Analysis and accuracy of the Weather Research and Forecasting Model (WRF) for Climate Change Prediction in Thailand
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1 ก ก ก th National Convention on Civil Engineering ก May 2014, Khon Kaen, THAILAND ก ก ก ก ก WRF Analysis and accuracy of the Weather Research and Forecasting Model (WRF) for Climate Change Prediction in Thailand 1, ก 2, ก 3,ก ก ก 4 5 1,2,3,4,5 ก ก ก ( ก ) 108 ก ก 8 ก ก ก ก ก ก ก ก ก ก 2554 ก ก ก ( ก ) ก ก WRF(Weather Research and Forecasting Model) ก ก ก WRF 3.2 3x3 ก 1 30 ก 2556 ก กก ก ก. 477 ก ก ก ก ก (Accuracy ) ก กก 50 ก ก ก ก ก ก ก ก ก ก : ก, ก WRF,,, Abstract Rainfall forecasting information plays a crucial role for Thailand s water resource management as supporting data in analyzing and monitoring water situation, surveillance and warning water disaster. In 2011, Hydro and Agro Informatics Institute (HAII) has developed Weather Research and Forecasting Model (WRF). This paper is concentrated on analyzing and evaluating the accuracy of rainfall information forecast from WRF Version 3.2 with the resolution 3x3 kilometers during August 1 to November 30, 2013 by comparing the observed data from 477 automated stations of Thai Meteorological Department (TMD) and HAII. Considering from error and spatial data, the accuracy of day 1, 2 and 3 forecast is at 69%, 66% and 62% respectively. The most effective forecast regions are Southeastern, Eastern and Northeastern; this made more reliability for rainfall monitoring and surveillance. Keywords: Rainfall forecasting system, WRF Model, Accuracy, Efficiency, Reliability 1. ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก WRF (Weather Research and Forecasting) ก * (Corresponding author) address: thippawan@haii.or.th 1
2 Nation Center for Atmospheric Research (NCAR) ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก [1] ก ก ก ก [2] [3] WRF ก ก ก ก 2-3 ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก [4] ก ก ก ก ก (Contiguous Rain Areas:CRA) กก ก ก ก ก ก ก ก ก ก ก ก ก [5] ก ก ก ก ก ก ก ก ก ก ก [6] [7] ก ก 120 ก 72 ก ก ก ก ก 24 ก ก ก ก [8][9] ก ก ก ก ก WRF ก ก ก ( ก ) 07:00. ก กก ก. ก ก กก ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก ก กก ก ก ก ก ก ก 2. ก ก 2.1 ก ก 1 ก ก ก ก ก ก ก ก ก ก WRF กก 10% ก ก ก ก ก ก ก 6x6 ก ก Model Evolution Tool (MET) [11] ก ก ก ก ก ก ก 07:00. ก ก ก ก MET ก ก 90% ก ก ก ก ก ก 1 ก ก ก ก 2.2 ก ก ก ก ก ก ก WRF 3x3 ก 3 00:00 UTC 7.00 ก ก 3 ก.ก
3 2.2.3 ก 2556 ก 477 (ก 93 ก. 384 ) ก ก WRF 07: ก ก. ก 2 ก ก 3 (Domain) ก ก WRF 1 ก WRF Model Physics Microphysics Cumulus Parameterization Surface Layer Land-Surface Model Planetary Boundary Layer Shortwave Radiation Longwave Radiation Physics Option Eta Grid-Scale Cloud and Precipitation Grell-Devenyi Monin-Obukhov Noah LSM Yonsei University MM5 Shortwave Radiation Rapid Radiative Transfer Model 2 ก ก ( ) กก ก ก ก ก ก ก ก 2.3 ก ก ก WRF ก 72 3 ก 5 Hits ก ก ก ก 3
![False alarms ก ก ก ก ก Misses ก ก ก ก ก ก ก ก ก ก ก ก [11] 2 ก ก (Muti-Contingency Table) 3 5 ก 3 ก (Muti-Contingency Table) ก ก ก ก ก ก ก hits misses misses misses ก false alarms hits misses misses](/docs-images/84/89729974/images/4-0.jpg "ก false alarms false alarms hits misses ก ก ก ก (2) POD = hits/ (hits + misses) (2) POD 0 1 POD 1 ก ก ก ก 2.3.")
