A performance evaluation of WRF model using different physical parameterization scheme during winter...

International Journal of Earth and Atmospheric Science | October-December, 2014 | Vol 1 | Issue 3 | Pages 104-114 © 2014 Jakraya Publications (P) Ltd

INTERNATIONAL JOURNAL OF EARTH AND ATMOSPHERIC SCIENCE Journal homepage: www.jakraya.com/journal/ijeas

ORIGINAL ARTICLE

A Performance Evaluation of WRF Model Using Different Physical Parameterization Scheme during Winter Season over a Semi-Arid Region, India Manish Sonia, Swagata Payraa, Palash Sinhab, and Sunita Vermaa* aCentre of Excellence in Climatology, Birla Institute of Technology Mesra, Jaipur Campus, Rajasthan, India. bDepartment of Meteorology, College of Earth and Mineral Sciences, Pennsylvania State University, PA, USA. *Corresponding Author: Sunita Verma Email: [email protected] Received: 14/10/2014 Revised: 27/11/2014 Accepted: 02/12/2014

Abstract A performance evaluation study has been carried out over a semi-

arid region, Jaipur India using Weather Research and Forecasting (WRF) model. The efficiency of the model is determined during winter season over Jaipur in the present study. For this purpose, we consider the study period from 11th to 14th January 2012, which was the coldest period during the winter season 2011-2012. Eight sensitivity experiments has been conducted using several physical parameterization schemes to find the best combination of the parameterization schemes and optimize the WRF model for the study area in the winter season. In spite of using the same initial, boundary conditions and model configuration, performance of the model varies with the different combination of the physical parameterization schemes. The model simulations show encouraging and better statistical results with the combination of Quasi-Normal Scale Elimination (QNSE) surface layer, QNSE planetary boundary scheme along with NOAH land-surface and Kain-Fritsch (new Eta) cumulus parameterisation scheme than any other combinations of physical parameterization schemes over study region. Keywords: Numerical weather model, Physical parameterization, Meteorological variables, WRF, Semi-arid.

1. Introduction Numerical Modelling has given us the tool to

better understand the atmosphere and its processes. This in turn has led to a reliable meteorological forecasting. Previous studies based on non-hydrostatic simulations have shown the advantages of high-resolution model in capturing the detailed evolution and structure of the atmospheric systems. However, the advantages of high-resolution model seem to be highly case and geographically dependent (Yu et al., 2014). Contrary to coarse resolution simulations with convective parameterization, the high-resolution model allows convective overturning to be resolved in an explicit manner. Numerical model for weather predictions have been incorporated using a variety of physical schemes taking into account different characteristics of locations of the world (Kumar et al., 2014). Physical schemes are the principal source of uncertainty in high-resolution numerical weather prediction models (Rajeevan et al., 2010). It is important to quantify the uncertainty associated with the simulations of basic temperature, relative humidity and wind profiles in the atmosphere and assess whether increasingly sophisticated physics schemes in a particular model improves the results consistently.

A weather model needs to be tuned/validated for a particular location so that a specific physical scheme

can be used to handle an emergency situation (Shrivastava et al., 2014). Thus, for the present study, the focus is on evaluation of parameterization schemes in the Weather Research and Forecasting (WRF) model version 3.3.1 to simulate the surface meteorological observations over Jaipur site (26.9°N, 75.8°E), Northwest India. Furthermore, we examined its sensitivity to numerous available physical schemes. Air temperature, relative humidity and winds are key variables. These variables are studied to understand the atmospheric variability and trends over the North-Western, India. Located in a transition zone between Central India and the Thar Desert system, the North-Western region in India offers a challenging research opportunity for weather forecast and is investigated in the present study using a numerical weather model. The study location is a sparsely observed region, and there is limited availability of the observed meteorological data. Thus, it has high relevance to understanding an NWP model capability to simulate the weather system, especially when the model is employed for a regional system.

