Correlating Extremes in Wind Divergence with Extremes in Rain over the Tropical Atlantic

• Published in: Remote Sensing
• Main Authors: King, Gregory P., Portabella, Marcos, Lin, Wenming, Stoffelen, Ad
• Other Authors: Ocean and Sea Ice Satellite Application Facility, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), CSIC - Unidad de Recursos de Información Científica para la Investigación (URICI)
• Format: Article in Journal/Newspaper
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2022
• Subjects: Air–sea interaction; Scatterometer winds; ASCAT; Meteosat Second Generation; Ocean wind divergence; Tropical convection; Mesoscale convective systems; Compound extremes; Heavy-tailed PDFs; Curl; Sea ice
• Online Access: http://hdl.handle.net/10261/263245; https://doi.org/10.3390/rs14051147

Special Issue Remote Sensing of Ocean-Atmosphere Interactions.-- 25 pages, 14 figures, 7 tables, 1 appendix.-- Data Availability Statement: Data supporting reported results can be found at: Meteosat Second Generation rain rates https://msgcpp.knmi.nl/ (accessed on 15 January 2022); wind divergence (as an L3 swath gridded (interpolated) product) at https://doi.org/10.48670/moi-00182 (accessed on 15 January 2022) (EU Copernicus Marine Service). The L3 product is produced by KNMI from L2 swath divergence and curl, which is available from KNMI on request ([e-mail]) 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 (P∼e−αδ with slopes that flatten with increasing rain rate), and power-law tails for rain rates (P∼(R∗)−β 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 This work was supported in part by the European Organization for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility (OSI-SAF) Visiting Scientist Program under reference OSI-SAF-AVS-15-02 and in part by the Spanish R&D projects L-BAND (ESP2017-89463-C3-1-R), which is funded by MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”, and INTERACT (PID2020-114623RB-C31), which is funded by MCIN/AEI/10.13039/501100011033. We also acknowledge funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S). The publication fee was partially supported by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI) Peer reviewed