A turbulence data reduction scheme for autonomous and expendable profiling floats Article Swipe
Kenneth Hughes
,
James N. Moum
,
Daniel L. Rudnick
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2022-944
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5194/egusphere-2022-944
Autonomous and expendable profiling float arrays such as deployed in the Argo Program require the transmission of reliable data from remote sites. However, existing satellite data transfer rates preclude complete transmission of rapidly sampled turbulence measurements. It is therefore necessary to reduce turbulence data onboard. Here we propose a scheme for onboard data reduction and test it with existing turbulence data obtained with a newly developed version of a SOLO-II profiling float. The scheme invokes simple power law fits to (i) shear probe voltage spectra and (ii) fast thermistor voltage spectra that yield a fit value plus a quality control metric. At roughly 1 m vertical interval resolution, this scheme reduces the necessary data transfer volume 240-fold to approximately 3 kB for every 100 m of a profile (when profiling at 0.2 m s-1). Turbulent kinetic energy dissipation rate ε and thermal variance dissipation rate χ are recovered in post-processing. As a test, we apply our scheme to a dataset comprising 650 profiles and compare its output to that from our standard turbulence processing algorithm. For ε, values from the two approaches agree within a factor of two 87 % of the time; for χ, 78 %. These levels of agreement are greater than or comparable to that between the ε and χ values derived from two shear probes and two fast thermistors, respectively, on the same profiler.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-2022-944
- OA Status
- gold
- Cited By
- 1
- References
- 23
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- OpenAlex ID
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All OpenAlex metadata
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- DOI
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https://doi.org/10.5194/egusphere-2022-944Digital Object Identifier
- Title
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A turbulence data reduction scheme for autonomous and expendable profiling floatsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-23Full publication date if available
- Authors
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Kenneth Hughes, James N. Moum, Daniel L. RudnickList of authors in order
- Landing page
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https://doi.org/10.5194/egusphere-2022-944Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.5194/egusphere-2022-944Direct OA link when available
- Concepts
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Turbulence, Dissipation, Turbulence kinetic energy, Standard deviation, Data reduction, Physics, Thermistor, Remote sensing, Computer science, Meteorology, Computational physics, Mathematics, Statistics, Thermodynamics, Geography, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ε, | 177 |
| abstract_inverted_index.χ, | 195 |
| abstract_inverted_index.(ii) | 87 |
| abstract_inverted_index.Argo | 12 |
| abstract_inverted_index.Here | 46 |
| abstract_inverted_index.data | 19, 26, 44, 53, 61, 114 |
| abstract_inverted_index.fast | 88, 222 |
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| abstract_inverted_index.rate | 139, 145 |
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| abstract_inverted_index.that | 92, 169, 208 |
| abstract_inverted_index.this | 109 |
| abstract_inverted_index.with | 58, 63 |
| abstract_inverted_index.(when | 129 |
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| abstract_inverted_index.power | 77 |
| abstract_inverted_index.probe | 83 |
| abstract_inverted_index.rates | 28 |
| abstract_inverted_index.s-1). | 134 |
| abstract_inverted_index.shear | 82, 218 |
| abstract_inverted_index.test, | 153 |
| abstract_inverted_index.time; | 193 |
| abstract_inverted_index.value | 96 |
| abstract_inverted_index.yield | 93 |
| abstract_inverted_index.arrays | 6 |
| abstract_inverted_index.energy | 137 |
| abstract_inverted_index.factor | 186 |
| abstract_inverted_index.float. | 72 |
| abstract_inverted_index.levels | 199 |
| abstract_inverted_index.output | 167 |
| abstract_inverted_index.probes | 219 |
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| abstract_inverted_index.values | 178, 214 |
| abstract_inverted_index.volume | 116 |
| abstract_inverted_index.within | 184 |
| abstract_inverted_index.Program | 13 |
| abstract_inverted_index.SOLO-II | 70 |
| abstract_inverted_index.between | 209 |
| abstract_inverted_index.compare | 165 |
| abstract_inverted_index.control | 100 |
| abstract_inverted_index.dataset | 160 |
| abstract_inverted_index.derived | 215 |
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| abstract_inverted_index.invokes | 75 |
| abstract_inverted_index.kinetic | 136 |
| abstract_inverted_index.metric. | 101 |
| abstract_inverted_index.onboard | 52 |
| abstract_inverted_index.profile | 128 |
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| abstract_inverted_index.quality | 99 |
| abstract_inverted_index.rapidly | 33 |
| abstract_inverted_index.reduces | 111 |
| abstract_inverted_index.require | 14 |
| abstract_inverted_index.roughly | 103 |
| abstract_inverted_index.sampled | 34 |
| abstract_inverted_index.spectra | 85, 91 |
| abstract_inverted_index.thermal | 142 |
| abstract_inverted_index.version | 67 |
| abstract_inverted_index.voltage | 84, 90 |
| abstract_inverted_index.240-fold | 117 |
| abstract_inverted_index.However, | 23 |
| abstract_inverted_index.complete | 30 |
| abstract_inverted_index.deployed | 9 |
| abstract_inverted_index.existing | 24, 59 |
| abstract_inverted_index.interval | 107 |
| abstract_inverted_index.obtained | 62 |
| abstract_inverted_index.onboard. | 45 |
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| abstract_inverted_index.reliable | 18 |
| abstract_inverted_index.standard | 172 |
| abstract_inverted_index.transfer | 27, 115 |
| abstract_inverted_index.variance | 143 |
| abstract_inverted_index.vertical | 106 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.Turbulent | 135 |
| abstract_inverted_index.agreement | 201 |
| abstract_inverted_index.developed | 66 |
| abstract_inverted_index.necessary | 40, 113 |
| abstract_inverted_index.profiler. | 228 |
| abstract_inverted_index.profiling | 4, 71, 130 |
| abstract_inverted_index.recovered | 148 |
| abstract_inverted_index.reduction | 54 |
| abstract_inverted_index.satellite | 25 |
| abstract_inverted_index.therefore | 39 |
| abstract_inverted_index.Autonomous | 1 |
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| abstract_inverted_index.comprising | 161 |
| abstract_inverted_index.expendable | 3 |
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| abstract_inverted_index.thermistor | 89 |
| abstract_inverted_index.turbulence | 35, 43, 60, 173 |
| abstract_inverted_index.dissipation | 138, 144 |
| abstract_inverted_index.resolution, | 108 |
| abstract_inverted_index.thermistors, | 223 |
| abstract_inverted_index.transmission | 16, 31 |
| abstract_inverted_index.approximately | 119 |
| abstract_inverted_index.measurements. | 36 |
| abstract_inverted_index.respectively, | 224 |
| abstract_inverted_index.post-processing. | 150 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5034866067, https://openalex.org/A5062201932, https://openalex.org/A5048647352 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I131249849, https://openalex.org/I150209017, https://openalex.org/I36258959 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8899999856948853 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.58710668 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |