A Parametric Model for Tropical Cyclone Waves

2013 ◽  
Author(s):  
I. R. Young ◽  
J. Vinoth
Keyword(s):  
Tropical Cyclone ◽  
Wave Field ◽  
Tropical Cyclones ◽  
Full Range ◽  
Wave Spectrum ◽  
Parametric Model ◽  
Full Data ◽  
Data Set ◽  
In Situ Data

One of the major challenges in fully understanding the complex wave fields produced by intense tropical cyclones is having sufficient data to fully define the spatial wave field in such systems. Although the in situ data set is increasing, it is still quite limited and does not cover the full range of possible tropical cyclone parameters. One way to address this problem is to use remote sensing data obtained from satellites. Radar altimeters on such satellites have now been in operation for more than 25 years. Such a data set is used to investigate the wave field within tropical cyclones. The full data set consists of the over flight by an altimeter of a total of 440 tropical cyclones. As such, the data set is the most extensive ever obtained under tropical cyclone conditions. Using this data set, a parametric model for the wave field is developed. The analysis confirms that the most extreme waves are generated to the right (northern hemisphere) of the storm, where the waves generated tend to move forward with the storm. As such, they experience an extended fetch. This concept is used in conjunction with JONSWAP scaling to develop a parametric model which can be used to predict the tropical cyclone wave field. This model is then used in conjunction with in situ data to provide an estimate of the wave spectrum at any point in the spatial wave field. This approach provides a very valuable approach for preliminary design and extreme value studies.

Low Wave Number Radar Image Energy Influence on Determining Current

2012 ◽  
Vol 433-440 ◽  
pp. 6054-6059
Author(s):  
Gan Nan Yuan ◽  
Rui Cai Jia ◽  
Yun Tao Dai ◽  
Ying Li
Keyword(s):  
Wave Spectrum ◽  
Surface Current ◽  
Sea Surface ◽  
Radar Image ◽  
Wave Number ◽  
Sea State ◽  
Low Frequencies ◽  
In Situ Data ◽  
Main Effects

In the radar imaging mechanism different phenomena are present, as a result the radar image is not a direct representation of the sea state. In analyzing radar image spectra, it can be realized that all of these phenomena produce distortions in the wave spectrum. The main effects are more energy for very low frequencies. This work investigates the structure of the sea clutter spectrum, and analysis the low wave number energy influence on determining sea surface current. Then the radar measure current is validated by experiments. By comparing with the in situ data, we know that the radar results reversed by image spectrum without low wave number spectrum have high precision. The low wave number energy influent determining current seriously.

scholarly journals Attributing Tropical Cyclogenesis to Equatorial Waves in the Western North Pacific

2011 ◽  
Vol 68 (2) ◽  
pp. 195-209 ◽  
Author(s):  
Carl J. Schreck ◽  
John Molinari ◽  
Karen I. Mohr
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Wave Spectrum ◽  
Vertical Wind Shear ◽  
Warm Season ◽  
Threshold Value ◽  
Rainfall Anomaly ◽  
Tropical Cyclogenesis ◽  
Positive Anomaly ◽  
Equatorial Wave

Abstract Tropical cyclogenesis is attributed to an equatorial wave when the filtered rainfall anomaly exceeds a threshold value at the genesis location. It is argued that 0 mm day−1 (simply requiring a positive anomaly) is too small a threshold because unrelated noise can produce a positive anomaly. A threshold of 6 mm day−1 is too large because two-thirds of storms would have no precursor disturbance. Between these extremes, consistent results are found for a range of thresholds from 2 to 4 mm day−1. Roughly twice as many tropical cyclones are attributed to tropical depression (TD)-type disturbances as to equatorial Rossby waves, mixed Rossby–gravity waves, or Kelvin waves. The influence of the Madden–Julian oscillation (MJO) is even smaller. The use of variables such as vorticity and vertical wind shear in other studies gives a larger contribution for the MJO. It is suggested that its direct influence on the rainfall in forming tropical cyclones is less than for other variables. The impacts of tropical cyclone–related precipitation anomalies are also presented. Tropical cyclones can contribute more than 20% of the warm-season rainfall and 50% of its total variance. The influence of tropical cyclones on the equatorial wave spectrum is generally small. The exception occurs in shorter-wavelength westward-propagating waves, for which tropical cyclones represent up to 27% of the variance. Tropical cyclones also significantly contaminate wave-filtered rainfall anomalies in their immediate vicinity. To mitigate this effect, the tropical cyclone–related anomalies were removed before filtering in this study.

