Simulation of weather systems over Indian region using mesoscale models

Vaidya, Shubhangi

doi:10.1007/s00703-006-0188-5

Cited by 24 publications

(17 citation statements)

References 14 publications

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“…Simulated and observed precipitation and cloud distributions agreed well when applying WRF for simulating a pre-monsoon thunderstorm over the east coast of India and a weak cyclonic circulation associated with low-pressure over south India [12].…”

Section: Introductionmentioning

confidence: 48%

Evaluation of WRF-Forecasts Over Siberia: Air Mass Formation, Clouds and Precipitation

PaiMazumder¹,

Henderson²,

Mölders³

2012

TOASCJ

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Abstract:The Weather Research and Forecasting (WRF) model was run as a regional model without data assimilation or nudging (31 36h-simulations) for July and December 2005 over a limited area domain covering Siberia to examine weather formation in an air-mass source region. The WRF-results were compared to NCEP1/NCAR-reanalysis, International Satellite Cloud Climatology Project, Global Precipitation Climatology Centre and Canadian Meteorological Centre data to assess model performance and identify shortcomings. WRF is capable of predicting air-mass formation. Simulation errors are within the error range of other models. The timing of best/worst agreement differs among quantities depending on their sensitivity to systematic (model deficiencies) and/or unsystematic errors (e.g. initial conditions). Overall, the WRF-results agree better with reanalysis for July than December. WRF-results and reanalysis agree best under persistent high pressure and worst during frontal passages and transition from one pressure regime to another. In July, WRF provides smaller diurnal amplitudes of 2m-temperature with up to 5.4 K lower, and 3.5 K higher values at 0000 and 1200 UTC than the reanalysis. In December, WRF overestimates 2m-temperature by 1.4 K. WRF-temperatures excellently agree with the reanalysis from 700 hPa to 300 hPa. Except during frontal passages, wind-speed shows positive bias. Typically root-mean-square errors and standard deviation of errors of wind-speed (temperature) increase (decrease) with height. In December, WRF has difficulty predicting the position and strength of the polar jet. WRF underestimates cloudiness and snow-depth, but overestimates precipitation. In July, predicted convective precipitation is related strongly to boundaries between different land-cover. WRF-predicted snow-depth strongly correlates with terrain and misses the observed fine features.

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Section: Introductionmentioning

confidence: 48%

Evaluation of WRF-Forecasts Over Siberia: Air Mass Formation, Clouds and Precipitation

PaiMazumder¹,

Henderson²,

Mölders³

2012

TOASCJ

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“…Most of the thunderstorms produce heavy rainfall during their life cycle of 1-3 h (Vaidya, 2007). The 3-hourly model-simulated accumulated rainfall between 0900 and 1200 UTC on 15 August 2007 is plotted in Figure 11(a).…”

Section: Resultsmentioning

confidence: 99%

“…An accurate, location specific and timely prediction is required to avoid loss of lives and property due to strong winds and heavy precipitation associated with such weather systems. Understanding the dynamics and physics of isolated heavy precipitation and other typical dynamical features associated with storm systems, such as squall lines, tornadoes and strong convection embedded in synoptic scale systems, is essential for better prediction (Vaidya, 2007). However, mesoscale research and forecasting in India could not keep pace with developments of the post-1970 period, especially in respect of mesoscale observational techniques, Doppler Weather Radar (DWR), wind profilers, mesonetwork, mesoscale analysis and mesoscale numerical weather prediction (Tyagi, 2000).…”

Section: Introductionmentioning

confidence: 99%

“…However, mesoscale research and forecasting in India could not keep pace with developments of the post-1970 period, especially in respect of mesoscale observational techniques, Doppler Weather Radar (DWR), wind profilers, mesonetwork, mesoscale analysis and mesoscale numerical weather prediction (Tyagi, 2000). In India, studies related to modelling of clouds are scarce, especially those involving intense thunderstorm events (Das, 1999), although simulation of active mesoscale systems such as tropical cyclones, heavy rainfall and severe thunderstorms with high-resolution mesoscale models has been attempted by many Indian researchers recently (Mohanty et al, 2004;Litta et al, 2007;Vaidya, 2007;Litta and Mohanty, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Numerical simulation of a tornado over Ludhiana (India) using WRF‐NMM model

Litta

Mohanty

Bhan

2009

Meteorological Applications

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ABSTRACT:A severe thunderstorm produced a tornado (F0 on the Fujita-Pearson scale) close to Ludhiana airport (Punjab), northwest region of India (30.80°N, 76.05°E) on 15 August 2007. Relatively less damage occurred as it passed through the open fields, but there were minor injuries to some cattle and damage to property. In this paper, analysis of meteorological conditions that led to the tornado on 15 August 2007 at 1100 UTC in Ludhiana has been made. An attempt is also made to simulate this rare event using the Non-hydrostatic Mesoscale Model (NMM) core of the Weather Research and Forecasting (WRF) system with a spatial resolution of 3 km for a period of 24 h, starting at 0000 UTC of 15 August 2007. The results are encouraging in terms of timing and location of occurrence of the event. The atmospheric settings as indicated by surface, upper air and radar echo studies are favourable for severe thunderstorm activity over Ludhiana. The simulated instability indices (lifted index, total totals index and CAPE) over Ludhiana during the tornado hour, which act as indicators of severe convective activity, have been found to be in good agreement with the actual analysis. The model-simulated meteorological parameters, relative humidity, mean sea level pressure, rainfall, moisture convergence, pressure vertical velocity and surface wind speed, are favourable for tornado occurrence at 1100 UTC over Ludhiana, and agree reasonably well with the observations.

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“…The physics and the dynamics of TS can be understood by simulating these systems with the help of mesoscale models. Simulation of TS with highresolution mesoscale models over the Indo-Bangla region has mainly been attempted in the past ten years [3][4][5][6][7][8][9][10][11]. A simulation study was carried out by Vaidya [3] for a pre-monsoon thunderstorm over east coast of India.…”

Section: Introductionmentioning

confidence: 99%