The On-Orbit Performance of FY-3E in an Early Morning Orbit

Peng Zhang Innovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, and Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, China;

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Xiuqing Hu Innovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, and Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, China;

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Ling Sun Innovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, and Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, China;

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Na Xu Innovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, and Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, China;

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Lin Chen Innovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, and Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, China;

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Aijun Zhu Innovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, and Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, China;

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Manyun Lin Innovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, and Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, China;

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Qifeng Lu Center for Earth System Modeling and Prediction, China Meteorological Administration, Beijing, China

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Zhongdong Yang Innovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, and Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, China;

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Jun Yang Innovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, and Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, China;

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Jinsong Wang Innovation Center for Fengyun Meteorological Satellite, National Satellite Meteorological Center, China Meteorological Administration, and Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing, China;

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Abstract

The polar-orbiting meteorological satellites operating in an observing system composed of early morning (EM), midmorning (AM), and afternoon (PM) orbits are extremely important to global numerical weather prediction (NWP). Following the proposal of World Meteorological Organization (WMO), the China Meteorological Administration (CMA) launched Fengyun-3E (FY-3E), the world’s first EM-orbit meteorological satellite for civil use, on 5 July 2021. With 11 scientific instruments on board, FY-3E is capable of providing atmospheric sounding, low-light imaging, sea surface wind detection, and space weather monitoring. Six months after launch, we have finished the postlaunch test for all the payloads. This paper presents the FY-3E data obtained during the 6-month test period, their performance, and key geophysical products driven ready for downstream applications. Experiments have been conducted to better disseminate the sounding data within the 6-h NWP assimilation window. Further efforts have been made to benefit data application for severe weather monitoring, diurnal cycle of Earth data, quasi-continuous sun monitoring for space weather, and climate research.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiuqing Hu, huxq@cma.gov.cn

Abstract

The polar-orbiting meteorological satellites operating in an observing system composed of early morning (EM), midmorning (AM), and afternoon (PM) orbits are extremely important to global numerical weather prediction (NWP). Following the proposal of World Meteorological Organization (WMO), the China Meteorological Administration (CMA) launched Fengyun-3E (FY-3E), the world’s first EM-orbit meteorological satellite for civil use, on 5 July 2021. With 11 scientific instruments on board, FY-3E is capable of providing atmospheric sounding, low-light imaging, sea surface wind detection, and space weather monitoring. Six months after launch, we have finished the postlaunch test for all the payloads. This paper presents the FY-3E data obtained during the 6-month test period, their performance, and key geophysical products driven ready for downstream applications. Experiments have been conducted to better disseminate the sounding data within the 6-h NWP assimilation window. Further efforts have been made to benefit data application for severe weather monitoring, diurnal cycle of Earth data, quasi-continuous sun monitoring for space weather, and climate research.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiuqing Hu, huxq@cma.gov.cn

Fengyun-3E (FY-3E), the fifth satellite in the second generation of Chinese meteorological satellites in near-polar sun-synchronous orbit, hereafter designated as the polar series (Zhang et al. 2009; Yang et al. 2012; Zhang et al. 2019), was launched successfully at the Jiuquan Satellite Launch Centre on 5 July 2021 (Zhang et al. 2022; Shao et al. 2022). The main task of FY-3E mission is to provide infrared (IR) and microwave soundings for global numerical weather prediction (NWP) models. It is believed that significant benefits will be achieved based on the extension of temporal distribution provided by the EM observations of FY-3E, in addition to the midmorning (∼1000 local time, LT) and early afternoon (∼1330 LT) satellites. Other expected benefits in weather, climate, solar physics, and environment applications are realized using the 11 instruments aboard.

Since the advances of satellite data assimilation from the early 1990s (Eyre and Lorenc 1989; Eyre et al. 1993, 2020, 2022), satellite measurements have played an indispensable role in global and regional NWP models (Eyre and English 2008; Bormann and Bauer 2010; Bormann et al. 2010). For instance, the European Centre for Medium-Range Weather Forecasts (ECMWF) has already used data from over 100 satellites in the NWP model (Rabier et al. 2018). Joo et al. (2013) showed that a large percentage of error reduction in short-range forecasts is attributed to satellite observations, while others are due to surface-based observations. The study also showed that a higher percentage of the satellite contribution comes from polar-orbiting satellites.

In view of the importance of polar-orbiting meteorological satellites to global NWP, the WMO recommended that the baseline configuration of the core polar operational constellation should be evolved from a two-orbit system (AM and PM orbits) to a three-orbit system (AM, PM, and EM) in the Vision for the Global Observing System (GOS) in 2025 (WMO 2009) and the Vision for the WMO Integrated Global Observing System (WIGOS) in 2040 (WMO 2019). The ideal three-orbit system is illustrated in Fig. 1. The AM, PM, and EM satellites cross the equator from south to north (i.e., the ascending node) at around 1000, 1400, and 0600 LT, respectively (Kidder and Vonder Haar 1995).

Fig. 1.
Fig. 1.

Schematic representation of the three polar-orbital planes with FY-3C (AM), FY-3D (PM), and FY-3E (EM) satellites.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

The current global NWP requirement for assimilating satellite data are every 6 h for the preparation of the initial meteorological field. The two-orbit system covers about 80% of the area globally, leaving a little gap without observations. After a risk analysis of long-term meteorological satellite plans in Europe, the United States, and China, the Coordination Group for Meteorological Satellites (CGMS) encouraged its member organizations to fill the EM orbit with an operational polar-orbiting satellite (CGMS 2011). It is anticipated that such a three-orbit system will fill the early morning gap and improve the forecast on both the hemispheric and the regional scales (WMO 2013). In recognition of the significance of EM orbit to the global and regional NWP, CMA expressed willingness to investigate the possibility of flying an EM mission with sounding capabilities to better distribute atmospheric soundings over the planned three orbits (CGMS 2012). An assessment of the benefits of flying an EM mission was made from the outcome of a seminar conducted by the “Tiger Team,” and the report was distributed (WMO 2013). After that, in 2014, CMA drafted the FY-3E Mission Requirement for the FY-3 Third Phase Program, which involves four satellites, one of which is an EM satellite. The program was approved and funded in 2018 (Zhang et al. 2022). Besides meteorology and oceanography, space weather is also considered in the FY-3E mission.

There are 11 instruments in total (see Table 1) on board FY-3E and almost half of them focus on NWP requirements. Filling the EM gap of atmosphere sounding is tasked to the microwave sounders MWHS-II and MWTS-III, the occultation sounder GNOS-II, and a hyperspectral infrared sounder HIRAS-II. The WindRAD’s abundant observation information in various viewing geometries is helpful for the improvement of wind retrieval. The active dual-frequency scatterometer, WindRAD, is a new sea surface wind vector instrument. MERSI-LL provides panchromatic low-light band (LLB) imaging at low solar illumination condition as well as six infrared bands inherited from FY-3D. There are five special instruments on FY-3E orbit for enhancing the solar and space weather observation including X-EUVI, SSIM, SIM, Tri-IPM, and SEM-II.

Table 1.

FY-3E instruments.

Table 1.

Initial on-orbit calibration and performance characterization are crucial to establish a baseline to maintain satellite operation throughout mission life. Therefore, it has been intensively studied and examined within 6 months after the launch. During the next stage, a series of postlaunch tests were conducted to establish the parameters needed by the ground system to process FY-3E data. In total, nearly 1 year was spent on completing tests including the product generation test before FY-3E’s ground processing segment transitioned to operation. Additional calibration and validation tests were performed to determine if the instruments met the required specifications (see Table 2). After examination, some Level 1 (L1) data were used to generate Level 2 (L2) products for evaluation and demonstration purposes. This paper presents certain data available for making the analysis and evaluation. There are several important lessons learned from the performance evaluation, which are discussed in the section on application demonstration.

Table 2.

Performance required specification of FY-3E instruments.

Table 2.
Table 2.

Satellite platform and instruments configuration

EM orbit specification.

FY-3E is a sun-synchronous dawn–dusk polar-orbiting environmental satellite whose primary task is to fill the EM gap in atmosphere-sounding data with two onboard microwave sounders, an occultation sounder, and a hyperspectral infrared sounder (Zhang et al. 2022). The Earth scene is typically observed by this satellite in the early morning or evening, with the ground track being close to the Earth’s twilight circle and low solar altitude angle of observing, which puts forward higher requirements for the payload capacity.

The satellite body is exposed to illumination most of the year. Therefore, thermal balance is an issue that must be considered in designing the satellite (Shao et al. 2022). The external heat flow and the sunlight intrusion faced by FY-3E are different from the situation faced by other FY-3 series satellites and thus need to be handled carefully to provide instruments a safe and consistent workplace all year round. The dawn–dusk orbit plane is perpendicular to the direction of the sun so that the solar panels do not need to keep rotating in collecting the power. Thus, the satellite receives the majority of its energy from solar radiation throughout its orbit.

Instrument configuration.

Among the 11 instruments listed in Table 1, three are brand new (WindRAD, SSIM, X-EUVI), seven are improved models (MERSI-LL, MWTS-III, HIRAS-II, GNOS-II, SIM-II, SEM-II, Tri-IPM), and one is inherited (MWHS-II) (Zhang et al. 2022). They are further cataloged in terms of characteristics and application.

MWHS-II is inherited from FY-3D with some improved specifications; it has 15 channels ranging from 89.0 to 191.0 GHz, with the window channel at 166 GHz. MWTS-III is an upgraded model with 17 channels, including channels at 23.8 GHz, 31.4 GHz, 53.246 ± 0.08 GHz, and 53.948 ± 0.081 GHz, and with better noise equivalent differential temperature (NEdT) specifications compared to MWTS-II of FY-3D. The orbit width of MWTS-III increases from 2,250 to 2,700 km, and the field of view (FOV) number increases from 90 to 98. GNOS-II, the combination of the GNSS reflectometry (GNSS-R) module and the GNSS radio occultation (GNSS-RO) module that flies on FY-3D support both Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS), by which the total number of occultation events is doubled. Sea surface wind speed can also be retrieved from GNSS reflectometry measurements. HIRAS-II is the improved version of the hyperspectral IR sounder HIRAS flying on FY-3D. The 3 × 3 detectors are used for HIRAS-II with 14 km spatial resolution at the nadir with much improved NEdT, especially in middle-wavelength (MW) and short-wavelength (SW) bands. It provides full spectral coverage from 650 to 2,550 cm−1 without gaps.

WindRAD, an active dual-frequency scatterometer [C and Ku band, both with vertical–vertical (VV) and horizontal–horizontal (HH) polarization] working with a rotating fan-beam conical scanning mode, provides wind vectors over the global ocean surface (Stoffelen and Anderson 1997; Stoffelen and Portabella 2006). The higher frequency has higher sensitivity, while the lower frequency has stronger weather robustness, making WindRAD more able to detect wind fields under all conditions. In addition, the WindRAD’s abundant observation information in a wider variety of viewing geometries is helpful for the potential improvement of wind retrieval.

MERSI-LL is an optical imager with six IR bands inherited from FY-3D, and one panchromatic LLB to provide visible/near-IR imagery in the low solar illumination conditions that are typical of the EM orbit. The LLB is further divided into three subbands to work with high/middle/low gain status (HGS, MGS, LGS) for observing near the terminator areas. It identifies the Earth target signals from sunlight to 1/4 moonlight irradiation conditions. For the LLB low gain status (LGS) band, sensor degradation is monitored by the onboard solar diffused transmission board (SDTB).

There are five solar and space weather instruments on FY-3E. X-EUVI can provide solar full disk X-ray and ultraviolet images, which are valuable for seeing solar activities and making space weather forecasts. SIM-II is an absolute radiometer for the total solar irradiance (TSI) measurements to support monitoring solar activity, the Earth radiation budget research, and the TSI record continuity. A four-radiometer device package, SIM-II includes three Solar Irradiance Absolute Radiometers (SIAR) inherited from SIM and an international instrument, the Digital Absolute Radiometer (DARA) from Physikalisch Meteorologisches Observatorium Davos and World Radiation Center (PMOD/WRC), Switzerland. The four radiometers are mounted on the same tracking system. SSIM is a spectrometer with three wave bands providing a continuous solar spectrum from 165 to 1,650 nm. Tri-IPM measures the airglow radiation intensity of oxygen atoms (OI) and nitrogen molecules; compared with FY-3D IPM (Jiang et al. 2023), Tri-IPM has got two more cross-track probes pointing to ±30°, thus increasing the observations in different sunshine conditions. SEM-II is an instrument package that comprises several types of detectors to measure the in situ space environments, including particles, radiation dosage, surface potential, and magnetic field vectors. For the first time, the Fengyun satellite makes measurements of the particle spectrum of space plasma, the medium energy-charged particles in solar winds, and the disturbances of geomagnetic fields in space weather events.

In-orbit performance of instruments

WindRAD.

The status of WindRAD has been quite steady since it was switched on. Complete global WindRAD coverage was obtained except for some occasional influences from high-energy particle events that either affected the raw observation data or interrupted the WindRAD in making the observations. During the postlaunch testing (PLT) from July to December 2021, WindRAD performance was evaluated with regard to the spatial resolution, the swath width, the minimum detectable wind speed, the radiometric resolution, the internal calibration accuracy, the observation accuracy, and telemetry parameters. The test results of several important parameters are given below. Example global images of WindRAD L1 data (30 April 2022) are shown in Figs. 2a–d, which are C band and Ku band with HH and VV polarized backscattering coefficients of a 10 km grid, respectively. The ocean/land features are very clear, and the quantitative assessment is described below. WindRAD data can be used in NWP and remote sensing applications. The instrument backscatter data stability over time is good, and the retrieved winds can fulfill operational requirements (Li et al. 2023).

Fig. 2.
Fig. 2.

Global backscattering coefficient of C and Ku bands with HH and VV polarized status on 30 Apr 2022. (a) C HH; (b) C VV; (c) Ku HH; (d) Ku VV.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

The radiometric resolution of WindRAD is determined with the standard deviation (STD) of the backscattering coefficient from the ocean. From the calculated backscattering coefficient (sigma0) of global open ocean targets, the normalized standard deviation (Kp) is evaluated. We used WindRAD data (after quality control) on 13 October 2021 to calculate Kp. The histograms of these Kp of C band at HH and VV polarization are shown in Fig. 3, indicating the Kp distribution for cases of wind speed ≥ 5m s−1—the smaller Kp, the higher the radiation resolution. The preliminary average estimates for Kp are 0.35 (0.33) for C band at HH (VV) polarization and 0.35 (0.33) for Ku band at HH (VV) polarization. These test results meet the index requirement of 0.5 dB for high-quality wind retrieval.

Fig. 3.
Fig. 3.

