scholarly journals USING SATELLITE OBSERVATION FOR EARLY WARNING OF CONVECTIVE STORM IN TEHRAN

2015 ◽  
Vol XL-1-W5 ◽  
pp. 565-569
Author(s):  
E. Owlad
Keyword(s):  
Weather Forecasting ◽  
Satellite Observation ◽  
Observation Data ◽  
Atmospheric Models ◽  
Convective Storm ◽  
Synoptic Station ◽  
Convective Storms ◽  
Large Application ◽  
Low Levels ◽  
Sudden Decrease

Severe convective storms are responsible for large amount of damage each year around the world. They form an important part of the climate system by redistributing heat, moisture, and trace gases, as well as producing large quantities of precipitation. As these extreme and rare events are in mesoscale there is many uncertainty in predicting them and we can’t rely on just models. On the other hand, remote sensing has a large application in Meteorology and near real time weather forecasting, especially in rare and extreme events like convective storms that might be difficult to predict with atmospheric models. On second of June 2014, near 12UTC a sudden and strong convective storm occurred in Tehran province that was not predicted, and caused economic and human losses. In This research we used satellite observations along with synoptic station measurements to predict and monitor this storm. Results from MODIS data show an increase in the amount of cloudiness and also aerosol optical depth and sudden decrease in cloud top temperature few hours before the storm occurs. EUMETSAT images show the governing of convection before the storm occurs. With combining the observation data that shows Lake of humidity and high temperature in low levels with satellite data that reveals instability in high levels that together caused this convective, we could track the storm and decrease the large amount of damage.

Effect of the Ushant SST front on regional weather forecasting in the event of anticyclonic events

2020 ◽  
Author(s):  
Francoise Orain ◽  
Marie-Noelle Bouin ◽  
Jean-Luc Redelsperger ◽  
Valérie Garnier
Keyword(s):  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Forecast ◽  
Satellite Observation ◽  
Observation Data ◽  
Satellite Sst ◽  
Hydrostatic Model ◽  
Definition Of ◽  
The Impact ◽  
North And South

<p>The representation of the Ushant front in Meteo-France numerical models is not accurate. The aim of this study is to evaluate the impact of a better representation of this front derived from SST satellite observation data on the weather forecast in Brittany.</p><p> </p><p>The study consisted in finding and selecting cases from 2016 to 2018 where the Ushant front was present in satellite SST analysis (high spatial and temporal resolution ) with differences in weather pattern between North and South Brittany. Then compare this to the operational Arome model (Meteo France non hydrostatic model).</p><p>Situations of disagreement between the model and the observations were selected. Some weather forecast simulations with Mesonh model (very close to Arome) were performed on these cases with a better definition of the Ushant front. We present some results.</p><p> </p><p> </p>

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 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 Infrasound Observations of Atmospheric Disturbances Due to a Sequence of Explosive Eruptions at Mt. Shinmoedake in Japan on March 2018

2020 ◽  
Vol 12 (4) ◽  
pp. 728
Author(s):  
Mario Batubara ◽  
Masa-yuki Yamamoto
Keyword(s):  
Acoustic Velocity ◽  
Propagation Direction ◽  
Horizontal Wind ◽  
Explosive Eruptions ◽  
Observation Data ◽  
Apparent Velocity ◽  
Atmospheric Models ◽  
Wind Speeds ◽  
Propagation Modeling ◽  
Atmospheric Disturbances

Thirty infrasound sensors have been operated over Japan since 2015. We developed the irregular array data processing in order to detect and estimate the parameters of the arrival source waves by using infrasound data related to the sequence of the volcanic eruption at Mt. Shinmoedake in March 2018. We found that the apparent velocity at the ground was equal to the acoustic velocity at particular reflection levels. The results were confirmed through a comparison of the findings of the apparent velocity with a wave propagation simulation on the basis of the azimuth, infrasound time arrivals, and the state of the atmospheric background using global atmospheric models. In addition, simple ideas for estimating horizontal wind speeds at certain atmospheric altitudes based on infrasound observation data and their validation and comparison were presented. The calculated upper wind speed and wind observed by radiosonde measurements were found to have a qualitative agreement. Propagation modeling for these events estimated celerities in the propagation direction to the sensors that were consistent with the tropospheric and stratospheric ducting. This study could inspire writers, in particular, and readers, in general, to take advantage of the benefits of infrasound wave remote-sensing for the study of the Earth’s atmospheric dynamics.

