Fengyun Satellite Research Articles

Historical and real-time estimation of snow depth in Eurasia based on multiple passive microwave data

Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia, and the lagging updates of datasets do not meet the operational requirements of the meteorological service department. This study developed a dynamic retrieval method for daily snow depth over Eurasia based on cross-sensor calibrated microwave brightness temperatures to enhance retrieval accuracy and meet the requirements of operational work. These brightness temperatures were detected by microwave radiometer imager carried on the FengYun 3 (FY-3) satellite and the special sensor microwave imager/sounder carried on the USA Defense Meteorological Satellite Program series satellites, which use the fewest sensors to provide the longest data and consequently introduce minimal errors during inter-sensor calibration. Firstly, inter-sensor calibration was conducted amongst brightness temperatures collected by the three sensors. A spatiotemporal dynamic relationship between snow depth and microwave brightness temperature gradient was then established, overcoming the large uncertainties induced by varying snow characteristics. This relationship can be utilised in FY-3 satellite data for operational service to obtain real-time snow depth. The generated daily snow depth dataset from 1988 to 2021 presents similar spatial patterns of snow depth to those observed in situ. Against in situ snow depth, the overall bias and root mean square error are −2.04 and 6.49 cm, respectively, facilitating considerable improvements in accuracy compared with the Advanced Microwave Scanning Radiometer 2 snow depth product, which adopts the static algorithm. Further analysis shows an overall decreasing trend from 1988 to 2021 for annual and monthly mean snow depths, demonstrating a noticeable reduction since around 2000. The reduction in monthly mean snow depth started earlier in shallow snow months than in deep snow months.

Research on the establishment of NDVI long-term data set based on a novel method

This study compares the relationship between different NDVI (Normalized Difference Vegetation Index), the NDVI of AVHRR (Advanced Very High Resolution Radiometer) (NDVIa), the NDVI of MODIS (Moderate Resolution Imaging Spectrometer) (NDVIm), and the NDVI of VIRR (Visible and Infrared Radiometer) (NDVIv), and found that there is a significant correlation between the NDVIa and the NDVIm, and between the NDVIv and the NDVIa, the relationship between the three is NDVIv < NDVIa < NDVIm. Machine learning is an important method in artificial intelligence. It can solve some complex problems through algorithms. This research uses linear regression algorithm in machine learning to construct the Fengyun Satellite NDVI correction method. By constructing a linear regression model, the NDVI value of Fengyun Satellite VIRR is corrected to a level that is basically the same as NDVIm. The corrected correlation coefficients (R2) were significantly improved, and the corrected correlation coefficients were significantly improved, and the confidence levels were all significant correlations less than 0.01. It is proved that the corrected normalized vegetation index of Fengyun Satellite has significantly improved accuracy and product quality compared with the normalized vegetation index of MODIS.

A New Method for Hour-by-Hour Bias Adjustment of Satellite Precipitation Estimates over Mainland China

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (&gt;1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data.

Evaluation of the Long-term Performance of Microwave Radiation Imager Onboard Chinese Fengyun Satellites

Evaluation of the Long-term Performance of Microwave Radiation Imager Onboard Chinese Fengyun Satellites

Ant-Inspired Bionic Design Method for the Support Structure of the Fengyun-3 Satellite Payload Infilled with Lattice Structure.

Owing to their high design freedom and excellent performance, lattice structures have shown outstanding capabilities and great potential in aeronautics and astronautics fields. In this paper, we propose a method to construct lattice structures by parameterizing biological features. An ant-leg configuration is used as the bionic object to generate a bionic lightweight design with a gradient lattice structure. To achieve the above goal, an innovative optimization method combining topology optimization, size optimization, and a bionic lattice structure is proposed in this paper. Taking the support structure of the Fengyun-3 satellite payload as the research object, this optimization method is applied to optimize the design. Further, the reconstructed optimization model and the original model are simulated to evaluate and compare the structural performance. The simulation results show that when combined with bionic lattice structure and structural optimization, the method can achieve the lightweight design goal while ensuring the stiffness and strength of the structure. The results demonstrate that the application of a bionic lattice design in a lightweight design has feasibility and expectable potential.

