Performance and Sensitivity Analysis of Very High...

International Journal of Earth and Atmospheric Science | October-December, 2017 | Volume 04 | Issue 04 | Pages 167-180 © 2017 Jakraya

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

ORIGINAL ARTICLE

Performance and Sensitivity Analysis of Very High Resolution WRF-ARW Model Over Indian Region During 2011 Summer Monsoon Season A. Lakshmi Kanth and S. Vijaya Bhaskara Rao

Department of Physics, S.V. University, Tirupati - 517502, India.

*Corresponding Author: S. Vijaya Bhaskara Rao Email: [email protected] Submitted: 01/11/2017 Accepted: 24/12/2017

Abstract In the present study, an attempt has been made to evaluate WRF-

ARW model performance with a resolution of 9x3km (WRF-3km) and 27x9km (WRF-9km) for the short-range forecasts during 2011 Summer Monsoon Season over the Indian subcontinent. Further, the model sensitivity concerning different parameterization schemes is analysed. The statistical analysis is carried out for simulated surface parameters, 24-h accumulated rainfall and radiosonde station data of India Meteorological Department (IMD). The comparison of 24-h simulated surface parameters in both experiments with surface data shows a significant reduction in errors for temperature, wind and rainfall in the simulation conducted with high-resolution experiment of WRF-3km. The comparison of simulated rainfall with respective to observed estimates showed an appreciable reduction in the predicted rainfall evidenced with WRF-3km over the regions of the Bay of Bengal and Central India and enhanced orographic rainfall than WRF-9km. Measure oriented rainfall analysis indicates also suggests the reduction of errors in WRF-3km. As an illustration to depict the performance of cloud-resolving grid, a 5-day very heavy rainfall spell over the Western Ghats particularly Mumbai region is analysed and the results indicate that there is a significant improvement in the simulated rainfall with the increase of horizontal grid resolution to cloud-resolving resolution but failed to capture the observed amount even with the finer grid resolution. Further study has extended by carrying out sensitivity experiments with different physics schemes for heavy rainfall episode to check the role of physical parameterization on simulation of heavy rainfall and to find out optimal combination of the best physics configuration for WRF-3km. The sensitivity experiments were conducted first, by varying the PBL schemes and further by microphysics schemes using 3km WRF model. In the sensitivity analysis of PBL, YSU has the least errors in simulated profile parameters and rainfall, while microphysics scheme analysis indicates Goddard ensemble scheme gives higher rainfall in Western Ghats region and well matches with the IMD gridded rainfall data. Keywords: Monsoon, WRF model, PBL Parameterization schemes and Heavy rainfall.

1. Introduction Indian summer monsoon is part and parcel of

Indian culture and traditions. Although the term “Monsoon” known to Indians for thousands of years, but the factor influencing the monsoon rainfall is still a mystery to the scientific community. During summer monsoon season India receives 70-90% its annual rainfall with large spatial and temporal variability (Parthasarathy et al., 1995). The major contribution to the summer monsoon rainfall is due to the off-shore

vertices and the monsoon depressions over Indian and neighbourhood ocean regions like Bay of Bengal and Arabian Sea. Regional operational models are not able to capture the Monsoonal rainfall variability found in short and medium range temporal scales. The possible reasons can be the coarser grid operational models, inappropriate physics parameterization and insufficient data (Baldauf et al., 2011; Yesubabu et al., 2014a).

Numerical weather prediction based on high resolution models, particularly explicit convection-

Kanth and Rao...Performance and Sensitivity Analysis of Very High Resolution WRF-ARW Model Over Indian Region During 2011 Summer Monsoon Season


168

resolving with grid resolutions ranging from 1-3 km, is one of the current focus areas of the scientific research community. The high resolution model forecast is not only required for improving local weather forecast and severe events, but also basic inputs for other numerical models like air quality, hydrology, flood, dispersion, crop, ocean and wave models (Srinivas et al., 2010; Langodan et al., 2015; Langodan et al., 2016a; Hima Bindu et al., 2016; Mishra et al., 2017). High resolution nested domain system incorporates details topography and better mesoscale features in weather forecasting models and in turn helps in better forecast skill. However, it consumes high computational power. The high resolution models can resolve important small scale features like topographic flow, gravity waves and help to investigate the evolution of weather in much detail (Fuyu et al., 2011; Ghosh et al., 2016). High resolution grid allows for a more precise representation of regional topographic forcing due to orography, land-sea contrasts and vegetation characteristics (Langodan et al., 2016b). Several studies (Manabe, 1975; Zangl, 2007; Srinivas et al., 2012b) are available in the literature discussing the role of orography in NWP simulations particularly about the Indian summer monsoon. Studies like (Giorgi and Marinucci, 1996; Zangl, 2007) have shown improved forecast quality of orographic precipitation and monsoon circulations by increasing model resolution. Hahn and Manabe (1975) showed monsoon rainfall increases substantially with the inclusion of orography.

The NWP model performance depends on the type of weather pattern, grid resolution and geographical region of interest. The best schemes for one region may not be suitable for other regions. The selected schemes chosen for the one type of weather pattern in a specific region may not perform better for the other type of weather pattern in the same region. Model performance depends on a combination of selected schemes and horizontal and vertical resolution of the model. The sensitivity of simulated precipitation to horizontal grid resolution using different mesoscale models has been investigated by many researchers (Bernardet et al., 2000; Colle, 2003; Mahmood and Rasul, 2012). The studies conclude that the orographic representation is better as the resolution increases and the rainfall associated with orography is well represented. Effects of higher resolution indicate an improvement in the simulation of the spatial and temporal distribution of rainfall. The sensitivity of physics parameterizations to model grid-spacing may overwhelm any benefits of higher resolution simulation (Duffy et al., 2003; Rao et al., 2014). However, the challenges grow regarding computational requirements and disk storage as the model resolution increases. Studies such as Baxter et al. (2007); Ballard et al. (2005); Sperber et al. (1994) show that major

improvements in high resolution model forecast are found at short range scale. The recent studies over Indian reflect the assimilation of observations further improves the model accuracy (Srinivas et al., 2010; Yesubabu et al., 2014b; Greshma et al., 2016). Atmospheric numerical models use a large number of physical parameterization schemes to represent the various atmospheric processes that take place in sub-grid scales. Apart from modelling systems, inherent errors and uncertainties in specifying the initial state of the atmosphere, and simplifications in physics and parameterization of sub-grid scale processes further contribute to errors in forecasts. It is believed that physics errors become more important as model resolution increases (Stensrud et al., 2000; Wandishin et al., 2001; Juan et al., 2010). There are a number of studies available in the literature on performance evaluation and role of parameterization schemes particularly Planetary Boundary Layer (PBL) parameterization Schemes (John et al., 2010; Xiao et al., 2010; Hu et al., 2010; Srinivas et al., 2012b) for short range forecast using mesoscale model conducted for western countries. However, there are a few studies (Das et al., 2002; Deb et al., 2010; Sinha et al., 2013) available in India discussing the role of parameterization schemes in simulation Monsoonal rainfall in short range forecasts using very high resolution WRF model. In the present study, we have chosen 2011 southwest monsoon season as the intension of study is evaluate the role of high resolution, cloud grid in reproducing the reasonable accuracy during the heavy rainfall episodes.

