Atmospheric blocking: why is it so hard to predict?

Atmospheric blocks are nearly stationary large-scale flow features that effectively block the prevailing westerly winds and redirect mobile cyclones. They are typically characterised by a synoptic-scale, quasi-stationary high pressure system in the midlatitudes that can remain over a region for several weeks. Blocking events can cause extreme weather: heat waves in summer and cold spells in winter, and the impacts associated with these events can escalate due to a block’s persistence. Because of this, it is important that we can forecast blocking accurately. However, atmospheric blocking has been shown to be the cause of some of the poorest forecasts in recent years. Looking at all occasions when the ECMWF model experienced a period of very low forecast skill, Rodwell et al. (2013) found that the average flow pattern for which these forecasts verified was an easily-distinguishable atmospheric blocking pattern (Figure 1). But why are blocks so hard to forecast?

Fig_1_blogjacob
Figure 1:  Average verifying 500 hPa geopotential height (Z500) field for occasions when the ECMWF model experienced very low skill. From Rodwell et al. (2013).

There are several reasons why forecasting blocking is a challenge. Firstly, there is no universally accepted definition of what constitutes a block. Several different flow configurations that could be referred to as blocks are shown in Figure 2. The variety in flow patterns used to define blocking brings with it a variety of mechanisms that are dynamically important for blocks developing in a forecast (Woollings et al. 2018). Firstly, many phenomena must be well represented in a model for it to forecast all blocking events accurately. Secondly, there is no complete dynamical theory for block onset and maintenance- we do not know if a process key for blocking dynamics is missing from the equation set solved by numerical weather prediction models and is contributing to the forecast error. Finally, many of the known mechanisms associated with block onset and maintenance are also know sources of model uncertainty. For example, diabatic processes within extratropical cyclones have been shown to contribute substantially to blocking events (Pfahl et al. 2015), the parameterisation of which has been shown to affect medium-range forecasts of ridge building events (Martínez-Alvarado et al. 2015).

Fig_2_blogjacob
Figure 2: Different flow patterns, shown using Z500 (contours), that have been defined as blocks. From Woollings et al. (2018).

We do, however, know some ways to improve the representation of blocking: increase the horizontal resolution of the model (Schiemann et al. 2017); improve the parameterisation of subgrid physical processes (Jung et al. 2010); remove underlying model biases (Scaife et al. 2010); and in my PhD we found that improvements to a model’s dynamical core (the part of the model used to solved the governing equations) can also improve the medium-range forecast of blocking. In Figure 3, the frequency of blocking that occurred during two northern hemisphere winters is shown for the ERA-Interim reanalysis and three operational weather forecast centres (the ECMWF, Met Office (UKMO) and the Korean Meteorological Administration (KMA)). Both KMA and UKMO use the Met Office Unified Model – however, before the winter of 2014/15 the UKMO updated the model to use a new dynamical core whilst KMA continued to use the original. This means that for the 2013/14 the UKMO and KMA forecasts are from the same model with the same dynamical core whilst for the 2014/15 winter the UKMO and KMA forecasts are from the same model but with different dynamical cores. The clear improvement in forecast from the UKMO in 2014/15 can hence be attributed to the new dynamical core. For a full analysis of this improvement see Martínez-Alvarado et al. (2018).

Fig_3_blogjacob
Figure 3: The frequency of blocking during winter in the northern hemisphere in ERA-Interim (grey shading) and in seven-day forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), the Met Office (UKMO) and the Korean Meteorological Administration (KMA). Box plots show the spread in the ensemble forecast from each centre.

In the remainder of my PhD I aim to investigate the link between errors in forecasts of blocking with the representation of upstream cyclones. I am particularly interested to see if the parameterisation of diabatic processes (a known source of model uncertainty) could be causing the downstream error in Rossby wave amplification and blocking.

Email: j.maddison@pgr.reading.ac.uk.

References:

Rodwell, M. J., and Coauthors, 2013: Characteristics of occasional poor medium-range weather  forecasts for Europe. Bulletin of the American Meteorological Society, 94 (9), 1393–1405.

Woollings, T., and Coauthors, 2018: Blocking and its response to climate change. Current Climate Change Reports, 4 (3), 287–300.

