A look back: PhD for 5 years

Time runs fast, that is true for everyone. From time to time, at the end of the year or at birthdays you take a look back, what the last year has brought to you. The good things, the bad and of cause what you want to achieve in the future. Five years ago today, I have finished my PhD, so it is a good time to do the same.

So what has happened since? I have worked into two completely new topics, palaeo-sea-level reconstruction and long-term, especially seasonal, climate prediction. In that time I have published three first author papers and four minor author ones, have lived more than two years abroad and have been to numerous conferences in Northern America and Europe. I have been active in teaching, have done co-supervision of students and learned many more skills. In short, I have worked as a scientist and am in my second post-doc phase.

That is a long list, but as always, under the pressure scientists are today, you always want more. Hopping between topics has proven a challenge for me, costed time to adapt and changing institutions always requires care not to completely starting from scratch. The first station, sea-level research, has proven as a surprise for me, as I really enjoyed working in the interdisciplinary environment. As a meteorologist, looking at topics based in oceanography is sometimes quite strange. Ideas are different even when the methodology, the math and physics, is in many perspectives the same. I focused on simple models and data assimilation and am quite happy with the results. The second station, seasonal climate prediction, has been more challenging as expected. Going from simple to complex models and working on real meteorology was new for me and it needed time to adapt. But finally it seems things come to fruition and I am positive for the future.

During the past five years I have specialised even more into the field I started in during my PhD: the development and application of new statistical methodology. I have done it now in many different fields and am due to cover the three main fields in publications of statistical data analysis in geosciences: data analysis and data assimilation are done, verification is hopefully done in the upcoming year. And with this we come to the future. What are the aims for the next years?

The main aim is of course to create more publications, get even better in teaching and get better in doing science and research. Currently there are four first author papers in the final phase before submission, so I hope this will become a successful year in this area. The biggest steps I had done in teaching in the last year, so I am working on steadying it. New challenges will be of couse to apply for funding and working even more in supervision of students. All this hopefully lead to the next steps and of cause some new collaborations.

So all in all, I am quite happy with the last five years, I have learned a lot and hopefully I will be able to learn even more in the next years. Science is still a lot of fun for me and I am still on track that it stays this way. So bring on the next five years and see where we end up.


EGU 2017: Complications of interdisciplinarity

The third day of the EGU is over and my day got busier than yesterday. It started with a look into a sea-ice session with an interesting view of predicting its decline. A key is not to look at the time as the decisive variable, but on the development of greenhouse gases in the atmosphere. The second half of the first session I went into a more applied geological session, which mainly asked questions about how boulders get onshore. Quite interesting were the implications on potential storm climate during the last interglacial. The second session I paid a visit to precipitation its retrieval and the resulting products. Precipitation is one of the most complicated variables to predict as well as to measure and has therefore always interesting developments to offer.

After lunch my next stop was again a medal lecture, this time on chaos and the presenter had some really nice examples. The remaining session was on ENSO, before I decided to visit the open session on ocean science. Some interesting talks, for example on the uncertainty of deep ocean heat content made it an interesting session. The final of the day was as always the poster session.

Conferences like the EGU are always great for researchers like me, who prefer to take look into different fields (as I personally focus on the developments of statistical methodologies, which do not require to stick to one field). Unfortunately, this leads even more to the problem that you have to decide what you would like to see. While often schedulers take care to give a consistent schedule for one discipline (even when it does not really work every time),  having several different divisions to follow needs some extra care. When I look onto the first three days, I have visited sessions of the following divisions (only the first division on the list): OS, GM, G, CL, AS, GI, CR, NP and NH. I am not quite sure, which division I belong to myself, but I have learned that it would be simpler to stick to one division only. Often the computer systems/apps are not designed to assist in the search of session of many (or all) divisions and it requires some extra work to do it properly. There is always a session you felt you have missed. Anyway, it is worth the effort and everyone has problems to get their ideal scheduling done. The current app is a nice feature, but there is still the question on how it will get better to really assist every type of scientist at such a huge conference.

Some comments on the Ocean Glider paper

To call it a new paper might be a little bit extragated, but its publication happend within the last year. Actually it was submitted around a year ago and published online in November, but the actual publication of the paper happend in April. The name is quite long, but tells you already a lot of its content:

Turbulence and Mixing by Internal Waves In The Celtic Sea Determined From Ocean Glider Microstructure Measurements

I do not want to talk about the whole paper, as my personal contribution was tiny compared to the great work of the other authors. Anyway, I would like to write a little bit about my part in it and what the task was.

Ocean gliders are one of the relatively new tools, which currently revolutionise the oceanographic observation system. As such they are currently tested for many applications, in case of this article for microstructure measurements. My part therein started when the main work was already done. After all the measuring, processing and calculations two time series over nearly nine days were given to me with the simple question: “What can you tell us about them.” Of course there were ideas around what could be in it, but as I do statistics, it is my task to make statements waterproof.

As always, you have to get familiar with the data in the first place before you can investigate detailed questions. I did quality assurance science during my PhD and from this I have my standard tools to play around with data and to learn about it. One of these tools is the histogram test, which is a nice test on inhomegeneities within datasets. The first thing you find with it is that there are obvious cycles within the time series, so you ask the experts to give you the obvious and physical most probable cycles you might find therin. Of course you can also tell exactly, which cycles have to be in it, by performing a spectral analysis, but when you make decsisions on simplifying and clustering data, it is better you understand the physics behind it. After doing this it was obvious that there are two different parts of the dataset (with different statistical properties), which are on the first view quite unrelated. The information to look at the data in the logarithmic sense, was then the main driver for the upcoming analysis.

When you assume distributions of the data it is important to test them. Done this it was simple to show that the time series are indeed, apart from the extremes, log-normal distributed. Performing the histogram test again, now with the logarithmic data, showed still the regime shift as before and so it was now the interesting question, whether the two parts itself were also log-normal distributed. Using qq-plots it was simple to show that they were and that just the mean and standard deviation in the logarithmic sense have changed. So my part got to an end, it was written up as one section at the end of the paper and I was happy with it.

So why are such analyses important? Why bringing in additional statistics into such a paper, while it is already a solid one? Because these small simple analysis contribute to the overall understandings of the data. Knowing the distribution of data values and its changes over time helps in modelling them or understanding the physics. Giving people simple tools at hand to see inhomogeneities would also allow for real time testing the data and might open new ways of measureing them. And yes, it gives nice figures, which illustrate the reader that there is really something within the data that might need further exploration. Statistics is not all about the equations, sometimes the right visualisation is equally important. All in all it was a nice example, how domain experts and their methodologies and a simple statistical analysis give quick and solid results.

M.R. Palmer, G.R. Stephenson, M.E. Inall, C. Balfour, A. Düsterhus, J.A.M. Green (2015): Turbulence and mixing by internal waves in the Celtic Sea determined from ocean glider microstructure measurements. Journal of Marine Systems, 144, 57-69