Author: The Social Metwork

Urban observations in Berlin

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Martina Frid – m.a.h.frid@pgr.reading.ac.uk

Beth Saunders – bethany.saunders@pgr.reading.ac.uk

Introduction 

With a large (and growing) proportion of the global population living in cities, research undertaken in urban areas is important; especially in hazardous situations (heatwaves, flooding, etc), which become more severe and frequent due to climate change.  

This post gives an overview of recent work done for The urbisphere; a Synergy Project funded by the European Research Council (urbisphere 2021), aiming to forecast feedbacks between weather, climate and cities.  

Berlin Field Campaign 

The project has included a year-long field campaign (Autumn 2021 – Autumn 2022) undertaken in Berlin (Fig. 1). A smart Urban Observation System was used to take measurements across the city. Sensors used include ceilometers, Doppler wind LIDARs, radiometers, thermal cameras, and large aperture scintillometers (LAS). These measurements were taken to provide new information about the impact of Berlin (and other cities) on the urban boundary layer. The unique observation network was able to provide dense, multi-scale measurements, which will be used to evaluate and inform weather and climate models.  

Figure 1: Locations of the urbisphere senors in Berlin, Germany (urbisphere 2021).

Large Aperture Scintillometry in Berlin

The Berlin field campaign has included 6 LAS paths (Fig. 1). LAS paths consist of a transmitter and receiver mounted in the free atmosphere (Fig. 2), 0.5 – 5 km apart (e.g. Ward et al. 2014).

A beam of near-infrared radiation (wavelength of ~ 850 nm) is passed from the transmitter to receiver, where the beam intensity is measured. Changes in the refractive index of air are used to derive turbulent sensible heat flux. As the received intensity is the result of fluctuations all along the beam, derived quantities are spatially-integrated, and are therefore at a larger-scale compared to other flux measurement techniques (e.g. eddy-covariance).

Figure 2: One of six large aperture scintillometer path (orange) transects. Ground height (blue) is shown between the receiver site (GROP) and transmitter site (OSWE) in Berlin. The Path’s effective beam height is 50 m above ground level.

Our Visit to Berlin

During the first week of August, we travelled to Berlin for three days of fieldwork, to prepare for an intense observation period (IOP). This trip included us installing sensors, and testing they worked as expected. We visited three observation sites: GROP (123 m above sea level, Fig. 2), OSWE (63 m, Fig. 2) and NEUK (60 m).

One of the main purposes of this visit was to align two of the LAS paths (including the one in Fig. 2). Initially, work is undertaken at the transmitter site (Fig. 3, top) to point the instrument in the approximate direction of the receiver using a sight (Fig. 3, right hand side photographs).

At the receiver site (Fig. 3, bottom), the instrument’s measurement of signal strength can be displayed on a monitor in real time. Using this output as a guide, small adjustments to the receiver’s alignment are made by loosening or tightening two bolts on the mount; one which adjusts the receiver’s pitch, and one with adjusts the yaw. This was carried out until we reached a peak reading in signal strength, indicating the path was aligned.

Figure 3: Photographs of the large aperture scintillometer transmitter at site OSWE (top) and receiver at site GROP (bottom).

Our contribution to the IOP

Back in Reading, daily weather forecasts were carried out for the IOP, to determine when ground-based observations could be made. As the field campaign coincided with the central European heat wave, some of the highest temperatures were recorded during the IOP, and there was a need to forecast thunderstorm and the possibility of lightning strikes.

Ideal conditions for observations were clear skies and a consistent wind direction with height. A variety of different wind directions during the IOP was also preferable, to capture different transects of Berlin. For the selected days, group members in Berlin deployed multiple weather balloons simultaneously across multiple sites within the city and the outskirts. This was also timed with satellite overpasses. Observations of the mixing layer height (urban and suburban) were taken using a ceilometer mounted in a van, which drove along different transects of Berlin.

As the field campaign is wrapping up in Berlin, several instruments are now being moved to the new focus city: Paris. We are looking forward to this new period of interesting observations! Thank you and goodbye from us at the top of the GROP observation site!

References

urbisphere, 2021: Project Context and Objectives. http://urbisphere.eu/ (accessed 27/09/22)

Ward, H. C., J. G. Evans, and C. S. B. Grimmond, 2014: Multi-Scale Sensible Heat Fluxes in the Suburban Environment from Large-Aperture Scintillometry and Eddy Covariance. Boundary-Layer Meteorol., 152, 65–89.

