Daniel R. Drew

Developing a research strategy to better understand, observe and simulate urban atmospheric processes at kilometre to sub-kilometre scales

A Met Office/Natural Environment Research Council Joint Weather and Climate Research Programme workshop brought together 50 key international scientists from the UK and international community to formulate the key requirements for an Urban Meteorological Research strategy. The workshop was jointly organised by University of Reading and the Met Office.

Low-Dimensional Space- and Time-Coupled Power System Control Policies Driven by High-Dimensional Ensemble Weather Forecasts

Many predictive control problems can be solved at lower cost if the practitioner is able to make use of a high-dimensional forecast of exogenous uncertain quantities. For example, power system operators must accommodate significant short-term uncertainty in renewable energy infeeds. These are predicted using sophisticated numerical weather models, which produce an ensemble of scenarios for the evolution of atmospheric conditions. We describe a means of incorporating such forecasts into a multistage optimization framework able to make use of spatial and temporal correlation information. We derive an optimal procedure for reducing the size of the look-ahead problem by generating a low-dimensional representation of the uncertainty, while still retaining as much information as possible from the raw forecast data. We then demonstrate application of this technique to a model of the Great Britain grid in 2030, driven by the raw output of a real-world high-dimensional weather forecast from the U.K. Met Office. We also discuss applications of the approach beyond power systems.

Improved very short-term spatio-temporal wind forecasting using atmospheric regimes

We present a regime-switching vector-autoregressive method for very short-term wind speed forecasting at multiple locations with regimes based on large-scale meteorological phenomena. Statistical methods for wind speed forecasting based on recent observations out-perform numerical weather prediction for forecast horizons up to a few hours, and the spatio-temporal interdependency between geographically dispersed locations may be exploited to improve forecast skill. Here we show that conditioning spatio-temporal interdependency on ‘atmospheric modes’ derived from gridded numerical weather data can further improve forecast performance. Atmospheric modes are based on the clustering of surface wind and sea level pressure fields, and the geopotential height field at the 500hPa level. The data fields are extracted from the MERRA-2 reanalysis dataset with an hourly temporal resolution over the UK, atmospheric patterns are clustered using self-organising maps and then grouped further to optimise forecast performance. In a case study based on 6 years of measurements from 23 weather stations in the UK, a set of three atmospheric modes are found to be optimal for forecast performance. The skill of one- to six-hour-ahead forecasts is improved at all sites compared to persistence and competitive benchmarks. Across the 23 test sites, one-hour-ahead root mean squared error is reduced by between 0.3% and 4.1% compared to the best performing benchmark, and by an average of 1.6% over all sites; the six-hour-ahead accuracy is improved by an average of 3.1%.

Improved very short-term spatio-temporal wind forecasting using atmospheric regimes: Improved very short-term spatio-temporal wind forecasting using atmospheric regimes

We present a regime-switching vector-autoregressive method for very short-term wind speed forecasting at multiple locations with regimes based on large-scale meteorological phenomena. Statistical methods for wind speed forecasting based on recent observations out-perform numerical weather prediction for forecast horizons up to a few hours, and the spatio-temporal interdependency between geographically dispersed locations may be exploited to improve forecast skill. Here we show that conditioning spatio-temporal interdependency on ‘atmospheric modes’ derived from gridded numerical weather data can further improve forecast performance. Atmospheric modes are based on the clustering of surface wind and sea level pressure fields, and the geopotential height field at the 500hPa level. The data fields are extracted from the MERRA-2 reanalysis dataset with an hourly temporal resolution over the UK, atmospheric patterns are clustered using self-organising maps and then grouped further to optimise forecast performance. In a case study based on 6 years of measurements from 23 weather stations in the UK, a set of three atmospheric modes are found to be optimal for forecast performance. The skill of one- to six-hour-ahead forecasts is improved at all sites compared to persistence and competitive benchmarks. Across the 23 test sites, one-hour-ahead root mean squared error is reduced by between 0.3% and 4.1% compared to the best performing benchmark, and by an average of 1.6% over all sites; the six-hour-ahead accuracy is improved by an average of 3.1%.

Improved very-short-term wind forecasting using atmospheric regimes

We present a regime-switching vector-autoregressive method for very short-term wind speed forecasting at multiple locations with regimes based on large-scale meteorological phenomena. Statistical methods for wind speed forecasting based on recent observations out-perform numerical weather prediction for forecast horizons up to a few hours, and the spatio-temporal interdependency between geographically dispersed locations may be exploited to improve forecast skill. Here we show that conditioning spatio-temporal interdependency on ‘atmospheric modes’ derived from gridded numerical weather data can further improve forecast performance. Atmospheric modes are based on the clustering of surface wind and sea level pressure fields, and the geopotential height field at the 500hPa level. The data fields are extracted from the MERRA-2 reanalysis dataset with an hourly temporal resolution over the UK, atmospheric patterns are clustered using self-organising maps and then grouped further to optimise forecast performance. In a case study based on 6 years of measurements from 23 weather stations in the UK, a set of three atmospheric modes are found to be optimal for forecast performance. The skill of one- to six-hour-ahead forecasts is improved at all sites compared to persistence and competitive benchmarks. Across the 23 test sites, one-hour-ahead root mean squared error is reduced by between 0.3% and 4.1% compared to the best performing benchmark, and by an average of 1.6% over all sites; the six-hour-ahead accuracy is improved by an average of 3.1%.

An hourly time series of GB-aggregated wind power generation from 1980-2013, based on a future distribution of wind farms with a high level of offshore capacity.

MERRA reanalysis data (>34 years available) have been used to estimate the hourly aggregated wind power generation for Great Britain based on a distribution of wind farms which is considered to be representative of a future scenario with a high penetration of offshore capacity. The file details the GB-total hourly capacity factor from 1980 to 2013 inclusive. The data have been produced to understand the long term variability of the wind generation with high levels of capacity and the possible implications for UK power system.