The evaluation of physical processes available in the models is an important step in developing regional simulations. The WRF model has the option for a broad-spectrum on setting up and forcing simulations including various parameterization schemes for sub-

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Soni et al... Evaluation of WRF Model Using Different Physical Parameterization Scheme over a Semi-Arid Region, India


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scale processes. Litta and Mohanty (2008), Mohan and Bhati (2012), Rao et al. (2014) and many others have recently addressed the issue of sensitivity studies using WRF model for short scale simulations. Mukhopadhayay et al. (2010) analyzed the impact of different cumulus convectional schemes (GD, BMJ and KF) namely on systematic biases using high resolution model WRF of the climatology of precipitation during Indian Monsoon. All these previous studies suggested that the performance of the model varies with different physical parameterization schemes and differs when the region of study is changed. The present study describes a sensitivity study here to quantify the uncertainty in the model output using different parameterization scheme.

The study region and the simulation period is described in sub-section 1.2 and 1.3 respectively. The design of the control experiment, the validation data set, and associated methodology is described in Section 2. The validation of the model results from control experiment (Exp. 1) and the results from the sensitivity studies are presented in Section 4. The findings from the present study are concluded in section 5. 1.1 Study Region

The study domain for WRF model simulations are shown in Fig 1. The domain (Fig 1) covers from 23°N to 31°N in the South-North, and from 72°E to 80°E in the West-East directions, centred over the North-Western India. The site location is on the eastern boundary of the Thar Desert and surrounded by the Aravali Mountains on its three sides. It experiences a tropical climate round the year. This domain is suitable to understand small-scale physical processes over the northwest India. During summer, the mean temperature ranges from 35 to 45°C and during winter, it ranges from 5 to 15 °C. 1.2 Simulation Period

Short-term validation studies enable evaluation of meteorological variables on process-oriented basis. During 11-14th January 2012, Wundergroud (http://www.wunderground.com) observations over Jaipur station shows the absolute minimum for the winter season during 2011-2012. Thus, a week-long simulation period starting from January 8 to January 14, 2012 is considered. A detailed analysis of the model simulated results are carried out for this period with a special emphasis from the 00:00 UTC, January 11 to 00:00 UTC January 14, 2012, as it was the coldest period of the year 2012. The simulation of 4th to 8th

January, 2013 is used for the validation purpose of the model. 2. Methodology 2.1 Model Configuration

The study uses WRF model, which is the next generation mesoscale model with capability to run with and without nesting. The model is developed primarily

at the National Center for Atmospheric Research (NCAR) in collaboration with different agencies like the National Oceanic and Atmospheric Administration (NOAA), the National Center for Environmental Prediction (NCEP), and many others. It is a limited-area, non-hydrostatic, primitive-equation model with multiple options for various physical parameterization schemes (Skamarock et al., 2008). It uses the Arakawa C-grid along with Rungakutta third order time integration scheme. The model is terrain following and conserves the scalar variables. This improves the surfaces processes and helps to study air quality, several events like windstorm, precipitations and many other atmospherics processes (Borge et al., 2008; Powers, 2007; Trenberth and Shea, 2006).

Different physics options are available in the model that can be used as per need. It is very essential to run the model using different parameterization schemes in order to find out the better combination for a particular location and season. The physics options available in the model are: 1) microphysics, 2) surface layer parameterization, 3) land surface parameterization, 4) planetary boundary layer parameterization, 5) cumulus scheme and different radiation schemes [Available on-line at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf]. All the schemes and the model experiments based on the different combination of these schemes are presented in Table 1. The details of all schemes can be found in chapter 5 of WRF document (Available online http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3/ARWUsersGuideV3.pdf.