Deploying an Instrument to the Reading University Atmospheric Observatory 

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Caleb Miller – c.s.miller@pgr.reading.ac.uk 

In the Reading area, December and January seem to be prime fog season. Since I’m studying the effects of fog on atmospheric electricity, that means that winter is data collection season! However, in order to begin collecting data in the first year of my PhD, there was only a short amount of time to prepare an instrument and deploy it to the observatory before Christmas. 

One of the instruments that I am using to measure fog is called the Optical Cloud Sensor (OCS). It was designed by Giles Harrison and Keri Nicoll, and it is described in more detail in this paper: (Harrison and Nicoll 2014). The OCS has four channels of LEDs which shine light into the surrounding air. When fog is present, the fog droplets scatter light back to the instrument, where the intensity from each channel can be measured. 

Powering the instrument 

The OCS was originally designed to be flown on a weather balloon, which meant that it was meant to be powered by battery and run for only short periods of time. In my case, however, I wanted the device to be able to continuously collect data over a period of weeks or months without interruption. Then, we would be able to catch any fog events, even if they hadn’t been forecasted. That meant the device would need to be powered by the +15V power supply available at the observatory, and my first step was to create a power adapter for the OCS so that this would be possible. 

Initially, I had been considering using an Arduino microcontroller as a datalogger, so I decided to put together a power adapter on an Arduino shield (a small electronic platform) for maximum convenience. I included multiple voltage levels on my power adapter and connected them to different power inputs on the OCS. Once this was completed, the entire system could now be powered with a single power supply that was available at the observatory! 

I was able to find all of the required parts for the power supply in stock in the laboratory in the Meteorology Department, and I soldered it together in a few days. The technical staff of the university were very helpful in this process! A photograph of the power adapter connected to an Arduino is shown in Figure 1. 

Figure 1. The power adapter for the optical cloud sensor, built on an Arduino shield 

Storing data from the instrument 

Once the power supply had been created, the next step was setting up a datalogging system. On a balloon, the data would be streamed in real-time down to a ground station by radio link. But when this system was deployed to the ground, that would no longer be necessary. 

Instead, I decided to use a CR1000X datalogger from Campbell Scientific. This system has a number of voltage inputs which can be programmed using a graphical interface over a USB connection, and it has a port for an SD card. I programmed the datalogger to sample each of the four analog channels coming from the OCS every five seconds and to store the measurements on an SD card. Collecting the measurements was then as simple as removing the SD card from the datalogger and copying the data to my laptop. This could be done without interrupting the datalogger, as it has its own internal storage, and it would continue measuring while the SD card was removed. 

I had also considered simultaneously logging a digital form of the measurements to an Arduino in addition to the analog measurements made by the datalogger. This would give us two redundant logging systems which would decrease the chances of losing valuable information in the event of an instrument malfunction. However, due to a shortage of time and a technical issue with the instrument’s digital channels, I was unable to prepare the Arduino logger by the time we were ready to deploy the OCS, so we used only the analog datalogger. 

Figure 2. The OCS with its new power supply being tested in the laboratory 

Deploying the instrument 

Once the power supply and datalogger were completed, the instrument was ready to be deployed! It was a fairly simple process to get approval to put the instrument in the observatory; then I met with Ian Read to find a suitable location to set up the OCS. There were several posts in the observatory which were free, and I chose one which was close to the temperature and humidity sensors in the hopes that the conditions would be fairly similar in those locations. Once everything was ready, the technicians and I took the OCS and datalogger and set it up in the field site. At first, when we powered it on, nothing happened. Apparently, one of the solder joints on my power adapter had been damaged when I carried it across campus. However, I resoldered that connection with advice from the university technical staff, and it worked beautifully! 

Figure 3. The datalogger inside its enclosure in the observatory 

Figure 4. The OCS attached to its post in the observatory  

Except for a short period of maintenance in January, the OCS has been running continuously from December until May, and it has already captured quite a few fog events! With the data from the OCS, I now have an additional resource to use in analyzing fog. The levels of light backscattered from the four channels of the instrument provide interesting information, which I am combining with electrical and visibility measurements to analyze the microphysical properties of fog development. 

Hopefully, over the next year, we will be able to measure many more fog events with this instrument that will help us to better understand fog! 

Harrison, R. G., and K. A. Nicoll, 2014: Note: Active optical detection of cloud from a balloon platform. Rev. Sci. Instrum., 85, 066104, https://doi.org/10.1063/1.4882318. 

Ensemble Data Assimilation with auto-correlated model error

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Haonan Ren – h.ren@pgr.reading.ac.uk

Data assimilation is a mathematical method to combine forecasts with observations, in order to improve the accuracy of the original forecast. Normally data assimilation methods are performed with the perfect-model assumption (weak-constraint). However, there are different sources that can produce model error, such as missing description of the dynamic system and numerical discretisation. Therefore, in recent years, the model error has been more often considered in the data assimilation process (strong-constraint settings). 

There are several data assimilation methods applied in various fields. My PhD project mainly focuses on the ensemble/Monte Carlo formulation of the Kalman Filter-based methods, more specifically, the ensemble Kalman Smoother (EnKS). Different from the filter, a smoother updates the state of the system using observations from the past, present and possibly the future. The smoother does not only improve the forecast at the observation time, instead, it updates the whole simulation period. 

The main purpose of my research is to investigate the performance of the data assimilation methods with auto-correlated model error. We want to know what will happen if we propose a misspecified auto-correlation in the model error, for both state update and parameter estimation. We start our project with a very simple linear auto-regressive model. As for the auto-correlation in model error, we propose an exponential decaying decorrelation. Naturally, the system has a exponential decaying parameter ωr, and the parameter we use in the forecast and data assimilation is ωg which can be different from the real one. 

A simple example can illuminate the decorrelation issue. In Figure 1, we show results of a smoothing process for a simple one-dimensional system over a time window of 20 nature time steps. We use an ensemble Kalman Smoother with two different observation densities in time. The memories in the nature model and the forecasts models do not coincide. We can see that with ωr = 0.0, when the actual model error is a white-in-time random variable, the evolution of the true state of the system behaves rather erratically with the present model settings. If we do not know the memory and assume the model error is a bias in the data assimilation process (ωg → ∞), the estimation made by the data assimilation method is not even close to the truth, even with very dense observations in the simulation period, as shown in the left two subplots in Figure 1. On the other hand, if the model error in the true model evolution behaves like a bias, and we assume that the model error is white in time in the data assimilation process, the results are quite different with different observation frequencies. As shown in two subplots on the right in Figure 1, with very frequent observations, we can see a fairly good performance of the data assimilation process, but with a single observation, the estimation is still not accurate. 

Figure 1: Plots of the trajectories of the true state of the system (black), three posterior ensemble members (pink, randomly chosen from 200 members), and the posterior ensemble mean (red). The left subplots show results for a true white noise model error and an assumed bias model error for two observation densities. Note that the posterior estimates are poor in both cases. The right subplots depict a bias true model error and an assumed white noise model error. The result with one observation is poor, while if many observations are present the assimilation result is consistent within the ensemble spread.

In order to evaluate the performance of the EnKS, we need to compare the root-mean-square error (RMSE) with the ensemble spread of the posterior. The best performance of the EnKS is when ratio of RMSE over the spread is equal to 1.0. The results are shown in Figure 2. As we can see, the Kalman Smoother works well when ωg = ωr for all the cases, with the ratio of RMSE over the spread equal to 1.0. With relatively high observational frequency, 5 observations or more in the simulation window, the RMSE is larger than the spread when ωg > ωr, and vice versa. In a further investigation, the mismatch between the two timescales ωr and ωg barely has any impact on the RMSE. The ratio is dominated by the ensemble spread.

Figure 2: Ratio of MSE over the posterior variance for the 1-dimensional system, calculated using numerical evaluation of the exact analytical expressions. The different panels show results for different numbers of observations.

Then, we move to estimating the parameter encoded in the auto-correlation of the model error. We estimate the exponential decaying parameter by the state augmentation using the EnKS, and the results are shown in Figure 3. Instead of the exponential parameter, ωg, we use the log scale of the memory timescale to avoid negative memory estimates. The initial log-timescale values are drawn from a normal distribution: ln ωgi ∈ N (ln ωg, 1.0). Hence we assume that the prior distribution of the memory time scale is lognormal distributed. According to Figure 3, with an increasing number of windows we obtain better estimates. Also, the convergence is faster with more observations. And in some cases the solution did not converge to the correct value. This is not surprising given the highly nonlinear character of the parameter estimation problem, especially with only one observation per window. When we observe every time step the convergence is much faster, and the variance in the estimate decreases, as shown in the lower two subplots. In this case we always found fast convergence with different first guess and true timescale combinations, demonstrating that more observations bring us closer to the truth, and hence make the parameter estimation problem more linear.

Figure 3: PDFs of the prior (blue) and posterior (reddish colours) estimated ωg, using an increasing number of assimilation windows. The different panels show results for different observation densities and prior mean larger (a, c) or smaller (b, d) than ωr. The vertical black line denotes the true value ωr.

As the results of the experiments show, the influence of an incorrect decorrelation timescale in the model error can be significant. We found that when the observation density is high, state augmentation is sufficient to obtain converging results. The new element is that online estimation is possible beyond a relatively simple bias estimate of the model error.

As a next step we will explore the influence of incorrectly specified model errors in nonlinear systems, and a more complex auto-correlation in the model error.

Communicating uncertainties associated with anthropogenic climate change

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Email: j.f.talib@pgr.reading.ac.uk

This week Prof. Ed Hawkins from the Department of Meteorology and NCAS-Climate gave a University of Reading public lecture discussing the science of climate change. A plethora of research was presented, all highlighting that humans are changing our climate. As scientists we can study the greenhouse effect in scientific labs, observe increasing temperatures across the majority of the planet, or simulate the impact of human actions on the Earth’s climate through using climate models.

simulating_temperature_rise
Figure 1. Global-mean surface temperature in observations (solid black line), and climate model simulations with (red shading) and without (blue shading) human actions. Shown during Prof. Ed Hawkins’ University of Reading Public Lecture.

Fig. 1, presented in Ed Hawkins’ lecture, shows the global mean temperature rise associated with human activities. Two sets of climate simulations have been performed to produce this plot. The first set, shown in blue, are simulations controlled solely by natural forcings, i.e. variations in radiation from the sun and volcanic eruptions. The second, shown in red, are simulations which include both natural forcing and forcing associated with greenhouse gas emissions from human activities. The shading indicates the spread amongst climate models, whilst the observed global-mean temperature is shown by the solid black line. From this plot it is evident that all climate models attribute the rising temperatures over the 20th and 21st century to human activity. Climate simulations without greenhouse gas emissions from human activity indicate a much smaller rise, if any, in global-mean temperature.

However, whilst there is much agreement amongst climate scientists and climate models that our planet is warming due to human activity, understanding the local impact of anthropogenic climate change contains its uncertainties.

For example, my PhD research aims to understand what controls the location and intensity of the Intertropical Convergence Zone. The Intertropical Convergence Zone is a discontinuous, zonal precipitation band in the tropics that migrates meridionally over the seasonal cycle (see Fig. 2). The Intertropical Convergence Zone is associated with wet and dry seasons over Africa, the development of the South Asian Monsoon and the life-cycle of tropical cyclones. However, currently our climate models struggle to simulate characteristics of the Intertropical Convergence Zone. This, alongside other issues, results in climate models differing in the response of tropical precipitation to anthropogenic climate change.

animation
Figure 2. Animation showing the seasonal cycle of the observed monthly-mean precipitation rates between 1979-2014.

Figure 3 is a plot taken from a report written by the Intergovernmental Panel on Climate Change (Climate Change 2013: The Physical Science Basis). Both maps show the projected change from climate model simulations in Northern Hemisphere winter precipitation between the years 2016 to 2035 (left) and 2081 to 2100 (right) relative to 1986 to 2005 under a scenario where minimal action is taken to limit greenhouse gas emissions (RCP8.5) . Whilst the projected changes in precipitation are an interesting topic in their own right, I’d like to draw your attention to the lines and dots annotated on each map. The lines indicate where the majority of climate models agree on a small change. The map on the left indicates that most climate models agree on small changes in precipitation over the majority of the globe over the next two decades. Dots, meanwhile, indicate where climate models agree on a substantial change in Northern Hemisphere winter precipitation. The plot on the right indicates that across the tropics there are substantial areas where models disagree on changes in tropical precipitation due to anthropogenic climate change. Over the majority of Africa, South America and the Maritime Continent, models disagree on the future of precipitation due to climate change.