Sensitivity Analysis of Microwave Spectrometer for Atmospheric Temperature and Humidity Sounding on the New Generation Fengyun Satellite

The vertical profiles, spatiotemporal distribution, and trends of temperature and humidity in the middle atmosphere are significant for numerical weather prediction and the analysis of global climate change. To better design and apply spaceborne microwave spectrometer on the new generation Fengyun satellite, the sensitivities of the spectrometers at 22.235, 50–60, 118.75, 183.31, 325.153, 380.33, 424.77, 448.0, and 556.94 GHz are analyzed, respectively. Qpack2 included in the atmospheric radiative transfer simulator is used. The results show that the retrieval accuracy of the 50.0–60.0 GHz spectrometer is obviously better than other spectrometers, and the effective detection height (EDH) is the highest, reaching 0.233 hPa. The humidity profiles bigger than 500 hPa are well detected by the seven channels of the Humidity and Temperature Profiler or 22.0–32.0 GHz spectrometer. The retrieval accuracy is better than 6%, which greatly improves the retrieval performance of humidity profiles in the lower troposphere over the sea. In addition, this frequency band is not affected by errors in sea surface temperature or wind speed. The humidity profiles over the sea and land in the middle atmosphere under clear-sky and cloudy-sky conditions are well detected by the 183.31 and 556.94 GHz spectrometer. The EDH can be increased to 1.14 hPa by the 556.94 GHz spectrometer. In the design and application of future spaceborne microwave spectrometers for temperature and humidity profiles detection, the spectrometers mentioned previously are good candidates, and the parameter configuration of these spectrometers can be used as a reference.

Physical and Optical Properties of Clouds in the Southwest Vortex from FY-4A Cloud Retrievals

Abstract The southwest vortex (SWV) is a critical weather system in China, but our knowledge of this system remains incomplete. Here, we investigate the cloud properties in the SWV. First, we search for the SWVs with time steps and center locations that are consistent between the SWV yearbook and ERA-Interim reanalysis data. Second, we supplement these SWVs’ life spans and movement paths. Third, we relocate the Fengyun (FY) satellite FY-4A cloud retrievals in the 10° × 10° region centered on each SWV and analyze the cloud occurrence frequency (COF), cloud-top height (CTH), and cloud optical thickness (COT). A distribution mode of cloud types is summarized from the COFs, with water clouds, supercooled clouds, mixed clouds, ice clouds, cirrus clouds, and overlap clouds occurring sequentially from west to east. The CTH probability density (PD) distribution features a significant north–south difference. In addition, the COT PD distributions exhibit a common trend: with increasing COT, the PD increases rapidly and then slowly before peaking, whereupon the PD decreases abruptly. From spring to summer, the region with the highest convective COF shifts from the northeast to the northwest, and an east–west gradient of the convective COF appears in autumn and winter. Furthermore, we investigate the cloud properties during SWV-related heavy rainfall. Heavy rain occurs mainly in the west of the SWV, and convective clouds are mainly in the northwest, partly in the southwest and near the SWV center. The average CTH in heavy rainfall is generally higher than 6 km, and the average COT is greater than 20. Significance Statement The southwest vortex (SWV) is an important weather system in China. However, we do not yet comprehensively know this weather system. The cloud properties can indicate the structures of weather systems and are key parameters in numerical weather prediction (NWP) models. Thus, investigating cloud properties is necessary and meaningful to understand the SWV and accurately predict SWV-related precipitation in NWP models. In this paper, a typical distribution mode of six cloud types in the SWV is summarized from the cloud occurrence frequency, and the distribution features of convective clouds, cloud-top height, and cloud optical thickness in the SWV are analyzed. Furthermore, the cloud properties in SWV-related heavy rain are also studied.

