Evaluation of the Outgoing Longwave Radiation from Fengyun-3B Products over the Asian-Australia Monsoon Region during 2011−2019

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  • Corresponding author: Wen CHEN, cw@post.iap.ac.cn
  • Funds:

    Supported by the National Key R&D Program of China (2016YFA0600604) and National Natural Science Foundation of China (41805126)

  • doi: 10.1007/s13351-021-1086-y

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  • The present study evaluates the spatial and temporal characteristics of Fengyun-3B (FY-3B)’s outgoing longwave radiation (OLR) data based on OLR data from the National Oceanic and Atmospheric Administration (NOAA) and Global Precipitation Climatology Project (GPCP) precipitation over the Asian−Australian region during 2011−2019. The spatial patterns of climatology and interannual standard deviation of FY3B’s OLR are quite consistent with the NOAA’s OLR, with annual and seasonal pattern correlation coefficients all exceeding 0.93. There are some differences in the magnitudes of OLR between FY3B and NOAA, especially for the climatology. The values of climatological OLR in FY3B are systematically larger than those in NOAA over the whole studied region. In addition, the temporal correlation coefficients between OLR and precipitation over Asian−Australian Monsoon (AAM) region are further examined. Both OLR datasets can capture the widespread negative correlation with precipitation, and the FY3B’s OLR exhibits a stronger relationship to precipitation over land. Moreover, FY3B captures the better OLR responses to the precipitation anomalies over India during the extreme El Niño and La Niña events. These results suggest that the quality of FY3B’s OLR is reliable and worthy of further global application.
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  • Fig. 1.  (a) Climatology of annual mean OLR over the Asian−Australian region during 2011−2019 in the NOAA’s OLR dataset. (b) As in (a), but for FY3B dataset. (c) Difference between (b) and (a). (d)-(f) As in (a−c), but for the interannual standard deviation.

    Fig. 2.  (a−c) As in Figs. 1a−1c, but for spring mean OLR. (d−f), (g−i), (j−l) as in (a−c), but for summer, autumn, winter, respectively.

    Fig. 3.  (a) Interannual standard deviation of spring mean OLR over the Asian-Australian monsoon region during 2011−2019 in the NOAA’s OLR dataset. (b) As in (a), but for FY3B’s OLR dataset. (c) Difference between (b) and (a). (d−f), (g−i), (j−l) As in (a−c), but for summer, autumn, winter results, respectively.

    Fig. 4.  (a) Pattern of temporal correlation coefficient between annual mean NOAA’s OLR and GPCC precipitation over the Asian-Australian monsoon region during 2011−2019. (b) As in (a), but for FY3B’s OLR dataset. (c) Difference between (b) and (a). (d−f), (g−i), (j−l), and (m−o) As in (a−c), but for spring, summer, autumn, and winter results, respectively.

    Fig. 5.  (a) Four sub-regions over the Asian-Australian monsoon region. (b) Time series of annual mean NOAA’s OLR (blue line), FY3B’s OLR (red line), and GPCP precipitation over the Indian monsoon region (IND) during 2011−2019. (c−d) as in (a), but for Southeastern Asia (SA), Maritime Continent (MC), Northern Australia (NA), respectively. For better comparison, here the r_Pre represent the original Pre multiplied by −1.

    Fig. 6.  (a−d) and (e−h) As in Fig. 5b−5e, but for the spring-mean (MAM) and summer-mean (JJA) results.

    Fig. 7.  (a−d) and (e−h) As in Fig. 5b−5e, but for the autumn-mean (SON) and winter-mean (DJF) results.

    Fig. 8.  (a) The winter (DJF) NOAA’s OLR (shading) and 850hPa winds (vectors) anomalies during El Niño year (2015/2016), (b−c) As in (a), but for FY3B’s OLR and GPCP precipitation anomalies, respectively. (d−f) As in (a−c), but for La Niña year (2010/2011), respectively.

    Table 1.  The temporal correlation coefficients between precipitation and NOAA’s OLR as well as FY3B’s OLR in the Asian-Australian monsoon region (30°S−30°N, 50°−160°E) during 2011−2019 on an annual and seasonal basis, respectively. Ind, Sa, Mc and Na denotes India region, Southeastern Asia, Maritime Continent and Northern Australia, respectively. N/P represents the correlation coefficients between NOAA’s OLR and GPCP precipitation, F/P represents the correlation coefficients between FY3B’s OLR and GPCP precipitation

    Ind(N/P)Ind(F/P)Sa(N/P)Sa(F/P)Mc(N/P)Mc(F/P)Na(N/P)Na(F/P)
    Annual−0.13−0.63−0.88−0.97−0.97−0.98−0.65−0.92
    Spring−0.65−0.52−0.91−0.91−0.89−0.92−0.79−0.94
    Summer−0.50−0.50−0.54−0.80−0.92−0.94−0.41−0.79
    Autumn−0.88−0.92−0.89−0.95−0.98−0.98−0.85−0.87
    Winter−0.08−0.58−0.81−0.80−0.86−0.82−0.86−0.90
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  • [1]