IPCC_plot
Figure 3. Changes in Northern Hemisphere Winter Precipitation between 2016 to 2035 (left) and 2081 to 2100 (right) relative to 1986 to 2005 under a scenario with minimal reduction in anthropogenic greenhouse gas emission. Taken from IPCC – Climate Change 2013: The Physical Science Basis.

How should scientists present these uncertainties?

I must confess that I am nowhere near an expert in communicating uncertainties, however I hope some of my thoughts will encourage a discussion amongst scientists and users of climate data. Here are some of the ideas I’ve picked up on during my PhD and thoughts associated with them:

  • Climate model average – Take the average amongst climate model simulations. With this method though you take the risk of smoothing out large positive and negative trends. The climate model average is also not a “true” projection of changes due to anthropogenic climate change.
  • Every climate model outcome – Show the range of climate model projections to the user. Here you face the risk of presenting the user with too much climate data. The user may also trust certain model outputs which suit their own agenda.
  • Storylines – This idea was first shown to me in a paper by Zappa, G. and Shepherd, T. G., (2017). You present a series of storylines in which you highlight the key processes that are associated with variability in the regional weather pattern of interest. Each change in the set of processes leads to a different climate model projection. However, once again, the user of the climate model data has to reach their own conclusion on which projection to take action on.
  • Probabilities with climate projections – Typically with short- and medium-range weather forecasts probabilities are used to support the user. These probabilities are generated by re-performing the simulations, each with either different initial conditions or a slight change in model physics, to see the percentage of simulations that agree on model output. However, with climate model simulations, it is slightly more difficult to associate probabilities with projections. How do you generate the probabilities? Climate models have similarities in the methods which they use to represent the physics of our atmosphere and therefore you don’t want the probabilities associated with each climate projection due to similarity amongst climate model set-up. You could base the probabilities on how well the climate model simulates the past, however just because a model simulates the past correctly, doesn’t mean it will correctly simulate the forcing in the future.

There is much more that can be said about communicating uncertainty among climate model projections – a challenge which will continue for several decades. As climate scientists we can sometimes fall into the trap on concentrating on uncertainties. We need to keep on presenting the work that we are confident about, to ensure that the right action is taken to mitigate against anthropogenic climate change.

The Role of the Cloud Radiative Effect in the Sensitivity of the Intertropical Convergence Zone to Convective Mixing

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Email: j.f.talib@pgr.reading.ac.uk

Talib, J., S.J. Woolnough, N.P. Klingaman, and C.E. Holloway, 2018: The Role of the Cloud Radiative Effect in the Sensitivity of the Intertropical Convergence Zone to Convective Mixing. J. Climate, 31, 6821–6838, https://doi.org/10.1175/JCLI-D-17-0794.1

Rainfall in the tropics is commonly associated with the Intertropical Convergence Zone (ITCZ), a discontinuous line of convergence collocated at the ascending branch of the Hadley circulation, where strong moist convection leads to high rainfall. What controls the location and intensity of the ITCZ remains a fundamental question in climate science.

ensemble_precip_neat_thesis
Figure 1: Annual-mean, zonal-mean tropical precipitation (mm day-1) from Global Precipitation Climatology Project (GPCP, observations, solid black line) and CMIP5 (current coupled models) output. Dashed line indicates CMIP5 ensemble mean.

In current and previous generations of climate models, the ITCZ is too intense in the Southern Hemisphere, resulting in two annual-mean, zonal-mean tropical precipitation maxima, one in each hemisphere (Figure 1).  Even if we take the same atmospheric models and couple them to a world with only an ocean surface (aquaplanets) with prescribed sea surface temperatues (SSTs), different models simulate different ITCZs (Blackburn et al., 2013).

Within a climate model parameterisations are used to replace processes that are too small-scale or complex to be physically represented in the model. Parameterisation schemes are used to simulate a variety of processes including processes within the boundary layer, radiative fluxes and atmospheric chemistry. However my work, along with a plethora of others, shows that the representation of the ITCZ is sensitive to the convective parameterisation scheme (Figure 2a). The convective parameterisation scheme simulates the life cycle of clouds within a model grid-box.

Our method of showing that the simulated ITCZ is sensitive to the convective parameterisation scheme is by altering the convective mixing rate in prescribed-SST aquaplanet simulations. The convective mixing rate determines the amount of mixing a convective parcel has with the environmental air, therefore the greater the convective mixing rate, the quicker a convective parcel will become similar to the environmental air, given fixed convective parcel properties.

