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

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.

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 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.

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.

Determination of solid Earth tide phase lag by satellite observation data

1999 ◽  
Vol 44 (5) ◽  
pp. 459-461
Author(s):  
Bin Wu ◽  
Yaozhong Zhu ◽  
Bibo Peng
Keyword(s):  
Solid Earth ◽  
Earth Tide ◽  
Satellite Observation ◽  
Observation Data ◽  
Phase Lag ◽  
Solid Earth Tide

scholarly journals Estimation of the anthropogenic aerosol emission effect on the rate of summer warming in Europe

2021 ◽  
Vol 65 (6) ◽  
pp. 734-741
Author(s):  
S. A. Lysenko ◽  
V. F. Loginov
Keyword(s):  
Dominant Role ◽  
Satellite Observation ◽  
Radiation Balance ◽  
Observation Data ◽  
Regression Equations ◽  
Total Change ◽  
Anthropogenic Aerosol ◽  
Radiation Emission ◽  
Aerosol Emission ◽  
Summer Warming

A relationship between aerosol air pollutions and summer air temperatures in Europe was studied. High correlation coefficients between the latitudinal distributions of the zone-averaged trends of the mentioned parameters were found. The potential effects of decrease in the aerosol emission on the cloud optical depth, in the air temperature, and the amount of precipitation in the territory of Europe were estimated on the basis of the obtained regression equations. It was shown that due to the aerosol emission decrease, the average summer temperature in Europe in 2000–2020 could increase by 0.53 °С, which is ~73 % of total summer warming in the region. The empirical estimates obtained in the work were confirmed by the satellite observation data and the numerical calculations of changes in radiation balance components at the top of the atmosphere. It was shown that the radiation emission decrease in the territory of Europe could increase the average radiation balance in Europe in summer months by 2.27 W/m², which is ~65 % of its total change. The increase in the carbon dioxide content in the atmosphere during the same period contributed much less to the observed change in the radiation balance (17.5 %), which supports the hypothesis about the dominant role of aerosols in summer warming in Europe.

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.

scholarly journals An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion

2019 ◽  
Vol 11 (24) ◽  
pp. 2898 ◽  
Author(s):  
Zhenyu Tan ◽  
Liping Di ◽  
Mingda Zhang ◽  
Liying Guo ◽  
Meiling Gao
Keyword(s):  
Image Fusion ◽  
Network Architecture ◽  
Satellite Observation ◽  
Superior Performance ◽  
Dynamic Monitoring ◽  
Future Research ◽  
Observation Data ◽  
Time Data ◽  
Spatiotemporal Resolution ◽  
Advantages And Disadvantages

Earth observation data with high spatiotemporal resolution are critical for dynamic monitoring and prediction in geoscience applications, however, due to some technique and budget limitations, it is not easy to acquire satellite images with both high spatial and high temporal resolutions. Spatiotemporal image fusion techniques provide a feasible and economical solution for generating dense-time data with high spatial resolution, pushing the limits of current satellite observation systems. Among existing various fusion algorithms, deeplearningbased models reveal a promising prospect with higher accuracy and robustness. This paper refined and improved the existing deep convolutional spatiotemporal fusion network (DCSTFN) to further boost model prediction accuracy and enhance image quality. The contributions of this paper are twofold. First, the fusion result is improved considerably with brand-new network architecture and a novel compound loss function. Experiments conducted in two different areas demonstrate these improvements by comparing them with existing algorithms. The enhanced DCSTFN model shows superior performance with higher accuracy, vision quality, and robustness. Second, the advantages and disadvantages of existing deeplearningbased spatiotemporal fusion models are comparatively discussed and a network design guide for spatiotemporal fusion is provided as a reference for future research. Those comparisons and guidelines are summarized based on numbers of actual experiments and have promising potentials to be applied for other image sources with customized spatiotemporal fusion networks.

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