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ORIGINAL ARTICLE Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models

M.S. Shekhar*, M. Sravan Kumar, P. Joshi and A. Ganju

Snow and Avalanche Study Establishment (DRDO), Research and Development Centre Chandigarh, India.

Abstract Performance of the fifth generation National Centre for Atmospheric *Corresponding Author: Research (NCAR)/Penn State Mesoscale Model (MM5) used at Snow and Avalanche Study Establishment (SASE) since past 7 years has been M S Shekhar evaluated. Analysis shows that performance of MM5 model is fairly good

Email: [email protected] for Pir Panjal, Shamshawari and Great Himalayan ranges but relatively poor for Karakoram. Few cases of Western Disturbances (WDs) were also studied using MM5 and Weather Research and Forecasting (WRF) models Received: 06/05/2014 and comparison was made before selecting one for operational use over Western and Central Himalaya. Results show that the precipitation Revised: 24/05/2014 simulated by WRF is more realistic and close to the observations than MM5. WRF, being more capable and stronger in the numerical scheme, is Accepted: 25/05/2014 used for regular quantitative weather forecasting at SASE.

Mesoscale model, Western Disturbances, Data Assimilation, Keywords : Cloudburst.

1. Introduction extensively used to simulate and forecast precipitation During winter, Western Himalayan region is associated with the WDs. particularly prone to severe weather events due to the Snow and Avalanche Study Establishment has movement of synoptic systems known as Western been using MM5 mesoscale model for operational Disturbances (WDs). These WDs generally originate weather forecasting over Western Himalayan region from the extra tropical region picking up the moisture since 2002. Another mesoscale model named Weather from the Mediterranean Sea and Caspian Sea and move Research and Forecasting (WRF) is also being used by from west to east as low pressure systems and give Snow and Avalanche Study Establishment (SASE) for copious amounts of snowfall over Himalayan region operational weather forecasting over Western and during the winter months (November–April) (Asnani, Central Himalaya since 2009. United States Geological 2009). Because of the complex topography the Survey (USGS) land-use and topography data is used distribution of the snowfall amount varies from west to in the models to generate domain information. National east. Pirpanjal Range of the Western Himalaya receives Center for Medium Range Weather Forecasting more snowfall comparative to the other ranges like (NCMRWF) operational global initial and boundary Shamshawari, Greater Himalaya and Karakoram. In condition data is used for the forecasting purpose. In winter, the average frequency of these low-pressure this paper, the sequences of weather forecasting done systems is seven to eight per month over Pakistan and by using Numerical Weather Prediction (NWP) models India (Pant et al., 1997). Heavy snowfall and gale at SASE for last 10 years have been discussed. The winds associated with these WDs can cause snow performance of the MM5 model for precipitation avalanches. Accurate prediction of WDs and associated forecast over Western Himalaya, model output precipitation plays an important role in prediction of statistics for improving weather forecast and avalanches in snow bound areas of the Western and comparison study of MM5 and WRF is discussed so as Central Himalayan region. Various case studies of to choose the robust model for operational purpose. WDs have been discussed in the past by many authors Optimization and data assimilation in WRF for (e.g. Pisharoty et al., 1956; Rao et al., 1969; Kalsi, improving weather forecast over Western and Central 1980; Azadi et al., 2001; Hatwar et al., 2001; Das et Himalaya has also been discussed in the paper. In al., 2002; Das et al., 2003; Srinivasan et al., 2004; addition, preliminary study of the cloudburst event over Dimri et al., 2008). These days, mesoscale models are Leh (India) on Aug 05, 2010 is also carried out.