AEIprecipCREon
Figure 2: Zonal-mean, time-mean (a) precipitation rates (mm day-1}$) and (b) AEI (W m-2) in simulations where the convective mixing rate is varied.

In our study, the structure of the simulated ITCZ is sensitive to the convective mixing rate. Low convective mixing rates simulate a double ITCZ (two precipitation maxima, orange and red lines in Figure 2a), and high convective mixing rates simulate a single ITCZ (blue and black lines).

We then associate these ITCZ structures to the atmospheric energy input (AEI). The AEI is the amount of energy left in the atmosphere once considering the top of the atmosphere and surface energy budgets. We conclude, similar to Bischoff and Schneider, 2016, that when the AEI is positive (negative) at the equator, a single (double) ITCZ is simulated (Figure 2b). When the AEI is negative at the equator, energy is needed to be transported towards the equator for equilibrium. From a mean circulation perspective, this take place in a double ITCZ scenario (Figure 3). A positive AEI at the equator, is associated with poleward energy transport and a single ITCZ.

blog_figure_ITCZ_simulation
Figure 3: Schematic of a single (left) and double ITCZ (right). Blue arrows denote energy transport. In a single ITCZ scenario more energy is transported in the upper branches of the Hadley circulation, resulting in a net-poleward energy transport. In a double ITCZ scenario, more energy is transport equatorward than poleward at low latitudes, leading to an equatorward energy transport.

In our paper, we use this association between the AEI and ITCZ to hypothesize that without the cloud radiative effect (CRE), atmospheric heating due to cloud-radiation interactions, a double ITCZ will be simulated. We also hypothesize that prescribing the CRE will reduce the sensitivity of the ITCZ to convective mixing, as simulated AEI changes are predominately due to CRE changes.

In the rest of the paper we perform simulations with the CRE removed and prescribed to explore further the role of the CRE in the sensitivity of the ITCZ. We conclude that when removing the CRE a double ITCZ becomes more favourable and in both sets of simulations the ITCZ is less sensitive to convective mixing. The remaining sensitivity is associated with latent heat flux alterations.

My future work following this publication explores the role of coupling in the sensitivity of the ITCZ to the convective parameterisation scheme. Prescribing the SSTs implies an arbitary ocean heat transport, however in the real world the ocean heat transport is sensitive to the atmospheric circulation. Does this sensitivity between the ocean heat transport and atmospheric circulation affect the sensitivity of the ITCZ to convective mixing?

Thanks to my funders, SCENARIO NERC DTP, and supervisors for their support for this project.

References:

Blackburn, M. et al., (2013). The Aqua-planet Experiment (APE): Control SST simulation. J. Meteo. Soc. Japan. Ser. II, 91, 17–56.

Bischoff, T. and Schneider, T. (2016). The Equatorial Energy Balance, ITCZ Position, and Double-ITCZ Bifurcations. J. Climate., 29(8), 2997–3013, and Corrigendum, 29(19), 7167–7167.

 

Presenting in Ponte Vedra, Florida – 33rd Conference on Hurricanes and Tropical Meteorology

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Email: j.f.talib@pgr.reading.ac.uk

You’ve watched many speak before you. You’ve practised your presentation repeatedly. You’ve spent hours, days, months, and sometimes years, understanding your scientific work. Yet, no matter the audience’s size or specialism, the nerves always creep in before a presentation. It’s especially no different at your first international conference!

IMG_20180420_133234

Between the 16th and 20th April 2018, me, Jonathan Beverley and Bethan Harris were fortunate enough to attend and present at the American Meteorological Society 33rd Conference on Hurricanes and Tropical Meteorology in Ponte Vedra, Florida. For each of us, our first international conference!

Being a regular user of Instagram through the conference, especially the Instagram Story function, I was regularly asked by my friends back home, “what actually happens at a scientific conference”? Very simple really – scientists from around the world, from different departments, universities, and countries, come to share their work, in the hope of progressing the scientific field, to learn from one another, and network with future collaborators. For myself, it was an opportunity to present recently submitted work and to discuss with fellow researchers on the important questions that should be asked during the rest of my PhD. One outcome of my talk for example, was a two-hour discussion with a graduate student from Caltech, which not only improved my own work, but also helped me understand other research in global circulation.

