## Forecasting space weather using “similar day” approach

Space weather is a natural threat that requires good quality forecasting with as much lead time as possible. In this post I outline the simple and understandable analogue ensemble (AnEn) or “similar day” approach to forecasting. I focus mainly on exploring the method itself and, although this work forecasts space weather through a timeseries of ground level observations, AnEn can be applied to many prediction tasks, particularly time series with strong auto-correlation. AnEn has previously been used to predict wind speed [1], temperature [1] and solar wind [2]. The code for AnEn is available at https://github.com/Carl-Haines/AnalogueEnsemble should you wish to try out the method for you own application.

The idea behind AnEn is to take a set of recent observations, look back in a historic dataset for analogous periods, then take what happened following those analogous periods as the forecast. If multiple analogous periods are used, then an ensemble of forecasts can be created giving a distribution of possible outcomes with probabilistic information.

Figure 1 – An example of AnEn applied to a space weather event with forecast time t0. The black line shows the observations, the grey line shows the ensemble members, the red line shows the median of the ensemble and the yellow and green lines are reference forecasts.

Figure 1 is an example of a forecast made using the AnEn method where the forecast is made at t0. The 24-hours of observations (black) prior to tare matched to similar periods in the historic dataset (grey). Here I have chosen to give the most recent observations the most weighting as they hold the most relevant information. The grey analogue lines then flow on after t0 forming the forecast. Combined, these form an ensemble and the median of these is shown in red. The forecast can be chosen to be the median (or any percentile) of the ensemble or a probability of an event occurring can be given by counting how many of the ensemble member do/don’t experience the event.

Figure 1 also shows two reference forecasts, namely 27-day recurrence and climatology, as benchmarks to beat. 27-day recurrence uses the observation from 27-days ago as the forecast for today. This is reasonable because the Sun rotates every 27-days as seen from earth so broadly speaking the same part of the Sun is emitting the relevant solar wind on timescales larger than 27-days.

To quantify how well AnEn works as a forecast I ran the forecast on the entire dataset by repeatedly changing the forecast time t0 and applied two metrics, namely mean absolute error (MAE) and skill, to the median of the ensemble members. MAE is the size of the mean difference between the forecast made by AnEn and what was actually observed. The mean of the absolute errors over all the forecasts (taken as median of the ensemble) is taken and we end up with a value for each lead time. Figure 2 shows the MAE for AnEn median and the reference forecasts. We see that AnEn has the smallest (best) MAE at short lead times and outperforms the reference forecasts for all lead times up to a week.

Figure 2 – The mean absolute error of the AnEn median and reference forecasts.

An error metric such as MAE cannot take into account that certain conditions are inherently more difficult to forecast such as storm times. For this we can use a skill metric defined by

${\text{Skill} = 1 - \frac{\text{Forecast error}}{\text{Reference error}}}$

where in this case we use climatology as the reference forecast. Skill can take any value between $-\infty$ and $1$ where a perfect forecast would receive a value of $1$ and an unskilful forecast would receive a value of $0$. A negative value of skill signifies that the forecast is worse than the reference forecast.

Figure 3 shows the skill of AnEn and 27-day recurrence with respect to climatology. We see that AnEn is most skilful for short lead times and outperforms 27-day recurrence for all lead times considered.

Figure 3 – The skill of the AnEn median and 27-day recurrence with respect to climatology.

In summary, the analogue ensemble forecast method matches current conditions with historical events and lifts the previously seen timeseries as the prediction. AnEn seems to perform well for this application and outperforms the reference forecasts of climatology and 27-day recurrence. The code for AnEn is available at https://github.com/Carl-Haines/AnalogueEnsemble

The work presented here makes up a part of a paper that is under review in the journal of Space Weather.

Here, AnEn has been applied to a dataset from the space weather domain. If you would like to find out more about space weather then take a look at these previous blog posts from Shannon Jones (https://socialmetwork.blog/2018/04/13/the-solar-stormwatch-citizen-science-project/) and I (https://socialmetwork.blog/2019/11/15/the-variation-of-geomagnetic-storm-duration-with-intensity/).

