An Evaluation of FY-3/VIRR Sea Surface Temperature Datasets for Climate Applications

风云三号卫星可见光红外辐射计海表温度资料的气候应用评估

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  • Corresponding author: Jian LIU, liujian@cma.gov.cn
  • Funds:

    Supported by the Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004) and National Key Reseach and Development Program of China (2018YFB0504905 and 2018YFB0504900)

  • doi: 10.1007/s13351-021-1055-5
  • Note: This paper will appear in the forthcoming issue. It is not the finalized version yet. Please use with caution.

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  • We evaluated the pentad and monthly means of the sea surface temperature (SST) products from the visible infrared radiometer onboard the Fengyun-3 satellite (FY-3/VIRR) in the period of 2016–2018 from the perspective of climate applications. These data had previously been reprocessed by the National Satellite Meteorological Center based on an updated SST retrieval algorithm. The overall consistency between the optimum interpolation SST version 2 (OIv2.1) and FY-3/VIRR data was better for the monthly means than for the pentad means and showed a clear dependence on the season and location. There was better consistency in winter than in summer and better in the tropical central and eastern Pacific than in the western Pacific warm pool, tropical North Indian Ocean, and tropical Atlantic Ocean. The monthly deviation of the global average SST anomaly was −0.03 ± 0.07°C and the global average root-mean-square errors (RMSEs) presented clear seasonal fluctuations with a maximum of ~0.5°C in summer. The poor consistency of the FY-3/VIRR data in summer may be partially attributed to the bias of the OIv2.1 data in the global ocean and Indian Ocean as a result of the spatially heterogeneous in situ measurements from ships, buoys, and Argo floats. Convective activities and clouds in the tropics may also influence the accuracy of the FY-3/VIRR SST retrievals. The Niño SST indices based on both the FY-3/VIRR and OIv2.1 data generally displayed a similar evolution, including the beginning and end of El Niño and La Niña events and their amplitudes, although the deviations were slightly larger when the Pacific SST anomaly was in the neutral state of the El Niño–Southern Oscillation (ENSO). The deviations varied greatly with season in the tropical Indian and Atlantic oceans, suggesting the need to perform further analyses of the quality of the data and validation of the FY-3/VIRR SST products in these two basins.
    本文基于国家卫星气象中心提供的一套利用最新的海温反演算法再处理的2016–2018年风云三号卫星可见光红外辐射计(FY-3/VIRR)海表温度产品,以最优插值海表温度数据OIv2.1作为标准,从气候应用的角度对其侯平均和月平均尺度产品进行了质量评估。FY-3/VIRR的月平均产品总体优于候平均产品,与OIv2.1的一致性表现出季节和空间差异性:冬季较好,夏季较差;热带中东太平洋较好,暖池、热带北印度洋和大西洋较差。全球平均的海温偏差为−0.03 ± 0.07°C,均方根误差呈现季节性波动,其中夏季最大,约0.5°C。夏季FY-3/VIRR海温在全球平均和印度洋区域与OIv2.1存在较大偏差,部分原因可能来自于船舶、固定浮标和Argo浮标的空间非均匀测量,而热带地区较多的对流活动和云覆盖也可能影响FY-3/VIRR海温反演的精度。基于FY-3/VIRR得到的Niño海温指数总体上与OIv2.1接近,能够反映ENSO事件的开始、结束和强度。在热带印度洋和大西洋两种海温资料之间的偏差随季节变化很大。因此,有必要对这两个区域的FY-3/VIRR海温产品做进一步的质量分析和验证。
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  • Fig. 1.  Time series of the monthly Niño3.4 index (°C) during the period of 2016–2018.

    Fig. 2.  Correlations of the (a) monthly and (b–d) pentad SSTAs averaged over the region (70°S–70°N, 0°–360°) between the FY-3/VIRR and OIv2.1 data in (a) 2016–2018, (b) 2016, (c) 2017, and (d) 2018, respectively. The gray line indicates the average anomaly correlation coefficient (ACC).

    Fig. 3.  The distribution of the temporal correlation coefficients of the monthly SSTAs between the FY-3/VIRR and OIv2.1 data from 2016 to 2018 (annual cycles are subtracted from the original data).

    Fig. 4.  Temporal correlation distributions of the pentad SSTAs between the FY-3/VIRR and OIv2.1 data in (a) 2016, (b) 2017, and (c) 2018, respectively (annual cycles are subtracted from the original data).

    Fig. 5.  The deviation (bar) and RMSE (line) of the monthly SSTA (°C) averaged over the region (70°S–70°N, 0°–360°) between the FY-3/VIRR and OIv2.1 data from 2016 to 2018.

