scholarly journals Geostationary Ocean Color Imager (GOCI) Marine Fog Detection in Combination with Himawari-8 Based on the Decision Tree

2020 ◽  
Vol 12 (1) ◽  
pp. 149 ◽  
Author(s):  
Donghee Kim ◽  
Myung-Sook Park ◽  
Young-Je Park ◽  
Wonkook Kim
Keyword(s):  
Decision Tree ◽  
Spatial Variability ◽  
Traffic Management ◽  
Optimal Algorithm ◽  
Ocean Color ◽  
Satellite Observation ◽  
Observation Data ◽  
Atmospheric Conditions ◽  
Marine Traffic ◽  
Cloud Removal

Geostationary Ocean Color Imager (GOCI) observations are applied to marine fog (MF) detection in combination with Himawari-8 data based on the decision tree (DT) approach. Training and validation of the DT algorithm were conducted using match-ups between satellite observations and in situ visibility data for three Korean islands. Training using different sets of two satellite variables for fog and nonfog in 2016 finally results in an optimal algorithm that primarily uses the GOCI 412-nm Rayleigh-corrected reflectance (Rrc) and its spatial variability index. The algorithm suitably reflects the optical properties of fog by adopting lower Rrc and spatial variability levels, which results in a clear distinction from clouds. Then, cloud removal and fog edge detection in combination with Himawari-8 data enhance the performance of the algorithm, increasing the hit rate (HR) of 0.66 to 1.00 and slightly decreasing the false alarm rate (FAR) of 0.33 to 0.31 for the cloudless samples among the 2017 validation cases. Further evaluation of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation data reveals the reliability of the GOCI MF algorithm under optically complex atmospheric conditions for classifying marine fog. Currently, the high-resolution (500 m) GOCI MF product is provided to decision-makers in governments and the public sector, which is beneficial to marine traffic management.

scholarly journals Comparison of ISS–CATS and CALIPSO–CALIOP Characterization of High Clouds in the Tropics

2020 ◽  
Vol 12 (23) ◽  
pp. 3946
Author(s):  
Pasquale Sellitto ◽  
Silvia Bucci ◽  
Bernard Legras
Keyword(s):  
Optical Properties ◽  
Space Station ◽  
Satellite Observation ◽  
Observation Data ◽  
Upper Troposphere ◽  
Cloud Properties ◽  
Synchronous Orbit ◽  
The Tropics ◽  
High Clouds

Clouds in the tropics have an important role in the energy budget, atmospheric circulation, humidity, and composition of the tropical-to-global upper-troposphere–lower-stratosphere. Due to its non-sun-synchronous orbit, the Cloud–Aerosol Transport System (CATS) onboard the International Space Station (ISS) provided novel information on clouds from space in terms of overpass time in the period of 2015–2017. In this paper, we provide a seasonally resolved comparison of CATS characterization of high clouds (between 13 and 18 km altitude) in the tropics with well-established CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation) data, both in terms of clouds’ occurrence and cloud optical properties (optical depth). Despite the fact that cloud statistics for CATS and CALIOP are generated using intrinsically different local overpass times, the characterization of high clouds occurrence and optical properties in the tropics with the two instruments is very similar. Observations from CATS underestimate clouds occurrence (up to 80%, at 18 km) and overestimate the occurrence of very thick clouds (up to 100% for optically very thick clouds, at 18 km) at higher altitudes. Thus, the description of stratospheric overshoots with CATS and CALIOP might be different. While this study hints at the consistency of CATS and CALIOP clouds characterizaton, the small differences highlighted in this work should be taken into account when using CATS for estimating cloud properties and their variability in the tropics.

Simulation of runoff for the Black Volta Basin using satellite observation data

2012 ◽  
Vol 10 (3) ◽  
pp. 245-254 ◽  
Author(s):  
Salamatu Shaibu ◽  
Samuel Nii Odai ◽  
Kwaku Amaning Adjei ◽  
Edward Matthew Osei ◽  
Frank Ohene Annor
Keyword(s):  
Satellite Observation ◽  
Observation Data ◽  
Volta Basin

Variational assimilation of observation data in the mathematical model of the Baltic Sea dynamics

2015 ◽  
Vol 30 (4) ◽  
Author(s):  
Valeriy I. Agoshkov ◽  
Eugene I. Parmuzin ◽  
Vladimir B. Zalesny ◽  
Victor P. Shutyaev ◽  
Natalia B. Zakharova ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Heat Flux ◽  
Baltic Sea ◽  
Satellite Observation ◽  
Sea Surface ◽  
Observation Data ◽  
The Baltic Sea ◽  
Variational Assimilation ◽  
The Baltic ◽  
The Mathematical Model

AbstractA mathematical model of the dynamics of the Baltic Sea is considered. A problem of variational assimilation of sea surface temperature (SST) data is formulated and studied. Based on variational assimilation of satellite observation data, an algorithm solving the inverse problem of heat flux restoration on the interface of two media is proposed. The results of numerical experiments reconstructing the heat flux functions in the problem of variational assimilation of SST observation data are presented. The influence of SST assimilation on other hydrodynamic parameters of the model is considered.

scholarly journals Optimal Estimation MSG-SEVIRI Clear-Sky Total Column Water Vapour Retrieval Using the Split Window Difference

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1256
Author(s):  
Jan El Kassar ◽  
Cintia Carbajal Henken ◽  
Rene Preusker ◽  
Jürgen Fischer
Keyword(s):  
Water Vapour ◽  
Large Scale ◽  
Pearson Correlation ◽  
Microwave Radiometer ◽  
Optimal Estimation ◽  
Forward Model ◽  
Observation Data ◽  
Atmospheric Conditions ◽  
Uncertainty Estimates ◽  
Total Column

