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

• Published in: Journal of Earth System Science 2022-06, Vol.131 (2), p.83, Article 83
• Main Authors: Chandrasekar, R, Sahu, Reetik Kumar, Balaji, C
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Atmospheric models; Bayesian analysis; Brightness temperature; Channels; Clear sky; Conditional probability; Cyclones; Data assimilation; Data collection; Earth and Environmental Science; Earth Sciences; General circulation models; Hurricanes; Infrared scanners; Initial conditions; Mathematical functions; Mathematical models; Mesoscale models; Neural networks; Orthogonal functions; Precipitation; Probability theory; Radiative transfer; Rain; Rainfall; Relative humidity; Simulation; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Surface radiation temperature; TRMM satellite; Tropical cyclone forecasting; Tropical cyclones; Tropical rainfall; Tropical Rainfall Measuring Mission (TRMM); Typhoon warnings; Typhoons; Vertical profiles; Weather forecasting; Winds; Zonal winds
• ISSN: 2347-4327; 0253-4126; 0973-774X
• DOI: 10.1007/s12040-021-01798-6

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 from the 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 each member 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 Typhoon Warning 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.