    Fig. 6.  SSTAs (°C) in the (a) OIv2.1 and (b) FY-3/VIRR data, and (c) differences between the FY-3/VIRR and OIv2.1 data in January 2016.

    Fig. 7.  SSTA differences (°C) between the FY-3/VIRR and OIv2.1 data in July (a) 2016, (b) 2017, and (c) 2018, respectively.

    Fig. 8.  Scatter plots of the deviations in SSTAs between the FY-3/VIRR and OIv2.1 data in the tropical Indian Ocean (y-axis) versus the OIv2.1 SSTAs (x-axis) in (a) January 2016, (b) July 2016, (c) July 2017, and (d) July 2018, respectively.

    Fig. 9.  Time series of the monthly mean tropical Pacific Ocean SST indices (a) Niño3.4, (b) Niño3, (c) Niño4, and (d) NiñoW based on the FY-3/VIRR (red line) and OIv2.1 (blue line) data and related biases (bar; °C), respectively.

    Fig. 10.  As in Fig. 9, but for the tropical Indian Ocean SST indices (a) IOBM and (b) IOD.

    Fig. 11.  As in Fig. 9, but for the tropical Atlantic Ocean SST indices (a) equatorial Atlantic Niño (ATL3), (b) tropical North Atlantic (TNA), (c) tropical South Atlantic (TSA), and (d) Tropical Atlantic Dipole (TAD).

    Table 1.  Definitions of the selected tropical SSTA indices. Notes: TNA—Tropical North Atlantic, TSA—Tropical South Atlantic, TAD—Tropical Atlantic Dipole.

    SST indexDefinition
    Niño3.4SSTA (5°S–5°N, 170°–120°W)
    Niño3SSTA (5°S–5°N, 150°–90°W)
    Niño4SSTA (5°S–5°N, 160°E–150°W)
    NiñoWSSTA (0°–10°N, 120°–140°E)
    IODSSTA (10°S–10°N, 50°–70°E)
    − SSTA (10°S–0°, 90°–110°E)
    IOBMSSTA (20°S–20°N, 40°–110°E)
    ATL3SSTA (3°S–3°N, 20°W–0°)
    TNASSTA (5°–20°N, 60°–30°W)
    TSASSTA (20°S–0°, 30°W–10°E)
    TADSSTATNA − SSTATSA
    Download: Download as CSV
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An Evaluation of FY-3/VIRR Sea Surface Temperature Datasets for Climate Applications

    Corresponding author: Jian LIU, liujian@cma.gov.cn
  • 1. Laboratory of Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081
  • 2. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044
  • 3. National Meteorological Satellite Center, China Meteorological Administration, Beijing 100081
  • 4. Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081
Funds: Supported by the Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004) and National Key Reseach and Development Program of China (2018YFB0504905 and 2018YFB0504900)

Abstract: We evaluated the pentad and monthly means of the sea surface temperature (SST) products from the visible infrared radiometer onboard the Fengyun-3 satellite (FY-3/VIRR) in the period of 2016–2018 from the perspective of climate applications. These data had previously been reprocessed by the National Satellite Meteorological Center based on an updated SST retrieval algorithm. The overall consistency between the optimum interpolation SST version 2 (OIv2.1) and FY-3/VIRR data was better for the monthly means than for the pentad means and showed a clear dependence on the season and location. There was better consistency in winter than in summer and better in the tropical central and eastern Pacific than in the western Pacific warm pool, tropical North Indian Ocean, and tropical Atlantic Ocean. The monthly deviation of the global average SST anomaly was −0.03 ± 0.07°C and the global average root-mean-square errors (RMSEs) presented clear seasonal fluctuations with a maximum of ~0.5°C in summer. The poor consistency of the FY-3/VIRR data in summer may be partially attributed to the bias of the OIv2.1 data in the global ocean and Indian Ocean as a result of the spatially heterogeneous in situ measurements from ships, buoys, and Argo floats. Convective activities and clouds in the tropics may also influence the accuracy of the FY-3/VIRR SST retrievals. The Niño SST indices based on both the FY-3/VIRR and OIv2.1 data generally displayed a similar evolution, including the beginning and end of El Niño and La Niña events and their amplitudes, although the deviations were slightly larger when the Pacific SST anomaly was in the neutral state of the El Niño–Southern Oscillation (ENSO). The deviations varied greatly with season in the tropical Indian and Atlantic oceans, suggesting the need to perform further analyses of the quality of the data and validation of the FY-3/VIRR SST products in these two basins.

