scholarly journals Reliability Evaluation of the Joint Observation of Cloud Top Height by FY-4A and HIMAWARI-8

2021 ◽  
Vol 13 (19) ◽  
pp. 3851
Author(s):  
Qinghui Li ◽  
Xuejin Sun ◽  
Xiaolei Wang
Keyword(s):  
Measurement Method ◽  
Satellite Observation ◽  
Geostationary Satellite ◽  
Cloud Type ◽  
Surface Type ◽  
Geostationary Satellites ◽  
Good Consistency ◽  
Passive Sensors ◽  
Cloud Optical Thickness ◽  
Important Measurement

It is well known that the measurement of cloud top height (CTH) is important, and a geostationary satellite is an important measurement method. However, it is difficult for a single geostationary satellite to observe the global CTH, so joint observation by multiple satellites is imperative. We used both active and passive sensors to evaluate the reliability of joint observation of geostationary satellites, which includes consistency and accuracy. We analyzed the error of CTH of FY-4A and HIMAWARI-8 and the consistency between the two satellites and conducted research on the problem of missing measurement (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) has CTH data, but FY-4A/HIMAWARI-8 does not) of the two satellites. The results show that FY-4A and HIMAWARI-8 have good consistency and can be jointly observed, but the measurement of CTH of FY-4A and HIMAWARI-8 has large errors, and the error of FY-4A is greater than that of HIMAWIRI-8. The error of CTH is affected by the CTH, cloud optical thickness (COT) and cloud type, and the consistency between the two satellites is mainly affected by the cloud type. FY-4A and HIMAWARI-8 have the problem of missing measurement. The missing rate of HIMAWARI-8 is greater than that of FY-4A, and the missing rate is not affected by the CTH, COT and surface type. Therefore, although FY-4A and HIMAWARI-8 have good consistency, the error of CTH and the problem of missing measurement still limit the reliability of their joint observation.

Detection of Fog Involving Heavy Pollutants by Using the New Geostationary satellite Himawari-8

2020 ◽  
Author(s):  
Hongbin Wang ◽  
Zhiwei Zhang ◽  
Duanyang Liu
Keyword(s):  
Satellite Observation ◽  
Geostationary Satellite ◽  
Japan Meteorological Agency ◽  
Composite Image ◽  
Brightness Temperatures ◽  
The Difference ◽  
Grid Points ◽  
High Level ◽  
Different Levels ◽  
Better Than

<p>Himawari-8 is the new geostationary satellite of the Japan Meteorological Agency (JMA) and carries the Advanced Himawari Imager (AHI), which is greatly improved over past imagers in terms of its number of bands and its temporal/spatial resolution. In this work, two different methods for the detection of the different levels of fog involving heavy pollutants by using the Himawari-8 were developed in China. The two different methods are the method of the difference between the 11.2 mm and 3.9 mm brightness temperatures (BTD<sub>3.9-11.2</sub>) and the method of 3.9 mm Pseudo-Emissivity (ems<sub>3.9</sub>).  The 3.9 mm Pseudo-Emissivity is the ratio of the observed 3.9 mm radiance and the 3.9 mm blackbody radiance calculated using the 11.2 mm brightness temperature. We identified the parameters optimal threshold at the 2400 stations and the grid points using the BTD<sub>3.9-11.2</sub> and ems<sub>3.9</sub> for different levels of fog involving heavy pollutants. Results on land and sea from the two methods were compared with surface observations from 2400 weather stations in China and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) VFM (Vertical Feature Mask) products. The results show that both the method of BTD<sub>3.9-11.2</sub> and the method of ems<sub>3.9</sub> can accurately identify the different levels of fog involving heavy pollutants and the accuracy of ems<sub>3.9 </sub>method is slightly better than the BTD<sub>3.9-11.2</sub>. The accuracy of two methods has increased significantly and the false alarm rate has significantly decreased with the decrease of the visibility. When the visibility is less than 50 m, the HR, FAR and KSS of the BTD<sub>3.9-11.2</sub> method (the ems<sub>3.9 </sub>method) were 0.89 (0.90), 0.15 (0.15) and 0.74 (0.75), respectively. When mid- or high-level clouds were removed using surface temperature of the ground observations, the HR and KSS of two methods for the different levels of fog has increased significantly, and the FAR has significantly decreased. When the visibility is less than 1000 m, the HR of the BTD<sub>3.9-11.2</sub> method (the ems<sub>3.9 </sub>method) is increased to 0.81(0.85) from 0.71 (0.74), the FAR is decreased to 0.12 (0.13) from 0.27 (0.28), and the KSS is increased to 0.69 (0.72) from 0.44 (0.46). The KSS of two method increase by 0.23 and 0.26, respectively. Three cases analysis show that the fog area can be clearly identified by using the BTD<sub>3.9-11.2</sub>, ems<sub>3.9</sub> and RGB composite image. The results of the detection of sea fog by using Himawari-8 data and using CALIPSO VFM products have consistency.</p>

scholarly journals Geolocation Correction for Geostationary Satellite Observations by a Phase-Only Correlation Method Using a Visible Channel

