Tj1 20.3 the Applications of Feng Yun 4 a Satellite

20.3 THE APPLICATIONS OF FENG YUN 4 A SATELLITE PRODUCTS FOR WEATHER MONITORING OVER THE ASIAN REGIONS

Chan Ying-wa and So Chi-kuen  Hong Kong Observatory, Hong Kong, China

1. INTRODUCTION 2. WEATHER MONITORING OVER THE WESTERN ASIA REGION In 2018, HKO implemented a ground reception system to receive data from the Feng 2.1 Tropical cyclone activities Yun 4A (FY4A) experimental geostationary satellite which was launched in December 2016 The AGRI on-board of FY4A provided 14 and located over the equator in the longitude spectral channels in the range of 0.47μm to position of around 105°E. The objectives are to 13.5μm for generation of conventional visible, enhance monitoring of weather over the western infrared and other types of satellite imageries. Asia regions and utilize data measured by various Among the four geostationary satellites covering advanced instruments on-board of the satellite for the Indian Ocean including FY4A, FY2G, the operational applications despite its experimental Indian National satellite INSAT-3DR and status. Meteosat-8, FY4A provided relatively more channels for imageries production (Table 1) such The full disk FY4A satellite imageries as the various multispectral Red/Green/Blue generated from the Advanced Geosynchronous (RGB) products for diagnosing cloud phase, Radiation Imager (AGRI) provided a good atmospheric dust, severe storms, etc. A coverage of both western and eastern Asia summary of the aviation applications of various regions. It included the mountainous Tibetan spectral channels could be found in Ellrod and plateau, the Himalayas, the Indian subcontinent, Pryor (2018). In addition, the spatial resolution the Indian Ocean and the Arabian peninsula, etc. of the visible channel (0.65μm) of FY4A was the This enabled close monitoring and analysis of highest (0.5 km) which facilitated diagnosing the various meteorological features such as the structures and intensity changes of TCs. The change of convection associated with the FY4A imageries further enhanced HKO’s Intertropical Convergence Zone (ITCZ) over the capability of monitoring TCs over the Indian Asian regions. Ocean particularly those over the Bay of Bengal.

The FY4A’s Lightning Mapping Imager (LMI) The Indian Ocean has been a breeding data provided an unprecedented opportunity to ground of tropical cyclone (TC). Some of which study the large-scale spatial variations and may move northwards towards the Indian characteristics of lightning activities over the subcontinent and the Bay of Bengal and affect a Asian regions. A comparison of lightning data vast number of vulnerable regions. In 2018, it from FY4A’s LMI, Vaisala’s Global Lightning was an active year with 14 TCs forming over the Dataset (GLD360) and HKO’s Lightning Location north Indian Ocean 1 . In particular, TC Sagar Information System (LLIS) was conducted with a moved across the Gulf of Aden and made landfall view to evaluating the LMI’s performance. over Somalia (Figure 1) which was the first TC to move to the west of 45°E since 1965 (IMD, 2018). The FY4A also came with Geostationary In addition, TC Mekunu with maximum winds of Interferometric Infrared Sounder (GIIRS) data. around 50 m/s moved across the Arabian Sea This was the first time such data were available (Figure 2) and brought over 270 mm of rainfall to from geostationary satellite. The use of GIIRS the city of Salalah in Oman in the 24-hour period data, in particular, in numerical weather prediction of 25-26 May 2018 which was more than double (NWP) is under study at HKO. This paper will of the city’s average annual rainfall (IMD, 2018). focus on the added value of the AGRI and LMI Classified as one of the rarest of rare events, TCs data. Luban and Titli developed over the Arabian Sea

 Corresponding author address: Ying-wa Chan 1 Based on TC information on the Indian and Chi-kuen So, Hong Kong Observatory, Meteorological Department website. 134A Nathan Road, Kowloon, Hong Kong, e- mail: [email protected], [email protected].