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models

2. Results and Discussions forecast predictors were made orthogonal and independent using the empirical orthogonal function 2.1 Performance of MM5 for winter (EOF) technique. Multiple linear regression analysis was then used to estimate the relationship between the precipitation forecast over Western Himalaya predictands which is to be predicted (the three variables Winter precipitation data from MM5 model at the 11 stations such as wind at 10m, precipitation outputs archived at SASE for the winter season 2004 to and temperature) and the principal components 2008 were used for the study. Fifteen locations over resulting from the EOF analysis. The multiple linear different ranges of Western Himalaya were selected. regression equation is given by Y = Ax . MOS Precipitation over / near model grids were extracted for technique is applied and tested on three trained data day 1, day 2, day 3, day 4 and day 5. For the past four sets from 2004-05, 2005-06 and 2006-07 and tested on winter seasons i.e. Nov 2004 to Apr 2005, Nov 2005 to an independent data set from 2007-08. Fig 2 shows the Apr 2006, Nov 2006 to Apr 2007 and Nov 2007 to Apr improvement of precipitation forecast after applying 2008, the model predicted precipitations and the MOS. The RMSE has decreased for all the stations respective observations were calculated. By after applying MOS. The RMSE is also less than the considering the four winter season's model predictions observed SD (Srinivasan et al., 2010). and observations, Root Mean Square Error (RMSE) was calculated for the selected 15 stations. The observed Standard Deviation (SD) was also computed. 2.2 Simulation of Severe Weather events over The root mean square error of day 1 to day 5 for each North West Himalaya station was compared with the observed standard Before implementing WRF model for deviation and is shown in Fig 1. Statistically, the operational weather forecasting at SASE, comparison performance of the model prediction is good when the study was made by using both MM5 and WRF models RMSE is less than the observed. Comparison was also for precipitation forecast over Western Himalaya so as made for different ranges of Western Himalaya such as to select the better model for weather forecasting over Pir Panjal, Shamshawari, Great Himalaya and both western and central Himalayas. Pennsylvania Karakoram ranges for day 1 to 5. The figure 1 shows State University (PSU) / National Center for that the RMSE is less than observed SD from day 1 to Atmospheric Research (NCAR) MM5 V3.6 model was 3. For day 4 and 5, the results vary slightly from used here to predict two cases of western disturbances stations to stations. The model performances are also and associated precipitation. The results were good for all the stations except for the stations in compared with another mesoscale model WRF. Two Karakoram Range. This implies that the performance cases of WDs occurred on 15-20 January 2008 and 1-6 of the model over the stations in Karakoram Range is January 2009 were selected. The precipitation (mm poor. This may be due to the fact that the model water equivalent) predicted by the two models for day orography, presence of glaciated terrain and overall 2 to 5 was chosen to compare with observed data over data sparse region are not truly represented in the different stations and is shown in Fig 3. Both MM5 and model. Similar is the case for other three winter WRF show wide spread precipitation for day 2, 3 and seasons during 2005-06, 2006-07 and 2007-08. To 4. However WRF simulated precipitation is more improve the precipitation forecast over different detailed and also closed to the observed precipitation stations of Western Himalaya, Model Output Statistics value. In the MM5 model, there are no conservation (MOS) is computed. Four stations of Karakoram Range properties; whereas in the WRF model, the were not considered in the forecast improvement study conservation of mass, momentum and entropy exist in due to poor performances of the model for these the prognostic equations. Some of the problems may be stations. Thus, 11 stations are considered to be realistic attributed to the dynamical framework of MM5, enough. Twenty-six parameters were extracted from especially the second-order advection scheme, which the model forecasts to be used as predictors to develop tends to produce spurious oscillations and requires the statistical equations. The 26 predictors numerical smoothing. The WRF model has a choice of corresponding to 24, 48 and 72 hour forecast length are a third or fifth-order advection scheme and is able to used to predict the parameters at respective forecast run without numerical smoothing to give improved lengths. The observed data (predictands) collected over results (Sujata et al., 2008). WRF being the latest in the 11 stations of SASE were used to develop the series of NWP models and is used by largest weather regression equations. and climate communities and is having more physics There are several approaches to the options, is chosen to be used by SASE for operational development of the statistical regressions (Klein et al., weather forecasting. 1959; Glahn et al., 1972). In this study, the selected 26

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd 72 Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models

Fig 1: Performance of MM5 mesoscale model for the year 2004 – 05 over different stations of Western Himalaya from Day 1 to Day 5.

