June 2019 Conferences: WESC, ICEM

I attended two conferences in June 2019; WESC in UCC, Cork and ICEM in DTU, Copenhagen. Here are some points from these conferences.


I presented a talk titled “WRF planetary boundary layer schemes for wind forecasting” in the “Mesoscale” session as part of Theme 1: “Wind resource, turbulence and wakes” at WESC in UCC. The other talks in the session included the following.

  • Dr. Martin Doerenkaemper presented work involved in the production of the New European Wind Atlas (NEWA). He outlined computing hardware challenges of the project, WRF setup testing and workflow planning.
  • Dr. Dries Allaerts presented on assimilation methods involving mesoscale-to-microscale coupling.
  • Prof S.C. Pryor presented an analysis of 2 different wind farm parameterization, Fitch and EWP, and varying model resolution. These were run for an area over Iowa, USA. It was found that EWP produced 1.5-2.1% higher capacity factors compared to Fitch, which could impact wind farm planning. Further investigations needed to determine which scheme is closer to reality.
  • Erkan Yilmaz presented work looking at WRF skill at forecasting wind in complex terrain in Central Turkey. Analysed the pros and cons of nearest point grid selection and bi-linear interpolation for better forecast results.


I presented a talk titled “An investigation of WRF PBL schemes for renewable energy forecasting in Ireland” in the “Forecasting for power system applications: wind models” at ICEM2019 in DTU.

  • Sue Ellen Haupt presented about work on mesoscale-to-microscale coupling at NCAR for wind energy applications. Discussed the issue of “terra incognita”, the area between O(1km) and O(100m) between mesoscale NWP and LES. Said that upper limit of terra incognita is boundary layer height.
  • James Wilzcak presented work as part of WFIP2, discussing the observations collected as part of the field campaign and how these led to model development within WRF as a result, e.g. looking at cold pools leading to sustained weak winds east of mountains in Washington state. WRF repository for their updated to MYNN on github.
  • Sven-Erik Gryning presented work using WRF to downscale GFS forecast data to predict wind ramping at different forecast horizons at FINO3 platform. Forecasts were evaluated using correlation coefficient and comparison of histograms. At 10 min lead time, WRF performed poorly, becomes skillful at 4 hours lead time before reducing for longer lead times. Results shown for wind speed and wind direction.
  • Jared Lee presented work on a project for renewable energy forecasting in Kuwait using WRF-Solar-Wind and DiCast (a multi-model forecast system using MOS to determine adaptive weighting of different models using a 90 day training period). Also using a “cubist” machine learning method.

Journal Club: 11-06-2019

This paper studied a case of extreme ramping that occurred in central USA in July 2011. The passage of a cold front, along with large instability triggered the formation of a mesoscale convective system. This resulted in strong gusts from downdrafts which caused extensive damage to the Buffalo Ridge wind farm.

Wind farm damage

This paper performance of different WRF PBL and microphysics schemes at representing this event. ERA-Interim renalysis data was downscaled for this event using WRF with 3 domains at 9km, 3km and 1km. 3 PBL schemes (YSU, MYJ and Shin-Hong) and 2 microphysics schemes (Ferrier-Aligo and WDM6) were tested. The Ferrier-Aligo is a more simplistic scheme with less classes of hydrometeor types compared to WDM6. The WRF data was analysed for the two innermost domains (3km and 1km).

Max wind speed from observation stations in “analysis region” (top) and the corresponding maximum WRF wind speed in the “analysis region”.

It was found that the highest resolution domain and most complex microphysics scheme (WDM6) produced the strongest wind speeds. Although the observed wind speeds did not pick up these strong wind speeds (top plot), apart from the black dots which lies outside the “analysis region” to the east. It was found that the high resolution (d03) with Ferrier-Aligo (FA) microphysics performed similarly to the coaser resolution (d02) and WDM6 (W6) for 10m winds. But when looking at the extremes of the profile, W6-d02 produces stronger winds aloft within the turbine layer than fa-d03.

Provides some possible methods for analysing high-res case studies.


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.