TJ1 and Bay of Bengal respectively in early October FY4A provided a good platform for monitoring the 2018 (Figure 3) and such last occurrence of two TC activities over the entire Indian Ocean up to mature TCs over the north Indian Ocean could be the east coast of Africa. dated back to November 1977 (IMD, 2018). The

Table 1 Comparison of channel settings of various imagers of FY4A, FY2G, INSAT-3DR and Meteosat-8 geostationary satellites

FY4A Advanced FY2G Visible and INSAT-3DR Meteosat-8 Spinning Geosynchronous Infrared Spin Scan INSAT imager Enhanced Visible Infra-Red Radiation Imager Radiometer (VISSR) Imager (SEVIRI) (AGRI) Longitude: ~105°E Longitude: ~99°E Longitude: ~74°E Longitude: ~41.5°E C* W# R% C* W# R% C* W# R% C* W# R% 1 0.47 1.0 Broadband 0.6 – 1.0 Channel (12) 0.9 2 0.65 0.5 1 0.65 1.0 1 0.635 3.0 3 0.825 1.0 1 0.725 1.25 2 0.81 3.0 4 1.375 2.0 5 1.61 2.0 2 1.625 1.0 3 1.64 3.0 6 2.25 2.0 7 3.75H 2.0 2 3.75 5.0 4 3.92 3.0 8 3.75L 4.0 3 3.82 4.0 5 6.25 3.0 9 6.25 4.0 3 6.95 5.0 4 6.8 8.0 6 7.35 3.0 10 7.1 4.0 7 8.70 3.0 11 8.5 4.0 8 9.66 3.0 12 10.7 4.0 4 10.8 5.0 5 10.8 4.0 9 10.8 3.0 13 12.0 4.0 5 12.0 5.0 6 12.0 4.0 10 12.0 3.0 14 13.5 4.0 11 13.4 3.0 *C: Channel; #W: Wavelength in μm; %R: Resolution in km .

TC Sagar

Gulf of Aden

Northeastern part of Somalia

Figure 1 The visible image captured by the Feng Yun 4A satellite at 04UTC on 17 May 2018. The cloud clusters associated with tropical cyclone Sagar over the northeastern part of Somalia and Gulf of Aden could be observed.

TJ2 These cloud clusters later Spiral cloud developed to bands become a tropical associated depression over the with TC Bay of Bengal in Mekunu 29-30 May 2018

Figure 2 The visible image captured by the Feng Yun 4A satellite at 08UTC on 23 May 2018. The spiral cloud bands associated with tropical cyclone Mekunu over the Arabian Sea (red circle) could be observed.

TC Titli

TC Luban

Figure 3 The Feng Yun 4A satellite true colour image (multispectral product based on 0.47μm, 0.65μm and 0.825μm channels) at 08UTC on 9 October 2018 showing the circulation of tropical cyclones Luban and Titli over the Arabian Sea and the Bay of Bengal respectively.

The Madden-Julian Oscillation (MJO) is one Salby 1994, Zhang 2005). of the dominant modes of tropical variability on intra-seasonal time scales (Madden and Julian The MJO was best illustrated via the time- 1972) and has significant effect on the longitude diagrams of the anomalies of 850hPa atmospheric circulation throughout the global zonal wind, outgoing longwave radiation (OLR) tropics. The MJO can be characterized by and 200hPa velocity potential and such large-scale convective anomalies that develop information was available on the website of the over the tropical Indian Ocean and propagate Climate Prediction Centre (CPC)/National eastward over the maritime continent to the Centers for Environmental Prediction (NCEP) western Pacific typically in the time scales from under the U.S. National Oceanic and 30 to 90 days with an average speed of around 5 Atmospheric Administration (NOAA). The MJO m/s (Knutson and Weickmann 1987, Hendon and signal was strengthened during May 2018 as

TJ3 indicated by the above anomalies (Figure 4). It Sagar (16-20 May 2018), Mekunu (21-27 May was thought that the active MJO phase might 2018) and the Bay of Bengal Depression (29-30 contribute to the sequential development of TCs May 2018) (Figures 1 and 2).

