The advection process: simulating wind on computers

Email: js102@zepler.net   Web: datumedge.co.uk   Twitter: @hertzsprrrung

This article was originally posted on the author’s personal blog.

If we know which way the wind is blowing then we can predict a lot about the weather. We can easily observe the wind moving clouds across the sky, but the wind also moves air pollution and greenhouse gases. This process is called transport or advection. Accurately simulating the advection process is important for forecasting the weather and predicting climate change.

I am interested in simulating the advection process on computers by dividing the world into boxes and calculating the same equation in every box. There are many existing advection methods but many rely on these boxes having the correct shape and size, otherwise these existing methods can produce inaccurate simulations.

During my PhD, I’ve been developing a new advection method that produces accurate simulations regardless of cell shape or size. In this post I’ll explain how advection works and how we can simulate advection on computers. But, before I do, let’s talk about how we observe the weather from the ground.

In meteorology, we generally have an incomplete picture of the weather. For example, a weather station measures the local air temperature, but there are only a few hundred such stations dotted around the UK. The temperature at another location can be approximated by looking at the temperatures reported by nearby stations. In fact, we can approximate the temperature at any location by reconstructing a continuous temperature field using the weather station measurements.

The advection equation

So far we have only talked about temperatures varying geographically, but temperatures also vary over time. One reason that temperatures change over time is because the wind is blowing. For example, a wind blowing from the north transports, or advects, cold air from the arctic southwards over the UK. How fast the temperature changes depends on the wind speed, and the size of the temperature contrast between the arctic air and the air further south. We can write this as an equation. Let’s call the wind speed v and assume that the wind speed and direction are always the same everywhere. We’ll label the temperature T, label time t, and label the south-to-north direction y, then we can write down the advection equation using partial derivative notation,

\frac{\partial T}{\partial t} = - \frac{\partial T}{\partial y} \times v

This equation tells us that the local temperature will vary over time (\frac{\partial T}{\partial t}), depending on the north-south temperature contrast (- \frac{\partial T}{\partial y}) multiplied by the wind speed v.

Solving the advection equation

One way to solve the advection equation on a computer is to divide the world into boxes, called cells. The complete arrangement of cells is called a mesh. At a point at the centre of each cell we store meteorological information such as temperature, water vapour content or pollutant concentration. At the cell faces where two cells touch we store the wind speed and direction. The arrangement looks like this:

britain-cgrid
A mesh of cells with temperatures stored at cell centres and winds stored at cell faces.  For illustration, the temperature and winds are only shown in one cell.  This arrangement of data is known as an Arakawa C-grid.  Figure adapted from WikiMedia Commons, CC BY-SA 3.0.

The above example of a mesh over the UK uses cube-shaped cells stacked in columns above the Earth, and arranged along latitude and longitude lines. But more recently, weather forecasting models are using different types of mesh. These models tesselate the globe with squares, hexagons or triangles.

meshes
The surfaces of some different types of global mesh. The cells are prismatic since they are stacked in columns above the surface.

Weather models must also rearrange cells in order to represent mountains, valleys, cliffs and other terrain. Once again, different models rearrange cells differently. One method, called the terrain-following method, shifts cells up or down to accommodate the terrain. Another method, called the cut-cell method, cuts cells where they intersect the terrain. Here’s what these methods look like when we use them to represent an idealised, wave-shaped mountain:

terrain-meshes
Two different methods for representing terrain in weather forecast models. The terrain-following method is widely used but suffers from large distortions above steep slopes. The cut cell method alleviates this problem but cells may be very much smaller than most others in a cut cell mesh.

Once we’ve chosen a mesh and stored temperature at cell centres and the wind at cell faces, we can start calculating a solution to the advection equation which enables us to forecast how the temperature will vary over time. We can solve the advection equation for every cell separately by discretising the advection equation. Let’s consider a cell with a north face and a south face. We want to know how the temperature stored at the cell centre, T_\mathrm{cell}, will vary over time. We can calculate this by reconstructing a continuous temperature field and using this to approximate temperature values at the north and south faces of the cell, T_\mathrm{north} and T_\mathrm{south},

\frac{\partial T_\mathrm{cell}}{\partial t} = - \frac{T_\mathrm{north} - T_\mathrm{south}}{\Delta y} \times v

where \Delta y is the distance between the north and south cell faces. This is the same reconstruction process that we described earlier, only, instead of approximating temperatures using nearby weather station measurements, we are approximating temperatures using nearby cell centre values.

There are many existing numerical methods for solving the advection equation but many do not cope well when meshes are distorted, such as terrain-following meshes, or when cells have very different sizes, such as those cells in cut-cell meshes. Inaccurate solutions to the advection equation lead to inaccuracies in the weather forecast. In extreme cases, very poor solutions can cause the model software to crash, and this is known as a numerical instability.

slug-slantedCells-linearUpwind
An idealised simulation of a blob advected over steep mountains. A numerical instability develops because the cells are so distorted over the mountain.

We can see a numerical instability growing in this idealised example. A blob is being advected from left to right over a range of steep, wave-shaped mountains. This example is using a simple advection method which cannot cope with the distorted cells in this mesh.

We’ve developed a new method for solving the advection equation with almost any type of mesh using cubes or hexagons, terrain-following or cut-cell methods. The advection method works by reconstructing a continuous field from data stored at cell centre points. A separate reconstruction is made for every face of every cell in the mesh using about twelve nearby cell centre values. Given that weather forecast models have millions of cells, this sounds like an awful lot of calculations. But it turns out that we can make most of these calculations just once, store them, and reuse them for all our simulations.

slug-slantedCells-cubicFit
Our new advection method avoids the numerical instability that occurred using the simple method.

Here’s the same idealised simulation using our new advection method. The results are numerically stable and accurate.

Further reading

A preprint of our journal article documenting the new advection method is available on ArXiv. I also have another blog post that talks about how to make the method even more accurate. Or follow me on Twitter for more animations of the numerical methods I’m developing.

Understanding the dynamics of cyclone clustering

Priestley, M. D. K., J. G. Pinto, H. F. Dacre, and L. C. Shaffrey (2016), Rossby wave breaking, the upper level jet, and serial clustering of extratropical cyclones in western Europe, Geophys. Res. Lett., 43, doi:10.1002/2016GL071277.

Email: m.d.k.priestley@pgr.reading.ac.uk

Extratropical cyclones are the number one natural hazard that affects western Europe (Della-Marta, 2010). These cyclones can cause widespread socio-economic damage through extreme wind gusts that can damage property, and also through intense precipitation, which may result in prolonged flood events. For example the intensely stormy winter of 2013/2014 saw 456mm of rain fall in under 90 days across the UK; this broke records nationwide as 175% of the seasonal average fell (Kendon & McCarthy, 2015). One particular storm in this season was cyclone Tini (figure 1), this was a very deep cyclone (minimum pressure – 952 hPa) which brought peak gusts of over 100 mph to the UK. These gusts caused widespread structural damage that resulted in 20,000 homes losing power. These extremes can be considerably worse when multiple extratropical cyclones affect one specific geographical region in a very short space of time. This is known as cyclone clustering. Some of the most damaging clustering events can result in huge insured losses, for example the storms in the winter of 1999/2000 resulted in €16 billion of losses (Swiss Re, 2016); this being more than 10 times the annual average.

figure-1
Figure 1. A Meteosat visible satellite image at 12 UTC on February 12th 2014 showing cyclone Tini over the UK. Image credit to NEODAAS/University of Dundee.

Up until recently cyclone clustering had been given little attention in terms of scientific research, despite it being a widely accepted phenomenon in the scientific community. With these events being such high risk events it is important to understand the atmospheric dynamics that are associated with these events; and this is exactly what we have been doing recently. In our new study we attempt to characterise cyclone clustering in several different locations and associate each different set of clusters with a different dynamical setup in the upper troposphere. The different locations we focus on are defined by three areas, one encompassing the UK and centred at 55°N. Our other two areas are 10° to the north and south of this (centred at 65°N and 45°N.) The previous study of Pinto et al. (2014) examined several winter seasons and found links between the upper-level jet, Rossby wave breaking (RWB) and the occurrence of clustering. RWB is the meridional overturning of air in the upper troposphere. It is identified using the potential temperature (θ) field on the dynamical tropopause, with a reversal of the normal equator-pole θ gradient representing RWB. This identification method is explained in full in Masato et al. (2013) and also illustrated in figure 2. We have greatly expanded on this analysis to look at all winter clustering events from 1979/1980 to 2014/2015 and their connection with these dynamical features.

schematic_box1
Figure 2. Evolution of Rossby waves on the tropopause. RWB occurs when these waves overturn by a significant amount. H: High potential temperature; L: Low potential temperature (Priestley et al., 2017).

We find that when we get clustering it is accompanied with a much stronger jet at 250 hPa than in the climatology, with average speeds peaking at over 50 ms-1 (figures 3a-c). In all cases there is also a much greater presence of RWB in regions not seen from the climatology (Figure 3d). In figure 3a there is more RWB to the south of the jet, in figure 3b there is an increased presence on both the northern and southern flanks, and finally in figure 3c there is much more RWB to the north. The presence of this anomalous RWB transfers momentum into the jet, which acts to strengthen and extend it toward western Europe.

figure-2
Figure 3. The dynamical setup for clustering occurring at (a) 65°N; (b) 55°N; and (c) 45°N. The climatology is shown in (d). Coloured shading is the average potential temperature on the tropopause, black contours are the average 250 hPa wind speeds and black crosses are where RWB is occurring.

The location of the RWB controls the jet tilt; more RWB to the south of the jet acts to angle it more northwards (figure 3a), there is a southward deflection when there is more RWB to the north of the jet (figure 3c). The presence of RWB on both sides extends it along a more central axis (figure 3b). Therefore the occurrence of RWB in a particular location and the resultant angle of the jet acts to direct cyclones to various parts of western Europe in quick succession.

In our recently published study we go into much more detail regarding the variability associated with these dynamics and also how the jet and RWB interact in time. This can be found at http://dx.doi.org/10.1002/2016GL071277.

This work is funded by NERC via the SCENARIO DTP and is also co-sponsored by Aon Benfield.

References

Della-Marta, P. M., Liniger, M. A., Appenzeller, C., Bresch, D. N., Köllner-Heck, P., & Muccione, V. (2010). Improved estimates of the European winter windstorm climate and the risk of reinsurance loss using climate model data. Journal of Applied Meteorolo

Kendon, M., & McCarthy, M. (2015). The UK’s wet and stormy winter of 2013/2014. Weather, 70(2), 40-47.

Masato, G., Hoskins, B. J., & Woollings, T. (2013). Wave-breaking characteristics of Northern Hemisphere winter blocking: A two-dimensional approach. Journal of Climate, 26(13), 4535-4549.

Pinto, J. G., Gómara, I., Masato, G., Dacre, H. F., Woollings, T., & Caballero, R. (2014). Large‐scale dynamics associated with clustering of extratropical cyclones affecting Western Europe. Journal of Geophysical Research: Atmospheres, 119(24).

Priestley, M. D. K., J. G. Pinto, H. F. Dacre, and L. C. Shaffrey (2017). The role of cyclone clustering during the stormy winter of 2013/2014. Manuscript in preparation.

Swiss Re. (2016). Winter storm clusters in Europe, Swiss Re publishing, Zurich, 16 pp., http://www.swissre.com/library/winter_storm_clusters_in_europe.html. Accessed 24/11/16.

From foehn to intense rainfall: the importance of Alps in influencing the regional weather

Email: a.volonte@pgr.reading.ac.uk

dsc_0196
Figure 1: View from Monte Lema (Italy-Switzerland) looking West. The Lake Maggiore region and the southern Alpine foothills are visible in the foreground whereas Monte Rosa and the Pennine Alps behind them are partially hidden by a characteristic foehn wall.  (A. Volonté, 4 January 2017)

The interaction between atmospheric flow and topography is at the origin of various important weather phenomena, as we have already seen in Carly Wright’s blog post. When a mountain range is particularly high and extended it can even block or deflect weather systems, as it happens with the Alps. For example, in Figure 1 we can see the main Alpine range with its over-4000m-high peaks blocking a cold front coming from the north. The main ridge acts as a wall, enhancing condensation and precipitation processes on the upstream side (stau condition) and leaving clear skies on the downstream lee side, where dry and mild katabatic foehn winds flow. The contrast is striking between sunny weather on Lake Maggiore and snowy conditions over Monte Rosa, just a few miles apart. The same phenomenon is shown in Figure 2 with a satellite image that highlights how a cold front coming from northwest gets blocked by the Alpine barrier. A person enjoying the sunny day in the southern side of the Alps, if unaware of this mechanism, would be very surprised  to know that the current weather is so different on the other side of the range.

poplex-2014295-terra-1km
Figure 2: Satellite image (MODIS-NASA) over the Alps and Po Valley on 22 October 2014
poplex-2016348-terra-1km
Figure 3: same as Figure 1 but on 13 December 2016

A comparison with Figure 3 helps to notice that in Figure 2 the shape of the cloud band closely mirrors the mountain range. As an additional remark,  this comparison shows that foehn bring clear skies even in the Po Valley, having blown away the typical mist/fog occurring in the region in Autumn and Winter months in high pressure regimes. The  stau/foehn dynamics is actually very fascinating, and you can read more about it in Elvidge and Renfrew (2015 ) and in Miltenberger et al. (2016), among others. Unfortunately, the interaction of weather systems with the Alps can often trigger very damaging phenomena, like heavy and long-lasting precipitation on one side of the slope, and this is what the rest of this post will be focused on. In fact, the most recent event of this kind just happened at the end of November, with intense and long-lasting rain affecting the southern slope of the Alps  and causing floods particularly in the Piedmont region, in northwestern Italy ( Figure 4).

tanaro2
Figure 4: River Tanaro flooding in the town of Garessio, 24 November 2016 (Piedmont, Italy). Source: http://www.corrierenazionale.it
arpa_piemonte
Figure 5: rainfall accumulated between 21 and 26 November 2016 in the Piedmont region. Source: Regional Agency for the protection of the Environment – Piedmont

Figure 5 shows that the accumulated rainfall in the event goes over 300 mm in a large band that follows the shape of the southern Alpine slope in the region (see map of Piedmont, from Google Maps), reaching even 600 mm in a few places. This situation is the result of moist southerly flow being blocked by the Alps and thus causing ascent and consequent precipitation to persist on the same areas for up to five days. It is quite common to see quasi-stationary troughs enter the Mediterranean region during Autumn months causing strong and long-lasting moist flows to move towards the Alps. Hence, it is crucial to understand  where the heaviest precipitation will occur. In other words, will it rain the most on top of the ridge or on the upstream plain? What processes are controlling the location of heavy precipitation with respect to the slope?

The study published by Davolio et al. (2016), available here and originated from my master degree’s thesis, tackles this issue focusing on northeastern Italy. In fact, the analysis includes three case studies in which heavy and long-lasting rain affected the eastern Alps and other three case studies in which intense rainfall was mainly located on the upstream plain. Although all the events showed common large-scale patterns and similar mesoscale settings, characterised by moist southerly low-level flow interacting with the Alps, the rainfall distribution turned out to be very dissimilar. The study highlights that the two precipitation regimes strongly differ in terms of interaction of the flow with the mountain barrier. When the flow is able to go over the Alps the heaviest rain occurs on top of the ridge. When the flow is instead blocked and deflected by the ridge (flow around), creating a so-called barrier wind, intense convection is triggered on the upstream plain (Figure 6) .

qj2731
Figure 6: Schematic diagram of the key mechanisms governing the two different wind and precipitation patterns over NE Italy. (a) Blocked low-level flow, barrier wind, convergence and deep convection over the plain, upstream the orography. (b) Flow over conditions with orographic lifting and precipitation mainly over the Alps. From Davolio et al. (2016)
convection
Figure 7: cross section going from the Adriatic Sea to the Alps in one of the events simulated. Equivalent potential temperature is shaded, thick black lines indicate clouds while arrows show tangent wind component. See Davolio et al. (2016)

The key mechanism that explains this different evolution is connected to the thermodynamic state of the impinging flow. In fact, when the southerly moist and warm air gets close to the Alpine barrier it is lifted above the colder air already present at the base of the orography. It can be said that the colder air behaves as a first effective mountain for the incoming flow. If this lifting process triggers convection, then the persistence of a blocked-flow condition is highly favoured (see Figure 7). On the contrary, if this initial lifting process does not trigger convection the intense moist flow will eventually be able to go over the ridge, where a more substantial ascent will take place, causing heavy rain on the ridge top. This study also looks at numerical parameters used in more idealised analyses (like in Miglietta and Rotunno (2009)), finding a good agreement with the theory.

To summarise, we can say that the Alpine range is able to significantly modify weather systems when interacting with them. Thus, an in-depth understanding of the processes taking place during the interaction, along with a coherent model is necessary to capture correctly the effects on the local weather, being either a rainfall enhancement, the occurrence of foehn winds or various other phenomena.

References

Davolio, S., Volonté A., Manzato A., Pucillo A., Cicogna A. and Ferrario M.E. (2016), Mechanisms producing different precipitation patterns over north-eastern Italy: insights from HyMeX-SOP1 and previous events. Q.J.R. Meteorol. Soc., 142 (Suppl 1): 188-205. doi:10.1002/qj.2731

Elvidge A. D., Renfrew, I. A. (2015). The causes of foehn warming in the lee of mountains. Bull. Am. Meteorol. Soc. 97: 455466, doi:10.1175/BAMS-D-14-00194.1.

Miglietta M. and Rotunno R., (2009) Numerical Simulations of Conditionally Unstable Flows over a Mountain Ridge. J. Atmos. Sci., 66, 1865–1885, doi: 10.1175/2009JAS2902.1. 

Miltenberger, A. K., Reynolds, S. and Sprenger, M. (2016), Revisiting the latent heating contribution to foehn warming: Lagrangian analysis of two foehn events over the Swiss Alps. Q.J.R. Meteorol. Soc., 142: 2194–2204. doi:10.1002/qj.2816

Stationary Orographic Rainbands

Email: c.j.wright@pgr.reading.ac.uk

Small-scale rainbands often form downwind of mountainous terrain. Although relatively small in scale (a few tens of km across by up to ~100 km in length), these often poorly forecast bands can cause localised flooding as they can be associated with intense precipitation over several hours due to the anchoring effect of orography (Barrett et al., 2013).   Figure 1 shows a flash flood caused by a rainband situated over Cockermouth in 2009.  In some regions of southern France orographic banded convection can contribute 40% of the total rainfall (Cosma et al., 2002).  Rainbands occur in various locations and under different synoptic regimes and environmental conditions making them difficult to examine their properties and determine their occurrence in a systematic way (Kirshbaum et al. 2007a,b, Fairman et al. 2016).  My PhD considers the ability of current operational forecast models to represent these bands and the environmental controls on their formation.

blogfig1
Figure 1: Flash flood event caused by a rainband situated over Cockermouth, Cumbria, UK in 2009

 

What is a rainband?

  • A cloud and precipitation structure associated with an area of rainfall which is significantly elongated
  • Stationary (situated over the same location) with continuous triggering
  • Can form in response to moist, unstable air following over complex terrain
  • Narrow in width ~2-10 km with varying length scales from 10 – 100’s km

 

blogfig2
Figure 2: Schematic showing the difference between cellular and banded convection

To examine the ability of current operational forecast models to represent these bands a case study was chosen which was first introduced by Barrett, et al. (2016).  The radar observations during the event showed a clear band along The Great Glen Fault, Scotland (Figure 3).  However, Barrett, et al. (2016) concluded that neither the operational forecast or the operational ensemble forecast captured the nature of the rainband.  For more information on ensemble models see one of our previous blog posts by David Flack Showers: How well can we predict them?.

blogfig3
Figure 3: Radar observations of precipitation accumulation over a six hour period (between 3-9 am) showing a rainband located over The Great Glen Fault, Scotland on 29 December 2012.

Localised convergence and increased convective available potential energy along the fault supported the formation of the rainband.  To determine the effect of model resolution on the model’s representation of the rainband, a forecast was performed with the horizontal gird spacing decreased to 500 m from 1.5 km.  In this forecast a rainband formed in the correct location which generated precipitation accumulations close to those observed, but with a time displacement.  The robustness of this forecast skill improvement is being assessed by performing an ensemble of these convection-permitting simulations.  Results suggest that accurate representation of these mesoscale rainbands requires resolutions higher than those used operationally by national weather centres.

Idealised numerical simulations have been used to investigate the environmental conditions leading to the formation of these rainbands.  The theoretical dependence of the partitioning of dry flow over and around mountains on the non-dimensional mountain height is well understood.  For this project I examine the effect of this dependence on rainband formation in a moist environment.  Preliminary analysis of the results show that the characteristics of rainbands are controlled by more than just the non-dimensional mountain height, even though this parameter is known to be sufficient to determine flow behaviour relative to mountains.

This work has been funded by the Natural Environmental Research Council (NERC) under the project PREcipitation STructures over Orography (PRESTO), for more project information click here.

References

Barrett, A. I., S. L. Gray, D. J. Kirshbaum, N. M. Roberts, D. M. Schultz, and J. G. Fairman, 2015: Synoptic Versus Orographic Control on Stationary Convective Banding. Quart. J. Roy. Meteorol. Soc., 141, 1101–1113, doi:10.1002/qj.2409.

— 2016: The Utility of Convection-Permitting Ensembles for the Prediction of Stationary Convective Bands. Mon. Wea. Rev., 144, 10931114, doi:10.1175/MWR-D-15-0148.1.

Cosma, S., E. Richard, and F. Minsicloux, 2002: The Role of Small-Scale Orographic Features in the Spatial Distribution of Precipitation. Quart. J. Roy. Meteorol. Soc., 128, 75–92, doi:10.1256/00359000260498798.

Fairman, J. G., D. M. Schultz, D. J. Kirshbaum, S. L. Gray, and A. I. Barrett, 2016: Climatology of Banded Precipitation over the Contiguous United States. Mon. Wea. Rev., 144,4553–4568, doi: 10.1175/MWR-D-16-0015.1.

Kirshbaum, D. J., G. H. Bryan, R. Rotunno, and D. R. Durran, 2007a: The Triggering of Orographic Rainbands by Small-Scale Topography. J. Atmos. Sci., 64, 1530–1549, doi:10.1175/JAS3924.1.

