Studies of FY-3 Observations over the Past 10 Years: A Review

Zou, Xiaolei

doi:10.3390/rs13040673

Cited by 12 publications

(4 citation statements)

References 54 publications

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“…FY-3D, as the fourth satellite, was launched on 15 November 2017. It is jointly networked with FY-3C to improve the accuracy of atmospheric detection and enhance the capabilities of greenhouse gas monitoring, comprehensive space environment detection, and meteorological remote sensing detection [38,39]. The FY-3D L1 level data used in this article can be downloaded from the National Satellite Meteorological Center (http://satellite.nsmc.org.cn/portalsite/default.aspx (accessed on 27 May 2022)).…”

Section: Data Sourcesmentioning

confidence: 99%

Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change

Dong

et al. 2022

Forests

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As one of China’s new generation polar-orbiting meteorological satellites, FengYun-3D (FY-3D) provides critical data for forest fire detection. Most of the existing related methods identify fire points by comparing the spatial features and setting thresholds empirically. However, they ignore temporal features that are associated with forest fires. Besides, they are difficult to generalize to multiple areas with different environmental characteristics. A novel method based on FY-3D combining the genetic algorithm and brightness temperature change detection is proposed in this work to improve these problems. After analyzing the spatial features of the FY-3D data, it adaptively detects potential fire points based on these features using the genetic algorithm, then filters the points with contextual information. To address the false alarms resulting from the confusing spectral characteristics between fire pixels and conventional hotspots, temporal information is introduced and the “MIR change rate” based on the multitemporal brightness temperature change is further proposed. In order to evaluate the performance of the proposed algorithm, several fire events occurring in different areas are used for testing. The Moderate-Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire products (MYD14) is chosen as the validation data to assess the accuracy of the proposed algorithm. A comparison of results demonstrates that the algorithm can identify fire points effectively and obtain a higher accuracy than the previous FY-3D algorithm.

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Section: Data Sourcesmentioning

confidence: 99%

Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change

Dong

et al. 2022

Forests

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“…Microwave imagers are an important category of space‐based instruments that measure upwelling microwave radiation from the surface and atmosphere. Examples of microwave imagers placed on board satellites include the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission Water Cycle ( GCOM‐W ) satellite (JAXA, 2013), the Special Sensor Microwave Imager Sounder (SSMIS) on board the Defense Meteorological Satellite Program ( DMSP ) satellites Kunkee et al (2008), the MicroWave Radiation Imager (MWRI) on board the Fengyun‐3D ( FY‐3D ) satellite Zou (2021), and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) Hou et al (2014). Recent studies have shown that microwave imager observations have great potential to improve atmospheric analyses and forecasts for both regional and global NWP Kazumori et al (2016); Yang et al (2016); Geer et al (2010); Pu et al (2019); Lean et al (2017); Geer and Bauer (2010).…”

Section: Introductionmentioning

confidence: 99%

Impact of HY‐2B SMR radiance assimilation on CMA global medium‐range weather forecasts

Li,

Han

2024

Quart J Royal Meteoro Soc

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The Chinese second ocean dynamic environment satellite Haiyang‐2B (HY‐2B), was successfully launched on 25 October 2018, carrying a Scanning Microwave Radiometer (SMR) to provide the information of ocean and atmosphere. For the first time, this study investigated the impact of radiance data from the HY‐2B SMR in the Four Dimensional Variational (4DVar) Data Assimilation system of the Global Forecast System developed by the China Meteorology Administration (CMA‐GFS). Prior to the radiance assimilation, we evaluated the data quality and developed suitable quality control (QC) procedures for the HY‐2B SMR observations. In addition, the cloud liquid water path (CLWP) and ocean wind speed (OWS) were introduced as new bias correction (BC) predictors to remove the systematic bias originating from the forward operator in the CMA‐GFS 4DVar system. The assimilation effects of SMR observations were evaluated through one‐month cycling experiments. Verification with independent observations and reanalysis data has demonstrated that assimilating SMR clear‐sky radiance can greatly reduce the analysis errors for temperature and humidity, while indirectly improve the quality of wind analysis. Furthermore, significant improvements in geopotential height forecasts were found for days 1‐5 in the lower troposphere of the tropical region.This article is protected by copyright. All rights reserved.

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“…Accurate forecasting of typhoon track and intensity is of great significance in reducing the enormous losses they cause in terms of assets, human casualties, and economic impacts [3]. In recent years, the utilization of radiance data from microwave sounders has emerged as a crucial aspect in studies concerning numerical weather prediction (NWP) and its correlation with climate [4,5]. Several microwave sounders have been developed and employed for atmospheric observations.…”

Section: Introductionmentioning

confidence: 99%

Assimilating FY-3D MWHS2 Radiance Data to Predict Typhoon Muifa Based on Different Initial Background Conditions and Fast Radiative Transfer Models

Huang

et al. 2023

Remote Sensing

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In this study, the impact of assimilating MWHS2 radiance data under different background conditions on the analyses and deterministic prediction of the Super Typhoon Muifa case, which hit China in 2022, was explored. The fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data and the Global Forecast System (GFS) analysis data from the National Centers for Environmental Prediction (NCEP) were used as the background fields. To assimilate the Microwave Humidity Sounder II (MWHS2) radiance data into the numerical simulation experiments, the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system were employed. The results show that after the data assimilation, the standard deviation and root-mean-square error of the analysis significantly decrease relative to the observation, indicating the effectiveness of the assimilation process with both background fields. In the MWHS_GFS experiment, a subtropical high-pressure deviation to the east is observed around the typhoon, resulting in its northeast movement. In the differential field of the MWHS_ERA experiment, negative sea-level pressure differences around the typhoon are observed, which increases its intensity. In the deterministic predictions, assimilating the FY3D MWHS2 radiance data reduces the typhoon track error in the MWHS_GFS experiment and the typhoon intensity error in the MWHS_ERA experiment. In addition, it is found that the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for Tovs (RTTOV) model show similar performance in assimilating MWHS2 radiance data for this typhoon case. It seems that the data assimilation experiment with the CRTM significantly reduces the typhoon track error than the experiment with the RTTOV model does, while the intensity error of both experiments is rather comparable.

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Studies of FY-3 Observations over the Past 10 Years: A Review

Cited by 12 publications

References 54 publications

Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change

Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change

Impact of HY‐2B SMR radiance assimilation on CMA global medium‐range weather forecasts

Assimilating FY-3D MWHS2 Radiance Data to Predict Typhoon Muifa Based on Different Initial Background Conditions and Fast Radiative Transfer Models

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