Data Visualization & Illustrations

Here I show a brief selection of animations, plots, and illustrations directly related to my research topics or made within collaborative contexts or by commission.

Animations

  • Animation constructed from the information gathered by DWD signal stations that are in current digitalisation process. The animation depicts the transit of a storm around 1906.03.12, preceded by a weaker one a couple of days before. The spatial plot (top) illustrates the wind intensity (color code) and direction (arrows) towards the winds were blowing. The temporal plot (bottom) shows the sea level pressure (SLP) recorded at each of the sites on the top plot. [The animation will be uploaded soon at the DWD page. Created with Python, animated with imagemagick]

  • Animations depicting the transit of an extratropical cyclone during 2003.12. The top animation was constructed combining information provided by the observational WNENA dataset to the depict the surface wind speed (arrow colors) and direction of the winds (arrow), and SLP isobars obtained from CFSR reanalysis. The bottom animation is a more general view and it was constructed solely using CFSR SLP data. [Created with Generic Mapping Tools, animated with imagemagick]

Plots

  • The following panel depicts the first Canonical Correlation Analysis (CCA) spatial pattern and associated Canonical Series (CS), from a wintertime surface wind climatological variability study over North Easter North America (Fig. 4 from Lucio-Eceiza et al. 2018). (a) depicts the first CCA pattern (CCA1) using SLP as predictor for MERRA Reanalysis (isolines, hPa), the shadings correspond to Z500 (regressed pattern), and the vector field to the WNENA observational database (predictand). The wind speed is given with the color scale. (b) First CS of the predictor (blue) and predictand (red). The correlation between both is also provided. (c) Wavelet spectral power of the predictor’s CS. The colours represent the normalized variances scaled by the global wavelet spectrum (Torrence and Compo, 1998). The cone of influence, beyond which edge effects become important, is indicated with dashed lines. The black contour lines enclose the significant areas (p<0.05), using a red-noise (first-order autoregressive) background spectrum. (d) Absolute correlation values between the first CS and different circulation indices. Colors correspond to different reanalyses. All correlations shown here are significant (p<0.05). [Created with GMT (map and wavelets) and gnuplot. Composition with Adobe Illustrator®]
  • The following panel depicts a long-term reconstruction of wintertime surface wind climatological variability study over North Easter North America (adapted from Fig. 15; Lucio-Eceiza et al. 2018) obtained from a large ensemble of downscaling estimations. The regional wind climatology reconstruction is plotted along its associated uncertainty (gray shading) for (a) zonal and (b) meridional wind anomalies. The observational database is presented with black lines. The sensitivity of the 4,340 parameter configuration ensemble is depicted in shades of greys for deciles, with the lightest grey for the maximum and minimum values. Each reanalysis/ database (inset) is depicted with a different color. The series are given as 2 year low pass filter outputs. [Created with gnuplot. Composition with Adobe Illustrator®]
  • Linear trends (in m/s per decade) of regional Statistically Downscaled estimations (top, showing the results of 4,340 estimation ensemble for winter and 7,350 for summer), reanalysis (12) simulated wind anomalies (middle) and absolute values (bottom, all indicated with solid triangles, coloured according to each large-scale source), for the whole year (TOT), NDJFM and JJASO seasons, and for zonal and meridional winds. These results are complemented with WNENA regional wind anomalies and absolute values as well (empty black triangles). The trends are calculated for the training (1980–2010) period. The regional wind components have been calculated averaging the nearest grid-points co-located to WNENA sites. The (non) significant, p < 0.05, trends are indicated with (inverted) regular triangles. From Lucio-Eceiza’s PhD Thesis. [Created with gnuplot. Composition with Adobe Illustrator®]
  • The following plot (adapted from Fig. 10; Lucio-Eceiza et al. 2018) shows the relative skill of the meridional surface wind estimations obtained from 12 global reanalysis (in colors) respect to an observational database of 95 sites. The skill evaluation is performed through a Taylor diagram: the correlation values between estimated and observed winds are provided in the clockwise angle scale, the standard deviation ratios (estimations/observations) are given by the radial coordinate, and the normalized RMSE values are indicated in concentric green circles. The large dots correspond to the regional averages while each small dot depicts a particular observational site. Each Taylor diagram is divided in 3 domains according to correlation values and standard deviation ratios: domain 1 (light green) encompasses sites with poor correlation values (r<0.5); domain 2 (blue) comprises those with high correlation values and ratios (r>=0.5 and standard deviation ratios between [0.75,1.25]). Finally domain 3 (light pink) corresponds to sites with good correlation but poor ratios. [Created with R. Composition with Adobe Illustrator®]

Illustrations

  • Illustration showing the typical path of an extreme wind event known as Wreckhouse Winds and Les Suêtes. The orographically originated winds located in SW Newfoundland and NW Cape Breton, at the leeside of the Cape Breton Plateau in relation with the prevailing synoptic wind. From Lucio-Eceiza’s PhD Thesis. [Constructed with GMT and Adobe Illustrator®]
  • Region of origin and approximate paths of the storms that pass by over North Eastern North America (shaded in gray). Summer (winter) is shown with dotted (solid) lines. Adapted from Conrad (2009), from Lucio-Eceiza’s PhD Thesis. [Constructed with GMT and Adobe Illustrator®]
  • Regolith – Atmosphere exchange of H2O in Mars. The exchange of H2O between the surface and the atmosphere can occur via: (i) physisorption, (ii) chemisorption, (iii) brine formation and (iv) deposition/sublimation. Commissioned by G. Martínez-Martínez. Shown in the Astrobiology Science Conference 2019 (AbSciCon 2019). [Entirely created with Adobe Illustrator®]

  • Schematics of the Heinrich event 1. AB, and C indicate critical steps in the Laurentide ice sheet around Heinrich event 1. Warm colors in B and C represent acceleration and thinning in ice streams of the Hudson Bay and Hudson Strait area. The complete illustration can be found in Fig. 1 from Alvarez-Solas and Ramstein (2011). [Constructed with GMT and Adobe Illustrator®]

Book/Report Covers

  • Back and front cover of Lucio-Eceiza’s PhD Thesis, showing a conceptualisation of the innards of an observational database compilation/quality control (left) and the main climatology over North Eastern North America (right). [Entirely constructed with Adobe Illustrator®]
  • Front cover comissioned by Global Forescasters© for a report. It freely depicts the Atlas, Antiatlas, the occurrence extreme events and a solar plant. [Constructed using Adobe Photoshop® and Illustrator®]