风云三号卫星可见光红外辐射计海表温度资料的气候应用评估

本文基于国家卫星气象中心提供的一套利用最新的海温反演算法再处理的2016–2018年风云三号卫星可见光红外辐射计(FY-3/VIRR)海表温度产品,以最优插值海表温度数据OIv2.1作为标准,从气候应用的角度对其侯平均和月平均尺度产品进行了质量评估。FY-3/VIRR的月平均产品总体优于候平均产品,与OIv2.1的一致性表现出季节和空间差异性:冬季较好,夏季较差;热带中东太平洋较好,暖池、热带北印度洋和大西洋较差。全球平均的海温偏差为−0.03 ± 0.07°C,均方根误差呈现季节性波动,其中夏季最大,约0.5°C。夏季FY-3/VIRR海温在全球平均和印度洋区域与OIv2.1存在较大偏差,部分原因可能来自于船舶、固定浮标和Argo浮标的空间非均匀测量,而热带地区较多的对流活动和云覆盖也可能影响FY-3/VIRR海温反演的精度。基于FY-3/VIRR得到的Niño海温指数总体上与OIv2.1接近,能够反映ENSO事件的开始、结束和强度。在热带印度洋和大西洋两种海温资料之间的偏差随季节变化很大。因此,有必要对这两个区域的FY-3/VIRR海温产品做进一步的质量分析和验证。
1.   Introduction
  • The global sea surface temperature (SST) is an important climate variable as a result of its large heat capacity and wide global coverage. SST anomalies (SSTAs) can induce variations in the earth’s climate over both the land and oceans by modulating the geopotential height and wind fields in the lower to upper tropospheres. The SST has been widely used in climate monitoring, assessments, predictions, and simulations, as well as in applications related to the environmental protection, agriculture, and industry (EPA, 2014; IPCC, 2018).

    The El Niño–Southern Oscillation (ENSO) in the tropical central and eastern Pacific Ocean provides the strongest external forcing of the SST on seasonal to interannual timescales and can cause severe flooding, droughts, heatwaves, landslides, and other natural disasters that affect both the lives, property, and economic activities of humans, as well as the natural environment (National Research Council, 2010)—For example, the ENSO exerts distinct impacts on the variability of the East Asian climate in different phases (Zhang et al., 1999; Wang et al., 2000; Zhang and Sumi, 2002; Wu et al., 2003; Hu et al., 2005; Zhou and Wu, 2010; Liu et al., 2013, 2015, 2019b). The SSTAs in other oceans, including the tropical Indian Ocean (Wu et al., 2000, 2004; Liu et al., 2019a; Ding et al., 2021), Atlantic Ocean (Li and Bates, 2007; Han et al., 2011; Liu et al., 2019a, b), and western Pacific warm pool (Nitta and Hu, 1996; Guo and Ni, 1998), also affect the atmospheric circulation and precipitation anomalies over Asia (Zhang et al., 2008; Liu et al., 2019b). A high-quality SST dataset is therefore essential in understanding the variation of SSTs and their global impacts.

    Climate applications require SST data with an accuracy of 0.1 K and a stability of 0.04 K decade−1 (Ohring et al., 2005). The SST can be estimated by the retrieval of observations from satellites (GCOS, 2011). The Fengyun-3 (FY-3) series is the second generation of polar-orbiting meteorological satellites launched by China. The FY-3B and FY-3C satellites were launched on 5 November 2010 and 23 September 2013, respectively; and are operated in a sun-synchronous afternoon orbit with local equator-crossing times of 1400 and 1000, respectively, in the descending node. The SST is a key global product of the FY-3 series satellites (Wang et al., 2014a, b, c, 2020).

    The visible infrared radiometer (VIRR) onboard the FY-3 satellites is a 10-channel radiometer for multipurpose imagery with a 1.1-km resolution at nadir. The swath of the VIRR is 2800 km (Yang et al., 2012). The VIRR has one mid-wavelength infrared channel (3.55–3.93 μm) and two long-wavelength infrared channels [the split window channels (10.3–11.3 and 11.5–12.5 μm)] which are used to estimate the SST. To meet the needs of climate applications, the National Satellite Meteorological Center of the China Meteorological Administration provides a set of global daily satellite merged SST products by combining the FY-3B and FY-3C VIRR SST data (hereafter the FY-3/VIRR SST).

    We evaluated the FY-3/VIRR SST products on both pentad and monthly timescales from the point of view of climate applications and investigated the biases and possible causes. This work will provide reference information for reprocessing historical FY-3/VIRR data. We also assessed and selected suitable SST indicators for the application to climate monitoring and diagnosis in real-time climate operations by using the independent FY series satellite products.

    The remainder of the paper is organized as follows. Section 2 describes the datasets, SST indices, and data processing methods used in this study. Section 3 addresses the deviations of the FY-3/VIRR SST data and their possible causes by using regional SST indices. A summary and discussion are provided in Section 4.