    Chelliah, M., and P. Arkin, 1992: Large-scale interannual variability of monthly outgoing longwave radiation anomalies over the global tropics. J. Climate, 5, 371–389. doi: 10.1175/1520-0442(1992)005<0371:LSIVOM>2.0.CO;2.
    [2]

    Chen, L. J., W. Gu, and W. J. Li, 2019: Why is the East Asian summer monsoon extremely strong in 2018? —Collaborative effects of SST and snow cover anomalies J. Meteor. Res., 33, 593–608. doi: 10.1007/s13351-019-8200-4.
    [3]

    Chiodi, A. M., and D. E. Harrison, 2013: El Niño impacts on seasonal U.S. atmospheric circulation, temperature, and precipitation anomalies: The OLR-event perspective. J. Climate, 26, 822–837. doi: 10.1175/JCLI-D-12-00097.1.
    [4]

    Chiodi, A. M., and D. E. Harrison, 2015: Global seasonal precipitation anomalies robustly associated with El Niño and La Niña events-an OLR perspective. J. Climate, 28, 6133–6159. doi: 10.1175/JCLI-D-14-00387.1.
    [5]

    Chu, P.-S., and J.-B. Wang, 1997: Recent climate change in the tropical western Pacific and Indian Ocean regions as detected by outgoing longwave radiation records. J. Climate, 10, 636–646. doi: 10.1175/1520-0442(1997)010<0636:RCCITT>2.0.CO;2.
    [6]

    Ding, Y. H., Y. J. Liu, S. J. Liang, et al., 2014: Interdecadal variability of the East Asian winter monsoon and its possible links to global climate change. J. Meteor. Res., 28, 693–713. doi: 10.1007/s13351-014-4046-y.
    [7]

    Ellingson, R. G., and M. B. Ba., 2003: A study of diurnal variation of OLR from the GOES sounder. J. Atmos. Oceanic Technol., 20, 90–98. doi: 10.1175/1520-0426(2003)020<0090:ASODVO>2.0.CO;2.
    [8]

    Fajary, F. R., T. W. Hadi, and S. Yoden, 2019: Contributing factors to spatiotemporal variations of outgoing longwave radiation (OLR) in the Tropics. J. Climate, 32, 4621–4640. doi: 10.1175/JCLI-D-18-0350.1.
    [9]

    Gastineau, G., B. J. Soden, D. L. Jackson, et al., 2014: Satellite-based reconstruction of the tropical oceanic clear-sky outgoing longwave radiation and comparison with climate models. J. Climate, 27, 941–957. doi: 10.1175/JCLI-D-13-00047.1.
    [10]

    Gong, H. N., L. Wang, W. Chen, et al., 2015: Diverse influences of ENSO on the East Asian-western Pacific winter climate tied to different ENSO properties in CMIP5 models. J. Climate, 28, 2187–2202. doi: 10.1175/JCLI-D-14-00405.1.
    [11]

    Gong, H. N., L. Wang, W. Chen, et al., 2018: Diversity of the Pacific-Japan pattern among CMIP5 models: Role of SST anomalies and atmospheric mean flow. J. Climate, 31, 6857–6877. doi: 10.1175/JCLI-D-17-0541.1.
    [12]

    Huffman, G. J., R. F. Adler, D. T. Bolvin, et al., 2009: Improving the global precipitation record: GPCP version 2.1. Geophys. Res. Lett., 36, L17808. doi: 10.1029/2009GL040000.
    [13]

    Kiladis, G. N., J. Dias, K. H. Straub, et al., 2014: A comparison of OLR and circulation-based indices for tracking the MJO. Mon. Wea. Rev., 142, 1697–1715. doi: 10.1175/MWR-D-13-00301.1.
    [14]

    Li, Y. F., Y. Luo, and Y. H. Ding, 2004: The relationships between the global satellite-observed outgoing longwave radiation and the rainfall over China in summer and winter. Adv. Space. Res., 33, 1089–1097. doi: 10.1016/S0273-1177(03)00735-X.
    [15]

    Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 1275–1277. doi: 10.1175/1520-0477(1996)077<1255:EA>2.0.CO;2.
    [16]

    Liu, L., J. P. Guo, W. Chen, et al., 2018: Large-scale pattern of the diurnal temperature range changes over East Asia and Australia in boreal winter: A perspective of atmospheric circulation. J. Climate, 31, 2715–2728. doi: 10.1175/JCLI-D-17-0608.1.
    [17]

    Liu, L., J. P. Guo, W. Chen, et al., 2019: Dominant interannual covariations of the East Asian–Australian land precipitation during boreal winter. J. Climate, 32, 3279–3296. doi: 10.1175/JCLI-D-18-0477.1.
    [18]