Kirshbaum, D. J., R. Rotunno, and G. H. Bryan, 2007b: The Spacing of Orographic Rainbands Triggered by Small-Scale Topography. J. Atmos. Sci., 64, 4222–4245, doi:10.1175/2007JAS2335.1.

Showers: How well can we predict them?

Email: d.l.a.flack@pgr.reading.ac.uk

Showers are one of the many examples of convective events experienced in the UK, other such events include thunderstorms, supercells and squall lines. These type of events form most often in the summer but can also form over the sea in the winter. They form because the atmosphere is unstable, i.e. warm air over a cooler surface, this results in the creation of thermals. If there is enough water vapour in the air and the thermal reaches high enough the water vapour will condense and eventually form a convective cloud. Convective events produce intense, often very localised, rainfall, which can result in flash floods, e.g. Boscastle 2004.

boscastle04
Boscastle flood 2004 – BBC News

Flash floods are very difficult to predict, unlike flood events that happen from the autumnal and winter storms e.g. floods from Storms Desmond and Frank last winter, and the current floods (20-22 November). So often there is limited lead time for emergency services to react to flash flood events. One of the main reasons why flash floods are difficult to predict is the association with convective events because these events only last for a few hours (6 hours at the longest) and only affect a very small area.

One of the aspects of forecasting the weather that researchers look into is the predictability of certain events. My PhD considers the predictability of convective events within different situations in the UK.

The different situations I am considering are generally split into two regimes: convective quasi-equilibrium and non-equilibrium convection.

In convective quasi-equilibrium any production of instability in the atmosphere is balanced by its release (Arakawa and Schubert, 1974). This results in scattered showers, which could turn up anywhere in a region where there is large-scale ascent. This is typical of areas behind fronts and to the left of jet stream exit regions. Because there are no obvious triggers (like flow over mountains or cliffs) you can’t pin-point the exact location of a shower.  We often find ourselves in this sort of situation in April, hence April showers.

equilibrium
Classic convective quasi-equilibrium conditions in the UK – scattered showers on 20 April 2012 – Dundee Satellite Receiving Station

On the other hand in non-equilibrium convection the instability is blocked from being released so energy in the system builds-up over time. If this inhibiting factor is overcome all the instability can be released at once and will result in ‘explosive’ convection (Emanuel, 1994).  Overcoming the inhibiting factor usually takes place locally, such as a sea breeze or flow up mountains, etc. so these give distinct triggers and help tie the location of these events down. These are the type of situations that occur frequently over continents in the spring and often result in severe weather.

nonequilibrium
Non-equilibrium convection – convergence line along the North Cornish Coast, 2 August 2013 – Dundee Satellite Receiving Station

It’s useful having these regimes to categorise events to help determine what happens in the forecasts of different situations but only if we understand a little bit about their characteristics. For the initial part of my work I considered the regimes over the British Isles and found that  we mainly have convective events in convective quasi-equilibrium (showers) – on average roughly 85% of convective events in the summer are in this regime (Flack et al., 2016). Therefore it is pertinent to ask how well can we predict showers?

To see how well we can predict showers, and other types of convection, the forecast itself is examined. This is done by adding small-scale variability into the model, throughout the forecast, to determine what would happen if the starting conditions (or any other time in the model) changed. This is run a number of times to create an ensemble.

ensembles
Deterministic forecast vs Ensemble forecast schematic, dotted lines represent model trajectories, the bright red represents the truth, darker red represents the forecast

Using ensembles we can determine the uncertainty in the weather forecast, this can either be in terms of spatial positioning, timing or intensity of the event. My work has mainly considered the spatial positioning and intensity of the convection, and is to be submitted shortly to Monthly Weather Review. The intensity in my ensemble shows similar variation in both regimes, suggesting that there are times when the amount of rainfall predicted can be spot on. Most of the interesting results appear to be linked to the location of the events. The ensembles for the non-equilibrium cases generally show agreement between location of the events, so we can be fairly confident about their location (so here your weather app would be very good). On the other hand, when it comes to showers there is no consistency between the different forecasts so they could occur anywhere  (so when your app suggests showers be careful – you may or may not get one).

So I’ll answer my question that I originally posed with another question: What do you want from a forecast? If the answer to this question is “I want to know if there is a chance of rain at my location” then yes we can predict that you might get caught by a shower. If on the other hand your answer is “I want exact details, for my exact location, e.g. is there going to be a shower at 15:01 on Saturday at Stonehenge yes or no?” Then the answer is, although we are improving forecasts, we can’t give that accurate a forecast when it comes to scattered showers, simply because of their very nature.