Pfahl, S., C. Schwierz, M. Croci-Maspoli, C. Grams, and H. Wernli, 2015: Importance of latent  heat release in ascending air streams for atmospheric blocking. Nature Geoscience, 8 (8), 610– 614.

Mart´ınez-Alvarado, O., E. Madonna, S. Gray, and H. Joos, 2015: A route to systematic error in forecasts of Rossby waves. Quart. J. Roy. Meteor. Soc., 142, 196–210.

Mart´ınez-Alvarado, O., and R. Plant, 2014: Parametrized diabatic processes in numerical simulations of an extratropical cyclone. Quart. J. Roy. Meteor. Soc., 140 (682), 1742–1755.

Scaife, A. A., T. Woollings, J. Knight, G. Martin, and T. Hinton, 2010: Atmospheric blocking and mean biases in climate models. Journal of Climate, 23 (23), 6143–6152.

Schiemann, R., and Coauthors, 2017: The resolution sensitivity of northern hemisphere blocking in four 25-km atmospheric global circulation models. Journal of Climate, 30 (1), 337–358.

Jung, T., and Coauthors, 2010: The ECMWF model climate: Recent progress through improved physical parametrizations. Quart. J. Roy. Meteor. Soc., 136 (650), 1145–1160.

New Forecast Model Provides First Global Scale Seasonal River Flow Forecasts

new_web_figure2_rivernetwork

Over the past ~decade, extended-range forecasts of river flow have begun to emerge around the globe, combining meteorological forecasts with hydrological models to provide seasonal hydro-meteorological outlooks. Seasonal forecasts of river flow could be useful in providing early indications of potential floods and droughts; information that could be of benefit for disaster risk reduction, resilience and humanitarian aid, alongside applications in agriculture and water resource management.

While seasonal river flow forecasting systems exist for some regions around the world, such as the U.S., Australia, Africa and Europe, the forecasts are not always accessible, and forecasts in other regions and at the global scale are few and far between.  In order to gain a global overview of the upcoming hydrological situation, other information tends to be used – for example historical probabilities based on past conditions, or seasonal forecasts of precipitation. However, precipitation forecasts may not be the best indicator of floodiness, as the link between precipitation and floodiness is non-linear. A recent paper by Coughlan-de-Perez et al (2017), “should seasonal rainfall forecasts be used for flood preparedness?”, states:

“Ultimately, the most informative forecasts of flood hazard at the seasonal scale are streamflow forecasts using hydrological models calibrated for individual river basins. While this is more computationally and resource intensive, better forecasts of seasonal flood risk could be of immense use to the disaster preparedness community.”

twitter_screenshotOver the past months, researchers in the Water@Reading* research group have been working with the European Centre for Medium-Range Weather Forecasts (ECMWF), to set up a new global scale hydro-meteorological seasonal forecasting system. Last week, on 10th November 2017, the new forecasting system was officially launched as an addition to the Global Flood Awareness System (GloFAS). GloFAS is co-developed by ECMWF and the European Commission’s Joint Research Centre (JRC), as part of the Copernicus Emergency Management Services, and provides flood forecasts for the entire globe up to 30 days in advance. Now, GloFAS also provides seasonal river flow outlooks for the global river network, out to 4 months ahead – meaning that for the first time, operational seasonal river flow forecasts exist at the global scale – providing globally consistent forecasts, and forecasts for countries and regions where no other forecasts are available.

The new seasonal outlook is produced by forcing the Lisflood hydrological river routing model with surface and sub-surface runoff from SEAS5, the latest version of ECMWF’s seasonal forecasting system, (also launched last week), which consists of 51 ensemble members at ~35km horizontal resolution. Lisflood simulates the groundwater and routing processes, producing a probabilistic forecast of river flow at 0.1o horizontal resolution (~10km, the resolution of Lisflood) out to four months, initialised using the latest ERA-5 model reanalysis.

The seasonal outlook is displayed as three new layers in the GloFAS web interface, which is publicly (and freely) available at www.globalfloods.eu. The first of these gives a global overview of the maximum probability of unusually high or low river flow (defined as flow exceeding the 80th or falling below the 20th percentile of the model climatology), during the 4-month forecast horizon, in each of the 306 major world river basins used in GloFAS-Seasonal.

new_web_figure1_basins
The new GloFAS Seasonal Outlook Basin Overview and River Network Layers.