The objective of the present study is two-fold. The first objective is to study the impact of very high resolution domain in short range forecasts. The second objective is to choose the best configuration of physics schemes for the high resolution modelling system (WRF-3km). The present study is organised into five sections; Section 1 starts with the importance of the study and previous work carried out by several authors. Section 2 provides a brief description of 2011 South West Monsoon features and heavy rainfall considered for the study. Data, Methodology followed by Model configuration used in the study is described in Section 3. Results and discussion are given in Section 4, and Section 5 provides summary and conclusion.

2. Features of 2011 Southwest Monsoon

and Synoptic Situation of Heavy Rainfall Episode

2011 southwest monsoon known to be one of good monsoon with seasonal rainfall of 104% of long period average (LPA) over the county as a whole. Four depressions and seventeen upper air cyclonic circulations were formed during the season. Out of 17



169

upper air circulations, the upper air circulation formed over the northeast Arabian Sea off north Gujarat coast during 25 August to 01 September produced very heavy rainfall over the Western Ghats and Mumbai region. This is considered for the present study. Western Ghats region of India, particularly Mumbai experienced a heavy to very heavy rainfall activity during 25 to 29 August 2011 (IMD special report on 29 Aug 2011 with Table 1. South-west Monsoon was active over Central and Western India and there is upper air cyclonic circulation over west Madhya Pradesh and adjoining North Maharashtra (Fig 1). Also, the IMD Synoptic analysis shows that the heavy rainfall episode was due to the strengthening of cross equatorial flow over Arabian Sea and increase in pressure gradient along the Konkan coast which resulted in an off shore trough extending from Gujarat coast to Kerala coast (IMD special report).

3. Data, Methodology and Model Configuration

The WRF-ARW model version 3.3, was configured with first domain of 9 km with 766X628 and second domain 3 km with grids 931X970 (Fig 1) with vertical levels of 42 for the WRF-3km modelling domain. The operational WRF-9km domain was configured with first domain of 370x308 and second domain of 616x493 with 42 vertical levels. The topographic information on terrain, land use and soil types are interpolated from the USGS arc '2m' resolution data for the first domain of WRF-9km and '30 sec' resolution data for the second domain WRF-9km and both first and second domains of WRF-3km. Model for all the experiments is initialized at 00 UTC integrated up to 48 hours with 0.5° x 0.5° global forecasts system (GFS) data of the National Centre for Environment Prediction (NCEP) and the boundary conditions are updated at every 3h interval. In first part of the study i.e., performance analysis study of WRF model for two different resolutions, Two types of experiments are carried out namely WRF-9km (27x9 km) and WRF-3km (9x3 km) for two months period (each with total 62 simulations) from 01 July to 30 August 2011. The physics options are same in both the experiments. In WRF-3km, no cumulus scheme is used in the inner domain (3 km grid) as suggested in the study by Weisman et al. (1997). In their study of the resolution dependence of numerical model in resolving the mesoscale convective systems, they reported that grid resolution less than 4 km is sufficient to reproduce mesoscale structure and evolution of convection as produced in 1-km simulations. The physics configuration used for the performance evaluation section of the study is Goddard Ensemble scheme for microphysics, Dudhia shortwave radiation scheme,

RRTM long wave radiation scheme (Mlawer et al., 1997), YSU non-local scheme for PBL turbulence (Hong et al., 2006), Kain-Fritisch (KF-Eta) (Kain and Fritisch, 1993) mass flux scheme for cumulus convection for two domains of WRF-9km but KF-Eta used in the outer domain (9 km) of WRF-3km and the NOAH scheme for land surface processes (Chen and Dudhia, 2001). For the scheme sensitive analysis experiments, WRF-3km simulations are carried out for the heavy rainfall episode during 25-29 August 2011. In this investigation, two sets of numerical experiments are carried out. The first set of experiments is carried out with different PBL options and keeping others set of physics options same as WRF-3km configuration used in resolution experiment. After identifying the best PBL scheme, experiments are conducted using different microphysics schemes with the selected PBL scheme.

4. Results and Discussion First section details the comparison and

performance evaluation of WRF-3km and WRF-9km nested domain experiments for 61-days during the months of July and August, 2011 and the second section discusses the sensitivity study of WRF-3km PBL and microphysics experiments conducted during the heavy rainfall episode 25-30 August 2011.

4.1 Performance Evaluation of WRF for Two Horizontal Nested Domain Resolutions

The performance analysis is carried out for high resolution WRF-3km with two days lead time against the operational WRF-9km forecasts for 61 sample days from 1st July to 30th August 2011. Statistical analysis is carried out using the IDWR data for surface parameters, and then the mean spatial rainfall is compared with gridded IMD rainfall and TRMM 3B42 estimates. Further, multi-category and multi-threshold measure oriented rainfall analysis are carried out, 24hour model accumulated rainfall for Santacruz and Colaba stations, Mumbai with corresponding AWS data is compared. 4.1.1 Comparison of WRF Predicted Parameters in

Both Experiments with IDWR Data There are many statistical skill scores available

to define the quality of model forecasts such as deterministic and probabilistic approach (e.g., McDonald, 1998). The parameters set chosen was based on an attempt to define a relatively independent set of quantities for comparison of statistical performance in terms of statistical indices such as Bias, Mean Absolute Error (MAE), Correlation Coefficient (CC), Standard Deviation (SD) and Root Mean Square Error (RMSE) for WRF-9km and WRF-3km experiment with IDWR data is shown in Table 2.