Sensitivity Testing of Microwave Temperature Sounder-II Onboard the Fengyun-3 Satellite to Sea Surface Barometric Pressure Based on Deep Neural Network

Sea surface barometric pressure contributes to calculating the surface transmissivity so that the observations of Microwave Temperature Sounder-II channels with non-zero surface transmissivity contain the sea surface barometric pressure information. Meanwhile, all channels of Microwave Temperature Sounder-II are sensitive to sea surface barometric pressure due to the correlation between channels. However, the traditional sensitivity test method based on the radiative transfer model cannot characterize the sensitivity of Microwave Temperature Sounder-II to sea surface barometric pressure due to the correlations between channels. In this study, the relationship between atmospheric parameters and Microwave Temperature Sounder-II observations is studied by a deep neural network, and the deep neural network-based model for Microwave Temperature Sounder-II simulations is established. Then, the deep neural network-based test method for the sensitivity of Microwave Temperature Sounder-II to sea surface barometric pressure is developed, and the sensitivity test experiments are carried out. The experimental results show that the sensitivity of all channels of Microwave Temperature Sounder-II to sea surface barometric pressure is captured by the deep neural network-based test method. In addition, the retrieval experiments of sea surface barometric pressure using Microwave Temperature Sounder-II observations are carried out, and the retrieval results further validate the feasibility of the deep neural network-based test method.

Tracking the Early Movements of Northeast China Cold Vortices Using FY-3D MWTS-2 Observations of Brightness Temperature

The Northeast China cold vortex (NCCV) often occurs in spring and summer, causing extreme weather such as rainstorm and hail in Northeast China. The brightness temperature (TB) observations of Microwave Temperature Sounder-2 (MWTS-2) on board Fengyun-3D (FY-3D), which can provide atmospheric temperature in various vertical layers, are firstly limb-corrected and then applied to track the origin and movement of four NCCV cases in June and July 2019. Results show that a cold core is observed at the location of NCCVs in TB observations of channels 4 and 5, whose peak weighting function (WF) altitudes are 700 and 400 hPa, respectively, indicating the cold structure of NCCVs in the middle and lower troposphere. The TB observations of channels 6 and 7, which measure the atmospheric temperature around 250 and 200 hPa, respectively, capture a warm core structure of NCCVs in the upper troposphere and lower stratosphere. Being less affected by the low-level cloud and rain, TB observations of channels 6 and 7 are applied to identify and track the warm cores of NCCVs. The NCCV tracks of movement identified by MWTS-2 observations compare well with those determined by the 500 hPa geopotential height and the 300 hPa potential vorticity (PV) anomaly from the ERA5 reanalysis. Both clearly show that the NCCVs were originated from high latitudes, then moved southeastward, and finally entered Northeast China. The entire process took several days. Therefore, TB observations of MWTS-2 can be used to identify the precursors of NCCVs and monitor their appearances, developments, and movements in time. With the flourishing development of Fengyun satellite series in China as well as the already existing 40 years of microwave sounder observations worldwide, this research provides a new way to investigate the synoptic and climatological features of NCCVs.

Cloud detection for satellite cloud images based on fused FCN features

ABSTRACT Cloud detection for satellite cloud images is a challenging image processing task owing to the blurring of cloud boundaries, multiplicity, and complexity of cloud types. Currently, the commonly used cloud detection methods include original full convolutional neural network (original FCN), FCN with an 8-pixel stride (FCN- 8s), FCN with a 2-pixel stride (FCN-2s), and so on. However, the aforementioned methods exclusively rely on a single network layer the final layer feature map; thus, shallow cloud image information, such as cloud profile information may not be captured. In this letter, a cloud detection method for satellite cloud images based on fused FCN features is proposed. The proposed method effectively fuses spatial and high-level semantic information, and a voting ensemble strategy is used to improve the accuracy and robustness of cloud detection. Finally, the experimental results demonstrate that the average overall accuracy (OA), average producer’ accuracy (PA), and average user’ accuracy (UA) of the proposed method for multiple training sample sizes and image sizes of the collected Fengyun satellite (FY-2 G) cloud image database increased by 7.15%, 9.04%, and 8.46%, respectively, relative to the average accuracies of the original FCN, FCN-8s, FCN-2s, SegNet, and DeepLabV3 methods.

