scholarly journals Multiscale Meteorological Systems Resulted in Meteorological Tsunamis

Tsunami ◽  
10.5772/63762 ◽  
2016 ◽  
Author(s):  
Kenji Tanaka ◽  
Daiki Ito
Keyword(s):  
Meteorological Systems ◽  
Meteorological Tsunamis

Conservation Laws on Surfaces: Meteorological Systems—Shallow-Water

2011 ◽  
Author(s):  
Matania Ben-Artzi ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
Ch. Tsitouras ◽  
Zacharias Anastassi
Keyword(s):  
Conservation Laws ◽  
Shallow Water ◽  
Meteorological Systems

Multiscale Convective Organization and the YOTC Virtual Global Field Campaign

2012 ◽  
Vol 93 (8) ◽  
pp. 1171-1187 ◽  
Author(s):  
Mitchell W. Moncrieff ◽  
Duane E. Waliser ◽  
Martin J. Miller ◽  
Melvyn A. Shapiro ◽  
Ghassem R. Asrar ◽  
...  
Keyword(s):  
Time Scales ◽  
Climate Models ◽  
Water Cycle ◽  
Building Blocks ◽  
Tropical Convection ◽  
Global Water Cycle ◽  
Weather And Climate ◽  
Impact Phenomena ◽  
Meteorological Systems ◽  
The Tropics

The Year of Tropical Convection (YOTC) project recognizes that major improvements are needed in how the tropics are represented in climate models. Tropical convection is organized into multiscale precipitation systems with an underlying chaotic order. These organized systems act as building blocks for meteorological events at the intersection of weather and climate (time scales up to seasonal). These events affect a large percentage of the world's population. Much of the uncertainty associated with weather and climate derives from incomplete understanding of how meteorological systems on the mesoscale (~1–100 km), synoptic scale (~1,000 km), and planetary scale (~10,000 km) interact with each other. This uncertainty complicates attempts to predict high-impact phenomena associated with the tropical atmosphere, such as tropical cyclones, the Madden–Julian oscillation, convectively coupled tropical waves, and the monsoons. These and other phenomena influence the extratropics by migrating out of the tropics and by the remote effects of planetary waves, including those generated by the MJO. The diurnal and seasonal cycles modulate all of the above. It will be impossible to accurately predict climate on regional scales or to comprehend the variability of the global water cycle in a warmer world without comprehensively addressing tropical convection and its interactions across space and time scales.

scholarly journals Meteotsunamis: atmospherically induced destructive ocean waves in the tsunami frequency band

2006 ◽  
Vol 6 (6) ◽  
pp. 1035-1051 ◽  
Author(s):  
S. Monserrat ◽  
I. Vilibić ◽  
A. B. Rabinovich
Keyword(s):  
Gravity Waves ◽  
Spatial Scales ◽  
World Ocean ◽  
Ocean Waves ◽  
Tsunami Waves ◽  
Atmospheric Disturbances ◽  
Source Mechanisms ◽  
Low Probability ◽  
Atmospheric Gravity ◽  
Meteorological Tsunamis

Abstract. In light of the recent enhanced activity in the study of tsunami waves and their source mechanisms, we consider tsunami-like waves that are induced by atmospheric processes rather than by seismic sources. These waves are mainly associated with atmospheric gravity waves, pressure jumps, frontal passages, squalls and other types of atmospheric disturbances, which normally generate barotropic ocean waves in the open ocean and amplify them near the coast through specific resonance mechanisms (Proudman, Greenspan, shelf, harbour). The main purpose of the present study is to describe this hazardous phenomenon, to show similarities and differences between seismic and meteorological tsunamis and to provide an overview of meteorological tsunamis in the World Ocean. It is shown that tsunamis and meteotsunamis have the same periods, same spatial scales, similar physical properties and affect the coast in a comparably destructive way. Some specific features of meteotsunamis make them akin to landslide-generated tsunamis. The generation efficiency of both phenomena depend on the Froude number (Fr), with resonance taking place when Fr~1.0. Meteotsunamis are much less energetic than seismic tsunamis and that is why they are always local, while seismic tsunamis can have globally destructive effects. Destructive meteotsunamis are always the result of a combination of several resonant factors; the low probability of such a combination is the main reason why major meteotsunamis are infrequent and observed only at some specific locations in the ocean.

