Advances in Natural and Applied Sciences

Size: px

Start display at page:

Download "Advances in Natural and Applied Sciences"

David Shelton
5 years ago
Views:

AENSI Journals Advances in Natural and Applied Sciences ISSN:1995-0772 EISSN: 1998-1090 Journal home page: www.aensiweb.

Climate Change Research Group (CCRG), Faculty of Science and Natural Resource, University Malaysia Sabah Box.88400.Kota Kinabalu, Sabah.

1 AENSI Journals Advances in Natural and Applied Sciences ISSN: EISSN: Journal home page: Evaluation of Weather Research Forecast (WRF) Modeling System on Surface Temperature and Precipitation over Malaysia Region 1 Steven Soon-Kai Kong, 2 Justin Sentian and 3 Kawi Bidin 1 Climate Change Research Group (CCRG), Faculty of Science and Natural Resource, University Malaysia Sabah Box Kota Kinabalu, Sabah. Malaysia 2 Climate Change Research Group (CCRG), Faculty of Science and Natural Resource, University Malaysia Sabah Box Kota Kinabalu, Sabah. Malaysia 3 Natural Disasters Research Unit, Faculty of Science and Natural Resource, University Malaysia Sabah, Box Kota Kinabalu, Sabah. Malaysia A R T I C L E I N F O Article history: Received 3 August 2015 Accepted 28 October 2015 Available online 31 October 2015 Keywords: Model evaluation, WRF, Surface Temperature, Precipitation, Malaysia A B S T R A C T Background: Dynamic downscaling is a process to produce a higher resolution of Regional Climate Model (RCM) from Global Climate Model (GCM). Objective: In this study the regional climate model WRF (Weather Research Forecast) was evaluated to assess the ability of the model to simulate the seasonal variables of meteorological features over Malaysia region. The research was carried out by comparing the monthly average WRF output with the observation dataset. Results: Generally, the model performed well in simulating the surface temperature, with cold bias lower than 1.5 C. However, the simulation of precipitation did not perform as well as the surface temperature. Overestimation was observed throughout all seasons, with the largest bias during the Northeast Monsoon season, and smallest bias in the second Intermediate Monsoon season. Moreover, the model uncertainty was significantly observed over mountainous and interior regions. Conclusion: The outcome of the present study can be useful for impact studies and development of adaptation policy to counter the influence due to climate change AENSI Publisher All rights reserved. To Cite This Article: Steven Soon-Kai Kong, Justin Sentian and Kawi Bidin., Evaluation of Weather Research Forecast (WRF) Modeling System on Surface Temperature and Precipitation over Malaysia Region. Adv. in Nat. Appl. Sci., 9(14): 20-24, 2015 INTRODUCTION Global Climate Model (GCM) is a primary application that used to study the weather and climate system with global coverage. By forcing the model with future greenhouses gases or radioactive forcing, the future climate change and its impacts toward various components are often being carried out. However, the GCM is unable to represent the climatic condition over the regional scale due to its course resolution (IPCC, 2002; Leung et al., 2003; Fowell, 2006). Hence, the model has difficulties in resolving complex terrain, islands and coastlines since the large grids are inadequate in representing small grids (Jones et al., 2004). In order to overcome this problem, a Regional Climate Model (RCM) with a finer scale is utilized. A higher resolution of RCM can be produced from GCM through a modeling technique called the dynamic downscaling process. Hence, the RCM simulations are actually the output of GCM, but RCM only covers limited areas over the globe. The importance of RCM application can be due to a number of reasons. These models tend to simulate course terrain such as mountainous and coastline areas, on a scale of 100 km or less. Moreover, the RCM has the capability to simulate extreme weather conditions, especially heavy rainfall. Some of the mesoscale weather features, for example the hurricanes and cyclones can also be simulated by applying RCM (Jones et al., 2004). Malaysia is a developing country that lies within the Southeast Asia region, with total population around 28 million people. The country undergoes rapid industrilization and urbanization, thus at the same time experiencing expansion of agricultural activities. The country's rapid economic change has caused general increase of anthropogenic and biogenic emission, which has been an important issue recently. The impact of climate change is also another important issue since Malaysia is declared as one of the most vulnerable regions (Kong and Sentian, 2015). Several studies about climate change have been conducted by applying RCM over Southeast Asia region (McGregor et al.,1998; Chotamonsak et al., 2011; Sentian and Kong, 2013; Sentian and Kong, 2015) as well as over Malaysia region (Kong and Sentian 2015; Kwan et al., 2014; Corresponding Author: Steven Soon-Kai Kong, Climate Change Research Group (CCRG), Faculty of Science and Natural Resource, University Malaysia Sabah Box Kota Kinabalu, Sabah. Malaysia