    Nitta, T., and S. Yamada, 1989: Recent warming of tropical sea surface temperature and its relationship to the Northern Hemisphere circulation. J. Meteor. Soc. Japan, 67, 375–383. doi: 10.2151/jmsj1965.67.3_375.
    [19]

    Qi, Y. J., R. H. Zhang, X. Y. Rong, et al., 2019: Boreal Summer Intraseasonal Oscillation in the Asian–Pacific Monsoon Region Simulated in CAMS-CSM. J. Meteor. Res., 33, 66–79. doi: 10.1007/s13351-019-8080-7.
    [20]

    Schmetz, J., and Q. H. Liu, 1988: Outgoing longwave radiation and its diurnal variation at regional scales derived from Meteosat. J. Geophys. Res. Atmos., 93, 11192–11204. doi: 10.1029/JD093iD09p11192.
    [21]

    Su, W. Y., N. G. Loeb, L. S. Liang, et al., 2017: The El Niño-Southern Oscillation effect on tropical outgoing longwave radiation: A daytime versus nighttime perspective. J. Geophys. Res. Atmos., 122, 7820–7833. doi: 10.1002/2017JD027002.
    [22]

    Sun, L., X. Q. Hu, N. Xu, et al., 2013: Postlaunch calibration of FengYun-3B MERSI reflective solar bands. IEEE Trans. Geosci. Remote Sens., 51, 1383–1392. doi: 10.1109/TGRS.2012.2217345.
    [23]

    Wang, B., R. G. Wu, and X. H. Fu, 2000: Pacific–East Asian teleconnection: How does ENSO affect East Asian climate? J. Climate, 13, 1517–1536. doi: 10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
    [24]

    Wang, B., J. Yang, T. J. Zhou, et al., 2008: Interdecadal changes in the major modes of Asian–Australian monsoon variability: Strengthening relationship with ENSO since the late 1970s. J. Climate, 21, 1771–1789. doi: 10.1175/2007JCLI1981.1.
    [25]

    Wang, B., S.-Y. Yim, J.-Y. Lee, et al., 2013: Future change of Asian-Australian monsoon under RCP 4.5 anthropogenic warming scenario. Climate Dyn., 42, 83–100. doi: 10.1007/s00382-013-1769-x.
    [26]

    Wang, D. H., J. F. Yin, and G. Q. Zhai, 2015: In-situ measurements of cloud-precipitation microphysics in the East Asian monsoon region since 1960. J. Meteor. Res., 29, 155–179. doi: 10.1007/s13351-015-3235-7.
    [27]

    Wang, H., Z. M. Ji, X. Zhu, et al., 2021: Future changes in the Asian-Australian monsoon system with 1.5°C and 2°C rise in temperature. J. Geophys. Res. Atmos., 126, e2020JD032872. doi: 10.1029/2020JD032872.
    [28]

    Wu, X., and J. J. Yan, 2011: Estimating the outgoing longwave radiation from the FY-3B satellite visible infrared radiometer Channel 5 radiance observations. Chinese Sci. Bull., 56, 3480–3485. doi: 10.1007/s11434-011-4686-6.
    [29]

    Xie, P. P., and P. A. Arkin, 1998: Global monthly precipitation estimates from satellite-observed outgoing longwave radiation. J. Climate, 11, 137–164. doi: 10.1175/1520-0442(1998)011<0137:GMPEFS>2.0.CO;2.
    [30]

    Xu, B., P. P. Xie, M. Xu, et al., 2015: A validation of passive microwave rain-rate retrievals from the Chinese FengYun-3B satellite. J. Hydrometeorol, 16, 1886–1905. doi: 10.1175/JHM-D-14-0143.1.
    [31]

    Xu, Q., and Z. Y. Guan, 2017: Interannual variability of summertime outgoing longwave radiation over the Maritime Continent in relation to East Asian summer monsoon anomalies. J. Meteor. Res., 31, 665–677. doi: 10.1007/s13351-017-6178-3.
    [32]

    Yang, H., F. Z. Weng, L. Q. Lyu, et al., 2011: The FengYun-3 microwave radiation imager on-orbit verification. IEEE Trans. Geosci. Remote Sens., 49, 4552–4560. doi: 10.1109/TGRS.2011.2148200.
    [33]

    Yang, H., X. L. Zou, X. Q. Li, et al., 2012: Environmental data records from FengYun-3B microwave radiation imager. IEEE Trans. Geosci. Remote Sens., 50, 4986–4993. doi: 10.1109/TGRS.2012.2197003.
    [34]

    Yang, J., P. Zhang, N. M. Lu, et al., 2012: Improvements on global meteorological observations from the current Fengyun 3 satellites and beyond. Int. J. Digit. Earth, 5, 251–265. doi: 10.1080/17538947.2012.658666.
    [35]

