denote better products. The gauge-corrected products have the distinct advantage of showing much closer distances than their satellite-only counterparts over the rainfall centers in mainland China and southern China. For the satellite-only products, the points of IMERG products are much closer to the gauge against GSMaP products over the rainfall centers in mainland China and southern China while the GSMaP products perform better over the rainfall centers in eastern China. These findings are consistent with the analysis of the scatter plots in Figure 3. GSMaP_MVK shows the most inferior performance in all regions, especially the rainfall center in southern China, with the highest SD and RMSE and relatively small CC. It is also found that IMERG_FRUncal outperforms other satellite-only products in the rainfall centers over mainland China and southern China. Over the rainfall Atmosphere 2021, 12, 134 8 of 13 center in eastern China, GSMaP_NRT performs best, with slightly better agreement than other satellite-based precipitation products. In addition, IMERG products have similar Taylor diagrams, with their close CCs and RMSEs, indicating that gauge-corrected IMERG_FRCal does not significantly improve the accuracy of uncalibrated data.

Figure 4. Taylor diagrams with correlation coefﬁcients, standard deviation (normalized), and root-mean-squared deviation Figure 4. Taylor diagrams with correlation coefficients, standard deviation (normalized), and root-mean-squared devia- (RMSD also means RMSE) of 72 h of accumulated precipitation between the satellite-based precipitation products and the tion (RMSD also means RMSE) of 72 h of accumulated precipitation between the satellite-based precipitation products referenceand the reference data over data (a) mainlandover (a) mainland China and China the rainfalland the centers rainfall in centers (b) southern in (b) southern China and China (c) eastern and (c) China. eastern China. 3.2. Contingency Scores 3.2. Contingency Scores Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Successful IndexProbability (CSI) can of help Detection us to examine (POD), False the details Alarm of Ratio the nature(FAR), ofand rainfall Critical occurrence Successful [ 39In-]. Thedex (CSI) contingency can help statistics us to examine (POD, CSI,the details and FAR) of the of thenature GSMaP of rainfall and IMERG occurrence products [39]. asThe a contingencyfunction of hourly statistics precipitation (POD, CSI, rateand areFAR) depicted of the GSMaP in Figure and5. AsIMERG the threshold products increases,as a func- tionthere of is hourly the tendency precipitation for decreasing rate are depicted POD and in Figure CSI and 5. increasingAs the threshold FAR for increases, all products. there isGenerally, the tendency GSMaP for decreasing products have POD higher and CSI PODs and andincreasing CSIs and FAR lower for all FARs products. over various Gener- ally,regions GSMaP than products IMERG products,have higher especially PODs and for CSIs rainfall and centers lower FARs over mainlandover various China regions and thaneastern IMERG China. products, Interestingly, especially IMERG for products rainfall havecenters relatively over mainland higher POD China and and lower eastern CSI China.and FAR Interestingly, than GSMaP IMERG products products for precipitation have relatively rates higher less than POD 5 mm/hand lower over CSI the and rainfall FAR thancenter GSMaP in southern products China. for However,precipitation all therates IMERG less than products 5 mm/h have over very the similar rainfall POD, center CSI, in southernand FAR, China. indicating However, that IMERG_FRCal all the IMERG fails products to signiﬁcantly have very improve similar the POD, detection CSI, and accuracy FAR, indicatingof precipitation that IMERG_FRCal events, which conformsfails to significantly to the Taylor improve diagram the analysis detection in Figureaccuracy4. On of pre- the contrary, GSMaP_Gauge contributes better performance than its uncalibrated counterparts for the relatively higher POD and CSI and lower FAR when the rain rate is 1 mm/h. Regionally, POD and CSI were lower while FAR was higher for all products in the three regions when rain rates were greater than 5 mm/h. Interestingly, GSMaP_MVK exhibits higher CSI and lower FAR than its counterpart with bias correction for the precipitation rates greater than 5 mm/h over rainfall centers in mainland China and eastern China. IMERG_ERUncal represents the lowest POD and CSI and the highest FAR over rainfall centers in mainland China and southern China among all the products, while the percentage of POD and CSI (FAR) becomes higher (lower) over rainfall center in eastern China. This may indicate that rainfall intensity depends on contingency statistics (i.e., POD, CSI, and FAR).