2020 ◽  
Vol 12 (15) ◽  
pp. 2472
Author(s):  
Hideaki Takenaka ◽  
Taiyou Sakashita ◽  
Atsushi Higuchi ◽  
Teruyuki Nakajima
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Ground Truth ◽  
Satellite Observation ◽  
Geostationary Satellite ◽  
Satellite Observations ◽  
Physical Analysis ◽  
Observation Data ◽  
Time Analysis ◽  
Real Time Analysis

This study describes a high-speed correction method for geolocation information of geostationary satellite data for accurate physical analysis. Geostationary satellite observations with high temporal resolution provide instantaneous analysis and prompt reports. We have previously reported the quasi real-time analysis of solar radiation at the surface and top of the atmosphere using geostationary satellite data. Estimating atmospheric parameters and surface albedo requires accurate geolocation information to estimate the solar radiation accurately. The physical analysis algorithm for Earth observations is verified by the ground truth. In particular, downward solar radiation at the surface is validated by pyranometers installed at ground observation sites. The ground truth requires that the satellite observation data pixels be accurately linked to the location of the observation equipment on the ground. Thus, inaccurate geolocation information disrupts verification and causes complex problems. It is difficult to determine whether error in the validation of physical quantities arises from the estimation algorithm, satellite sensor calibration, or a geolocation problem. Geolocation error hinders the development of accurate analysis algorithms; therefore, accurate observational information with geolocation information based on latitude and longitude is crucial in atmosphere and land target analysis. This method provides the basic data underlying physical analysis, parallax correction, etc. Because the processing speed is important in geolocation correction, we used the phase-only correlation (POC) method, which is fast and maintains the accuracy of geolocation information in geostationary satellite observation data. Furthermore, two-dimensional fast Fourier transform allowed the accurate correction of multiple target points, which improved the overall accuracy. The reference dataset was created using NASA’s Shuttle Radar Topography Mission 1-s mesh data. We used HIMAWARI-8/Advanced HIMAWARI Imager data to demonstrate our method, with 22,709 target points for every 10-min observation and 5826 points for every 2.5 min observation. Despite the presence of disturbances, the POC method maintained its accuracy. Column offset and line offset statistics showed stability and characteristic error trends in the raw HIMAWARI standard data. Our method was sufficiently fast to apply to quasi real-time analysis of solar radiation every 10 and 2.5 min. Although HIMAWARI-8 is used as an example here, our method is applicable to all geostationary satellites. The corrected HIMAWARI 16 channel gridded dataset is available from the open database of the Center for Environmental Remote Sensing (CEReS), Chiba University, Japan. The total download count was 50,352,443 on 8 July 2020. Our method has already been applied to NASA GeoNEX geostationary satellite products.

Measurement of Laser Ablation Micro Impulse Using the Torsion Pendulum Interferometry

2011 ◽  
Vol 301-303 ◽  
pp. 1078-1082
Author(s):  
Ji Fei Ye ◽  
Guang Yu Wang ◽  
Dian Kai Wang
Keyword(s):  
Laser Ablation ◽  
Measurement Technique ◽  
Laser Plasma ◽  
Measurement Method ◽  
Torsion Pendulum ◽  
Differential Interferometry ◽  
Plasma Thruster ◽  
Important Measurement

A method is described for measuring the micro impulse induced by the laser ablation. This method is based upon the torsion pendulum interferometry technique. The method measures the micro impulse through the detection of the swinging angle of torsion pendulum. The swinging angle is obtained by the laser differential interferometry. For the 10-4~10-7 magnitude micro-impulse, It could be the important measurement method in the research of micro laser plasma thruster (mLPT). The results of some preliminary experiments are presented with detailed reference to experiment methodology and accuracy. The measurement technique is well suited to cases seeking the measurement of mN×s magnitude micro-impulse.

scholarly journals Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

2008 ◽  
Vol 8 (5) ◽  
pp. 1231-1248 ◽  
Author(s):  
B. H. Kahn ◽  
M. T. Chahine ◽  
G. L. Stephens ◽  
G. G. Mace ◽  
R. T. Marchand ◽  
...  
Keyword(s):  
Lower Layer ◽  
Cloud Amount ◽  
Satellite Observation ◽  
Retrieval Algorithm ◽  
Cloud Type ◽  
Atmospheric Infrared Sounder ◽  
Cloud Height ◽  
Wide Range ◽  
Low Cloud ◽  
Height Fields