Normalized standard deviation Kp histograms of C band of the backscattering coefficient from the ocean on 13 Oct 2021. (a) HH and (b) VV.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Evaluation for radiometric accuracy is done based on the internal calibration (Ailing et al. 2014) accuracy; for that, we used the in-orbit internal calibration observation data. Internal calibration accuracy was estimated with the internal calibration signal accuracy, the insertion loss measurement error of the radar front end, the coupling measurement error of the internal calibration loop, etc. The estimation of internal calibration accuracy is 0.3 and 0.2 dB, respectively, for C band and Ku band, which meet the index requirements of 0.6 dB, suggesting that WindRAD is quite a stable instrument.

To measure the antenna pattern in orbit and to calculate the calibration coefficients, two calibration stations were planned with equipment of active radar calibrators. Construction for the first one has been completed in Inner Mongolia, China, and an external calibration experiment is underway. Before getting the experimental results, WindRAD’s initial performance was evaluated using natural targets such as the open ocean and rain forest or simultaneous observation data from similar space-based instruments. The NWP ocean calibration (NOC) method (Verspeek et al. 2012) for scatterometer performance evaluation is carried out regularly. Figure 4 shows the preliminary results of NWP ocean calibration using the WindRAD ascending data of 10 km grid from 25 to 30 January 2022. The vertical axis shows the difference between the observed and the calculated ocean backscattering. It reveals a small bias in C band and a relatively large bias in Ku band, with apparent incident angle dependence in both bands. The bias is also visible in Fig. 2 at the edge of the swaths. At the same time, it can be observed that the biases on different dates have good repeatability. These biases shown in Fig. 4 are NOC calibration coefficients, which can be used to correct WindRAD backscattering coefficients and help to obtain better wind retrieval results.

Fig. 4.
Fig. 4.

Preliminary NWP ocean calibration results. (a) C VV and (b) Ku VV.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

MWTS-III and MWHS-II.

The MWTS-III is the third version of the microwave sounder on FY-3E for atmospheric temperature profiles. MWHS-II, with 15 channels ranging from 89.0 to 191.0 GHz, makes soundings of atmospheric temperature and humidity from the oxygen absorption line at 118 GHz and the water vapor absorption line at 183 GHz. The observations from the two instruments are valuable for the NWP application, atmospheric parameter retrieval, and climate studies.

The on-orbit NEdT of these two microwave sounders is calculated with 40 consecutive scan-line observations of their internal warm blackbody. Altogether, 400 observations were taken and yielded 10 NEdT values. The next largest of the 10 values is used to estimate instrument sensitivity. The NEdTs of all channels of MWTS-III in Fig. 5a (green bars) meet the specifications (red line) and are better than FY-3D MWTS-II (blue bars). The NEdT values of MWHS-II presented in Fig. 5c are much smaller than the specifications, represented by red bars.

Fig. 5.
Fig. 5.

NEdT and calibration accuracy for FY-3E MWTS-III and MWHS-II. (a) NEdT for MWTS-III. (b) Calibration bias (blue bar) and standard deviation (STD, orange line) of MWTS-III w.r.t. JPSS-1/ATMS. (c) NEdT for MWHS-II (blue bar) vs specification (red bar). (d) STD of MWHS-II BT compared with JPSS-1/ATMS.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

To validate FY-3E MWTS-II and MWHS-II measurements, an intersatellite comparison with the NOAA-20 Advanced Technology Microwave Sounder (ATMS) from 1 May to 30 September 2022 was undertaken. When the sensors observe the same target at the simultaneous nadir overpass (SNO), the observed brightness temperature (BT) difference should be small with a constant bias (Guo et al. 2019). The SNO observations with a time difference of less than 20 min, a spatial distance less than 10 km, and a scan angle difference less than 1° were selected for comparison. All the SNO pixels were located over the high-latitude area. All MWTS-III channels except for channel 6 and channel 8 have the corresponding channels of ATMS. The deviations between MWTS-III and ATMS are obtained and presented in Fig. 5b. The biases and STDs of channels 1–14 for MWTS-III are less than 1 K. However, the biases of channels 15–17 are much higher; further analysis is ongoing. There are five corresponding channels (channel 11–15) of MWHS-II, which are the same as ATMS (channels 22–18). The MWHS-II calibration results are shown in Fig. 5d from SNOs in fifty days (from 10 July to 30 August 2021) with a time difference of less than 20 min, spatial distance less than 3 km, and scan angle difference less than 5° around the nadir. Spatial subsets are extracted for 3 × 3 MWHS-II pixels for a homogeneity check, and brightness temperature standard deviation between MWHS-II and ATMS is less than 1.0 K.

The in-orbit status of MWTS-III and MWHS-II is being monitored in real time. It was found that the instrument temperatures of MWTS-III fluctuated within certain ranges over time (see Fig. 6a). The temperature fluctuation of more than 8 K over different seasons on the FY-3E dawn–dusk orbit is larger than that found in the previous MWTS on AM and PM orbits. The BT calculated with the Radiative Transfer for the TIROS (Television Infrared Observation Satellite) Operational Vertical Sounder (RTTOV) version 10.1 based on the fifth generation of the ECMWF Reanalysis (ERA5) of the global climate data were used as a reference for evaluating the stability performance of MWTS-III (Hu et al. 2021). The instrument temperature may affect the difference between observed and simulated BT (OB). As shown in Fig. 6b, the daily OB trend of channel 10 fluctuates about 0.8 K, varying synchronously with the instrument temperature. Through this analysis, we have determined that MWTS-III has the following issues, which we are currently working to resolve: 1) the deviations of channels 16–17 are larger than 1 K; 2) the land–sea OB differences are about 0.2–0.3 K in channels 9–17; and 3) fluctuations of 0.5 K in calibration deviation of channel 10 are related to the changes in instrument temperature. We are investigating the principle of the effect of instrument temperature on the calibration accuracy of each channel and have made some initial progress on the temperature correction. We plan to publish the improved results for some channels by the end of 2023.

Fig. 6.
Fig. 6.

Time series trend of the instrument temperature and OB of FY-3E MWTS-III. (a) Instrument temperature; (b) daily OB of channel 10.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

The nonnegligible ascending–descending bias in FY-3D MWTS-II (Carminati et al. 2019) no longer appears in FY-3E MWTS-III (Qian et al. 2022). Additionally, land–sea contrast bias suspected to be caused by interchannel interferences was found in some FY-3C MWTS-II upper-atmosphere channels (Lu et al. 2015). We checked this kind of bias in the situation of FY-3E MWTS-III and found that the OB values of channels 7–8 over land were higher than that over the sea (see Fig. 7a). Based on the nonnegligible out-of-band response of these two channels, the bias correction algorithm was applied to the operational processing program on 8 December 2022. The OB distribution after correction shows noticeable improvement for land–sea contrasts (see Fig. 7b).

Fig. 7.
Fig. 7.

The global distribution of OB of FY-3E MWTS-III channel 8 (a) before and (b) after interchannel interference correction.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

HIRAS-II.

HIRAS-II on board FY-3E is much improved from its predecessor HIRAS-I (Qi et al. 2020) in that the detector array changes from 2 × 2 to 3 × 3, and the IR spectrum is continuous without gaps from middle-wave to longwave infrared regions, which makes it a potential reference for intercomparison between IR instruments. The HIRAS-II provides radiance spectra measurements in three bands: the longwave IR (LW) band from 650 to 1,168 cm−1; the MW IR band from 1,168.625 to 1,920 cm−1; and the SW IR band from 1,920.625 to 2,550 cm−1.