3.3. Probability Distribution The Probability distribution functions (PDFs) reveal the inhomogeneity of precipitation in space and time and provide insights into the dependence of estimate errors on precipitation rate and the potential effects from these errors on hydrological applications [26]. Figure6 depicts the PDFs of precipitation rates by occurrence (PDFc) and volume (PDFv) for gauge, GSMaP_NRT, GSMaP_MVK, GSMaP_Gauge, IMERG_ERUncal, IMERG_FRUncal, and IMERG_FRCal. All of the satellite-only precipitation products signiﬁcantly overestimate the occurrence when rain rate is less than 1 mm/h (light precipitation) over rainfall centers in mainland China and southern China, while tending to underestimate the occurrence during 1 mm/h to 10 mm/h (moderate precipitation) over rainfall centers in mainland China and southern China. However, among the gauge- corrected products, GSMP_Gauge shows a relatively lower occurrence and IMERG_FRCal Atmosphere 2021, 12, x FOR PEER REVIEW 9 of 14

Atmosphere 2021, 12, 134 9 of 13 cipitation events, which conforms to the Taylor diagram analysis in Figure 4. On the contrary, GSMaP_Gauge contributes better performance than its uncalibrated counterparts displaysfor the a relatively higher occurrence higher POD when and the CSI precipitation and lower rate FAR is lesswhen than the 1 rain mm/h. rate Interestingly,is 1 mm/h. Re- GSMaP_Gaugegionally, POD tends andto CSI overestimate were lower rain while rates FAR while was IMERG_FRCal higher for all underestimates products in the rain three ratesregions at 1–5 when mm/h rain in the rates three we regions.re greater As than for rain 5 mm/h. volume, Interestingly, the GSMaP products,GSMaP_MVK in parallel exhibits withhigh IMERG,er CSI and agrees lower well FAR with than the its gauge counterpart when with rain bias rate correction is less than for 1 the mm/h, precipitation but with rates a slightlygreater lowerthan PDFv 5 mm/h during over 10 rainfall mm/h tocenters 30 mm/h in mainland over rainfall China centers and in eastern mainland China. ChinaIMERG_ERUncal and southern represents China. Furthermore, the lowest POD the IMERG and CSI products and the highest obviously FAR underestimate over rainfall cen- theters rain in volume mainland when China the and rain southern rate is moreChina thanamong 15 all mm/h the products, (heavy precipitation) while the percentage in the of threePOD regions, and CS implyingI (FAR) becomes that the IMERG higher (lower) products over have rainfall difﬁculty center in preciselyin eastern estimating China. This the may heavyindicate rainfall that rates. rainfall intensity depends on contingency statistics (i.e., POD, CSI, and FAR).

FigureFigure 5. Contingency 5. Contingency statistics statistics computed computed from from the the Global Global Satellite Satellite Mapping Mapping of Precipitationof Precipitation (GSMaP) (GSMaP and) and Integrated Integrated Multi-satellitEMulti-satellitE Retrievals Retrievals for for Global Global Precipitation Precipitation Measurement Measurement (IMERG) (IMERG hourly) hourly precipitation precipitation products products over over mainland mainland China and two rainfall centers: (a–c) Probability of Detection (POD), (d–f) Critical Success Index (CSI), and (g–i) False China and two rainfall centers: (a–c) Probability of Detection (POD), (d–f) Critical Success Index (CSI), and (g–i) False Alarm Ratio (FAR). Alarm Ratio (FAR).

3.4.3.3. Mean Probability Hourly RainfallDistribution TheThe temporal Probability variation distribution of precipitation functions is(PDFs) critical reveal to the the hydrological inhomogeneity cycle. of precipita- Short periodstion in of space heavy and rainfall time canand triggerprovide ﬂoods, insights landslides, into the dependence and other hydro-related of estimate errors disas- on tersprecipitation [40]. Figure 7rate depicts and the plotspotential of the effects 72-h from time seriesthese errors of area-mean on hydrological hourly rainfall applications for all[26] rainfall. Figure products 6 depicts over the threePDFsregions. of precipitation In general, rates the by GSMaP occurrence products (PDFc) (GSMaP_NRT, and volume GSMaP_MVK,(PDFv) for andgauge, GSMaP_Gauge) GSMaP_NRT, captured GSMaP_MVK, the closer performance GSMaP_Gauge, of temporal IMERG_ERUncal, variation of rainfallIMERG_FRUncal, with the gauge and inIMERG_FRCal. rainfall center All over of mainland the satellite China-only and precipitation southern China, products with sig- highnificantly CC (0.66 overestimate vs. 0.61, 0.80 the vs. occurrence 0.68, and 0.79 when vs. rain 0.81) rate and is low less RB than (− 10.11 mm/h vs. −(light0.18, precipita-−0.01 vs.tion)−0.20, over and rainfall 0.03 vs. center−0.06).s in However,mainland inChina the rainfalland southern center China, over eastern while China,tending IMERG to under- productsestimate give the better occurrence performance during 1 when mm/h capturing to 10 mm/h the (moderate time series precipitation) area-averaged over rainfall rainfall thancenters the GSMaP in mainland products, China with and a more southern similar China. tendency However, to that among of gauge. the gaugeAdditionally,-corrected the GSMaP_Gauge and IMERG_FRCal have a similar variation of hourly rainfall with gauge measurements when compared to their satellite-only products with a good match

