Appraisal of Data Assimilation Techniques for Dynamical Downscaling of the Structure and Intensity of Tropical Cyclones. Issue 2 (21st February 2020)

• Record Type: Journal Article
• Authors: Malakar, Pampi; Kesarkar, Amit P.; Bhate, Jyoti; Deshamukhya, Atri
• Is Part Of: Earth and space science. Volume 7:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-21
• Subjects: data assimilation -- hybrid data assimilation techniques -- tropical cyclone track verification -- tropical cyclone intensification -- tropical cyclone structure; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2019EA000945
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: The dynamical downscaling technique is used for the understanding of physical mechanisms associated with the atmospheric phenomena. We have developed high‐resolution analysis (6 km) for three tropical cyclones (TCs), namely, Phailin (2013), Nilofar (2014), and Chapala (2015) originated over the North Indian Ocean using the dynamical downscaling approach. The study aimed at the identification of appropriate methodology for generating analysis so that it becomes useful for identifying the role of environmental and internal dynamics on intensification processes and structural changes of TCs. The simulations using Weather Research and Forecasting model and four‐dimensional variational (4DVAR), hybrid three‐dimensional ensemble‐variational (3DEnVAR), and a hybrid four‐dimensional ensemble‐variational (4DEnVAR) data assimilation (DA) techniques are compared. The impact of DA is quantified by comparing errors in position, minimum sea level pressure, and maximum wind speed with the best track data set of India Meteorological Department. The intensities of TCs simulated by three downscaling methods are validated in terms of changes in minimum sea level pressure, maximum surface winds, and boundary layer and middle tropospheric relative humidity. The skills scores, namely, equitable threat score, false alarm ratio, the probability of detection, and biases (BIAS), are calculated to identify the best suitable DA technique. It is found that the hybrid DA techniques improve the overall quality of analysis compared to those developed using only variational DA techniques. The simulation using the hybrid 4DEnVAR DA technique is found to be better for simulation of the track, intensity changes, and structural characteristics of TCs. Plain Language Summary: The reanalysis data sets are useful for understanding the physical mechanisms associated with the formation and evolution of tropical cyclones. The high‐resolution reanalysis data sets are being generated using mesoscale models and advanced data assimilation techniques for this purpose. We have simulated three tropical cyclones, namely, Phailin (2013), Nilofar (2014), and Chapala (2015), originated over the North Indian Ocean to generate high‐resolution analysis using dynamical downscaling techniques. The simulations are carried out using the mesoscale Weather Research and Forecasting model. The applicability of 4DVAR, 3DEnVAR, and 4DEnVAR data assimilation techniques along with the Weather Research and Forecasting model for generating high‐resolution reanalysis data set for TCs structure and intensity prediction has been tested. In general, it is found that the 4DEnVAR technique is most suitable for the development of high‐resolution analysis, which in turn can be used to understand the consequences of internal dynamics and environmental factors in structure and intensity changes of the TCs. Key Points: Mesoscale downscaling is used for the generation of analysis for understanding physical mechanisms associated with tropical cyclone evolution Hybrid data assimilation techniques are composed of variational and ensemble Kalman filter‐based methods found to be most accurate 4DEnVAR is best among 4DVAR, 3DEnVAR, and 4DEnVar data assimilation techniques for the generating of analysis for tropical cyclones studied