Preprocessing WRF initial conditions for coastal stratocumulus forecasting
| Autor: | Handa Yang, Jan Kleissl |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
010504 meteorology & atmospheric sciences
Meteorology 020209 energy Cloud cover Numerical weather prediction 02 engineering and technology Solar irradiance 01 natural sciences Engineering Materials Science(all) Marine layer Solar forecasting 0202 electrical engineering electronic engineering information engineering General Materials Science North American Mesoscale Model 0105 earth and related environmental sciences Cloud forecasting Energy Renewable Energy Sustainability and the Environment Built Environment and Design Liquid water content Weather Research and Forecasting Model Environmental science Rapid Refresh |
| Zdroj: | Yang, H; & Kleissl, J. (2016). Preprocessing WRF initial conditions for coastal stratocumulus forecasting. SOLAR ENERGY, 133, 180-193. doi: 10.1016/j.solener.2016.04.003. UC San Diego: Retrieved from: http://www.escholarship.org/uc/item/7xt0358g SOLAR ENERGY, vol 133 |
| ISSN: | 0038-092X |
| DOI: | 10.1016/j.solener.2016.04.003 |
| Popis: | The impact of atmospheric liquid water content at model initialization in the Weather Research and Forecasting (WRF) model is explored through the application of two preprocessing schemes. The first scheme, the Well-mixed Preprocessor (WEMPP), was designed and developed based on a well-mixed boundary layer to provide an initial guess at liquid water content when initializing with data from the North American Mesoscale (NAM) model, as liquid water content is not present in NAM output. The second scheme was adapted from a satellite Cloud Data Assimilation (CLDDA) package intended to make the initial model cloud field consistent with observations, using input data from the CIMSS GOES sounder cloud product. Preprocessed simulations were compared against baseline WRF simulations initialized with NAM and the Rapid Refresh (RAP) model (which contains liquid water output), as well as the raw parent model outputs. These intra-day forecasts were validated against both 5-min and 10-min averaged ground station and 30-min (hourly averaged) satellite irradiance observations over the course of a month. Due to their extensive spatial coverage, optical thickness, and reflectivity, stratocumulus (Sc) clouds are responsible for much of the variability in available solar resource at the surface in coastal California, where most rooftop photovoltaic systems are located. Currently, the trend of numerical weather prediction models is to underpredict both the presence and thickness of Sc. Therefore, the validation is conducted for a summer month in southern California, when Sc are most prevalent. Ground station validation showed average improvements by WEMPP in predicting surface irradiance over the baseline NAM (RAP) WRF initializations of 33% (−3%) MBE and 16% (9%) MAE, and by CLDDA of 47% (18%) MBE and 26% (20%) MAE. Additionally, simulations preprocessed by CLDDA were consistently able to outperform 24-h persistence forecast at 3 out of 4 ground stations. Validation against SolarAnywhere® satellite irradiance observations showed that the combination of both preprocessors provided the most improvement in the prediction of Sc spatial coverage, thickness, and lifetime in coastal regions where marine layer stratocumulus is most frequently observed, but cloud cover over the ocean was overestimated by all preprocessors. |
| Databáze: | OpenAIRE |
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