Attitude determination for three-axis stabilized geostationary meteorological satellite image navigation

2005 ◽  
Author(s):  
Yaguang Wu ◽  
Zhigang Wang
Keyword(s):  
Attitude Determination ◽  
Satellite Image ◽  
Geostationary Meteorological Satellite ◽  
Image Navigation ◽  
Meteorological Satellite

scholarly journals Introduction of the Advanced Meteorological Imager of Geo-Kompsat-2a: In-Orbit Tests and Performance Validation

2021 ◽  
Vol 13 (7) ◽  
pp. 1303
Author(s):  
Dohyeong Kim ◽  
Minju Gu ◽  
Tae-Hyeong Oh ◽  
Eun-Kyu Kim ◽  
Hye-Ji Yang
Keyword(s):  
Spatial Resolution ◽  
Optical Sensors ◽  
Near Infrared ◽  
Weather Forecasting ◽  
Ground Segment ◽  
Satellite Broadcasting ◽  
Geostationary Meteorological Satellite ◽  
Image Navigation ◽  
Performance Validation ◽  
Meteorological Satellite

Geo-Kompsat-2A (Geostationary-Korean Multi-Purpose SATtellite-2A, GK2A), a new generation of Korean geostationary meteorological satellite, carry state-of-the-art optical sensors with significantly higher radiometric, spectral, and spatial resolution than the Communication, Ocean, and Meteorological Satellite (COMS) previously available in the geostationary orbit. The new Advanced Meteorological Imager (AMI) on GK2A has 16 observation channels, and its spatial resolution is 0.5 or 1 km for visible channels and 2 km for near-infrared and infrared channels. These advantages, when combined with shortened revisit times (around 10 min for full disk and 2 min for sectored regions), provide new levels of capacity for the identification and tracking of rapidly changing weather phenomena and for the derivation of quantitative products. These improvements will bring about unprecedented levels of performance in nowcasting services and short-range weather forecasting systems. Imagery from the satellites is distributed and disseminated to users via multiple paths, including internet services and satellite broadcasting services. In post-launch performance validation, infrared channel calibration is accurate to within 0.2 K with no significant diurnal variation using an approach developed under the Global Space-based Inter-Calibration System framework. Visible and near infrared channels showed unexpected seasonal variations of approximately 5 to 10% using the ray matching method and lunar calibration. Image navigation was accurate to within requirements, 42 µrad (1.5 km), and channel-to-channel registration was also validated. This paper describes the features of the GK2A AMI, GK2A ground segment, and data distribution. Early performance results of AMI during the commissioning period are presented to demonstrate the capabilities and applications of the sensor.

scholarly journals Instrument observation strategy of new generation three-axis stabilized geostationary meteorological satellite of China

2019 ◽  
Author(s):  
Jian Shang ◽  
Pan Huang ◽  
Huizhi Yang ◽  
Chengbao Liu ◽  
Jing Wang ◽  
...  
Keyword(s):  
Attitude Control ◽  
The Earth ◽  
Observation Strategy ◽  
Geostationary Meteorological Satellite ◽  
Image Navigation ◽  
Complicated Part ◽  
New Generation ◽  
Observation Strategies ◽  
Meteorological Satellite ◽  
Instrument Observation

Abstract. Fengyun-4 (FY-4) satellite series is the new generation of geostationary meteorological satellite of China. Thenewly adopted three-axis stabilized attitude control platform can increase observation efficiency and flexibility, while bringing great challenge to image navigation as well as integrated observation mode design. Considering the requirements of the earth observation, navigation and calibration besides observation flexibility, instrument observation strategies are proposed, including the earth, the moon, stars, cold space, blackbody, diffuser observations, on which the instruments' in-orbit daily observations must be based. The most complicated part is star observation strategy, while navigation precision is dependent on in-orbit star observations. Flexible, effective, stable and automatic star observation strategy directly influences obtaining star data and navigation precision. According to the requirement of navigation, two specific star observation strategies for the two main instruments onboard FY-4 were proposed to be used in the operational ground system. The strategies have been successfully used in FY-4 in-orbit test for more than a year. Both the simulation results and in-orbit application results are given, including instrument observation strategies, star observation strategies and moon tasks, to demonstrate the validity of the proposed observation strategies, which lay important foundations for the instruments' daily operation.

scholarly journals Estimating Sunshine Duration Using Hourly Total Cloud Amount Data from a Geostationary Meteorological Satellite