    Yoo, J.-M., and J. A. Carton, 1988: Outgoing longwave radiation derived rainfall in the tropical Atlantic, with emphasis on 1983–84. J. Climate, 1, 1047–1054. doi: 10.1175/1520-0442(1988)001<1047:SDOTRB>2.0.CO;2.
    [36]

    You, R., S. Y. Gu, Y. Guo, et al., 2012: Long-term calibration and accuracy assessment of the FengYun-3 microwave temperature sounder radiance measurements. IEEE Trans. Geosci. Remote Sens., 50, 4854–4859. doi: 10.1109/TGRS.2012.2200257.
    [37]

    Zheng, F., H. Wang, H. Luo, et al., 2020: Decadal change in ENSO related seasonal precipitation over southern China under influences of ENSO and its combination mode. Climate Dyn., 54, 1973–1986. doi: 10.1007/s00382-019-05096-2.
    [38]

    Zhou, T. J., B. Wu, and B. Wang, 2009: How well do atmospheric general circulation models capture the leading modes of the interannual variability of the Asian–Australian monsoon? J. Climate, 22, 1159–1173. doi: 10.1175/2008JCLI2245.1.
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Evaluation of the Outgoing Longwave Radiation from Fengyun-3B Products over the Asian-Australia Monsoon Region during 2011−2019

    Corresponding author: Wen CHEN, cw@post.iap.ac.cn
  • 1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
  • 2. National Satellite Meteorological Centre, Chinese Meteorological Administration, Beijing 100081
  • 3. Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100190
  • 4. School of Earth Sciences, Zhejiang University, Hangzhou
Funds: Supported by the National Key R&D Program of China (2016YFA0600604) and National Natural Science Foundation of China (41805126)

Abstract: The present study evaluates the spatial and temporal characteristics of Fengyun-3B (FY-3B)’s outgoing longwave radiation (OLR) data based on OLR data from the National Oceanic and Atmospheric Administration (NOAA) and Global Precipitation Climatology Project (GPCP) precipitation over the Asian−Australian region during 2011−2019. The spatial patterns of climatology and interannual standard deviation of FY3B’s OLR are quite consistent with the NOAA’s OLR, with annual and seasonal pattern correlation coefficients all exceeding 0.93. There are some differences in the magnitudes of OLR between FY3B and NOAA, especially for the climatology. The values of climatological OLR in FY3B are systematically larger than those in NOAA over the whole studied region. In addition, the temporal correlation coefficients between OLR and precipitation over Asian−Australian Monsoon (AAM) region are further examined. Both OLR datasets can capture the widespread negative correlation with precipitation, and the FY3B’s OLR exhibits a stronger relationship to precipitation over land. Moreover, FY3B captures the better OLR responses to the precipitation anomalies over India during the extreme El Niño and La Niña events. These results suggest that the quality of FY3B’s OLR is reliable and worthy of further global application.

    • The Asian monsoon including three sub-monsoons (East Asian monsoon, Indian monsoon, and western North Pacific monsoon), together with the Indonesian−Australian monsoon, forms an Asian-Australian Monsoon (AAM) system through cross-equatorial flows linking the Northern and the Southern hemispheres (e.g., Wang et al., 2008, 2015, 2021; Zhou et al., 2009; Ding et al., 2014; Gong et al., 2015, 2018; Liu et al., 2018, 2019; Chen et al., 2019; Qi et al., 2019). The AAM region covers one third of the global tropical and subtropics, and is commonly defined as extending 30°N to 30°S and approximately from 50° to 160°E (Wang et al., 2008, 2013). About 60% world population lives in this region where all the needed water is provided by the Asian−Australian monsoon rainfall. Therefore, the precipitation variability in the Asian−Australian monsoon region has a profound socioeconomic implication on local human and production activities (Zhou et al., 2009; Zheng et al., 2020).