[1] Delle Monache, L., Eckel, F. A., Rife, D. L., Nagarajan, B., & Searight, K.(2013) Probabilistic Weather Prediction with an Analog Ensemble. doi: 10.1175/mwr-d-12-00281.1

[2] Owens, M. J., Riley, P., & Horbury, T. S. (2017a). Probabilistic Solar Wind and Ge-704omagnetic Forecasting Using an Analogue Ensemble or “Similar Day” Approach. doi: 10.1007/s11207-017-1090-7

## Extending the predictability of flood hazard at the global scale

When I started my PhD, there were no global scale operational seasonal forecasts of river flow or flood hazard. Global overviews of upcoming flood events are key for organisations working at the global scale, from water resources management to humanitarian aid, and for regions where no other local or national forecasts are available. While GloFAS (the Global Flood Awareness System, run by the European Centre for Medium-Range Weather Forecasts (ECMWF) and the European Commission Joint Research Centre (JRC) as part of the Copernicus Emergency Management Services) was producing operational, openly-available flood forecasts out to 30 days ahead, there was a need for more extended-range forecast information. Often, due to a lack of hydrological forecasts, seasonal rainfall forecasts are used as a proxy for flood hazard – however, the link between precipitation and floodiness is nonlinear, and recent research has shown that seasonal rainfall forecasts are not necessarily the best indicator of potential flood hazard. The aim of my PhD research was to look into ways in which we could provide earlier warning information, several weeks to months ahead, using hydrological analysis in addition to the meteorology.

Broadly speaking, there are two key ways in which to provide early warning information on seasonal timescales: (1) through statistical analysis based on large-scale climate variability and teleconnections, and (2) by producing dynamical seasonal forecasts using coupled ocean-atmosphere GCMs. Over the past 4.5 years, I worked on providing hydrologically-relevant seasonal forecast products using these two approaches, at the global scale. This blog post will give a quick overview of the two new forecast products we produced as part of this research!

Can we use El Niño to predict flood hazard?

ENSO (the El Niño Southern Oscillation), is known to influence river flow and flooding across much of the globe, and often, statistical historical probabilities of extreme precipitation during El Niño and La Niña (the extremes of ENSO climate variability) are used to provide information on likely flood impacts. Due to its global influence on weather and climate, we decided to assess whether it is possible to use ENSO as a predictor of flood hazard at the global scale, by assessing the links between ENSO and river flow globally, and estimating the equivalent historical probabilities for high and low river flow, to those that are already used for meteorological variables.

With a lack of sufficient river flow observations across much of the globe, we needed to use a reanalysis dataset – but global reanalysis datasets for river flow are few and far between, and none extended beyond ~40 years (which includes a sample of ≤10 El Niños and ≤13 La Niñas). We ended up producing a 20th Century global river flow reconstruction, by forcing the Camaflood hydrological model with ECMWF’s ERA-20CM atmospheric reconstruction, to produce a 10-member river flow dataset covering 1901-2010, which we called ERA-20CM-R.

Using this dataset, we calculated the percentage of past El Niño and La Niña events, during which the monthly mean river flow exceeded a high flow threshold (the 75th percentile of the 110-year climatology) or fell below a low flow threshold (the 25th percentile), for each month of an El Niño / La Niña. This percentage is then taken as the probability that high or low flow will be observed in future El Niño/La Niña events. Maps of these probabilities are shown above, for El Niño, and all maps for both El Niño and La Niña can be found here. When comparing to the same historical probabilities calculated for precipitation, it is evident that additional information can be gained from considering the hydrology. For example, the River Nile in northern Africa is likely to see low river flow, even though the surrounding area is likely to see more precipitation – because it is influenced more by changes in precipitation upstream. In places that are likely to see more precipitation but in the form of snow, there would be no influence on river flow or flood hazard during the time when more precipitation is expected. However, several months later, there may be no additional precipitation expected, but there may be increased flood hazard due to the melting of more snow than normal – so we’re able to see a lagged influence of ENSO on river flow in some regions.

While there are locations where these probabilities are high and can provide a useful forecast of hydrological extremes, across much of the globe, the probabilities are lower and much more uncertain (see here for more info on uncertainty in these forecasts) than might be useful for decision-making purposes.