Atmosphere 2021, 12, x FOR PEER REVIEW 10 of 14

products, GSMP_Gauge shows a relatively lower occurrence and IMERG_FRCal displays a higher occurrence when the precipitation rate is less than 1 mm/h. Interestingly, Atmosphere 2021, 12, 134 GSMaP_Gauge tends to overestimate rain rates while IMERG_FRCal underestimates10 ofrain 13 rates at 1–5 mm/h in the three regions. As for rain volume, the GSMaP products, in parallel with IMERG, agrees well with the gauge when rain rate is less than 1 mm/h, but with a slightly inlower the PDFv temporal during variation 10 mm/h of to hourly 30 mm/h rainfall over over rainfall the centers three regions. in mainland All theChina gauge-adjusted and southern productsChina. Furt (GSMaP_Gaugehermore, the IMERG and IMERG_FRCal) products obviously have underestimate a high CC (0.79 the vs.rain 0.94, volume 0.81 when vs. 0.96, the andrain 0.95rate vs.is more 0.97) than and 15 a low mm/h RMSE (heavy (0.04 precipitation) vs. 0.06, 0.40 in vs. the 0.59, three and regions, 0.19 vs. implying 0.34mm) that in thethe threeIMERG regions. products have difficulty in precisely estimating the heavy rainfall rates.

FigureFigure 6. 6.Probability Probability distribution distribution function function by by (a –(ca)– occurrencec) occurrence (PDFc) (PDFc) and and (d– (fd)– volumef) volume (PDFv) (PDFv) of hourly of hourly precipitation precipitation for Atmosphere 2021, 12, x FOR PEER REVIEW 11 of 14 differentfor different intensities intensities over over mainland mainland China China and and two two rainfall rainfall centers. centers.

3.4. Mean Hourly Rainfall The temporal variation of precipitation is critical to the hydrological cycle. Short pe- riods of heavy rainfall can trigger floods, landslides, and other hydro-related disasters [40]. Figure 7 depicts the plots of the 72-h time series of area-mean hourly rainfall for all rainfall products over the three regions. In general, the GSMaP products (GSMaP_NRT, GSMaP_MVK, and GSMaP_Gauge) captured the closer performance of temporal variation of rainfall with the gauge in rainfall center over mainland China and southern China, with high CC (0.66 vs. 0.61, 0.80 vs. 0.68, and 0.79 vs. 0.81) and low RB (−0.11 vs. −0.18, −0.01 vs. −0.20, and 0.03 vs. −0.06). However, in the rainfall center over eastern China, IMERG products give better performance when capturing the time series area-averaged rainfall than the GSMaP products, with a more similar tendency to that of gauge. Addi- tionally, the GSMaP_Gauge and IMERG_FRCal have a similar variation of hourly rainfall with gauge measurements when compared to their satellite-only products with a good match in the temporal variation of hourly rainfall over the three regions. All the gauge- adjusted products (GSMaP_Gauge and IMERG_FRCal) have a high CC (0.79 vs. 0.94, 0.81 vs. 0.96, and 0.95 vs. 0.97) and a low RMSE (0.04 vs. 0.06, 0.40 vs. 0.59, and 0.19 vs. 0.34mm) in the three regions.