      The strongest atmospheric heat sources including Maritime Continent, the South China Sea and the Bay of Bengal are within the AAM region. The heavy convective precipitation associated with release of water vapor condensation is the prominent characteristics of AAM precipitation (Chu and Wang, 1997). Observational precipitation data in the AAM region is relatively scarce as oceans have a large proportion in equatorial regions. In comparison, satellite monitoring has advantage over ground-based observations in spatial coverage, particularly, in sparsely populated marine areas. With the continuous improvement of satellite detection technology, meteorological satellite products play an important role in research as a supplement to ground-based observations. Outgoing longwave radiation (OLR) at the top of the atmosphere observed by the satellites is one of the regular meteorological satellite products (Chelliah and Arkin, 1992; Gastineau et al., 2014; Fajary et al., 2019). It is not only an important parameter in the earth’s radiation budget, but also has been used to represent tropical convection and estimate convective precipitation in tropical and subtropical regions (Schmetz and Liu, 1988; Yoo and Carton, 1988; Nitta and Yamada, 1989; Chiodi and Harrison, 2013, 2015; Kiladis et al., 2014; Su et al., 2017). The magnitude of OLR is mainly determined by surface temperature and the status of the cloud. Typically, the lower cloud top temperature and the thicker cloud correspond to lower OLR value and OLR value lower than 240 W m-2 indicates stronger convective activity. Previous studies have showed a pronounced negative relationship between precipitation and OLR anomaly on seasonal and interannual time scales (Xie and Arkin, 1998; Li et al., 2004; Xu and Guan, 2017). Therefore, the OLR based on satellite detection is used to represent the convective precipitation over tropics (30°S−30°N) especially in the marine areas in which the effective ground-based observations is spare. Up to present, the vast majority of OLR studies are based on the products from the National Oceanic and Atmospheric Administration (NOAA) OLR data. Limited studies have been carried out to evaluate OLR in the AAM region using the OLR data of the Fengyun-3B (FY3B) meteorological satellite independently developed by China (e.g., Yang et al., 2011, 2012 本年份文献引用不明确; You et al., 2012; Sun et al., 2013; Xu et al., 2015). In order to better understand the capacity of the current FY3B satellite observation, especially the OLR, this study attempts to address the following questions: (1) How is the consistency between the FY3B OLR data and NOAA’s OLR data on a mean-state over the AAM region? (2) Which data has more advantage in representing the spatiotemporal variability of precipitation over the AAM region including the climate anomalies in extreme El Niño year? Answering these questions is crucial for expanding the applications of FY3B meteorological satellite products to the globe.

      The present study aims to evaluate the OLR from FY3B products based on comparison with NOAA’s OLR over the AAM region during 2011−2019. Particular attention is paid to the relationship between these two OLR data and precipitation variability. The rest of this paper is organized as follows. Section 2 describes the data and methods. Climatology and interannual variation of OLR in NOAA and FY3B datasets are presented in Section 3. In section 4, we compare temporal variations of OLR in NOAA and FY3B and precipitation in Global Precipitation Climatology Project (GPCP). Section 5 discusses OLR and precipitation anomalies during ENSO events. Summary and concluding remarks are given in section 6.

    2.   Data and methods
    • Currently, most global estimates of the OLR are derived from polar orbiting satellite with two observations per day per satellites (Ellingson and Ba, 2003). FY3B is the second generation polar-orbiting satellites of China, which was launched in 2010 (Yang J. et al., 2012). The visible infrared radiometer (VIRR) on board the FY-3B polar orbiting is used to detect the short wave and long wave radiation reflected and scattered by the Earth-Atmospheric system (e.g., Yang J. et al., 2012; Yang H. et al., 2012; Sun et al., 2013; Xu et al., 2015). OLR at the top of the atmosphere is calculated by the brightness temperature of the VIRR Chanel 5 (Wu and Yan, 2011). The FY3B OLR data adopted in the present study is provided by the National Satellite Meteorological Centre (NSMC), with a horizontal resolution of 0.5° × 0.5° covering the period from 2011 to the present. To verify the FY3B OLR data, the global monthly mean OLR data from the Advanced Very High Resolution Radiometer (AVHRR) instrument aboard the NOAA polar orbiting spacecraft are used in the current study. NOAA’s OLR data is analyzed by Climate Prediction Center (CPC), which covers the period from June 1974 to the present on a global grid of 2.5° × 2.5° (Liebmann and Smith, 1996). Considering the OLR estimates has a good representation of convective rainfall in most tropical and prevailing monsoon regions, here our study domain is confined in the Asian-Australian monsoon region (30°S−30°N, 50°−160°E). To further evaluate NOAA and FY3B OLR data, we employed GPCP precipitation data over oceans to characterize convective precipitation (Huffman et al., 2009). The GPCP datasets cover the period 1979 to the present, with a resolution of 2.5° × 2.5°. Given that FY3B data has a shorter time period, our study focus on the period from 2011 to 2019. And for the convenience of comparison, the FY3B datasets are bilinearly interpolated to the resolution of 2.5° × 2.5° in accordance with NOAA dataset.

    3.   Climatology and interannual variation of OLR in NOAA and FY3B datasets
    • Figures. 1a and 1b show the geographical distributions of annual mean OLR from NOAA and FY3B datasets during 2011−2019, respectively. The spatial distributions of OLR are quite similar in two datasets, both show a smaller OLR value over tropical regions and larger OLR value over southern subtropics. This distribution of lower OLR is consistent with the location of convective precipitation center. The pattern correlation coefficient between NOAA and FY3B in the AAM region is 0.992. It is noted that there are obvious systematic differences in magnitudes of OLR between NOAA and FY3B and the OLR in FY3B is overall larger than that in NOAA in the whole studied region, especially over India, southeastern Asia, and southern Australia (Fig. 1c).

