Evaluating solar energy NWP forecasts during major dust aerosol events using MACC renalysis with WRF

There are projects underway and major plans to generate solar power utilising the high solar insolation over vast expanses of semi-arid and desert regions of the world to mitigate climate change and meet growing energy demands of developing nations. However, in the desert regions, accuracy in prediction of radiative effects of aerosol species from dust storms is a constraint. The variability of solar irradiance due to scattering and absorbing aerosols must be predicted accurately to optimise solar operations for grid integration and market participation. The Lake Eyre basin in Australia is the largest source of airborne dust in the southern hemisphere. The focus of this work is the 2009 Australian dust storm event where the radiative effects of aerosols stemming from the Lake Eyre Basin impacted the entire Eastern coast of Australia and dust particulates were transported to most regions of the North Island of New Zealand as well.

In this study, Global and regional Earth system Monitoring using Satellite and in-situ data (GEMS) ECMWF Monitoring Atmospheric Composition and Climate (MACC) global reanalysis data of atmospheric composition is incorporated to simulate NCAR's Weather Research and Forecast (WRF) ARW model. The 3-hourly MACC reanalysis relies on MOZART chemical transport and 4D-Var data assimilation for aerosol prognosis. The MACC aerosol reanalysis atmospheric compositions biases are compared against AERONET ground observations. Combinations of multiphysics ensembles for regionally optimised radiation physics options in WRF are selected to simulate the dust storm event with MACC reanalysis data. Forecasts generated with MACC reanalysis are compared with gridded NCEP final analysis data and Global Forecast System (GFS) boundary conditions downscaled to perform WRF mesoscale simulations. The GFS case also utilises optimised multiphysics ensembles but with the Thompson aerosol-aware scheme that relies on a monthly climatology based on Goddard Chemistry Aerosol Radiation and Transport (GOCART). The forecast MACC reanalysis MOZART dust transport case and the GOCART monthly climatology case with surface solar radiation day ahead (6 to 48 hours) forecasts generated are both inter-compared to measure errors with solar pyrheliometer derived ground observation data from Bureau of Meteorology (BoM). The BoM sites selected are based on five key Köppen climate classification zones suitable for solar power. The bias errors in forecast direct normal irradiance (DNI) from MACC aerosol prognosis are reduced at most ground stations compared to the monthly GFS GOCART climatology case. For non-major aerosol event sites, the differences in DNI are less significant between the chemical transport 4D-Var and NCEP monthly climatology case.