| Summary: | In this paper, the wind direction (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>W</mi><mi>D</mi></mrow></semantics></math></inline-formula>) behaviour with respect to the variability of other meteorological parameters (i.e., rainfall (<i>R</i>), temperature (<i>T</i>), relative humidity (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>h</mi></mrow></semantics></math></inline-formula>), solar radiation (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>R</mi></mrow></semantics></math></inline-formula>) and wind speed (<i>U</i>)) was studied in a multi-scale way. To carry out this study, the Hilbert–Huang transform (HHT) framework was applied to a Guadeloupe archipelago dataset from 2016 to 2021. Thus, the time-dependent intrinsic correlation (TDIC) analysis based on multivariate empirical mode decomposition (MEMD) was performed. For time scales between ∼3 days and ∼7 months, the localized positive and negative correlations between <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>W</mi><mi>D</mi></mrow></semantics></math></inline-formula> and the meteorological parameters have been identified. The alternation between these correlations was more significant for <i>T</i> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>h</mi></mrow></semantics></math></inline-formula>. With regard to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>R</mi></mrow></semantics></math></inline-formula> and <i>U</i>, there was a dominance of a negative correlation with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>W</mi><mi>D</mi></mrow></semantics></math></inline-formula>. We assumed that the micro-climate previously identified in the literature for the study area plays a key role in these behaviours. A strong positive correlation between <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>W</mi><mi>D</mi></mrow></semantics></math></inline-formula> and <i>R</i> was found from ∼7 months to ∼2.5 years. At the annual scale, the relationships between <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>W</mi><mi>D</mi></mrow></semantics></math></inline-formula> and all meteorological parameters were long range and no significant transition in correlation was observed showing the impact of the Earth’s annual cycle on climatic variables. All these results clearly show the influence of <i>R</i>-<i>T</i>-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>h</mi></mrow></semantics></math></inline-formula>-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>R</mi></mrow></semantics></math></inline-formula>-<i>U</i> on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>W</mi><mi>D</mi></mrow></semantics></math></inline-formula> over different time scales.
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