Journal Club: 28-05-2019

Solar irradiance forecasting in the tropics using numerical weather prediction and statistical learning (2018).

This paper examines the skill of three different WRF set-ups (WRF-dudhia, WRF-rrtmg, WRF-solar) compared to it’s driving data, GFS. The aim is to produce an hourly day-ahead forecast for Singapore. A multivariate post-processing technique which combines principal component analysis (PCA) with stepwise variable selection is applied to all four models. These are also compared to smart persistence and a climatological forecast. WRF-solar combined with PCA and stepwise selection method produced the best results.

In this study the output is average for the whole island of Singapore. Clear sky index is used to estimate the forecast as it removes the diurnal and seasonality in irradiance. The three steps to the post-processing method are: 1) removal of the yearly and daily cycles, 2) dimensionality reduction and 3) Model Output Statistics (MOS). Stepwise variable selection is used to select the best possible set of explanatory variables. PCA is used for dimensionality reduction of the 3D model output. 80% of the explained variance is accounted for in at most 5 PCs. This method is compared to 1) no dimensionality reduction and 2) 3D variables are averaged vertically.

All post-processing techniques improve the forecast with WRF-solar-PCA performing best. Although there are certain months when other configurations perform well. Different models perform better during different weather conditions (Fig. 1)

Figure 1: RMSE of GFS, WRF-solar, WRF-rrtmg and WRF-dudhia as a function of the clear sky index for years 2014, 2015 and 2016. An asterisk indicate a statistically significant difference, while ‘n.s.’ indicates statistically indistinguishable values. To avoid cluttering, only the difference between WRF-solar and the other models is indicated.

MÉRA workshop 2019

by Gráinne Allen (an intern with MetClim)

I attended the MÉRA workshop in Glasnevin on Thursday 2nd of May. The workshop featured speakers from Met Éireann, the European Centre for Medium-Range Weather Forecasts (ECMWF), the Swedish Meteorological and Hydrological Institute (SMHI), UCD, Maynooth University, the Danish Meteorological Institute (DMI) and the University of Highlands and Islands (UHI) Stornoway. The abstract are here.

Eoin Whelan of Met Éirean gave an introduction to MÉRA, the Met Éireann Re-Analysis. MÉRA is a reanalysis of the Irish climate at high resolution from 1981 to 2016. The data is freely available and used primarily by academics and for environmental or renewable energy research purposes.

Cornel Soci of ECMWF gave an introduction to ERA5, which is the global reanalysis produced by the ECMWF. It was discussed how the model must be complete and consistent, as well as high functioning. It was found that ERA5 has forecast skill  a day further ahead than ERA Interim.

Semjon Schimanke of SMHI presented the Copernicus Regional Reanalysis for Europe which is produced as part of the Copernicus Climate Change Service (C3S). The available data covers all of Europe, some of North Africa and Greenland. UERRA is shown to be more accurate than ERA Interim and ERA5, and generally better for regional forecasts than for global forecasts. It has larger uncertainty over regions with less available data.

Éadaoin Doddy of UCD gave an evaluation of integrated cloud condensate in the MÉRA dataset. She found that it tends to overestimate cloud water path for frontal cloud cover compared to satellite data, but underestimates it for broken cloud.

Liam Woods from UCD is researching Atmospheric Rivers and Extreme weather precipitation events. He aims to identify atmospheric river events from the ERA5 dataset and to investigate their connection with extreme precipitation events. He also intends to build a storm tracker using a Lagrangian approach, and try to determine whether water vapour in atmospheric rivers is carried by the river or picked up as it travels.

Daniel Hawtree (UCD) uses MÉRA data to predict times of high bacterial content in water used for bathing. He and his team are building a model using MÉRA data because it contains many of the variables that have been used before in similar predictions, such as rainfall, temperature and pressure. The model could be improved by inclusion of observed tidal and better water quality data.