For the control experiment i.e. Exp.1, Lin et al. (1983) microphysics that is used generally for high resolutions simulations and contains all the hydrometers processes has been chosen. Dudhia (Dudhia, 1989) and RRTM (Mlawer et al., 1997) schemes are chosen for short-wave and long-wave radiation schemes for all the experiments. The chosen short-wave scheme is widely used for high-resolution simulations because it takes into account the shadowing effects. The long-wave scheme is used for multiple bands, trace gases and microphysics. Surface layer scheme has ETA similarity that is based on Monin-Obukhov with Zilitinkevich thermal roughness length. The Land surface scheme is unified NCEP/NCAR/AFWA scheme which contains soil and moisture in four layers and also accounts for frozen soil and multi-layer snow. The planetary boundary layer scheme is Mellor-Yamada-Janjic (MYJ) scheme, which compute the turbulent kinetic energy with time taking into account the local vertical mixing. Cumulus parameterization scheme is Kain-Fritsch scheme that consider both shallow and deep convections in sub-grid scale. This scheme works better for mid latitudes. For other simulations, only some physics options are changed and rest of all other physics options are unchanged as of control experiment. Exp.2 is done by changing the surface layer scheme to QNSE and planetary boundary layer to QNSE of the control -

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https://www.researchgate.net/publication/244473598_Numerical_Study_of_Convection_Observed_during_the_Winter_Monsoon_Experiment_Using_a_Mesoscale_Two-Dimensional_Model?el=1_x_8&enrichId=rgreq-ebf8acca-24a2-41c7-9f54-8805ab716240&enrichSource=Y292ZXJQYWdlOzI3MTE5ODk1NTtBUzoxODgzMjI0MzM0Nzg2NTZAMTQyMTkxMDk3NTgwMQ==



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Fig 1: Study Area for the Experiments, considered domain is shown in black rectangle.

experiment. This scheme uses new theory for stably stratified regions. Exp.3 is designed by changing the land surface layer to RUC scheme. The RUC scheme is similar to NOAH Land surface layer and only difference is that RUC has two more layers (total six layers) than NOAH for soil temperature and moisture. For the Exp.4, we experimented with surface layer, land surface and planetary boundary layer scheme. The Pleim-Xiu scheme, a two layer scheme with vegetation and sub-grid tiling options, is chosen for surface and land scheme. The planetary boundary layer is ACM2, which is a convection scheme having option for non-local upward mixing and local downward mixing, is used in this experiment. For Exp.5, MM5 similarity-surface-layer scheme is chosen. The planetary boundary

layer scheme used in this case is YSU, which is non-local scheme and contains explicit entrainment layer and parabolic profile layer in unstable mixed layer. For the Exp.6, microphysics option has been set to WSM 5 scheme, which is sophisticated version of WSM 3. The WSM3 scheme allows mixed-phase processes other than ice and snow processes and suitable for mesoscale grid sizes. The surface and land surface layer chosen is Pleim-Xiu. The planetary boundary layer chosen is QNSE. For Exp.7, combination of WSM 6 class scheme microphysics, MM5 similarity-surface-layer scheme, YSU PBL Scheme and Grell3D cumulus scheme is considered. The last case is the combination of MM5 similarity-surface-layer scheme, 5-layer thermal diffusion land-surface scheme that accounts for soil -



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Table 1: Different set of physical schemes options for different set of (8) simulations considered in the study.

Simulation Name

mp_physics sf_sfclay_ physics, code

sf_surface_ physics, code

bl_bpl_ physics, code

cu_physics, code

Parameterization Experimented, Summarized Code

Exp1 Lin, 2 ETA, 2 NOAH, 2 MYJ, 2 Kain Fritsch, 1 Control Experiment, (22221) Exp 2 Lin, 2 QNSE, 4 NOAH, 2 QNSE, 4 Kain Fritsch, 1 Experiment with surface layer

and planetary boundary layer, (24241)

Exp 3 Lin ,2 ETA, 2 RUC, 3 MYJ, 2 Kain Fritsch, 1 Experiment with land surface layer, (22321)

Exp 4 Lin, 2 Pleim, 7 Pleim, 7 ACM2, 7 Kain Fritsch 1 Experiment with surface layer, land surface layer and planetary boundary layer, (27771)

Exp 5 Lin, 2 MM5, 1 NOAH, 2 YSU, 1 Kain Fritsch, 1 Experiment with surface layer and planetary boundary layer, (21211)

