Correlating Extremes in Wind Divergence with Extremes in Rain over the Tropical Atlantic
Air–sea fluxes are greatly enhanced by the winds and vertical exchanges generated by mesoscale convective systems (MCSs). In contrast to global numerical weather prediction models, space-borne scatterometers are able to resolve the small-scale wind variability in and near MCSs at the ocean surface....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/5/1147 |
| _version_ | 1797473938456969216 |
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| author | Gregory P. King Marcos Portabella Wenming Lin Ad Stoffelen |
| author_facet | Gregory P. King Marcos Portabella Wenming Lin Ad Stoffelen |
| author_sort | Gregory P. King |
| collection | DOAJ |
| description | Air–sea fluxes are greatly enhanced by the winds and vertical exchanges generated by mesoscale convective systems (MCSs). In contrast to global numerical weather prediction models, space-borne scatterometers are able to resolve the small-scale wind variability in and near MCSs at the ocean surface. Downbursts of heavy rain in MCSs produce strong gusts and large divergence and vorticity in surface winds. In this paper, 12.5 km wind fields from the ASCAT-A and ASCAT-B tandem mission, collocated with short time series of Meteosat Second Generation 3 km rain fields, are used to quantify correlations between wind divergence and rain in the Inter-Tropical Convergence Zone (ITCZ) of the Atlantic Ocean. We show that when there is extreme rain, there is extreme convergence/divergence in the vicinity. Probability distributions for wind divergence and rain rates were found to be heavy-tailed: exponential tails for wind divergence (<i>P</i>∼<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>e</mi><mrow><mo>−</mo><mi>α</mi><mi>δ</mi></mrow></msup></semantics></math></inline-formula> with slopes that flatten with increasing rain rate), and power-law tails for rain rates (<i>P</i>∼<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow><mo>(</mo><msup><mi>R</mi><mo>*</mo></msup><mo>)</mo></mrow><mrow><mo>−</mo><mi>β</mi></mrow></msup></semantics></math></inline-formula> with a slower and approximately equal decay for the extremes of convergence and divergence). Co-occurring points are tabulated in two-by-two contingency tables from which cross-correlations are calculated in terms of the odds and odds ratio for each time lag in the collocation. The odds ratio for extreme convergence and extreme divergence both have a well-defined peak. The divergence time lag is close to zero, while it is 30 min for the convergence peak, implying that extreme rain generally appears after (lags) extreme convergence. The temporal scale of moist convection is thus determined by the slower updraft process, as expected. A structural analysis was carried out that demonstrates consistency with the known structure of MCSs. This work demonstrates that (tandem) ASCAT winds are well suited for air–sea exchange studies in moist convection. |
| first_indexed | 2024-03-09T20:23:15Z |
| format | Article |
| id | doaj.art-5c1aaf6755b34ef78bc34194f1e9861d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T20:23:15Z |
| publishDate | 2022-02-01 |
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| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5c1aaf6755b34ef78bc34194f1e9861d2023-11-23T23:42:08ZengMDPI AGRemote Sensing2072-42922022-02-01145114710.3390/rs14051147Correlating Extremes in Wind Divergence with Extremes in Rain over the Tropical AtlanticGregory P. King0Marcos Portabella1Wenming Lin2Ad Stoffelen3Institute of Marine Sciences, Pg. Marítim de la Barceloneta 37-49, 08003 Barcelona, SpainInstitute of Marine Sciences, Pg. Marítim de la Barceloneta 37-49, 08003 Barcelona, SpainSchool of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaRoyal Netherlands Meteorological Institute (KNMI), NL-3731 GA De Bilt, The NetherlandsAir–sea fluxes are greatly enhanced by the winds and vertical exchanges generated by mesoscale convective systems (MCSs). In contrast to global numerical weather prediction models, space-borne scatterometers are able to resolve the small-scale wind variability in and near MCSs at the ocean surface. Downbursts of heavy rain in MCSs produce strong gusts and large divergence and vorticity in surface winds. In this paper, 12.5 km wind fields from the ASCAT-A and ASCAT-B tandem mission, collocated with short time series of Meteosat Second Generation 3 km rain fields, are used to quantify correlations between wind divergence and rain in the Inter-Tropical Convergence Zone (ITCZ) of the Atlantic Ocean. We show that when there is extreme rain, there is extreme convergence/divergence in the vicinity. Probability distributions for wind divergence and rain rates were found to be heavy-tailed: exponential tails for wind divergence (<i>P</i>∼<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>e</mi><mrow><mo>−</mo><mi>α</mi><mi>δ</mi></mrow></msup></semantics></math></inline-formula> with slopes that flatten with increasing rain rate), and power-law tails for rain rates (<i>P</i>∼<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow><mo>(</mo><msup><mi>R</mi><mo>*</mo></msup><mo>)</mo></mrow><mrow><mo>−</mo><mi>β</mi></mrow></msup></semantics></math></inline-formula> with a slower and approximately equal decay for the extremes of convergence and divergence). Co-occurring points are tabulated in two-by-two contingency tables from which cross-correlations are calculated in terms of the odds and odds ratio for each time lag in the collocation. The odds ratio for extreme convergence and extreme divergence both have a well-defined peak. The divergence time lag is close to zero, while it is 30 min for the convergence peak, implying that extreme rain generally appears after (lags) extreme convergence. The temporal scale of moist convection is thus determined by the slower updraft process, as expected. A structural analysis was carried out that demonstrates consistency with the known structure of MCSs. This work demonstrates that (tandem) ASCAT winds are well suited for air–sea exchange studies in moist convection.https://www.mdpi.com/2072-4292/14/5/1147air–sea interactionscatterometer windsASCATMeteosat Second Generationocean wind divergencetropical convection |
| spellingShingle | Gregory P. King Marcos Portabella Wenming Lin Ad Stoffelen Correlating Extremes in Wind Divergence with Extremes in Rain over the Tropical Atlantic Remote Sensing air–sea interaction scatterometer winds ASCAT Meteosat Second Generation ocean wind divergence tropical convection |
| title | Correlating Extremes in Wind Divergence with Extremes in Rain over the Tropical Atlantic |
| title_full | Correlating Extremes in Wind Divergence with Extremes in Rain over the Tropical Atlantic |
| title_fullStr | Correlating Extremes in Wind Divergence with Extremes in Rain over the Tropical Atlantic |
| title_full_unstemmed | Correlating Extremes in Wind Divergence with Extremes in Rain over the Tropical Atlantic |
| title_short | Correlating Extremes in Wind Divergence with Extremes in Rain over the Tropical Atlantic |
| title_sort | correlating extremes in wind divergence with extremes in rain over the tropical atlantic |
| topic | air–sea interaction scatterometer winds ASCAT Meteosat Second Generation ocean wind divergence tropical convection |
| url | https://www.mdpi.com/2072-4292/14/5/1147 |
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