The second layer provides further sub-basin-scale detail, by displaying the global river network (all pixels with an upstream area >1500km2), again coloured according to the maximum probability of unusually high or low river flow during the 4-month forecast horizon. In the third layer, reporting points with global coverage are displayed, where more forecast information is available. At these points, an ensemble hydrograph is provided showing the 4-month forecast of river flow, with thresholds for comparison of the forecast to typical or extreme conditions based on the model climatology. Also displayed is a persistence diagram showing the weekly probability of exceedance for the current and previous three forecasts.

blog_screenshot
The new GloFAS Seasonal Outlook showing the river network and reporting points providing hydrographs and persistence diagrams.

Over the coming months, an evaluation of the system will be completed – for now, users are advised to evaluate the forecasts for their particular application. We welcome any feedback on the forecast visualisations and skill – feel free to contact me at the email address below!

To find out more, you can see the University’s press release here, further information on SEAS5 here, and the user information on the seasonal outlook GloFAS layers here.

*Water@Reading is “a vibrant cross-faculty centre of research excellence at the University of Reading, delivering world class knowledge in water science, policy and societal impacts for the UK and internationally.”

Full list of collaborators: 

Rebecca Emerton1,2, Ervin Zsoter1,2, Louise Arnal1,2, Prof. Hannah Cloke1, Dr. Liz Stephens1, Dr. Florian Pappenberger2, Prof. Christel Prudhomme2, Dr Peter Salamon3, Davide Muraro3, Gabriele Mantovani3

1 University of Reading
2 ECMWF
3 European Commission JRC

Contact: r.e.emerton@pgr.reading.ac.uk

Should we be ‘Leaf’-ing out vegetation when parameterising the aerodynamic properties of urban areas?

Email: C.W.Kent@pgr.reading.ac.uk

When modelling urban areas, vegetation is often ignored in attempt to simplify an already complex problem. However, vegetation is present in all urban environments and it is not going anywhere… For reasons ranging from sustainability to improvements in human well-being, green spaces are increasingly becoming part of urban planning agendas. Incorporating vegetation is therefore a key part of modelling urban climates. Vegetation provides numerous (dis)services in the urban environment, each of which requires individual attention (Salmond et al. 2016). However, one of my research interests is how vegetation influences the aerodynamic properties of urban areas.

Two aerodynamic parameters can be used to represent the aerodynamic properties of a surface: the zero-plane displacement (zd) and aerodynamic roughness length (z0). The zero-plane displacement is the vertical displacement of the wind-speed profile due to the presence of surface roughness elements. The aerodynamic roughness length is a length scale which describes the magnitude of surface roughness. Together they help define the shape and form of the wind-speed profile which is expected above a surface (Fig. 1).

blogpostpic

Figure 1: Representation of the wind-speed profile above a group of roughness elements. The black dots represent an idealised logarithmic wind-speed profile which is determined using the zero-plane displacement (zd) and aerodynamic roughness length (z0) (lines) of the surface.

For an urban site, zd and z0 may be determined using three categories of methods: reference-based, morphometric and anemometric. Reference-based methods require a comparison of the site to previously published pictures or look up tables (e.g. Grimmond and Oke 1999); morphometric methods describe zd and z0 as a function of roughness-element geometry; and, anemometric methods use in-situ observations. The aerodynamic parameters of a site may vary considerably depending upon which of these methods are used, but efforts are being made to understand which parameters are most appropriate to use for accurate wind-speed estimations (Kent et al. 2017a).

Within the morphometric category (i.e. using roughness-element geometry) sophisticated methods have been developed for buildings or vegetation only. However, until recently no method existed to describe the effects of both buildings and vegetation in combination. A recent development overcomes this, whereby the heights of all roughness elements are considered alongside a porosity correction for vegetation (Kent et al. 2017b). Specifically, the porosity correction is applied to the space occupied and drag exerted by vegetation.

