Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4: Fengyun-4 is the new generation of Chinese geostationary meteorological satellites with greatly enhanced capabilities for high-impact weather event monitoring, warning, and forecasting.
China is developing a new generation of geostationary meteorological satellites called Fengyun-4 (FY-4), which is planned for launch beginning in 2016. Following upon the current FY-2 satellite series, FY-4 will carry four new instruments: the Advanced Geosynchronous Radiation Imager (AGRI), the Geosynchronous Interferometric Infrared Sounder (GIIRS), the Lightning Mapping Imager (LMI), and the Space Environment Package (SEP). The first satellite of the FY-4 series launched on 11 December 2016 is experimental, and the following four or more satellites will be operational.
The main objectives of the FY-4 series are to monitor rapidly changing weather systems and to improve warning and forecasting capabilities. The FY-4 measurements are aimed at accomplishing 1) high temporal and spatial resolution imaging in 14 spectral bands from the visible, near-infrared, and infrared (IR) spectral regions; 2) lightning imaging; and 3) high-spectral-resolution IR sounding observations over China and adjacent regions. FY-4 will also enhance the space weather monitoring and warning with SEP. Current products from FY-2 will be improved by FY-4, and a number of new products will also be introduced. FY-4's sounding and imaging data will be used to improve applications in a wide range of ocean, land, and atmosphere monitoring plus forecasting extreme weather (especially typhoons and thunderstorms); overall, FY-4 will contribute to more accurate understanding and forecasting of China's weather, climate, environment, and natural disasters. This new generation of Chinese geostationary weather satellites is being developed in parallel with the new generation of geostationary meteorological satellite systems from the international community of satellite providers and is intended to be an important contribution to the global observing system.
On 7 September 1988, the Long March rocket carried the Fengyun-IA (FY-1A) polar-orbiting satellite into orbit, marking the start of the Chinese Fengyun (FY; meaning wind and cloud in Chinese) meteorological satellite observing systems program. On 10 June 1997, the FY-2 A geostationary satellite was successfully launched and the Chinese meteorological satellite program took a large step toward its goal of establishing both polar-orbiting and geostationary observational systems. Over time the Fengyun satellites have become increasingly important for protecting lives and property from natural disasters in China.
The Chinese FY satellites are launched as a series. The odd numbers denote the polar-orbiting satellite series, and the even numbers denote the geostationary satellite series. After launch, a letter is appended to indicate the order in the satellite series; for instance, FY-2F is the sixth satellite that has been launched in the first generation of the Chinese geostationary satellites (FY-2). The FY-2, the first generation of the Fengyun geostationary weather satellite series, includes seven satellites launched since 1997; another will follow around 2017 to conclude the FY-2 mission. The Chinese geostationary weather satellite system operates two satellites located at 86.5[degrees]E (FY-2 West) and 105[degrees]E (FY-2 East); they provide full-disc observations every 30 min and observations every 15 min in their overlap region (see Fig. 1 for the current FY-2 coverage). The FY-2D (West) and FY-2E (East) observation schedule is shown in Table 1. The two satellites also back each other up. FY-2 satellites carry the Visible and Infrared Spin Scan Radiometer (VISSR) capable of imagery in five spectral bands. Derived products include atmospheric motion vectors (AMVs), sea surface temperatures (SSTs), total precipitable water vapor (TPW), quantitative precipitation estimations (QPEs), fire locations and intensity, surface albedo, and several others.
The FY-4 introduces a new generation of Chinese geostationary meteorological satellites, with the first FY-4A launched on 11 December 2016. The remaining satellites of this series are planned to be launched from 2018 to 2025 and beyond. FY-4 has improved capabilities for weather and environmental monitoring, including a new capability for vertical temperature and moisture sounding of the atmosphere with its high-spectral-resolution infrared (IR) sounder, the Geostationary Interferometric Infrared Sounder (GIIRS). Following 15 years, the three-axis stabilized FY-4 series will offer full-disc coverage every 15 min or better (compared to 30 min of FY-2) and the option for more rapid regional and mesoscale observation modes. The Advanced Geosynchronous Radiation Imager (AGRI) has 14 spectral bands (increased from the five bands of FY-2) that are quantized with 12 bits per pixel (up from 10 bits for FY-2) and sampled at 1 km at nadir in the visible (VIS), 2 km in the near-infrared (NIR), and 4 km in the remaining IR spectral bands (compared with 1.25 km for VIS, no NIR, and 5 km for IR of FY-2). FY-4 will improve most products of FY-2 and introduce many new products [such as atmospheric temperature and moisture profiles, atmospheric instability indices, layer precipitable water vapor (LPW), rapid developing clouds, and others]. Products from FY4 series are expected to provide enhanced applications and services. These new products are compared with those of FY-2 in Table 2.
FY-4's AGRI will be operated in conjunction with GIIRS. FY-4's GIIRS is the first high-spectral-resolution advanced IR sounder on board a geostationary weather satellite, complementing the advanced IR sounders in polar orbit. These include the Atmospheric Infrared Sounder (AIRS) on board the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Aqua platform (Chahine et al. 2006), the Infrared Atmospheric Sounding Interferometer (IASI) on board Europe's Meteorological Operational (MetOp) satellites (Clerbaux et al. 2007; Smith et al. 2009), and the Cross-Track Infrared Sounder (CrIS) on board the Suomi National Polar-Orbiting Partnership (SNPP; www.nasa.gov/mission_pages/NPP/main/index.html; Bloom 2001). They have had a large positive impact in global and regional numerical weather prediction (NWP) applications (Le Marshall et al. 2006; McNally et al. 2014; Wang et al. 2014; Li et al. 2016) and climate research (Yoo et al. 2013). However, severe weather warning in a preconvective environment (Li et al. 2011, 2012), nowcasting, and short-range forecasting require nearly continuous monitoring of the vertical temperature and moisture structure of the atmosphere on small spatial scales that only a geostationary advanced IR sounder can provide. The GIIRS will provide breakthrough measurements with the temporal, horizontal, and vertical resolution needed to resolve the quickly changing water vapor and temperature structures associated with severe weather events. GIIRS will be an unprecedented source of information on the dynamic and thermodynamic atmospheric fields necessary for improved nowcasting and NWP services (Schmit et al. 2009). High-spectral-resolution IR measurements will also provide estimates of diurnal variations in tropospheric trace gases like ozone and carbon monoxide (Li et al. 2001; J.-L. Li et al. 2007; Huang et al. 2013) that will support forecasting of air quality and monitoring of atmospheric minor constituents.