How China’s Fengyun Satellite Precipitation Product Compares with Other Mainstream Satellite Precipitation Products

AbstractSatellite-based and reanalysis precipitation estimates are an alternative and important supplement to rain gauge data. However, performance of China’s Fengyun (FY) satellite precipitation product and how it compares with other mainstream satellite and reanalysis precipitation products over China remain largely unknown. Here five satellite-based precipitation products (i.e., FY-2 precipitation product, IMERG, GSMaP, CMORPH, and PERSIANN-CDR) and one reanalysis product (i.e., ERA5) are intercompared and evaluated based on in situ daily precipitation measurements over mainland China during 2007–17. Results show that the performance of these precipitation products varies with regions and seasons, with better statistical metrics over wet regions and during warm seasons. The infrared–microwave combined precipitation [i.e., IMERG, GSMaP, and CMORPH, with median KGE (Kling–Gupta efficiency) values of 0.53, 0.52, 0.59, respectively] reveals better performance than the infrared-based only product (i.e., PERSIANN-CDR, with a median KGE of 0.31) and the reanalysis product (i.e., ERA5, with a median KGE of 0.43). IMERG performs well in retrieving precipitation intensity and occurrence over China, while GSMaP performs well in the middle to low reaches of the Yangtze River basin but poorly over sparsely gauged regions, e.g., Xinjiang in northwest China and the Tibetan Plateau. CMORPH performs well over most regions and has a greater ability to detect precipitation events than GSMaP. The FY-2 precipitation product can capture the overall spatial distribution of precipitation in terms of both precipitation intensity and occurrence (median KGE and CSI of 0.54 and 0.55), and shows better performance than other satellite precipitation products in winter and over sparsely gauged regions. Annual precipitation from different products is generally consistent, though underestimation exists in the FY-2 precipitation product during 2015–17.Significance StatementIntercomparison between the FY-2 precipitation product and mainstream precipitation products is valuable to guide applications of satellite precipitation products to China and its subregions. This study illustrates uncertainties in various satellite precipitation products, and could guide optimization of algorithms of precipitation retrieval and data fusion/merging to improve the accuracy and resolution of satellite precipitation products.

SDTGAN: Generation Adversarial Network for Spectral Domain Translation of Remote Sensing Images of the Earth Background Based on Shared Latent Domain

The synthesis of spectral remote sensing images of the Earth’s background is affected by various factors such as the atmosphere, illumination and terrain, which makes it difficult to simulate random disturbance and real textures. Based on the shared latent domain hypothesis and generation adversarial network, this paper proposes the SDTGAN method to mine the correlation between the spectrum and directly generate target spectral remote sensing images of the Earth’s background according to the source spectral images. The introduction of shared latent domain allows multi-spectral domains connect to each other without the need to build a one-to-one model. Meanwhile, additional feature maps are introduced to fill in the lack of information in the spectrum and improve the geographic accuracy. Through supervised training with a paired dataset, cycle consistency loss, and perceptual loss, the uniqueness of the output result is guaranteed. Finally, the experiments on the Fengyun satellite observation data show that the proposed SDTGAN method performs better than the baseline models in remote sensing image spectrum translation.

Quiet-time seasonal behaviour of the ionosphere measured by the Ionosphere PhotoMeter (IPM) on the Chinese meteorological satellite FY-3D compared with the IRI2016 model predictions

In this paper, we examine the climatology structure of the mid-night ionosphere at approximately two o’clock local time of a low-solar-activity year and discrepancies between Ionosphere PhotoMeter (IPM) observations and IRI (International Reference Ionosphere) model-2016 predictions using (1) a global structure for the F2 layer peak electron density (NmF2) for four seasons, (2) the mean NmF2 values at different latitudes by month, and (3) the monthly distribution of the global average of NmF2. The data is collected by the IPM, a far-ultraviolet nadir- viewing photometer that has been aboard the second-generation, polar-orbiting Chinese meteorological satellite Feng-Yun 3D (FY-3D) since its launch on November 25th, 2017 and has provided excellent opportunities for many scientific investigations of the ionosphere. We collected night glow data under low geomagnetic activity (KP ≤ 4) during 2018, and the data obtained five days before and after a full-moon night were removed to prevent the influence of stray light. Before analysing the climatology of the ionosphere and comparing the results with those obtained from IRI2016, the accuracy of NmF2 measured by IPM was evaluated by comparison with data from ground-based ionosondes. A statistical comparative analysis of NmF2 measured by IPM and ionosonde indicated a difference of 20% at middle latitudes and 28% at low latitudes. The global behaviour of NmF2 measured by IPM and IRI for four seasons shows that the IRI model generally captures the morphology of the latitudinal structure and hemispheric asymmetry in NmF2 observed by IPM. A comparison between IPM-measured NmF2 and IRI-predicted NmF2 indicates that the former is systematically lower than the latter. The difference in the measurements and predictions can be as large as 300%.