With forecasts improving all the time and the fact that they are looking more realistic it does not mean that every detail of a forecast is perfect. As with forecasting in all areas (from politics to economy) things can take an unexpected turn so caution is advised. When it comes to the original question of showers then it’s always best to be prepared.

This work has been funded by the Natural Environmental Research Council under the project Flooding From Intense Rainfall, for more project details and project specific blogs visit: www.met.reading.ac.uk/flooding

References

Arakawa, A. and W. H. Schubert, 1974: Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment, Part I. J. Atmos. Sci., 31, 674-701.

Emanuel, K. A., 1994: Atmospheric convection, Oxford University Press, 580 pp.

Flack, D. L. A., R. S. Plant, S.L. Gray, H. W. Lean, C. Keil and G. C. Craig, 2016: Characterisation of Convective Regimes over the British Isles. Quart. J. Roy. Meteorol. Soc., 142, 1541-1553.  

 

Discovering COP22

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

Over the past two weeks 25,000 delegates have been gathering in Marrakech to discuss mitigation and adaptation for climate change. On the 4th November 2016 the Paris Agreement came into force and as a result discussions during the conference debated its implementation. The Walker Institute and the Department of Meteorology (University of Reading), with the support of the NERC SCENARIO doctoral training partnership and an UNFCCC partnership, supported two PhD students to be official UN observers at COP22, and enabled remote participation with students back at Reading University. To find out more about our work with COP22 continue reading this blog post and check out:

Today (18/11/16) the UK government are set to announce that the United Kingdom has ratified the Paris Agreement. Yesterday, Boris Johnson (UK foreign secretary) signed the Paris Agreement after no objections were raised by the House of Commons or House of Lords. The United Kingdom in accordance with the Intended Nationally Determined Contributions (INDCs) of the European Union, are set to reduce greenhouse gas emissions by 40% by 2030 relative to 1990 emission levels. Today also marks the end of the 22nd Conference of the Parties (COP) for the United Nations Framework Convention on Climate Change and here are some quick summary points that PhD students took away from observing the process in Marrakech:

1) The significance of the Paris Agreement.

“Now that we have Paris, we need to take action immediately”

Teresa Anderson, ActionAid UK.

The Paris Agreement marks a change in the intentions during the COP process. Due to the success and ratification of the Paris Agreement more discussions can be based on the adaptation and mitigation against climate change, rather than negotiating global targets on climate change prevention. The Paris Agreement states that a global response is needed to respond to the threat of climate change and that global temperature rise should be kept well below 2°C and that efforts should be pursued to limit the global temperature rise to 1.5°C. COP22 Marrakech, began by stating that this is the “COP of Action”, and therefore the focus seen during side events, negotiations, dignitary speeches and press conferences was on the need for action.

“Countries have strongly supported the [Paris] Agreement because they realize their own national interest is best secured by pursuing the common good. Now we have to translate words into effective policies and actions.”

Mr Ban Ki-Moon, Secretary General of the United Nations.

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2) A continued effort is needed to concentrate on the individual.

As SCENARIO PhD students we were challenged to understand the process that takes place during a UNFCCC conference. To do this we interviewed many conference delegates including policymakers, research organisations, industry experts, entrepreneurs, environmental consultants and funding sources to name a few. A common theme that ran through most of our interviews is that action is needed to prioritise the individual as well as thinking in terms of national- and community-level. To ensure the successful mitigation and adaptation to climate change, strategies need to come into place that protect the rights of the individual. This poses a global challenge, stretching from protecting the livelihoods of indigenous cultures and those impacted by sea level rise on low-lying islands, to supporting workers who rely on the non-renewable energy industry. In terms of climate research we need to ensure that we make our scientific conclusions accessible on an individual-level so that our work has a greater impact.

“a key goal for us is making climate change research accessible to the user community”

Clare Kapp, WMO Press Office Communications Leader.

3) Action is needed now, however the Paris Agreement only implies action post-2020.

Throughout our attendance in plenary meetings and side events there was an emphasis that whilst the Paris Agreement is an important stepping stone to combatting climate change, action is needed before 2020 for the Paris Agreement to be reached. Currently INDCs are proposed for between 2021-2030, however for the intended global temperature targets to be achieved it was argued that action is needed now. Although, pre-2020 action raises much contention, with the most popular argument against pre-2020 action being that more time and effort is needed for negotiations to ensure that a better understanding of national efforts to climate change mitigation is determined.

“We need to take action before 2020. Working for action post-2020 is not going to be enough. We need to start acting now.”