Recordings of the presentations given by University of Reading PhD students can be found at:

Alongside presenting my own work, I had the opportunity to listen and learn from other scientific researchers. The conference had oral and poster presentations from a variety of tropical meteorology subject areas including hurricanes, global circulation, sub-seasonal forecasting, monsoons and Madden-Julian Oscillation. One of the things that I most enjoy at conferences is to hear from leading academics give an overview of certain topic or issue. For example, Kerry Emanuel spoke on the inferences that can be made from simple models of tropical convection. Through applying four key principles of tropical meteorology including the weak temperature gradient approximation and conservation of free-tropospheric moist static energy, we can understand tropical meteorology processes including the Intertropical Convergence Zone, Walker circulation and observed temperature and humidity profiles.

Of course, if you’re going to fly to the other side of the pond, you must take advantage of being in the USA. We saw a SPACEX rocket launch, (just at a distance of 150 miles away,) experienced travelling through a squall line, visited the launch sites of NASA’s first space programs, and explored the sunny streets of Miami. It was a great privilege to have the opportunity to present and attend the AMS 33rd Conference on Hurricanes and Tropical Meteorology, and I am hugely thankful to NERC SCENARIO DTP and the Department of Meteorology for funding my work and travel.

 

Inspirational Female Scientists #women1918

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100 years ago today the UK parliament reformed the electoral system in Great Britain by permitting women over the age of 30 to vote. Unfortunately, there were terms to the act that meant women either had to be a member or married to a member of the Local Government Register, a property owner, or a graduate voting in a University constituency. However, crucial and progressive steps had been taken for women’s rights, and it is the same for today as it was 100 years ago, that more is needed to be done to ensure global gender equality.

At Social Metwork HQ, we have taken our time to reflect and be encouraged by inspirational female scientists. Different students across the department have written short paragraphs on female scientists that have inspired them to where they are today. If you have any other suggestions for inspirational scientists, please feel free to leave us a comment.

Amelie Emmy Noether – Kaja Milczewska

emmy-noether-2A true revolutionary in the field of theoretical physics and abstract algebra, Amelie Emmy Noether was a German-born inspiration thanks to her perseverance and passion for research. Instead of teaching French and English to schoolgirls, Emmy pursued the study of mathematics at the University of Erlangen. She then taught under a man’s name and without pay because she was a women.  During her exploration of the mathematics behind Einstein’s general relativity alongside renowned scientists like Hilbert and Klein, she discovered the fundamentals of conserved quantities such as energy and momentum under symmetric invariance of their respective quantities: time and homogeneity of space. She built the bridge between conservation and symmetry in nature, and although Noether’s Theorem is fundamental to our understanding of nature’s conservation laws, Emmy has received undeservedly small recognition throughout the last century.

Claudine Hermann – Helene Bresson

Claudine-HermannClaudine Hermann is a French physicist and Emeritus Professor at the École Polytechnique in Paris. Her work, on physics of solids (mainly on photo-emission of polarized electrons and near-field optics), led to her becoming the first female professor at this prestigious school. Aside from her work in Physics, Claudine studied and wrote about female scientists’ situation in Europe and the influence of both parents’ works on their daughter’s professional choices. Claudine wishes to give girls “other examples than the unreachable Marie Curie”. She is the founder of the Women and Sciences association and represented it at the European Commission to promote gender equality in Science and to help women accessing scientific knowledge. Claudine is also the president of the European Platform of Women Scientists which represents hundreds of associations and more than 12,000 female scientists.

Katherine Johnson – Sally Woodhouse

26646856911_ca242812ee_o_1For most people being handpicked to be one of three students to integrate West Virginia’s graduate schools would probably be the most notable life achievements. However for Katherine Johnson’s this was just the start of a remarkable list of accomplishments. In 1952 Johnson joined the all-black West Area Computing section at NACA (to become NASA in 1958). Acting as a computer, Johnson analysed flight test data, provided maths for engineering lectures and worked on the trajectory for America’s first human space flight.

She became the first woman to receive an author credit on a Flight Research Division report in 1960 and went on to author or co-author 26 research reports. Johnson is perhaps best known (in part due to the excellent feel good film Hidden Figures) for her work on the flight trajectory for John Glenn’s 1962 orbital mission.

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She was required to check the calculations of NASA’s IBM computer and Glenn is reported to have asked for her to personally check the coordinates.

“GET THE GIRL TO CHECK THE NUMBERS… IF SHE SAYS THE NUMBERS ARE GOOD, I’M READY TO GO.”