A new algorithm for the retrieval of day-time total column water vapour (TCWV) from measurements of a MSG-SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager) instrument is presented. The retrieval is based on a forward operator, at the core of which lies Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV). This forward model relates TCWV and surface temperature to brightness temperatures in the split window at 11 and 12µm with the use of a first guess for temperature and humidity profiles from the ERA5 reanalysis. The forward model is then embedded in a full Optimal Estimation (OE) method, which yields pixel by pixel uncertainty estimates and performance indicators. The algorithm is applicable to any instrument which features the split window configuration, given a first guess for atmospheric conditions (i.e., from NWP) and an estimate of surface emissivity at 11 µm. The algorithm was developed within the framework of RealPEP (Near-Realtime Quantitative Precipitation Estimation and Prediction) in which the advancement of the estimation and nowcasting of extreme precipitation and flooding in Germany are studied. Thus, processing and validation has been limited to the German domain. Three independent ground-based TCWV observation data sets were used as reference, i.e., AERONET (Aerosol Robotic Network), GNSS Germany (Global Navigation Satellite System) and measurements from two MWR (Microwave Radiometer) sites. The validation concludes with good agreement, with absolute biases between 0.11 and 2.85 kg/m2, root mean square deviations (rmsds) between 1.63 and 3.24 kg/m2 and Pearson correlation coefficients ranging from 0.96 to 0.98. The retrievals uncertainty estimates were evaluated against AERONET. The comparison suggests that, in sum, uncertainties are estimated well, while still some error sources seem to be over- and underestimated. In limited case studies it could be shown that SEVIRI TCWV is capable to both display large scale variabilities in water vapour fields and reproduce the daily course of water vapour exposed by ground-based observations.

Remote-Sensing Monitoring of Green Tide and Its Drifting Trajectories in Yellow Sea Based on Observation Data of Geostationary Ocean Color Imager

2020 ◽  
Vol 40 (3) ◽  
pp. 0301001
Author(s):  
陈莹 Chen Ying ◽  
孙德勇 Sun Deyong ◽  
张海龙 Zhang Hailong ◽  
王胜强 Wang Shengqiang ◽  
丘仲锋 Qiu Zhongfeng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Ocean Color ◽  
Green Tide ◽  
Observation Data ◽  
Remote Sensing Monitoring

scholarly journals A Satellite Observation Data Transmission Scheduling Algorithm Oriented to Data Topics

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Hao Chen ◽  
Baorong Zhai ◽  
Jiangjiang Wu ◽  
Chun Du ◽  
Jun Li
Keyword(s):  
Data Transmission ◽  
Domain Knowledge ◽  
Scheduling Algorithm ◽  
Satellite Observation ◽  
Observation Data ◽  
Transmission Scheduling ◽  
Expected Time ◽  
Knowledge Based ◽  
Sensing Applications ◽  
Speed Up

The scheduling of Earth Observation Satellite (EOS) data transmission is a complex combinatorial optimization problem. With the development of remote sensing applications, a new special requirement named data transmission oriented to topics has appeared. It supposes that the data obtained from each observation activity by satellites belong to certain observation data topics, and every observation data topic has completeness and timeliness requirements. Unless all of the observation data belonging to one topic has been transmitted to the ground before the expected time, the value of the observation data will be decayed sharply and only a part of the rewards (or even no reward) for the data transmission will be obtained. Current researches do not meet the new data topic transmission requirements well. Based on the characteristics of the problem, a mathematic scheduling model is established, and a novel hybrid scheduling algorithm based on evolutionary computation is proposed. In order to further enhance the performance and speed up the convergence process of our algorithm, a domain-knowledge-based mutation operator is designed. Quantitative experimental results show that the proposed algorithm is more effective to solve the satellite observation data topic transmission scheduling problem than that of the state-of-the-art approaches.

Geographical area selection and construction of a corresponding routing grid used for in-flight management system flight trajectory optimization

2016 ◽  
Vol 231 (5) ◽  
pp. 809-822
Author(s):  
Bogdan D Dancila ◽  
Ruxandra M Botez
Keyword(s):  
Traffic Management ◽  
Trajectory Optimization ◽  
Management System ◽  
Optimization Algorithms ◽  
Geographical Area ◽  
Atmospheric Conditions ◽  
Flight Trajectory ◽  
Trajectory Data ◽  
Design Variables ◽  
Grid Nodes

This paper proposes a new method for selecting an ellipse-shaped geographical area and constructing a routing grid that circumscribes the contour of the designated area. The resulting grid describes the set of points used by the flight trajectory optimization algorithms to determine an aircraft’s optimal flight trajectory as a function of given particular atmospheric conditions. This method was developed with the intent of its employment in the context of Flight Management System trajectory optimization algorithms, but can be used in Air Traffic Management environments as well. The routing grid limits the trajectory’s maximal total ground distance (between the departure and destination airports), maximizes the geographical area (for a better consideration of the wind conditions) and minimizes the number of grid nodes. The novelty of the proposed method resides in the fact that it allows a distinct and independent parameterization and control of the ellipse’s total surface, and the required size of the take-off/landing procedure maneuvering areas at the departure/destination airports. The ellipse contour constructed using this method is, therefore, well adapted to the particular configuration of the trajectory for which the optimization is performed. Each design variables’ influence is presented, as well as a set of routing grids generated for trajectories corresponding to different total flight distances, and were further compared with real flight trajectory data retrieved using the website Flight Aware.

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