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Mountain Weather Research and Forecasting Over Western and Central Himalaya by Using Mesoscale Models

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Mountain Weather Research and Forecasting Over Western and Central Himalaya by Using Mesoscale Models INTERNATIONAL JOURNAL OF EARTH AND ATMOSPHERIC SCIENCE Journal homepage: www.jakraya.com/journal/ ijeas 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.
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