scholarly journals An Intercomparison of GPS RO Retrievals with Colocated Analysis and In Situ Observations within Tropical Cyclones

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Henry R. Winterbottom ◽  
Qingnong Xiao
Keyword(s):  
Tropical Cyclones ◽  
Global Positioning System ◽  
Global Analysis ◽  
Reanalysis Data ◽  
Satellite Mission ◽  
Data Set ◽  
Global Positioning ◽  
In Situ Observations ◽  
Atmospheric State

Observations from four Global Position System (GPS) Radio Occultation (RO) missions: Global Positioning System/Meteorology, CHAallenging Minisatellite Payload, Satellite de Aplicaciones Cientificas-C, and Constellation Observing System for Meteorology, Ionosphere and Climate and Taiwan's FORMOsa SATellite Mission #3 (COSMIC/FORMOSAT-3) are collected within a 600 km radius and ±180 minute temporal window of all observed tropical cyclones (TCs) from 1995 to 2006 that were recorded in the global hurricane best-track reanalysis data set (Jarvinen et al. (1984); Davis et al. (1984)). A composite analysis of tropical cyclone radial mean temperature and water vapor profiles is carried out using the GPS RO retrievals which are colocated with global analysis profiles and available in situ radiosonde observations. The differences between the respective observations and analysis profiles are quantified and the preliminary results show that the observations collected within TCs correspond favorably with both the analysis and radiosonde profiles which are colocated. It is concluded that GPS RO observations will contribute significantly to the understanding and modeling of TC structures, especially those related to vertical variability of the atmospheric state within TCs.

scholarly journals A global historical data set of tropical cyclone exposure (TCE-DAT)

2017 ◽  
Author(s):  
Tobias Geiger ◽  
Katja Frieler ◽  
David N. Bresch
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
The United States ◽  
Long Term Effects ◽  
Wind Fields ◽  
Damage Functions ◽  
Data Set ◽  
Wind Field Model ◽  
Wind Speeds ◽  
The Difference

Abstract. Tropical cyclones pose a major risk to societies worldwide with about 22 million directly-affected people and damages of $29 billion on average per year over the last 20 years. While data on observed cyclones tracks (location of the center) and wind speeds is publically available these data sets do not contain information about the spatial extent of the storm and people or assets exposed. Here, we apply a simplified wind field model to estimate the areas exposed to wind speeds above 34, 64, and 96 knots. Based on available spatially-explicit data on population densities and Gross Domestic Product (GDP) we estimate 1) the number of people and 2) the sum of assets exposed to wind speeds above these thresholds accounting for temporal changes in historical distribution of population and assets (TCE-hist) and assuming fixed 2015 patterns (TCE-2015). The associated country-event level exposure data (TCE-DAT) covers the period 1950 to 2015 and is freely available at http://doi.org/10.5880/pik.2017.005. It is considered key information to 1) assess the contribution of climatological versus socio-economic drivers of changes in exposure to tropical cyclones, 2) estimate changes in vulnerability from the difference in exposure and reported damages and calibrate associated damage functions, and 3) build improved exposure-based predictors to estimate higher-level societal impacts such as long-term effects on GDP, employment, or migration. We validate the adequateness of our methodology by comparing our exposure estimate to estimated exposure obtained from reported wind fields available since 1988 for the United States. We expect that the free availability of the underlying model and TCE-DAT will make research on tropical cyclone risks more accessible to non-experts and stakeholders.