2.   Datasets and methods
  • We used daily SST data from the VIRR onboard FY-3B and FY-3C polar-orbiting satellites. Reprocessing version 1 (RV1) of the FY-3B and FY-3C data was conducted over the period from January 2014 to December 2019. RV1 is based on operational Level 1 data with monthly nonlinear SST coefficients and the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIAv2.0; Donlon et al., 2012) was used as the first-guess SST of the nonlinear SST algorithm. The nonlinear SST algorithms were tuned by the regression of the SST against the quality-controlled buoy data from the in situ SST Quality Monitor (iQUAM; Xu et al., 2014).

    The VIRR satellite SST based on the statistical regression of the buoy SST was defined as the bulk SST using the Algorithm Theoretical Basis Document from the National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service (Ignatov, 2010). This procedure converts the retrieval of the temperature from the “skin” to the “bulk” SST and is sensitive to the skin SST (Ignatov, 2010). The granule FY-3B and FY-3C VIRR SST data were resampled to a latitude–longitude grid at 0.05° (5 km) resolution separated into daytime and nighttime data for each satellite. The quality index was delivered with each grid to classify the processing conditions as excellent, good, bad, or unprocessed (cloud, land, or missing data). The introduction of specific algorithms has been reported by Wang et al. (2020).

    The difference between the nighttime and daytime SSTs was taken into account in the processing of the FY-3/VIRR merged SST data. The daily FY-3/VIRR merged SST of RV1 was processed by using the priority FY-3B nighttime, FY-3C nighttime, and FY-3B and FY-3C daytime SSTs when the wind speed was > 6 m s−1 to take account of the diurnal warming caused by strong breezes and sunshine. The global resolution was 5 km. The daily vector wind data were from the National Centers of Environmental Prediction-Department of Energy Reanaly-sis-2 dataset for the period from 1979 to present (Kanamitsu et al., 2002; www.esrl.noaa.gov/psd). We only used SST data with an “excellent” quality level. We set the global SST spatial range as (70°S–70°N, 0–360°) to avoid missing SST data in the polar regions, which are usually covered by sea ice.

    The reference data for the evaluation of the FY-3/VIRR SST was the daily mean SST from version 2.1 of the optimum interpolation SST (OIv2.1; Reynolds et al., 2002, 2007) with a horizontal resolution of (0.25° × 0.25°). OIv2.1 is an upgraded version of OIv2 (Reynolds et al., 2002) with reduced biases in the global ocean and Indian Ocean compared with Argo observations (Huang et al., 2021). It has been widely used in climate assessments and monitoring by the National Centers of Environmental Prediction Climate Prediction Center and the China Meteorological Administration National Climate Center. The OIv2.1 SST is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (Huang et al., 2021). The SST product is obtained by the optimal interpolation process and reflects the bulk SST (Ignatov, 2010). The monthly and pentad climatologies used in this work were averages of the OIv2.1 data from 1991 to 2010. The daily average Canadian Meteorological Center Level 4 SST data (Brasnett and Surcel Colan, 2016) were selected to verify the credibility of the FY-3/VIRR SST.

    The FY-3/VIRR SST data were interpolated onto the same spatial resolution as the OIv2.1 data for comparison and only the FY-3/VIRR data with a quality control code of five (excellent) were selected for interpolation; this accounts for 51% of the total global FY-3/VIRR SST. The daily FY-3/VIRR SST was processed into pentad and monthly averages according to the demand of climate operational applications. The mean of the first to fifth days of each month represents the first pentad, the mean of the sixth to tenth days is the second pentad, and so on. The last pentad is the average from the 26th day to the last day of each month, with a total of six periods in each month. The missing values are therefore greatly reduced in both space and time through the interpolation process.

    The SSTs in the equatorial central and eastern Pacific associated with the ENSO are key signals in the climate monitoring and prediction (Hu et al., 2020). It has been shown that the variations in some regional mean SSTA indices can be used to represent key climate variability modes, such as the El Niño and La Niña status. For example, based on the OIv2.1 SST, the variation in the Niño3.4 index indicates that the ENSO evolved from a positive phase to a neutral state and then to a negative phase from 2016 to 2018 (Fig. 1). In addition to the Niño3.4 index, the Niño3 and Niño4 indices have been used to represent different flavors of the ENSO (Kao and Yu, 2009; Hu et al., 2012). The western Pacific warm pool (NiñoW), Indian Ocean basin mode (IOBM; Chambers et al., 1999), Indian Ocean Dipole (IOD; Saji et al., 1999), and equatorial Atlantic Niño (ATL3; Zebiak, 1993) indices were selected to represent tropical SSTA forcing in different ocean basins (Enfield et al., 1999). Table 1 gives the definitions of these SSTA indices.

    Figure 1.  Time series of the monthly Niño3.4 index (°C) during the period of 2016–2018.