2 21 Steven Soon-Kai Kong et al, 2015 Kong and Sentian, 2013). In this study, the ability of Weather Research Forecast (WRF) modeling system in simulating meteorological parameters across Malaysia region will be evaluated. Methodology: In this study, two-way nesting was used in the WRF model version (Skamarocket et al., 2008), where the simulations were carried out in two nested horizontal domains. The first domain (domain 1) covered the Southeast Asia region, with at 45 km resolution ( grid points), while the second domain (domain 2) was the Malaysia region, the East and West Malaysia, which spaced at 15 km ( grid points) (Figure 1). The analysis was concentrated on the second domain and 30 vertical levels with the top reaching 10 milibar were used in the model. The NCEP FNL Operational Model Global Tropospheric Analyses was used as initial and boundary conditions for WRF model simulation. The spatial resolution of the data was 1 1. Meanwhile, the temporal resolution was 6 hours of time-step with the sequence of 00, 06, 12, 18 UTC. The time slice for the present-day simulation was selected for the months of January (Winter or Northeast Monsoon), April (1st Intermediate Monsoon), July (Summer or Southwest Monsoon) and October (2nd Intermediate Monsoon) in the year The simulations were run based on a 5-day period, with the first day as the model spin-up. The meteorological variables such as temperature and precipitation were being investigated. The Sea Surface Temperature (SST) was updated every six hours in WRF by using the Real-Time, Global, Sea Surface Temperature (RTG_SST) analysis. SST was developed and archived at NCEP. Fig. 1: The study domain (Height) with two nested, domain 1 and domain 2. The physics schemes of physics components that included within the WRF were the Kain-Fritsch scheme for cumulus parameterization (Kain, 2014); Yonsei University (YSU) scheme for PBL parameterization; Rapid Radiation Transfer Model (RRTM) for Long wave Physics Radiation (Hong and Pan, 1996); WRF single-momentum 6-class scheme (WSM6) for microphysics parameterization (Hong et al., 2004); Dudhia short-wave radiation scheme for Short wave Physics Radiation (Dudhia, 1989) and Noah Land Surface Model (LSM) set as four layers of soil and one canopy layer (Chen and Dudhia, 2001). The Climate Research Unit Climatology (CRU) was used as observational dataset in this study (Hulme et al., 1995). CRU dataset was an observation data constructed from Climate Research Unit (CRU) at University of East Anglia. CRU dataset was a gridded data set developed with high resolution ( ). The observation data contained a full set of monthly-mean surface climate and only covered land areas (New et al., 2002; Mitchell and Jones, 2005). RESULTS AND DISCUSSION Table 1 shows the evaluation and assessment of WRF modeling system as relative to CRU observation dataset in terms of bias, normalized mean square error (NMSE), fractional bias (FB) and factor of two (Fa2). In general, the model was observed to perform well in simulating surface temperature, with the bias between -0.7 C and C. Also, the values of FB and NMSE were nearly 0. Moreover, the model managed to produce a good values of Fa2, which were above 0.94 for all periods. Figure 2 shows the spatial distribution of mean surface temperature at 2 meter over the whole Malaysia region. The simulated average temperature was higher across the northern and southern parts and west coast of Malaysian Peninsula. The results were consistent with the CRU observation datasets across most of the period except during the Southeast Monsoon season. In July, the CRU dataset showed higher value of surface temperature than the simulation result especially over north and west coasts of Malaysian Peninsula and coastal area of Malaysian Borneo. Both WRF datasets indicated that the surface temperature was low in the central of West Malaysian Peninsula and north and central