170

Table 1: IMD ARW observed 24hour accumulated rainfall for Santacruz, Colaba stations of Mumbai, during 25-29

Aug 2011

24hour Accumulated rainfall (mm) with day wise AWS Stations 25-08-2011 26-08-2011 27-08-2011 28-08-2011 29-08-2011 Santacruz 15 16.3 83 220.4 232.6 Colaba 50 25.4 37.9 89 178.6

Table 2: Performance evaluation of simulated surface parameters with IDWR data

Statistics Indices BIAS MAE CC SD(f) SD(o) RMSE

3km 9km 3km 9km 3km 9km 3km 9km 3km 9km 3km 9km Max T -0.22 -0.55 1.83 1.89 0.87 0.83 4.70 4.87 4.67 4.67 2.70 2.78 Min T 0.86 1.28 1.61 1.84 0.88 0.84 3.85 4.13 3.84 3.85 2.29 2.59 Pres 0.49 0.54 1.39 1.42 0.72 0.73 3.44 3.71 4.50 4.51 3.17 3.17 Rain 0.66 2.12 12.12 13.36 0.54 0.40 25.62 23.72 23.36 23.75 24.84 26.01 Wind speed -0.04 -0.13 0.65 0.72 0.44 0.42 0.86 0.85 1.18 1.31 1.14 1.23 U comp 0.73 0.87 1.48 1.66 0.55 0.52 1.96 2.12 1.49 1.49 1.85 2.04 V comp 0.19 0.36 0.99 1.07 0.34 0.30 1.14 1.23 1.07 1.08 1.33 1.42

Fig 1: Model domains used for a) WRF-9km and b) WRF-3km experiments.

The 24h model parameters considered for the comparison with IDWR data are Maximum Temperature (MaxT) at 2m, Minimum Temperature (MinT) at 2m, surface pressure (Pres) at 2m, 24h accumulated rainfall (Rain), wind regarding wind speed, U and V components at 10m level. On comparing the bias in both the experiments, it is noticed that the WRF-3km experiment shows less bias than a WRF-9km experiment, particularly there is a considerable decrease in Bias for 24-hour accumulated rainfall. A similar trend is found for MAE and SD statistical indices. MaxT, MinT, wind and rainfall are better correlated in WRF-3km experiment than in the WRF-9km, though there is no significant difference found in the CC value for the surface pressure. The RMSE values of MaxT and MinT in both the experiments indicate a significant reduction in errors for the simulated wind and rainfall in WRF-3km.

4.1.2 Comparison of Daily Mean Rainfall The simulated 24h Accumulated rainfall for the

first and second day’s forecast is compared with the corresponding mean IMD 1ox1o gridded and TRMM 3B42 rainfall estimates (Fig 2) for WRF-3km and WRF-9km configuration. The figure 2a shows heavy rainfall in IMD gridded mean daily rainfall plot distributed mainly over Western Ghats and north-east India and moderate heavy rainfall over west-central India. The TRMM rainfall (fig 2d) show two heavy rainfall bands, one over east-central Gujarat to Madhya Pradesh, the other peak from north-east states to North Bay of Bengal in addition to Western Ghats’s heavy rainfall peak. First day forecasts of WRF-9km experiment over predict the heavy rainfall over East Central India and west central Bay of Bengal, model captured heavy rainfall spell observed in the East Central India. Second day rainfall forecasts of WRF 9km over predict the rainfall to large extent over –



171

Fig 2: Comparison of Daily Mean rainfall (mm) for July and August 2011 a) IMD 1deg rainfall b) WRF-9km day-1

forecast, c) WRF-9km day-2 forecast, d) TRMM 3B42 estimates, e) WRF-3km day-1 forecast and f) WRF-3km day-2 forecast.

Bengal and also central India. First and second day forecasts of WRF-3km simulated rainfall indicate significant reduction in over prediction of rainfall over central, north-east India and Bay of Bengal region with an observed pattern of IMD and TRMM rainfall. The TRMM estimates show mean rainfall of 6-12 mm/day over south Andhra Pradesh and Tamil Nadu and the pattern was well captured in WRF-9km first and second forecasts, In case of WRF-3km experiment, there is a reduction mean rainfall of 5mm/day in the first day forecasts and reduced drastically in the second day forecasts over Tamil Nadu and southern Andhra Pradesh. One of the reasons for this reduction in WRF-3km can be due to increasing high resolution in turns increases the wind speed which amplifies the orographic effect and produces very heavy rainfall over the windward side and comparatively less rainfall over the leeward side of the Western Ghats. The other reason for the under prediction of the rainfall over south-east region could be no convective scheme is used in the inner domain of WRF-3km and suggests that even 3km grid resolution over tropical coastal regions are not resolving the convection completely. Since IDWR data

obtained from the IMD, indicates the rainfall over the south-east India is mainly due to the convective storms. 4.1.3 Comparison of WRF Domain Averaged 24h

Accumulated Rainfall The simulated domain average 24h accumulated

rainfall for the first forecast day (Fig 3a) and second forecast day (Fig 3b) for July and August 2011 forecasts are plotted against the corresponding domain averages TRMM 3B42 rainfall (mm). TRMM estimates domain averaged rainfall ranges from 5mm to 15mm with an average of 10mm per day for 61days and peak rainfall on 15th Jul 2011. The daily time series of domain averaged rainfall (mm) for the WRF-9km first day forecasts indicate there is considerable over prediction of rainfall for all the days expect for the period from 9th July to 12th July 2011. The difference between TRMM estimated and first day rainfall of WRF-9km is about 3kmm during 05th to 16th of August and the difference found to be maximum on 4.2mm on 21st July.

The daily time series of domain averaged rainfall (mm) for WRF-3km experiment well matches –



172

Fig 3: Comparison of WRF domain averaged 24h accumulated rainfall (mm) for a) Forecast day-1 and b) Forecast

day-2 with TRMM 3B42 Rainfall Estimates.

with observed averaged estimates of TRMM gridded rainfall both in terms pattern and quantitatively except for first four simulated days. The first forecast day 24h rainfall amount is significantly reduced in WRF-3km experiment as against the over prediction observed in WRF-9km. Similarly, on comparing with daily series of mean estimates of TRMM for the second forecasted day rainfall (Fig 3b), it is observed the WRF-9km experiment over predicted the first day rainfall except from 11th July to 17th July 2011. The over predicted rainfall for WRF-9km is ~5mm on 20th July to 25th July than the observed TRMM estimates. The nested WRF-3km experiment comparatively better matches with observed TRMM estimates both in terms of spatial pattern and quantitatively though slightly under predicts rainfall for 15 forecast days (10-22 July 2011). 4.1.4 Multi Category Analysis of Simulated Rainfall

with IDWR Data The performance of WRF model simulated

24hour Accumulated Rainfall in WRF-9km and WRF-3km is evaluated using measure oriented rainfall analysis in terms of multi threshold (Table 3) and multi category analysis (Table 4) using IDWR data of IMD 200 synoptic stations. Multi threshold rainfall analysis has been carried out to calculate statistical indices like Threat Score (TS), Probability of Detection (POD) or