Providing openly-available seasonal river flow forecasts, globally

For the next ‘chapter’ of my PhD, we looked into the feasibility of providing seasonal forecasts of river flow at the global scale. Providing global-scale flood forecasts in the medium-range has only become possible in recent years, and extended-range flood forecasting was highlighted as a grand challenge and likely future development in hydro-meteorological forecasting.

To do this, I worked with Ervin Zsoter at ECMWF, to drive the GloFAS hydrological model (Lisflood) with reforecasts from ECMWF’s latest seasonal forecasting system, SEAS5, to produce seasonal forecasts of river flow. We also forced Lisflood with the new ERA5 reanalysis, to produce an ERA5-R river flow reanalysis with which to initialise Lisflood, and to provide a climatology. The system set-up is shown in the flowchart below.

I also worked with colleagues at ECMWF to design forecast products for a GloFAS seasonal outlook, based on a combination of features from the GloFAS flood forecasts, and the EFAS (the European Flood Awareness System) seasonal outlook, and incorporating feedback from users of EFAS.

After ~1 year of working on getting the system set up and finalising the forecast products, including a four-month research placement at ECMWF, the first GloFAS -Seasonal forecast was released in November 2017, with the release of SEAS5. GloFAS-Seasonal is now running operationally at ECMWF, providing forecasts of high and low weekly-averaged river flow for the global river network, up to 4 months ahead, with 3 new forecast layers available through the GloFAS interface. These provide a forecast overview for 307 major river basins, a map of the forecast for the entire river network at the sub-basin scale, and ensemble hydrographs at thousands of locations across the globe (which change with each forecast depending on forecast probabilities). New forecasts are produced once per month, and released on the 10th of each month. You can find more information on each of the different forecast layers and the system set-up here, and you can access the (openly available) forecasts here. ERA5-R, ERA-20CM-R and the GloFAS-Seasonal reforecasts are also all freely available – just get in touch! GloFAS-Seasonal will continue to be developed by ECMWF and the JRC, and has already been updated to v2.0, including a calibrated version of the hydrological model.

So, over the course of my PhD, we developed two new seasonal forecasts for hydrological extremes, at the global scale. You may be wondering whether they’re skilful, or in fact, which one provides the most useful forecasts! For information on the skill or ‘potential usefulness’ of GloFAS-Seasonal, head to our paper, and stay tuned for a paper coming soon (hopefully! [update: this paper has just been accepted and can be accessed online here]) on the ‘most useful approach for forecasting hydrological extremes during El Niño’, in which we compare the skill of the two forecasts at predicting observed high and low flow events during El Niño.

With thanks to my PhD supervisors & co-authors:

Hannah Cloke1, Liz Stephens1, Florian Pappenberger2, Steve Woolnough1, Ervin Zsoter2, Peter Salamon3, Louise Arnal1,2, Christel Prudhomme2, Davide Muraro3

1University of Reading, 2ECMWF, 3European Commission Joint Research Centre

## The Circumglobal Teleconnection and its Links to Seasonal Forecast Skill for the European Summer

Recent extreme weather events such as the central European heatwave in 2003, flooding in the UK in 2007, and even the recent dry summer in the UK in 2018, have highlighted the need for more accurate long-range forecasts for the European summer. Recent research has led to improvements in European winter seasonal forecasts, however summer forecast skill remains relatively low. One potential source of predictability for Europe is the Indian summer monsoon, which can affect European weather via a global wave train known as the “Circumglobal Teleconnection” (CGT).

The CGT was first identified by Ding and Wang (2005) as having a major role in modulating observed weather patterns in the Northern Hemisphere summer. Using a 200 hPa geopotential height index centred in west-central Asia (35°-40°N, 60°-70°E), they constructed a one-point correlation map of geopotential height with reference to this index (reproduced in Figure 1). From this, they identified a wavenumber-5 structure where the pressure variations over the Northeast Atlantic, East Asia, North Pacific and North America are all nearly in phase with the variations over west-central Asia (these are known as the “centres of action”). They also showed that the CGT is associated with significant temperature and precipitation anomalies in Europe, so accurate representation this mechanism in seasonal forecast models could provide an important source of subseasonal to seasonal forecast skill.

