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

亚澳季风区FY-3B极轨气象卫星大气向外长波辐射数据评估

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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
  • Note: This paper will appear in the forthcoming issue. It is not the finalized version yet. Please use with caution.

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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 NOAA and precipitation from the Global Precipitation Climatology Project (GPCP) over the Asian–Australian region during 2011–2019. The spatial patterns of climatology and interannual standard deviation of FY-3B OLR are quite consistent with the NOAA OLR, with annual and seasonal pattern correlation coefficients all exceeding 0.93. There are some differences in the magnitudes of OLR between FY-3B and NOAA, especially for the climatology. The values of climatological OLR in FY-3B 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 FY-3B OLR exhibits a stronger relationship to precipitation over land. Moreover, FY-3B 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 FY-3B OLR is reliable and worthy of further global application.
    本研究基于NOAA提供的大气向外长波辐射数据(OLR)以及GPCP降水数据,评估了FY-3B气象卫星OLR数据在2011–2019时段在亚澳季风区的时空分布特征。研究结果表明:(1)FY-3B和NOAA的OLR数据在气候态及年际标准差表现出很好的空间一致性分布,年平均和四个季节平均的空间相关系数均超过0.93;(2)两套数据的差异从气候态来看,主要在于FY-3B的OLR数据的气候态数值相较于与NOAA存在系统性偏大;(3)两套数据均能较好反映OLR与热带地区降水的负相关关系,并且在陆地上FY-3B的OLR数据与降水表现出更好的相关;(4)在超强厄尔尼诺和拉尼娜事件中,FY-3B的OLR数据在印度地区与降水异常有更好的一致对应关系。以上结果表明FY-3B的OLR数据质量可靠,能够在全球气象卫星数据推广应用中发挥重要的作用。
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  • Fig. 1.  Climatology of annual mean OLR in (a) NOAA and (b) FY-3B datasets, (c) difference between two datasets, and (d–f) interannual standard deviation over the AAM region during 2011–2019.

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

    Fig. 3.  Interannual standard deviation of spring (MAM), summer (JJA), autumn (SON), and winter (DJF) mean OLR in (a, d, g, j) NOAA and (b, e, h, k) FY-3B datasets and (c, f, i, l) their difference between two datasets over the AAM region during 2011–2019.

    Fig. 4.  (a) Pattern of temporal correlation coefficient between annual mean NOAA OLR and GPCC precipitation over the AAM region during 2011–2019. (b) As in (a), but for FY-3B 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 AAM region. (b) Time series of annual mean NOAA OLR (blue line), FY-3B 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.  As in Fig. 5be, but for the spring-mean (MAM) and summer-mean (JJA) results.

    Fig. 7.  As in Fig. 5be, but for the autumn-mean (SON) and winter-mean (DJF) results.

    Fig. 8.  (a) Winter (DJF) NOAA OLR (shading) and 850-hPa wind (vector) anomalies during El Niño year (2015/2016). (b, c) As in (a), but for FY-3B OLR and GPCP precipitation anomalies, respectively. (d–f) As in (a–c), but for La Niña year (2010/2011), respectively.

    Table 1.  Temporal correlation coefficients between precipitation and NOAA OLR as well as FY-3B OLR in the AAM region (30°S–30°N, 50°–160°E) during 2011–2019 on an annual and seasonal basis, respectively. IND, SA, MC, and NA denote Indian region, southeastern Asia, Maritime Continent, and northern Australia, respectively. N/P represents the correlation coefficients between NOAA OLR and GPCP precipitation, and F/P represents the correlation coefficients between FY-3B 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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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 NOAA and precipitation from the Global Precipitation Climatology Project (GPCP) over the Asian–Australian region during 2011–2019. The spatial patterns of climatology and interannual standard deviation of FY-3B OLR are quite consistent with the NOAA OLR, with annual and seasonal pattern correlation coefficients all exceeding 0.93. There are some differences in the magnitudes of OLR between FY-3B and NOAA, especially for the climatology. The values of climatological OLR in FY-3B 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 FY-3B OLR exhibits a stronger relationship to precipitation over land. Moreover, FY-3B 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 FY-3B OLR is reliable and worthy of further global application.