Daniel Courtney (Maynooth University) is modelling the environmental impacts of agricultural intensification in the Boyne Catchment. Food Harvest 2020 aims to improve the stock of dairy products and the value of meat by 2020. These goals will have a significant environmental impact on biodiversity in the Boyne catchment. The study particularly focused on ammonia which is used as a fertiliser. It does not persist in the atmosphere but gaseous and wet deposition make it very toxic to plants. Signs of heather bleaching and damage to sphagnum moss due to ammonia are apparent in the Killycunny raised bog, which is surrounded by farmland. Lower emissions methods of fertiliser spreading were recommended.

Laura Cooke (UCD) gave a presentation on future and historical renewable energy resources and weather driven demand in Ireland. The target for 2050 is for the majority of energy to be from renewable sources. She aims to investigate the effect of climate change on resources of renewable energy, using historical data as well as projections for mid- and long-term future. The aim is to produce a model for each hour and each season to determine peak energy demand times. Seasonal variations are removed to determine an overall demand model for day of the week and time of day. A solar capacity model depends on irradiation and solar panel angle and efficiency among other things.

Kristian Pagh Nielsen (DMI) is working on the Copernicus Arctic Regional Reanalysis. It is important to monitor the melting of glaciers because they contribute to sea level rise. A high resolution is required to accurately represent the processes that occur, but obtaining the volume of data required for this presents a challenge. He mentioned that there will be an increase in traffic now that a channel large enough for shipping has opened up due to the melting of the ice caps. The warming in the Arctic has been observed to be twice as high as global trends. The model predicts a value of 0.85 albedo for glaciers. Difficulty was encountered in using satellite images to detect snow, which may be darkened by sediment or bacteria living in the ice. They also plan to produce a model that reflects the coupling between surface albedo and cloud transparency. Their models take data such as local synoptic charts, reprocessed satellite data and sea state data as inputs.

The topic of Edward Graham’s (UHI) talk was ‘Extreme Low Thicknesses during the “Beast from the East”. He explained the differences between cold and warm high pressure systems. Thickness is the distance between pressure levels. During the Beast from the East (the second worst cold spell in 70 years), thicknesses were extremely small because the air was very cold. As the North Atlantic cools due to the melting of the ice caps, it is likely that more storms will come in over the Atlantic, which Graham termed ‘pests from the West’.

Laura Zubiate (Met Éireann) spoke about ‘Verification of Extreme Windstorms in the MÉRA Dataset’. WINDSURFER is a programme to assess the current and future risk of extreme wind to forestry and water. Assessing these risks is important so that they can be anticipated and prepared for. The results of MÉRA and Irish radiosonde observations at Valentia were compared for storms such as Darwin and Ophelia in the study.

Frédéric Dias (UCD) gave a talk on extreme wave events during the winter of 2013/2014. He used the movement of boulders on the west coast of Ireland to understand the size of and force exerted by these waves. He also made a simulation of the wave events using an unstructured grid at 225 m resolution inshore and 10 km resolution offshore. The model gives hourly outputs of wave parameters and are in close agreement with MÉRA and HARMONIE-AROME results.

Clément Calvino (UCD) is working on a coupled wave-ocean model for Galway bay. The aim is to improve its numerical interaction and to refine the strength of coupling. Waves are influenced by bottom and shoreline shape, and currents. It incorporates the NE Atlantic Marine Institute model, river climatologies and MÉRA atmospheric forcing. The model picks up correct results for the first 90 days when compared to data from the Spiddal observatory and acoustic Doppler current profilers in Galway bay. Future work will involve studying Lagrangian trajectories.

Nicole Beisiegel’s (UCD) talk was titled ‘Latest Insights into the use of MÉRA Data for Simulation of Storm Surges’. She discussed recently developed models such as Holland’s model for wind stress and the discontinuous Galerkin model which simulates storm surges. Beisiegel’s research is in the development of a non-uniform mesh to increase computational efficiency, and including detailed resolution of bottom topography. The results of her simulation currently underestimate the magnitude of storm surges when using MÉRA data.