After 3 months of outgas heating and decontamination in orbit, the detectors of HIRAS-II were powered up on 12 October 2021, and experimental observations began on 13 October 2021. Some maneuvers such as alignment of the interferometer, warming up and cooling down of blackbody, blackbody temperature traceability, and compressed model data transmission were carried out to derive relevant parameters needed for instrument calibration and data preprocessing. The instrument status has been stable since 18 December 2021, and the observed BTs for typical MW (at 1,500 cm−1) and LW (at 911.25 cm−1) spectral channel imagery are shown in Fig. 8.

Fig. 8.
Fig. 8.

HIRAS-II global brightness temperature for the (a) longwave band at 911.25 cm−1 and (b) middle-wave band at 1,500 cm−1.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

A total of 14 required specifications, including field of view (FOV) angle, scanning period, scanning pointing accuracy, band registration, spectral calibration accuracy, radiometric calibration accuracy, sensitivity, and dynamic range, were tested. Results show that all performances meet the requirements (Table 2), except the NEdTs of 16 MW channels from 1,652.5 to 1,671.25 cm−1 and the first FOV detector of the SW band. FY-3E HIRAS-II becomes the second instrument in the world that can provide continuous high-spectral-resolution infrared radiance measurements after the Infrared Atmospheric Sounding Interferometer (IASI) on the Meteorological Operational satellites (MetOp; Menzel et al. 2018).

Sensitivity and calibration accuracy are the core performance metrics of the instrument. The observation data from cold space, the inner blackbody, and the Earth were used to calculate the radiances. The 1σ noise equivalent radiance variance (NEdN) was obtained by the statistical standard deviation of radiance spectral samples and then converted to NEdT with BT conversion. The test data are from 2000 to 2130 UTC 18 December 2021. The comprehensive sensitivity NEdT test results of FY-3E HIRAS-II are shown in Fig. 9, in which the red dashed line represents the NEdT requirements. It shows that all detectors for all channels meet the required specifications except MW detectors in the range of 1,652.5–1,688.125 cm−1 and the first SW detector, which is far from the other eight detectors. Due to slight damage in the IR material of the SW FOV1 detector, signs of outliers were found occasionally before the launch though they were not very obvious, and it was impossible to be fixed or recovered. After launch, the outlier of SW FOV1 became much more significant, and the performance of this detector apparently deteriorated. The NEdT of this FOV does not meet the specification; also affected is the calibration accuracy of this FOV, which can be seen in Fig. 10. Therefore, this FOV is not suggested to be used in applications.

Fig. 9.
Fig. 9.

HIRAS-II sensitivity (NEdT) evaluation results. The red dashed line represents specification.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Fig. 10.
Fig. 10.

Mean biases and standard deviations of cross-matching data comparison of FY-3E HIRAS-II and MetOp-B IASI. (a) Mean bias at the North Pole; (b) mean bias at the South Pole; (c) STD of bias at the North Pole; (d) STD of bias at the North Pole.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Based on the SNO cross-comparison method recommended by Global Space-Based Inter-Calibration System (GSICS) (Cao et al. 2004; Goldberg et al. 2011), the radiometric calibration accuracy of HIRAS-II was analyzed and verified. During the commissioning test, the cross-matching data of FY-3E HIRAS-II and MetOp-B IASI from 24 November to 1 December 2021 were selected for comparative analysis. The SNO intersections were located near the North and South Poles, with relatively lower target temperature. For HIRAS-II comparison with IASI, the matching criteria were the time difference between the data pair is less than 15 min, the relative difference of the slant path air mass at the zenith angle θz [defined as 1/cos(θz)] between the two measurements is less than 1%, and the distance between data pair is less than 6 km (Qi et al. 2020; Wu et al. 2020). There are 349 mapping samples after strict matching and screening accuracy evaluation. Figure 10 shows that, on the whole, the radiometric calibration accuracy of the Antarctic samples is slightly better than the Arctic samples: mean biases are less than 0.2 K in longwave channels, except for some edge channels, and less than 0.3 K for most middle-wave channels; less than 1 K in shortwave windows and the nearby weak absorption spectral region; and approximately within 2 K for other absorption channels. The standard deviations are approximately within 1 K.

To assess the consistency of the deviations of HIRAS-II and IASI observations, MetOp-B IASI and FY-3E HIRAS-II simulation at the collocated SNO targets on 20 December 2021, was calculated with the RTTOV, and the OB as well as; the double difference between these two OB deviations were analyzed as shown in Fig. 11. The results of OB double difference show good agreement with SNO for LW and MW spectral channels. The deviation of SW spectrum channels is still large.

Fig. 11.
Fig. 11.

The OB biases of HIRAS-II and IASI and their double difference using the RTTOV simulation.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

MERSI-LL.

The Medium Resolution Spectral Imager-Low Light (MERSI-LL) is a visible and infrared spectral imager with a similar design as its predecessor MERSI-II on FY-3D (Hu et al. 2012; Xu et al. 2018). It has seven channels, including six IR channels like those on MERSI-II and one new panchromatic LLB with a spectral range of 500–900 nm. The LLB covers the large anticipated dynamic range of early morning signals and operates at low, mid-, or high gain status (LGS, MGS, and HGS). LLB was activated on 9 July 2021, and the IR band was activated on 7 September 2021 after outgas heating. During postlaunch testing, MERSI-LL undertook SDTB baffle opening operation 32 times, four active lunar observations, and two blackbody warming-up and cooling-down operations on 31 December 2021. Figure 12 shows the FY-3E MERSI-LL LLB global observation data (L1), which includes image fusion from the three gain stage images.

Fig. 12.
Fig. 12.

Global map of LLB imagery for FY-3E MERSI-LL on 23 Feb 2022.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

The LLB LGS signal-to-noise ratios (SNRs) are derived from SDTB observations in Fig. 13a, and HGS are derived from a uniform target over a large area in Antarctica in Fig. 13b. Because of different solar incident angles, the LLB LGS detectors have different levels of SDTB response. The LGS SNRs are computed at all levels and then fitted as a function of radiance level. NEdTs of MERSI-LL IR bands 2–7 are calculated as a function of temperature during blackbody cool down on 10 September 2021, and the results of all bands are better than the required specification.

Fig. 13.
Fig. 13.

The SNR result of (a) LGS band and (b) HGS band for MERSI-LL low-light band detectors (red dots represent SNR results, and dashed lines represent specifications for each gain stage).

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

SNOs with JPSS-1 Visible Infrared Imaging Radiometer Suite (VIIRS) day–night band (DNB; Mills et al. 2013) and MetOp-B IASI were used to evaluate the calibration accuracy during the on-orbit test for low-light band and IR bands, respectively. Because of heavy solar intrusion contamination for the LLB HGS, the calibration accuracy evaluation for LLB is only carried out on LGS currently, using the SNO method (Xu et al. 2014). Figure 14 shows the differences between MERSI-LL LGS and JPSS-1 VIIRS DNB from the collocated data from 10 to 30 August 2021. MERSI-LL IR band calibration biases are all less than 0.4 K using MetOp-A IASI as a reference from the collocated data in October 2021.

Fig. 14.
Fig. 14.

LGS calibration accuracy evaluation of MERSI-LL LLB based on SNOs with JPSS-1 VIIRS. (a) Scatterplot of matching samples of the radiance between MERSI-LL and VIIRS, the black line is 1:1 line, the dashed line is linear regression line, the x axis is for VIIRS radiance, and y axis is for MERSI-LL. (b) The relative difference of top-of-atmosphere (TOA) reflectance between MERSI and VIIRS.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

GNOS-II.