scholarly journals Small scale spatial variability of bare-ice albedo at Jamtalferner, Austria

2020 ◽  
Author(s):  
Lea Hartl ◽  
Lucia Felbauer ◽  
Gabriele Schwaizer ◽  
Andrea Fischer
Keyword(s):  
Satellite Data ◽  
Full Range ◽  
Temporal Variations ◽  
Small Scale ◽  
Landsat 8 ◽  
Dry Ice ◽  
In Situ Data ◽  
Depth Analysis ◽  
The Difference

Abstract. As Alpine glaciers recede, they are quickly becoming snow free in summer and, accordingly, spatial and temporal variations in ice albedo increasingly affect the melt regime. To accurately model future developments, such as deglaciation patterns, it is important to understand the processes governing broadband and spectral albedo at a local scale. However, little in situ data of ice albedo exits. As a contribution to this knowledge gap, we present spectral reflectance data from 325 to 1075 nm collected along several profile lines in the ablation zone of Jamtalferner, Austria. Measurements were timed to closely coincide with a Sentinel 2 and Landsat 8 overpass and are compared to the respective ground reflectance products. The brightest spectra have a maximum reflectance of up to 0.7 and consist of clean, dry ice. In contrast, reflectance does not exceed 0.2 at dark spectra where liquid water and/or fine grained debris are present. Spectra can roughly be grouped into dry ice, wet ice, and dirt/rocks, although transitions between types are fluid. Neither satellite captures the full range of in situ reflectance values. The difference between ground and satellite data is not uniform across satellite bands, between Landsat and Sentinel, and to some extent between ice surface types (underestimation of reflectance for bright surfaces, overestimation for dark surfaces). We wish to highlight the need for further, systematic measurements of in situ spectral albedo, its variability in time and space, and in- depth analysis of time-synchronous satellite data.

scholarly journals Conditional Generative Adversarial for in-Situ Layerwise AM

2021 ◽  
Author(s):  
Goberta, Christian ◽  
Arrietab Edel ◽  
McWilliams Brandon
Keyword(s):  
Near Infrared ◽  
Turbine Blades ◽  
Optical Tomography ◽  
In Situ Monitoring ◽  
Generative Adversarial Networks ◽  
Data Set ◽  
Tomography System ◽  
In Situ Data ◽  
Real Time Information

Conditional generative adversarial networks (CGANs) learn a mapping from conditional input to observed image and perform tasks in image generation, manipulation and translation. In-situ monitoring uses sensors to obtain real-time information of additive manufacturing (AM) processes that relate to process stability and part quality. Understanding the correlations between process inputs and in-situ process signatures through machine learning can enable experimental-driven predictions of future process inputs. In this research, in-situ data obtained during a metallic powder bed fusion AM process is mapped with a CGAN. A single build of two turbine blades is monitored using EOSTATE Exposure OT, a near-infrared optical tomography system of the EOS M290 system. Layerwise images generated from the in-situ monitoring system were paired with a conditional image that labeled the specimen cross-section, laser-scan stripe overlap and z-distance to part surfaces. A CGAN was trained using the turbine blade data set and employed to generate new in-situ layerwise images for unseen conditional inputs.