Figure 7. 7. AreaArea-mean-mean hourly hourly rainfall rainfall over over (a) (mainlanda) mainland China China and andthe rainfall the rainfall centers centers in (b) insouthern (b) southern China and China (c) eastern and (c) China. eastern China. 4. Summary and Conclusions This study evaluates the performances of the latest version 6.0 GSMaP and version 6.0 IMERG products in an extreme typhoon event (Mangkhut) over China. With the gauge data as the ground truth, the GSMaP and IMERG products have been assessed at different temporal and spatial resolutions. The main results are summarized as follows: (1) Spatially, GSMaP_Gauge outperforms the IMERG_FRCal estimates in each region according to CCs and displays lower FSEs and RMSEs. IMERG_FRCal performs better than GSMaP over the rainfall centers in mainland China and southern China. (2) GSMaP products have higher PODs and CSIs and lower FARs over various regions than IMERG products. GSMaP_Gauge has a higher POD and CSI and lower FAR when the rain rate is 1 mm/h, while gauge-corrected IMERG_FRCal does not significantly improve the detection accuracy of precipitation events. (3) For PDFc and PDFv, GSMaP products agree better with the gauge and the IMERG products have difficulty estimating the heavy rainfall rates. All of the satellite-only products overestimate the occurrence of light precipitation while underestimating the occurrence of moderate precipitation over rainfall centers in mainland China and southern China. (4) According to the time-series analysis, GSMaP products perform better than IMERG products in the rainfall centers over mainland China and southern China while IMERG products show better performance in the rainfall center over eastern China. The bias-adjusted products GSMaP_Gauge and IMERG_FRCal have a high CC and a low RMSE. The identification and quantification of spatiotemporal characteristics of GSMaP and IMERG products provide algorithm developers and users alike with detailed information. It is not surprising that GSMaP products generally perform better than IMERG products during the extreme typhoon Mangkhut over China. This may be explained as followed. Firstly, the IMERG precipitation estimates using GPROF2017 algorithms and is thus in- herently affected by GPROF2017 datasets. Secondly, GSMaP has a better detection capability than GPROF2017 and a higher CC and lower RMSE over land [36,41]. The agreement between satellite-only GSMaP products and gauge measurements is low and even negative for rainfall intensities over southern China with high FSE and RMAE on scatter plots, illustrating that it is necessary to improve the satellite-only GSMaP products. These results

Atmosphere 2021, 12, 134 11 of 13

4. Summary and Conclusions This study evaluates the performances of the latest version 6.0 GSMaP and version 6.0 IMERG products in an extreme typhoon event (Mangkhut) over China. With the gauge data as the ground truth, the GSMaP and IMERG products have been assessed at different temporal and spatial resolutions. The main results are summarized as follows: (1) Spatially, GSMaP_Gauge outperforms the IMERG_FRCal estimates in each region according to CCs and displays lower FSEs and RMSEs. IMERG_FRCal performs better than GSMaP over the rainfall centers in mainland China and southern China. (2) GSMaP products have higher PODs and CSIs and lower FARs over various regions than IMERG products. GSMaP_Gauge has a higher POD and CSI and lower FAR when the rain rate is 1 mm/h, while gauge-corrected IMERG_FRCal does not significantly improve the detection accuracy of precipitation events. (3) For PDFc and PDFv, GSMaP products agree better with the gauge and the IMERG products have difficulty estimating the heavy rainfall rates. All of the satellite-only products overestimate the occurrence of light precipitation while underestimating the occurrence of moderate precipitation over rainfall centers in mainland China and southern China. (4) According to the time-series analysis, GSMaP products perform better than IMERG products in the rainfall centers over mainland China and southern China while IMERG products show better performance in the rainfall center over eastern China. The bias-adjusted products GSMaP_Gauge and IMERG_FRCal have a high CC and a low RMSE. The identification and quantification of spatiotemporal characteristics of GSMaP and IMERG products provide algorithm developers and users alike with detailed information. It is not surprising that GSMaP products generally perform better than IMERG products during the extreme typhoon Mangkhut over China. This may be explained as followed. Firstly, the IMERG precipitation estimates using GPROF2017 algorithms and is thus inher- ently affected by GPROF2017 datasets. Secondly, GSMaP has a better detection capability than GPROF2017 and a higher CC and lower RMSE over land [36,41]. The agreement between satellite-only GSMaP products and gauge measurements is low and even negative for rainfall intensities over southern China with high FSE and RMAE on scatter plots, illustrating that it is necessary to improve the satellite-only GSMaP products. These results are based only on the extreme Typhoon Mangkhut precipitation storm, and the sources of GSMaP and IMERG bias and their behaviors in different events and regions remain to be further investigated and analyzed. Moreover, typhoon features such as typhoon eye, strong convective area, and precipitation particle size distributions still need further investigation with observations from upgraded S-band dual-polarization radars [42]. Future work will make use of the observations from dual-pol weather radars and raindrop size distribution observations from disdrometers to investigate the microphysical property of precipitation over southern China during different rainfall storms brought by tropical cyclones.

