Search

For most locations on Earth the ability of a numerical weather prediction (NWP) model to accurately simulate surface irradiance relies heavily on the NWP model being able to resolve cloud coverage and thickness. At horizontal resolutions at or below a few kilometers NWP models begin to explicitly resolve convection and the clouds that arise from convective processes. However, even at high resolutions, biases may remain in the model and result in under- or over prediction of surface irradiance. In this study we explore the correction of such systematic biases using a moisture adjustment method in tandem with the Weather Research and Forecasting (WRF) Model for a location in Xinjiang, China. After extensive optimization of the configuration of the WRF Model we show that systematic biases still exist--in particular for wintertime in Xinjiang. We then demonstrate the moisture adjustment method with cloudy days for January 2019. Adjusting the relative humidity by 12% through the vertical led to a root-mean-square error (RMSE) improvement of 57.8% and a 90.5% reduction in bias for surface irradiance. [ABSTRACT FROM AUTHOR]

ABSTRACT This study presents a semi-objective set of criteria for filtering the time series of a self-organizing map ( SOM, a method for collapsing a complex data set onto a series of typical instances/nodes). The purpose of the filter (or selection process) is to avoid the problem of associating anomalous time steps with SOM nodes that do not adequately describe that time step. The filter is based on measures of the match of the original field to the nearest SOM node. We demonstrate the method by using 21 years of MSLP reanalysis data for the Australian region from the European Centre for Medium Range Weather Forecasting product, ERA-Interim. We show that there are instances in which the SOM is incapable of describing the original data and that these instances can be removed using a set of criteria. We then show that this filtering/removal process improves both the quantitative and qualitative matching of the SOM to the MSLP data. Finally, we show that SOM-associated precipitation at three Australian cities (Melbourne, Sydney and Perth) is significantly influenced by the MSLP distributions that do not relate to the SOM and were removed by the filtering. The results show that care is required when interpreting trends inferred from data sorted into SOM nodes. [ABSTRACT FROM AUTHOR]