The model used here is a version of the European Centre for Medium-Range Weather Forecasts (ECMWF)’s coupled seasonal forecast model. Reforecasts are initialised on 1st May and are run for four months, so cover May-August, with start dates from 1981-2014. The skill of the model 200 hPa geopotential height is shown in Figure 2, defined as the correlation between the model ensemble mean and ERA-Interim. The model has good skill in May (to be expected given that the reforecasts are initialised in May) but in June, July and August areas of zero or negative correlation develop across much of the northern hemisphere extratropics. The areas of reduced skill align closely with the location of the centres of action of the CGT shown in Figure 1, suggesting that there is a link between the model skill and the model representation of the CGT.

To determine how well the model represents the CGT, Figure 3 shows the correlation between the D&W region and the other centres of action of the CGT, as defined in Figure 1. Focussing on August (as August has the strongest CGT pattern) it can be seen that the model correlations, indicated by the box and whisker plots, are weaker than in observations (red diamond) for the D&W vs. North Pacific (NPAC), North America (NAM) and Northwest Europe (NWEUR) regions. This indicates that the model has a weak representation of the wavetrain associated with the CGT.

There are likely to be several reasons for the weak representation of the CGT in the model. One important factor is the presence of a northerly jet bias in the model across much of the Northern Hemisphere. This can be seen in Figure 4, which shows the model jet biases relative to ERA-Interim in the coloured contours, and the observed zonal wind in the black contours. The dipole structure of the biases which exists across much of the hemisphere, particularly in June, July and August, indicates that the model jet stream is located too far to the north. This means that Rossby waves forced in this region will have different wave propagation characteristics to reality – they may propagate at the incorrect speed, in the wrong direction or may not propagate at all, and this is likely to be an important factor in the weak representation of the CGT in the model.

Other potential factors involved are a poor representation of the link between monsoon precipitation and the geopotential height in west-central Asia (which was shown by Ding and Wang (2007) to be important in the maintenance of the CGT) and errors in the forcing of Rossby waves associated with the monsoon. For a more detailed explanation of these, see my paper in Climate Dynamics (Beverley et al. 2018). It seems likely that the pattern of reduced skill in Figure 2, with negative correlations located at the centres of action of the CGT, including over Europe, is related to the poor representation of the CGT in the model. This raises the question of whether an improvement in the model’s representation of the CGT would lead to an improvement in forecast skill for the European summer. To address this question, sensitivity experiments have been carried out, in which the observed circulation is imposed in several centres of action along the CGT pathway to explore the impact on forecast skill for European summer weather.

References

Beverley, J. D., S. J. Woolnough, L. H. Baker, S. J. Johnson and A. Weisheimer, 2018: The northern hemisphere circumglobal teleconnection in a seasonal forecast model and its relationship to European summer forecast skill. Clim. Dyn. https://doi.org/10.1007/s00382-018-4371-4

Ding, Q., and B. Wang, 2005: Circumglobal teleconnection in the northern hemisphere summer. J. Clim. 18, 3483–3505.

Ding, Q., and B. Wang, 2007: Intraseasonal teleconnection between the summer Eurasian wave train and the Indian monsoon. J. Clim. 20, 3751-3767. https://doi.org/10.1175/JCLI4221.1

## APPLICATE General Assembly and Early Career Science event

On 28th January to 1st February I attended the APPLICATE (Advanced Prediction in Polar regions and beyond: modelling, observing system design and LInkages associated with a Changing Arctic climaTE (bold choice)) General Assembly and Early Career Science event at ECMWF in Reading. APPLICATE is one of the EU Horizon 2020 projects with the aim of improving weather and climate prediction in the polar regions. The Arctic is a region of rapid change, with decreases in sea ice extent (Stroeve et al., 2012) and changes to ecosystems (Post et al., 2009). These changes are leading to increased interest in the Arctic for business opportunities such as the opening of shipping routes (Aksenov et al., 2017). There is also a lot of current work being done on the link between changes in the Arctic and mid-latitude weather (Cohen et al., 2014), however there is still much uncertainty. These changes could have large impacts on human life, therefore there needs to be a concerted scientific effort to develop our understanding of Arctic processes and how this links to the mid-latitudes. This is the gap that APPLICATE aims to fill.