Anomalous Favourable for (a) westerly flow (b) precipitation

Wetter than normal conditions

(c)

Figure 4 The 850hpa zonal wind anomalies in 5°N – 5°S [westerly anomalies (orange/red) and easterly anomalies (blue), panel (a)]. The 200hpa velocity potential anomalies in 5°N – 5°S [positive (brown)/negative (green) anomalies indicate unfavourable/favourable for precipitation, panel (b)]. OLR anomalies in 2.5°N – 17.5°N [positive OLR anomalies (yellow/red) indicate drier than normal while negative OLR anomalies (blue) indicate wetter than normal, panel (c)]. The red eclipses in Figures 4(a), 4(b) and 4(c) suggest anomalous west to east flow, favourable conditions for precipitation and wetter than normal conditions over the equatorial Indian Ocean from mid to late May 2018, reflecting the active phase of MJO.

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2.2 Asian summer monsoon (ASM)

Based on the 1979-1995 mean pentad southwesterlies over Indo-China and the active reanalysis data from NCEP, Zhang et al. (2004) convection over the SCS. In 2018, these two constructed a conceptual model to describe the stages of ASM onset seemed to occur around 29 evolution of the ASM onset which could be May 2018 (Figures 6a and 6b) and 4 June 2018 broadly divided into three different stages (for central part of the SCS as illustrated in (Figure 5). In brief, Stage I (1 April–15 May) was Figures 6c and 6d) based on FY4A imageries and the monsoon onset over Indo-China. Stage II the European Centre for Medium-Range Weather (16 May–25 May) was over the SCS and Stage Forecasts (ECMWF) analysed 850hPa flow III (26 May–15 June) was over the Arabian Sea patterns at 12 UTC on 29 May 2018 and 4 June and India. 2018.

The FY4A imageries provided a close watch In 2018, the full establishment of the summer of the evolution of convective activities over the monsoon over the south China coastal areas Indian Ocean, Indo-China and the SCS to enable seemed to be in mid-June of around 19 June detailed tracking of the advance of the tropical 2018 which was quite late compared with that of ASM onset. Using the above conceptual model, early June (around 1 June 2017) in 2017 (Figure Stage I was marked by strong convection as a 7). The same late 2018 onset of the summer result of the convergence of the southwesterlies monsoon over India was also observed (Figure 8). associated with a cyclonic vortex over the Bay of The availability of FY4A observation of ASM Bengal and the southeasterlies associated with onset could potentially contribute towards the western Pacific subtropical high (WPSH) improved seasonal rainfall forecast in south (Zhang et al., 2004). Stage II manifested itself China. through the eastward extension of

Figure 5 Schematic illustration of the advance of the ASM onset as proposed by Zhang et al. (2004). The meaning of abbreviations is as follows: SH+LH (surface sensible and latent heat fluxes), L (low-level low), SW (southwesterlies), SE (southeasterlies), WPSH (western Pacific subtropical high), H (upper level high), SJ (Somalia cross- equatorial jet). The occurrence sequence of the events is roughly indicated by the descending order of letters in parentheses. In general, the advance of the ASM onset can be divided into three stages with time. Stage I (1 April–15 May) is the monsoon onset over the Indo-China; Stage II (16 May–25 May) is over the South China Sea (SCS); Stage III (26 May–15 June) is over the Arabian Sea and India.

TJ5 Cyclonic vortex over Bay of Bengal

(a) (b) Stage I of the Asian summer monsoon (ASM) onset

Enhanced convection over the central part of the SCS

Figure 6 The FY4A infrared imagery at 12UTC on 29 May 2018 indicating strong convection associated with a cyclonic circulation over the Bay of Bengal [Panel (a)]. The convergence area occurred over Indo-China as shown by the ECMWF analysed 850hPa streamline chart at 12UTC on 29 May 2018 [Panel (b)]. The FY4A infrared imagery at 12UTC on 4 June 2018 showing enhanced convection associated with a tropical depression over the central part of the South China Sea (SCS) [Panel (c)]. The cyclonic circulation over the central part of the SCS as shown by the ECMWF analysed 850hPa streamline chart at 12UTC on 4 June 2018 [Panel (d)].