Abstract. The precision of the two-layer cloud height fields derived from the Atmospheric Infrared Sounder (AIRS) is explored and quantified for a five-day set of observations. Coincident profiles of vertical cloud structure by CloudSat, a 94 GHz profiling radar, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), are compared to AIRS for a wide range of cloud types. Bias and variability in cloud height differences are shown to have dependence on cloud type, height, and amount, as well as whether CloudSat or CALIPSO is used as the comparison standard. The CloudSat-AIRS biases and variability range from −4.3 to 0.5±1.2–3.6 km for all cloud types. Likewise, the CALIPSO-AIRS biases range from 0.6–3.0±1.2–3.6 km (−5.8 to −0.2±0.5–2.7 km) for clouds ≥7 km (<7 km). The upper layer of AIRS has the greatest sensitivity to Altocumulus, Altostratus, Cirrus, Cumulonimbus, and Nimbostratus, whereas the lower layer has the greatest sensitivity to Cumulus and Stratocumulus. Although the bias and variability generally decrease with increasing cloud amount, the ability of AIRS to constrain cloud occurrence, height, and amount is demonstrated across all cloud types for many geophysical conditions. In particular, skill is demonstrated for thin Cirrus, as well as some Cumulus and Stratocumulus, cloud types infrared sounders typically struggle to quantify. Furthermore, some improvements in the AIRS Version 5 operational retrieval algorithm are demonstrated. However, limitations in AIRS cloud retrievals are also revealed, including the existence of spurious Cirrus near the tropopause and low cloud layers within Cumulonimbus and Nimbostratus clouds. Likely causes of spurious clouds are identified and the potential for further improvement is discussed.

GeoNEX: Earth observations from operational geostationary satellite systems

2020 ◽  
Author(s):  
Rama Nemani ◽  
Tsengdar Lee ◽  
Satya Kalluri ◽  
Kazuhito Ichii ◽  
Jong-Min Yeom
Keyword(s):  
High Frequency ◽  
Spectral Characteristics ◽  
Ocean Color ◽  
Geostationary Satellite ◽  
Data Sets ◽  
Satellite Systems ◽  
Earth Observations ◽  
Geostationary Satellites ◽  
Operational Decision Making ◽  
New Research

&lt;p&gt;The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort involving scientists from NASA, NOAA, JAXA/JMA, KMA/KARI exploring the feasibility of producing EOS-quality research products from operational geostationary satellite systems for climate monitoring. The latest generation of geostationary satellites (Himawari 8/9, GOES-16/17, Fengyun-4, GeoKompsat-2A) carry sensors that closely mimic the spatial and spectral characteristics of widely used polar-orbiting, global monitoring sensors such as MODIS and VIIRS. More importantly, they provide observations as frequently as 5-15 minutes. Data from various currently operating geostationary platforms provide a geo-ring of hyper-temporal, multispectral observations. Such high frequency observations, particularly when combined with data from polar orbiters, offer exciting possibilities for improving the retrieval of geophysical variables by overcoming cloud cover, enable studies of diurnally varying phenomena over land, in the atmosphere and the oceans, and help in operational decision-making in agriculture, hydrology and disaster management. Beyond the weather-focused geo-sensors, a number of new spectrometers are scheduled to be launched in the next five years in geostationary orbit to study atmospheric pollution (GEMS, TEMPO), ocean color (GOCI) and carbon cycling (GeoCARB). This talk will highlight new research, data sets, algorithms and computational platforms that utilize data from geostationary satellites to advance our ability to monitor the environment and create climate resiliency.&lt;/p&gt;

scholarly journals A Parallax Shift Effect Correction Based on Cloud Height for Geostationary Satellites and Radar Observations

2020 ◽  
Vol 12 (3) ◽  
pp. 365 ◽  
Author(s):  
Tomasz Bieliński
Keyword(s):  
Simulation Experiment ◽  
Solution Procedure ◽  
Satellite Imaging ◽  
Geodetic Coordinates ◽  
Geostationary Satellites ◽  
New Methods ◽  
Cloud Height ◽  
Before And After ◽  
Cloud Optical Thickness ◽  
Shift Effect