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.

The Development of Meteorological and Oceanographic Data Collection and Recording System Operating on Training Ship

2014 ◽  
Author(s):  
Michio Fujii ◽  
Mitsuru Hayashi ◽  
Misako Urakami ◽  
Nobukazu Wakabayashi
Keyword(s):  
Data Collection ◽  
Weather Forecasting ◽  
High Reliability ◽  
General Purpose ◽  
Observation System ◽  
Observation Data ◽  
Special Equipment ◽  
Merchant Ship ◽  
Marine Environmental ◽  
System Operating

The observation at sea for marine meteorology is achieved by weather reports from merchant ship’s crew every 3 or 6 hours, mainly. However, the number of available observation data is insufficient for weather forecasting and marine environmental simulation, compared with landside. Nowadays, the special data collection function is required if the automatic observation data collection system is installed on ship. But, it is difficult to install special equipment onto general merchant ship. Therefore, we develop a prototype marine observation system, which can be installed various types of ships easily without any special data collection function for improving data collection source and/or period of the observation at sea. In this paper, a) the configuration of high reliability and durability marine observation system by using general-purpose PC and general meteorological / oceanographic sensors, b) outlook of utilizing the data, which collected by this system, are described.

Supporting reduction of risks of tailings dams using earth observation data

2020 ◽  
Author(s):  
Mark Davison ◽  
Marta Roca ◽  
Gregor Petkovsek
Keyword(s):  
Weather Forecasting ◽  
Flood Hazard ◽  
Disaster Risk ◽  
Earth Observation ◽  
Mitigation Measures ◽  
Dam Failure ◽  
Observation Data ◽  
Irreversible Damage ◽  
Loss Of Life ◽  
Tailings Dams

<p>Tailings dams are earth embankments used to store toxic mine waste and effluent. Their failure, as already seen in January 2019 with the fatal failure of Brumadinho dam in Brazil, can cause loss of life, irreversible damage to ecosystems and large economic damages. In countries with limited resources, it is challenging for the authorities to be able to assess the risk and effectively monitor this type of infrastructure, especially when located in remote areas.</p><p>We are developing DAMSAT (Dam Monitoring from SATellites), a web-based system for a sustainable and cost effective way of remotely monitor tailings and water retention dams to support early decision making and reduce the social, economic and environmental impacts downstream of potential failures.</p><p>DAMSAT monitors the displacement of the structures using earth observation technologies such as Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) technologies, combined with real-time in-situ devices. These observations combined with weather forecasting tools allow the issue of alerts for unusual behaviour or weather conditions that could lead to dam failure. These alerts are part of the Disaster Risk Management cycle to trigger the implementation of mitigation measures to reduce the likelihood of failure of the dam or the potential consequences downstream.  </p><p>In order to have a better understanding of these potential consequences and provide all the information necessary for asset managers to take decisions, DAMSAT also assesses the hazard component of disaster risk due to dam failure using a set of modelling tools. A dam breach simulation model (EMBREA) is combined with a mud flow model to spread the flood hazard downstream of the dam if a failure occurs.  The consequences of the flood are assessed in terms of loss of life using an evacuation model, the Life Safety Model. Different flood warning scenarios and evacuation strategies are mapped to inform emergency planning.</p><p>DAMSAT is currently being piloted in two mining regions in Peru with the involvement of government organisations and other relevant stakeholders. </p>

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