    SST indexDefinition
    Niño3.4SSTA (5°S–5°N, 170°–120°W)
    Niño3SSTA (5°S–5°N, 150°–90°W)
    Niño4SSTA (5°S–5°N, 160°E–150°W)
    NiñoWSSTA (0°–10°N, 120°–140°E)
    IODSSTA (10°S–10°N, 50°–70°E)
    − SSTA (10°S–0°, 90°–110°E)
    IOBMSSTA (20°S–20°N, 40°–110°E)
    ATL3SSTA (3°S–3°N, 20°W–0°)
    TNASSTA (5°–20°N, 60°–30°W)
    TSASSTA (20°S–0°, 30°W–10°E)
    TADSSTATNA − SSTATSA

    Table 1.  Definitions of the selected tropical SSTA indices. Notes: TNA—Tropical North Atlantic, TSA—Tropical South Atlantic, TAD—Tropical Atlantic Dipole.

3.   Results
  • The largest focus in climate applications is on the variation of SSTAs. We therefore used the spatial distribution of the anomaly correlation coefficient (ACC) to measure the consistency (difference) between the two SST datasets. Figure 2a presents the variation in the ACC of the monthly global SSTAs (70°S–70°N, 0°–360°) between the FY-3/VIRR and OIv2.1 data from 2016 to 2018. The overall consistency between the two datasets was good, with a mean ACC of 0.895. There was a clear dependence on the season, with an average ACC of 0.920 in winter (December–January–February) and an average ACC of 0.868 in summer (June–July–August).

    Figure 2.  Correlations of the (a) monthly and (b–d) pentad SSTAs averaged over the region (70°S–70°N, 0°–360°) between the FY-3/VIRR and OIv2.1 data in (a) 2016–2018, (b) 2016, (c) 2017, and (d) 2018, respectively. The gray line indicates the average anomaly correlation coefficient (ACC).

    The variations in the ACCs of the pentad SSTAs in 2016, 2017, and 2018 had similar features (Figs. 2bd) and were better in winter than in summer, although the overall ACCs were lower than the ACCs of the monthly SSTAs. There were also some differences in the ACCs during these three years. In 2016, which was a decaying year of an El Niño event, the SST in the central and eastern tropical Pacific had an abnormally warm to neutral state with an average ACC of 0.858. In 2018, which was a decaying year of a La Niña event, the SST in the equatorial central and eastern Pacific varied from anomalously cold to warm with an average ACC of 0.856. The SSTA of the tropical central and eastern Pacific (Niño3.4 index) was neutral throughout most of 2017 (Fig. 1) and the average ACC was 0.845, slightly lower than that in 2016 and 2018. The ENSO cycle was therefore associated with the consistency between the two SST datasets, with a higher consistency during both the warm and cold phases of the ENSO than during the neutral phase. This was probably a result of the large amplitude of the SSTA and the signal-to-noise ratio in the tropical central and eastern Pacific in both the warm and cold phases of the ENSO (Hu et al., 2019).

    Figure 3 shows the spatial distribution of the point-to-point correlation of the monthly SSTAs between the FY-3/VIRR and OIv2.1 data from 2016 to 2018. The consistency between the two datasets was excellent in most regions, with a correlation coefficient > 0.9, but there was a clear dependence on location. The correlation coefficient was > 0.95 in the tropical central and eastern Pacific and > 0.9 in the North Pacific, central North Atlantic, South Indian, South Pacific, and South Atlantic oceans. By contrast, relatively low correlation coefficients were present in some key tropical areas of the SST monitoring operation. For example, the correlation coefficients were generally < 0.8 in the western Pacific warm pool, northern Indian Ocean, and equatorial Atlantic Ocean.

    Figure 3.  The distribution of the temporal correlation coefficients of the monthly SSTAs between the FY-3/VIRR and OIv2.1 data from 2016 to 2018 (annual cycles are subtracted from the original data).

    Figure 4 shows a similar distribution of the correlation coefficients of the pentad SSTAs between the FY-3/VIRR and OIv2.1 datasets, where the correlation coefficient was generally lower than that of the monthly average. There were also slight differences among the three years. As a result of warming of the tropical central and eastern Pacific and Indian oceans in 2016 (figure not shown), the correlation coefficients in these regions, as well as in the western Pacific warm pool, were higher than those in 2017 and 2018. The correlation coefficients in the tropical Atlantic showed little difference among the three years.

    Figure 4.  Temporal correlation distributions of the pentad SSTAs between the FY-3/VIRR and OIv2.1 data in (a) 2016, (b) 2017, and (c) 2018, respectively (annual cycles are subtracted from the original data).