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(a) (b) Figure 7 The Hong Kong Observatory’s daily weather maps at 00UTC on 1 June 2017 [panel (a)] and 00UTC on 19 June 2018 [panel (b)] which indicate the full establishment of the summer southwest monsoon over the south China coastal areas in 2017 and 2018 respectively.

Figure 8 A map showing the advance and late onset of the southwest monsoon over India in 2018 (map available on the India Meteorological Department (IMD) website at http://www.imd.gov.in/pages/monsoon_main.php.

TJ7 3. LIGHTNING DETECTED BY DIFFERENT OBSERVATION PLATFORMS

3.1 FY4A’s LMI

The LMI instrument on-board of FY4A impulses generated by individual lightning return satellite is equipped with 400x600 pixel planar strokes and large cloud pulses (Vaisala, 2015). CCD array camera operating at 777.4 nm to Both the Time Of Arrival (TOA) and Magnetic count the optical flashes generated by lightning. Direction Finding (MDF) methods were used for This optic imaging technique detects total deriving lightning locations (Said and Murphy, lightning and a number of signal enhancements 2016). The performance of GLD360 over North have been implemented to retrieve the lightning America was estimated to be a cloud-to-ground signal from the CCD data (Yang et al., 2017). (CG) lightning detection efficiency (DE) of 70% Figure 9 shows the LMI detection area. and a median CG stroke location accuracy (LA) of 2–5 km (Holle et al., 2016). The resolution of Using the 1-minute FY4A’s LMI data, total GLD360 data was better than 0.01°. lightning spanning a 15-minute time interval was computed. For quality assurance, simple quality 3.3 HKO’s Lightning Location Information control procedures were devised to filter System (LLIS) suspicious LMI data using the cloud top temperatures (CTtemp) from FY4A satellite HKO collaborated with the Guangdong infrared imageries. For area with CTtemp > 293K Meteorological Bureau (GMB) and the Shenzhen (regarded as cloud free region), the Meteorological Bureau (SZMB) to form a dense corresponding LMI data were removed. The lightning observation network comprising a total possibility of lightning occurring over cloud free of 19 lightning sensors for monitoring lightning areas was thus eliminated. The resolution of activities over the Pearl River Delta region, inland LMI data received by HKO was 0.1°. Guangdong and the south China coastal areas (Figure 10). Four different types of sensors 3.2 Global Lightning Dataset (GLD360) including LS7001, LS7002, IMPACT and LS8000 manufactured by Vaisala were installed in the The GLD360 lightning data from Vaisala network. Both TOA and MDF methods were updated at 1-minute interval were employed for used for deriving lightning locations. For CG comparison with FY4A LMI data. The GLD360 lightning, the LA was about 250 m and DE up to data were generated by a long-range global 95%. The DE for cloud-to-cloud (CC) lightning network of lightning sensors. Each sensor was above 50%. The LLIS data resolution was measured the arrival time and arrival angle of better than 0.01°.

Figure 9 The detection area of the Lightning Mapping Imager (LMI) of the Feng Yun 4A satellite.

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Figure 10 The lightning detection network established by the Hong Kong Observatory (HKO), the Guangdong Meteorological Bureau (GMB) and the Shenzhen Meteorological Bureau (SZMB).

3.4. Comparison of lightning data

A qualitative comparison of LMI, GLD360 seemed to tie in quite well with the locations of and LLIS data was conducted using two cases radar echoes. occurred on 7 August 2018 and 22 August 2018 respectively. For these two cases, convective The LLIS data were able to show two of the activities triggered by high temperatures brought three clusters of lightning captured also by LMI thundery showers to the coastal areas of and GLD360 that occurred over the inland areas Guangdong which provided good opportunity to of southeastern China (green eclipses). The assess the lightning detected by the above three third cluster which lied over 500 km from Hong platforms. Kong was too far to be detected by LLIS.