On self-similarity of waves in tropical cyclones

2021 ◽  
Author(s):  
Maria Yurovskaya ◽  
Vladimir Kudryavtrsev ◽  
Bertrand Chapron
Keyword(s):  
Wave Field ◽  
Tropical Cyclones ◽  
Numerical Solutions ◽  
Parametric Model ◽  
Linear Wave ◽  
Wave System ◽  
Large Computer ◽  
State Assignment ◽  
Self Similar ◽  
Similar Solutions

<p>Wave fields generated by tropical cyclones (TC) are of strong interest for marine engineering, navigation safety, determination of coastal sea levels and coastal erosion. Considerable efforts have been made to improve knowledge about the surface waves in TC, both from measurements and numerical experiments. Full sophisticated spectral wave models certainly have the capability to provide detailed wave information, but they require large computer power, precise well-resolved surface winds and/or needs to consider large ensembles of solutions. In this context, more simplified but robust solutions are demanded.</p><p>This work is based on 2D-parametric model of waves evolution forced by wind field varying in space and time, non-linear wave interactions and wave breaking dissipation [submitted to J. Geoph. Res., see also preprint DOI: https://doi.org/10.1002/essoar.10504620.1]. Numerical solutions of model provide efficient visualization on how waves develop under TC and leave it as swell. Superposition of wave-rays exhibits coherent spatial patterns of wave parameters depending on TC characteristics, - maximal wind speed (um), radius (Rm), and translation velocity (V).</p><p>In this presentation we demonstrate how solutions of 2D-parametric model can be described analytically through self-similar functionsusing proper scaling involving the main TC parameters: um, Rm, and V. These self-similar solutions can be treated as TC-wave Geophysical Model Function (TC-wave GMF), to help analytically derive azimuthal-radial distributions of the primary wave system parameters (SWH, wavelength, direction) under TC characterized by arbitrary sets of um, Rm and V conditions. Self-similar solutions describe the main properties of wave field under TC, in particular: right-to-left half asymmetry of wave field under TC; strong dependence of wave energy and wavelength on V, um and Rm caused by group velocity resonance; division of TCs on “slow” and “fast” when TC-induced waves outrun TC and form wake of swell trailing TC.</p><p>Comparisons between self-similar solutions and measurements of TC-generated waves reported in the literature, demonstrate excellent agreement to warrant their use for research and practical applications.</p><p>The core support for this work was provided by the Russian Science Foundation through the Project №21-47-00038 at RSHU. The support of the Ministry of Science and Education of the Russian Federation under State Assignment No. 0555-2021-0004 at MHI RAS, and State Assignment No. 0736-2020-0005 at RSHU are gratefully acknowledged.</p>

scholarly journals Comparisons of Satellite and Modeled Surface Temperature and Chlorophyll Concentrations in the Baltic Sea with In Situ Data

2021 ◽  
Vol 13 (15) ◽  
pp. 3049
Author(s):  
Malgorzata Stramska ◽  
Marta Konik ◽  
Paulina Aniskiewicz ◽  
Jaromir Jakacki ◽  
Miroslaw Darecki
Keyword(s):  
Baltic Sea ◽  
Satellite Data ◽  
In Situ Measurements ◽  
Data Sets ◽  
The Baltic Sea ◽  
Data Set ◽  
In Situ Data ◽  
The Baltic ◽  
Good Agreement

Among the most frequently used satellite data are surface chlorophyll concentration (Chl) and temperature (SST). These data can be degraded in some coastal areas, for example, in the Baltic Sea. Other popular sources of data are reanalysis models. Before satellite or model data can be used effectively, they should be extensively compared with in situ measurements. Herein, we present results of such comparisons. We used SST and Chl from model reanalysis and satellites, and in situ data measured at eight open Baltic Sea stations. The data cover time interval from 1 January 1998 to 31 December 2019, but some satellite data were not always available. Both the model and the satellite SST data had good agreement with in situ measurements. In contrast, satellite and model estimates of Chl concentrations presented large errors. Modeled Chl presented the lowest bias and the best correlation with in situ data from all Chl data sets evaluated. Chl estimates from a regionally tuned algorithm (SatBaltic) had smaller errors in comparison with other satellite data sets and good agreement with in situ data in summer. Statistics were not as good for the full data set. High uncertainties found in chlorophyll satellite algorithms for the Baltic Sea highlight the importance of continuous regional validation of such algorithms with in situ data.