Fusion Retrieval of Sea Surface Barometric Pressure from the Microwave Humidity and Temperature Sounder and Microwave Temperature Sounder-II Onboard the Fengyun-3 Satellite

Both the Microwave Humidity and Temperature Sounder (MWHTS) and the Microwave Temperature Sounder-II (MWTS-II) operate on the Fengyun-3 (FY-3) satellite platform, which provides an opportunity to retrieve the sea surface barometric pressure (SSP) with high accuracy by fusing the observations from the 60 GHz, 118.75 GHz, and 183.31 GHz channels. The theory of retrieving SSP using passive microwave observations is analyzed, and the sensitivity test experiments of MWHTS and MWTS-II to SSP as well as the test experiments of the contributions of MWHTS and MWTS-II to SSP retrieval are carried out. The theoretical channel combination is established based on the theoretical analysis, and the SSP retrieval experiment is carried out based on the Deep Neural Network (DNN) for the theoretical channel combination. The experimental results show that the retrieval accuracy of SSP using the theoretical channel combination is higher than that of MWHTS or MWTS-II. In addition, based on the test results of the contributions of MWHTS and MWTS-II to the retrieval SSP, the optimal theoretical channel combination can be built, and can further improve the retrieval accuracy of SSP from the theoretical channel combination.

Intercalibration of Brightness Temperatures From FY-3 MWRI for Surface Snowmelt Detection Over Polar Ice Sheets

Surface snowmelt is a vital environmental parameter that affects energy exchanges between polar ice sheets and the atmosphere. Due to the difficulties of continuous in-situ measurements, passive microwave remote sensing technology has become a major method for obtaining ice sheet surface snowmelt states over large areas. Feng Yun-3 (FY-3) series satellites, the second generation of Chinese polar-orbiting meteorological satellite missions, have great potential for providing long-term polar ice sheet surface snowmelt state products. In this study, we establish a monthly inter-calibration model to synergize brightness temperatures from the Microwave Radiation Imager (MWRI) aboard different FY-3 satellites. Based on the calibrated continuous brightness temperature record, an improved snowmelt algorithm is proposed by using an adaptive thresholding method, which does not rely on in-situ observation data. After inter-calibration, the consistency of the melt extent obtained by different sensors is considerably better than before, with the bias decreasing from 85 pixels to 3 pixels in the Greenland Ice Sheet (GrIS) and from 16 pixels to 6 pixels in the Antarctic Ice Sheet (AIS). Evaluation of the snowmelt result is conducted with the automatic weather station (AWS) air temperature, and a promising accuracy is found with an overall accuracy above 92% in the AIS and approximately 86% in the GrIS. This study provides new possibilities for a long-term continuous snowmelt product by connecting FY-3B, FY-3C, FY-3D, and its successors FY-3F and FY-3G. The inter-calibration coefficients and FY-3 crossing times are available at https://doi.org/10.6084/m9.figshare.20657712.v1.

Intercalibration of FY-3C MWRI Over Forest Warm-Scenes Based on Microwave Radiative Transfer Model

In order to cover the warm end of Earth-scene brightness temperature (TB) range of passive microwave radiometers, intercalibration over warm scenes is necessary. This article presents a methodology to intercalibrate the microwave radiation imager (MWRI) on the Chinese second-generation meteorological satellite Fengyun 3C (FY-3C) with the Global Precipitation Measurement (GPM) Microwave Imager (GMI) over the warm scenes of dense forests using the double-difference (DD) method. Based on the microwave radiative transfer model (RTM), an intercalibration method is developed, in which a modified land surface emissivity (LSE) model for dense forests is proposed. The forests with optically thick canopy are identified in terms of polarization TB differences and normalized difference vegetation index (NDVI) extracted from the latest vegetation product of Moderate-Resolution Imaging Spectroradiometer (MODIS). The matching TBs between FY-3C MWRI and GMI over dense forest warm scenes are collected and analyzed together with the TBs over ocean surfaces obtained by Zeng and Jiang (2020). The results show that: 1) FY-3C MWRI’s observations are generally underestimated, and the intercalibration biases are polynomial functions of observations; 2) the intercalibration biases at the warm end are relatively smaller than those at the cold end; and 3) the calibration in the ascending orbits (MWRIA) is relatively better than that in the descending orbits (MWRID). At the tropical rain forest scene TBs defined in this work, the intercalibration biases (mean ± standard deviation at the mean) in the FY-3C MWRI channels of 10 V, 10 H, 18 V, 18 H, 23 V, 36 V, 36 H, 89 V, and 89 H are, respectively, −1.3 ± 0.7, −1.9 ± 1.1, 1.6 ± 0.6, 2.5 ± 0.8, −0.2 ± 0.5, −2.0 ± 0.6, −2.4 ± 0.7, −0.2 ± 0.6, and −0.1 ± 0.6 K for the ascending orbits, while they are, respectively, −4.0 ± 0.8, −5.4 ± 1.2, −1.4 ± 0.7, −1.2 ± 0.8, −2.9 ± 0.5, −4.9 ± 0.7, −5.5 ± 0.7, −2.7 ± 0.8, and −2.3 ± 0.7 K for the descending orbits. The in-orbit calibration coefficients of GMI are successfully transferred to FY-3C MWRI.