      Figure 1.  (a) Climatology of annual mean OLR over the Asian−Australian region during 2011−2019 in the NOAA’s OLR dataset. (b) As in (a), but for FY3B dataset. (c) Difference between (b) and (a). (d)-(f) As in (a−c), but for the interannual standard deviation.

      Figures. 1d and 1e show the inter-annual standard deviation of OLR in NOAA and FY3B datasets, respectively. The spatial distribution of interannual standard deviation of OLR is basically consistent between the NOAA and FY3B, with pattern correlation coefficient of 0.946. Large standard deviations are mainly located in the southwestern side of the Maritime Continent and from the South China Sea eastward to the Philippine Sea. The interannual variability of OLR in FY3B is overall weaker than that in NOAA, especially over the Maritime Continent and Australia (Fig. 1f).

      Fig. 2 presents the climatology of OLR in four seasons over the Asian−Australian region. In general, the spatial distribution and seasonal evolution of OLR are quite consistent between NOAA and FY3B, both reflecting the seasonal shifts of convective activities over the AAM region (Fig. 2). The pattern correlation coefficients of climatological OLR between NOAA and FY3B are 0.99, 0.992, 0.993, and 0.995, respectively, from spring to winter. The differences of magnitudes between FY3B and NOAA in four seasons are somewhat similar to that in the annual mean, also showing larger OLR in FY3B over India, southeastern Asia, and Southern Australia, especially in summer (JJA) (Figs. 2c, 2f, 2i, 2l). This result indicates that the FY3B satellite can well capture the spatial distribution of climatological OLR although there are some differences in the magnitudes.

      Figure 2.  (a−c) As in Figs. 1a−1c, but for spring mean OLR. (d−f), (g−i), (j−l) as in (a−c), but for summer, autumn, winter, respectively.

      The interannual standard deviations of OLR in four seasons and the differences between NOAA and FY3B are displayed in Fig. 3. In MAM, strong interannual variability of OLR is mainly over the South China Sea and the Philippine Sea (Fig. 3a). The large value region moves to the southeastern India Oceans in JJA, and expands to a bigger scope and attains a greater intensity in SON (Figs. 3d, 3g). The large value of OLR variability in southeastern India Ocean during autumn is likely related to the India Ocean dipole mode (IOD), which is the strongest SST mode in tropical India Ocean during autumn. In winter, there is almost symmetrical distribution about the equator with great interannual variability over western Pacific (Fig. 3j). This symmetrical distribution of standard deviation in OLR is most likely caused by ENSO because a pair of anticyclone or cyclone is located over the western Pacific, which is triggered by the changes of Walker circulation associated with the ENSO events (Wang et al., 2000). The patterns of interannual standard deviation of seasonal mean OLR in FY3B datasets exhibit similar characteristics with those in NOAA datasets (Figs. 3b, 3e, 3h, 3k). The pattern correlation coefficients of interannual standard deviation of OLR between NOAA and FY3B are 0.954, 0.939, 0.957, and 0.971, respectively, from spring to winter. There are some differences of magnitudes of interannual standard deviation of OLR between NOAA and FY3B.

      Figure 3.  (a) Interannual standard deviation of spring mean OLR over the Asian-Australian monsoon region during 2011−2019 in the NOAA’s OLR dataset. (b) As in (a), but for FY3B’s OLR dataset. (c) Difference between (b) and (a). (d−f), (g−i), (j−l) As in (a−c), but for summer, autumn, winter results, respectively.

      Notable differences are mainly in the Maritime Continent and northern Australia. During MAM, the interannual standard deviation of FY3B OLR is larger than that of NOAA in the Maritime Continent and northern Australia (Fig. 3c). In contrast, during JJA and SON, the interannual standard deviation of FY3B OLR is smaller than that of NOAA in the Maritime Continent and northern Australia (Figs. 3f, 3i). This result suggests that the FY3B satellite can well capture the distribution of interannual variability of OLR although there are somewhat differences in the magnitudes. However, the differences of magnitudes of OLR data between NOAA and FY3B cannot directly tell which data is more realistic.

      Since the OLR can well represent the convective precipitation over tropics, the GPCP precipitation data are employed to further investigate the relationship of two OLR data with the precipitation data. Fig. 4 shows the patterns of temporal correlation coefficient of NOAA and FY3B OLR with GPCP precipitation during 2011−2019. It is clear that there are widespread significant negative correlations of both OLR in the NOAA and FY3B with precipitation over the AAM region. It reflects that lower OLR is observed in regions where there is stronger convective activity. Although the correlation coefficients between OLR and precipitation are consistent over most of the ocean in the two data sets, the largest differences are mainly on land, especially in India and Australia (Figs. 4c, 4f, 4i, 4l, 4o). The negative correlation coefficient between FY3B OLR and precipitation is stronger in India and Australia than that between the NOAA’s OLR and precipitation. This result implies that the FY3B OLR is better than the NOAA’s OLR in capturing the interannual variability of land convective precipitation.