Hit rate, False Alarm Ratio (FAR), Heidke Skill score (HS), Percent Correct (PC), Bias Score (BIAS), Equitable Threat Score (ETS) using the perfect thresholds 2.5, 5, 10, 20, 50 and 100mm respectively for the both the experiments. TS measures the fraction of forecast events that were correctly predicted and TS could not measure hits can occur purely due to random chance. On observing the TS values for the WRF-9km and WRF-3km experiments, it has seen that there is no significant difference found between the two experiments except at 50mm threshold value. ETS value which is corrected TS by removing the Random hits, shows significant improvement for WRF-3km experiment than the WRF-9km for the thresholds till 50mm. Hit Score (POD) which indicates the fraction of the observed hits, also show significant improvement in rainfall forecast for WRF-3km experiment for all the threshold values except 100mm. FAR denotes the fraction of the predicted "yes" events which are not observed in actual. From the thresholds values of FAR, there are more number of false alarms in the rainfall forecast of WRF-3km experiment than in WRF-9km till the 10mm threshold value after that WRF-9km shows more number of false alarms than WRF-3km experiment. Bias score measures the ratio of the frequency of forecast events to the frequency of observed events, which in turn indicates whether the –



173

Table 3: Multi threshold analysis of rainfall for WRF-3km and WRF-9km experiments

Thresholds in rainfall (mm)

TS POD FAR HS PC BIAS ETS

3km 9km 3km 9km 3km 9km 3km 9km 3km 9km 3km 9km 3km 9km 2.5 0.48 0.49 0.65 0.55 0.35 0.19 0.4 0.3 0.71 0.64 1.01 0.68 0.25 0.18 5 0.44 0.43 0.62 0.52 0.39 0.28 0.41 0.33 0.73 0.67 1.01 0.72 0.25 0.19 10 0.38 0.36 0.54 0.47 0.45 0.39 0.38 0.33 0.76 0.72 0.98 0.77 0.24 0.2 20 0.31 0.3 0.47 0.44 0.52 0.51 0.36 0.35 0.82 0.8 0.98 0.89 0.22 0.21 50 0.2 0.17 0.32 0.29 0.65 0.71 0.29 0.24 0.91 0.91 0.91 1 0.17 0.13 100 0.13 0.13 0.2 0.24 0.75 0.77 0.21 0.22 0.97 0.98 0.81 1.07 0.12 0.12

Table 4: Multi category analysis of rainfall for WRF-3km and WRF-9km experiments

IMD Rainfall Category TS FAR Hit Rate PC BIAS

3km 9km 3km 9km 3km 9km 3km 9km 3km 9km No Rain 0.440 0.233 0.340 0.432 0.570 0.251 0.710 0.666 0.870 0.327 Very Light Rain 0.130 0.122 0.800 0.813 0.260 0.259 0.710 0.692 1.290 1.382 Light Rain 0.100 0.101 0.820 0.856 0.180 0.257 0.790 0.715 0.980 1.785 Moderate Rain 0.170 0.187 0.710 0.742 0.300 0.407 0.710 0.652 1.030 1.580 Rather Heavy 0.090 0.090 0.820 0.830 0.160 0.161 0.890 0.894 0.900 0.949 Heavy Rain 0.150 0.079 0.750 0.855 0.280 0.148 0.950 0.942 1.130 1.016 3km Percent Correct = 0.38 9km Percent Correct = 0.27

forecast system tends to under forecast (Bias<1) or over forecast (Bias>1) events. Bias comparison table for the both the experiments indicates WRF-9km experiment under predict the rainfall and the WRF-3km experiment shows good bias score. Overall statistical incidences show that the forecast accuracy is improved significantly in the WRF-3km experiment till threshold value 50mm, thereafter WRF-9km experiment shows slightly better forecast. Though, the false alarms are slightly high for WRF-3km experiment till thresholds of 10mm. Hit rate of WRF-3km is more for No rain and heavy rain category, while hit rate is more for moderate Rain with WRF-9km experiment. In WRF-3km experiment FAR is comparatively less in all rain categories than in WRF-9km, which depicts model capability to capture rainfall events more with increasing resolution. Highest TS has been observed for extremely high category for both resolutions. PC is treated as most crucial skill score while assessing overall performance of the model and indicates total fraction of the forecasts which were correct in the category. In the present study, for WRF-3km resolution overall PC is 0.38 which is better than WRF-9km with 0.27 PC. Measured oriented rainfall analysis suggests WRF-3km has better skill in terms high POD and minimum FAR and BIAS the WRF-9km experiment. 4.1.5 Comparison of WRF Model Simulated

Rainfall Over Mumbai Region Comparison of 24h accumulated rainfall (mm)

for WRF-3km and WRF-9km experiments with IMD

AWS stations of Santacruz and Colaba in Mumbai are shown in Table 5. IMD AWS observation of Santacruz, moderate rainfall of 15mm for 25 and 26 of August and heavy rainfall of 83mm on 27 and very heavy rainfall on 28 and 29 of August 2011. WRF-3km well captured very heavy rainfall on 28 of August and comparatively better predicted than WRF-9km for the other four days. Though WRF-3km experiment over predicted 24h accumulated rainfall particularly for first forecast day than the WRF-9km further a reduction in the over-prediction of rainfall is reduced slightly on second forecast day. In case of Colaba station, AWS observations indicate moderate rainfall from 25 to 27, heavy rainfall on 28 and very heavy rainfall on 29 of August. The first forecast day rainfall for WRF-3km under predicted the rainfall amount for 25 and 26 of August and over predicted from 27 to 28 of August and WRF-9km experiment over predicted the rainfall amount for first three days and under predicted the amount for the next two days. It is seen that first forecast day 24h accumulated rainfall of WRF-3km better compared with AWS observation than WRF-9km prediction. 4.2 Sensitivity Analysis During Heavy Rainfall

Episode During 25-30 August of 2011 As Planetary Boundary Layer (PBL) and

microphysics play major role in simulation Monsoon rainfall for short range forecasts. For selecting best PBL and microphysics schemes over Indian region, sensitivity analysis of WRF-3km domain is carried out.