Eadaoin’s presentation at the MÉRA workshop 2019

I gave a short presentation, “An Evaluation of Integrated Cloud Condensate in MÉRA”, about the follow-on research I’ve done on the work I presented at EMS last autumn. My talk looked at how the MÉRA reanalysis dataset needs time to spin-up for the variables: integrated cloud ice and integrated cloud water. The 09-33H forecast is best and recommended for these variables.

I compared the cloud water path (CWP) from MÉRA with satellite data which was taken to represent reality. There’s generally good agreement of CWP between both datasets. However, MÉRA tends to overestimate CWP, especially during cloudy conditions. Preliminary results indicate there may be a trend in the bias in relation to cloud type, but further analysis is needed on this. Future work also includes relating the bias to large scale weather patterns.

Feedback from the audience included a question about the accuracy of the satellite data and whether that had an impact on my results. This would be a good idea for future work. Other feedback for future work included looking to see if a similar bias exists in cloud optics or in global radiation.

Eadaoin’s time at EGU 2019

On Monday I attended the session ‘Numerical weather prediction, data assimilation and ensemble forecasting’. Among other interesting talks was one titled ‘Fraction of large forecast errors in global NWP’ by Thomas Haiden, which discussed the errors in upper level variables at ECMWF and the large-scale errors. He found that the fraction of large errors is important for communicating the forecast improvements. The fraction of large errors is highly correlated with average errors. Even if the skill of an NWP model improves from day 5 to day 4 over a few years, it is still important to look at the distribution of errors and how that changes with model updates.

I also attended the session ‘Big data and machine learning geosciences’ where there was an interesting talk ‘Machine learning methods for predicting energy demand/production based on hydro-meteorological input’ by Konrad Bogner, where they looked at a number of different machine learning methods to predict the energy demand and supply in the near future. These can also be used to look at predictive uncertainties in the energy demand and supply chain. Methods included multivariate linear regression (MLR), multivariate adaptive regression splines (MARS), Quantile regression (QR), quantile regression neural networks (QRNN) quantile random forest (WRF) and deep learning quantile regression (DLQR). The demand model includes variables such as daily measurements of temperature, precipitation, global radiation and wind speed as well as details about weekdays and holidays.

On Thursday I went to an interesting session which was very relevant to my research called ‘Advances in statistical post-processing for deterministic and ensemble forecasts’. A talk ‘Statistical post-processing of dual-resolution ensemble forecasts’ by Sándor Baran, looked at the skill of post-processing a dual resolution ensemble with members with a horizontal resolution of 18km and 45km. The most popular post-processing methods include BMA and EMOS. Results found that the improvement of post-processing dual resolution ensembles is not as pronounced as using a single resolution ensemble.

The next talk was by Nina Schuhen called ‘Rapid Adjustment of Forecast Trajectories: Improving short-term forecast skill through statistical post-processing’ which was about applying post-processing techniques to older forecast runs to before the next forecast is complete. The adjustment is from short-term observations which can be applied as soon as observations are available. Results found a better forecast from RAFT applied to earlier forecast runs than newer forecast runs.

The ‘Energy meteorology’ session was on Friday. The first talk was ‘Forecasting of solar irradiance at Reunion Island using numerical weather prediction models’ by Frederik Kurzrock, who found that spatial averaging usually improves GHI forecasts.

In the session ‘Forecasting the weather and aviation meteorology’ I heard an interesting talk about ‘IMPROVER: A probabilistic, multi-model post-processing system for meteorological forecasts’ by Benjamin Ayliffe, which is an open-source probability-based post-processing system from the Met Office. It has multiple modular steps including verification, thresholds to create probability, neighbourhood processing – land and topographic aware, time lag individual models among others.

An interesting piece of information I picked up during the week was that ERA5 tends to have low wind speed, possibly due to a roughness parameter issue.

While at EGU I presented my poster – ‘Multivariate spatial post-processing for renewable energy forecasts’. I got some positive interest including interest from energy traders in industry. I also got some good feedback recommending me to extend the lead time of my forecasts out further.