Impact of AWS observations in WRF-3DVAR data assimilation system: a case study on abnormal warming condition in Odisha

S. Dash, Dipak Sahu () and S. Sahu

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2013, vol. 65, issue 1, 767-798

Abstract: An abnormal warming condition with 3–5 °C rise in temperature above its normal value was observed in the Indian state of Odisha during 12–16 November 2009. This study aims at examining the impact of additional weather observations obtained from the automatic weather stations (AWS) installed in the recent past on the numerical simulation of such abnormal warming. AWS observations, such as temperature at 2 m (T2m), dew point temperature at 2 m (Td2m), wind vector at 10 m (speed and direction), and sea level pressure (SLP) have been assimilated into the state-of-the-art Weather Research and Forecasting (WRF) model using the three-dimensional variational data assimilation (3DVAR). Six sets of experiments have been conducted here. There is no data assimilation in the control experiment, whereas AWS and radiosonde observations have been assimilated in rest of the five experiments. The model integrations have been made for 72 h in each experiment starting from 0000 UTC November 12 to 0000 UTC November 15, 2009. Assimilation experiments have also been performed to assess the impact of individual surface parameters on the model simulations. Impact of AWS observations on model simulation has been examined with reference to the control simulation and quantified in terms of root-mean-square error and forecast skill score for temperature, sea level pressure, and relative humidity at three selected stations Bonaigarh, Brahmagiri, and Nuapada in Odisha. Results indicate improvements in the surface air temperature and SLP simulations in the timescale of 72 h at all the three stations due to additional weather data assimilation into the model. Improvements in simulation are significant up to 24 h. The assimilation of additional wind fields significantly improved the temperature simulation at all the three stations. The simulated SLP has also improved significantly due to the assimilation of surface temperature and moisture. Copyright Springer Science+Business Media B.V. 2013

Keywords: Abnormal warming; Automatic weather stations (AWS); Data assimilation; Three-dimensional variational (3DVAR) technique (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s11069-012-0393-0

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