Meteorological Tidal Predictions in the Mekong Estuary Using an Evolved ANN Time Series

2019 ◽  
Vol 53 (6) ◽  
pp. 27-34
Author(s):  
Tim Chen ◽  
C.Y.J. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Variation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Systems ◽  
Modeling And Forecasting ◽  
Physically Based ◽  
Artificial Neural Network Ann ◽  
Meteorological Tsunamis

AbstractThe reproduction of meteorological waves utilizing physically based hydrodynamic models is very difficult in light of the fact that it requires enormous amounts of information, for example, hydrological and water-driven time arrangement limits, stream geometry, and balance coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a back-propagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modeling and forecasting the maximum and time variation of meteorological tsunamis in the Mekong Estuary in Vietnam. The parameters, including both the nearby climatic and breeze field factors, for finding the most extreme meteorological waves are first examined, depending on the preparation of the evolved neural systems. The time series for meteorological tsunamis are used for training and testing the models, and data for three cyclones are used for model prediction. This study finds that the proposed advanced ANN time series model is easy to utilize with display and prediction tools for simulating the time variation of meteorological tsunamis.

scholarly journals Using Evolving ANN-Based Algorithm Models for Accurate Meteorological Forecasting Applications in Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Tim Chen ◽  
N. Kapron ◽  
J. C.-Y. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Time Variation ◽  
Tidal Waves ◽  
Radial Basis ◽  
Physically Based ◽  
Artificial Neural Network Ann ◽  
Meteorological Tsunamis

The reproduction of meteorological tsunamis utilizing physically based hydrodynamic models is complicated in light of the fact that it requires large amounts of information, for example, for modelling the limits of hydrological and water driven time arrangement, stream geometry, and balanced coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a backpropagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modelling and forecasting the maximum peak and variation with time of meteorological tsunamis in the Mekong estuary in Vietnam. The parameters, including both the nearby climatic weights and the wind field factors, for finding the most extreme meteorological waves, are first examined, through the preparation of evolved neural systems. The time series of meteorological tsunamis were used for training and testing the models, and data for three cyclones were used for model prediction. Given the 22 selected meteorological tidal waves, the exact constants for the Mekong estuary, acquired through relapse investigation, are A = 9.5 × 10−3 and B = 31 × 10−3. Results showed that both the Multilayer Perceptron Network (MLP) and evolved radial basis function (ERBF) methods are capable of predicting the time variation of meteorological tsunamis, and the best topologies of the MLP and ERBF are I3H8O1 and I3H10O1, respectively. The proposed advanced ANN time series model is anything but difficult to use, utilizing display and prediction tools for simulating the time variation of meteorological tsunamis.

Monitoring Neptune's atmosphere with small and large telescopes: results for 2019

2020 ◽  
Author(s):  
Ricardo Hueso ◽  
Imke de Pater ◽  
Erandi Chavez ◽  
Amy Simon ◽  
Larry Sromovsky ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Near Infrared ◽  
Late Summer ◽  
Life Time ◽  
Space Science ◽  
The South ◽  
Absorption Bands ◽  
Single Observation ◽  
Cloud Systems ◽  
Meteorological Systems