3 Bias (%) 22 Steven Soon-Kai Kong et al, 2015 parts of Malaysian Borneo. However, the simulation underestimated the CRU observation data. This could be due to the inefficiency of the model in simulating highlands and mountainous areas. The performance of WRF model in projecting the total precipitation was also carried out by comparing with CRU observation data. The high values of FB and NMSE indicating a low performance of WRF modeling system (Table 1). Moreover, the simulation of precipitation did not perform as well as the surface temperature for all periods especially in January (Figure 3). Similar observation was found in a previous study in the Malaysia region (Kong and Sentian, 2015), where large values of bias were shown between the model and observation dataset in projecting the total precipitation during Northeast Monsoon season. Fig. 2: Mean surface temperature ( C) for (a) Northeast Monsoon January, (b) 1st Intermediate Monsoon April, (c) Southeast Monsoon July and (d) 2nd Intermediate Monsoon October in WRF simulation (left) and CRU observation (right) January April July October Surface Temperature Total Precipitation Fig. 3: Bias in percentage for mean surface temperature and total precipitation over Malaysia region. The spatial distribution of total precipitation across Malaysian region is shown in Figure 4. In general, the modeled total precipitation was largely over projected in most of the research domain except for the month of October. There were large biases between WRF and CRU datasets especially in the mountainous area and interior region. Similar simulation was also noted by Kong and Sentian (2015), Giorgi et al. (2004a), Solman et al. (2008) and Bartholy et al. (2001). As mentioned by Salimun et al. (2010) and Alves et al. (2009), the discrepancies might be caused by poor representative of the convective parameterization and hydrological cycle. 4. Conclusion: The present study aims to evaluate the skill of WRF modeling system in projecting the surface temperature and total precipitation in Malaysia region during the four separate months in the year of The CRU observation gird dataset was used to evaluate the performance of the model. Generally, the model agrees well with CRU in simulating the surface temperature by producing a good value of statistical analysis formulae. However, the precipitation did not perform as well as the surface temperature except in October. Large biases were found over interior and mountainous regions. The assessment of model skill is essential to understand the behavior of the model physics, thus leads to

23 Steven Soon-Kai Kong et al, 2015 model improvement. A better model projection is important for future climate investigation and impacts study. Fig.

4 23 Steven Soon-Kai Kong et al, 2015 model improvement. A better model projection is important for future climate investigation and impacts study. Fig. 4: Mean total precipitation (mm/day) for (a) Northeast Monsoon January, (b) 1st Intermediate Monsoon April, (c) Southeast Monsoon July and (d) 2nd Intermediate Monsoon October in WRF simulation (left) and CRU observation (right). Table 1: Comparison of surface temperature and precipitation as relative to observed (CRU) for bias, fractional bias (FB), normalised mean square error (NMSE) and factor of two (Fa2). Surface Temperature Period Jan Apr Jul Oct FNL( C) CRU( C) Bias( C) Bias(%) FB NMSE Fa Total Period Precipitation Jan Apr Jul Oct FNL(mm/day) CRU(mm/day) Bias(mm/day) Bias(%) FB NMSE Fa REFERENCE Alves, L.M. and J. Marengo, Assessment of regional seasonal predictability using the PRECIS regional climate modeling system over South America. Theor. Appl. Climatol, DOI /s Bartholy, J., R. Pongra cz, I. Pieczka and C. Torma, Dynamical downscaling of projected 21st century climate for the Carpathian Basin. Climate Change - Research and Technology for Adaptationand Mitigation, Dr Juan Blanco (Ed.), ISBN: , InTech, 502. Chen, F. and J. Dudhia, Coupling an advanced land surface hydrology model with the Penn State NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Monthly Weather Review, 129: Chotamonsak, C., Salathe, Jr., J. Kresuwan, S. Chantara and K. Siriwitayakom, Projected climate change over Southeast Asia simulated using a WRF regional climate model. Royal Meteorological Society. Atmos Sci Let, Doi: /asl.313. Dudhia, J., Numerical study of convection observed during the winter monsoon experiment