The overarching goal of APPLICATE is to develop enhanced predictive capacity for weather and climate in the Arctic and beyond, and to determine the influence of Arctic climate change on Northern Hemisphere mid-latitudes, for the benefit of policy makers, businesses and society.

APPLICATE Goals & Objectives

Attending the General Assembly was a great opportunity to get an insight into how large scientific projects work. The project is made up of different work packages each with a different focus. Within these work packages there are then a set of specific tasks and deliverables spread out throughout the project. At the GA there were a number of breakout sessions where the progress of the working groups was discussed. It was interesting to see how these discussions worked and how issues, such as the delay in CMIP6 experiments, are handled. The General Assembly also allows the different work packages to communicate with each other to plan ahead, and for results to be shared.

One of the big questions APPLICATE is trying to address is the link between Arctic sea-ice and the Northern Hemisphere mid-latitudes. Many of the presentations covered different aspects of this, such as how including Arctic observations in forecasts affects their skill over Eurasia. There were also initial results from some of the Polar Amplification (PA)MIP experiments, a project that APPLICATE has helped coordinate.

At the end of the week there was the Early Career Science Event which consisted of a number of talks on more soft skills. One of the most interesting activities was based around engaging with stakeholders. To try and understand the different needs of a variety of stakeholders in the Arctic (from local communities to shipping companies) we had to try and lobby for different policies on their behalf. This was also a great chance to meet other early career scientists working in the field and get to know each other a bit more.

What a difference a day makes, heavy snow getting the ECMWF’s ducks in the polar spirit.

#### References

Aksenov, Y. et al., 2017. On the future navigability of Arctic sea routes: High-resolution projections of the Arctic Ocean and sea ice. Marine Policy, 75, pp.300–317.

Cohen, J. et al., 2014. Recent Arctic amplification and extreme mid-latitude weather. Nature Geoscience, 7(9), pp.627–637.

Post, E. & Others, 24, 2009. Ecological Dynamics Across the Arctic Associated with Recent Climate Change. Science, 325(September), pp.1355–1358.

Stroeve, J.C. et al., 2012. Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophysical Research Letters, 39(16), pp.1–7.

## Evaluating aerosol forecasts in London

Aerosols in urban areas can greatly impact visibility, radiation budgets and our health (Chen et al., 2015). Aerosols make up the liquid and solid particles in the air that, alongside noxious gases like nitrogen dioxide, are the pollution in cities that we often hear about on the news – breaking safety limits in cities across the globe from London to Beijing. Air quality researchers try to monitor and predict aerosols, to inform local councils so they can plan and reduce local emissions.

Recently, large numbers of LiDARs (Light Detection and Ranging) have been deployed across Europe, and elsewhere – in part to observe aerosols. They effectively shoot beams of light into the atmosphere, which reflect off atmospheric constituents like aerosols. From each beam, many measurements of reflectance are taken very quickly over time – and as light travels further with more time, an entire profile of reflectance can be constructed. As the penetration of light into the atmosphere decreases with distance, the reflected light is usually commonly called attenuated backscatter (β). In urban areas, measurements away from the surface like these are sorely needed (Barlow, 2014), so these instruments could be extremely useful. When it comes to predicting aerosols, numerical weather prediction (NWP) models are increasingly being considered as an option. However, the models themselves are very computationally expensive to run so they tend to only have a simple representation of aerosol. For example, for explicitly resolved aerosol, the Met Office UKV model (1.5 km) just has a dry mass of aerosol [kg kg-1] (Clark et al., 2008). That’s all. It gets transported around by the model dynamics, but any other aerosol characteristics, from size to number, need to be parameterised from the mass, to limit computation costs. However, how do we know if the estimates of aerosol from the model are actually correct? A direct comparison between NWP aerosol and β is not possible because fundamentally, they are different variables – so to bridge the gap, a forward operator is needed.

In my PhD I helped develop such a forward operator (aerFO, Warren et al., 2018). It’s a model that takes aerosol mass (and relative humidity) from NWP model output, and estimates what the attenuated backscatter would be as a result (βm). From this, βm could be directly compared to βo and the NWP aerosol output evaluated (e.g. see if the aerosol is too high or low). The aerFO was also made to be computationally cheap and flexible, so if you had more information than just the mass, the aerFO would be able to use it!