The FY-4 GIIRS is one of the Group on Earth Observations (GEO) sounders planned by Global Earth Observation System of Systems (GEOSS) member states in response to the call from the World Meteorological Organization (WMO) for advanced sounders in the geostationary orbit. Another is the Infrared Sounder (IRS) planned by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) for the geosynchronous Meteosat Third Generation (MTG) satellite systems in the 2020 time frame and beyond. Together with the new generation of geostationary weather satellite systems being developed by other countries, FY-4 will become an important GEO component of the global Earth-observing system.
Overall, FY-4 represents an exciting expansion in Chinese geostationary remote sensing capabilities. FY-4A will be considered experimental and the subsequent satellites in the FY-4 series will be operational. Compared with the current operational FY-2 series, the FY-4 satellites are designed to have a longer operating life. Table 3 summarizes some of the significant improvements in instrument performance expected from FY-4 compared with the current operational FY-2 series. For the FY-4 operational series of satellites, the main observation capabilities are similar to those of FY4A, with some significant performance improvements. The AGRI channel number will be increased from 14 to 18 with IR spatial resolution of 2 km, and the full-disc temporal resolution will be enhanced from 15 to 5 min. GIIRS spectral and spatial resolutions will be increased to 0.625 cm-1 and 8 km, respectively. Lightning Mapping Imager (LMI) coverage will be enlarged to full disc. Space-monitoring instruments will be increased; for example, a solar X-ray and extreme ultraviolet imager will be on the following FY-4 series satellites.
This paper provides an introduction to the Chinese FY-4 observation capabilities, the derived products, and the associated applications. The ground system components are briefly described in the next section. The following sections provide an overview of the four FY-4 A instruments, products, and related application areas, and the final section offers a summary and conclusions.
FY-4A GROUND SEGMENT. A new ground segment has been designed and is being built to accommodate the technical requirements of the FY-4 satellites. The primary ground missions are as follows:
1) receiving raw data from the satellite;
2) determining and predicting the satellite orbit based on ranging measurements to the satellite;
3) monitoring the satellite and controlling the payloads;
4) undertaking the mission management and operation control of the satellite and ground systems;
5) processing data for geolocation and registration;
6) processing data for measurement calibration;
7) producing quantitative products;
8) providing an archive and distribution service for the data and products;
9) carrying out applications for the weather, climate, and environment; and
10) accomplishing monitoring and predicting services for space weather.
Figure 2 presents a flowchart of FY-4 ground segment. In addition to the backup Data and Telemetry System (DTS) located in Guangzhou in southern China, the new DTS facilities will be located in Beijing, China. The ground segment is being developed in five phases, which include user requirement analysis, algorithm development for quantitative products, system design, engineering, and in-orbit testing.
The navigation and registration of AGRI, GIIRS, and LMI data from a three-axis stabilized satellite is a great challenge. A method has been designed in which the satellite platform, payloads, and ground segment cooperate with each other. As part of the ground segment, the Navigation and Registration System (NRS) calculates the scan and step angles from each instrument to the predicted stars that can be observed by the two payloads and arranges the observation timetable for AGRI and GIIRS based on those predicted stars. The NRS solves the equations that describe the relationship between the optical line of sight of instrument and structural thermal distortion and determines the equivalent variation of yaw, pitch, and roll once every 24 h. The pixels of an image should be Earth-located to within 112 [micro]rad (3[sigma], within 64.5[degrees] of geocentric angle) at the subsatellite point during the daytime. Geographical location error is estimated from landmark navigation. Image navigation and registration (INR) specification is listed in Table 4.
Unlike FY-2, FY-4A will have enhanced calibration, including a full-path blackbody for AGRI and GIIRS in the thermal infrared bands (TIBs) and a standard reflective board for AGRI in the reflective solar band (RSB). The calibration accuracy of FY-4A will be better than 1 K for TIB and 5% for RSB; this will benefit quantitative applications greatly.
After launch and an in-orbit test of FY-4A, the new data and products will be used in NWP, weather, climate, environment, and other areas; data distribution and applications are shown in Fig. 3. The processing of FY-4A raw data includes navigation, calibration, inversion, and generation of various-level products. The Level 1B (L1B) and some L2 products will be broadcast by FY-4A directly and users will be able to receive the High Rate Information Transmission in horizontal link or vertical polarization link (HRIT-H or HRIT-V) or Low Rate Information Transmission (LRIT). The contents, bit rate, and frequency of broadcast specifications are listed in Table 5. The L2 and L3 products generated by the ground segment of FY-4A will also be distributed by the National Meteorological Information Center (NMIC) through CMACast (Satellite Data Broadcasting System of China Meteorological Administration). All datasets of FY-4A, both real time and historical, will be available to the global community on the National Satellite Meteorological Center (NSMC) satellite data server website (http://satellite.nsmc.org.cn).
THE FY-4 AGRI. FY-4A's AGRI has 14 spectral bands that are similar to those planned for other advanced imagers, such as the new generation of U.S. Geostationary Operational Environmental Satellite (GOES-R series) Advanced Baseline Imager (ABI; Schmit et al. 2005, 2017) and the European MTG Flexible Combined Imager (FCI; www.eumetsat .int/website/home/Satellites/FutureSatellites /MeteosatThirdGeneration/index.html), as well as other similar instruments on board geostationary meteorological satellites. Figure 4 shows the AGRI spectral band locations compared with ABI, FCI, and others. Five bands of AGRI are heritage from FY-2 VISSR (0.55-0.75,3.5-4.0,6.3-7.6,10.3-11.3, and 11.5-12.5 [micro]m) and similar to those on the current GOES Imager (Menzel and Purdom 1994) and the Japanese Multifunctional Transport Satellite (MTSAT) Imager. The C[O.sub.2]-sensitive spectral band (13.5 [micro]m) of AGRI is also found on the GOES-12, -13, -14, and -15 imagers; the GOES-8 and -15 sounders (Menzel et al. 1998); and the Moderate Resolution Imaging Spectroradiometer (MODIS) (Menzel et al. 2008). The additional bands of AGRI are comparable with those of GOES-16 ABI (Schmit et al. 2005, 2017) and the Himawari-8 Advanced Himawari Imager (AHI; Bessho et al. 2016). Similar to ABI (Schmit et al. 2005, 2017), AGRI includes 0.47 [micro]m for aerosol detection and visibility estimation; 0.83 [urn for aerosol detection and estimation of vegetation health; 1.378 [micro]m for sensing very thin cirrus clouds; 1.61 [um for snow/cloud discrimination; 2.23 [micro]m for aerosol and cloud particle size estimation and vegetation detection; 3.75 [micro]m for cloud properties/screening, hot spot detection, moisture determination, and snow detection; 6.25 [micro]m and 7.10 [micro]m for middle- and upper-tropospheric water vapor detection and tracking; 8.5 [micro]m for detection of volcanic ash clouds containing sulfuric acid aerosols and estimation of cloud phase; 10.8 [micro]m for determination of SST and surface temperature, and 12.0 [micro]m for estimation of low-level moisture.