GNOS-II on board FY-3E is an upgraded version of the previous GNOS. It performs GNSS radio occultation and ocean reflection detection, obtains atmospheric and ionospheric parameter profiles, detects sea surface wind speed, and provides high-quality datasets for NWP, climate change, and space weather. In contrast to GNOS, GNOS-II receives GNSS signals not only from the U.S. GPS but also from the Chinese BDS (Yang et al. 2019) and the European Galileo (Benedicto et al. 2000) constellations. The in-orbit BDS satellites include two generations: BDS-2, which began in 2011, and BDS-3. Deployment of BDS-3 began in 2015 and was finalized in 2020. Compared to BDS-2, the BDS-3 has more satellites and advanced signals (Yang et al. 2019), capable of GNSS sea surface reflection observation, BDS-3 signal receiving, and BeiDou positioning.

GNOS-II includes the new GNSS-R technology. The Earth surface reflected signals contain information on the surface roughness, from which sea surface wind speed, wave height, land soil moisture, cryosphere, and other geophysical parameters can be retrieved. GNSS-RO of GNOS-II has 24 occultation channels, 14 for GPS satellites, 8 for BDS satellites, and 2 for Galileo satellites. The number of occultation events will increase with the full support of BDS-2/3 and the global deployment of BDS3 satellites. According to simulations, the occultation events will be 580 day−1 for GPS and 500 day−1 for BDS-2/3 (Fig. 15a). GNOS-II is the first operational payload for civilian applications supporting a total of 63 satellites of BDS-2 and BDS-3. By combining GNSS-RO and GNSS-R technology, the FY-3E GNOS-II provides all-weather data in three dimensions from the Earth surface to the upper air, which will benefit operational NWP and other meteorological research.

Fig. 15.
Fig. 15.

(a) One-day occultation event distribution of FY-3E/GNOS-II. Blue stands for GPS events and red stands for BDS events. (b),(left) Mean bias (red; observations minus model values) and standard deviation (blue) statistics of FY-3E GNOS-II bending angle profiles deviation to ECMWF with (right) the number-of-event profiles.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Figure 15b shows the bending angle deviation from the ERA-5 for FY-3E GNOS-II GPS. The left panel shows the mean bias (red) and the standard deviation (blue), and the right panel shows the samples used as a function of altitude. The bias and standard deviation profiles have been calculated from a large ensemble of FY-3E GNOS-II GPS data and their collocated ERA-5 profiles. The vertical profiles collocated with the GNOS RO profiles were extracted from ERA-5 fields by bilinear interpolation in latitude and longitude to the mean RO event location, using the nearest-neighbor time layer of the RO event time. It shows a small negative bias of around 0.15% against ERA-5 and a standard deviation of approximately 1.5% in the height range from 5 to 35 km. The SNR variations of GNSS reflected signals received by FY-3E GNOS-II show that the SNR of GNOS-II received GPS and BDS reflected signals exceeds 15 dB. The delay-Doppler Map (DDM) is the fundamental measurement for GNSS reflections. It is the correlated power of the reflected GNSS signals generated by the instrument, whose shape and magnitude characterize the roughness of the Earth surface (Zavorotny et al. 2014). Figure 16 shows an example of FY-3E GNOS-II DDM from BDS and GPS. The sea surface wind speed (SWS) from GNOS-II was experimentally retrieved from reflected GNSS signals.

Fig. 16.
Fig. 16.

FY-3E GNOS-II delay-Doppler map (DDM). (a) BDS DDM and (b) GPS DDM.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

SIM-II and SSIM.

The SIM-II on board FY-3E is a successor instrument from FY-3C SIM with improved degradation monitoring and measure cycle and an added international radiometer DARA. FY-3E SIM-II consists of three same SIARs with the new working cycle: SIAR1 performs the normal measurement on 14 orbits every day, SIAR2 measures 4 orbits every 16 days, and SIAR3 measures 4 orbits every 4 days. The newly designed solar mode can give a TSI value on 2-min measuring cycle instead of 10 min for FY-3C. Benefited from these improvements, the on-orbit degradation correction is automated and applied operationally such that the coefficients update every 4 days based on the working cycle. DARA, an instrument from PMOD/WRC, is mounted at the same tracking system and provides a new TSI reference to continue the long-term data record. The simultaneous observations of SIM-II three SIARs were obtained on 19 August 2021 to build the on-orbit reference data for detector degradation correction. The instrument has been turned on to operational observation mode since 20 August 2021 under the designed working cycle. Figure 17a presents an internal comparison of SIM-II. The measurements of three SIARs show comparable fluctuation results, and the difference in SIAR’s TSI value is in the acceptable range, which is common to similar instruments.

Fig. 17.
Fig. 17.

(a) Total solar irradiance observed by three SIARs of SIM-II. AR is the abbreviation of SIAR. (b) TSI trend comparison between FY-3E SIM-II/SIAR, FY-3E DARA, and TSIS-1TIM from 20 Aug to 7 Dec 2021.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Based on nearly 4 months of on-orbit data analysis and algorithm optimization, the instrument degradation has been carefully determined. Total Irradiance Monitor (TIM) on the Total and Spectral Solar Irradiance Sensor 1 (TSIS-1) is used as a reference (Coddington 2017). From 20 August to 7 December 2021, comparisons between FY-3E SIM-II, FY-3E DARA, and TSIS-1TIM show comparable solar energy changes (see Fig. 17b). DARA is more sensitive to environmental temperature, especially after continuing cold space observations on 18 October 2021. The TSI values of the three instruments are 1,361.54, 1,360.9, and 1,361.89 W m−2, respectively. The relative deviation between SIM-II and TIM is −0.026%, and that between SIM-II and DARA is 0.047%. The result indicates that instrument stability is good.

SSIM is a new payload on board FY-3E for measuring the solar spectral irradiance from 165 to 2,400 nm (measurements from 1,650 to 2,400 nm are experimental) and is designed for capturing a continuous record of solar spectral irradiances to detect solar variability, providing a solar reference spectrum, and improving our knowledge of climate response. A series of tests were carried out to characterize instrument in-orbit performance, which includes spectral calibration, SNR, and reference sources observation. SSIM has been switched to operational mode since 24 September 2021 and performs observation with the designed duty cycle. SSIM observations from dark signal mode, solar mode, cold space mode, and spectral and radiometric calibration mode have been analyzed and evaluated. The solar tracking accuracy is much better than the requirement of 0.1 degrees, usually at a level of 10−3 or 10−4 degrees, which can support stability for observations for nearly 20 min.

The SNR is calculated with data observed on solar energy level at a typical wavelength with 100 continuing measurements, and the test results are shown in Fig. 18a. Seven wavelengths meet the requirements, except for 180 nm. Its test result is 89.3, lower than the request of 100. The absolute response of detectors has been rebuilt based on in-orbit data with TSIS-1 hybrid spectrum as a reference (Coddington et al. 2021). Figure 18b shows the comparison results of SSIM and TSIS-1 on 28 September 2021. SSIM can capture more details in the absorption structure under the support of higher spectral resolution. FY-3E is also the first time that the total and spectral solar irradiances are obtained from the same satellite platform.

Fig. 18.
Fig. 18.

(a) SNR test results of FY-3E SSIM. (b) Solar spectral irradiances from SSIM and TSIS-1 on 28 Sep 2021.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

X-EUVI.