174

PBL processes play vital role in numerical weather model for controlling the transfer of momentum, heat and water vapour to represent the general circulation of the atmosphere. Four prominent PBL schemes namely YSU (Yonsei University), MYJ (Mellor-Yamada-Janjic), MYNN2.5 (Mellor-Yamada-Nakanishi-Niino) and ACM2 (asymmetric convective model) are chosen for the study. Other physical parameterization schemes used in the study are Goddard scheme for microphysics, RRTMG for short wave and long wave schemes and NOAH LSM for land surface and no cumulus schemes are used for the inner domain. 4.2.1 Evaluation of PBL Schemes During 25-29 Aug

2011 Simulated surface and upper air parameters for

different PBL schemes are compared with available 500 AWS stations of IMD and with Wyoming University (~25 stations) observations respectively. Three statistical metrics namely, Root Mean Square Error (RMSE), Correlation Coefficient (CC) and Mean Error (ME) are used for evaluation for 24h Accumulated Rainfall, Temperature, Relative Humidity (RH) and Wind speed (WS) shown in Table 6. From all the statistical parameters it has been observed that YSU PBL experiments shown least error for all the parameters including rainfall than other PBL schemes MYJ, MYNN2, and ACM2 respectively. ME for all the experiments over predict the relative humidity and rainfall and under predict the temperature by 0.5oC and wind speed by 1.5 m/s. Significant correlation is obtained for temperature and RH. The correlation for 24h accumulated rainfall is found to be poor.

The performance evaluation of simulated temperature (Fig 4a, 4b, 4c) and RH profiles (Fig 4d, 4e, 4f) at 24h forecast, calculated with available Wyoming radiosonde data considering statistical metrics CC, BIAS and RMSE. From CC of temperature and RH, correlation values are high for YSU PBL and low for ACM2 for all the PBL experiments. Mean bias of Temperature and RH simulated profiles show under prediction for lower levels and considerable high over predicted temperature and RH for higher model pressure levels. RMSE values of temperature show that the deviation is more or less within the limit of 1.5 to 2.0oC till 400hPa, thereafter there deviation increases more at higher levels. RMSE profiles of relative humidity indicate the deviation from the observed and the simulated profiles increase up (RH % of 20) near linearly till 300hPa, thereafter deviation decrease at higher levels. From all the statistical profile indices it is seen that YSU PBL has highest correlations and minimum BIAS and RMSE throughout the models levels. In case of wind speed (Fig 4g, 4h, 4i), the performance analysis shows significant difference only

at lower levels with different PBL schemes. YSU PBL found to be a minimum error regarding high correlation, low BIAS and RMSE values at model lower level. PBL experiments overestimate the wind at lower levels and under estimate between 600 to 400hPa. 4.2.2 Comparison Accumulated Rainfall for

Difference PBL Schemes During 25-29 Aug 2011 Comparison of WRF model simulated 24h

accumulated rainfall of the WRF-3km experiment with respect to different PBL schemes over Santacruz, IMD AWS location is shown in Table 7. From all the experiments, for the first and second day forecasts found to be better forecasted with YSU scheme than other three schemes, though YSU scheme over predicted the first day rainfall except for 29 of August and under predicted the second day rainfall except 27 Aug. Out of the three schemes MYJ, MYNN2 and ACM2, MYJ scheme predicted rainfall found to be better than the other two. It is also noticed that MYJ 24h simulated rainfall for the first forecast day well matches with AWS observation expect for the 29 of August. 4.2.3 Performance Evolution for Microphysics

Experiments During 25-29 Aug 2011 In second set of numerical experiments, five

microphysical schemes namely WRF Single-Moment 6-Class (WSM-6), Goddard, Thompson, Milbrandt and Yau and Morrison are compared. Other physical parameterization schemes used in this experiment are Dudhia scheme for short wave radiation, rapid radiative transfer model (RRTM) long wave radiation, Noah land surface scheme for land surface physics and YSU with ETOPO for planetary boundary layer scheme. Statistical evaluation has also carried out for different microphysics scheme experiments simulated surface parameters with all available AWS stations (~500). Table 8 gives error metrics of RMSE, correlation coefficient and mean error in surface variables of temperature, RH, wind speed and rainfall with respect to AWS observations. Errors in 24h accumulated rainfall found to be least for Goddard scheme. Relative humidity of RMSE and ME are found to be minimum and more significant for Goddard scheme. In case of temperature, model performance is found to be similar in all microphysics experiments except ME of Goddard microphysics scheme found to be minimum than other scheme experiments for wind speed, Goddard and WSM-6 schemes give the least value of RMSE and ME and significant correlation found for Goddard. From microphysics experiments shown in Table 8, it is seen that model simulated relative humidity and rainfall is highly sensitive to microphysics and least sensitive to –



175

Table 5: Comparison of WRF model simulated rainfall for Santacruz, Colaba location during 25-29 Aug 2011

Date

AWS Santacruz, Mumbai Colaba, Mumbai

3km 9km AWS 3km 9km 1 day 2 day 1 day 2 day 1 day 2 day 1 day 2 day

25 Aug’13 15 0.9 43.5 44.8 50 0.3 59.9 24.9 26 Aug’13 16.3 11.8 13.6 26.4 70.1 25.4 12 12.13 26.4 70.1 27 Aug’13 83 143.2 93.6 151.6 57.1 37.9 124.5 31.1 151.6 57.1 28 Aug’13 220.4 232.3 194.8 175.6 209.8 89.1 108.7 225.5 75.6 199.8 29 Aug’13 232.6 128.5 161.3 87.9 110.6 178.6 114.2 54.65 87.9 110.6

Table 6: Performance evaluation of the surface parameters with AWS data for PBL schemes

Simulation Rain fall Temperature RH WS

RMSE CC ME RMSE CC ME RMSE CC ME RMSE CC ME YSU 38.77 0.45 4.51 3.5 0.555 -0.19 22.95 0.61 3.27 4.3 0.535 -1.9 MYJ 44.55 0.29 4.78 3.43 0.542 -0.68 24.21 0.55 5.84 4.37 0.54 -2.1 MYNN2 48.97 0.26 4.84 3.51 0.549 -0.71 23.45 0.54 5.76 3.99 0.523 -1.37 ACM2 39.55 0.47 4.57 3.56 0.552 -0.73 23.31 0.59 4.72 4.13 0.524 -1.52

Table 7: Comparison of WRF model simulated rainfall for Santacruz, Colaba location during 25-29 Aug 2011

Date 24 hour accumulated rainfall (mm)

AWS YSU MYJ MYNN2 ACM2 1 day 2 day 1 day 2 day 1 day 2 day 1 day 2 day

25 Aug’13 15 0.9 4.2 1.5 1.4 26 Aug’13 16.3 11.8 13.6 10.4 7.2 2.8 28 4.1 34.5 27 Aug’13 83 143.2 93.6 79.2 82.4 26.4 52.7 50.1 62.5 28 Aug’13 220.4 232.3 194.8 192.4 147.6 133.8 151.4 129.7 181.5 29 Aug’13 232.6 128.5 161.3 98.4 127.2 72.2 29.4 56.8 103.7