The development is assessed across several areas typical of a European city, ranging from a densely-built city centre to an urban park. The results demonstrate that where buildings are the dominant roughness elements (i.e. taller and occupying more space), vegetation does not obviously influence the calculated geometry of the surface, nor the aerodynamic parameters and the estimated wind speed. However, as vegetation begins to occupy a greater amount of space and becomes as tall as (or larger) than buildings, the influence of vegetation is obvious. Expectedly, the implications are greatest in an urban park, where overlooking vegetation means that wind speeds may be slowed by up to a factor of three.

Up to now, experiments such as those in the wind tunnel focus upon buildings or trees in isolation. Certainly, future experiments which consider both buildings and vegetation will be valuable to continue to understand the interaction within and between these roughness elements, in addition to assessing the parameterisation.

References

Grimmond CSB, Oke TR (1999) Aerodynamic properties of urban areas derived from analysis of surface form. J Appl Meteorol and Clim 38:1262-1292.

Kent CW, Grimmond CSB, Barlow J, Gatey D, Kotthaus S, Lindberg F, Halios CH (2017a) Evaluation of Urban Local-Scale Aerodynamic Parameters: Implications for the Vertical Profile of Wind Speed and for Source Areas. Boundary-Layer Meteorology 164: 183-213.

Kent CW, Grimmond CSB, Gatey D (2017b) Aerodynamic roughness parameters in cities: Inclusion of vegetation. Journal of Wind Engineering and Industrial Aerodynamics 169: 168-176.

Salmond JA, Tadaki M, Vardoulakis S, Arbuthnott K, Coutts A, Demuzere M, Dirks KN, Heaviside C, Lim S, Macintyre H (2016) Health and climate related ecosystem services provided by street trees in the urban environment. Environ Health 15:95.

Future of Cumulus Parametrization conference, Delft, July 10-14, 2017

Email: m.muetzelfeldt@pgr.reading.ac.uk

For a small city, Delft punches above its weight. It is famous for many things, including its celebrated Delftware (Figure 1). It was also the birthplace of one of the Dutch masters, Johannes Vermeer, who coincidentally painted some fine cityscapes with cumulus clouds in them (Figure 2). There is a university of technology with some impressive architecture (Figure 3). It holds the dubious honour of being the location of the first assassination using a pistol (or so we were told by our tour guide), when William of Orange was shot in 1584. To this list, it can now add hosting a one-week conference on the future of cumulus parametrization, and hopefully bringing about more of these conferences in the future.

Delftware_display

Figure 1: Delftware.

Vermeer-view-of-delft

Figure 2: Delft with canopy of cumulus clouds. By Johannes Vermeer, 1661.

Delft_AULA

Figure 3: AULA conference centre at Delft University of Technology – where we were based for the duration of the conference.

So what is a cumulus parametrization scheme? The key idea is as follows. Numerical weather and climate models work by splitting the atmosphere into a grid, with a corresponding grid length representing the length of each of the grid cells. By solving equations that govern how the wind, pressure and heating interact, models can then be used to predict what the weather will be like days in advance in the case of weather modelling. Or a model can predict how the climate will react to any forcings over longer timescales. However, any phenomena that are substantially smaller than this grid scale will not be “seen” by the models. For example, a large cumulonimbus cloud may have a horizontal extent of around 2km, whereas individual grid cells could be 50km in the case of a climate model. A cumulonimbus cloud will therefore not be explicitly modelled, but it will still have an effect on the grid cell in which it is located – in terms of how much heating and moistening it produces at different levels. To capture this effect, the clouds are parametrized, that is, the vertical profile of the heating and moistening due to the clouds are calculated based on the conditions in the grid cell, and this then affects the grid-scale values of these variables. A similar idea applies for shallow cumulus clouds, such as the cumulus humilis in Vermeer’s painting (Figure 2), or present-day Delft (Figure 3).

These cumulus parametrization schemes are a large source of uncertainty in current weather and climate models. The conference was aimed at bringing together the community of modellers working on these schemes, and working out which might be the best directions to go in to improve these schemes, and consequently weather and climate models.

Each day was a mixture of listening to presentations, looking at posters and breakout discussion groups in the afternoon, as well as plenty of time for coffee and meeting new people. The presentations covered a lot of ground: from presenting work on state-of-the-art parametrization schemes, to looking at how the schemes perform in operational models, to focusing on one small aspect of a scheme and modelling how that behaves in a high resolution model (50m resolution) that can explicitly model individual clouds. The posters were a great chance to see the in-depth work that had been done, and to talk to and exchange ideas with other scientists.