Infrared precipitation estimation using convolutional neural network for FengYun satellites

Infrared (IR) is an important data source for satellite quantitative precipitation estimation, and has been widely applied in the fields of meteorology, hydrology, and agriculture. In the past decades, a series of IR retrieval algorithms have been developed to support the production of IR-based and IR-microwave merged precipitation products. Recently, deep learning techniques such as the convolutional neural network (CNN) show great potential in obtaining IR precipitation estimates with higher accuracy than traditional retrieval algorithms. In this study, we present an upgraded version of IR Precipitation Estimation using CNN (IPEC), i.e., IPEC version 2 (IPEC-V2), with a new end-to-end manner. IPEC-V2 is used to generate a precipitation dataset based on IR data from China’s Fengyun (FY) geostationary satellites (FY-2F, 2G, and 4A). To overcome the difficulty of model training in regions with sparse observations, IPEC-V2 models are firstly pre-trained over the data-rich Continental US (CONUS) using IR data from the Geostationary Operational Environment Satellite (GOES). The models are then transferred to China through re-training with multi-band IR signals from FY satellites. Finally, a long-term record of high-resolution FY IR precipitation estimates is produced during the period from November 9, 2012 to February 28, 2021, which is named as IPEC-FY. IPEC-FY shows better performance than the baseline Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) with 76.2% gain in Pearson’s correlation coefficient and 18.4% gain in root mean squared error in China. This study shows that transfer learning is an effective way to build CNN models in regions without enough observations, and the high-quality IPEC-FY can act as an alternative precipitation dataset for research in China.

Advances in Ecological Applications of Fengyun Satellite Data

Advances in Ecological Applications of Fengyun Satellite Data

Applications of the Advanced Radiative Transfer Modeling System (ARMS) to Characterize the Performance of Fengyun–4A/AGRI

This study applies the Advanced Radiative Transfer Modeling System (ARMS), which was developed to accelerate the uses of Fengyun satellite data in weather, climate, and environmental applications in China, to characterize the biases of seven infrared (IR) bands of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Chinese geostationary meteorological satellite, Fengyun–4A. The AGRI data are quality controlled to eliminate the observations affected by clouds and contaminated by stray lights during the mid–night from 1600 to 1800 UTC during spring and autumn. The mean biases, computed from AGRI IR observations and ARMS simulations from the National Center for Environmental Prediction (NCEP) Final analysis data (FNL) as input, are within −0.7–1.1 K (0.12–0.75 K) for all seven IR bands over the oceans (land) under clear–sky conditions. The biases show seasonal variation in spatial distributions at bands 11–13, as well as a strong dependence on scene temperatures at bands 8–14 and on satellite zenith angles at absorption bands 9, 10, and 14. The discrepancies between biases estimated using FNL and the European Center for Medium–Range Weather Forecasts Reanalysis–5 (ERA5) are also discussed. The biases from water vapor absorption bands 9 and 10, estimated using ERA5 over ocean, are smaller than those from FNL. Such discrepancies arise from the fact that the FNL data are colder (wetter) than the ERA5 in the middle troposphere (upper–troposphere).

Fengyun Meteorological Satellite Products for Earth System Science Applications

Following the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.