Table 8: Performance evaluation of surface parameters with AWS data

Simulation Rainfall Temperature RH WS

RMSE CC ME RMSE CC ME RMSE CC ME RMSE CC ME Goddard 37.36 0.49 4.13 3.57 0.563 -0.52 22.74 0.64 3.25 4.06 0.563 -1.66 WSM-6 37.64 0.46 5.31 3.563 0.563 -0.56 23.79 0.44 3.99 4.04 0.461 -2.08 Thomson 38.82 0.37 7.07 3.608 0.561 -0.76 23.65 0.59 4.08 4.41 0.455 -2.1 Milbrandt 38.81 0.41 8.26 3.571 0.564 -0.7 23.75 0.54 3.93 4.36 0.465 -2.07 Morrison 42.05 0.27 6.5 3.601 0.563 -0.76 23.6 0.62 3.44 4.41 0.458 -2.13

temperature. 24 hour accumulated rainfall (mm) of different microphysics schemes averaged for 5 days from 25-29 August 2011 corresponding TRMM 3B42 rainfall is shown in Fig 5. It is observed that differences in heavy rainfall patch over the Western Ghats simulated rainfall between microphysics scheme experiments are very minute. West central India heavy rainfall patch is well simulated by Goddard microphysics scheme compared to the other schemes. It is also noticed that North central Bay of Bengal rainfall patch is over predicted by all micro physics schemes. The overall rainfall pattern well simulated by Goddard and Thompson schemes are found to be closer to

observations. It is also noticed that the TRMM rainfall show convective rainfall over the north Tamilnadu region though none of the micro physics experiments simulated observed as seen in TRMM. There is no convection used in the inner domain for the all experiments, this could be the possible reason for under prediction rainfall of south east Tamilnadu.

Model simulated 3hourly accumulated rainfall in all microphysics experiments for the 27 August forecast day are compared with AWS rainfall of Santacruz, Mumbai, IMD observatory (shown in Fig 6). IMD AWS observations indicate that the two day –



176

Fig 4: Performance evolution of model simulated profiles of temperature, relative humidity and wind speed with

radiosonde data for all PBL schemes.

Fig 5: Comparison of 5day averaged different micro physics experiments simulated 24h accumulated rainfall (mm)

for the forecast day-1 with rainfall estimates of TRMM 3B42.



177

Fig 6: Comparison of Santacruz AWS rainfall with different micro physics experiments for 48h forecast of 27

August 00UTC initial condition. (48hours) accumulated rainfall is found to be 400mm All the micro physics experiments except Goddard scheme over predicted the rainfall from 15 UTC of 27 August. The 3hourly accumulated rainfall for the Goddard microphysics scheme well captured the rainfall till 36h forecast hour, thereafter Goddard scheme also over predicted the rainfall. It is also seen that none of the experiments over predicted the rainfall after 12hours of model simulation till 48 hours of forecast. 4.2.4 Comparison Accumulated Rainfall for Micro

Physics Schemes During 25-29 Aug 2011 Comparison of WRF model simulated 24-h

accumulated rainfall with respect to five Microphysics schemes namely Goddard, WSM6, Thomson, Milbrandt and Morrison in the WRF-3km experiment over Santa Cruz of IMD AWS is shown in Table 9. The first forecast day comparison shows that Goddard microphysics scheme simulated 24h rainfall well matches with AWS observations, though it over-forecasted the rainfall on 27 August 2013. Out of the other four microphysics scheme experiments, Thomson microphysics found be better than the other three schemes in terms of quantitative rainfall for first and second day 24h accumulated rainfall. It is also noticed that Goddard microphysics second day rainfall forecast found to be better that than other four microphysics schemes with slightly under predicted the amount. 5. Summary and Conclusion

The performance of the high resolution (9x3km) nested WRF-ARW modelling system (WRF-3km) and IMD operational model configuration 27x9km

forecasts (WRF-9km) has examined for the period 62-days (01 July to 31 August 2011) during the Monsoon season of 2011. The statistical evaluation of simulated surface parameters and rainfall has studied using the IDWR data. The 24-h accumulated rainfall (mm) and times series of domain averaged rainfall from both high resolution experiments (WRF-9km and WRF-3km) have been compared with IMD gridded rainfall, and TRMM 3B42 estimates for day-1 and day-2 forecasts. For rainfall, the sensitivity of horizontal resolution in WRF model has been evaluated with respect to measuring oriented methods using statistical metrics namely Threat Score (TS), Hit Rate (HR), False Alarm Ratio (FAR), Percent Correct (PC) and Bias Score (BS). The analyses of the statistical performance of surface parameters indicate that model simulated errors have been reduced significantly for the surface wind and rainfall after increasing the mesoscale grid resolution while for other simulated parameters such as maximum, minimum temperature and pressure, the skill of high resolution WRF-3km is relatively improved compared to WRF-9km, and the spatial comparison of daily mean simulated rainfall suggests the increasing resolution from WRF-9km to WRF-3km, reduces the over-prediction of rainfall over central India and east central Bay of Bengal. It is also seen that the 24-h accumulated rainfall of WRF-3km increased over Western Ghats orographic while significantly reduced over the south central India rainfall as compared to WRF-9km. The reduction of simulated rainfall could be due to increasing horizontal resolution of model which in turns improves the simulation of wind speed and amplifies the orographic effect resulting in producing heavy rainfall over windward –



178

Table 9: Comparison of WRF model simulated rainfall (mm) for Santacruz, Colaba locations during 25-29 Aug

2011

Date

24 hour accumulated rainfall (mm) AWS Goddard WSM6 Thomson Milbrandt Morrison

1 day 2 day 1 day 2 day 1 day 2 day 1 day 2 day 1 day 2 day 25 Aug’13 15 0.9 4.5 2.6 13.5 5 26 Aug’13 16.3 11.8 13.6 6.2 63 42.3 24.2 44.8 33.3 19.5 31.5 27 Aug’13 83 143.2 93.6 65.3 90.2 74.5 58.3 73.9 136.9 59.6 48.5 28 Aug’13 220.4 232.3 194.8 167.4 111.1 183.3 126.1 108.6 127.8 136.6 184.6 29 Aug’13 232.6 128.5 161.3 99.8 126.4 96.6 98.8 85.3 139.3 92.8 129.6

side and comparatively less rainfall over the leeward side of the Western Ghats. Measure oriented rainfall analysis shows that using very high resolution grid (WRF-3km) forecast improved in terms of reduced number of false alarms and with high hits ratio than in WRF-9km experiment. It is also observed that forecast quality of number of “no rain” events significantly improved in high resolution configuration (WRF-3km). The PBL sensitivity analysis conducted during the heavy rainfall period with AWS and radiosonde data suggests that YSU PBL scheme has highest prediction skill with least error metrics for wind and relative humidity in turn prediction of rainfall and least sensitive for simulated temperature. This observation is also reflected in 24-h accumulated rainfall. The performance of microphysics scheme experiments shows that Goddard ensemble scheme produces least statistical errors mainly in terms of simulated temperature, relative humidity and rainfall. The statistical analysis also indicates that simulated surface winds least sensitive to the model microphysics. From

the comparison table of 24-h accumulated rainfall with different microphysics schemes, it is seen that Goddard microphysics give higher and optimal quantitative rainfall estimates with AWS observations than other microphysics schemes. Overall comparison of sensitivity analysis indicates that the model configuration of WRF-3km with YSU PBL scheme and Goddard ensemble microphysics schemes produces better results in capturing the heavy rainfall events as observed over Mumbai during 25-29 Aug 2011. 6. Acknowledgement

