Study on Typhoon Center Monitoring Based on HY-2 and FY-2 Data

Hu, Tangao; Wang, Xiao; Zhang, Dengrong; Zheng, Gang; Zhang, Yuzhou; Wu, Yiyue; Xie, Bin

doi:10.1109/lgrs.2017.2764620

Cited by 15 publications

(8 citation statements)

References 15 publications

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“…These powerful and hazardous meteorological phenomena cause damage to natural and man-made features all around the subtropical world [1][2][3][4]. Hurricane Maria in September 2017 caused catastrophic damage and loss of life in Dominica, the Dominican Republic, Guadeloupe, Haiti, Puerto Rico, and the United States Virgin Islands [5].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing

Smith

2018

Remote Sensing

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102

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Abstract:As the worst natural disaster on record in Dominica and Puerto Rico, Hurricane Maria in September 2017 had a large impact on the vegetation of these islands. In this paper, multitemporal Landsat 8 OLI and Sentinel-2 data are used to investigate vegetation damage on Dominica and Puerto Rico by Hurricane Maria, and related influencing factors are analyzed. Moreover, the changes in the normalized difference vegetation index (NDVI) in the year 2017 are compared to reference years (2015 and 2016). The results show that (1) there is a sudden drop in NDVI values after Hurricane Maria's landfall (decreased about 0.2) which returns to near normal vegetation after 1.5 months; (2) different land cover types have different sensitivities to Hurricane Maria, whereby forest is the most sensitive type, then followed by wetland, built-up, and natural grassland; and (3) for Puerto Rico, the vegetation damage is highly correlated with distance from the storm center and elevation. For Dominica, where the whole island is within Hurricane Maria's radius of maximum wind, the vegetation damage has no obvious relationship to elevation or distance. The study provides insight into the sensitivity and recovery of vegetation after a major land-falling hurricane, and may lead to improved vegetation protection strategies.

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Section: Introductionmentioning

confidence: 99%

The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing

Smith

2018

Remote Sensing

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102

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“…To compensate for the shortcoming of the uneven spatial distribution of the AWSs, this study also uses reanalysis data from the European Centre for Medium-Range Satellite data are obtained from the National Satellite Meteorological Center (China). The FY-2 series is the first generation of Chinese geostationary meteorological satellites, which has a frequency of half an hour for producing the cloud images [32]. In this study, we used the satellite cloud images right on every hour.…”

Section: Datamentioning

confidence: 99%

Relationship between Near-Surface Winds Due to Tropical Cyclones and Infrared Brightness Temperature Obtained from Geostationary Satellite

Zhang

Zhao

et al. 2021

Atmosphere

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Based on ten-year tropical cyclones (TCs) observations from 2009 to 2018, the black body temperature (TBB, also called cloud-top brightness temperature) data obtained from the infrared channel 1 (with the wavelength of 10.30–11.30 µm) of the FY-2 satellite image, and the wind observation data at the automatic weather stations (AWSs) in Guangdong province, this study explores the relationship between the TBBs and the winds induced by TCs at AWSs. It is found that the wind speeds at AWSs cannot be obtained directly by using TBB value inversion, but the maximum potential wind gust (MPG) and the maximum potential average-wind (MPAW) at AWSs can be estimated when a TBB is known. Influenced by the terrain, the surrounding environment, and detected height, the MPG and the MPAW values of different AWSs may differ for the same TBB. The wind data from ERA5 reanalysis is also used to explore the relationship between the TBBs and the winds over grids area during the TCs’ periods. Similar to the AWSs, there is a capping function between the winds over the grids and the TBBs. The reanalysis data can generally show the average wind conditions of the weather stations inside the grids, and therefore, can be used to supplement the data for the areas where there is no AWS observation available. Such a study could provide references for estimating the potential wind disasters induced by TCs in the study area.

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“…There are, on average, 26-28 tropical storms and typhoons each year, two or three of which can reach typhoon intensity. Typhoons here are seasonal and usually occur from July to September [14]. We chose three representative typhoons (Usagi, Fitow, and Nari) that occurred in 2013 to validate the effectiveness of the proposed model.…”

Section: Study Areamentioning

confidence: 99%

Retrieval of Sea Surface Wind Fields Using Multi-Source Remote Sensing Data

et al. 2020

Remote Sensing

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Timely and accurate sea surface wind field (SSWF) information plays an important role in marine environmental monitoring, weather forecasting, and other atmospheric science studies. In this study, a piecewise linear model is proposed to retrieve SSWF information based on the combination of two different satellite sensors (a microwave scatterometer and an infrared scanning radiometer). First, the time series wind speed dataset, extracted from the HY-2A satellite, and the brightness temperature dataset, extracted from the FY-2E satellite, were matched. The piecewise linear regression model with the highest R2 was then selected as the best model to retrieve SSWF information. Finally, experiments were conducted with the Usagi, Fitow, and Nari typhoons in 2013 to evaluate accuracy. The results show that: (1) the piecewise linear model is successfully established for all typhoons with high R2 (greater than 0.61); (2) for all three cases, the root mean square error () and mean bias error (MBE) are smaller than 2.2 m/s and 1.82 m/s, which indicates that it is suitable and reliable for SSWF information retrieval; and (3) it solves the problem of the low temporal resolution of HY-2A data (12 h), and inherits the high temporal resolution of the FY-2E data (0.5 h). It can provide reliable and high temporal SSWF products.

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Study on Typhoon Center Monitoring Based on HY-2 and FY-2 Data

Cited by 15 publications

References 15 publications

The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing

The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing

Relationship between Near-Surface Winds Due to Tropical Cyclones and Infrared Brightness Temperature Obtained from Geostationary Satellite

Retrieval of Sea Surface Wind Fields Using Multi-Source Remote Sensing Data

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