Compared with the current FY-2 VISSR, the FY-4A AGRI will have nine more spectral bands, increased spectral resolution, improved radiometric accuracy, and improved imager registration and navigation. Table 6 summarizes the FY-4A AGRI spectral bands, along with the spatial resolution, the signal-to-noise specification, and the associated main applications. The spectral coverage for the six VIS/ NIR bands of FY-4A AGRI are shown in the top-left panel of Fig. 5, along with spectral reflectance plots for snow and vegetation [the spectral plots are from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) library (see online at http://speclib.jpl.nasa.gov/)]. The AGRI spectral bands will enable generation of new products and refinement of existing products of FY-2. These VIS/NIR bands will introduce or improve detection of haze, clouds, surface vegetation, cirrus, snow cover, and aerosol particle sizes. The first three VIS/NIR bands can be composited to produce color images with respect to red, green, and blue (RGB) applications, that is, dust and volcanic ash monitoring recommended by WMO. In fact, these composited images are quite different from the natural or near-natural ones generated by polar-orbiting imagers such as MODIS on board the NASA EOS Terra and Aqua platforms, Medium Resolution Spectral Imager (MERSI) on board the Chinese FY-3 series (Dong et al. 2009), and Visible Infrared Imaging Radiometer Suite (VIIRS) on board the U.S. SNPP satellite. For the geostationary orbiting imagers, the true color images are now being produced using a new process called the Simple Hybrid Contrast Stretch (SHCS) method developed by the University of Wisconsin (UW) Space Science and Engineering Center (SSEC) and the National Oceanic and Atmospheric Administration (NOAA)'s Center for Satellite Applications and Research (STAR). This simple algorithm combines four of the spectral bands from the AHI into a true color RGB image (Miller et al. 2016), providing added clarity.
The top-right and bottom panels of Fig. 5 also show the spectral coverage of the seven IR bands for FY-4A AGRI. In general, AGRI has IR spectral coverage similar to that on the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG). Weighting functions, shown in Fig. 6 for a U.S. standard atmosphere at zero satellite zenith angle, indicate the peaks of the two water-vapor-sensitive bands, the C[O.sub.2]-sensitive band, and the various surface viewing bands and display the vertical distribution of the upwelling radiances sensed by the AGRI IR bands. The two 3.9-jUm bands have the same spectral response function (SRF), but different dynamic ranges; one covers 260-450 K for fire detection and characterization and the other covers 200-340 K for cloud and Earth surface property determination. For improved fire applications, the spatial resolution of the 3.9-[micro]m high-range band is 2 km. The calculations to produce Fig. 6 relied on FY-4A SRFs used in a fast radiative transfer model developed in a joint effort between the NSMC and the SSEC.
With 14 spectral bands (see Fig. 7 for an example of simulated images in all spectral bands), FY-4 will improve the current operational FY-2 satellite products and introduce many new products. In addition to spectral band imagery and color composite images, the quantitative products expected from FY-4A AGRI data are summarized in Table 2. Most FY-4A AGRI products will have improved spatial and temporal resolutions compared to the current operational FY-2 products [cloud mask, land surface temperature (LST), SST, QPE, AMV, and radiation] and are expected to provide enhanced applications and services.
Figure 8 shows the anticipated FY-4A-like cloud products (optical depth at 0.55 [micro]m in the top-left panel, cloud-top height in kilometers in the top-right panel, ice water path in the bottom-left panel, and liquid water path in the bottom-right panel) retrieved using the FY-4 algorithm (Min et al. 2017) on data from SEVIRI (Schmetz et al. 2002) on board the Meteosat-8 at 1200 UTC 1 August 2006. A convective cloud system containing mainly ice particles is well depicted. AGRI will offer the same cloud products for applications over China and its adjacent regions.
FY-4 A AGRI will also provide critical information for monitoring and forecasting severe storms, such as a major flooding event that occurred in Beijing, on 21 July 2012. The heaviest rainfall in over 60 years caused at least 77 fatalities, cancelled over 500 airport flights, and forced more than 65,000 people to be evacuated. Much of the city averaged around 18-23 cm of rainfall within a 10-h period, with the heaviest total rainfall accumulation being 46 cm in the Fangshan District of Beijing. Analysis of this storm with FY-2E satellite images is available on the Cooperative Institute for Meteorological Satellite Studies (CIMSS) satellite blog (http://cimss.ssec.wisc .edu/goes/blog/?s=beijing+). The 1.25-km resolution, 0.73-[micro]m visible channel image from the Chinese FY-2E satellite showed an elongated band of clouds with embedded thunderstorms oriented from southwest to northeast across much of northeastern China. The 5-km-resolution FY-2E 10.8-[micro]m IR channel images (not shown) revealed the development of very cold cloud-top brightness temperatures (-60[degrees] to -75[degrees]C) with embedded thunderstorms and a period of back building of convection in the vicinity of Beijing after around 1500 UTC. The 5-km-resolution FY-2E 6.8-[micro]m water vapor channel images (see Fig. 9) indicated a pronounced warming/drying signature (yellow color) associated with a deepening shortwave trough that was approaching from the northwest. This approaching trough may have played a role in enhancing the synoptic-scale upward vertical motion across the Beijing region that created a more favorable environment for formation and maintenance of strong convection. The small green square denotes the location of Beijing International Airport (see Fig. 9). The spatial resolution of AGRI will be higher than FY-2; the spatial resolution of a 0.55-0.75-[micro]m band is 0.5 km, that of the IR band (10.3-11.3 [micro]m) is 4 km, and the time for a full disc is 15 min, hence more spatial and temporal details of storms will be detected. Since the quantization depth for all bands increases to 12 bits (and 3.9 [micro]m high, it is 16 bits), the temperature resolution will be higher also. And AGRI has two water vapor bands so that not only two-dimensional but three-dimensional water vapor structure will be detected.
