• Radhika Ramachandran

      Articles written in Journal of Earth System Science

    • Observations of the atmospheric surface layer parameters over a semi arid region during the solar eclipse of August 11th, 1999

      Praveena Krishnan P K Kunhikrishnan S Muraleedharan Nair Sudha Ravindran Radhika Ramachandran D B Subrahamanyam M Venkata Ramana

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      This paper discusses the observations of the Atmospheric Surface Layer (ASL) parameters during the solar eclipse of August 11th, 1999. Intensive surface layer experiments were conducted at Ahmedabad (23‡21′N, 72‡36′E), the western part of India, which was close to the totality path. This rare event provided by nature is utilised to document the surface layer effects during the eclipse period using measurements of high frequency fluctuations of temperature, tri-axial wind components as well as mean parameters such as temperature, humidity, wind speed and subsoil temperature. Analysis showed that during the eclipse period, the turbulence parameters were affected leading to the suppression of the turbulence process, the main dynamic process in the atmospheric boundary layer, while the mean parameters showed variations within the natural variability of the observational period. The spectra of the wind components and temperature indicated decrease in spectral power by one order in magnitude during the eclipse period. The rate of dissipation of turbulent kinetic energy is found to decrease by more than one order during the eclipse period. The stability parameter showed a change from unstable to stable condition during the period of eclipse and back to unstable condition by the end of eclipse

    • Prediction of heavy rainfall days over a peninsular Indian station using the machine learning algorithms


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      Advance prediction of heavy rainfall days over a given location is of paramount importance as heavy rainfall impacts ecosystems, leads to foods, accounts largely for the total rainfall over the region and its prediction is highly desired for the efficient management of weather-dependent activities. Traditionally, Numerical Weather Prediction models serve the purpose of weather predictions, but they have their constraints and limitations. In this regard, artificial intelligence and machine learning tools have gained popularity in recent years. In the present study, we have employed the Gaussian Process Regression (GPR) approach, one of the machine learning methods, on a long time-series rainfall data for the determination of heavy and light rainfall days. Climatological data of daily rainfall for a period of 116 years from 1901 to 2016 over Sriharikota (13.66°N, 80.23°E), a coastal island location in India, is used for training the GPR model for the identification of the heavy and light category of rainy days. The performance of the GPR model is investigated by predicting the heavy and light rainfall days per year over Sriharikota. K-nearest neighbour, random forest, and decision tree models are also used and results are compared. The validation of GPR results shows that the performance of the proposed model is satisfying (root mean square error = 0.161; mean absolute error = 0.126; mean squared error = 0.026), especially for the heavy rainfall days. Furthermore, GPR model is extended to prediction of spatial distribution of monthly rainfall over the Indian region. Results obtained from the present study encourages the utilization of the GPR model as one of the promising machine learning tools for the prediction of heavy rainfall days over a given location.

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