      Figure 4.  (a) Pattern of temporal correlation coefficient between annual mean NOAA’s OLR and GPCC precipitation over the Asian-Australian monsoon region during 2011−2019. (b) As in (a), but for FY3B’s OLR dataset. (c) Difference between (b) and (a). (d−f), (g−i), (j−l), and (m−o) As in (a−c), but for spring, summer, autumn, and winter results, respectively.

    4.   Temporal evolution of OLR in NOAA and FY3B and precipitation in GPCP
    • To further elucidate the relationship between OLR and precipitation in details, we divided the Asian-Australian monsoon region into four sub-regions, including India, Southeastern China, Maritime Continent and Northern Australia. Fig. 5 shows the time series of annual mean NOAA’s OLR, FY3B OLR and precipitation over four sub-regions. Generally, the value of OLR is lowest over the Maritime Continent and highest over northern Australia. FY3B OLR is larger than NOAA’s OLR about 8 W m-2. The temporal correlation coefficients between precipitation and OLR in two datasets are highest in the Maritime Continent and lowest in the Indian region based on annual mean. Note that the correlation between FY3B OLR and precipitation is all stronger than that of NOAA’s OLR (Table 1), especially for India and Northern Australia. This result is consistent with that shown in Fig. 5.

      Figure 5.  (a) Four sub-regions over the Asian-Australian monsoon region. (b) Time series of annual mean NOAA’s OLR (blue line), FY3B’s OLR (red line), and GPCP precipitation over the Indian monsoon region (IND) during 2011−2019. (c−d) as in (a), but for Southeastern Asia (SA), Maritime Continent (MC), Northern Australia (NA), respectively. For better comparison, here the r_Pre represent the original Pre multiplied by −1.

      Ind(N/P)Ind(F/P)Sa(N/P)Sa(F/P)Mc(N/P)Mc(F/P)Na(N/P)Na(F/P)
      Annual−0.13−0.63−0.88−0.97−0.97−0.98−0.65−0.92
      Spring−0.65−0.52−0.91−0.91−0.89−0.92−0.79−0.94
      Summer−0.50−0.50−0.54−0.80−0.92−0.94−0.41−0.79
      Autumn−0.88−0.92−0.89−0.95−0.98−0.98−0.85−0.87
      Winter−0.08−0.58−0.81−0.80−0.86−0.82−0.86−0.90

      Table 1.  The temporal correlation coefficients between precipitation and NOAA’s OLR as well as FY3B’s OLR in the Asian-Australian monsoon region (30°S−30°N, 50°−160°E) during 2011−2019 on an annual and seasonal basis, respectively. Ind, Sa, Mc and Na denotes India region, Southeastern Asia, Maritime Continent and Northern Australia, respectively. N/P represents the correlation coefficients between NOAA’s OLR and GPCP precipitation, F/P represents the correlation coefficients between FY3B’s OLR and GPCP precipitation

      For the analysis of seasonal time series of precipitation, NOAA’s OLR and FY3B OLR are further presented in Fig. 6 and Fig. 7. In MAM, the value of OLR is highest in India region above 270 W m-2 and is lowest in the Maritime Continent about 215 W m-2. The temporal correlation coefficients between FY3B OLR and precipitation are stronger than those between NOAA’s OLR and precipitation. In JJA, the OLR is relatively small both over India and Southeastern China and is large over Northern Australia. The temporal correlation coefficients between FY3B OLR and precipitation are stronger than those between NOAA’s OLR and precipitation in almost all sub-regions (Fig. 6). In SON, the OLR values in four sub-regions are all higher than JJA, and also the temporal correlations between OLR and precipitation are all stronger than those in JJA. Also, the temporal correlations between FY3B OLR and precipitation are almost all stronger than that between NOAA’s OLR and precipitation in four sub-regions (Table 1). For winter, the temporal correlation coefficients between FY3B OLR and precipitation are evidently stronger than those between NOAA’s OLR and precipitation in India (Fig. 7). Overall, FY3B OLR has a higher correlation with precipitation variability than NOAA’s OLR, especially over the lands of the AAM regions (Table 1). This result further supports that the FY3B OLR has a better quality than NOAA’s OLR over the AAM region.

      Figure 6.  (a−d) and (e−h) As in Fig. 5b−5e, but for the spring-mean (MAM) and summer-mean (JJA) results.

      Figure 7.  (a−d) and (e−h) As in Fig. 5b−5e, but for the autumn-mean (SON) and winter-mean (DJF) results.

