Descripción de las Estrategias Analizadas

The last part of the study of NO2 long-range transport plumes in satellite data focusses on the meteorological conditions that accompany the emission of such a plume and might yield insights into the processes causing or supporting long-range transport.

For this study, I once again make use of the source regions and their associated plumes as defined in Subsection 7.2.5 and Figure 7.22.

Using NCEP DOE AMIP-II Reanalysis and GOME-2 / MetOp-A data, I then obtain composites of the following quantities:

• mean sea level pressure (MSLP)

• geopotential height at 700 hPa

• surface temperature

• FRESCO+ cloud fraction

• tropospheric NO2 vertical column density as observed in this GOME-2 / MetOp-A data product

Due to the directional nature of wind velocities, it is not possible to create a meaningful composite that would allow the analysis of the dominant wind patterns.

Composite analysis of meteorological events has been performed in various studies, e.g. in order to analyze polar lows in the North Atlantic and Arctic Ocean (Blechschmidt et al., 2009) or transport of NOx within South Africa (Abiodun et al., 2014).

GOME-2 / MetOp-A data are given once per day, NCEP DOE AMIP-II Reanalysis-data every 6 hours. I iterate over all plumes from the given region and retrieve their date and time of emission. From the full set of satellite or meteorological observations I then select the timestep closest to the date of emission and add it to the correlated (or signal) composite. All timesteps from the data that were not added to the correlated composite are then added to the uncorrelated (or meteorological background) compos-ite. This procedure yields two distinct sets of meteorological observations: the signal composite contains only meteorological conditions associated to a long-range transport event, the background composite contains only meteorological conditions that are not associated to long-range transport.

To avoid the impact of seasonal meteorological variation, I select data only from the regional winter months: DJF for the Northern Hemisphere and JJA for the Southern Hemisphere. Otherwise, the difference between the composites would be dominated by seasonal differences: plumes occur mostly in winter and autumn, which means that data from winter would be overrepresented in the signal composite; likewise data from summer and spring would be underrepresented in the signal composite. Thus, the meteorological difference between summer and winter half would be strongly visible in the composites and dominate the effects leading to emission of long-range transport events.

I repeat the same procedure in steps of 24 h forwards and backwards, creating com-posites for "24 hours before/after plume emission" and similar.

I then compare the composite average of the signal and the background composite.

Also, I employ the Mann-Whitney U test (Mann and Whitney, 1947) to analyze in

7 NO₂ long-range transport in GOME-2 data

Figure 7.25: GOME-2 tropospheric NO2 vertical column density for the days of plume emission in the South African region. Only events in JJA (2007–2011) are shown, to mitigate biases from meteorological seasonality. There is an anomaly towards high values over the Highveld region before plume emission. After emission, the NO2 vertical column densities over the High-veld region are on average lower, while an upwards anomaly can be seen southeast of South Africa. High fluctuations in the GOME-2 data and few observations result in visible noise in the anomalies near polar night.

which regions the distributions in the signal and background composite follow compatible or significantly different distributions. The test computes a statistic that serves as a measure of the difference between two distributions of data and can be used to estimate the probability that both data sets follow the same underlying distribution. In the composite analysis, the probability of an identical distribution is computed for each grid cell individually, highlighting areas of significant difference between the two composites.

Only spacially extended anomalies with probabilities of less than 1 % of resulting from statistical variance of identical distributions (i.e. that show significant results in the Mann-Whitney U test), are reported in this study.

In the analysis, a few striking features can be seen in the composites of mean sea-level pressure, surface temperature and FRESCO+ cloud fraction. There are also significant features appearing in the tropospheric NO2vertical column density composites, as should be expected from the underlying selection process. No analysis of composites of wind speed has been performed as the vectorial nature of these forbids superimposing them in a simple manner.

Figure 7.25 shows the deviations of signal and background composite of NO2 in the

7.2 Statistical analysis

Figure 7.26: As in Figure 7.25, but for Europe and the North Sea. Only events in DJF (2007–2011) are shown. The data show significantly elevated levels of NO2

vertical column density over Europe and the North Sea in the days before and during a long-range transport event.

days preceeding and following the emission of long-range transport events during winter in South Africa. An outflow of NO2 is visible one and two days after emission of a long-range transport event, directed towards the polar circle in eastward direction. This serves as an indication of the effectiveness of this analysis.

On the day before emission, a strong, isolated peak in tropospheric NO2 vertical column density is located exactly above the Highveld plateau. This indicates that there is a build-up of NO2 preceeding a long-range transport transport event and favorable meteorological conditions alone may not be sufficient for a NO2 long-range transport event.

Similar features can be seen during summer in the Northern Hemisphere in Europe (Figure 7.26) and China (not shown). There are still significant elevations of tropospheric NO2 vertical column densities in the emission region, but they extend over a longer timespan and are not as locally confiend as in South Africa. Also, the outflow is far less pronounced and extends over a longer timespan. This indicates that it is a lot easier to correctly identify and trace back long-range transport events near the isolated emission region South Africa than near convoluted emission regions and shores as in Europe and China.

The evolution of the composite mean sea-level pressure for long-range transports near South Africa is shown in Figure 7.27. There is a low-pressure anomaly building up to the West on the day before and the days of emission, moving towards the southern tip of South Africa, together with surrounding high-pressure anomalies moving towards

7 NO₂ long-range transport in GOME-2 data

Figure 7.27: NCEP DOE AMIP-II Reanalysis mean sea-level pressure anomaly (given in Pa) for the days of plume emission in the South African region. Only events in JJA (2007–2011) are shown, to mitigate biases from meteorologic seasonality. The image shows significant high and low pressure patterns in the southern midlatitudes, moving from West to East.

the East. This low pressure anomaly is likely to be the result of cyclones causing the transport.

In Europe (Figure 7.28), again, this is much less dynamic. There is an anomaly on the order of ΔP ≈ −5 hPa towards a reduced mean sea-level pressure over Western Europe, while there is an upwards anomaly on the same order over Western Russia and the Arctic. Central Europe lies right between these two anomalies. If these anomalies results from highs and lows, there will be a channel of high wind speeds right over a major emission region that will propel NO2 offshore, in the direction that most European long-range transports travel.

For China, this result is much less clear, showing only a vague low-pressure anomaly moving from the Pacific towards the West onto the Asian continent.

The analysis of FRESCO+ cloud fractions only yields significant results for the China region where patterns of elevated cloud fraction can be seen offshore of Beijing on the days after emission. It is quite likely that a passing storm and elevation of boundary layer air will lead to cloud formation. As cloud formation is a complex process, I did not expect any significant results from this analysis and it may be coincidental.

There were no clear results of any anomaly in composite meteorological studies of the North American emission region. This may be an effect of the very elongated emission region, covering the entire East Coast of the North American continent. Also, several

7.2 Statistical analysis

Figure 7.28: As in Figure 7.27, but for the Central European region. Only events in DJF (2007–2011) are shown. The data show a low-pressure anomaly over Western Europe over the course of the transport. Contrasting this, there is a high-pressure anomaly over the Arctic and Northern Russia.

storm tracks cross North America, moving into different directions. While all may lead to the emission of a NO2 long-range transport, they may be acoompanied by various different meteorological conditions.

Keeping in mind that the emission times of long-range transports are only a vague estimation, the results of this analysis nonetheless yield significant insight into the emis-sion mechanism of long-range transport events and further consolidate the underlying theory.