scholarly journals A Completeness and Complementarity Analysis of the Data Sources in the NOAA In Situ Sea Surface Temperature Quality Monitor (iQuam) System

2021 ◽  
Vol 13 (18) ◽  
pp. 3741
Author(s):  
Haifeng Zhang ◽  
Alexander Ignatov
Keyword(s):  
Sea Surface ◽  
Data Sets ◽  
Data Set ◽  
In Situ Data ◽  
Satellite Sst ◽  
Complementarity Analysis ◽  
The Cost ◽  
Marine Environment Monitoring ◽  
Drifting Buoys

In situ sea surface temperatures (SST) are the key component of the calibration and validation (Cal/Val) of satellite SST retrievals and data assimilation (DA). The NOAA in situ SST Quality Monitor (iQuam) aims to collect, from various sources, all available in situ SST data, and integrate them into a maximally complete, uniform, and accurate dataset to support these applications. For each in situ data type, iQuam strives to ingest data from several independent sources, to ensure most complete coverage, at the cost of some redundancy in data feeds. The relative completeness of various inputs and their consistency and mutual complementarity are often unknown and are the focus of this study. For four platform types customarily employed in satellite Cal/Val and DA (drifting buoys, tropical moorings, ships, and Argo floats), five widely known data sets are analyzed: (1) International Comprehensive Ocean-Atmosphere Data Set (ICOADS), (2) Fleet Numerical Meteorology and Oceanography Center (FNMOC), (3) Atlantic Oceanographic and Meteorological Laboratory (AOML), (4) Copernicus Marine Environment Monitoring Service (CMEMS), and (5) Argo Global Data Assembly Centers (GDACs). Each data set reports SSTs from one or more platform types. It is found that drifting buoys are more fully represented in FNMOC and CMEMS. Ships are reported in FNMOC and ICOADS, which are best used in conjunction with each other, but not in CMEMS. Tropical moorings are well represented in ICOADS, FNMOC, and CMEMS. Some CMEMS mooring reports are sampled every 10 min (compared to the standard 1 h sampling in all other datasets). The CMEMS Argo profiling data set is, as expected, nearly identical with those from the two Argo GDACs.

Evaluation and Validation of HF Radar Swell and Wind wave Inversion Method

2021 ◽  
Author(s):  
Zaid R. Al-Attabi ◽  
George Voulgaris ◽  
Daniel C. Conley
Keyword(s):  
Wind Wave ◽  
Wave Spectrum ◽  
Empirical Method ◽  
Hf Radar ◽  
Inversion Method ◽  
Bulk Wave ◽  
Single Beam ◽  
In Situ Data ◽  
Wave Inversion

AbstractAn examination of the applicability and accuracy of the empirical wave inversion method in the presence of swell waves is presented. The ability of the method to invert Doppler spectra to wave directional spectra and bulk wave parameters is investigated using one-month data from a 12 MHz WERA High Frequency (HF) radar system and in-situ data from a wave buoy. Three different swell inversion models are evaluated: LPM (Lipa et al. 1981), WFG (Wang et al. 2016) and EMP, an empirical approach introduced in this study. The swell inversions were carried out using two different scenarios: (1) a single beam from a single radar site and two beams from a single radar site, and (2) two beams from two sites (a single beam per site) intersecting each other at the buoy location. The LPM method utilized using two beams from two different sites was found to provide the best estimations of swell parameters (swell height RMS error 0.24m) and showed a good correlation with the partitioned swell in-situ values. For the wind wave inversion, the empirical method presented here is used with an empirical coefficient of 0.3 which seems to be suitable for universal application for all radar operating frequencies. The inverted swell parameters are used to create a swell spectrum which is combined with the inverted wind wave spectrum to create a full directional wave spectrum. The wave inversion method presented in this study although empirical does not require calibration with in situ data and can be applied to any beam forming system and operating frequency.

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