4 False alarms ก ก ก ก ก Misses ก ก ก ก ก ก ก ก ก ก ก ก [11] 2 ก ก (Muti-Contingency Table) 3 5 ก 3 ก (Muti-Contingency Table) ก ก ก ก ก ก ก hits misses misses misses ก false alarms hits misses misses ก false alarms false alarms hits misses ก ก ก ก (2) POD = hits/ (hits + misses) (2) POD 0 1 POD 1 ก ก ก ก ก ก (False alarm ratio: FAR) False alarms ก ก ก ก ก ก ก ก ก ก ก ก ก (3) FAR = false alarms / (hits + false alarms) (3) FAR 0 1 FAR 0 ก ก ก ก ก ก ก ก ก ก ก ก ก ก (Critical Success Index, Threat Scores: CSI) ก (4) CSI = hits / (hits + misses + false alarms) (4) CSI ก ก ก ก (misses) ก ก ก ก ก (false alarms) ก ก ก (Accuracy: ACC) Accuracy ก ก ก ก ก ก ก ก ก (5) ก ก [12] 6 ACC = ( hits / N) 100 (5) hits ก ก N ก ก 3 กก 50 ก ก false alarms false alarms false alarms hits ก ก ก ก ก (Bias) Bias ก ก ก (1) Bias = false alarms / (hits + false alarms) (1) Bias กก 1 ก 1 1 ก กก 1 ก ก ก ก ก (Probability of detection: POD) POD ก ก ก ก ก ก ก 6 ก ก 4
5 ก ก ก ก 1. ก 1 2. ก ก 1 3. ก 2 ก ก 3 ก ก ก hits 3. ก 3.1 ก 3 ก ก WRF 7 ก ก ก ก ก 75 ก ก ก 8 ก ก ก 2556 ก 3.2 ก ก ก ก 9 ก ก 50 ก ก ก (POD) ก ก ก ก 1 ก ก ก (POD) กก 0.80 ก กก 75 3 ก ก ก (POD) ก ก ก ก 7 ก 3 ก 3.3 ก 10 ก ก 50 3 ก (FAR) ก 3 กก 50 ก ( ) ( ) : ก. 10 ก ก 5
6 3.4 ก ก ก ก ก ก ก 11 ก ก 75 3 ก ก ก ก ก ก ก (CSI) ก 3 กก 75 ก ก ก ก ก ก ArcMAP 10.0 ก Inverse Distance Weighted (IDW) ก กก (Power) ก 2 ก (Neighbor) ก ก ก ก ก ก ก (ACC) ก ก ก ก ก ก กก 50 ก ก ก ก (POD) ก 11 ก ก ก ก ก ก ก 3 ก 3.5 ก ก ก ก ก [8] 4 ก ก 2556 ก ก ( ) ก ก ก ก ก ก 4 ก ก 3 ก ก ก ก ก ก 1 ก 71 ก ก ก ก ก ก 24 1 ก ก ก ก กก ก ก 3 ก ก ก ก 13 ก ก (ACC) ก ก ก ก ก ก ก 8 ก ก ก 2-3 ก ก ก 2 ก ก eighteen ก.. 26 ก ก ก 13 ก ก 3 ก 6
![6 ก ก ก ก ก ก (ACC) ก ก ก ก 3 71 69 64 กก 50 ก (FAR) ก (ACC) ก ก 62 60 55 ก ก ก ก ก ก ก ก ก ก ก ก (extreme event) ก ก ก ก ก ก ก ก ก ก ก ก ก ก ( ก ) ก ก ก ก ก ก ก ก ก WRF ก [1] WRF Model Users Pages.](/docs-images/84/89729974/images/7-1.jpg "[Online]. Available:http://www.mmm.ucar.edu/wrf/users/.Online.Accessed December 4,2013. [2] J.Marshall and R.A. Plumb, Atmosphere,Ocean,and Climate Dynamics: An Introductory Text.")
7 ก ก ก ก ก ก ก ก ก ก (CSI) ก ก ก ก ก ก ก (ACC) ก ก 3 ก 15 ก (ACC) ก ก ก ก ก 10 ก (CSI) ก ก ก (ACC) ก 15 ก ก 3 ก 4. ก ก ก 3 ก ก WRF 3x3 ก ก กก ก. ก ก ก ก ก ก ก ก ก (POD) ก ก (FAR) ก (CSI) ก ก ก ก 50 ก 0.6 ก ก ก ก ก ก (ACC) ก ก ก ก กก 50 ก (FAR) ก (ACC) ก ก ก ก ก ก ก ก ก ก ก ก ก ก (extreme event) ก ก ก ก ก ก ก ก ก ก ก ก ก ก ( ก ) ก ก ก ก ก ก ก ก ก WRF ก [1] WRF Model Users Pages.[Online]. Available: December 4,2013. [2] J.Marshall and R.A. Plumb, Atmosphere,Ocean,and Climate Dynamics: An Introductory Text.Elsevier Academic Press,2008. [3] D. J. Stensrud, Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models. Cambridge University Press, [4] E.E. Ebert Contiguous Rain Area Entity-based Verification 2009.[Online].Available: ation/cra/cra_verification.html. [5] E.E. Ebert,2009: Feature-specific verification of ensemble forecasts. 4th Intl Verification Methods Workshop, Helsinki, Finland, 8-10 June 2009.[Online].Available : Session-6/O6.7_Ebert.pdf. 7
8 [6] E.E. Ebert, Verification of Nowcasts and Very Short Range Forecasts, WWRP Int'l Symposium on Nowcasting and Very Short Range Forecasting, Toulouse, 2005 [7] J.L.Wilson,Verification of Ensemble Forecasts: A look to the future, Environment Canada,2009 [8] Clark,T-C. Chen, 2008: Contributions of mixed physics versus perturbed initial/lateral boundary conditions to ensemble-based precipitation forecast skill. Mon. Wea. Rev., 136, [9] Tartaglione, N., S. Mariani, C. Accadia, A. Speranza, and M. Casaioli, 2005: Comparison of rain gauge observations with modeled precipitation over Cyprus using contiguous rain area analysis. Atmos. Chem. Phys., 5, pp Kabaila P.(1999), The Relevance Property for Predection Intervals, Journal of Time Series Analysis,20, [10] University Corporation for Atmospheric Research (UCAR), May 2013, Model Evaluation Tools Version 4.1 (METv4.1) User s Guide 4.1, [Online], Available: /docs/overview.php [11] ก..[ ]. ก: [12] ก..[ ]. ก 8
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