Impact of Assimilation of Satellite Retrieved Ocean Surface Winds on the Tropical Cyclone Simulations Over the North Indian Ocean. Issue 8 (9th August 2021)

• Record Type: Journal Article
• Authors: Bhate, Jyoti; Munsi, Arpita; Kesarkar, Amit; Kutty, Govindan; Deb, Sanjib K.
• Is Part Of: Earth and space science. Volume 8:Issue 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-09
• Subjects: data assimilation -- En3DVar -- ocean surface winds -- scatterometer -- tropical cyclone; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2020EA001517
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: The study intends to examine the impact of assimilation of ocean surface winds on the simulation of the tropical cyclones Ockhi, Mekunu, and Luban formed over the north Indian Oceans. We have conducted two assimilation experiments, cyclic control (CyclicCntl) and Hybrid, using the Weather Research and Forecast model. The CyclicCntl experiment and Hybrid experiment are the analysis generated without and with adding ocean surface winds, respectively. The ocean surface winds from Ocean Scatterometer, Advanced Scatterometer, and Windsat are assimilated using the ensemble three‐dimensional variational assimilation technique. The analysis generated by Hybrid and CyclicCntl is compared with the ocean surface winds observations from buoys, and it is found that the assimilation of ocean surface winds reduces the root mean square error between observation and analysis for zonal and meridional winds by 2–3 m s −1 (42%) compared to CyclicCntl simulations. The comparison with the International Best Track Archive for Climate Stewardship locations of cyclones derived from the analyses indicates that track error is maximum in genesis and landfall stage for both the Hybrid and CyclicCntl experiments, and the average track error is less than 80 km for the three cyclones. In a Hybrid experiment, during the intensifications from deep depression to the very severe cyclonic storm, the wind speed enhancement is dominant over the southeast and northwest sectors of the eyewall regions and subsequently in the rainband region. Thus, the assimilation of ocean surface winds from the three sensors improves the prediction of intensity and the evolution of tropical cyclones in general. Plain Language Summary: The study presents the impact of assimilation of ocean surface winds on the analysis of tropical cyclones (TCs) formed over the north Indian Ocean viz. Ockhi, Mekunu, and Luban. Ocean surface winds transfer heat, moisture, and momentum between ocean and atmosphere, and it is one of the essential parameters for TC evolution and movement. The ocean surface winds data are acquired from the three sensors viz. Advanced Scatterometer, Ocean Scatterometer, and Windsat. We have carried out two experiments with the Weather Research and Forecast (WRF) model. In the first experiment, cyclic control (CyclicCntl), generates analysis at each 6‐h interval without any data assimilation, while the Hybrid experiment generates a 6‐hourly analysis with data assimilation. The ocean surface winds retrieved from the above‐mentioned sensors are assimilated in the WRF model. The track and intensity generated using both the analyses are compared with International Best Track Archive for Climate Stewardship observations. The average track error is less than 80 km for all three cyclones. The intensity simulation is a challenging task in numerical prediction. The intensity simulation is better in the Hybrid experiment compared to CyclicCntl. During the intensifications from deep depression to the very severe cyclonic storm, the wind speed enhancement is dominant over the southeast and northwest sectors of the eyewall regions. Key Points: The assimilation of ocean surface winds reduces the root mean square error in zonal and meridional winds between analysis and observation by 2–3 m s −1 (42%) Assimilation of ocean surface winds improves the intensity simulation of the tropical cyclone During the intensification of the cyclone, the wind speed enhancement is dominant over the southeast and northwest sectors of the eyewall regions