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Weiwei Zhu ◽  
Bingfang Wu ◽  
Nana Yan ◽  
Zonghan Ma ◽  
Linjiang Wang ◽  
...  
Keyword(s):  
Sunshine Duration ◽  
Regional Scale ◽  
Cloud Amount ◽  
Important Indicator ◽  
Study Region ◽  
Total Cloud Amount ◽  
Cloud Coverage ◽  
Ground Observation ◽  
Geostationary Meteorological Satellite ◽  
Meteorological Satellite

Sunshine duration is an important indicator of the amount of solar radiation received in a region and an important input parameter for the study of atmospheric energy balance, climate change, ecosystem evolution, and social sustainability. Currently, extrapolation and interpolation of data from meteorological stations are the most common methods used to calculate sunshine duration on a regional scale. However, it is difficult to obtain high precision sunshine duration in areas lacking ground observation or where sunshine duration is highly heterogeneous on the ground. In this paper, a new method is proposed to estimate sunshine duration with hourly total cloud amount (CTA) data from sunrise to sunset derived from the Fengyun-2G geostationary meteorological satellite (FY-2G). This method constructs a new index known as daytime mean total cloud coverage amount and provides quadratic equations relating daytime mean total cloud coverage amount to relative sunshine duration in different seasons. The method was validated with ground observation data for 2016 from 18 meteorological stations in the Three-River Headwaters Region of Qinghai Province, China. For individual stations, the coefficient of determination (R2) between estimated and measured sunshine was at least 0.894, the RMSE (root mean square error) was 0.977 h/day or less, the MAE (mean absolute error) was 0.824 h/day or less, the RE (relative error) was 0.150 or lower, and the value of d was 0.963 or greater, which validated that the proposed method can effectively predict daily sunshine duration. These equations can also provide higher precision estimates of regional-scale sunshine duration. This was demonstrated by comparing, for the entire study region, the spatial distribution of sunshine duration estimated from season-based equations with results from three different interpolation methods based on ground observations. Overall, the study confirms that total cloud amount measures from a geostationary satellite can be used to successfully estimate sunshine duration.

A Deep Learning Network for Cloud-to-Ground Lightning Nowcasting with Multisource Data

2020 ◽  
Vol 37 (5) ◽  
pp. 927-942
Author(s):  
Kanghui Zhou ◽  
Yongguang Zheng ◽  
Wansheng Dong ◽  
Tingbo Wang
Keyword(s):  
Deep Learning ◽  
Weather Radar ◽  
Probability Of Detection ◽  
Learning Network ◽  
Source Data ◽  
Geostationary Meteorological Satellite ◽  
Lightning Initiation ◽  
Deep Learning Network ◽  
Meteorological Satellite ◽  
Cg Lightning

AbstractPrecise and timely lightning nowcasting is still a great challenge for meteorologists. In this study, a new semantic segmentation deep learning network for cloud-to-ground (CG) lightning nowcasting, named LightningNet, has been developed. This network is based on multisource observation data, including data from a geostationary meteorological satellite, Doppler weather radar network, and CG lightning location system. LightningNet, with an encoder–decoder architecture, consists of 20 three-dimensional convolutional layers, pooling and upsampling layers, normalization layers, and a softmax classifier. The central–eastern and southern China was selected as the study area, with considerations given to the topography and spatial coverage of the weather radar and lightning observation networks. Brightness temperatures (TB) of six infrared bands from the Himawari-8 satellite, composite reflectivity mosaic, and CG lightning densities were used as the predictors because of their close relationships with lightning activity. The multisource data were first interpolated into a uniform spatial/temporal resolution of 0.05° × 0.05°/10 min, and then training and test datasets were constructed, respectively. LightningNet was trained to extract the features of lightning initiation, development, and dissipation. The evaluation results demonstrated that LightningNet was able to achieve good performance of 0–1-h lightning nowcasts using the multisource data. The probability of detection, the false alarm ratio, the area under relative operating characteristic curve, and the threat score (TS) of LightningNet with all three types of data reached 0.633, 0.386, 0.931, and 0.453, respectively. Because geostationary meteorological satellite and radar both possess the capability of capturing lightning initiation (LI) features, LightningNet also showed good performance in LI nowcasting. When all three types of data were used, more than 50% LI was predicted accurately and the TS exceeded 0.36. LightningNet’s nowcast performance using triple-source data was clearly superior to that using only single-source or dual-source data, and these findings indicate that LightningNet has good capability of combining multisource data effectively to produce more reliable lightning nowcasts.

Sign in / Sign up

Export Citation Format

Share Document