3.4.1 Lightning spatial distribution 3.4.1.2 Case 2 (22 August 2018)

3.4.1.1 Case 1 (7 August 2018) Figures 12 (a) to (c) show the lightning distribution based on LMI, GLD360 and LLIS data Figures 11 (a) to (c) show the lightning from 13:45 to 14:00 UTC on 22 August 2018. distribution based on LMI, GLD360 and LLIS data The large cluster of lightning associated with from 13:45 to 14:00 UTC on 7 August 2018. The intense radar echoes over the southwestern part locations of major clusters of lightning as shown of the Pearl River Delta region [green eclipses in by LMI and GLD360 data [the red, green and pink Figures 12 (a) to (c)] was well captured by all LMI, eclipses in Figures 11 (a) and (b)] were generally GLD360 and LLIS data. In fact, a band of east- in good agreement. GLD360 data showed a bit west orientated strong radar echoes moved more of lightning than LMI data particularly over southwards across the coastal areas of the southwestern part of China. Taking Guangdong that evening, bringing heavy rain and reference of the composite radar reflectivity thunderstorms to the territory. Over 80 mm of imagery at 14:00 UTC on 7 August 2018 provided rainfall were recorded over the New Territories. by the China Meteorological Administration LLIS detected more than 5,000 CG lightning (CMA), lightning shown by LMI and GLD360 strokes over Hong Kong that day.

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(a) LMI data (b) GLD360 data

Figure 11 The distribution of lightning events based on 15-minute total counts of FY4A’s LMI data (panel a), GLD360 data (panel b) and LLIS data (panel c) from 13:45 to 14:00 UTC on 7 August 2018. The FY4A infrared imagery with LMI data overlayed on top and composite radar reflectivity imagery at 14:00 UTC on 7 August 2018 provided by CMA are shown in panel (a) and panel (d) respectively.

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(a) LMI data (b) GLD360 data

Figure 12 The distribution of lightning events based on 15-minute total counts of FY4A’s LMI data (panel a), GLD360 data (panel b) and LLIS data (panel c) from 13:45 to 14:00 UTC on 22 August 2018. The FY4A infrared imagery with LMI data overlayed on top and composite radar reflectivity imagery at 14:00 UTC on 22 August 2018 provided by CMA are shown in panel (a) and panel (d) respectively.

TJ11 3.4.2 Lightning Density Map (LDM) Figure 14 also showed slightly enhanced lightning activities over such mountainous region although 3.4.2.1 Large area LDM the east-west orientated structure was not HKO started receiving LMI data in May apparent. For lightning occurring over southern 2018 but realized that sometimes there was an China, northern Vietnam and northern part of the apparent reduction of LMI data during the overnight SCS, it was consistent with the enhanced period. After reporting such observation to the convective activity as supported by the positive National Satellite Meteorological Center (NSMC) of OLR anomalies for August 2018 over these regions the CMA, some adjustments were made in late July (Figure 15). 2018 and the problem seemed to be resolved. As the FY4A’s LMI sensor moved to observe lightning 3.4.2.2 LDM over Pearl River Delta Region over the southern hemisphere commencing on 20 September 2018, the LDM for the month of August The LDMs over the Pearl River Delta 2018 (Figure 13) was plotted for analysing the region [21°N - 24°N, 112°E - 116°E] based on LLIS, characteristics of lightning distribution on a large GLD360 and LMI data for August 2018 were scale. compared [Figures 16 (a) to (c)]. All LDMs showed basically four major areas of lightning The lightning distribution pattern in Figure 13 clusters [brown rectangles as well as black, blue was largely consistent with the LDM compiled by and pink eclipses in Figures 16 (a) to (c)]. The Vaisala based on 5-year lightning data from 2012 “centroid” positions of those LMI clusters were to 2016 (Figure 14). In particular, more frequent generally in good agreement with those of LLIS and lightning was observed over the northeastern part GLD360 lighting clusters but there were slight of India but relatively less over the Tibetan plateau. displacements (in the order of 20-40 km). Such Interestingly, there was a line of lightning activities small location difference was reasonable as LMI with higher count near 105°E. According to the data were based on lightning observations from the preliminary investigation of NSMC, it might be due sky view aloft while LLIS and GLD360 data were to overlapping count by the CCD array camera based on ground-based measurements. modules. Another peculiar feature was the line of lightning events near 42°N, 72°E-88°E. It was not In comparison of GLD360 and LLIS data sure if this was to a certain extent caused by both having resolution of better than 0.01°, LLIS topographic influence of the east-west orientated was able to capture the fine details of lightning Tian Shan Mountains or due to problem of the LMI distribution particularly those high density regions. data processing algorithm. The Vaisala LDM in For LMI data with coarser resolution of 0.1°, only major clusters could be identified.