X-EUVI monitors the sun activity in X-ray and ultraviolet wavelengths. The X-EUVI telescope can compile full disk solar images that scientists can use to monitor solar activities and enhance their space weather forecasts. X-EUVI has six channels covering 0.6–8.0 nm in the X-ray region and two ultraviolet channels at 19.5 nm in the EUV region (Chen et al. 2022). It was found that the solar images became more blurred in the 10 days after the first solar EUV image was obtained on 10 July 2021. Therefore, the instrument observation was turned off, self-cleaning was started on 24 July 2021, and observation was restarted on 19 August 2021. The detection mode was changed from one channel (EUV) to full channels (X and EUV), and the first solar X-ray (0.6–8.0 nm) image was obtained on 22 August 2021. During the in-orbit testing period from July to December 2021, the instrument performed self-cleaning 5 times and radiometric calibration 10 times. It was determined that instrument decontamination is needed every 45 days; after a 1-day self-cleaning, the X-EUVI can return to normal observation.

X-EUVI can observe active regions of the sun and solar corona at high (4.1 and 2.5 arc-s for X-ray and EUV bands) spatial resolution with a wide field of view of 42 × 42 arc-min and a 1,033 × 1,073 CCD camera. X-EUVI observation data testing showed that the field uniformity is better than 90%. The pointing accuracy is better than 1.2 arc-s, and the stabilization is better than 1.0 arc-s s−1. The X-ray and EUV irradiance sensors on X-EUVI have completed radiometric calibration on the ground, with a calibration accuracy of 11.8% and a radiometric accuracy of about 15%.

Because the FY-3E satellite platform rotates relative to the sun at a uniform rate, the original solar images observed in X and EUV channels need rotation correction continually. The dark field is obtained by dark detection mode. The flat-field calibration is conducted by utilizing a succession of solar rotation images (Song et al. 2022). Normal solar images were obtained after rotation, dark deduction, and flat field correction, as shown in Fig. 19. The bright regions of the EUV image in Fig. 19a are solar active regions of the sun, and the dark regions are coronal holes. The X-ray image Fig. 19b shows the pictures of the sun’s hot (∼107 K) outer atmosphere, and the bright regions in it are the top of solar actives regions illustrated in the left EUV image. These full-disk solar images may be used to investigate the physical changes of coronal holes and active solar regions.

Fig. 19.
Fig. 19.

Solar EUV (a) and (b) X-ray images observed by X-EUVI on 22 Aug 2021.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

SEM-II.

SEM-II, on board FY-3E, can measure the charged particle flux along with the particle energy from 30 eV to 300 MeV in multidirections. SEM-II can also detect the satellite surface potential, the radiation dose in sensors, and the geomagnetic field variations. It is the first time in the FY-3 series that we can measure the particle spectrum of space plasma, medium energy charged particles brought by the solar wind, and geomagnetic field disturbances during space weather events. The space environment information derived from SEM-II can be utilized for satellite security designs, scientific studies, the development of radiation belt models, space weather monitoring, and disaster warning.

During the commissioning of SEM-II, the particle, and the magnetic field detection accuracies were evaluated by cross-calibration methods, and the radiation dose and the surface potential detections were compared for consistency with historical records. The cross-calibration methods are applied in the in-flight calibration and validation of SEM-II. SEM-II particle detection was compared with Medium Energy Proton and Electron Detector (MEPED; Evans and Greer 2000) on board NOAA-18/19. The magnetic field detection from SEM-II compared well with the output from the International Geomagnetic Reference Field (IGRF; Alken et al. 2021). The comparison approach is similar to the work by Huang et al. (2012). According to the in-orbit calibration results, the accuracies of particle detections are better than 25%, and the magnetic field accuracy is approximately 1%. The average background noise of particle detection is better than 10 counts cm−2 s−1 sr−1. The pattern of the outer radiation belt at high latitudes and the South Atlantic Anomaly (SAA) region at low latitudes can be obtained. The distribution pattern of SEM-II magnetic field detection correctly shows high values in the polar region and low values in the SAA region, as shown in Fig. 20.

Fig. 20.
Fig. 20.

The global geomagnetic field intensity map from FY-3E SEM-II.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Tri-IPM.

Tri-IPM is a UV spectrometer that measures the airglow radiation intensity of oxygen atoms and nitrogen molecules on the day–night interface from which one can infer the variation of the ionosphere/middle and upper atmosphere at the day side, night side, and twilight period. Tri-IPM is a modified version of the FY-3D IPM, with two cross-track probes pointing to ±30° added to increase the observations of different sunshine conditions and an in-orbit calibration probe to ensure uniformity of the three probes and their calibration attenuation over time. Tri-IPM experiments such as Probe A2 observation were carried out on 11 July 2021 to derive the differences in Probe A, B, and C calibration and preprocessing. Probe A2 is used to observe the consistency of Probe A, B, and C. As shown in Fig. 21, the measurements from three probes show good consistency using the bridge of Probe A2 at two different orbits.

Fig. 21.
Fig. 21.

The consistency comparison of Tri-IPM probe A2 and probes A, B, and C on 11 Jul 2021. The rows show two different orbits.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Tri-IPM calibration comparison with the Atmospheric Ultraviolet Radiance Integrated Code (AURIC) model (Strickland et al. 1999) and Global‐Scale Observations of the Limb and Disk (GOLD; Eastes et al. 2008) observation were conducted to investigate the quality of Tri-IPM products. Quantitative conversion of Tri-IPM observations depends on the laboratory characterization and calibration test. The calibration accuracy varies from 8.74% to 13.25% under different light intensities. Compared with AURIC model results and GOLD observation, it is found that the radiation distribution pattern of Tri-IPM observations conforms to the physical law and is in the same order of magnitude with the mode and observations, which shows consistent change trends in the observations. However, due to the large differences in orbit position, the quantitative conclusion between Tri-IPM and GOLD observations cannot be drawn. The observations of global radiation intensity from 11 days (from 10 to 20 August 2021) combination during quiet space weather are shown in Fig. 22 with a ratio of OI 135.6 nm and Lyman–Birge–Hopfield (LBH) band system of N2.

Fig. 22.
Fig. 22.

Tri-IPM global radiation intensity measurements. (a) LBH band and (b) oxygen atoms OI 135.6 nm.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Ground segment for data processing and product generation

Ground segment overview.

The ground segment for FY-3E data receiving and processing was built upon the infrastructure for previous FY-3 satellites, especially that for the FY-3D, with information technology (IT) resources and enhanced cloud service having been added. The FY-3E ground system comprises several technical components, shown in Fig. 23.

Fig. 23.
Fig. 23.

Schematic diagram FY-3E ground segment.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

The FY-3E global data acquisition network is composed of seven ground stations: five domestic ground stations in Beijing, Guangzhou, Urumqi, Jiamusi, and Kashi, respectively, and two high-latitude abroad stations in Kiruna (67°32ʹN, 21°02ʹE) and Troll (46°45ʹN, 130°22ʹE). They shall follow the operational schedule made by the Integrated Operation and Control System (IOCS) to receive and transmit satellite data to the Beijing Data Processing Center, where the data get processed, distributed, and archived. The product latency of most global observations is less than 3 h for the end users.

The raw data from satellite instruments are processed by the Data Pre-Processing System (DPPS) to get the L1 data with radiometric calibration and geolocation information ready for scientific application. The Product Generation System (PGS) further processes the L1 data to create various geophysical products to form L2 and L3 products for applications in meteorology, marine, environment, agriculture, forestry and fishery, transportation, etc. The FY-3E ground segment contains the Quality Control System (QCS) and the Simulation and Technique Support System (STSS), which monitor the instrument status and L1 data quality and validate the products. Before the satellite launch, the STSS provided simulated data at all levels for the ground segment to develop and test algorithms as well as corresponding software.