Author Contributions: S.C. conceived the framework of this study; X.L. performed the experiments and analyzed the data; Z.L. (Zhenqing Liang) and C.H. prepared the data and proofread the paper; S.C., Z.L. (Zhi Li), and B.H. helped analyze the results and revised the manuscript; X.L. and S.C. wrote the paper. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by High-level Talents Training and Teacher Qualities and Skills Promotion Plan for Nanning Normal Universities, grant number 6020303890216; First-class Discipline (Geography) in 2019 at Nanning Normal Universities, grant number 6020303891422; “100 Top Talents Program” at Sun Yat-sen University, grant number 74110-52601108; Guangxi Natural Science Foundation Project, grant number 2018JJA150110; National Natural Science Foundation of China, grant number 41875182; and the Guangzhou Scientiﬁc Plan Project, grant number 201904010162. Informed Consent Statement: Not applicable. Atmosphere 2021, 12, 134 12 of 13

Data Availability Statement: Data available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analyzed in this study. The typhoon path data can be found here: http://tcdata.typhoon.org.cn/, CMORPHGC data can be found here: http://data.cma.cn/, GPM data can be found here: https://gpm.nasa.gov/data, and GSMaP data can be found here: ftp://hokusai.eorc.jaxa.jp/. Acknowledgments: We thank all organizations for providing the GPM, GSMaP and CMORPHGC products as well as the typhoon path data freely to the public. We also highly appreciate the detailed reviews and the helpful comments and suggestions from the four reviewers. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References 1. Yang, J.; Li, L.; Zhao, K.; Wang, P.; Wang, D.; Sou, I.M.; Yang, Z.; Hu, J.; Tang, X.; Mok, K.M. A Comparative Study of Typhoon Hato (2017) and Typhoon Mangkhut (2018)–Their Impacts on Coastal Inundation in Macau. J. Geophys. Res. Ocean. 2019, 124, 9590–9619. [CrossRef] 2. Experts Say Typhoon Mangkhut Is Not the Strongest Typhoon Made Landfall in Guangdong Province. Available online: http://news.weather.com.cn/2018/09/2937054.shtml (accessed on 18 September 2018). 3. The King of the Typhoon Mangkhut Is the Largest Typhoon Since 2018! With a Diameter of More Than 1000 Kilometers, It Can Fit Guangdong and Hainan into It. Available online: http://www.fishfirst.cn/article.php?aid=105485 (accessed on 14 September 2018). 4. Guo, H.; Chen, S.; Bao, A.; Hu, J.; Gebregiorgis, A.S.; Xue, X.; Zhang, X. Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia. Remote Sens. 2015, 7, 7181–7211. [CrossRef] 5. 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. [CrossRef] 6. Hong, Y.; Hsu, K.-L.; Sorooshian, S.; Gao, X. Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteorol. 2004, 43, 1834–1853. [CrossRef] 7. 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. [CrossRef] 8. Tan, J.; Petersen, W.A.; Kirstetter, P.-E.; Tian, Y. Performance of IMERG as a function of spatiotemporal scale. J. Hydrometeorol. 