The effect of cloud parallax shift occurs in satellite imaging, particularly for high angles of satellite observations. This study demonstrates new methods of parallax effect correction for clouds observed by geostationary satellites. The analytical method that could be found in literature, namely the Vicente et al./Koenig method, is presented at the beginning. It approximates a cloud position using an ellipsoid with semi-axes increased by the cloud height. The error values of this method reach up to 50 meters. The second method, which is proposed by the author, is an augmented version of the Vicente et al./Koenig approach. With this augmentation, the error can be reduced to centimeters. The third method, also proposed by the author, incorporates geodetic coordinates. It is described as a set of equations that are solved with the numerical method, and its error can be driven to near zero by adjusting the count of iterations. A sample numerical solution procedure with application of the Newton method is presented. Also, a simulation experiment that evaluates the proposed methods is described in the paper. The results of an experiment are described and contrasted with current technology. Currently, operating geostationary Earth Observation (EO) satellite resolutions vary from 0.5 km up to 8 km. The pixel sizes of these satellites are much greater than for maximal error of the least precise method presented in this paper. Therefore, the chosen method will be important when the resolution of geostationary EO satellites reaches 50 m. To validate the parallax correction, procedure data from on-ground radars and the Meteosat Second Generation (MSG) satellite, which describes stormy events, was compared before and after correction. Comparison was performed by correlating the logarithm of the cloud optical thickness (COT) with radar reflectance in dBZ (radar reflectance – Z in logarithmic form).

Geostationary Satellite Coverage for Surface Applications of Differential Global Positioning system

1998 ◽  
Vol 1625 (1) ◽  
pp. 35-40
Author(s):  
James A. Arnold
Keyword(s):  
Global Positioning System ◽  
Urban Areas ◽  
Geostationary Satellite ◽  
Tree Canopy ◽  
Positioning System ◽  
Irregular Terrain ◽  
Geostationary Satellites ◽  
Global Positioning ◽  
Signal Fading ◽  
Differential Global Positioning System

The coverage issues surrounding the use of geostationary satellites for surface applications using Differential Global Positioning System (DGPS) data are examined. The Federal Aviation Administration’s Wide Area Augmentation System plan to use geostationary satellites to provide differential corrections for aviation users is presented. Impediments to coverage include man-made structures, irregular terrain (i.e., mountains), and attenuation or signal fading due to tree canopy. Each of the impediments is examined and an assessment is made of their effects on coverage. Various user groups require ubiquitous coverage, and a geostationary satellite broadcast of corrections does not provide this. Large gaps will exist in rural and urban areas, and signal fading due to tree canopy will be significant over much of the country. Alternative communication techniques, such as the U.S. Coast Guard Low Frequency Radiobeacon DGPS service, should be used to meet surface user requirements.

scholarly journals Comparison of Aqua/Terra MODIS and Himawari-8 Satellite Data on Cloud Mask and Cloud Type Classification Using Split Window Algorithm

2019 ◽  
Vol 11 (24) ◽  
pp. 2944 ◽  
Author(s):  
Babag Purbantoro ◽  
Jamrud Aminuddin ◽  
Naohiro Manago ◽  
Koichi Toyoshima ◽  
Nofel Lagrosas ◽  
...  
Keyword(s):  
Image Data ◽  
Satellite Observation ◽  
Radiation Budget ◽  
Cloud Type ◽  
Cloud Classification ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Split Window Algorithm ◽  
Cloud Mask ◽  
Type Classification ◽  
Cloud Analysis

Cloud classification is not only important for weather forecasts, but also for radiation budget studies. Although cloud mask and classification procedures have been proposed for Himawari-8 Advanced Himawari Imager (AHI), their applicability is still limited to daytime imagery. The split window algorithm (SWA), which is a mature algorithm that has long been exploited in the cloud analysis of satellite images, is based on the scatter diagram between the brightness temperature (BT) and BT difference (BTD). The purpose of this research is to examine the usefulness of the SWA for the cloud classification of both daytime and nighttime images from AHI. We apply SWA also to the image data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra to highlight the capability of AHI. We implement the cloud analysis around Japan by employing band 3 (0.469 μm) of MODIS and band 1 (0.47 μm) of AHI for extracting the cloud-covered regions in daytime. In the nighttime case, the bands that are centered at 3.9, 11, 12, and 13 µm are utilized for both MODIS and Himawari-8, with somewhat different combinations for land and sea areas. Thus, different thresholds are used for analyzing summer and winter images. Optimum values for BT and BTD thresholds are determined for the band pairs of band 31 (11.03 µm) and 32 (12.02 µm) of MODIS (SWA31-32) and band 13 (10.4 µm) and 15 (12.4 µm) of AHI (SWA13-15) in the implementation of SWA. The resulting cloud mask and classification are verified while using MODIS standard product (MYD35) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. It is found that MODIS and AHI results both capture the essential characteristics of clouds reasonably well in spite of the relatively simple scheme of SWA based on four threshold values, although a broader spread of BTD obtained with Himawari-8 AHI (SWA13-15) could possibly lead to more consistent results for cloud-type classification than SWA31-32 based on the MODIS sensors.

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