Fig 2: Performance of Model Output Statistics on precipitation forecast over different station locations Western Himalaya

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd 73 Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models

MM 5 WRF

Fig 3: Precipitation as simulated by MM5 (left panel) and WRF (right panel) for a WD case.

2.3 Data assimilation in WRF and prediction of as to use the same for improving the initial and precipitation associated with WD boundary conditions of the model. The model's initial Indian Himalayas is referred as a data sparse condition is one of very important factors to impact on region. The lack of both surface and upper air data is a the model forecast error. Studies by several authors major problem in forecasting WD activities (Hatwar et (Tracton et al., 1980; Halem et al., 1982; Andersson et al., 2001) as the region that WDs traverse have a poor al., 1991; Mo et al., 1995; Derber et al., 1998; Bouttier data network. Therefore, the research community is et al., 2001) indicate that the weather forecasting can taking the help of numerical weather prediction models be improved by assimilating satellite radiance for this purpose. Satellite data can be used to fill those observations into a numerical weather prediction surface and upper air data gaps. Satellite radiance data (NWP) system. For example, the studies by Weng et al. give brightness temperature and one can compute the (2007) used satellite radiances from microwave temperature and water vapor profiles from these data so observations in a hurricane vortex analysis. They found

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd 74 Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models that there is improvement in the global forecast model monsoon season. During the monsoon season most of outputs after assimilating the radiances data in the the rain occurs over the valley regions because of the model. AMSU-A retrieved air temperatures have also interaction of the monsoon currents with the slopes. been used in the assimilation of 32 tropical cyclone When such an air mass containing enough moisture to cases by Xudong et al. (2007). In order to show a precipitate, saturates at a particular level, it gives heavy significant decrease in the track forecast errors. In precipitation at that specific location. Leh, situated on addition, they also confirmed that the assimilation of the banks of Indus river, on a riverside plain, is a semi- more satellite radiances data will improve the forecast arid region (34 009' N, 77 034' E) completely surrounded in prediction of a tropical cyclone. Kumar et al. (2014) by the high mountains in all the directions with very used GTS Satellite and observations from ARMEX-I to low monsoon-moisture content. Since, it does not improve initial condition through 3DVAR data receive much precipitation; this region is also referred assimilation system in WRF model to better reproduce as ‘Cold Desert’. the structure of convective organization as well as A ‘cloud burst’ is a localized weather prominent synoptic features associated with heavy phenomena representing highly concentrated rainfall rainfall events. The brightness temperature and over a small area (not exceeding 20 – 30 km 2) lasting humidity calculated from the Advanced Microwave for few hours. This leads to flash floods, landslides, Sounding Unit AMSU–A and AMSU–B on board the house collapses, dislocation of traffic and human latest generation of NOAA polar orbiting satellites casualties on a large scale. Cumulus convective clouds have been used in the present data assimilation in WRF are developed deeply on a localized area which has a model. The details of AMSU-A and B can be found in capability of giving enormous amount of rainfall over a Saunders et al. (1993). limited horizontal area, within a short span of time. It WRF model has been integrated for WD case represents cumulonimbus convection in conditions of from 13 to 15 January, 2011 with different initial marked moist thermodynamic instability and deep, conditions. The initial conditions for the simulations rapid dynamic lifting by steep orography (Someswar et are updated every 24 hrs and the model was integrated al., 2006). A severe cloud burst took place over Leh on for next 5 days. The forecast run without data 06 th August, 2010 at around 0130 – 0200 hrs Indian assimilation is referred as control run and that with data Standard Time (in the mid-night of 05 th August). A assimilation is referred as 3DVAR run hereafter. The heavy downpour occurred over the terrain slopes and model was run for three nested domain with 81, 27 and because of lack of vegetation and friction, the rain 9km horizontal resolutions. The model results without water along with the rocks and mud gushed through the and with data assimilation is shown in Figs 4 and 5 populated areas. As the event took place around respectively for the case 13 -15 January 2011. Results midnight, many people lost their lives and their show that the model precipitation forecast has properties. improved after the inclusion of radiance data in WRF. Cloud microphysical processes play an The precipitation simulated by model is compared with important role through direct influences on the cold the SASE observational data for few stations and is pool strength due to rainfall evaporation and latent shown in Fig 6. Results show that the model heating due to condensation (Rajeevan et al., 2010) . performance over 7 stations out of 12 improved for Sensitivity of cloud microphysics in predicting extreme precipitation forecasting on day 1. Experiments are heavy rainfall events has been done by many being designed to incorporate more satellite data and researchers (Deb et al., 2008; Kumar et al., 2008) using upper air observations over the Himalayas for different microphysics and cumulus parameterization improving accuracy in precipitation forecasting. Such schemes in WRF mesoscale model and MM5 improvement in weather forecast has opened new mesoscale model (Litta et al., 2007). A preliminary outlooks in mountain meteorology. Study of lither to study of Leh cloudburst of 06 Aug 2010 has been unpredictable events such as cloudbursts etc. can be carried out in this paper by using WRF model. IMD taken up using WRF and TRMM data. One such case observations show that the intense convective system study involving simulation of cloudburst event at Leh developed in the easterly current associated with in 2010 is discussed in succeeding paragraph. monsoon conditions over the region (Sravana et. al., 2010). The convective cloud band extending from 3. Numerical simulation of Leh southeast to northwest Himalayan region developed over Nepal and adjoining India in the afternoon of 5 th cloudburst event using WRF model August, 2010. It gradually intensified and moved in The complex orography of Himalayan west-north-west direction towards Jammu and Mountains in the northern states of India significantly Kashmir. An intense convective group of clouds influences the extreme events during the peak summer developed to the east of Leh by 2130 hrs IST of 5 th -