Katherine was also involved in calculations for the Apollo missions trajectories, including Apollo 11. In 2015 she was presented with the Presidential Medal of Freedom by Barack Obama.

Marie Tharp – Caroline Dunning

World War II was an important period in terms of scientific advance. In addition, it enabled more women to be trained in professions such as geology, at a time when very few women were in earth sciences. One such woman was Marie Tharp. Following the advancement of sonar technology during WWII, in the early 1950s, ships travelled across the Atlantic Ocean recording ocean depth. maria-tharp-oceanWomen however were not allowed on such ships, thus Marie Tharp was stationed in the lab, checking and plotting the data. Her drawings showed the presence of the North Atlantic Ridge, with a deep V-shaped notch that ran the length of the mountain range, indicating the presence of a rift valley, where magma emerges to form new crust. At this time the theory of plate tectonics was seen as ridiculous. Her supervisor initially dismissed her results as ‘girl talk’ and forced her to redo them. The same results were found. Her work led to the acceptance of the theory of plate tectonics and continental drift.

Ada Lovelace – Dominic Jones

ada-lovelace-20825279-1-402Ada Lovelace was a 19th century Mathematician popularly referred to as the “first computer programmer”. She was the translator of “Sketch of the Analytical Engine, with Notes from the Translator”, (said “notes” tripling the length of the document and comprising its most striking insights) one of the documents critical to the development of modern computer programming. She was one of the few people to understand and even fewer who were able to develop for the machine. That she had such incredible insight into a machine which didn’t even exist yet, but which would go on to become so ubiquitous is amazing!

Drs. Jenni Evans, Sukyoung Lee, and Yvette Richardson – Michael Johnston

Leading Scientists at Penn State University, Drs. Jenni Evans, Sukyoung Lee, and Yvette Richardson serve as role models for students in STEM subjects. The three professors are active in linking their research interests to not only education but also science communication, and government policy. Between them, they highlight some of the many avenues a career in STEM can lead to. Whether its authoring a widely used textbook, leading advisory panels, or challenging students throughout their time in higher education – these leaders never cease to be an inspiration.

 

Why become a Royal Meteorological Society Student member?

The Social Metwork1 Comment

This week the Royal Meteorological Society (RMetS) published their strategic plan for the period of 2018 to 2020, and here at Social Metwork HQ we thought it would be a splendid idea to reflect on the benefits of being a student member of the Royal Meteorological Society.

An important benefit in my opinion is that when becoming a member of RMetS you join a well-established community who hold enthusiasm about the weather and climate at its core. Members come from all corners of the world and at different stages of their career spanning the entire range: from the amateur weather enthusiasts to professionals.  nicole-kuhn-450747As a student, being an RMetS member can lead to conversations that could develop your career and bring unexpected opportunities. This has been greatly enhanced with the RMetS mentoring scheme.

RMetS host many different types of meetings, including annual conferences, meetings hosted by regional centres, and national meetings. Additional gatherings are held by special interest groups, ranging from Weather Arts & Music to Dynamical Problems. Meetings on a regional and national scale provide a platform for discussion and learning amongst those in the field. DEhXj9AXkAARyMM.jpg largeFor a student, the highlight in the RMetS calendar is the annual student conference. Every year, sixty to eighty students come together to present their work and develop professional relationships that continue for years to come. This year’s conference is hosted at the University of York on the 5th and 6th July 2018 (more information). After two student conferences under my belt (see previous blog post), I would highly recommend any early career research scientist attending this event. It serves as a platform to share their own work in a friendly atmosphere and be inspired by the wider student community.

nasa-63030Other benefits to becoming an RMetS student member include eligibility to the Legacies Fund, grants and fellowships, and receiving a monthly copy of Weather magazine. Most importantly though, through becoming a RMetS member you support a professional society who are committed to increasing awareness of the importance of weather and climate in policy and decision-making. Alongside this week’s publication of RMetS’ strategic plan, both the Met Office and NASA have published press releases stating that 2017 was the warmest year on record without El Niño. The atmosphere and oceans of our planet are changing at unprecedented rates: rising sea levels, reductions in Arctic sea-ice, and an increased frequency of extreme weather events to name but a few climate change impacts. Becoming an RMetS student member does not only benefit your career and knowledge, but also supports a society that is committed to promoting and raising awareness of weather and climate science.