Certain ideas for improving the parametrization schemes resurfaced repeatedly. The need for scale-awareness, where the response of the parametrization scheme takes into account the model resolution, was discussed. One idea for doing this was the use of stochastic schemes to represent the uncertainty of the number of clouds in a given grid cell. The concept of memory also cropped up – where the scheme remembers if it had been active at a given grid cell in a previous point in time. This also ties into the idea of transitions between cloud regimes, e.g. when a stratocumulus layer splits up into individual cumulus clouds. Many other, sometimes esoteric, concepts were discussed, such as the role of cold pools, how much tuning of climate models is desirable and acceptable, how we should test our schemes, and what the process of developing the schemes should look like.

In the breakout groups, everyone was encouraged to contribute, which made for an inclusive atmosphere in which all points of view were taken on board. Some of the key points of agreement from these were that it was a good idea to have these conferences, and we should do it more often! Hopefully, in two years’ time, another PhD student will write a post on how the next meeting has gone. We also agreed that it would be beneficial to be able to share data from our different high resolution runs, as well as to be able to compare code for the different schemes.

The conference provided a picture of what the current thinking on cumulus parametrization is, as well as which directions people think are promising for the future. It also provided a means for the community to come together and discuss ideas for how to improve these schemes, and how to collaborate more closely with future projects such as ParaCon and HD(CP)2.

Can we really use El Niño to predict flooding?

R. Emerton, H. Cloke, E. Stephens, E. Zsoter, S. Woolnough, F. Pappenberger (2017). Complex picture for likelihood of ENSO-driven flood hazard. Nature Communications. doi: 10.1038/NCOMMS14796

Email: r.e.emerton@pgr.reading.ac.uk

When an El Niño is declared, or even forecast, we think back to memorable past El Niños (such as 1997/98), and begin to ask whether we will see the same impacts. Will California receive a lot of rainfall? Will we see droughts in tropical Asia and Australia? Will Peru experience the same devastating floods as in 1997/98, and 1982/83?

news_headlines_elnino

El Niño and La Niña, which see changes in the ocean temperatures in the tropical Pacific, are well known to affect weather, and indeed river flow and flooding, around the globe. But how well can we estimate the potential impacts of El Niño and La Niña, and how likely flooding is to occur?

This question is what some of us in the Water@Reading research group at the University of Reading have been looking to answer in our recent publication in Nature Communications. As part of our multi- and inter-disciplinary research, we work closely with the Red Cross / Red Crescent Climate Centre (RCCC), who are working on an initiative called Forecast-based Financing (FbF, Coughlan de Perez et al.). FbF aims to distribute aid (for example providing water purification tablets to prevent spread of disease, or digging trenches to divert flood water) ahead of a flood, based on forecasts. This approach helps to reduce the impact of the flood in the first place, rather than working to undo the damage once the flood has already occurred.

Photo credit: Red Cross / Red Crescent Climate Centre

In Peru, previous strong El Niños in 1982/83 and 1997/98 had resulted in devastating floods in several regions. As such, when forecasts in early 2015 began to indicate a very strong El Niño was developing, the RCCC and forecasters at the Peruvian national hydrological and meteorology agency (SENAMHI) began to look into the likelihood of flooding, and what FbF actions might need to be taken.

Typically, statistical products indicating the historical probability (likelihood [%] based on what happened during past El Niños) of extreme precipitation are used as a proxy for whether a region will experience flooding during an El Niño (or La Niña), such as these maps produced by the IRI (International Research Institute for Climate and Society). You may also have seen maps which circle regions of the globe that will be drier / warmer / wetter / cooler – we’ll come back to these shortly.

These rainfall maps show that Peru, alongside several other regions of the world, is likely to see more rainfall than usual during an El Niño. But does this necessarily mean there will be floods? And what products are out there indicating the effect of El Niño on rivers across the globe?