Exp 6 WSM5, 4 Pleim, 7 Pleim, 7 QNSE, 4 Kain Fritsch ,1 Experiment with microphysics ,surface layer, land surface layer and planetary boundary layer, (47741)

Exp 7 WSM6, 6 MM5, 1 NOAH, 2 YSU, 1 Grell 3D, 5 Experiment with microphysics ,surface layer, planetary boundary layer and cumulus parameterization, (61215)

Exp 8 Lin, 2 MM5, 1 5 Thermal, 1 YSU, 1 BMJ ,2 Experiment with surface layer , land surface layer, planetary boundary layer and cumulus parameterization, (21112)

temperature and YSU PBL Scheme. The cumulus scheme chosen in this case is BMJ scheme, which is operational ETA scheme. This scheme accounts for column moist adjustment. 2.2 Design of the Experiments

In the present study, in addition to a control experiment (Exp. 1), seven different set of simulations are conducted using several physics options as described in section 2.1 and listed in Table 1. The model was integrated for a week with 6 hourly National Center for Climate Prediction (NCEP) reanalysis FNL data for initial and boundary conditions for the period 8th January 2012 00:00 UTC -14th

January, 2012 00:00 UTC which contains the minimum temperature for the winter period of 2011-2012 as discussed in section 1.2. A spin up period of 72 hours is considered while carrying out the simulations over the selected domain (Fig 1). The model consists of 28 vertical levels. Spatial resolution of the domain taken is 9 km in both latitude and longitude directions. The detailed configuration of the model is presented in Table 2. The projection is MERCATOR and non-hydrostatic dynamics has considered for all simulations.

The data sets for Jaipur are obtained from the Wunderground (http://www.wunderground.com/) to

validate the model results at station level (Jaipur station). This website collects weather information -

Table 2: Default configuration of WRF model Dynamics Non –hydrostatic Main prognostic variables u, v, w, T´, P´ and q Number of domain Single Domain Central point of the domain 25.0 N 80.0 E Resolution 9 km X 9 km Number of grid points 80 X 75 Map Projection Mercator Horizontal grid distribution Arakawa C-grid Nesting No Nesting Vertical co-ordinate Terrain-following

hydrostatic-pressure co-ordinate

Time Integration 3rd order Runge-Kutta Spatial differencing scheme 6th order centered

differencing Initial conditions 3-dimensional real data Lateral Boundary condition Periodic, open,

symmetric & specified options available

Top boundary condition Gravity wave absorbing (diffusion or Rayleigh damping)

Bottom Boundary condition Physical or free-slip



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Table 3: Statistical results for Temperature, Relative Humidity and Wind Speed 11-14 JANUARY 2012

Name Exp1 Exp 2 Exp 3 Exp 4 Exp 5 Exp 6 Exp 7 Exp 8

Temperature Code 22221 24241 22321 27771 21211 47741 61215 21112 NMSE 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.01 COR 0.97 0.97 0.95 0.95 0.96 0.95 0.96 0.97 FAC2 100 100 100 100 100 100 100 100

Relative Humidity Code 22221 24241 22321 27771 21211 47741 61215 21112 NMSE 0.08 0.06 0.19 0.17 0.07 0.18 0.07 0.29 COR 0.81 0.86 0.78 0.66 0.86 0.65 0.86 0.64 FAC2 97.3 94.44 97.3 83.78 94.59 83.33 94.59 77.78

Wind Speed Code 22221 24241 22321 27771 21211 47741 61215 21112 NMSE 2.84 1.85 2.44 2.37 2.72 2.37 2.72 2.72 COR 0.26 0.23 0.27 0.47 0.1 0.47 0.1 0.23 FAC2 5.41 40.38 10.81 10.81 8.11 10.81 8.11 11.11

directly from weather stations, which are located in different parts of the world. These weather stations are owned by several government agencies and sometimes by the international airports authorities. In Jaipur, the weather station is located at Sanganer Airport (26.82 °N, 75.81 °E). The surface variables namely temperature, pressure, relative humidity, wind speed and direction are available at an interval of 30-60 minutes over this station and considered for the present study.