The Computer and Network System (CNS) adopts a heterogeneous hybrid cloud architecture, integrating cloud computing with network to provide three types of IT platforms: the business operation platform, the project development and testing platform, and the scientific simulation platform. CNS provides a simple and efficient computing resource pool scheduling service for operating the ground segment and improves its overall reliability. The Archival and Retrieval Service System (ARSS) realizes whole-life cycle management for all data products and for remotely preserved backups of key data products (Xian et al. 2021). At the same time, a new service model is being constructed to integrate data and data processing, consisting of “cloud + terminal” based on a hybrid cloud, with data sharing services to global users. Currently, FY-3E L1 and L2 data are openly available in the satellite archive at Fengyun Satellite Data Center (http://satellite.nsmc.org.cn/portalsite/default.aspx?currentculture=en-US).

Typical geophysical products.

During the FY-3E commissioning test, we generated 40 types of geophysical products for demonstration and evaluation purposes. They are listed into six categories of application fields: image, cloud and radiation, sea and land surface, atmospheric sounding and dynamic parameters, and solar and space weather (see the last column of Table 2). With the observational capability of EM orbit, we developed and tested new products such as the low-light near constant contrast (NCC), the nighttime light (NTL), sea surface wind field, sea ice edge, and type. The early morning orbit allows us to observe the sun continuously, and we created the X-EUVI X-ray and the extreme ultraviolet images, the solar constant, and the solar spectrum measurements. Next, examples of typical L2 products made during FY-3E commissioning are presented for demonstration.

Atmospheric temperature and humidity profiles.

The microwave and hyperspectral IR-sounding instruments aboard the FY-3E satellite have the potential to improve weather forecasting significantly. The global distribution of atmospheric temperature and humidity profiles can be retrieved from the upwelling microwave and thermal radiation observed by these instruments. The HIRAS measurements have high vertical resolution but are influenced by clouds, while lower-resolution MWTS and MWHS measurements are much less sensitive to clouds. Thus HIRAS, MWTS, and MWHS complement each other in the derivation of atmospheric temperature and humidity profiles under all conditions.

A neural network (NN)-based machine learning method has been developed to retrieve temperature and humidity profiles from combined microwave and hyperspectral infrared observations. The network inputs are HIRAS, MWTS, and MWHS brightness temperatures, along with some auxiliary data, and the training is accomplished using a global training dataset from the ECMWF. Using the method described above, Figs. 24a and 24b show the global distributions of temperature and humidity, respectively, at the height of 500 hPa retrieved on 24 December 2021.

Fig. 24.
Fig. 24.

(a) Global distribution of temperature (unit: K) and (b) humidity (unit: g kg−1) at a height of 500 hPa retrieved from 24 Dec 2021.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Ocean surface wind vector from WindRAD and GNOS-II.

Ocean surface wind vectors (OVWs) from scatterometers have proven to greatly benefit marine weather analysis and NWP. The WindRAD on board FY-3E, which utilizes a spinning four-beam antenna to achieve the quasi-synchronous observation of Ku-band frequency and C-band frequency, acquires observations of ocean surface wind vectors. The synchronous OVWs observations from the two frequency bands provide additional information for the application area, owing to their different responses to the sea surface and atmosphere conditions.

The spatial resolution of the OVWs product is 20 km. We used the algorithm developed and promoted by the Royal Netherlands Meteorological Institute (KNMI). The first step is calibrating the L1 data with the NWP ocean calibration coefficients. Then the NWP calibration coefficients are generated from collocated WindRAD observations and the ECMWF forecast winds. After quality control, the observations are used in the maximum likelihood estimation (MLE) module and the 2D-Var ambiguity removal module. Then the selected wind solution and the corresponding quality flag are generated for each wind vector cell. The typical OVWs daily and monthly products are shown in Figs. 25a and 25b, respectively.

Fig. 25.
Fig. 25.

Global OVWs from WindRAD. (a) Daily OVWs product on 11 Nov 2021, where the arrow direction represents the wind direction, and the background color represents the wind speed. (b) Global averaged wind fields in October 2021 from the OVWs monthly product.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

The OVWs from WindRAD were validated by comparing with the collocated OVWs from buoys and NWP model forecast. The buoy data used in the validation were from the ECMWF MARS (Meteorological Archival and Retrieval System) archive. Observed winds from 74 buoy stations, including Tropical Atmosphere Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TRITON), National Data Buoy Center (NDBC), and others, were collocated with the WindRAD winds. The buoy winds have been converted to 10 m equivalent neutral winds using the Lyman–Kutcher–Burman (LKB) model (Liu et al. 1979). The spatial criteria in collocating buoy winds with scatterometer winds are 25/2km, and the temporal criteria are 30 min. And the collocation time is from 1 to 31 December 2021. For quality control, we limited the WindRAD wind speed to the range of 3.0–20.0 m s−1 (to avoid the biased retrievals induced by the very low and high radar backscatter). Results show that the WindRAD OVWs product meets the design specification (2 m s−1 for wind speed and 25° for wind direction in the wind speed range of 3–20 m s−1). We also collocated the ECMWF forecast winds (Verhoef et al. 2020) to compare with the NWP winds in this validation. Compared to the buoy data, the bias of wind speed and wind direction are −0.26 m s−1 and 1.61°, respectively, and the standard deviations are 1.5 m s−1 and 23.39°. Compared to the ECMWF model forecast, the bias of wind speed and wind direction are −0.09 m s−1 and −5.18°, respectively, and the standard deviations are 1.76 m s−1 and 19.84°. With the continuous refinement of the calibration algorithm for the dual-frequency observations, the OVWs product is expected to be further improved soon.

The sea surface wind speed (SWS) from GNOS-II provides wind speeds 10 m above the sea surface. They are retrieved using the reflected GNSS signals. The spatial resolution of the wind product is around 25 km. GNOS-II can simultaneously track eight specular points at most with a sampling frequency of 1 Hz, providing wind speeds at eight specular points each second (Yang et al. 2022). GNOS-II uses a multi-observable method to retrieve wind speeds (Clarizia et al. 2014). The wind speed is then retrieved for each observation using a geophysical model function (GMF). In the end, wind speeds from the two observations are combined to get the best estimate with a minimum variance estimator. Figures 26a and 26b show the global distribution of GNOS-II SWS products in 1 and 5 days, respectively. Figure 27 shows the preliminary validation results of the GNOS-II SWS product compared to ERA5 reanalysis wind speeds. The root-mean-square error (RMSE) is 1.64 m s−1 for the GPS SWS product and 1.48 m s−1 for BDS SWS product, respectively. The bias is 0.15 m s−1 for the GPS SWS product and 0.06 m s−1 for the BDS SWS product, respectively.

Fig. 26.
Fig. 26.

Global distribution of GNOS-II SWS product for (a) 1 day (21 Jul 2021) and (b) 5 days (21–25 Jul 2021).

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Fig. 27.
Fig. 27.

Density scatterplots for the comparison between GNOS-II SWS product [(a) GPS product and (b) BDS product] and collocated ERA5 wind speeds for the data from 10 Jul to 31 Oct 2021.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Polar sea ice edge and type from WindRAD.

