M S SHEKHAR
Articles written in Journal of Earth System Science
Volume 128 Issue 8 December 2019 Article ID 0205 Research Article
An analog ensemble system was developed for the realisation of local-scale surface meteorological variables for independent test data (test data) at six stations over the north-west Himalaya (NWH), India. Extreme values (the maximum value and the minimum value) and the mean value in 10 analog days (the analog mean) and the climatological mean of each surface meteorological variable were compared with its corresponding observed values on the same day ($d0$, lead time 0 hour (h)), $d1 (d0 + 1$, lead time 24 h), $d2 (d0 + 2$, lead time 48 h) and $d3 (d0 + 3$, lead time 72 h) of test data. Pearson correlation coefficients(CCs), Mean Absolute Differences (MADs) and Root Mean Square Differences (RMSDs) of the extreme values in analog days, and the analog mean and climatological mean of each meteorological variable on $d0$ with its corresponding observed values on $d0, d1, d2$ and $d3$ of test data were computed at six stations over the NWH. CCs of extreme values in analog days and the analog mean of each meteorological variable on d0 with its observed values on $d0, d1, d2$ and $d3$ were found to be higher than the CCs of the climatological mean of each meteorological variable on d0 with its observed values on $d0, d1, d2$ and $d3$. MADs (RMSDs) of extreme values in analog days and the analog mean of each meteorological variable on $d0$ with its observed values on $d0, d1, d2$ and $d3$ were found to be lesser than the MADs (RMSDs) of the climatological mean of each meteorological variable on d0 with its observed values on $d0, d1, d2$ and $d3$. However, the MADs (RMSDs) of the extreme values of each meteorological variable in analog days were found to be higher than the MADs (RMSDs) of its analog mean. Results show that the analog mean of each meteorological variable holds better predictive skill than the extreme values in analog days and its climatological mean. MADs (RMSDs) of different surface meteorological variables in surface weather analogs comparable to Mean Absolute Errors (MAEs) and RootMean Square Errors (RMSEs) for their prediction with the help of different types of weather forecast models show that the surface weather analogs hold good promise for the local-scale prediction of surface meteorological variables over the NWH.
Volume 128 Issue 8 December 2019 Article ID 0207 Research Article
Spatio-temporal variability of binary weather patterns (precipitation event/no-precipitation event) and precipitation amounts of short time intervals of 15, 24, 48 and 72 hours (h) are examined by analysing data on the observed precipitation amount of 3377 common days of different winters (winter 1993–winter 2015) at 12 stations in the north-west Himalaya (NWH). Surface meteorological variables over the NWH are collected daily at 0300 and 1200 UTC and data on the precipitation amount collected daily at 0300 UTC are taken to conduct this study. Data on the precipitation amount collected at 0300 UTC daily represent the cumulative precipitation amount of a short time interval of the previous 15 h (1200–0300 UTC) hence the precipitation amount of the 15 h time interval is considered in addition to the precipitation amounts of 24, 48 and 72 h time intervals to examine the Spatio-temporal variability of the precipitation amounts at 12 stations over the NWH. The spatio-temporal variability in the binary weather patterns of short time intervals is examined by computing the normalised percentage differences in the observed precipitation events of short time intervals at 11 stations from corresponding observed precipitation events at a reference station and Spatio-temporal variability in the precipitation amount of short time interval at 12 stations is examined by computing Mean Absolute Differences (MADs) and Root Mean Square Differences (RMSDs) of observed precipitation amounts of short time intervals at each station from corresponding observed precipitation amounts at a reference station. Normalised percentage difference in precipitation events and MAD (RMSD) of the precipitation amount of 24 h time intervals at 11 stations from a reference station fall in the range $(-) 50.0%–(+) 20.7%$ and 4.2–7.2 mm (12.2–18.5 mm), respectively. The maximum difference in binary weather patterns is found for 24 h time interval and simultaneous precipitation events are not found up to 72 h time interval at 12 stations over the NWH. The spatial variability of binary weather patterns is found to decrease and the spatial variability of the precipitation amount is found to increase with the increasing length of short time intervals, i.e., 15–72 h. These findings show that binary weather patterns and precipitation amounts of short time intervals exhibit large Spatio-temporal variability over the NWH. Results of this study can be useful for various applications directly (or indirectly) influenced by weather and/or precipitation amounts of short time intervals over the NWH during the winter period.
Volume 129 All articles Published: 1 January 2020 Article ID 0030 Research Article
In recent past, rainfall-induced debris flow events in Ladakh–Nubra region have caused loss of lives and damages to civil infrastructures and army locations. Therefore, there is a need of high spatial and temporal monitoring of precipitation, and further to assess susceptible rainfall-induced debris flow zones in the area. We assessed the rainfall data collected at two gauge stations and observed a significant increase in the rainfall amount over the study region during summer-monsoonal period 1997–2017. Increasing trend was also observed from CRU gridded precipitation dataset. A GIS-based multi-criteria evaluation (MCE) method was performed by combining topographical, environmental and hydrological parameters for mapping of rainfall-induced susceptible zones. Suitability analysis of precipitation forecasts from WRF model at higher resolution (3 km) was also performed. A good agreement (r = 0.76) was observed between 4-day model forecast and field observed rainfall. Further, the simulated precipitation from WRF was incorporated into GIS model for assessment of debris flow susceptible zones for two cases of heavy precipitation events. The modelled high, medium, low and very low risk susceptible zones identified for the year 2015 events are validated with field survey and pre-post satellite imageries, and found in good agreement (ROC = 76.6%). The model was able to identify affected areas during the Leh cloud burst event in year 2010. In addition, a threshold value of rainfall for initiation of debris flow in the region was also reported.
Volume 129 All articles Published: 10 January 2020 Article ID 0040 Research Article
A localized extreme precipitation event occurred over Munsiyari (Uttarakhand, India) on 2nd July 2018 causing Cash floods, landslides and damage to the hydropower project. A preliminary study has been carried out by using Weather Research and Forecasting (WRF) model with three-dimensional variation data assimilation technique (3DVAR) to examine the feasibility of the model to predict the localized phenomena. Sensitivity experiments were carried out with two different microphysics in the model. Results show that P3 1-category plus double moment cloud water microphysics scheme with 3DVAR in WRF simulates the quantity of precipitation closer to the observed precipitation over Munsiyari. The vertical velocity and relative humidity were also simulated well during 3DVAR data assimilation as compared to without data assimilation over study region.
Volume 129, 2020
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