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.