Search

Numerical Weather Prediction (NWP) models over limited areas enable the simulation of local atmospheric processes in more detail and with a higher degree of accuracy when compared to global models. Limited-area NWP models can outperform their global counterparts due to higher resolution (ability to explicitly simulate processes) and tailored physics (global models, unless run as a physics ensemble, have one set of parameterization schemes for the whole globe). However, increased accuracy from an NWP model is not guaranteed and can vary based on the location and variable of interest. In this paper, we present a method for combining the output of a limited-area NWP model, the Weather Research and Forecasting model (WRF) and its global model—the European Center for Medium Range Weather Forecasting (ECMWF) deterministic model. We simulate day-ahead global horizontal irradiance for a location in Qinghai, China. WRF model configurations optimized by the type of day (cloud amount) are then implemented based on the ECMWF model forecast of cloud amount. A regression model to combined ECMWF and WRF model forecasts is then trained. The optimized coefficients (weights) of ECMWF and WRF show increasing WRF importance with higher cloud amounts and the combination out-performs the ECMWF input by 5.2% and the best WRF configuration by 7.2% on a 2.5-month testing set. The performance of the combined model increased with observed cloud amount where the combined model out-performed the ECMWF model by 12.6% for cloudy days indicating the relative importance of physical downscaling for the simulation of clouds. [ABSTRACT FROM AUTHOR]

Granularity of the grid (both horizontally and vertically) is a key consideration when conducting localised Numerical Weather Prediction (NWP) modelling. Generally speaking, an NWP model with a finer grid can explicitly resolve more processes and require less parameterisation. However, a finer grid also requires more computation and it is not always clear that a finer grid will produce a more accurate forecast. In this study, we explore the sensitivity of rainfall prediction over Singapore to grid resolution. We use the Weather and Research Forecasting model (WRF) to forecast rainfall over Singapore and explore the performance of horizontal resolutions ranging from 1 km to 12 km. We test the performance on a set of dates from across the years 2020–2021 against both ground observations and radar-derived rain rates. When compared to ground observations, we show that, overall, the higher resolution produces the highest Critical Success Index (CSI) for rain rates in excess of 0.5 mm/h. When compared against radar-derived rain rates, the 1 km domain produces superior CSI values for all rain rates. The daily-average hourly Fractional Skill Score (FSS) was then calculated for some dates and showed agreement with the CSI results with the exception of a north-east monsoon day where, for heavier rain rates, the 3 km domain has superior FSS. We also investigate a particularly heavy rain event on 10 January 2021 and show that the 3 km grid has highest CSI for rain rates of 4 mm/h and 16 mm/h (based on both ground-based and radar-derived rain rates), however, the 1 km has superior FSS for this event. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP) models, which attempt to simulate the full state of the atmosphere, come with many options for dealing with processes that are unable to be explicitly resolved by the model. These model parameterizations are an ongoing area of research in atmospheric science. However, with continuous contributions from the research community and subsequent upgrade of the NWP codes, there are often many options for each unresolved process--leaving the user confronted with potentially thousands of ways to configure the model. We use the weather research and forecasting (WRF) model to forecast global horizontal irradiance (GHI) and undertake the task of narrowing down these options for a location in Qinghai, China. We show that optimizing the configuration of the WRF model based on the type of day (sunny, partly cloudy, or cloudy) is 13.6% better than using a single best configuration for all types of days. We also show that this performance improvement holds true for a longer 3-month test period (17.8% improvement). [ABSTRACT FROM AUTHOR]

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]