With more spectral bands, increased spatial resolution, and improved navigation, FY-4A will offer enhanced and new information/products for monitoring and forecasting high-impact weather events like the storm discussed in the previous paragraph. A new product called Rapid Developing Convection (RDC) aims to monitor mesoscale convection over China for nowcasting applications. The RDC uses a multispectral thresholding algorithm that tracks convective clouds with cloud-top temperature cooling and checks their spectral characteristics. If the cloudtop cooling rate (CTC) threshold is exceeded, then the pixels within the cloud object are flagged for RDC. The RDC product will detect not only convective initiation (CI) but also vigorous deep convective clouds and cloud systems. The CI algorithm (Table 7 shows the specific attributes) of RDC is similar to that of GOES-16 (Mecikalski and Bedka 2006). Figure 10 shows the RDC test results using Himawari-8 AHI data from 0000 UTC 20 August 2015; RDC levels are indicated by colors, with green for CTC > 0 K, medium green for -2 < CTC < 0 K, light green for -4 [less than or equal to] CTC < -2 K, yellow for -6 [less than or equal to] CTC < -4 K, orange for CTC < -6 K, and red for CI.
THE FY-4A GIIRS. The GIIRS on board FY-4A will be used for hourly (or better) atmospheric soundings. Two observation modes of GIIRS are planned. One mode is designed for synoptic-scale coverage over China with a temporal resolution of 55 min and coverage of 5,000 x 5,000 [km.sup.2]. The second mode is the mesoscale mode with a temporal resolution of 10 min and coverage of 2,000 x 2,000 [km.sup.2]. Some specifics of GIIRS are shown in Table 8.
The main optical structure of GIIRS is almost the same as that of AGRI; an off-axis primary telescope is supplemented by secondary and tertiary mirrors so that the effects of sunlight shining directly into the instrument aperture around local midnight are mitigated. Two scan mirrors, in contrast to the gimbaled two-axis scan mirror adopted for other instruments [i.e., Japanese Advanced Meteorological Imager (JAMI)] in GEO platform, are utilized for mesoscale and synoptic-scale observations so that the traditional image rotation effects are removed completely. Moreover, a Sterling cooler is adopted by GIIRS to enable its two detector arrays to operate at 65 and 75 K for longwave and midwave infrared spectra, respectively.
Since 1994, the United States has had a broadband IR sounder on the GOES series (Menzel and Purdom 1994; Menzel et al. 1998). The GOES Sounder measures emitted radiation in 18 IR spectral bands and reflected solar radiation in one visible band. With spectral measurements from bandwidths at the order of tens of wavenumbers, the GOES Sounder has been found to be very useful for preconvective warnings and weather forecasts by providing near-real-time products that include clear-sky radiances, profiles of temperature and moisture, atmospheric instability indices, LPW, TPW, cloud-top retrievals (pressure, temperature, and effective cloud amount), surface skin temperature, water vapor atmospheric motion vectors, and total ozone (Menzel et al. 1998; Velden et al. 1998; Li et al. 2001; J.-L. Li et al. 2007; Li et al. 2008). FY-4A with GIIRS will have spectral coverage similar to the advanced IR sounders AIRS, CrIS, IASI, and IRS (see Fig. 11). The GIIRS 913 channels will have spectral widths of less than a wavenumber in both the longwave and midwave IR spectrum and will enable similar but improved applications as those of the GOES Sounder. Jacobians indicate the sensitivities of brightness temperature (BT) viewed by the satellite to the changes of atmospheric parameters, such as air temperature, water vapor, and ozone. The Jacobians of temperature, water vapor, and ozone are defined as dBT/dT, dBVdlog (Q), and dBT/dlog ([O.sub.3]), where T, Q, and [O.sub.3] are the profiles of air temperature (in kelvins), water vapor mixing ratio (in grams per kilogram), and ozone (in parts per million by volume), respectively. Figure 12 shows the temperature Jacobian from the FY-4A GIIRS longwave IR band, water vapor mixing ratio Jacobian (Li 1994) from the midwave IR band, and ozone mixing ratio Jacobian from the longwave IR band, along with a BT spectrum and noise equivalent delta temperatures [NeDT (NEAT) at 250, 280, and 300 K] for a U.S. standard atmosphere. From the Jacobians shown in Fig. 12, it can be seen that the temperature vertical information in the troposphere can be derived mainly from the longwave IR channels, while the tropospheric moisture can be derived from the midwave IR channels. The upper-tropospheric and lower-stratospheric ozone can also be derived from the longwave IR channels. The high spectral resolution of GIIRS is necessary to approach the observation requirements for weather forecasting, including retrieval of vertical profiles of atmospheric moisture [rootmean-square error (rmse) of 10% relative humidity (RH) for 2-km layers] and temperature (rmse of 1K for 1-km layers), trace gas concentrations, cloud-top pressures (Yao et al. 2013), surface IR emissivity (J. Li et al. 2007; Li and Li 2008), and surface temperatures. The spatial resolution is 16 km for the first GIIRS on the experimental FY-4A and will be improved to 8 km on the following operational FY-4 systems. The primary products of FY-4A GIIRS include atmospheric temperature and moisture profiles, instability indices, and trace gases derived in clear skies and partially cloudy skies (Smith et al. 2012; Weisz et al. 2013). It could be used together with AGRI for AMV height assignment.
Figure 13 shows the rmse of temperature retrievals for 1-km layers and RH retrievals for 2-km layers from simulated FY-4A GIIRS radiances and radiances having the same characteristics of the GOES Sounder. The retrieval algorithm is based on the one-dimensional variational (1DVAR) approach (Li and Huang 1999; Li et al. 2000). The simulated FY-4A GIIRS radiances are degraded with an assumed observation error and then inverted into soundings. The rmse is calculated by comparison between the "true" profiles used in the radiance simulation and the retrieved profiles. The results indicate that the FY-4A GIIRS soundings will improve upon the current GOES Sounder retrievals for temperature and relative humidity.