亚澳季风区FY-3B极轨气象卫星大气向外长波辐射数据评估

本研究基于NOAA提供的大气向外长波辐射数据(OLR)以及GPCP降水数据,评估了FY-3B气象卫星OLR数据在2011–2019时段在亚澳季风区的时空分布特征。研究结果表明:(1)FY-3B和NOAA的OLR数据在气候态及年际标准差表现出很好的空间一致性分布,年平均和四个季节平均的空间相关系数均超过0.93;(2)两套数据的差异从气候态来看,主要在于FY-3B的OLR数据的气候态数值相较于与NOAA存在系统性偏大;(3)两套数据均能较好反映OLR与热带地区降水的负相关关系,并且在陆地上FY-3B的OLR数据与降水表现出更好的相关;(4)在超强厄尔尼诺和拉尼娜事件中,FY-3B的OLR数据在印度地区与降水异常有更好的一致对应关系。以上结果表明FY-3B的OLR数据质量可靠,能够在全球气象卫星数据推广应用中发挥重要的作用。
    • 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 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 from 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 AAM rainfall. Therefore, the precipitation variability in the AAM 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 shown a pronounced negative relationship between precipitation and OLR anomaly on seasonal and interannual timescales (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 are spare. Up to present, the vast majority of OLR studies are based on the products from the NOAA OLR data. Limited studies have been carried out to evaluate OLR in the AAM region using the OLR data of the Fengyun-3B (FY-3B) meteorological satellite independently developed by China (e.g., Yang et al., 2011, 2012; You et al., 2012; Sun et al., 2013; Xu et al., 2015). In order to better understand the capacity of the current FY-3B satellite observation, especially the OLR, this study attempts to address the following questions: (1) How is the consistency between the FY-3B OLR data and NOAA OLR data on a mean state over the AAM region? (2) Which data have 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 FY-3B meteorological satellite products to the globe.

      The present study aims to evaluate the OLR from FY-3B products based on comparison with NOAA 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 variations of OLR in NOAA and FY-3B datasets are presented in Section 3. In Section 4, we compare temporal variations of OLR in NOAA and FY-3B 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). FY-3B 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 shortwave and longwave radiation reflected and scattered by the earth−atmosphere system (e.g., Yang H. et al., 2012; Yang J. 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 FY-3B OLR data adopted in the present study are 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 FY-3B 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 OLR data are analyzed by the 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 that the OLR estimates has a good representation of convective rainfall in most tropical and prevailing monsoon regions, our study domain is confined in the AAM region (30°S−30°N, 50°−160°E). To further evaluate NOAA OLR and FY-3B 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 FY-3B data have a shorter time period, our study focus on the period from 2011 to 2019. And for the convenience of comparison, the FY-3B datasets are bilinearly interpolated to the resolution of 2.5° × 2.5° in accordance with NOAA datasets.

    3.   Climatology and interannual variations of OLR in NOAA and FY-3B datasets
    • Figures 1a and 1b show the geographical distributions of annual mean OLR from NOAA and FY-3B datasets during 2011−2019. The spatial distributions of OLR are quite similar in two datasets, and 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 FY-3B in the AAM region is 0.992. It is noted that there are obvious systematic differences in magnitudes of OLR between NOAA and FY-3B and the OLR in FY-3B is overall larger than that in NOAA in the whole studied region, especially over India, southeastern Asia, and southern Australia (Fig. 1c).

      Figure 1.  Climatology of annual mean OLR in (a) NOAA and (b) FY-3B datasets, (c) difference between two datasets, and (d–f) interannual standard deviation over the AAM region during 2011–2019.