    5.   OLR and precipitation anomalies during recent strong El Niño event
    • To further verify the quality of OLR based on precipitation data, the spatial patterns of OLR and precipitation anomalies during extreme El Niño and La Niña events are investigated. Fig. 8 shows the winter precipitation, OLR and 850hPa wind anomalies during recent strong El Niño year (2015/2016) and La Niña year (2010/2011). During El Niño year, there is an evident warming in the tropical eastern central Pacific. The resultant weakened Walker circulation triggers a pair of the lower-level anticyclone north and south of the equatorial western Pacific (Liu et al. 2018). Therefore, there are negative (positive) precipitation (OLR) anomalies in the western North Pacific and Northern Australia which are under the control of two lower-level anticyclones (Figs. 8a−8c). The patterns of OLR anomalies in both NOAA and FY3B are basically consistent with that of precipitation anomalies. It is noted there are negative precipitation anomalies over India due to the westward stretch of northern anticyclone from the equatorial western Pacific (Fig. 8c). Only FY3B captures the weak positive OLR anomalies over India (Fig. 8b) whereas the NOAA’s OLR exhibits the negative anomalies in India (Fig. 8a). During La Nina year, the OLR anomalies in NOAA and FY3B both exhibit negative correlation with precipitation anomalies over the whole AAM region (Figs. 8d−8f). However, there are excessively strong positive OLR anomalies corresponding to weak precipitation anomalies over India in NOAA datasets (Figs. 8d, 8f). These results further confirm that FY3B OLR data has a better match with precipitation data especially for the Indian regions.

      Figure 8.  (a) The winter (DJF) NOAA’s OLR (shading) and 850hPa winds (vectors) anomalies during El Niño year (2015/2016), (b−c) As in (a), but for FY3B’s OLR and GPCP precipitation anomalies, respectively. (d−f) As in (a−c), but for La Niña year (2010/2011), respectively.

      The above results indicate that FY3B’s OLR is consistent with NOAA’s OLR data in terms of spatial distribution of climatology and interannual variability, with slight differences in the magnitudes. In addition, the relationship between FY3B’s OLR and convective precipitation in the tropical Asian-Australian monsoon region is better than that of NOAA’s OLR, especially over the land, which indicates that the quality of FY3B’s OLR is reliable and worthy of further global application.

    6.   Summary and concluding remarks
    • In this study, we evaluate the ability of FY3B’s OLR data and NOAA’s OLR data to depict precipitation from the perspective of climatology and temporal evolution over the Asian-Australian monsoon region during 2011−2019. Some major results are summarized as follows:

      In a climatological mean sense, the spatial distributions of OLR in both NOAA and FY3B datasets mainly show a smaller value over tropical regions with minima approximately 200 W m-2 and a lager value over southern subtropics with maxima about 270 W m-2. The spatial patterns of climatological annual mean FY3B’s OLR are consistent with the NOAA’s OLR. In addition, the values of FY3B’s OLR are systematically larger than NOAA’s OLR by about 10W m-2 on an average in the whole studied region. Similarly, the spatial patterns of inter-annual standard deviation of OLR are also basically consistent between NOAA and FY3B datasets. Seasonally, low value regions in OLR move northward from MAM to JJA and southward from SON to DJF, which is mainly determined by seasonal variation in solar radiation. On an annual and seasonal basis, the pattern correlation coefficients between climatological NOAA and FY3B’s OLR all exceed 0.99 and the pattern correlation coefficients between interannual NOAA and FY3B’s OLR all exceed 0.93, both of which indicates that there is consistency between these two datasets.

      In view of the consistency of OLR and precipitation over tropics, the GPCP precipitation data are employed to further verify the quality of two OLR data. Although the correlation coefficients between OLR and precipitation in both data sets are consistent over most of the ocean, the largest differences are mainly on land, especially in India and Australia. The negative correlation coefficient between FY3B’s OLR and precipitation is stronger in India and Australia than that between the NOAA’s OLR and precipitation. This result indicates that the FY3B’s OLR is better than the NOAA’s OLR in capturing the interannual variability of land convective precipitation.

      To further illustrate the relationship between OLR and precipitation in details, four sub-regions located in the Asian-Australian monsoon region were defined (Ind, Sa, Mc, and Na). Based on both annual and seasonal averages, the temporal correlation coefficients between FY3B’s OLR and precipitation are overall stronger than that between NOAA’s OLR and precipitation, especially over the land of AAM region. Moreover, the spatial patterns of OLR and precipitation anomalies associated with the extreme El Niño and La Niña events are further compared between NOAA and FY3B. The patterns of OLR anomalies both in NOAA and FY3B are basically consistent with those of precipitation anomalies over majority of the AAM region. However, FY3B captures the better OLR responses to the precipitation anomalies over India during El Niño and La Niña events. These results suggest that the quality of FY3B’s OLR is reliable and worthy of further global application.

    Acknowledgements
    • The authors extend sincere gratitude to National Oceanic and Atmospheric Administration and National Satellite Meteorological Centre for providing outgoing longwave radiation data, and Global Precipitation Climatology Project for providing precipitation data publicly accessible.

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