Authors thank IMD for providing AWS Data, Gridded rainfall data and IDWR reports. The WRF-ARW model was obtained from NCAR (www.wrf-model.org). The NCEP GFS 0.5o data obtained from NCEP (nomads.ncdc.noaa.gov), NOMADS Server and TRMM data downloaded from http://trmm.gsfc.nasa.gov.

References Ashrit RG, Das Gupta M and Bohra AK (2006). MM5

simulation of the 1999 Orissa Super Cyclone: Impact of bogus vortex on track and intensity prediction. Mausam, 57: 129-134.

Baldauf M, Axel S, Jochen F, Detlev M, Matthias R and Thorsten R (2011). Operational convective-scale numerical weather prediction with the Cosmo model: description and sensitivities. Monthly Weather Review, 139: 3887-3905.

Ballard S, Li Z, Dixon M, Swarbrick S, Stiller O and Lean H (2005). Development of 1-4km resolution data assimilation for now casting at the met office. Presented at world weather research program symposium on nowcasting and very short range forecasting (WSN05), Toulouse, Meteo-France, September 2005.

Baxter GM, Dance SL, Lawless AS, Nichols NK and Ballard SP (2007). Towards data assimilation for high resolution Nested models, numerical analysis report 2/2007, Presented at the ICFD International Conference on Numerical Methods for Fluid Dynamics, Reading, March.

Bernardet, Lgia R, Lewis D, Grasso, Jason E, Nachamkin, Catherine A Finley and William R Cotton (2000). Simulating convective events using a high-resolution mesoscale model. Journal of Geophysical Research, 105(D11): 14963-14982.

Chakraborty A, Nanjundiah RS and Srinivasan J (2002). Role of Asian and African Orography in Indian Summer Monsoon. Geophysical Research Letters, 29(20): 1989.

Chen F and Dudhia J (2001). Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: Model description and implementation. Monthly Weather Review, 129: 56-585.

Colle BA, Olson JB and Tongue JS (2003). Multiseason verification of the MM5. Part II: Evaluation of high-resolution precipitation forecasts over the north eastern United States. Weather Forecasting, 18: 458-480.

Das Someshwar, AK Mitra, G Iyengar and Jagvir Singh (2002). Skill of medium range forecasts over the Indian monsoon region using different parameterizations of deep convection. Weather and Forecasting, 17(6): 1194-1210.



179

Deb SK, Srivastava TP and Kishtawal CM (2008). The WRF model performance for the simulation of heavy precipitating events over Ahmedabad during August 2006. Journal of Earth System Science, 117(5): 589-602.

Dudhia J (1989). Numerical study of convection observed during winter monsoon experiment using a mesoscale two-dimensional model. Journal of Atmosphere Science, 46: 3077-3107.

Duffy P, Govindasamy B, Iorio J, Milovich J, Sperber K, Taylor K, Wehner M and Thompson S (2003). High resolution simulations of global climate. Part I: Present climate. Climate Dynamic, 21: 371-390.

Fuyu Li, William DC, Michael FW, David LW, Jerry Go and Christopher A (2011). Impact of horizontal resolution on simulation of precipitation extremes in an aqua-planet version of Community Atmospheric Model (CAM3), Tellus, 63(5).

Ghosh P, Ramkumar TK, Yesubabu V and Naidu CV (2016). Convection-generated high-frequency gravity waves as observed by MST radar and simulated by WRF model over the Indian tropical station of Gadanki. Quarterly Journal of the Royal Meteorological Society, 142: 3036-3049.

Giorgi F and Marinucci M (1996). An investigation of the sensitivity of simulated precipitation to model resolution and its implications for climate studies. Monthly Weather Review, 124: 148-166.

Goswami BB, Mukhopadhyay P, Khairoutdinov M and Goswami BN (2012). Simulation of Indian summer monsoon intraseasonal oscillations in a superparameterized coupled climate model: need to improve the embedded cloud resolving model, Climate Dynamics, 1-11.

Greeshma MM, Srinivas CV, Yesubabu V, Naidu CV, Baskaran R and Venkatraman B (2015). Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model. Annales Geophysicae, 33: 805-828.

Hahn DG and Manabe S (1975). The role of mountains in the South Asian monsoon circulation, Journal of Atmosphere Science, 32: 1515-1541.

Hima Bindu H, Ratnam MV, Yesubabu V, Rao TN, Kesarkar A and Naidu CV (2016). Characteristics of cyclone generated gravity waves observed using assimilated WRF model simulations over Bay of Bengal. Atmospheric Research, 180(1): 178-188.

Hong SY, Noh Y and Dudhia J (2006). A new vertical diffusion package with explicit treatment of entrainment processes. Monthly Weather Review, 134: 2318-2341.

Hu Xiao-Ming, John W, Nielsen-Gammon and Fuqing Zhang (2010). Evaluation of three planetary boundary layer schemes in the WRF Model. The Journal of Applied Meteorology and Climatology, 49: 1831-1844.

John W, Nielsen-Gammon, Xiao-Ming Hu, Fuqing Zhang and Jonathan EP (2010). Evaluation of planetary boundary layer scheme sensitivities for the purpose of parameter estimation. Monthly Weather Review, 138(9): 3400-3417.

Juan JR, Celeste S and Julia Nogués-Paegle (2010). WRF model sensitivity to choice of parameterization over South America: Validation against surface variables. Monthly Weather Review, 138(8): 3342-3355.

Kain JS and Fritsch JM (1993). Convective parameterization for mesoscale models: The Kain-Fritcsh scheme, the representation of cumulus convection in numerical models, KA Emanuel and DJ Raymond, Eds. American Meteor Society, 246.

Kobayashi C and Sugi M (2004). Impact of horizontal resolution on the simulation of the Asian summer monsoon and tropical cyclones in the JMA global model, Climate Dynamics, 23(2): 165-176.