      Figures 1d and 1e show the interannual standard deviation of OLR in NOAA and FY-3B datasets. The spatial distribution of interannual standard deviation of OLR is basically consistent between the NOAA and FY-3B, 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 FY-3B OLR is overall weaker than that in NOAA, especially over the Maritime Continent and Australia (Fig. 1f).

      Figure 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 FY-3B, both reflecting the seasonal shifts of convective activities over the AAM region (Fig. 2). The pattern correlation coefficients of climatological OLR between NOAA and FY-3B are 0.99, 0.992, 0.993, and 0.995, respectively, from spring to winter. The differences of magnitudes between FY-3B and NOAA in four seasons are somewhat similar to that in the annual mean, also showing larger OLR in FY-3B over India, southeastern Asia, and southern Australia, especially in summer (JJA) (Figs. 2c, f, i, l). This result indicates that the FY-3B satellite can well capture the spatial distribution of climatological OLR although there are some differences in the magnitudes.

      Figure 2.  As in Fig. 1, but for (a–c) spring, (d–f) summer, (g–i) autumn, and (j–l) winter mean OLR.

      The interannual standard deviations of OLR in four seasons and the differences between NOAA and FY-3B 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 Indian Ocean in JJA, expands to a bigger scope, and attains a greater intensity in SON (Figs. 3d, g). The large value of OLR variability in southeastern Indian Ocean during autumn is likely related to the Indian Ocean dipole (IOD) mode, which is the strongest SST mode in tropical Indian 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 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 FY-3B datasets exhibit similar characteristics with those in NOAA datasets (Figs. 3b, e, h, k). The pattern correlation coefficients of interannual standard deviation of OLR between NOAA and FY-3B 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 FY-3B.

      Figure 3.  Interannual standard deviation of spring (MAM), summer (JJA), autumn (SON), and winter (DJF) mean OLR in (a, d, g, j) NOAA and (b, e, h, k) FY-3B datasets and (c, f, i, l) their difference between two datasets over the AAM region during 2011–2019.

      Notable differences are mainly in the Maritime Continent and northern Australia. During MAM, the interannual standard deviation of FY-3B 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 FY-3B OLR is smaller than that of NOAA in the Maritime Continent and northern Australia (Figs. 3f, i). This result suggests that the FY-3B 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 FY-3B 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. Figure 4 shows the patterns of temporal correlation coefficient of NOAA and FY-3B OLR with GPCP precipitation during 2011–2019. It is clear that there are widespread significant negative correlations of both OLR in the NOAA and FY-3B 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, f, i, l, o). The negative correlation coefficient between FY-3B OLR and precipitation is stronger in India and Australia than that between the NOAA OLR and precipitation. This result implies that the FY-3B OLR is better than the NOAA OLR in capturing the interannual variability of land convective precipitation.

      Figure 4.  (a) Pattern of temporal correlation coefficient between annual mean NOAA OLR and GPCC precipitation over the AAM region during 2011–2019. (b) As in (a), but for FY-3B 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 FY-3B and precipitation in GPCP
    • To further elucidate the relationship between OLR and precipitation in details, we divided the AAM region into four sub-regions, including India, southeastern China, Maritime Continent, and northern Australia. Figure 5 shows the time series of annual mean NOAA OLR, FY-3B OLR, and precipitation over four sub-regions. Generally, the value of OLR is the lowest over the Maritime Continent and highest over northern Australia. FY-3B OLR is larger than NOAA OLR about 8 W m−2. The temporal correlation coefficients between precipitation and OLR in two datasets are the highest in the Maritime Continent and the lowest in the Indian region based on annual mean. Note that the correlation between FY-3B OLR and precipitation is all stronger than that of NOAA 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 AAM region. (b) Time series of annual mean NOAA OLR (blue line), FY-3B 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.  Temporal correlation coefficients between precipitation and NOAA OLR as well as FY-3B OLR in the AAM region (30°S–30°N, 50°–160°E) during 2011–2019 on an annual and seasonal basis, respectively. IND, SA, MC, and NA denote Indian region, southeastern Asia, Maritime Continent, and northern Australia, respectively. N/P represents the correlation coefficients between NOAA OLR and GPCP precipitation, and F/P represents the correlation coefficients between FY-3B OLR and GPCP precipitation