  • We analyzed the possible reasons for the low consistency between the FY-3/VIRR and OIv2.1 climatology data in the study regions and suggested how to reasonably use the FY-3/VIRR SST data products.

    Figure 5 presents the difference and root-mean-square-error (RMSE) of the monthly FY-3/VIRR SSTAs averaged over the region (70°S–70°N, 0–360°) compared with the OIv2.1 SSTA from 2016 to 2018. In general, the FY-3/VIRR SSTAs were colder than the OIv2.1 SSTAs, except for during a few months of summer, with a deviation range of −0.03 ± 0.07°C. The RMSE of the monthly FY-3/VIRR SSTA from 2016 to 2018 showed a clear seasonal fluctuation, with a maximum of about 0.5°C in summer. Huang et al. (2021) showed that the OIv2.1 data have a residual cold bias of about −0.04°C over the global oceans and about −0.08°C in the Indian Ocean, which may result from heterogeneous in situ measurements from ships and Argo floats. The biases of the OIv2.1 data in the global ocean and Indian Ocean may contribute to the notable deviation between the two SST datasets. The lower consistency in summer may also be partly attributed to the merge algorithm of the FY-3/VIRR, i.e., the processing method to avoid diur-nal warming (Wang et al., 2020).

    Figure 5.  The deviation (bar) and RMSE (line) of the monthly SSTA (°C) averaged over the region (70°S–70°N, 0°–360°) between the FY-3/VIRR and OIv2.1 data from 2016 to 2018.

    We investigated the differences in the spatial distribution and deviation of the SSTAs between winter, with good consistency, and summer, with a poor consistency (Figs. 6, 7). January 2016 had the highest correlation for the two sets of SSTA data, with a correlation coefficient of 0.932 and an RMSE of 0.335°C (Fig. 2a). In the OIv2.1 SSTA data, positive SSTAs were seen in the tropical central and eastern Pacific, tropical Indian Ocean, and western Atlantic Ocean, whereas negative SSTAs were seen in both the north and south midlatitudes of the Pacific and Atlantic oceans, which is typical of the mature phase of an extremely strong El Niño event (Fig. 6a).

    Figure 6.  SSTAs (°C) in the (a) OIv2.1 and (b) FY-3/VIRR data, and (c) differences between the FY-3/VIRR and OIv2.1 data in January 2016.

    Figure 7.  SSTA differences (°C) between the FY-3/VIRR and OIv2.1 data in July (a) 2016, (b) 2017, and (c) 2018, respectively.

    The FY-3/VIRR data reflected the spatial pattern of SSTAs well (Fig. 6b), but detailed differences from the OIv2.1 data could be distinguished from the distribution of the deviations (Fig. 6c). This showed a clear dependence on the latitude with the characteristics of a quasi-symmetrical zonal distribution—That is, the region from the equator to the south and north presented “negative–positive–negative” deviations. An overall negative deviation was observed in the tropical oceans, especially in the equatorial central and eastern Atlantic, equatorial central and eastern Pacific, and equatorial western Indian oceans. The significant SSTA variability in these areas may be associated with the large differences between the two SST datasets.

    In contrast with the winter months, when there was good consistency between the datasets, July had the lowest ACC consistency (Fig. 2). Figure 7 shows the spatial distribution of the SSTA deviations in July 2016, 2017, and 2018. The distribution of the deviation in July was different from that in January: The differences in SSTA between the two SST datasets were more significant in the Northern Hemisphere than in the Southern Hemisphere. The FY-3/VIRR SSTAs were colder than the OIv2.1 SSTAs in the North Indian as well as equatorial eastern Pacific and North Atlantic oceans. The SSTA pattern in the North Pacific region near 40°N had a “positive in the west and negative in the east” pattern, which did not occur in the SSTA deviation field in January (Fig. 6c). The differences in SSTA in these oceanic regions need further verification before being applied to climate monitoring.

    There were clear differences in the SSTAs between the FY-3/VIRR and OIv2.1 datasets in the tropical Indian Ocean in summer, when the evolution of the SSTAs had a significant impact on precipitation over Asia, especially in China during the flood season (Wu and Kirtman, 2004; Annamalai et al., 2005; Yang et al., 2007; Xie et al., 2009; Ding et al., 2021). There were two typical spatial modes of the SSTAs in the tropical Indian Ocean (Chambers et al., 1999; Saji et al., 1999), where the IOBM generally develops in autumn and reaches its peak in winter and spring: (1) A mode in which the SST over the whole Indian Ocean basin tends to be warmer or colder; and (2) a mode in which the SSTA is relatively weak in summer (Chambers et al., 1999). In January 2016, the SSTA of the Indian Ocean in the OIv2.1 dataset was > 1°C and the spread of the FY-3/VIRR SSTA deviation was relatively small. Therefore it seems that the deviations in the FY-3/VIRR SSTA might not have a significant relationship with the SSTA in the OIv2.1 dataset. Correspondingly, the linear fitting coefficient was −0.16 and did not reach the 0.1 significance level (Fig. 8a).