2017, 18, 307–319. [CrossRef] 9. Kubota, T.; Shige, S.; Hashizume, H.; Aonashi, K.; Takahashi, N.; Seto, S.; Hirose, M.; Takayabu, Y.N.; Ushio, T.; Nakagawa, K. Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: Production and validation. IEEE Trans. Geosci. Remote Sens. 2007, 45, 2259–2275. [CrossRef] 10. Chen, S.; Hu, J.; Zhang, Z.; Behrangi, A.; Hong, Y.; Gebregiorgis, A.S.; Cao, J.; Hu, B.; Xue, X.; Zhang, X. Hydrologic evaluation of the TRMM multisatellite precipitation analysis over Ganjiang Basin in humid southeastern China. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 4568–4580. [CrossRef] 11. Gosset, M.; Viarre, J.; Quantin, G.; Alcoba, M. Evaluation of several rainfall products used for hydrological applications over West Africa using two high-resolution gauge networks. Q. J. R. Meteorol. Soc. 2013, 139, 923–940. [CrossRef] 12. Sobel, A.H.; Yuter, S.E.; Bretherton, C.S.; Kiladis, G.N. Large-scale meteorology and deep convection during TRMM KWAJEX. Mon. Weather Rev. 2004, 132, 422–444. [CrossRef] 13. Zhong, R.; Chen, X.; Lai, C.; Wang, Z.; Lian, Y.; Yu, H.; Wu, X. Drought monitoring utility of satellite-based precipitation products across mainland China. J. Hydrol. 2019, 568, 343–359. [CrossRef] 14. Hong, Y.; Adler, R.F.; Negri, A.; Huffman, G.J. Flood and landslide applications of near real-time satellite rainfall products. Nat. Hazards 2007, 43, 285–294. [CrossRef] 15. Padhee, S.K.; Nikam, B.R.; Dutta, S.; Aggarwal, S.P. Using satellite-based soil moisture to detect and monitor spatiotemporal traces of agricultural drought over Bundelkhand region of India. Gisci. Remote Sens. 2017, 54, 144–166. [CrossRef] 16. Hou, A.Y.; Kakar, R.K.; Neeck, S.; Azarbarzin, A.A.; Kummerow, C.D.; Kojima, M.; Oki, R.; Nakamura, K.; Iguchi, T. The global precipitation measurement mission. Bull. Am. Meteorol. Soc. 2014, 95, 701–722. [CrossRef] 17. Tan, M.; Duan, Z. Assessment of GPM and TRMM Precipitation Products over Singapore. Remote Sens. 2017, 9, 720. [CrossRef] 18. Anjum, M.N.; Ding, Y.; Shangguan, D.; Ahmad, I.; Ijaz, M.W.; Farid, H.U.; Yagoub, Y.E.; Zaman, M.; Adnan, M. Performance evaluation of latest integrated multi-satellite retrievals for Global Precipitation Measurement (IMERG) over the northern highlands of Pakistan. Atmos. Res. 2018, 205, 134–146. [CrossRef] 19. Liu, Z. Comparison of integrated multisatellite retrievals for GPM (IMERG) and TRMM multisatellite precipitation analysis (TMPA) monthly precipitation products: Initial results. J. Hydrometeorol. 2016, 17, 777–790. [CrossRef] 20. Sungmin, O.; Foelsche, U.; Kirchengast, G.; Fuchsberger, J.; Tan, J.; Petersen, W.A. Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria. Hydrol. Earth Syst. Sci. 2017, 21. [CrossRef] 21. Prakash, S.; Mitra, A.K.; Rajagopal, E.; Pai, D. Assessment of TRMM-based TMPA-3B42 and GSMaP precipitation products over India for the peak southwest monsoon season. Int. J. Climatol. 2016, 36, 1614–1631. [CrossRef] Atmosphere 2021, 12, 134 13 of 13