For organisations working at the global scale, such as the RCCC and other humanitarian aid agencies, global overviews of potential impacts are key in taking decisions on where to focus resources during an El Niño or La Niña. While these maps are useful for looking at the likely changes in precipitation, it has been shown that the link between precipitation and flood magnitude is nonlinear (Stephens et al.),  – more rain does not necessarily equal floods – so how does this transfer to the potential for flooding?

The motivation behind this work was to provide similar information, but taking into account the hydrology as well as the meteorology. We wanted to answer the question “what is the probability of flooding during El Niño?” not only for Peru, but for the global river network.

To do this, we have taken the new ECMWF ERA-20CM ensemble model reconstruction of the atmosphere, and run this through a hydrological model to produce the first 20th century global hydrological reconstruction of river flow. Using this new dataset, we have for the first time estimated the historical probability of increased or decreased flood hazard (defined as abnormally high or low river flow) during an El Niño (or La Niña), for the global river network.

elnino_flood_hazard_gif_beccalize
Figure 1: The probability of increased (blue) or decreased (red) flood hazard during each month of an El Nino. Based on the ensemble mean of the ERA-20CM-R 20th century river flow reconstruction.

The question – “what is the probability of flooding during El Niño?”, however, remains difficult to answer. We now have maps of the probability of abnormally high or low river flow (see Figure 1), and we see clear differences between the hydrological analysis and precipitation. It is also evident that the probabilities themselves are often lower, and much more uncertain, than might be useful – how do you make a decision on whether to provide aid to an area worried about flooding, when the probability of that flooding is 50%?

blob_floodhazard_comparison_map
Figure 2: Historical probability of increased / decreased flood hazard map for February, with overlay showing the typical impact map for winter during an El Nino. This highlights the complexity of the link between El Nino and flooding compared to the information usually available.

The likely impacts are much more complex than is often perceived and reported – going back to the afore-mentioned maps that circle regions of the globe and what their impact will be (warmer, drier, wetter?) – these maps portray these impacts as a certainty, not a probability, with the same impacts occurring across huge areas. For example, in Figure 2, we take one of the maps from our results, which indicates the probability of increased or decreased flood hazard in one month during an El Niño, and draw over this these oft-seen circles of potential impacts. In doing this, we remove all information on how likely (or unlikely) the impacts are, smaller scale changes within these circles (in some cases our flood hazard map even indicates a different impact), and a lot of the potential impacts outside of these circles – not to mention the likely impacts can change dramatically from one month to the next. For those organisations that take actions based on such information, it is important to be aware of the uncertainties surrounding the likely impacts of El Niño and La Niña.

“We conclude that while it may seem possible to use historical probabilities to evaluate regions across the globe that are more likely to be at risk of flooding during an El Niño / La Niña, and indeed circle large areas of the globe under one banner of wetter or drier, the reality is much more complex.”

PS. During the winter of 2015/16, our results estimated an ~80% likelihood of increased flood hazard in northern coastal Peru, with only ~10% uncertainty surrounding this. The RCCC took FbF actions to protect thousands of families from potentially devastating floods driven by one of the strongest El Niños on records. While flooding did occur, this was not as severe as expected based on the strength of the El Niño. More recently, during the past few months (January – March 2017), anomalously high sea surface temperatures (SSTs) in the far eastern Pacific (known as a “coastal El Niño” in Peru but not widely acknowledged as an El Niño because central Pacific SSTs are not anomalously warm) have led to devastating flooding in several regions and significant loss of life. And Peru wasn’t the only place that didn’t see the impacts it expected in 2015/16; other regions of the world, such as the US, also saw more rainfall than normal in places that were expected to be drier, and California didn’t receive the deluge they were perhaps hoping for. It’s important to remember that no two El Niños are the same, and El Niño will not be the only influence on the weather around the globe. While El Niño and La Niña can provide some added predictability to the atmosphere, the impacts are far from certain.

Presidente Kuczynski recorre zonas afectadas por lluvias e inund
Flooded areas of Trujillo, Peru, March 2017. Photo credit: Presidencia Peru, via Floodlist

Full reference:

R. Emerton, H. Cloke, E. Stephens, E. Zsoter, S. Woolnough, F. Pappenberger (2017). Complex picture for likelihood of ENSO-driven flood hazard. Nature Communications. doi: 10.1038/NCOMMS14796

Press Release:

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.