    Figure 8.  Scatter plots of the deviations in SSTAs between the FY-3/VIRR and OIv2.1 data in the tropical Indian Ocean (y-axis) versus the OIv2.1 SSTAs (x-axis) in (a) January 2016, (b) July 2016, (c) July 2017, and (d) July 2018, respectively.

    The spread of the FY-3/VIRR SSTA deviations in the tropical Indian Ocean was larger in summer than in winter, without a significant relationship with the OIv2.1 SSTA (Figs. 8bd). The spread of the FY-3/VIRR SSTA cold deviations was clearer than the distribution of the warm deviations of the FY-3/VIRR SSTA during the three summers. The spatial distribution of the SSTA deviations in Fig. 7 shows that the largest cold deviation in summer was located in the coastal regions of the northern Indian Ocean, where the seasonal variability of the SSTA was large and had a significant impact on atmospheric heating and climate anomalies (Huang and Hu, 2008; Izumo et al., 2008; Du et al., 2011). The accuracy of the SSTAs in these regions in the FY-3/VIRR dataset needs further improvement by cross-validation with different SST datasets and in situ observations as a result of their important role in climate variability and predictability (i.e., Huang et al., 2013, 2016).

  • Various SST monitoring indices have been designed for key regions to describe the evolution of the external forcing signal of SSTAs for use in climate operations and research, including the Niño3.4, NiñoW, IOBM, IOD, and ATL3 indices (i.e., Zebiak, 1993; Chambers et al., 1999; Saji et al., 1999; see Table 1). We selected several representative SST monitoring indices in the tropical Pacific, Indian, and Atlantic oceans to evaluate the quality of the FY-3/VIRR SST data from the monthly evolution of these indicators (Figs. 911).

    Figure 9.  Time series of the monthly mean tropical Pacific Ocean SST indices (a) Niño3.4, (b) Niño3, (c) Niño4, and (d) NiñoW based on the FY-3/VIRR (red line) and OIv2.1 (blue line) data and related biases (bar; °C), respectively.

    Figure 10.  As in Fig. 9, but for the tropical Indian Ocean SST indices (a) IOBM and (b) IOD.

    Figure 9 shows the four monitoring indices (Niño3.4, Niño3, Niño4, and NiñoW) for ENSO events in the tropical Pacific Ocean. All the indices show consistent evolution characteristics as a result of the relatively good consistency of the two datasets in the equatorial central and eastern Pacific (Figs. 9ac). A transition from El Niño (Niño3.4 index > 0.5°C) to La Niña (Niño3.4 index less than −0.5°C) events was experienced from 2016 to 2018. The Niño indices based both on the FY-3/VIRR and OIv2.1 SSTAs and the amplitudes of the positive and negative SSTAs were consistent at the beginning and end of El Niño and La Niña events. However, the differences in the Niño3.4 index between the two datasets were slightly larger in the neutral ENSO state (Niño3.4 index between −0.5 and 0.5°C) than during El Niño and La Niña events (Fig. 9a).

    The two datasets showed a larger deviation in the NiñoW index than in the equatorial central and eastern Pacific (Fig. 9d), with July 2016, August 2016, and August 2017 showing large positive deviations as well as November 2016, November 2017, and December 2017 showing large negative deviations. The monthly variability of the NiñoW index was larger in the FY-3/VIRR dataset than in the OIv2.1 dataset. This is consistent with the poor point-to-point correlation in the western Pacific warm pool, which may be related to deep convective activities and clouds in this region (Wang et al., 2020). We need to further quantify the impact of clouds on the SST in the satellite retrieval data in this region.

    Figure 10 shows the monthly evolution of the two Indian Ocean SST indices. The IOBM index was more consistent between the FY-3/VIRR and OIv2.1 datasets than the IOD index, although there was a clear deviation between the two datasets when the IOBM index reached a maximum or minimum. There were similar deviations in the monthly IOD index, particularly for the cold deviations accompanied by a minimum value of the IOD index—For example, the IOD index from the FY-3/VIRR dataset was colder than that from the OIv2.1 dataset by > 1°C in June 2018. Such a large SSTA deviation may have a crucial impact on operational applications to climate monitoring and forecasts.