4. Results and Discussion

Statistical measures like normalized mean square error (NMSE), number of estimates within a factor of 2 (FAC2), correlation (r) are used to investigate the model performance of different combinations of physical parameterizations with respect to control experiment (Exp. 1) and observations. A good model must have least NMSE nearly equal to zero, and correlation close to 1.0 and FAC2 (equal to100%; Chang and Hanna, 2004). Instantaneous hourly data obtained from the model simulations are used to compare with observed data. A result of statistical evaluation is presented in Table 3.

Table 3 shows the correlation varies from 0.95 to 0.97 for temperature between observation and model simulations using all combinations of the physical parameterizations. The correlation is computed using the hourly temperature data from observation and simulation over Jaipur station. The result indicates that WRF is capable of reproducing the observed temperature variations. Exp.1, Exp.2 and Exp.8 gives the highest correlation (0.97) and FAC2 (100%) values and least NMSE (0.01).

Table 3 computed the relative humidity and it illustrates that the ranges of correlation values varies from 0.64 to 0.86. Among all the simulations, Exp. 2, Exp.5 and Exp.7 show overall performances with higher correlation (0.86), FAC2 and lower NMSE. In Exp.2, FAC2 is 94.44 and NMSE is 0.06, whereas in

Exp.5 and Exp.7, the FAC2 and NMSE are 94.59 and 0.07, respectively for both experiments. Thus, the combination of parameterizations scheme in Exp.2 shows better statistical results for relative humidity.

Computed correlations of wind speed between observations and model simulations are provided in Table 5. Results shows that the correlation ranges from 0.1 to 0.47, indicating the performance of the model is weaker in simulating the wind magnitude as compared to temperature and relative humidity. The WRF model under predicts the wind speed when compared to observed wind speed. Exp.2, Exp.4 and Exp.6 performs better than other experiments in simulating the wind speed over the study region. For Exp.4 and Exp.6, the correlation is 0.47, FAC2 is 10.81 and NMSE is 2.72 whereas for Exp.2, correlation is 0.23, FAC2 is 40.38 and NMSE is 1.85 so Exp.2 overall show better correlation, FAC2 and least NMSE. Fig 2(a,b,c) shows the Taylor diagram of temperature, relative humidity and wind speed. Taylor diagram provides better representation of correlation and NMSE from all considered experiments.

It can be noted that all the experiments commonly work satisfactorily to simulate temperature, relative humidity and wind speed. However, Exp2 performs better in terms of statistical evaluation with the least NMSE, highest FAC2 and correlation(r) than the other experiments.

The parameterization schemes of Exp.2 are QNSE surface layer, QNSE planetary boundary scheme along with NOAH land-surface model and Kain-Fritsch (new Eta) cumulus parameterisation. QNSE scheme works better for stratified region. Its winter time so stratified atmosphere prevails. This scheme looks for the combined effect of turbulences and waves thus solving the disparity that exists in between the transportation process in both the horizontal and vertical directions (Sukoriansky et al., 2006). This scheme provide better near surface predictions.

Fig 3, 4 and 5 show comparison between hourly variation of observed, control run and best combination



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Fig 2 (a): Taylor Diagram of Temperature (11-14

January 2012) for the study area.

Fig 2(b): Taylor Diagram of Relative Humidity (11-14


(Exp. 2) through statistical analysis. The diurnal variation of temperature (Fig. 3) shows the maximum temperatures in the afternoon and minimum temperatures in the morning. Both the simulations (control and Exp. 2) show good prediction skill for temperatures. The maximum temperature reaches around 21°C on 13th January at 09.00 a.m. and it is well captured by the model (20.62°C), while the minimum temperature is slightly under estimated.

The observational minimum temperature is 6°C on 13th January 2012 at 00:00 a.m. but model predicts 4.25 °C. This indicates that the model has a cool bias for the semi-arid region and probably due to the higher -

Fig 2 (c): Taylor Diagram of Wind speed (11-14


cooling rate in the model as compared to the observation.

