Merlinde Kay

Radiative effects of absorbing aerosols and the impact of water vapor

The radiative effects (both forcing at the tropopause and absorption in the lower troposphere) of standard aerosol models are examined as a function of relative humidity. Several of the models are also modified by the inclusion of additional soot, in line with recent observations. Increasing relative humidity causes many aerosol types to expand and also increases their single-scattering albedo. We have examined the impacts of these changes and also the interaction between aerosol growth and absorption of solar radiation by water vapor. These effects are seen not to be additive, especially in the case of flux divergence due to absorbing aerosols.

Actinic Flux and Net Flux Calculations in Radiative Transfer—A Comparative Study of Computational Efficiency

Abstract The accuracy and speed of three well-known computational techniques (DISORT, the δ–four-stream approximation, and the two-stream approximation), and the matrix inversion method, which is less well known, have been investigated. Results are presented for both broadband actinic and net fluxes over a range of parameters including solar zenith cosine, relative humidity, and altitude for two different surface/aerosol systems: terrestrial and oceanic. The matrix inversion method can only calculate actinic fluxes; therefore, this is the main focus of this paper. Investigations into the comparative accuracy of the four techniques for the oceanic model with and without a cloud layer included are also presented. (DISORT is taken as the benchmark for this research.) Based on results presented here, it is found that for actinic flux calculations, the δ–four-stream approximation is slightly more accurate than the matrix inversion method, and that both are far more accurate than the two-stream approximation. H...

Emerging challenges in wind energy forecasting for Australia

Global warming and diminishing fossil fuel resources are driving the greatest shift in energy supply in the history of human society. As a result, renewable resources such as wind energy are making ever greater contributions to electricity production worldwide (Sanchez 2006). Australia is blessed with substantial wind resources due to its latitude, extensive coastline and vast unpopulated areas, and these resources have the potential to contribute significantly to our future electricity supply (Archer and Jacobson 2005). However, wind energy is not quite as simple to implement as some other energy sources. Unlike a coal-fired power station, for example, where power only stops being produced when the coal supply runs out, or there is a maintenance issue, wind farm production stops if the wind is too strong, not strong enough or suddenly changes direction. This added variability, and the inherently non-storable nature of wind energy, presents a major challenge for power system operation – how do we prevent fluctuations in the power production of a wind farm from affecting the continuity of electricity supply to the consumer? Unfortunately, we can’t control the weather, and so we need strategies to work around it instead. This can be achieved in two steps. The first is to predict the weather as accurately as possible, aiming to know any weather-related lapses in power production well in advance. The second is to use that knowledge to arrange for other sources to make up the shortfall at the appropriate time. At first sight, this might seem rather troublesome, but the energy industry already does exactly this to some extent. The operation of non-renewable resources is guided by accurate weather forecasting; for example, on a very hot day, power producers will increase supply to account for increased loads due to higher than normal usage of air conditioners. The use of an electricEmerging challenges in wind energy forecasting for Australia

Improving NWP Forecasts for the Wind Energy Sector

Weather forecasting has traditionally been primarily used in the energy industry to estimate the impact of weather, particularly temperature, on future electrical demand. As a growing proportion of electricity generation comes from intermittent renewable sources such as wind, weather forecasting techniques need to be extended to this highly variable and site-specific resource. We demonstrate that wind speed forecasts from Numerical Weather Prediction (NWP) models can be significantly improved by implementing a bias correction methodology. For the study presented here, we used the Australian Bureau of Meteorology (BoM) MesoLAPS 5 km limited domain NWP model, focused over the Victoria/Tasmania region of Australia. The site for this study is the Woolnorth wind farm, situated in north-west Tasmania. We present a comparison of the accuracy of uncorrected hourly NWP forecasts and bias-corrected forecasts over the period March 2005 to May 2006. This comparison includes both the wind speed regimes of importance for typical daily wind farm operation, as well as infrequent but highly important weather risk scenarios that require turbine shutdown. In addition to the improved accuracy that can be obtained with a basic bias correction method, we show that further improvement can be gained from an additional correction that makes use of real-time wind turbine data and a smoothing function to correct for timing-related issues that result from use of the basic correction alone. With full correction applied, we obtain a reduction in the magnitude of the wind speed error by as much as 50 % for ‘hour ahead’ forecasts specific to the wind farm site.

Mesoscale simulations of Australian direct normal irradiance, featuring an extreme dust event

AbstractDirect normal irradiance (DNI) is the main input for concentrating solar power (CSP) technologies—an important component in future energy scenarios. DNI forecast accuracy is sensitive to radiative transfer schemes (RTSs) and microphysics in numerical weather prediction (NWP) models. Additionally, NWP models have large regional aerosol uncertainties. Dust aerosols can significantly attenuate DNI in extreme cases, with marked consequences for applications such as CSP. To date, studies have not compared the skill of different physical parameterization schemes for predicting hourly DNI under varying aerosol conditions over Australia. The authors address this gap by aiming to provide the first Weather and Forecasting (WRF) Model DNI benchmarks for Australia as baselines for assessing future aerosol-assimilated models. Annual and day-ahead simulations against ground measurements at selected sites focusing on an extreme dust event are run. Model biases are assessed for five shortwave RTSs at 30- and 10-k...

Improving Weather Forecasts for Wind Energy Applications

Weather forecasts play an important role in the energy industry particularly because of the impact of temperature on electrical demand. Power system operation requires that this variable and somewhat unpredictable demand be precisely met at all times and locations from available generation. As wind generation makes up a growing component of electricity supply around the world, it has become increasingly important to be able to provide useful forecasting for this highly variable and uncertain energy resource. Of particular interest are forecasts of weather events that rapidly change wind energy production from one or more wind farms. In this paper we describe work underway to improve the wind forecasts currently available from standard Numerical Weather Prediction (NWP) through a bias correction methodology. Our study has used the Australian Bureau of Meteorology MesoLAPS 5 km limited domain model over the Victoria/Tasmania region, providing forecasts for the Woolnorth wind farm, situated in Tasmania, Australia. The accuracy of these forecasts has been investigated, concentrating on the key wind speed ranges 5 – 15 ms−1 and around 25 ms−1. A bias correction methodology was applied to the NWP hourly forecasts to help account for systematic issues such as the NWP grid point not being at the exact location of the wind farm. An additional correction was applied for timing issues by using meteorological data from the wind farm. Results to date show a reduction in spread of forecast error for hour ahead forecasts by as much as half using this double correction methodology – a combination of both bias correction and timing correction.