Li et al. (2012) have summarized several potential applications of high-temporal-resolution and high-spectral-resolution IR data from the literature. For example, Sieglaff et al. (2009) showed that the spectral "online" and "offline" absorption features in the IR window region of the spectrum are related to low-level temperature and moisture. Schmit et al. (2008) demonstrated that the equivalent potential temperature differences between 800 and 600 hPa can be a useful indicator of thunderstorm potential. Li et al. (2011) noted that the advanced IR sounder is able to depict an unstable region similar to the "truth" field in a simulation using an International H20 Project (IHOP) case; equally important is that the atmospheric stability derived from an advanced IR sounder may suggest the region of stable air important for reducing false alarms when forecasting convective events.
In another study, Li et al. (2012) showed that the derived atmospheric stability indices such as convective available potential energy (CAPE) and lifted index (LI) from geostationary advanced IR sounders may provide critical information 1-6 h before the development of severe convective storms, such as the local severe storm that occurred on 7-8 August 2010 in Zhou Qu, China, which caused more than 1,400 deaths and left another 300 or more people missing.
With the FY-4A GIIRS, atmosphere instability can be continuously monitored in real time for storms that have not been anticipated by NWP like the one occurring on 6-7 August 2013 in Beijing, which was not anticipated in the numerical weather forecasts. This storm produced a very uneven distribution of precipitation with heavy local rainfall accompanied by hail and gusts of 14-24 m [s.sup.-1]; Pinggu, Miyun, and Shunyi, China, experienced heavy rainfall. The high-resolution Rapid Update Cycling Data Assimilation and Forecasting System at the Beijing Meteorological Bureau, version 2.0 (BJ-RUCv2.0) NWP (Chen et al. 2014) at 3-km resolution forecasted 24-h precipitation accumulations of only 30 mm in the northwestern edge of Beijing and totally missed the heavy precipitation over the city area. With FY-4A GIIRS measurements, the forecasts (location, time occurred, QPE, and more) could be improved with better moisture initialization in the NWP model (Li and Liu 2009; Liu and Li 2010).
For the same storm, another important indicator is the warning of the preconvective environment. The FY-2E 11-[micro]m BT image indicates that the storm developed at 1500 UTC, while the simulated FY-4A GIIRS LI indicates that the atmosphere is extremely unstable at 1200 UTC, 3 h before the storm. Figure 14 shows the simulated FY-4A LI (color) at 1200 UTC along with the FY-2E 6.8-[micro]m BT image at 1500 UTC. The European Centre for Medium-Range Weather Forecasts (ECMWF) analysis was used in FY-4A GIIRS retrieval simulation. A large unstable region (yellow and red) was identified before the severe storm development.
THE FY-4A LMI. The FY-4A LMI will be the first lightning detection sensor on Chinese satellites. LMI will be able to detect the presence of total lightning activity (in-cloud and cloud-to-ground lightning), which is useful for early predictions of storms and severe weather events (Schultz et al. 2009; Gatlin and Goodman 2010; Stano et al. 2014). The specific parameters of LMI, similar to that of the GOES-R Geostationary Lightning Mapper (GLM; Goodman et al. 2013), are shown in Table 9.
LMI will measure the total lightning activity over China continuously day and night. The spatial resolution is 7.8 km at nadir. The LMI's 400 x 600 pixel charge-coupled device (CCD) camera will operate at 777.4 nm to count flashes and measure their intensity. False alarms are nonlightning artifacts generated in the processing of LMI data due to natural or anthropogenic causes. To deal with these false alarms, a set of filtering algorithms have been developed to categorize them as dedupe, ghost, lollipop, track and solar flares, and so on. A surface validation system is built to carry out a comparison of LMI flashes with surface total lightning observation data. To meet the 10% requirement on false alarms, the FY-4 LMI will combine a set of signal enhancements to retrieve the lightning signal from the CCD data flow. These enhancements include 1) spatial filtering by matching the instantaneous field of view of each detector to the typical cloud-top area illuminated by a lightning stroke, 2) spectral filtering by using a narrowband interference filter with a 1-nm bandwidth and centered at 777.4 nm to enhance the lightning signal from the background, 3) 2-ms integration on the focal plane to enhance the radiation signal of the lightning over the background, and 4) utilizing the Real-Time Event Processor (RTEP) to carry out background subtraction and lightning event pickup. In addition to these, we will also build a set of false-alarm-filtering algorithms to decrease false events in the L1B data flow.
The LMI's Level 2 algorithm identifies "group" and "flash" from Level 1 geolocated time-tagged lightning event data; a tree-structured algorithm will be developed that clusters optical events into groups and groups into flashes as used for Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) and GOES-16 GLM. Data from the ground-based lightning network in China and LIS equipped on TRMM (Albrecht et al. 2016) have been combined to generate simulated LMI proxy data for algorithm development and product evaluation. The proxy data were generated as follows (Finke 2011):
1) According to the position and intensity of a "strike" (just like the return stroke of a cloud-to-ground flash), the position and time of optical pulse associated with the strike are generated. The optical pulse parameters (size and energy) are generated using a random number generator that depends on a distribution function.
2) The photons are generated according to the parameters (size and energy) of each optical pulse.
3) The output of the detected event data are generated according to the pixels with energy above a detection threshold.
Figure 14a shows the distribution of photons. Figure 15b shows the expanded bottom-right corner of Fig. 15a. Figure 15c shows the distribution of the accumulated radiance of "optical events." Figure 15d shows the expanded bottom-right corner of Fig. 15c. There are 4,721 optical events (radiance is the sum of every optical event, in units of y] nr2 sr-1); those are filtered and clustered to 25 flashes and 363 groups. The different colors indicate the intensity of radiance of the optical events.
The LMI products will be used to identify risk areas for convective storms by detecting total lightning and to improve warnings regarding severe storm hazards, convection precipitation, and lightning strike alerts. The LMI data will be integrated with other observations (satellite, radar, in situ) and models; it will be used for applications like cell tracking and QPEs. It will also be used to research lightning-produced NOx, to study the Earth's electric field, to accumulate a long-term database useful for tracking decadal changes of lightning, and to assess the impact of climate change on thunderstorm activity.
