HWRF Analysis and Forecast Impact of CYGNSS Observations Assimilated as Scalar Wind Speeds and as VAM Wind Vectors

• Other Authors: Annane, Bachir (author)
• Format: Others
• Language: English; English
• Published: Florida State University
• Subjects: Meteorology
• Online Access: http://purl.flvc.org/fsu/fd/2019_Fall_Annane_fsu_0071E_15536

After decades of focused research into tropical cyclone (TC) dynamics and evolution, operational centers are now able to predict TC track out to a lead time of five days with a high degree of accuracy. However, during this time, forecast skill for TC intensity has not kept the same pace. There are likely many reasons for this slowing improvement in TC intensity forecasts, but the one that is cited often in the community is a lack of frequent and accurate observations of winds in the inner core of TCs. Specifically, current satellite observing systems are unable to penetrate through heavy rainfall, and in situ measurements by aircraft and dropsondes are limited in space and time. The paucity of observations of surface wind speeds in the most dynamically active portion of a TC leads to (1) inaccuracies in the initial conditions used in subsequent model forecasts and (2) insufficient information for evaluating parameterizations of convection and surface fluxes. The NASA Cyclone Global Navigation Satellite System (CYGNSS) mission is designed to address these shortcomings by providing more accurate and timely observations of surface winds in all precipitation conditions. Eight micro-satellites launched in December 2016 (CYGNSS), providing an unprecedented opportunity to obtain ocean surface wind at increased revisit frequency compared to polar-orbiting satellites. Release 2.1 of the CYGNSS data contain improved wind speed quality and can be used to run data impact studies for the cases where the operational center had a weak intensity forecast. This study explores the expected benefits of this retrieved data to numerical simulations of tropical cyclones using two different data assimilation methods within the experimental framework of Observing System Simulation Experiments (OSSE) and Observing System Experiments (OSE). The goals of this study are three-fold: first, investigate the potential for CYGNSS to improve analyses and forecasts of tropical cyclones in an OSSE framework (pre-Launch); second, application of the variational analysis method (VAM) method on the CYGNSS data; third, evaluate the actual influence of assimilating CYGNSS data into NOAA’s operational hurricane model (Post-Launch). From a highly detailed and realistic hurricane nature run (NR), CYGNSS winds were simulated with error characteristics that are expected to occur in reality, and directional information is added using a two dimensional VAM for near-surface vector winds that blends simulated CYGNSS wind speeds with an a priori background vector wind field at 6-h analysis times. The OSSE system makes use of NOAA’s Hurricane Weather and Research Forecast (HWRF) model and Gridpoint Statistical Interpolation (GSI) data assimilation system in a configuration that was operational in 2012. CYGNSS winds were assimilated as scalar wind speeds and as wind vectors determined by a variational analysis method. Both forms of wind information had positive impacts on the short-term HWRF forecasts, as shown by key storm and domain metrics. Data assimilation cycle intervals of 1, 3, and 6 hours were tested, and the 3-h impacts were consistently best. The OSE quantifies the impact of assimilating both CYGNSS retrieved wind speed and derived CYGNSS wind vectors in tropical cyclone Michael (2018) on 6-hourly analyses and 5-day forecasts, using the 2019 version of the operational HWRF model. It is found that the assimilation of CYGNSS data results in improved track, intensity, and structure forecasts for both retrieved and derived CYGNSS data, implying the potential benefits of using such data for future research and operational applications. === A Dissertation submitted to the Department of Earth, Ocean, and Atmospheric Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy. === 2019 === October 30, 2019. === Includes bibliographical references. === Guosheng Liu, Professor Co-Directing Dissertation; Ruby Krishnamurti, Professor Co-Directing Dissertation; An-I Andy Wang, University Representative; Vasubandhu Misra, Committee Member; Mark Bourassa, Committee Member.