Background

The circulation of the atmosphere in the mid-latitudes is dominated by so-called "planetary waves" (PW). They lead to a more or less periodic change between high-pressure and low-pressure cells and significantly determine the large-scale weather situations in the mid-latitudes. Even though PW are mainly excited in the troposphere, they often propagate through the troposphere up to the mesopause (90 km). In this way PW contribute to a coupling of the different atmospheric layers. The impact of PW is most dramatic during so-called "stratospheric warmings" (SSW). These are spontaneous, strong warmings of the stratospheric polar region within a few days during winter, often preceded by a cooling of the mesosphere. This is associated with a strong weakening or reversal of the westerly wind flow in the stratosphere. An increased activity of the planetary waves (PWA) due to wave resonance, which leads to the breaking of the waves, seems to be the cause of the stratospheric warming. The energy and momentum released during a break, weakens the polar vortex. This can lead to its displacement and eventually to its splitting.

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Research Questions

1. Why does SSW occur? / How can SSW be characterized?

2. What is the DAI? How is it calculated?

3. Which PW are predominantly responsible for SSW?

4. What is the importance of SSW in the context of climate change?

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Dataset

The dynamic activity index (DAI) is a measure of hemispheric PWA with daily resolution. The index is based on temperature data from the European Centre for 新万博体育下载_万博体育app【投注官网】ium-Range Weather Forecasts (ECMWF). This is the ERA-5 reanalysis data set from 1979 to the present. For this purpose, we provide the DAI in 10 hPa pressure level since January 2000.

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Methods

First, the time series of PWA is presented and anomalies are detected visually. By means of wavelet analysis the PWA is investigated over the period of a SSW. The result is a spectrogram, which shows the periods of planetary waves with their spectral intensity depending on the time. From this it becomes clear which PW components have contributed to the SSW.

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Topics of Presentations

• PW in tropospheric circulation

• SSW and impact on weather

• DAI and ERA-5 reanalysis dataset

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Group 1: Work description & presentations

Background

With the help of ground-based infrared spectrometers as they are operated within the Network for the Detection of Mesospheric Change (https:\\ndmc.dlr.de), the temperature of the UMLT can be measured every 10 – 15?s during (at least nearly) cloudless nights. This is achieved through the observation of a self-luminescent layer, the hydroxyl airglow, which is centered around 86 – 87?km height. The full-width at half maximum of the layer accounts for ca. 7 – 8?km.

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The temperature at this height is determined by large and small-scale dynamical processes, so by the residual circulation and planetary waves as well as gravity waves and infrasound. Those are at least in parts generated by airflow over mountainous regions. The analysis of temperature time series allows conclusions to be drawn about the temporal variations of the aforementioned processes.

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In the Alpine region, a number of identical infrared spectrometers, so called GRIPS (Ground based Infrared P-branch Spectrometer), is operated by DLR together with scientific cooperation partners. The fields of view of three GRIPS systems (GRIPS 6, GRIPS 7 and GRIPS 16) formed an equilateral triangle for about one and a half years and allowed the derivation of additional spatial information about gravity waves.

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Research Questions

1. How does the yearly course of temperature in the UMLT look like and why?
2. Investigate GRIPS time series of two different night! Can you identify periods, which are typical for gravity waves? What can you say about the horizontal wavelength (please use the fact that the fields of view of the instruments formed an equilateral triangle)?

3. How can gravity waves be measured in the UMLT?

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Database

Time series of the same night recorded by the three GRIPS instruments mentioned above are provided.

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Methods

The three time-series are analyzed by “eye” and additionally by a Fast Fourier Transform. This analysis provides information about the periods and phases of the processes, which dominate the temperature during the respective night.

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Topics for Presentations

• Gravity waves
• Airglow instruments

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Group 2: Work description and presentations

Background

Air pollution and its effects on human health has not only become more important regarding the COVID-19 pandemic and due to the global lockdowns, but was already a top priority topic of the World Health Organization (WHO) within the last years. According to the WHO website, referring to an estimation from 2018, nine out of ten people on this earth inhale highly polluted air. As a result, the polluted air makes its way through the lungs and therefore into the sensitive human’s system and can, just to mention a few, irritate human airways, lead to cardiovascular diseases, strokes as well as heart and lung diseases. Thus, the WHO further states that air pollution is responsible for around 4.2 million deaths per year worldwide.

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Hence, in this group we will focus on nitrogen dioxide (NO₂) which is one of the most important pollutants in the troposphere regarding to the effects on human health. Subsequently, it must therefore be understood what the sources and sinks for NO₂ pollution in our atmosphere are. Upon a first examination, the sources of NO₂ can be divided into natural and anthropogenic sources, whereby the anthropogenic share clearly predominates.

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This leads to the conclusion that anthropogenic polluters need to be examined more closely in order to generate more efficient policy measures to improve health and well-being not only for humans but all creatures and ecosystems on earth. Therefore, in this group we will analyze a given NO₂ time series with a signal analysis method and compare the results to an economic indicator like the gross domestic product for the area of interest in northern Italy.

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Research Questions

1. How is NO₂ created and what is its lifetime?
2. What are the consequences of NO₂ for human health?
3. What role does economic activity play in this and is there a connection to air pollution?
4. Which meteorological factors play a role in NO₂ pollution and why?
5. Is it possible to draw conclusions about ground-based NO₂ pollution based on satellite data?
6. Is it possible to establish connections to large-scale processes in the stratosphere such as the quasi-biennial oscillation (QBO)?

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Area of Interest

Po Valley (Latitudes: 44°-46°N, Longitudes: 7°-14°E)

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Database ???????

The tropospheric emission monitoring internet service (TEMIS) is as the name already suggests a web-based service which provides atmospheric satellite data products. Especially in this seminar we therefore focus on tropospheric monthly mean satellite data of nitrogen dioxide (NO₂).

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Methods?? ??????

Within this group we are going to create a tropospheric NO₂ time series based on satellite data provided by TEMIS. In order to examine common signals, we subject the time series to the so-called Empirical Mode Decomposition (EMD) method. Contrary to the Fast Fourier Transform (FFT) and the Wavelet Analysis (WA) – both are spectral analysis methods – the EMD can be assigned to the signal analysis methods.

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Topics for Presentations

• Economic activity and its role in NO₂ pollution
• Air pollution and the effects on human health

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Literature

• Bichler, R., Bittner, M. (2022): Comparison between economic growth and satellite-based measurements of NO2 pollution over northern Italy. In: Atmospheric Environment, 272, 118948, https://doi.org/10.1016/j.atmosenv.2022.118948
• Castellanos, P., Boersma, K.F. (2012): Reductions in nitrogen oxides over Europe driven by environmental policy and economic recession. In: Scientific Reports, 2, 265, https://doi.org/10.1038/srep00265
• Liu, S., Valks, P., Beirle, S., Loyola, D. (2021): Nitrogen dioxide decline and rebound observed by GOME-2 and TROPOMI during COVID-19 pandemic. In: Air Quality, Atmosphere and Health, 14, 1737-1755, https://doi.org/10.1007/s11869-021-01046-2
• Georgoulias, A.K., van der A, R.J, Stammes, P., Boersma, K.F., Eskes, H.J. (2019): Trends and trend reversal detection in 2 decades of tropospheric NO₂ satellite observations. In: Atmospheric Chemistry and Physics, 19, 6269-6294, https://doi.org/10.1007/s11869-021-01046-2
• Oke, T., Mills, G., Christen, A., Voogt, J. (2017). Urban Climates. Cambridge, Cambridge 新万博体育下载_万博体育app【投注官网】 Press

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Group 3: Work description and presentations