Mountains and the Atmospheric Circulation within Models


Mountains come in many shapes and sizes and as a result their dynamic impact on the atmospheric circulation spans a continuous range of physical and temporal scales. For example, large-scale orographic features, such as the Himalayas and the Rockies, deflect the atmospheric flow and, as a result of the Earth’s rotation, generate waves downstream that can remain fixed in space for long periods of time. These are known as stationary waves (see Nigam and DeWeaver (2002) for overview). They have an impact not only on the regional hydro-climate but also on the location and strength of the mid-latitude westerlies. On smaller physical scales, orography can generate gravity waves that act to transport momentum from the surface to the upper parts of the atmosphere (see Teixeira 2014), playing a role in the mixing of chemical species within the stratosphere.

Figure 1: The model resolved orography at different horizontal resolutions. From a low (climate model) resolution to a high (seasonal forecasting) resolution. Note how smooth the orography is at climate model resolution.

Figure 1 shows an example of the resolved orography at different horizontal resolutions over the Himalayas. The representation of orography within models is complicated by the fact that, unlike other parameterized processes, such as clouds and convection, that are typically totally unresolved by the model, its effects are partly resolved by the dynamics of the model and the rest is accounted for by parameterization schemes.However, many parameters within these schemes are not well constrained by observations, if at all. The World Meteorological Organisation (WMO) Working Group on Numerical Experimentation (WGNE) performed an inter-model comparison focusing on the treatment of unresolved drag processes within models (Zadra et al. 2013). They found that while modelling groups generally had the same total amount of drag from various different processes, their partitioning was vastly different, as a result of the uncertainty in their formulation.

Climate models with typically low horizontal resolutions, resolve less of the Earth’s orography and are therefore more dependent on parameterization schemes. They also have large model biases in their climatological circulations when compared with observations, as well as exhibiting a similarly large spread about these biases. What is more, their projected circulation response to climate change is highly uncertain. It is therefore worth investigating the processes that contribute towards the spread in their climatological circulations and circulation response to climate change. The representation of orographic processes seem vital for the accurate simulation of the atmospheric circulation and yet, as discussed above, we find that there is a lot of uncertainty in their treatment within models that may be contributing to model uncertainty. These uncertainties in the orographic treatment come from two main sources:

  1. Model Resolution: Models with different horizontal resolutions will have different resolved orography.
  2. Parameterization Formulation: Orographic drag parameterization formulation varies between models.

The issue of model resolution was investigated in our recent study, van Niekerk et al. (2016). We showed that, in the Met Office Unified Model (MetUM) at climate model resolutions, the decrease in parameterized orographic drag that occurs with increasing horizontal resolution was not balanced by an increase in resolved orographic drag. The inability of the model to maintain an equivalent total (resolved plus parameterized) orographic drag across resolutions resulted in an increase in systematic model biases at lower resolutions identifiable over short timescales. This shows not only that the modelled circulation is non-robust to changes in resolution but also that the parameterization scheme is not performing in the same way as the resolved orography. We have highlighted the impact of parameterized and resolved orographic drag on model fidelity and demonstrated that there is still a lot of uncertainty in the way we treat unresolved orography within models. This further motivates the need to constrain the theory and parameters within orographic drag parameterization schemes.


Nigam, S., and E. DeWeaver, 2002: Stationary Waves (Orographic and Thermally Forced). Academic Press, Elsevier Science, London, 2121–2137 pp., doi:10.1016/B978-0-12-382225-3. 00381-9.

Teixeira MAC, 2014: The physics of orographic gravity wave drag. Front. Phys. 2:43. doi:10.3389/fphy.2014.00043

Zadra, A., and Coauthors, 2013: WGNE Drag Project. URL:

van Niekerk, A., T. G. Shepherd, S. B. Vosper, and S. Webster, 2016: Sensitivity of resolved and parametrized surface drag to changes in resolution and parametrization. Q. J. R. Meteorol. Soc., 142 (699), 2300–2313, doi:10.1002/qj.2821. 


Stationary Orographic Rainbands


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.

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


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?.

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.


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?


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.

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.

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.

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.

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:


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.  


The effect of local topography on severe tropical convective rainfall development.


The occurrence of severe convective rainfall is common over the tropical rainforest region. While the basic mechanism of the development of severe convective rainfall over the tropics is well understood in previous studies, the effect of local topography may yield a unique development process.

One part of my PhD project is to look at how local topography modifies severe rainfall events over the western Peninsular Malaysia. This was examined via a case study of severe rainfall that took place on 2nd May 2012. On that day, heavy rainfall caused flash floods and landslides over Klang Valley (red box in Fig. 1). Although the total rainfall on the 2nd May was above the Apr-May average, it was not extremely high.


Fig. 1. The study area, specifically over the western Peninsular Malaysia. The red box is Klang valley area.

Looking at observational data was not enough to understand the processes involved in the development of severe rainfall event on 2nd May 2012 and therefore a simulation study was conducted using the UK Met Office Unified Model (1.5km horizontal resolution).

One theory which could explain  the rainfall event on 2nd May 2012 is the influence of a series of rainfall events that developed earlier. There were rainfall events over the Peninsular Malaysia and Sumatra Island in the early evening of 1st May 2012 along the Titiwangsa mountains (Peninsular Malaysia) and Barisan Mountains (Sumatra Island). These rainfall events influenced the development of rainfall over the Malacca Strait overnight. The rainfall event over the strait strengthened by the morning of 2nd May. In the afternoon of 2nd May, the western peninsula had the right atmospheric conditions to develop convective rainfall, and the rainfall over the strait influenced the intensification of rainfall over the western peninsula. Thus, we believe that the local topography has a large impact on the development of the 2nd May rainfall event.

So, how do we test the hypothesis? One way is to perform sensitivity experiments. Four sensitivity experiments were conducted, modifying the orography of both the peninsula and Sumatra, and removing Sumatra altogether (Fig. 2).


Fig. 2. Sensitivity experiments on the local orography and Sumatra Island. Control run on the first panel, flatPM (flat peninsula to sea level), flatSI (flat Sumatra), flatALL(both peninsula and Sumatra are flat), and noSI (Sumatra is removed)

The results show that orography influenced and modified the development of late evening rainfall over both landmasses on both days. On 2nd May, total rainfall in the experiments are as follows:
1. flatPM : Klang valley received less rainfall than control,
2. flatSI : Klang valley received less rainfall than control but more than flatPM,
3. flatALL : Klang valley received more rainfall than control, flatPM and flatSI experiments,
4. noSI : Klang valley received triple the amount of rainfall of the control and other experiments.
These results hint the complex relationship between local topography and rainfall. Moreover, both the peninsula and Sumatra are important for the development of the morning rainfall over the Malacca Strait, regardless of the orographic variability.

Whilst looking at one case study is not enough to draw a general conclusion, this will definitely be a step forward on broadening the information that we already have. A more robust conclusion would require further studies to be taken.

(This PhD project is supervised by Pete Inness and Christopher Holloway, and funded by MARA Malaysia).