WindRAD is the first space-based dual-frequency rotating fan-beam scatterometer (RFSCAT). With the characteristics of diverse incidence and azimuth angles, a new algorithm for Arctic and Antarctic sea ice edge and type classification using a random forest classifier is presented (Zhai et al. 2023). It is noted that the sea ice edge is a classification between ice and seawater, and the sea ice type is a classification between seawater, first-year ice (FYI), and multiyear ice (MYI), respectively. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and FY-3D microwave radiometer (MWRI) sea ice concentration product. 12 feature parameters, including the mean value of backscatter coefficient  σ¯α,λ, the standard deviation of backscatter coefficient Δσα,λ, the copol ratio γλ, and the wavelength gradient ratio GRα, are innovatively extracted from orbital measurement for the first time to distinguish water, FYI, and MYI, the equations of which are listed as below:
σ¯α,λ=1Ni=1i=1σα,λ,i(θi,ϕi),
Δσα,λ=1Ni=1N[σα,λ,i(θi,ϕi)σ¯α,λ]2,
γλ=σ¯vv,λσ¯hh,λ,
GRα=σ¯α,Kuσ¯α,C,
where α and λ represent polarization (hh or vv) and wavelength band (Ku or C), respectively.

Due to the different combinations of feature parameters, three sea ice product groups, C-band, Ku-band, and dual-band sea ice parameters, are released. Figure 28 shows the WindRAD Artic sea ice product on 15 February 2023.

Fig. 28.
Fig. 28.

FY-3E WindRAD Arctic sea ice product on 15 Feb 2023. (a) Sea ice edge at C band; (b) sea ice edge at Ku band; (c) sea ice edge at dual band; (d) sea ice type at C band; (e) sea ice type at Ku band; (f) sea ice type at dual band. FYI and MYI represent first-year ice and multiyear ice, respectively.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

MERSI-LL nighttime lights.

Nighttime lights (NTL) have been widely applied in characterizing urban structures and indicating socioeconomic activities (Zhao et al. 2019; Levin et al. 2020). The MERSI-LL/LLB of FY-3E can image the city lights at twilight and dawn in the mid- and high latitudes of the winter hemisphere. After removing solar intrusion, screening data contaminated by clouds, and identifying lightning and high-energy particles (Elvidge et al. 2015), one month of nighttime LLB data after quality control were fused to a complete NTL image (Yu et al. 2023). The NTL of East Asia in December of 2021 is shown in Fig. 29. The city light spatial distributions from Fig. 29 are highly consistent with VIIRS NTL in the same season. Still, the comparison result with VIIRS NTL suggested that it may bear a negative bias. Nonetheless, these global NTLs at dawn allow us to study city lights during different periods of the night (Yu et al. 2023).

Fig. 29.
Fig. 29.

The nighttime lights of East Asia in December of 2021.

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

Land surface temperature of dawn–dusk orbiting.

The MERSI-LL instrument on board the FY3E satellite provides 10.8 and 12.0 μm thermal IR split window band data with 250 m spatial resolution for land surface temperature (LST) retrieval, providing a higher-quality data source for global land surface temperature monitoring twice a day. Taking the advantages of 250-m-resolution data of the two thermal IR bands of the MERSI-LL after calibration and geolocation processing, a refined thermal IR LST retrieval model is established through radiative transfer simulation, and the 250 m LST maps at dawn and dusk are produced. The LST of dawn and dusk times in East Asia are shown in the following maps (Fig. 30).

Fig. 30.
Fig. 30.

FY-3E LST composite in East Asia in February of 2022. (a) The LST of descending orbit (dawn); (b) the LST of ascending orbit (dusk).

Citation: Bulletin of the American Meteorological Society 105, 1; 10.1175/BAMS-D-22-0045.1

From the spatial distribution, the area of high latitudes and the Arctic are the two main cold sources. As an essential high-altitude cold source, the Qinghai–Tibet Plateau greatly impacts global climate. A large difference in LST between early morning and dusk is evident, with early morning being the lowest due to the outgoing radiation from the Earth’s surface. The LST product from FY-3E is complementary to other current LST products from AM and PM satellites and provides a valuable diurnal variation of global LST.

Conclusions

After about 1 year of on-orbit commissioning and products testing, a series of tests were finished for the evaluation of FY-3E key performance and confirmed that FY-3E met the required specification. Through these tests, we established the parameters to process the data and products in the ground processing system. FY-3E has transitioned to operational mode since December 2022. In this paper, we have presented the on-orbit performance of FY-3E instruments through various exams, with results approved that all payloads meet the requirement for providing reliable datasets for NWP and other downstream applications.

A much uniform temporal spacing of observations will show a tremendous impact in situations where the forecast error is rapidly increasing, such as quickly evolving weather events. The FY-3E data have been tested for assimilation in the CMA NWP model. The data of GNOS-II and MWHS-II were already taken into CMA NWP operation, and the data or products of other sounding instruments (MWTS-III, HIRAS-II, and WindRAD) are expected to be completely incorporated into the NWP operation half year later.

FY-3E data were also used to test the L2 geophysical product generation during the commissioning. Validation and demonstration of some of the products have proven the viability of FY-3E data for various users services. With new observation capability in early morning orbit, unique products from special instruments have been developed and verified with similar products from other satellites. New techniques are also used in deriving geophysical products, such as the NN-based method for retrieving the temperature and humidity profiles from combined microwave and infrared observations. The early morning orbit offers the potential to look at the sun almost continuously, an advantage in monitoring the solar activity by using X-EUVI X-ray and extreme ultraviolet images. The derived products of solar constant and solar spectrum measurements are of the information with climatic significance.

There are some important lessons drawn from the performance evaluation and application demonstration. Because of the EM special orbital conditions, the FY-3E satellite is exposed to sunlight most of the year. Sometimes even the nadir surface of the satellite body would be illuminated by the solar, and the thermal balance became a big issue and posed a significant challenge to designing the satellite. The on-orbit testing showed that the satellite platform needs to be further improved to adapt to a completely different situation created by external heat flow and solar intrusions to provide a stable and consistent working environment for the instruments throughout the year.

It should be mentioned that the deployment of FY-3E in the EM orbit is particularly significant for the international meteorological observing system, and CMA is becoming the primary satellite provider in the early morning orbit and sharing the global responsibility with European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) in the midmorning orbit and with NOAA in the afternoon orbit. The successful launch of FY-3E has fulfilled the agreement by the satellite community of CGMS to meet the baseline configuration in supporting the “WMO Vision for the GOS in 2040.”

Acknowledgments.

This work was funded by the FY3-03 project and the National Key R&D Program of China (Grants 2018YFB0504900, 2018YFB0504901, and 2022YFB3902901).

Data availability statement.

Datasets for this study are available in the FY-3E satellite archive at Fengyun Satellite Data Center. These datasets were derived from the public domain resources: http://satellite.nsmc.org.cn/portalsite/default.aspx?currentculture=en-US.

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Save
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    • Search Google Scholar
    • Export Citation
  • Bormann, N., A. Collard, and P. Bauer, 2010: Estimates of spatial and interchannel observation-error characteristics for current sounder radiances for numerical weather prediction. II: Application to AIRS and IASI data. Quart. J. Roy. Meteor. Soc., 136, 10511063, https://doi.org/10.1002/qj.615.

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    • Export Citation
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    • Export Citation
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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
  • Elvidge, C. D., M. Zhizhin, K. Baugh, and F.-C. Hsu, 2015: Automatic boat identification system for VIIRS low light imaging data. Remote Sens., 7, 30203036, https://doi.org/10.3390/rs70303020.

    • Search Google Scholar
    • Export Citation
  • Evans, D., and M. Greer, 2000: Polar orbiting environmental satellite Space Environment Monitor-2: Instrument descriptions and archive data documentation. NOAA Tech. Memo., 51 pp., https://ngdc.noaa.gov/stp/satellite/poes/docs/SEM2Archive.pdf.

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