5 24 Steven Soon-Kai Kong et al, 2015 using a mesoscale two-dimensional model. Journal of Atmospheric Science, 46: Fowell, M., Methodology for evaluating GCMs at regional scales: Method development and application to SE Asian hydrological variables. PhD Thesis, Lancaster University, UK. Giorgi, F., X. Bi and J.S. Pal, 2004a. Mean, interannual variability and trends in a regional climate change experiment over Europe. I. Present day climate ( ). Clim Dyn, 22: doi: /s x. Hong, S.Y., J. Dudhia and S.H. Chen, A revised approach to ice microphysical processes for the bulk parameterization of cloud and precipitation. Monthly Weather Reviews, 132: Hong, S.Y. and H.L. Pan, Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Monthly Weather Reviews, 124: Hulme, M., D. Conway, P.D. Jones, T. Jiang, E.M. Barrow and C. Turney, Construction of a European climatology for climate change modelling and impact applications. Int J Climatology, 15: IPCC (Intergovernmental Panel on Climate Change), Climate Change: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, and C. A. Johnson (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 881. Jones, R.G., M. Noguer, D.C. Hassel, D. Hudson, S.S. Wilson, G.J. Jenkins and J.F.B. Mitchell, Generating high resolution climate change scenarios using PRECIS. Met Office Hadley Centre, Exeter, 44. Kain, J.S., The Kain Fritsch convective parameterization: an update. Journal of Applied Meteorology, 43(1): Kong, S.S.K. and J. Sentian, Climate change simulations in terms of solar radiation and cloud fraction based on PRECIS over Malaysia region. Journal of Borneo Science, 33: Kong., S.S.K. and J. Sentian, Present-day and future climate on seasonal surface temperature and precipitation over Malaysia using PRECIS regional model. International Journal of Engineering Technology Science and Research, 2(6), pp Kwan, M. S., F.T. Tangang and J.N. Liew, Present-day regional climate simulation over Malaysia and western Maritime Continent region using PRECIS forced with ERA40 reanalysis. Theor Appl Climatol, 115: 1 14, DOI /s Leung, I.R., I.O. Mearns, F. Giorgi and R.L. Wilby, Regional climate research: needs and opportunities. Bulletin of the American Meteorological Society, 82: McGregor, J.L., J. J. Katzfey and K.C. Nguyen, Fine resolution simulations of climate change for Southeast Asia. Final report for a Research Projected commissioned by Southeast Asian Regional Committee for START (SARCS). Aspendale, Vic.: CSIRO Atmospheric Research, Mitchell, T.D. and P.D. Jones, An improved method of constructing a database of monthly climate observations and associated high resolution grids. International Journal of Climatology, 25: New, M., D. Lister, M. Hulme and I. Makin, A high-resolution data set of surface climate over global land areas. Climate Research, 21: 1 5. Salimun, E, F.T. Tangang, and L. Juneng, Simulation of heavy precipitation episode over eastern Peninsular Malaysia using MM5: sensitivity to cumulus parameterization schemes", Meteorog. Atmos. Phys, 107: Sentian, J. and S.S.K. Kong, High resolution climate change projection under SRES A2 scenario during summer and winter monsoons over Southeast Asia using PRECIS regional climate modeling system. The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), The Standard International Journals (The SIJ), 1(4): Sentian, J. and S.S.K. Kong, The effect of landcover changes on surface temperature and precipitation in the Southeast Asia region. Advanced Science Letters, 21: Skamarock, W., J.B. Klemp, J. Dudhia, D.O. Gill, D. Barker, M.G. Duda, X.Y. Huang and W. Wang, A Description of the Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN-475+STR, doi: /d68s4mvh. Solman, S.A., M.N. Nuñez and M.F. Cabré, Regional climate change experiments over southern South America. I: present climate. Clim Dyn, 30:

A Study of the Uncertainty in Future Caribbean Climate Using the PRECIS Regional Climate Model

A Study of the Uncertainty in Future Caribbean Climate Using the PRECIS Regional Climate Model by Abel Centella and Arnoldo Bezanilla Institute of Meteorology, Cuba & Kenrick R. Leslie Caribbean Community