      For the analysis of seasonal time series of precipitation, NOAA OLR and FY-3B OLR are further presented in Figs. 6, 7. In MAM, the value of OLR is the highest in Indian region above 270 W m−2 and is the lowest in the Maritime Continent about 215 W m−2. The temporal correlation coefficients between FY-3B OLR and precipitation are stronger than those between NOAA 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 FY-3B OLR and precipitation are stronger than those between NOAA 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. Moreover, the temporal correlations between FY-3B OLR and precipitation are almost all stronger than those between NOAA OLR and precipitation in four sub-regions (Table 1). For winter, the temporal correlation coefficients between FY-3B OLR and precipitation are evidently stronger than those between NOAA OLR and precipitation in India (Fig. 7). Overall, FY-3B OLR has a higher correlation with precipitation variability than NOAA OLR, especially over the lands of the AAM regions (Table 1). This result further supports that the FY-3B OLR has a better quality than NOAA OLR over the AAM region.

      Figure 6.  As in Fig. 5be, but for the spring-mean (MAM) and summer-mean (JJA) results.

      Figure 7.  As in Fig. 5be, 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. Figure 8 shows the winter precipitation, OLR and 850-hPa wind anomalies during the recent strong El Niño year (2015/2016) and La Niña year (2010/2011). During the 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. 8ac). The patterns of OLR anomalies in both NOAA and FY-3B are basically consistent with those 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 FY-3B captures the weak positive OLR anomalies over India (Fig. 8b) whereas the NOAA OLR exhibits the negative anomalies in India (Fig. 8a). During the La Nina year, the OLR anomalies in NOAA and FY-3B both exhibit negative correlation with precipitation anomalies over the whole AAM region (Figs. 8df). However, there are excessively strong positive OLR anomalies corresponding to weak precipitation anomalies over India in NOAA datasets (Figs. 8d, f). These results further confirm that FY-3B OLR data have a better match with precipitation data especially for the Indian regions.

      Figure 8.  (a) Winter (DJF) NOAA OLR (shading) and 850-hPa wind (vector) anomalies during El Niño year (2015/2016). (b, c) As in (a), but for FY-3B 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 FY-3B OLR is consistent with NOAA OLR data in terms of spatial distribution of climatology and interannual variability, with slight differences in the magnitudes. In addition, the relationship between FY-3B OLR and convective precipitation in the tropical AAM region is better than that of NOAA OLR, especially over the land, which indicates that the quality of FY-3B OLR is reliable and worthy of further global application.

    6.   Summary and concluding remarks
    • In this study, we evaluate the ability of FY-3B OLR data and NOAA OLR data to depict precipitation from the perspective of climatology and temporal evolution over the AAM 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 FY-3B 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 FY-3B OLR are consistent with the NOAA OLR. In addition, the values of FY-3B OLR are systematically larger than NOAA OLR by about 10 W m−2 on an average in the whole studied region. Similarly, the spatial patterns of interannual standard deviation of OLR are also basically consistent between NOAA and FY-3B 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 FY-3B OLR all exceed 0.99 and the pattern correlation coefficients between interannual NOAA and FY-3B 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 datasets are consistent over most of the ocean, the largest differences are mainly on land, especially in India and Australia. The negative correlation coefficient between FY-3B OLR and precipitation is stronger in India and Australia than that between the NOAA OLR and precipitation. This result indicates that the FY-3B OLR is better than the NOAA 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 AAM region are defined as IND, SA, MC, and NA. Based on both annual and seasonal averages, the temporal correlation coefficients between FY-3B OLR and precipitation are overall stronger than that between NOAA 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 FY-3B. The patterns of OLR anomalies both in NOAA and FY-3B are basically consistent with those of precipitation anomalies over majority of the AAM region. However, FY-3B 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 FY-3B 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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