Evaluation of cloudiness prediction resulting from WRF model


1 Yazd university

2 Yazd University


Weather predicts are influence in all aspects of life everyday. More importantly Weather warnings are important forecasts because they are used to protect life and property. Mesoscale numerical weather prediction models are key for weather forecasting, however the accuracy of the predictions are affected by the errors in the numerical models.
Study of the clouds is challenging and there are many applications in which the prediction of cloud is essential, including the field of air transport, rainfall and water resources. Nowadays, the clouds and precipitation of its behavior are even more strategic. Severe water shortage in some areas and increase rainfall in other areas is due to climate changes, therefore research in cloud and its precipitations are so important. The errors in the cloud forecast can have widespread impacts on the quality and accuracy of other model outputs. One of the important influences on temperature is interaction between the cloud and radiation. Therefore, parameters of cloud models, are very important to evaluate the model used for the prediction. In this study cloud cover, predicted by the Weather Research and Forecast (WRF) model, is reviewed.
The evaluating of clouds has always been a challenging task, due to the three dimensional (3D) structure and the need to finding adequate observations for the purpose. Historically conventional surface data have been used for verification purposes because of the ease of accessibility. This, at best provide point observations of low, medium and high cloud, total cloud and cloud base height. Recently Mittermaier (2012) reviewed the use of these observations for verification of Total Cloud Amount or cover (TCA) and cloud base height (CBH).
The availability of two dimensional time-height observations from ground-based active remote sensing instruments such as vertically pointing cloud radar can provide vertical detail at a location over time, from which cloud profiles (cloud amount as a function of altitude) can be derived. These give a view of clouds “from below”. Satellite data can provide a view from above.
As the first step for the evaluation of a numerical model of forecasting, the forecast data and observations must be prepared in which the observations should be temporally and spatially matched in an appropriate manner, as far as possible. The aim of the present study is to evaluate the cloud cover predicted by the model, and to do that, radar and satellite images have been used as observation data. The study has used table of agreement 3*3 for verification of cloud parameters in three categories of no clouds, partly cloudy and cloudy. The first WRF model is implemented in 5 days for ages predicting less than 24 hour and more than 24 and less than 48 hours. For running WRF model, 3 Domain was considered including 36 km horizontal range of parent domain and two nest domains with range of 12 and 4 km. It is worth noting that the relation between total cloud cover and images of Radar measurements was investigated in the time - period of 7/5/2009 - 12/5/2009. For evaluation of WRF used the assessment of the quantities, such as Bias (B), False Alarms Rate (FAR), Proportion Correct (PC), Kuipers Skill Score (KSS) and Heidke Skill Score (HSS). The 5 - day period were also evaluated with data obtained from satellite images. The results showed that verification of the model WRF for all ages of forecasting and for all domains is same. The model almost predicts all clear weather condition, however, it has come to overestimation. The number of times correct or incorrect partly cloudy weather is predicted by the model was very small so the output of the model for the partly cloudy is poor and quantity of cloudy weather was acceptable. The cases in which the weather was clear or partly cloudy, but the model predicted cloudy weather has very few occurrences. This result shows the amount of false alarms rate is low. In order to confirm these results, the evaluation was done for a longer period in three months, and the results are match.


Main Subjects

Azadi, M., Taghizadeh, E., Memarian, M. H. and Dmitrieva-Arrago, L. R., 2013, Comparing the results of precipitation forecast based on mesoscale models on the territory of Iran during the cold season, Russian Meteorology and Hydrology, 38(9), 605-613.
Crocker, R. and Mittermaier, M., 2013, Exploratory use of a satellite cloud masks to verify NWP models. Meteorological Applications.
Ferro, C. A. T., Richardson, D. S. and Weigel, A. P., 2008, On the effect of ensemble size on the discrete and continuous ranked probability scores, Meteorological Applications 15(1), 19-24.
Hamer, G. L., 1996, Forecaster assessments of cloud and visibility reports from the enhanced synoptic automated weather stations (Esaws) observations (land) Memo 3c, Met office.
Hogan, R. J., O’Connor, E. J. and Illingworth, A. J., 2009, Verification of cloud-fraction orecast,Quarterly Journal of the Royal Meteorological Society, 135(643), 1494-1511.
Keil, C. and Craig, G. C., 2007, A displacement-based error measure applied in a regional ensemble forecasting system, Monthly Weather Review, 135(9), 3248-3259.
(WWRP), Report of  world weather research programme, 2012,. Recommended Methods for Evaluating Cloud and Related Parameters.
Morcrette, C. J., O'Connor, E. J. and Petch, J. C., 2012, Evaluation of two cloud parametrization schemes using arm and cloudnet observations, Quarterly Journal of the Royal Meteorological Society, 138(665), 964-979.
Nurmi, P., 2003, Recommendations on the

verification of local weather forecasts.
Ringer, M. A., McAvaney, B. J., Andronova, N., Buja, L. E., Esch, M., Ingram, W. J., Li, B., Quaas, J., Roeckner, E. and Senior, C. A., 2006, Global mean cloud feedbacks in idealized climate change experiments, Geophysical research letters, 33(7), L07718.
Williams, K. and Brooks, M., 2008, Initial tendencies of cloud regimes in the Met Office Unified Model, J. Clim, 21, 833-840.
Zingerle, C. and Nurmi, P., 2008, Monitoring and verifying cloud forecasts originating from operational numerical models, Meteorological Applications, 15(3), 325-330.