Topographical lightning influence or Might be due to count software overlapping count of and up processing lightning events by LMI problem to be identified

Figure 13 Total counts of lightning for the month of August 2018 based on FY4A’s LMI data.

TJ12 Region near the Tian Shan Mountains with relatively more lightning from FY4A’s LIM data for August 2018

Figure 14 Lightning density map over Asia compiled by Vaisala based on 5-year GLD360 data from 2012 to 2016.

Figure 15 Mean (top) and anomalous (bottom) outgoing longwave radiation (OLR) for August 2018 measured by NOAA-18 satellite. Anomalies are departures from the 1981- 2010 base period monthly means. Positive OLR anomalies are indicative of enhanced convective activities. Diagrams extracted from the Climate Diagnostic Bulletin produced by the U.S. Climate Prediction Center.

TJ13 and and up up

(a) LLIS data (b) GLD360 data

and up

Figure 16 Lightning density maps covering the Pearl River Delta region bounded by 21°N - 24°N, 112°E - 116°E for the month of August 2018 based on LLIS [panel (a)], GLD360 [panel (b)] and FY4A’s LMI [panel (c)] data. The coloured arrows indicated the suggested displacements of the LMI lightning clusters for better matching the LLIS and GLD360 lightning clusters patterns.

TJ14 4. DISCUSSION

With the aid of high resolution FY4A 2018 showed similar spatial distribution of major imageries, TC activities over the Indian Ocean lightning clusters with higher density. With large could be monitored closely. The 2018 TC aerial coverage, LMI data provided a broad season over such basin has been rather overview of lightning-embedded convection over extraordinary and record breaking in terms of the Asia continent which might be useful for higher number of TC developing over the region, aviation route planning. strong TC Mekunu affecting the Arabian peninsula, simultaneous presence of two TCs Effort was being made to characterise the Luban and Titli in early October 2018 and the GOES-R Geostationary Lightning Mapper (GLM) extreme movement of TC Sagar to the west of performance through the conduction of Post 45°E. Launch Test (PLT) phase and the Post Launch Product Test (PLPT) phase lead by the GLM In addition, FY4A imageries enabled science team (Rudlosky, 2017). The PLPT monitoring of the evolution of the ASM particularly would include comparison of GLM data with its onset time at different regions and it posted ground-based lightning observation network. great impact on the summer monsoon rainfall The comparison results would provide further over India. Based on FY4A imageries, ECMWF insight to GLM capabilities and would be an 850hPa analysed streamline patterns and HKO essential reference for evaluating FY4A’s LMI daily weather charts, it was diagnosed that the performance. ASM onset over the south China coastal areas in 2018 was later than that in 2017. Similar late 5. CONCLUSION onset of the summer monsoon over India was also observed in 2018. It was thus essential to The present study has taken reference of monitor the change of convection over the FY4A imageries to diagnose the characteristics of equatorial Indian Ocean (EIO) which was an TC activities over the north Indian Ocean and the important precursor for the development of a Bay ASM onset over Indo-China and the SCS. The of Bengal depression and its subsequent opportunity was taken to analyze the MJO status eastward movement, causing the onset of the and found that the active phase of MJO during summer monsoon over Indo-China and later over May 2018 might lead to the successive formation the SCS. On the other hand, the convection of TCs Sagar, Mekunu and Bay of Bengal over EIO might lead to development of TC that depression from middle to late May of 2018. moved northwestwards to affect the Arabian peninsula and TCs Sagar and Mekunu were such Based on two thunderstorm cases on 7 and examples. For monitoring of MJO status and its 22 August 2018 respectively, FY4A LMI data subsequent evolution especially the eastward showed reasonable agreement with GLD360 and propagation of MJO wave, the availability of FY4A LLIS data in terms of the spatial lightning imageries would be helpful. distribution despite failing to capture some isolated events. The comparison of LDMs over For lightning measurement, the two the Pearl River Delta Region based on LMI, thunderstorm cases on 7 and 22 August 2018 GLD360 and LLIS data for August 2018 also respectively showed that LMI data provided showed similar distribution of major lightning reasonable qualitative assessment of the spatial clusters with high density. distribution of lightning-bearing convection comparable with ground-based measurements The GIIRS on-board of FY4A satellite is and consistent with radar observations. another piece of essential instrument. It has However, LMI data were considered not sensitive been reported that the application of GIIRS data enough in capturing isolated or weak lightning has improved the accuracy of TC track forecast. spots. As FY4A was still in experimental stage, HKO would explore more the potential impact of further tuning of LMI sensitivity might be required. GIIRS data on NWP work.