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd 75 Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models

Fig 4: Precipitation simulated by WRF model during control run for Day 1 to Day 4.

Fig 5: Same as figure 4 but during 3DVAR run.

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd 76 Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models

Fig 6: Comparison of WRF precipitation for control and 3DVAR run with SASE observational precipitation data

August. The cloudburst was highly localized, as the side of the vortex, no dominant wind speeds are seen as nearby air force Station meteorological observatory compared to the eastern side. Thus, WSM3 cloud reported only 12.8 mm of rainfall during 0530 hrs IST microphysics is able to simulate wind pattern close to of 5 th to 0530 hrs IST of 6 th August (Ashrit, 2010). the location of the actual occurrence of the cloud burst TRMM 3B42 satellite products (precipitation event over Leh in comparison to WSM6 micro physics. and rain rate) are used for comparison study and are Fig 11(a) and (b) depicts 3hr accumulated discussed besides the results from two microphysics rainfall for two cloud microphysics schemes WSM3 (WSM3 and WSM6) experiments. Fig 7 (a-b) shows and WSM6 respectively. Results show that there is a that, the three hours accumulated precipitation values spatial as well as temporal shifting of the localized from TRMM satellite during the two time intervals precipitation over the area where the event occurred. In (between 15hr to 18hr and 18hr to 21hr) are both of the experiments, the precipitation is captured comparable to each other. However, the rainfall rates three hours before the actual time of event. However, have almost doubled (Fig 8a and b). This might the amount of simulated precipitation during 1800 to triggered the heavy downpour or cloudburst event over 2100 UTC of 05 th Aug 2010 is around 70 mm for the region during 1800-210 hr. The vertical cross WSM6 and 35 mm for WSM3 respectively. The model section of meridional and vertical winds as simulated simulated precipitation for both the experiments is by the model for domain 4 at 1800 hrs (UTC) for both compared with the observed TRMM precipitation. the cloud microphysics schemes is shown in Fig 9 and Results show that precipitation simulated in WSM6 10 respectively. This is plotted over the latitude 33 0 N scheme (70 mm) is in good agreement with the TRMM varying the longitudes and heights. In the longitude- observed precipitation (90 mm). The shifting of the height cross section of the meridional wind (Fig 9), for precipitation in the model may be due to non WSM3 microphysics, the maximum magnitude of wind representation of the true orography in the model. The is 14 m/s at 400 hPa at a distance of 10 km east from Similar cloudburst experiment has also been conducted the vortex. However, in WSM6 microphysics, the by several researchers (Ashrit, 2010; Sravana et al., maximum magnitude of wind is 4 m/s at 250 hPa at a 2010) using the WRF model in high resolutions nested distance of 100 km east from the vortex. In the western

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd 77 Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models

(a )

itude

at L

(b )

itude at L

Longitud

Fig 7: Three hourly accumulated precipitation from TRMM during (a) 1500UTC to 1800 UTC and (b) 1800 UTC to 2100 UTC on August 05, 2010.