    The four SST indices were compared for the tropical Atlantic Ocean. The FY-3/VIRR data showed remarkable deviations from the OIv2.1 data, especially for the ATL3 index in the equatorial Atlantic (Fig. 11). This is partly linked to the cold SST deviation of the FY-3/VIRR data relative to the OIv2.1 data in the lower latitudes of the Atlantic Ocean (Fig. 5). The TNA and TSA indices of the FY-3/VIRR dataset were colder than those in the OIv2.1 data and the TAD showed an irregular deviation during this period.

    Figure 11.  As in Fig. 9, but for the tropical Atlantic Ocean SST indices (a) equatorial Atlantic Niño (ATL3), (b) tropical North Atlantic (TNA), (c) tropical South Atlantic (TSA), and (d) Tropical Atlantic Dipole (TAD).

4.   Summary and discussion
  • We evaluated the quality of the FY-3/VIRR SST product in the period of 2016–2018 when the SSTA in the tropical central and eastern Pacific Ocean experienced a typical “warm–neutral–cold” evolution. This FY-3/VIRR SST product had been reprocessed by the National Satellite Meteorological Center of the China Meteorological Administration based on an updated SST retrieval algorithm. We compared the FY-3/VIRR SST product with the OIv2.1 SST, which is commonly used in climate monitoring, assessment, and prediction operations. We only selected FY-3/VIRR global SST data with an “excellent” quality level and smoothed to pentad and monthly timescales to meet the operation needs of the climate monitoring and prediction. Our main conclusions are as follows.

    (1) The consistency of the monthly SSTA between the FY-3/VIRR and OIv2.1 datasets was better than that of the pentad SSTA. The consistency was clearly dependent on the season and location. The consistency was better in winter than in summer and better in the tropical central and eastern Pacific Ocean than in the western Pacific warm pool, tropical North Indian Ocean, and tropical Atlantic Ocean.

    (2) The monthly deviation of the global average SSTA between the two datasets was −0.03 ± 0.07°C and the global average RMSE showed clear seasonal fluctuations with a maximum of ~0.5°C in summer. The SSTA deviations in the Northern Hemisphere were larger than those in the Southern Hemisphere in summer and the cold deviations in the FY-3/VIRR data were mainly present in the North Indian, equatorial eastern Pacific, and North Atlantic oceans. The poor consistency in summer may be partially associated with the biases in the OIv2.1 data in the global ocean, particularly in the Indian Ocean, as a result of heterogeneous in situ measurements from ships, buoys, and Argo floats. Convective activities and clouds in the tropics, including the western Pacific, North Indian, and tropical Atlantic oceans, may affect the accuracy of satellite infrared sensors and, in turn, the SST retrievals.

    (3) Based on the Niño indices, the FY-3/VIRR and OIv2.1 SST data display a similar ENSO evolution, including the beginning and end of El Niño and La Niña events and their amplitudes, although the deviation is slightly larger in the ENSO neutral condition. The deviations of the SST indices between the two datasets vary with the season in the tropical Indian and Atlantic oceans.

    To further verify the validity of the FY3/VIRR SST product, an additional comparative analysis was made of the FY3/VIRR SST data and the daily OIv2.1 and Canadian Meteorological Center Level 4 SST from 2016 to 2018. The results were generally consistent with the results for the pentad and monthly mean SSTs. Compared with the daily OIv2.1 data, the deviation and RMSE of the FY3/VIRR SSTA were 0.01 and 0.54°C, respectively. The RMSE also showed a clear seasonal fluctuation, with the largest deviation in summer (figure not shown). The deviation and RMSE of the FY3/VIRR SSTA from the daily Canadian Meteorological Center SSTA were 0.03 and 0.47°C, respectively; and the seasonal fluctuation of the RMSE was slightly smaller than that of the daily OIv2.1. This dependence suggests that we need to further evaluate the FY3/VIRR SST product with other SST reanalysis products in addition to in situ observations (Huang et al., 2013, 2016).

    The accuracy of FY-3/VIRR SST product is highly dependent on the performance of the VIRR instrument, onboard operational status, positioning and calibration accuracy, and historical reprocessing of the SST. The quality assessment of the FY-3/VIRR satellite product reported here will be helpful in operational applications to the climate monitoring and prediction. However, the historical time series of the FY satellite data products is relatively short and the sample size is limited. Evaluation of the consistency of longer time series of SST data products and identification of the improvement or degradation of the FY-3/VIRR SST products compared with the OSTIA SST (Donlon et al., 2012) will be considered in future work. Infrared instruments measure the skin SST at a high spatial resolution, but the surface is obscured by clouds, whereas microwave instruments provide an approximation to the sub-skin SST, including through clouds, thus having a fuller coverage at a reduced spatial resolution and > 50–100 km from land. Microwave SST products could be combined with the infrared SST in multipayload SST product fusion processes to reduce the impact of clouds.

    Acknowledgments. The authors thank the two reviewers for comments and suggestions.

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