The comparison of LDMs over the Pearl River Delta Region [21°N - 24°N, 112°E - 116°E] based on LMI, GLD360 and LLIS data for August

TJ15 6. ACKNOWLEDGEMENTS Knutson, T. R., and K. M. Weickmann, 1987: 30- The authors would like to thank Mr. C.W. 60 day atmospheric oscillations: Composite Suen, Ms. W.H. Choi and Mr. C.H. Lam for life cycles of convection and circulation helping to plot the FY4A LMI, GLD360 and LLIS anomalies. Mon. Wea. Rev., 115, 1407-1436. lightning density maps and the associated diagrams. Lu, F., X. Zhang, B. Chen, H. Liu, R. Wu, Q. Han, X. Feng, Y. Li and Z. Zhang, 2017: FY-4 7. REFERENCES geostationary meteorological satellite imaging characteristics and its application Ellrod G.P. and K. Pryor, 2018: Applications of prospects. J. Marine Met., 37, No. 2, 1-12. Geostationary Satellite Data to Aviation. Pure appl. Geophys. Madden, R. A., and P. R. Julian, 1972: doi.org/10.1007/s00024-018-1821-1. Description of global-scale circulation cells in the tropics with a 40−50-day period. J. Goodman S.J., D. Mach, W.J. Koshak and R.J. Atmos. Sci., 29, 1109−1123. Blakeskee, 2010: GLM Lightning Cluster- Filter Algorithm. Algorithm theoretical basis Rudlosky S.D., S.J. Goodman, W.J. Koshak, R.J. document Version 2.0. NOAA NESDIS Blakeslee, D.E. Buechler, D. Mach and M. Center for Satellite Applications and Bateman, 2017: Characterizing the GOES-R Research. (GOES-16) Geostationary Lightning Mapper (GLM) on-orbit performance. 2017 IEEE Hendon, H. H., and M. L. Salby, 1994: The life International Geoscience and Remote cycle of the Madden- Julian oscillation, J. Sensing Symposium, July 2017, pp.279-282. Atmos. Sci., 51, 2225– 2237. Said R. and M. Murphy, 2016: GLD360 Upgrade: Holle R.L., K.L. Cummins and W.A. Brooks, 2016: Performance Analysis and Applications. Seasonal, Monthly, and Weekly Distributions 24th International Lightning Detection of NLDN and GLD360 Cloud-to-Ground Conference & 6th International Lightning Lightning. Mon. Wea. Rev., 144, 2855-2870. Meteorology Conference, 18-21 April San doi.org/10.1175/MWR-D-16-0051.1. Diego, California, USA.

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