THE FY-4A SPACE ENVIRONMENT PACKAGE (SEP). Geostationary satellites are vulnerable to space weather events such as energetic particles damage and severe geomagnetic disturbance. Therefore, it is necessary to monitor and forecast the near-Earth space environment conditions. The SEP on FY-4 A consists of energetic particle detectors and a magnetometer that will be used for in situ monitoring of the near-Earth (at geostationary altitude) space environment for the National Center for Space Weather. The energetic particle detectors will measure the energetic particle environment in orbit. Electrons will be measured over the range of 0.4-4 MeV. Protons will be measured over the range 1-165 MeV and over 165 MeV. The detectors will also give a directional observation of particle flux from multidirectional sensors mounted on the three-axis-stabilized satellite platform. The space particle environment in geosynchronous orbit is dynamic and composed of trapped particles, solar energetic particles, and a cosmic-ray background. These energetic particles could cause spacecraft surface and deep dielectric charging, single event upsets (SEUs), electronic component degradations, and other effects. Some measurements will continue to be used for operational alerts and warnings of potentially hazardous conditions. For example, the energetic proton flux (>10 MeV) alert will be issued to civil aviation users that need to consider the polar cap absorption induced by the solar proton events. The energetic electron flux (>2 MeV) has been used as the proxy that indicates the possible levels of electrostatic discharge in spacecraft instrumentation. In addition, the energetic particle measurements have also served as the basis for postevent analyses and study of the space environment. The pitch-angle distribution calculated from the directional flux and the magnetic field vector will give a better understanding of the dynamic processes of particles near the geosynchronous orbit. Another component of SEP is a magnetometer that measures the magnetic field vector in the range of [+ or -]400 nanotesla (nT). It consists of two triaxial fluxgate sensors and the associated electronics. The electronics are inside the spacecraft, and the sensors are mounted on a 6-m deployable boom, with one sensor at the tip of the boom and the other one located 1 m inboard. The dual-sensor configuration will enable a separation of stray field effects generated by the spacecraft from the ambient space magnetic field. The measurements are the sum of the ambient space field and stray fields originating from the spacecraft; the difference between two sensors can be considered to originate from the spacecraft and thus the ambient space field can be inferred.
The magnetic field products from FY-4 have various purposes, both operational and scientific. The products can be used to evaluate the level of geomagnetic activity and the solar wind dynamic pressure, to estimate the magnetopause crossings and shocks, to provide input to the space weather forecasting model, to provide a database for improving knowledge of the magnetosphere and solar-terrestrial interactions, and last, but not least, to process the pitch-angle production after combination with the energetic electron data.
SUMMARY AND CONCLUSIONS. FY-4A, launched on 11 December 2016, represents an improved and new capability of the Chinese geostationary weather satellite system. With advanced imaging and sounding instruments on board FY-4A providing high temporal, spatial, and spectral resolution measurements, the benefit is expected to be large for severe weather monitoring, warning, and forecasting. With the first lightning imager on board the Chinese geostationary satellite, the added valuable lightning information is expected to significantly improve warnings of severe storm hazards, convection precipitation, and lightning strikes. A primary use of the AGRI and GIIRS data will be to improve NWP through data assimilation of both radiances (AGRI, GIIRS) and L2 products (TPW, LPW, and AMVs from AGRI). Those data will be assimilated in the operational Global and Regional Assimilation and Prediction System (GRAPES) models and will also be distributed to the user community for operational applications. Assimilation of data and derived products from the AGRI, GIIRS, and LMI in both global and regional NWP models is expected to show valuable improvement in forecast skill. FY-4A will also enhance space weather monitoring and warning. Together with the new generation of geostationary weather satellites planned by the international satellite community, the FY-4 series will become an important geostationary component of the global Earth-observing system.
ACKNOWLEDGMENTS. The authors thank the BAMS Editor Timothy J. Schmit for carefully reviewing the manuscript. The three anonymous reviewers are thanked for their valuable comments on improving the manuscript. The authors also would like to thank Drs. Xiang Fang, Min Min, Wenguang Bai, Chunqiang Wu, Danyu Qin, Xiaoxin Zhang, Lei Yang, Jian Shang, Xiaohu Zhang, Jiawei Li, and Dongjie Cao of the National Satellite Meteorological Center (NSMC) for their assistance on tables and figures for this manuscript. Dr. W. Paul Menzel and Dr. Jun Li of the Cooperative Institute for Meteorological Satellite Studies (CIMSS) provided valuable input and suggestions. Dr. Elisabeth Weisz of CIMSS assisted in the development of the fast FY-4A radiative transfer model. EUMETSAT and ECMWF are also thanked for sharing the data used for proxy in FY-4 algorithm development. The U.S. GOES-16 program (www.goes-r.gov) is specifically acknowledged for science and application information that is made available to the international community.
AFFILIATIONS: YANG, ZHANG, WEI, LU AND GUO--National Satellite Meteorological Center, China Meteorological Administration, Beijing, China
CORRESPONDING AUTHOR: Zhiqing Zhang, firstname.lastname@example.org
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Caption: FIG. 1. The 10.8-[micro]m (left) FY-2D and (right) FY-2E BT (K) images showing coverage of (left) FY-2 East and (right) FY-2 West.
Caption: FIG. 2. Flowchart of FY-4 ground segment.
Caption: FIG. 3. FY-4A data distribution and application flowchart.
Caption: FIG. 4. The spectral band locations of AGRI compared with FCI (European imager), Advanced Meteorological Imager (AMI, South Korea), AHI (Japanese imager), and ABI (U.S. imager).
Caption: FIG. 5. (a) The spectral coverage of the six visible/near-infrared bands of AGRI along with spectral plots of reflectance from grass (green curve) and snow (red curve); and (b, c, and d) the coverage of seven AGRI IR bands in the infrared region superimposed on a calculation of Earth-emitted spectral BT for the U.S. standard atmosphere, along with the spectral coverage of the VISSR (green) on board the FY-2, AGRI (red) on board the FY-4, and SEVIRI (blue) on board the MSG.
Caption: FIG. 6. (left) Temperature weighting functions (Jacobian) from seven AGRI bands and (right) water vapor mixing ratio weighting functions from two AGRI water vapor absorption bands for a U.S. standard atmosphere.
