A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

Xuanli Li,John R Mecikalski,Timothy J Lang

doi:10.3390/rs12081243

Abstract

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a ±35° latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated.

Highlights

Owing to better global analyses and advances in numerical models and physics parameterizations, tropical cyclone (TC) forecasts have improved steadily in the past two decades (See, e.g., [1,2])
Cyclone Global Navigation Satellite System (CYGNSS) uses the global positioning system (GPS) “L1” channel, which suffers no appreciable attenuation, even under heavy rainfall conditions. This allows CYGNSS to retrieve ocean surface winds for a variety of tropical phenomena, e.g., the tropical cyclone core region and deep convection associated with the Madden–Julian Oscillation (MJO; [11,12]), with a level of accuracy that was previously impossible in practice
The application of CYGNSS v2.1 Level 2 data was examined in a numerical simulation of TC Irving and a westerly wind burst (WWB) event that occurred during the 2018 January MJO event

Summary

Introduction

Owing to better global analyses and advances in numerical models and physics parameterizations, tropical cyclone (TC) forecasts have improved steadily in the past two decades (See, e.g., [1,2]). Especially the surface wind from scatterometers, can provide information about the location and intensity of tropical cyclones and tropical convections These observation systems [e.g., Quick Scatterometer—QuikSCAT, Advanced Scatterometer—ASCAT, and OceanSat-2 Scatterometer—OSCAT] suffer from severe limitations under extreme conditions (e.g., heavy precipitation, strong winds > 35 m s−1, etc.), and long revisit times (~12+ h) limit their usefulness in detecting rapid changes in intensity and location [5,6]. CYGNSS uses the GPS “L1” channel, which suffers no appreciable attenuation, even under heavy rainfall conditions This allows CYGNSS to retrieve ocean surface winds for a variety of tropical phenomena, e.g., the tropical cyclone core region and deep convection associated with the Madden–Julian Oscillation (MJO; [11,12]), with a level of accuracy that was previously impossible in practice. It is expected that the CYGNSS will be able to accurately monitor the rapidly changing wind of precipitating regions over the tropics (e.g., [13]), and to improve forecasting operations (e.g., [14])

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Conclusion