Kumar S, Routray A, Chauhan R and Panda J (2014). Impact of parameterization schemes and 3DVAR data assimilation for simulation of heavy rainfall events along West Coast of India with WRF modeling system. International Journal of Earth and Atmospheric Science, 01(1): 18-34.

Kumar A, Dudhia J, Rotunno R, Niyogi D and Mohanty UC (2008). Analysis of the 26 July 2005 heavy rain event over Mumbai, India using the Weather Research and Forecasting (WRF) model. Quarterly Journal of the Royal Meteorological Society, 134(636): 1897-1910.

Langodan S, Cavaleri L, Viswanadhapalli Y and Hoteit I (2015). Wind-wave source functions in opposing seas, Journal of Geophysical Research: Oceans, 120: 6751-6768.

Langodan S, Viswanadhapalli Y and Hoteit I (2016b). The impact of atmospheric data assimilation on wave simulations in the Red Sea. Ocean Engineering Elsevier, 116: 200-215.

Langodan S, Viswanadhapalli Y, Dasari HP, Knio O and Hoteit I (2016a). A high-resolution assessment of wind and wave energy potentials in the Red Sea. Applied Energy, 181: 244-255.

Litta AJ, Sumam Mary I and Mohanty UC (2011). A comparative study of convective parameterization schemes in WRF-NMM model. International Journal of Computer Applications, 33(6): 32-40.

Mahmood T and Rasul G (2012). Predictability of summer monsoon rainfall by using high resolution regional model (HRM). Pakistan Journal of Meteorology, 9(17): 25-36.

McDonald BE (1998). Sensitivity of precipitation forecast skill to horizontal resolution. Ph.D. dissertation. University of Utah, 135.

Mishra AK, Panda J and Rafiq M (2017). Increasing risk of droughts and floods and decline in ground water level in warming environment. International Journal of Earth and Atmospheric Science, 4(02): 127-132.

Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ and Clough SA (1997). Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research, 102(D14): 16663-16682.

Mukhopadhyay PS, Taraphdar, Goswami BN and Krishnakumar K (2010). Indian summer monsoon precipitation climatology in a high-resolution regional climate model: Impacts of convective parameterization



180

on systematic biases. Weather Forecasting, 25: 369-387.

Osuri KK, Mohanty UC, Routray A, Makarand AK and Mohapatra M (2012). Customization of WRF-ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean, Natural Hazards, 63(3): 1337-1359.

Parthasarathy B, Munot AA and Kothawale DR (1995) Monthly and seasonal rainfall series for all India homogeneous regions and meteorological subdivisions: 1871-1994; Research Report No. RR-065, Indian Institute of Tropical Meteorology, Pune, India.

Rajeevan M, Kesarkar A, Thampi SB, Rao TN, Radhakrishna B and Rajasekhar M (2010). Sensitivity of WRF cloud microphysics to simulations of a severe thunderstorm event over Southeast India. Annales Geophysicae, 28(2): 603-619.

Rama Rao, YV Hatwar, HR Salah AK and Sudhakar Y (2007). An experiment using the high resolution Eta and WRF models to forecast heavy precipitation over India, Pure and Applied Geophysics, 164(8-9): 1593-1615.

Rao S, Kolusu, Venkatraman Prasanna and Preethi B (2014). Simulation of Indian summer monsoon intraseasonal oscillations using WRF regional atmospheric model. International Journal of Earth and Atmospheric Science, 01: 35-53.

Senior C (1995). The dependence of climate sensitivity on the horizontal resolution of a GCM. Journal of Climate, 8: 2860-2880.

Sinha P, Mohanty UC, Kar SC, Dash SK and Kumari S (2013). Sensitivity of the GCM driven summer monsoon simulations to cumulus parameterization schemes in nested RegCM3, Theoretical and Applied Climatology, 112(1-2): 285-306.

Sperber K, Hameed S, Potter G and Boyle J (1994). Simulation of the northern summer monsoon in the ECMWF model: Sensitivity to horizontal resolution. Monthly Weather Review, 122: 2461-2481.

Srinivas CV, Venkatesan R, Yesubabu V, Nagaraju C, Somayajai KM, Chellapandi P and Baldev Raj (2010). Assimilation of conventional and satellite wind observations in a mesoscale atmospheric model for studying atmospheric dispersion. Atmospheric Environment, 44(24): 2846-2864.

Srinivas CV, Yesubabu V, Venkatesan R and Ramakrishna SSVS (2012a). Impact of assimilation of conventional and satellite meteorological observations on the numerical simulation of a Bay of Bengal Tropical Cyclone of November 2008 near Tamilnadu using WRF model. Meteor Atmosphere Physics, 110: 19-44.

Srinivas CV, Bhaskar Rao DV, Yesubabu V, Baskaran R and Venkatraman B (2012b). Tropical cyclone predictions over the Bay of Bengal using the high-resolution advanced research weather research and forecasting model. Quarterly Journal of the Royal Meteorological Society, 139: 1810-1825.

Stensrud DJ and Yussouf N (2003). Short-range predictions of 2-m temperature and dewpoint temperature over New England. Monthly Weather Review, 131: 2510-2524.

Viswanadhapalli Y, Dasari HP, Langodan S, Challa VS and Hoteit I (2017). Climatic features of the Red Sea from a regional assimilative model. International Journal of Climatology, 37: 2563-2581.

Wandishin MS, Mullen SL, Stensrud DJ and Brooks HE (2001). Evaluation of a short-range multimodel ensemble system. Monthly Weather Review, 129: 729-747.

Weisman, Morris L, William C, Skamarock and Klemp JB (1997). The resolution dependence of explicitly modeled convective systems. Monthly Weather Review, 125: 527-548.

Xiao-Ming Hu, John W Nielsen-Gammon and Fuqing Z (2010). Evaluation of three planetary boundary layer schemes in the WRF model. Journal of Applied Meteorology and Climatology, 49(9): 1831-1844.

Yesubabu V, Sahidul I, Sikka DR, Akshara K, Sagar K and Srivastava AK (2014a). Impact of variational assimilation technique on simulation of a heavy rainfall event over Pune, India, Natural Hazards, 71(1): 639-658.

Yesubabu V, Srinivas CV, Ramakrishna SSVS and Hari Prasad KBRR (2014b). Impact of period and timescale of FDDA analysis nudging on the numerical simulation of tropical cyclones in the Bay of Bengal. Natural Hazards, 74(3): 2109-2128.

Zangl Gunther (2007). To what extent does increased model resolution improve simulated precipitation fields? A case study of two north-Alpine heavy-rainfall events. Meteorologische Zeits Chrift, 16(5): 571-580.

Performance and Sensitivity Analysis of Very High...

Documents

Transcript of Performance and Sensitivity Analysis of Very High...