Caption: FIG. 7. Simulated FY-4A AGRI 14 spectral bands images from the high-spatial-resolution Weather Research and Forecasting (WRF) Model for a case of storm development (Beijing 21 Jul 2012 storm case).
Caption: FIG. 8. Cloud products [(top left) optical depth at 0.55 [micro]m, (top right) cloud-top height in kilometers, (bottom left) ice water path, and (bottom right) liquid water path] derived from SEVIRI data at 1200 UTC I Aug 2006 using the FY-4A cloud retrieval algorithm.
Caption: FIG. 9. FY-2E 6.8-/um water vapor image at 1931 UTC 21 Jul 2012 visualized with the Man Computer Interactive Data Access System (MclDAS) from CIMSS/SSEC. The green square is the location of Beijing.
Caption: FIG. 10. The RDC image at 0000 UTC 20 Aug 20IS, using 2-km-resolution Himawari-8 data as test.
Caption: FIG. 11. AIRS, CrIS, IASI, IRS, and GIIRS spectral coverage.
Caption: FIG. 12. (top left) The temperature Jacobian from FY-4A GIIRS longwave IR band, (top right) water vapor mixing ratio Jacobian from midwave IR band, (bottom left) ozone mixing ratio Jacobian from longwave IR band for a U.S. standard atmosphere, and (bottom right) a BT spectrum and NeDT at 250, 280, and 300 K.
Caption: FIG. 13. (left) Simulated temperature at l-km-layer rmse and (right) RH at 2-km-layer rmse for FY-4A GIIRS and the current GOES sounder over the China region.
Caption: FIG. 14. Simulated LI (left) from FY-4A GIIRS at 1200 UTC and (right) from FY-2E 6.8-[micro]m WV BT image at 1500 UTC.
Caption: FIG. 15. Detected lightning events generated from proxy data: (a) a simulated storm in an area of 10[degrees] x 10[degrees] with the photons (black dots) and optical events (red squares) during I h, (b) zoom-in of the bottom-right corner of (a),(c) accumulated radiance of detected events for a simulated storm distribution for an hour on 16 Aug 2007, and (d) zoom-in of the bottom-right corner of (c). The units of (c) and (d) are [micro]J [m.sup.-2] [sr.sup.-1] .
TABLE 1. FY-2DIFY-2E overlap region observation schedule. Full-disc and north-disc observations are abbreviated as F and N, respectively. Time (UTC) Region Satellite 0000 F FY-2E 0015 F FY-2D 0030 FY-2E 0045 FY-2D 0100 F FY-2E 0115 F FY-2D 0130 FY-2E 0145 FY-2D -- -- -- 2300 F FY-2E 2315 F FY-2D 2330 N FY-2E 2345 N FY-2D TABLE 2. Products of FY-4 and FY-2. FY-2 FY-4 Products Payloads Products Payloads Cloud detection VISSR Cloud masks AGRI Cloud classification VISSR Cloud type AGRI Total cloud amount VISSR Total cloud amount AGRI Precipitation VISSR Rainfall rate/ AGRI estimation quantitative precipitation estimate Atmospheric motion VISSR Atmospheric AGRI vector motion vector Outgoing longwave VISSR Outgoing longwave AGRI radiation radiation Blackbody brightness VISSR Blackbody brightness AGRI temperature temperature Surface solar VISSR Surface solar AGRI irradiance irradiance Humidity product VISSR Legacy vertical GIIRS analyzed by cloud moisture profile information Total precipitable VISSR Layer precipitable AGRI water water Upper-tropospheric VISSR Layer precipitable humidity water Dust detection VISSR Aerosol detection AGRI (including smoke and dust) Sea surface VISSR Sea surface AGRI temperature temperature (skin) Snow cover VISSR Snow cover AGRI Land surface VISSR Land surface (skin) AGRI temperature temperature Cloud-top temperature VISSR Cloud-top temperature AGRI Cloud-top height AGRI Cloud-top pressure AGRI Cloud optical depth AGRI Cloud liquid water AGRI Cloud particle AGRI size distribution Cloud phase AGRI Downward longwave AGRI radiation: surface Upward longwave AGRI radiation: surface Reflected shortwave AGRI radiation: top of atmosphere Aerosol optical depth AGRI Convective initiation AGRI Fire/hot spot AGRI characterization Fog detection AGRI Land surface AGRI emissivity Land surface AGRI temperature Land surface albedo AGRI Tropopause folding AGRI turbulence prediction Legacy vertical GIIRS temperature profile Ozone profile and GIIRS total Atmosphere GIIRS instability index Lightning detection LMI Space and solar SEP products TABLE 3. Advancement of FY-4A compared with the current operational FY-2 series. SEM = Space Environment Monitor. SSP = subsatellite point. FY-4A (experimental) Stabilization Three axis Designed life 7 years (designed life) Observation 85% efficiency Observation Imaging + mode sounding + lightning mapping AGRI: 14 channels Resolution: 0.5-4 km Full disc: 15 min GIIRS: 913 channels SSP resolution: 16 km Spectral resolution: 0.8, 1.6 [cm.sup.-1] Main LMI instruments Area coverage SSP resolution: 7.8 km SEP High-energy particles Magnetic field FY-4 (operational) FY-2 (operational) Stabilization Three axis Spin Designed life 7 years (operation life) 4 years Observation 85% 5% efficiency Observation Imaging + mode sounding + Imaging only lightning mapping AGRI: 18 channels VISSR: 5 channels Resolution: 0.5-2 km Resolution: 1.25-5 km Full disc: 5 min Full disc: 30 min GIIRS: >1,500 channels -- SSP resolution: 8 km Spectral resolution: 0.625 [cm.sup.