Among the aerFO’s several uses (Warren et al., 2018, n.d.), was the evaluation of NWP model output. Figure 2 shows the aerFO in action with a comparison between βm and observed attenuated backscatter (βo) measured at 905 nm from a ceilometer (a type of LiDAR) on 14th April 2015 at Marylebone Road in London. βm was far too high in the morning on this day. We found that the original scheme the UKV used to parameterise the urban surface effects in London was leading to a persistent cold bias in the morning. The cold bias would lead to a high relative humidity, so consequently the aerFO condensed more water than necessary, onto the aerosol particles as a result, causing them to swell up too much. As a result, bigger particles mean bigger βm and an overestimation. Not only was the relative humidity too high, the boundary layer in the NWP model was developing too late in the day as well. Normally, when the surface warms up enough, convection starts, which acts to mix aerosol up in the boundary layer and dilute it near the surface. However, the cold bias delayed this boundary layer development, so the aerosol concentration near the surface remained high for too long. More mass led to the aerFO parameterising larger sizes and total numbers of particles, so overestimated βm. This cold bias effect was reflected across several cases using the old scheme but was notably smaller for cases using a newer urban surface scheme called MORUSES (Met Office – Reading Urban Surface Exchange Scheme). One of the main aims for MORUSES was to improve the representation of energy transfer in urban areas, and at least to us it seemed like it was doing a better job!

References

Barlow, J.F., 2014. Progress in observing and modelling the urban boundary layer. Urban Clim. 10, 216–240. https://doi.org/10.1016/j.uclim.2014.03.011

Chen, C.H., Chan, C.C., Chen, B.Y., Cheng, T.J., Leon Guo, Y., 2015. Effects of particulate air pollution and ozone on lung function in non-asthmatic children. Environ. Res. 137, 40–48. https://doi.org/10.1016/j.envres.2014.11.021

Clark, P.A., Harcourt, S.A., Macpherson, B., Mathison, C.T., Cusack, S., Naylor, M., 2008. Prediction of visibility and aerosol within the operational Met Office Unified Model. I: Model formulation and variational assimilation. Q. J. R. Meteorol. Soc. 134, 1801–1816. https://doi.org/10.1002/qj.318

Warren, E., Charlton-Perez, C., Kotthaus, S., Lean, H., Ballard, S., Hopkin, E., Grimmond, S., 2018. Evaluation of forward-modelled attenuated backscatter using an urban ceilometer network in London under clear-sky conditions. Atmos. Environ. 191, 532–547. https://doi.org/10.1016/j.atmosenv.2018.04.045

Warren, E., Charlton-Perez, C., Kotthaus, S., Marenco, F., Ryder, C., Johnson, B., Lean, H., Ballard, S., Grimmond, S., n.d. Observed aerosol characteristics to improve forward-modelled attenuated backscatter. Atmos. Environ. Submitted

## Quantifying the skill of convection-permitting ensemble forecasts for the sea-breeze occurrence

On the afternoon of 16th August 2004, the village of Boscastle on the north coast of Cornwall was severely damaged by flooding (Golding et al., 2005). This is one example of high impact hazardous weather associated with small meso- and convective-scale weather phenomena, the prediction of which can be uncertain even a few hours ahead (Lorenz, 1969; Hohenegger and Schar, 2007). Taking advantage of the increased computer power (e.g. https://www.metoffice.gov.uk/research/technology/supercomputer) this has motivated many operational and research forecasting centres to introduce convection-permitting ensemble prediction systems (CP-EPSs), in order to give timely weather warnings of severe weather.

However, despite being an exciting new forecasting technology, CP-EPSs place a heavy burden on the computational resources of forecasting centres. They are usually run on limited areas with initial and boundary conditions provided by global lower resolution ensembles (LR-EPS). They also produce large amounts of data which needs to be rapidly digested and utilized by operational forecasters. Assessing whether the convective-scale ensemble is likely to provide useful additional information is key to successful real-time utilisation of this data. Similarly, knowing where equivalent information can be gained (even if partially) from LR-EPS using statistical/dynamical post-processing both extends lead time (due to faster production time) and also potentially provides information in regions where no convective-scale ensemble is available.

