Articles written in Journal of Earth System Science

    • Retrieval of humidity and temperature profiles over the oceans from INSAT 3D satellite radiances

      C Krishnamoorthy Deo Kumar C Balaji

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      In this study, retrieval of temperature and humidity profiles of atmosphere from INSAT 3D-observed radiances has been accomplished. As the first step, a fast forward radiative transfer model using an Artificial neural network has been developed and it was proven to be highly effective, giving a correlationcoefficient of 0.97. In order to develop this, a diverse set of physics-based clear sky profiles of pressure (P), temperature (T) and specific humidity (q) has been developed. The developed database was further used for geophysical retrieval experiments in two different frameworks, namely, an ANN and Bayesianestimation. The neural network retrievals were performed for three different cases, viz., temperature only retrieval, humidity only retrieval and combined retrieval. The temperature/humidity only ANN retrievals were found superior to combined retrieval using an ANN. Furthermore, Bayesian estimation showed superior results when compared with the combined ANN retrievals.

    • A sensitivity study of WRF model microphysics and cumulus parameterization schemes for the simulation of tropical cyclones using GPM radar data


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      The present study focuses on determining the best combination of microphysics (MP) and cumulus parameterization (CP) schemes for the simulation of Tropical Cyclones (TCs) in the Indian subcontinent region, using the Weather Research and Forecasting (WRF) model. From the available schemes, four CP schemes, namely Kain–Fritsch, Betts–Miller–Janjic, New Simplified Arakawa–Schubert, and Grell–Devenyi, and four MP schemes, namely WSM6, Purdue Lin, Thompson, and Morrison, are selected for the sensitivity study. Seven TCs are simulated using all combinations of the chosen physics schemes. The simulated tracks and intensities are compared against the India Meteorological Department (IMD) observations. The results show that the Kain–Fritsch scheme, in combination with all MP schemes, predicts the tracks best among all the available CP schemes, but the performance of microphysics schemes is indistinguishable. A further study is conducted using the Global Precipitation Measurement (GPM) radar data to identify the best MP scheme by comparing the reflectivities. An existing radar simulator is modified to calculate the simulated reflectivities from the WRF model output corresponding to the MP schemes. The simulated reflectivities are compared against the GPM radar reflectivities, and the results show that the Thompson scheme reproduces the reflectivities closest to observations. The performance of the best set of schemes obtained from this study is compared with a random set of schemes for cyclone Bulbul, and the best set of schemes outperformed the random schemes in every aspect.

    • Assimilation of multi-channel radiances in mesoscale models with an ensemble technique to improve track forecasts of Tropical cyclones


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      This study focuses on the impact of direct assimilation of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Visible and Infrared Scanner (VIRS) channels radiances in the prediction of Tropical cyclones (TCs) in the Bay of Bengal (BOB) region. For this purpose, two TCs, viz., Jal and Thane are simulated by using the Weather Research and Forecasting (WRF) model. artificial Neural Network (ANN) based fast forward radiative transfer codes are developed for both the TMI and VIRS channels to speed up the simulation of radiances from vertical profiles of the atmosphere. For the WRF model initialization, initial ensembles are generated by perturbing atmospheric variables such as temperature (T, K), pressure (P, hpa), relative humidity (RH, %), meridional (U, m/s) and zonal winds (V, m/s) using Empirical Orthogonal function (EOF) technique. Further, each ensemble member is integrated up to a time that is close to the subsequent overpass of TRMM. Simulated profiles are obtained fromthe assimilated ensemble members which are used to generate the brightness temperatures through the fast ANN based fast forward radiative transfer codes. A Bayesian-based ensemble data assimilation technique is then developed for assimilating both the rainy and clear sky radiances, wherein the likelihoods are used to determine the conditional probabilities of all the candidates in the ensemble by comparing the TRMM observed radiances with the simulated radiances. Based on the posterior probability densities of eachmember of the ensemble, the initial conditions (ICs) at 00 UTC are corrected using a linear weighted average of initial ensembles for the all atmospheric variables. With these weighted average ICs, the WRF model is then executed all the way up to the required forecast period. Simulation results thus obtained with the assimilation are compared with the observations provided by the Joint TyphoonWarning Center (JTWC) and also the control run (i.e.,WRF simulations sans assimilation). The impact of assimilation of TMI and VIRS radiances (i) individually and (ii) simultaneously is elucidated.

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