-1] Main LMI instruments Full-disc coverage -- SSP resolution: 7.8 km SEP SEM High-energy particles, High-energy particles magnetic field, solar Solar X-ray fluxes imager TABLE 4. Image navigation and registration specification. FY-4A Requirement Conditions Navigation 112 [mu]rad At the subsatellite point within 64.5[degrees] of geocentric angle except [+ or -] 2 h around satellite midnight Band-to-band -- band registration 1/4 pixel TABLE 5. FY-4A direct broadcast capabilities. Channel Bit rate Max daily data (GB) 1 HRIT-H 11.6 Mbps 123 2 HRIT-VI 9.3 Mbps 65.9 3 HRIT-V2 750 Kbps 2.6 4 LRIT 150 Kbps 1.58 Contents Frequency 1 LI of all 14 channel data of AGRI 1,680 MHz 2 a) GIIRS data 1,679 MHz b) LMI data c) Part of L2 products 3 A part of AGRI data 1,679 MHz 4 Low-resolution image of AGRI 1,697 MHz Table 6. Specifications for AGRI on board FY-4A. S/N = signal to noise. NEAT = noise equivalent differential temperature. WV = water vapor. Spectral Spectral Spatial Sensitivity coverage band resolution ([micro]m) (km) VIS/NIR 0.45-0.49 1 S/N [greater than or equal to]90 (p = 100%) 0.55-0.75 0.5 S/N [greater than or equal to] 150 (p = 100%) 0.75-0.90 1 S/N [greater than or equal to] 200 (p = 100%) 1.36-1.39 2 S/N [greater than or equal to] 150 (p = 100%) 1.58-1.64 2 S/N [greater than or equal to] 200 (p = 100%) 2.10-2.35 2 S/N [greater than or equal to] 200 (p = 100%) 3.50-4.00 2 NEAT [less than or equal to] 0.7 K (300 K) 3.50--4.00 4 NEAT [less than or equal to] 0.2 K (300 K) Midwave 5.8-6.7 4 NEAT [less than or equal to] 0.3 K (260 K) IR 6.9-7.3 4 NEAT [less than or equal to] 0.3 K (260 K) Longwave 8.0-9.0 4 NEAT [less than or equal to] 0.2 K (300 K) IR 10.3-11.3 4 NEAT [less than or equal to] 0.2 K (300 K) 11.5-12.5 4 NEAT [less than or equal to] 0.2 K (300 K) 13.2-13.8 4 NEAT [less than or equal to] 0.5 K (300 K) Spectral Spectral Main applications coverage band ([micro]m) VIS/NIR 0.45-0.49 Aerosol, visibility 0.55-0.75 Fog, clouds 0.75-0.90 Aerosol, vegetation 1.36-1.39 Cirrus 1.58-1.64 Cloud, snow 2.10-2.35 Cloud phase, aerosol, vegetation 3.50-4.00 Clouds, fire, moisture, snow 3.50--4.00 Land surface Midwave 5.8-6.7 Upper-level WV IR 6.9-7.3 Midlevel WV Longwave 8.0-9.0 Volcanic ash, cloud-top phase IR 10.3-11.3 SST, LST 11.5-12.5 Clouds, low-level WV 13.2-13.8 Clouds, air temperature Spectral Spectral Main applications coverage band ([micro]m) VIS/NIR 0.45-0.49 Aerosol, visibility 0.55-0.75 Fog, clouds 0.75-0.90 Aerosol, vegetation 1.36-1.39 Cirrus 1.58-1.64 Cloud, snow 2.10-2.35 Cloud phase, aerosol, vegetation 3.50-4.00 Clouds, fire, moisture, snow 3.50--4.00 Land surface Midwave 5.8-6.7 Upper-level WV IR 6.9-7.3 Midlevel WV Longwave 8.0-9.0 Volcanic ash, cloud-top phase IR 10.3-11.3 SST, LST 11.5-12.5 Clouds, low-level WV 13.2-13.8 Clouds, air temperature TABLE 7. The CI interest fields of FY-4A. Interest field Thresholds Physical description 10.8-[micro]m <260 K Cloud tops cold enough to [B.sub.T] support supercooled water and ice mass growth; cloud-top glaciation 10.8-[micro]m >6 K [(15 min). Cloud growth rate [B.sub.T] time sup.-1] (vertical) trends (absolute value) 7.3-6.2-[micro]m <10 K Cloud thickness [B.sub.T] difference 10.8-6.2-[micro]m <20 K Cloud-top height relative [B.sub.T] difference to mid-/upper tropospher 10.8-6.2-[micro]m >5 K [(15 min). Cloud growth rate [B.sub.T] time trend sup.-1] (vertical) toward dry air (absolute value) aloft 10.8-8.6-[micro]m <4 K Cloud-top glaciation [B.sub.T] difference 13.3-l0.8-[micro]m <13 K Cloud-top height relative [B.sub.T] difference to mid-/upper tropospher better indicator of early cumulus development but sensitive to cirrus 13.3-10.8-[micro]m >4 K [(15 min). Cloud growth rate [B.sub.T] time trend sup.-1] (vertical) toward dry (absolute vaiue) air aloft 12-10.8-[micro]m >-3 K Cloud thickness [B.sub.T] difference TABLE 8. Specification for GIIRS on board FY-4A. LWIR = longwave IR. MWIR = midwave IR FY-4A (Experimental) Range Resolution Channels Spectral parameters LWIR: 700-1,130 0.8 538 (normal mode) [cm.sup.-1] MWIR: 1,650-2,250 1.6 375 [cm.sup.-1] VIS: 0.55-0.75 1 [micro]m Spatial resolution LWIR/MWIR: 16-km SSP (subsatellite VIS: 2-km SSP point) Operational mode China area 5,000 x 5,000 [km.sup.2] Mesoscale area 2,000 x 2,000 [km.sup.2] Temporal resolution China area <1 h Mesoscale area <1/6 h F Sensitivity (mW LWIR: 0.5-1.1 MWIR: 0.1-0.14 [m.sup.-2] sr VIS: S/N > 200 ([rho] = 100%) [cm.sup.2]) Calibration 1.5 K (3[sigma]) accuracy radiation Calibration 10 ppm (3[sigma]) accuracy spectrum Quantization bits 13 bits TABLE 9. The performance of LMI on board FY-4A. Spatial resolution About 7.8 km at SSP Sensor size 400 x 300 x 2 Center wavelength 777.4 nm Bandwidth 1 nm [+ or -] 0.1 nm Detection efficiency >90% False alarm ratio <10% Dynamic range >100 Signal-to-noise ratio >6 Frequency of frames 2 ms (500 frames per second) Quantization 12 bits
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|Author:||Yang, Jun; Zhang, Zhiqing; Wei, Caiying; Lu, Feng; Guo, Qiang|
|Publication:||Bulletin of the American Meteorological Society|
|Date:||Aug 1, 2017|
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