There have been many studies on the verification of CP-EPSs (Klasa et al., 2018, Hagelin et al., 2017, Barret et al., 2016, Beck et al., 2016 amongst the others), but none of them has dealt with the quantification of the skill gained by CP-EPSs in comparison with LR-EPSs, when fully exploited, for specific weather phenomena and for a long enough evaluation period.

In my PhD, I have focused on the sea-breeze phenomenon for different reasons:

1. Sea breezes have an impact on air quality by advecting pollutants, on heat stress by providing a relief on hot days and also on convection by providing a trigger, especially when interacting with other mesoscale flows (see for examples figure 1 or figures 6, 7 in Golding et al., 2005).
2. Sea breezes occur on small spatio-temporal scales which are properly resolved at convection-permitting resolutions, but their occurrence is still influenced by synoptic-scale conditions, which are resolved by the global LR-EPS.

Therefore this study aims to investigate whether the sea breeze is predictable by only knowing a few predictors or whether the better representation of fine-scale structures (e.g. orography, topography) by the CP-EPS implies a better sea-breeze prediction.

In order to estimate probabilistic forecasts from both the models, two different methods have been applied. A novel tracking algorithm for the identification of sea-breeze front, in the domain represented in figure 2, was applied to CP-EPSs data. A Bayesian model was used instead to estimate the probability of sea-breeze conditioned on two LR-EPSs predictors and trained on CP-EPSs data. More details can be found in Cafaro et al. (2018).

The results of the probabilistic verification are shown in figure 3. Reliability (REL) and resolution (RES) terms have been computed decomposing the Brier score (BS) and Information gain (IGN) score. Finally, scores differences (BSD and IG) have been computed to quantify any gain in the skill by the CP-EPS. Figure 3 shows that CP-EPS forecast is significantly more skilful than the Bayesian forecast. Nevertheless, the Bayesian forecast has more resolution than a climatological forecast (figure 3e,f), which has no resolution by construction.

This study shows the additional skill provided by the Met Office convection-permitting ensemble forecast for the sea-breeze prediction. The ability of CP-EPSs to resolve meso-scale dynamical features is thus proven to be important and only two large-scale predictors, relevant for the sea-breeze, are not sufficient for skilful prediction.

It is believed that both the methodologies can, in principle, be applied to other locations of the world and it is thus hoped they could be used operationally.

References:

Barrett, A. I., Gray, S. L., Kirshbaum, D. J., Roberts, N. M., Schultz, D. M., and Fairman J. G. (2016). The utility of convection-permitting ensembles for the prediction of stationary convective bands. Monthly Weather Review, 144(3):1093–1114, doi: 10.1175/MWR-D-15-0148.1

Beck,  J., Bouttier, F., Wiegand, L., Gebhardt, C., Eagle, C., and Roberts, N. (2016). Development and verification of two convection-allowing multi-model ensembles over Western europe. Quarterly Journal of the Royal Meteorological Society, 142(700):2808–2826, doi: 10.1002/qj.2870

Cafaro C., Frame T. H. A., Methven J., Roberts N. and Broecker J. (2018), The added value of convection-permitting ensemble forecasts of sea breeze compared to a Bayesian forecast driven by the global ensemble, Quarterly Journal of the Royal Meteorological Society., under review.

Golding, B. , Clark, P. and May, B. (2005), The Boscastle flood: Meteorological analysis of the conditions leading to flooding on 16 August 2004. Weather, 60: 230-235, doi: 10.1256/wea.71.05

Hagelin, S., Son, J., Swinbank, R., McCabe, A., Roberts, N., and Tennant, W. (2017). The Met Office convective-scale ensemble, MOGREPS-UK. Quarterly Journal of the Royal Meteorological Society, 143(708):2846–2861, doi: 10.1002/qj.3135

Hohenegger, C. and Schar, C. (2007). Atmospheric predictability at synoptic versus cloud-resolving scales. Bulletin of the American Meteorological Society, 88(11):1783–1794, doi: 10.1175/BAMS-88-11-1783

Klasa, C., Arpagaus, M., Walser, A., and Wernli, H. (2018). An evaluation of the convection-permitting ensemble cosmo-e for three contrasting precipitation events in Switzerland. Quarterly Journal of the Royal Meteorological Society, 144(712):744–764, doi: 10.1002/qj.3245