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd 78 Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models

(a)

Latitude

(b)

Latitude

Longitude

Fig 8: Same as figure 7 but for average rain rate (mm/hr).

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd 79 Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models

(a)

Pressure (hPa) Pressure

(b)

Pressure (hPa) Pressure

Longitude

Fig 9: Latitudinal–height cross-section of meridional wind at 1800 hrs for (a) WSM3 and (b) WSM6 cloud microphysics.

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd 80 Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models

(a)

Pressure (hPa) Pressure

(b)

Pressure (hPa) Pressure

Longitude Fig 10: Same as figure 9 but for vertical (w) wind component.

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd 81 Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models

(a)

(b)

Longitude

Fig 11: WRF simulated 3-hrly accumulated precipitation for (a) WSM3 and (b) WSM6 microphysics. domains. Their results also show underestimation of paper. The performance of the fifth generation National precipitation by WRF. Centre for Atmospheric Research (NCAR)/Penn State Mesoscale Model (MM5) for precipitation forecast 4. Conclusions over Western Himalaya, model output statistics for The sequences of weather forecasting done by improving weather forecast and comparison study of using Numerical Weather Prediction (NWP) models at MM5 and Weather Research and Forecast (WRF) SASE for last 10 years have been discussed in this model is discussed so as to choose the robust model for

International Journal of Earth and Atmospheric Science | July-September, 2014 | Vol 1 | Issue 2 | Pages 71-84 © 2014 Jakraya Publications (P) Ltd 82 Shekhar et al…Mountain Weather Research and Forecasting over Western and Central Himalaya by using Mesoscale Models operational purpose. Optimization and data events during 1800-2100 hr is well captured in the assimilation in WRF for improving weather forecast model and is in good agreement with the TRMM over Western and Central Himalaya has also been rainfall. However, the wind associated with the discussed in the paper. Preliminary study of recent phenomena is well captured by WSM3 scheme. WSM6 cloudburst over Leh during 05 Aug 2010 has also been microphysics scheme is supposed to be the advanced carried out. It can be concluded that the quantitative scheme in comparison to the WSM3 scheme and thus precipitation forecast generated by MM5 model shows supposed to simulate winds also closed to the observed good performances over Western Himalaya except values. Therefore, more experiments are being Karakoram Range due to poor representation of model designed to run WRF model with various combinations orography in the model and presence of glaciated of microphysics schemes and cumulus schemes so as to terrain and overall data sparse region in Karakoram. find out the best combination for both precipitation and MOS techniques have been applied to improve wind simulation close to the actual observation of the the precipitation forecast over station locations. The cloud burst event. RMSE decreased for all the stations after applying MOS. The RMSE is also less than the observed SD. Acknowledgements Comparison of MM5 and WRF precipitation The authors wish to thank Sh. Ashwagosha indicates that WRF simulated precipitation is more Ganju, Director, Snow and Avalanche Study detailed and also closed to the observed precipitation Establishment (DRDO), Chandigarh (India) for value and therefore, can be used for operational providing necessary data and Dr M R Bhutiyani, weather forecast at SASE for Western as well as Director, Defence Research and Development Central Himalayan region. WRF model precipitation Laboratory (DRDO), Delhi (India) for making valuable forecast is also improved after the inclusion of radiance suggestions for the study. Thanks are also due to data assimilation. NCEP/NCAR and TRMM whose products (MM5, The precipitation simulated by the cloudburst WRF models and precipitation figs.) have been used th event of 5 August 2010 is well captured in WRF for various studies in this paper. model with WSM6 microphysics scheme. The model results show that the actual initiation of the rainfall

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