<p>Neptune’s atmosphere is highly dynamic with atmospheric systems observable as bands and discrete cloud systems that evolve in time scales of days, weeks and years. Most of them are observed as tropospheric clouds and elevated hazes that appear highly contrasted in observations obtained in hydrogen and methane absorption bands in the red and near-infrared spectrum of the planet. Given the small size of Neptune as observed from Earth (2.3 arcsec), it is difficult to characterize most of these clouds. Basic questions such as if they are convective storms, vortices or clouds detached from atmospheric waves or bands can be difficult for an specific feature in a given observation [1]. Only Adaptive Optics or lucky-imaging instruments in 8-m telescopes or larger, and HST, can provide suitable data, but the difficulty to access enough observational time in these facilities suggests that a combination of data from several observing programs can help. Smaller telescopes can also play an important role since they can be used to follow the main cloud systems and cover the gaps between observations obtained by the larger telescopes. This can provide the life-time or drift rates of the largest meteorological systems allowing to compare observations of the same features observed months apart in the largest telescopes.</p> <p>During the last few years we have combined observations obtained from a variety of telescopes to study the major cloud systems and understand their life-time and evolution [2, 3], including those of “companion” clouds linked to rare dark vortices that are only observable in blue wavelengths from space [2, 4, 5]. In this work we present our data for 2019 which consists of the following observations:</p> <ul> <li>HST observations from the Outer Planets Atmospheres Legacy program (OPAL).</li> <li>Several sets from Keck and Lick telescopes from different programs including some relatively frequent observations from the TWILIGHT program.</li> <li>GTC observations with the HiperCam instrument doing lucky-imaging.</li> <li>Calar Alto 2.2m telescope with the PlanetCam lucky-imaging instrument.</li> <li>One single observation from Gemini while testing an AO system.</li> <li>Additional observations from the Pic du Midi 1.05 m telescope.</li> <li>Images provided by amateur astronomers and available through the PVOL [6] database.</li> </ul> <p>The combination of these data suggests more variability and less cloud activity in 2019 than in previous years with a lower number of features in the data sets obtained with smaller telescopes. We provide the identification of particular meteorological systems over late summer 2019 and present drift rates of different mid-latitude features in the south hemisphere that are close but separated enough to the Voyager zonal winds to deserve attention. Other cloud systems in the south polar region and north tropics seem to follow the Voyager wind profile.</p> <p>Future punctual observations achievable with new observational facilities such as the JWST will benefit from the evolutionary time-lines of the major cloud systems of Neptune and their drift rates in the atmosphere provided by similar future campaigns.</p> <p><strong>References</strong></p> <p>[1] Hueso and Sánchez-Lavega, Atmospheric Dynamics and Vertical Structure of Uranus and Neptune's weather layers. Space Science Reviews, 2019.</p> <p>[2] Hueso et al., Neptune long-lived atmospheric features in 2013-2015 from small (28-cm) to large (10-m) telescopes. Icarus, 2017.</p> <p>[3] Molter et al., Analysis of Neptune's 2017 Bright Equatorial Storm, Icarus, 2019.</p> <p>[4] Wong et al., A New Dark vortex on Neptune, The Astronomical Journal, 2018.</p> <p>[5] Hsu et al., Lifetimes and Occurrence Rates of Dark Vortices on Neptune from 25 Years of Hubble Space Telescope Images, The Astronomical Journal, 2018.</p> <p>[6] Hueso et al., The Planetary Virtual Observatory and Laboratory (PVOL) and its integration into the Virtual European Solar and Planetary Access (VESPA), Planetary Space Science, 2018.</p>

Simultaneous meteorological tsunamis and storm surges at Buenos Aires coast, southeastern South America

2013 ◽  
Vol 74 (1) ◽  
pp. 269-280 ◽  
Author(s):  
Walter C. Dragani ◽  
Enrique E. D’Onofrio ◽  
Fernando Oreiro ◽  
Guadalupe Alonso ◽  
Mónica Fiore ◽  
...  
Keyword(s):  
South America ◽  
Buenos Aires ◽  
Storm Surges ◽  
Meteorological Tsunamis

scholarly journals Weather forecasting for Eastern Amazon with OLAM model

2014 ◽  
Vol 29 (spe) ◽  
pp. 11-22 ◽  
Author(s):  
Renato Ramos da Silva ◽  
Adilson Wagner Gandú ◽  
Julia Clarinda Cohen ◽  
Paulo Kuhn ◽  
Maria Aurora Mota
Keyword(s):  
Data Analysis ◽  
Case Studies ◽  
Numerical Experiments ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Precipitation Data ◽  
Grid Refinement ◽  
Squall Lines ◽  
Meteorological Systems ◽  
Individual Evaluation

The OLAM model has as its characteristics the advantage to represent simultaneously the global and regional meteorological phenomena using the application of a grid refinement scheme. During the REMAM project the model was applied for a few case studies to evaluate its performance on numerical weather prediction for the eastern Amazon region. Case studies were performed for the twelve months of the year of 2009. The model results for those numerical experiments were compared with the observed data for the region of study. Precipitation data analysis showed that OLAM is able to represent the average mean accumulated precipitation and the seasonal features of the events occurrence, but can't predict the local total amount of precipitation. However, individual evaluation for a few cases had shown that OLAM was able to represent the dynamics and forecast a few days in advance the development of coastal meteorological systems such as the squall lines that are one of the most important precipitating systems of the Amazon.

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