22. Chen, S.; Hu, J.; Zhang, A.; Min, C.; Huang, C.; Liang, Z. Performance of near real-time Global Satellite Mapping of Precipitation estimates during heavy precipitation events over northern China. Theor. Appl. Climatol. 2018, 135, 877–891. [CrossRef] 23. Morrissey, M.L.; Maliekal, J.A.; Greene, J.S.; Wang, J. The uncertainty of simple spatial averages using rain gauge networks. Water Resour. Res. 1995, 31, 2011–2017. [CrossRef] 24. Villarini, G.; Mandapaka, P.V.; Krajewski, W.F.; Moore, R.J. Rainfall and sampling uncertainties: A rain gauge perspective. J. Geophys. Res. Atmos. 2008, 113. [CrossRef] 25. Guo, H.; Chen, S.; Bao, A.; Hu, J.; Stepanian, P.M. Comprehensive Evaluation of High-Resolution Satellite-Based Precipitation Products over China. Atmosphere 2015, 7, 6. [CrossRef] 26. Chen, S.; Yang, H.; Cao, Q.; Gourley, J.J.; Kirstetter, P.E.; Yong, B.; Tian, Y.; Zhang, Z.; Yan, S.; Hu, J. 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. [CrossRef] 27. Zhang, A.; Xiao, L.; Min, C.; Chen, S.; Kulie, M.; Huang, C.; Liang, Z. Evaluation of latest GPM-Era high-resolution satellite precipitation products during the May 2017 Guangdong extreme rainfall event. Atmos. Res. 2019, 216, 76–85. [CrossRef] 28. 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. [CrossRef] 29. Chen, S.; Behrangi, A.; Tian, Y.; Hu, J.; Hong, Y.; Tang, Q.; Hu, X.-M.; Stepanian, P.M.; Hu, B.; Zhang, X. Precipitation spectra analysis over China with high-resolution measurements from optimally-merged satellite/gauge observations—part II: Diurnal variability analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016, 9, 2979–2988. [CrossRef] 30. Huang, C.; Hu, J.; Chen, S.; Zhang, A.; Liang, Z.; Tong, X.; Xiao, L.; Min, C.; Zhang, Z. How Well Can IMERG Products Capture Typhoon Extreme Precipitation Events over Southern China? Remote Sens. 2019, 11, 70. [CrossRef] 31. Tang, G.; Ma, Y.; Long, D.; Zhong, L.; Hong, Y. Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales. J. Hydrol. 2016, 533, 152–167. [CrossRef] 32. Li, R.; Wang, K.; Qi, D. Validating the integrated multisatellite retrievals for global precipitation measurement in terms of diurnal variability with hourly gauge observations collected at 50,000 stations in China. J. Geophys. Res. Atmos. 2018, 123, 10,423–10,442. [CrossRef] 33. Shige, S.; Yamamoto, T.; Tsukiyama, T.; Kida, S.; Ashiwake, H.; Kubota, T.; Seto, S.; Aonashi, K.; Okamoto, K.i. The GSMaP precipitation retrieval algorithm for microwave sounders—Part I: Over-ocean algorithm. IEEE Trans. Geosci. Remote Sens. 2009, 47, 3084–3097. [CrossRef] 34. Global Satellite Mapping of Precipitation (GSMaP) for GPM Algorithm Theoretical Basis Document (ATBD) Algorithm Ver.6. Avail- able online: https://www.eorc.jaxa.jp/GPM/doc/algorithm/GSMaPforGPM_20140902_E.pdf (accessed on 19 January 2021). 35. Huffman, G.J.; Bolvin, D.T.; Nelkin, E.J.; Tan, J. Integrated Multi-Satellite Retrievals for GPM (IMERG) Technical Documentation; Project, G., Ed.; NASA/GSFC: Greenbelt, MD, USA, 2019. 36. Huffman, G.J.; Bolvin, D.T.; Braithwaite, D.; Hsu, K.; Joyce, R.; Kidd, C.; Nelkin, E.J.; Sorooshian, S.; Tan, J.; Xie, P. Algorithm Theoretical Basis Document (ATBD) Version 06 for the NASA Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG); Project, G., Ed.; NASA/GSFC: Greenbelt, MD, USA, 2019. 37. Chen, S.; Hong, Y.; Cao, Q.; Kirstetter, P.-E.; Gourley, J.J.; Qi, Y.; Zhang, J.; Howard, K.; Hu, J.; Wang, J. Performance evaluation of radar and satellite rainfalls for Typhoon Morakot over Taiwan: Are remote-sensing products ready for gauge denial scenario of extreme events? J. Hydrol. 2013, 506, 4–13. [CrossRef] 38. Taylor, K.E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos. 2001, 106, 7183–7192. [CrossRef] 39. Ebert, E.E.; Janowiak, J.E.; Kidd, C. Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical Models. Bull. Am. Meteorol. Soc. 2007, 88, 47–64. [CrossRef] 40. Chen, S.; Kirstetter, P.; Hong, Y.; Gourley, J.; Tian, Y.; Qi, Y.; Cao, Q.; Zhang, J.; Howard, K.; Hu, J. Evaluation of spatial errors of precipitation rates and types from TRMM spaceborne radar over the southern CONUS. J. Hydrometeorol. 2013, 14, 1884–1896. [CrossRef] 41. You, Y.; Wang, N.-Y.; Kubota, T.; Aonashi, K.; Shige, S.; Kachi, M.; Kummerow, C.; Randel, D.; Ferraro, R.; Braun, S. Comparison of TRMM Microwave Imager Rainfall Datasets from NASA and JAXA. J. Hydrometeorol. 2020, 21, 377–397. [CrossRef] 42. Xia, Q.; Zhang, W.; Chen, H.; Lee, W.-C.; Han, L.; Ma, Y.; Liu, X. Quantiﬁcation of Precipitation Using Polarimetric Radar Measurements during Several Typhoon Events in Southern China. Remote Sens. 2020, 12, 2058. [CrossRef]