The present day mesoscale models have flaw in representing the relative humidity (Payra and Mohan, 2014). In contrary, Fig 4 shows that the WRF model reproduces the minimum and maximum epoch in RH very well and in agreement with the observations. The correlation of relative humidity is 0.86, indicating the model simulates the RH close to observations. The highest humidity reaches around 87% on 12th January at 02.00 a.m. and is captured well by the best experiment i.e. Exp 2. (82 %) when compared with the observations at the same time. The Figs 3 and 4 clearly depict that the simulated temperature and RH in Exp.2 agrees well with the observed temperature and humidity and provide better result in comparison to control case physical parameterization of the WRF model.

Figs (5a,b,c) show the wind rose for observed, control run and the Exp. 2, respectively. Fig 5a shows that the observed wind is northerly. The dominating wind speed for control experiment is northeast while the prevailing wind speed of Exp.2 is mostly northerly.

We investigated the model performance using the same best combination for simulating the winter of 2013. During the year 2013, the lowest temperature has been recorded during 04-08 January. So this period is chosen for model validation. The correlation, FAC2 and NMSE between observed and model temperatures are found to 0.96, 89.18 and 0.06, respectively, for this period. The highest temperature reaches around 19°C on 04th January at 09.00 a.m. in the observation and it is well captured by the model (18.5°C). However the minimum temperature is again under predicted. While observational minimum temperature is 4°C on 6th January at 01:00 a.m. but model predicts 1.67°C at the



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same time. Fig 6 shows the diurnal variation for the temperature.

Fig 7 shows the diurnal variation of relative humidity with observed and Exp. 2. The model successfully captures the minimum and maximum epoch for period 04-08 January, 2013. However, for the entire duration, model under predicts the relative humidity. For 04-08th January 2013 case, correlation, FAC2 and NMSE was found to be 0.85, 91.81 and 0.20

respectively. The maximum relative humidity is found on 05th January 2013 at 01.00 a.m. which is 93%.

Fig 8(a,b) shows the wind rose of the observed and Exp.2. Fig 8(a) shows that the general trend of the observed wind is northerly in January. Fig 8(b) also shows the wind direction obtained from the model simulation and represents the similar wind directions as seen in the observations. Thus, model successfully captures the trend in wind profiles.

Fig 3: The Simulated vs Observed temperature during 11-14 January, 2012 over Jaipur, India.

Fig 4: The Simulated vs Observed relative humidity during 11-14 January, 2012 over Jaipur, India.



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Fig 5 (a): Observed, (b) control experiment and (c) experiment 2 (Exp. 2) wind rose during 11-14 January, 2012 over

Jaipur, India.

(a)

(b)

(c)



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Fig 6: Observed Vs Exp. 2 temperature during 04-08 January 2013.

Fig 7: Observed vs Exp. 2 relative humidity during 04-08 January, 2013.

5. Conclusions The present study explores the sensitivity of

different physical schemes to meteorological variables under winter conditions in a semi-arid environment. To examine the sensitivity of the simulations to different physical schemes, eight different combinations of physical schemes are considered. A case for 11-14 January 2012 is simulated with eight different physics options in the model using the similar initial and lateral boundary conditions from the NCEP/NCAR reanalysis. Again the best combination is applied to the winter of

year 2013 to examine the performance of the optimize model. WRF model reproduces the observed variations in meteorological variables well in all of the experiments consistently over the study region. However, the combination QNSE surface layer, QNSE planetary boundary scheme along with NOAH land-surface model and Kain-Fritsch (new Eta) cumulus parameterization (i.e. Exp.2) performs better than the other combinations. Overall, the Exp.2 produces the smallest biases over the study area, which is critical for simulations in regional scale.



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Fig 8: (a) Observed vs (b) Exp. 2 wind rose during 04-08 January, 2013. Acknowledgments

The authors would like to acknowledge the computing support for the present study from the

Department of Science and Technology (DST), Government of India (DST No: SR/S4/AS: 39/2009).

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