Lorenz, E. N. (1969). Predictability of a flow which possesses many scales of motion.Tellus, 21:289 – 307, doi: 10.1111/j.2153-3490.1969.tb00444.x

## A New Aviation Turbulence Forecasting Technique

Anyone that has ever been on a plane will probably have experienced turbulence at some point. Most of the time it is not likely to cause injury, but during severe turbulence unsecured objects (including people) can be thrown around the cabin, costing the airline industry millions of dollars every year in compensation (Sharman and Lane, 2016). Recent research has also indicated that in the future the frequency of clear-air turbulence will increase with climate change. Forecasting turbulence is one of the best ways to reduce the number of injuries by giving pilots and flight planners ample warning, so they can put on the seat-belt sign or avoid the turbulent region altogether. The current method used in creating a turbulence forecast is a single ‘deterministic’ forecast – one forecast model, with one forecast output. This shows the region where they suspect turbulence to be, but because the forecast is not perfect, it would be more ideal to show how certain we are that there is turbulence in that region.

To do this, a probabilistic forecast can be created using an ensemble (a collection of forecast model outputs with slightly different model physics or initial conditions). A probabilistic forecast essentially shows model confidence in the forecast, and therefore how likely it is that there will be turbulence in a given region. For example, if all 10 out of 10 forecast outputs predict turbulence in the same location, the pilots would be confident in taking action (such as avoiding the region altogether). However, if only 1 out of 10 models predict turbulence, then the pilot may choose to turn on the seat-belt sign because there is still a chance of turbulence, but not enough to warrant spending time and fuel to fly around the region. A probabilistic forecast not only provides more information in the certainty of the forecast, but it also increases the chances of forecasting turbulence that a single model might miss.

Gill and Buchanan (2014) showed this ensemble forecast method does improve the forecast skill. In my project we have taken this one step further and created a multi-model ensemble, which is combining two different ensembles, each with their own strengths and weaknesses (Storer et al., 2018). We combine the Met Ofﬁce Global and Regional Ensemble Prediction System (MOGREPS-G), with the European Centre for Medium Range Weather Forecasting (ECMWF) Ensemble Prediction System (EPS).

There are three main sources of turbulence. The first is mountain wave turbulence, where gravity waves are produced from mountains that ultimately lead to turbulence. The second is convectively-induced turbulence, which includes in-cloud turbulence and also gravity waves produced as a result of deep convection that also lead to turbulence. The third is shear-induced turbulence, which is the one we are trying to forecast in this example. Figure 1 is an example plot showing orography and thus mountain wave turbulence (top left), convection and thus convectively induced turbulence (top right), the MOGREPS-G ensemble forecast of shear turbulence (bottom left) and the ECMWF ensemble forecast of shear turbulence (bottom right). The red circle indicates a ‘moderate or greater’ turbulence event, and we can see that because it is over the North Atlantic it is not a mountain wave turbulence event, and there is no convection nearby, but both the ensemble forecasts correctly predict the location of the shear-induced turbulence. This shows that there is high confidence in the forecast, and action (such as putting the seat-belt sign on) can be taken.

To understand the usefulness of the forecast, Figure 2 is a relative economic value plot. It shows the value of the forecast for a given cost/loss ratio (which will vary depending on the end user). The multi-model ensemble is more valuable than both of the single model ensembles for all cost/loss ratios, showing that every end user will benefit from this forecast. Although our results do show an improvement in forecast skill, it is not statistically significant. However, by combining ensemble forecasts we gain consistency and more operational resilience (i.e., we are still able to produce a forecast if one ensemble is not available), and is therefore still worth implementing in the future.

### References

Gill PG, Buchanan P. 2014. An ensemble based turbulence forecasting system. Meteorol. Appl. 21(1): 12–19.

Sharman R, Lane T. 2016. Aviation Turbulence: Processes, Detection, Prediction. Springer.

Storer, L.N., Gill, P.G. and Williams, P.D., 2018. Multi-Model Ensemble Predictions of Aviation Turbulence. Meteorol. Appl., (Accepted for publication).

## 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?

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).

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).

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.

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.