Honduras Party Representative.

“We need more time to work on the rule book for the Paris Agreement. Discussions on this should continue.”

Switzerland Party Representative.

4) There is a difference in opinion on whether 1.5°C can be reached.

For me the most interesting question we asked conference delegates was “do you think the target of 1.5°C can be reached?” This question brought a difference of opinion including some party members arguing that the change in our non-renewable energy dependence is far too great for the target to be achieved. Meanwhile, other political representatives and NGO delegates argued that accepting the target is unachievable before even trying makes negotiations and discussions less successful. There was also anticipation for the future IPCC report titled, Special Report on Global Warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways.

“Of course we want to fight for 1.5°C, why fight for 2°C? It just makes sense to fight for 1.5°C”

Martina Duncan, Party Representative for Grenada.

COP22 has been a fantastic opportunity for PhD students in our department to interact and understand the process that takes place during a UNFCCC conference. Whilst the past couple of weeks have been dominated by the results of the US election and the associated uncertainties, there has been an increasing global recognition of climate change and that action should be taken. In the next few years the challenge to mitigate and adapt towards climate change will be an increasing priority, and let us hope that these annual UNFCCC conferences are key stepping stones for climate change action.

“This is a problem people are recognising, and that it is time to change”

Jonathan Pershing, US Climate Envoy

Thank you all those who have supported our work at COP22 this year. Thank you to the Walker Institute, NERC SCENARIO doctoral training partnership and UNFCCC for this brilliant opportunity. Thank you to all those who have supported us with publicity including NERC, Royal Meteorological Society, members of staff and PhD students at the University of Reading and Lucy Wallace who has ensured the appropriate communication of our project. Plus a huge thanks to all delegates and staff at COP22 who volunteered their time to talk to us.

Understanding the urban environment and its effect on indoor air.

Email: h.l.gough@pgr.reading.ac.uk

Recent estimates by the United Nations (2009) state that 50 to 70 % of the world’s population now live in urban areas with over 70 % of our time being spent indoors, whether that’s at work, at home or commuting.

We’ve all experienced a poor indoor environment, whether it’s the stuffy office that makes you sleepy, or the air conditioning unit that causes the one person under it to freeze. Poor environments make you unproductive and research is beginning to suggest that they can make you ill. The thing is, the microclimate around one person is complex enough, but then you have to consider the air flow of the room, the ventilation of the building and the effect of the urban environment on the building.

So what tends to happen is that buildings and urban areas are simplified down into basic shapes with all the fine details neglected and this is either modelled at a smaller scale in a wind tunnel or by using CFD (computer fluid dynamics). However, how do we know whether these models are representative of the real-world?

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This is Straw city, which was built in Silsoe U.K during 2014. You can just see the car behind the array (purple circle), these cubes of straw are 6 m tall, or roughly the height of an average house. Straw city is the stepping stone between the scale models and the real world, and was an urban experiment in a rural environment. We measured inside the array, outside of the array and within the blue building so we could see the link between internal and external flow: which meant the use of drones and smoke machines! The focus of the experiment was on the link between ventilation and the external conditions.

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Smoke releases, drone flying, thermal imaging and tracer gas release: some of the more fun aspects of the fieldwork

After 6 months of data collection, we took the straw cubes away and just monitored the blue cube on its own and the effect of the array can clearly be seen in this plot, where pink is the array, and blue is the isolated cube. So this is showing the pressure coefficient (Cp),  and can be thought of as a way of comparing one building to another in completely different conditions. You can see that the wind direction has an effect and that the array reduces the pressure felt by the cube by 60-90 %. Pressure is linked to the natural ventilation of a building: less pressure means less flow through the opening.

 

Alongside the big straw city, we also went to the Enflo lab at the University of Surrey to run some wind tunnel experiments of our own, which allowed us to expand the array.

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Photos of the wind tunnel arrays. Left is the biggest array modelled, centre is the Silsoe array, top right is the wind tunnel and roughness elements. Bottom right is the model of the storage shed at the full-scale site and centre is the logging system used.

So we have a data set that encompasses all wind directions and speeds, all atmospheric stabilities, different temperature differences and different weather conditions. It’s a big data set and will take a while to work through, especially with comparisons to the wind tunnel model and CFD model created by the University of Leeds. We will also compare the results to the existing guidelines out there and to other similar data sets.

I could ramble on for hours about the work, having spent far too long in a muddy field in all weathers but for more information please email me or come along to my departmental seminar on the 8th November.

This PhD project is jointly funded by the University of Reading and the EPSRC and is part of the Refresh project: www.refresh-project.org.uk