Recent Progress in Applying Advanced Computation Methods to Radar-Based Wave Measurements

2021 ◽  
Author(s):  
Morten Loell Vinther ◽  
Torbjørn Eide ◽  
Aurelia Paraschiv ◽  
Dickon Bonvik-Stone
Keyword(s):  
Doppler Radar ◽  
Risk Mitigation ◽  
Weather Conditions ◽  
Data Access ◽  
Environmental Data ◽  
Data Sets ◽  
Wave Measurements ◽  
Current Profiler ◽  
Pulse Doppler ◽  
Calm Weather

Abstract High quality environmental data are critical for any offshore activity relying on data insights to form appropriate planning and risk mitigation routines under challenging weather conditions. Such data are the most significant driver of future footprint reduction in offshore industries, in terms of costs savings, as well as operational safety and efficiency, enabled through ease of data access for all relevant stakeholders. This paper describes recent advancements in methods used by a dual-footprint Pulse-Doppler radar to provide accurate and reliable ocean wave height measurements. Achieved improvements during low wind weather conditions are presented and compared to data collected from other sources such as buoys and acoustic doppler wave and current profiler (ADCP) or legacy. The study is based on comparisons of recently developed algorithms applied to different data sets recorded at various sites, mostly covering calm weather conditions.

Wind on lakes brings predator and prey together in the pelagic zone

2015 ◽  
Vol 72 (11) ◽  
pp. 1652-1662 ◽  
Author(s):  
Derrick Tupper de Kerckhove ◽  
Edyta Agnes Blukacz-Richards ◽  
Brian John Shuter ◽  
Liset Cruz-Font ◽  
Peter Arnold Abrams
Keyword(s):  
Aquatic Ecosystems ◽  
Large Range ◽  
Spatial Scales ◽  
Acoustic Doppler Current Profiler ◽  
Weather Conditions ◽  
Trophic Levels ◽  
Stratified Lakes ◽  
Water Currents ◽  
Current Profiler ◽  
Calm Weather

Wind blowing over the pelagic zones of stratified lakes creates recirculating water currents. In Lake Opeongo, we observed the effects of these currents on four different trophic levels using a variety of methods (acoustic Doppler current profiler, optical plankton counter, hydroacoustics, telemetry, and netting programs). During windy events, (1) water currents were stronger than the known swimming speeds of small zooplankton (355 to 399 μm) but not larger species or fish, (2) large zooplankton (>542 μm) and schooling planktivores congregated at the thermocline at the downwind end of the basin, and (3) large piscivores directed their foraging towards areas exposed to wind, where they appeared to acquire the same meal as under calm weather conditions but with less effort. We propose that (i) the horizontal physical homogeneity of pelagic zones, (ii) the slow swimming ability of zooplankton relative to the speed of wind-induced water currents, and (iii) the likely growth benefits to predators of foraging on patches of prey lead to the downwind aggregation of pelagic organisms. We discuss this conceptual framework with examples from both lake and ocean ecosystems to suggest that this phenonenon occurs across a large range of spatial scales in aquatic ecosystems.

Laboratory Study of ADCP Wave Measurements

2002 ◽  
Author(s):  
H. H. Shih ◽  
B. Strong
Keyword(s):  
Technology Development ◽  
Acoustic Doppler Current Profiler ◽  
Cost Effective ◽  
Environmental Data ◽  
Measurement Capability ◽  
Wave Measurements ◽  
Wave Measurement ◽  
Wave Basin ◽  
Current Profiler ◽  
Reference Measurements

The National Ocean Service (NOS) collects real-time environmental data to support mission activities including navigation safety, coastal hazard mitigation, and coastal resource management. Near shore wave information is important for these activities and is of growing interest to marine user groups. Acoustic Doppler current profiler (ADCP) are a primary tool for NOS current measurement programs. Recent technology development has added wave measurement capability to these instruments and provided a convenient way for wave monitoring. However, only limited field comparative studies have been conducted. The need to further understand the performance of these instruments and their operation requirements exists. Tests under controlled laboratory conditions offer certain advantages over field intercomparisons including reduced measurement uncertainties, isolation of variables, and is generally cost effective. This paper describes the study of wave measurement performance of a RDI 1200 KHz ADCP in a wave basin with prescribed waves consist of regular, irregular, and multi-directional waves. The steepness and peak energy frequency for each type of waves, and the orientation of ADCP acoustic beams relative to incident waves were varied. A Linear Array of five ultrasonic sensors and a SonTek 5 MHZ ADVOcean instrument were used to provide reference for intercomparison. The ADCP shows good measurement resolutions and agrees well with the reference measurements.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Assessment of Sampling Effects on Various Satellite-Derived Integrated Water Vapor Datasets Using GPS Measurements in Germany as Reference

2020 ◽  
Vol 12 (7) ◽  
pp. 1170 ◽  
Author(s):  
Cintia Carbajal Henken ◽  
Lisa Dirks ◽  
Sandra Steinke ◽  
Hannes Diedrich ◽  
Thomas August ◽  
...  
Keyword(s):  
Water Vapor ◽  
Pairwise Comparison ◽  
Weather Conditions ◽  
Satellite System ◽  
High Temporal Resolution ◽  
Data Sets ◽  
Frequency Distributions ◽  
Temporal Sampling ◽  
Global Navigation Satellite ◽  
Satellite Instruments

Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high temporal resolution. Therefore, they serve as a reference to assess the quality and sampling issues of IWV products from multiple satellite instruments that show different orbital and instrument characteristics. A direct pairwise comparison between one year of IWV data from GPS and satellite instruments reveals overall biases (in kg/m 2 ) of 1.77, 1.36, 1.11, and −0.31 for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Computed monthly means show similar behaviors. No significant impact of averaging time and the low temporal sampling on aggregated satellite IWV data is found, mostly related to the noisy weather conditions in the German domain. In combination with SEVIRI cloud coverage, a change of shape of IWV frequency distributions towards a bi-modal distribution and loss of high IWV values are observed when limiting cases to daytime and clear sky. Overall, sampling affects mean IWV values only marginally, which are rather dominated by the overall retrieval bias, but can lead to significant changes in IWV frequency distributions.

scholarly journals Demonstration of a Speckle Based Sensing with Pulse-Doppler Radar for Vibration Detection

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1409
Author(s):  
Nisan Ozana ◽  
Reuven Bauer ◽  
Koby Ashkenazy ◽  
Nissim Sasson ◽  
Ariel Schwarz ◽  
...  
Keyword(s):  
Doppler Radar ◽  
Vibration Detection ◽  
Pulse Doppler ◽  
Pulse Doppler Radar
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