Impact of assimilating CYGNSS data on tropical cyclone analyses and forecasts in a regional OSSE framework

The impact of assimilating ocean surface wind observations from the Cyclone Global Navigation Satellite System (CYGNSS) is examined in a high-resolution Observing System Simulation Experiment (OSSE) framework for tropical cyclones (TCs). CYGNSS is a planned National Aeronautics and Space Administration constellation of microsatellites that utilizes existing GNSS satellites to retrieve surface wind speed. In the OSSE, CYGNSSwind speed data are simulated using output from a “nature run” as truth. In a case study using the regional Hurricane Weather Research and Forecasting modeling system and the Gridpoint Statistical Interpolation data assimilation scheme, analyses of TC position, structure, and intensity, together with large-scale variables, are improved due to the assimilation of the additional surface wind data. These results indicate the potential importance of CYGNSS ocean surface wind speed data and furthermore that the assimilation of directional information would add further value to TC analyses and forecasts.

[1]  Robert Atlas,et al.  A Multiyear Global Surface Wind Velocity Dataset Using SSM/I Wind Observations , 1996 .

[2]  Robert Atlas,et al.  Future Observing System Simulation Experiments , 2016 .

[3]  Stanley B. Goldenberg,et al.  Toward Improving High-Resolution Numerical Hurricane Forecasting: Influence of Model Horizontal Grid Resolution, Initialization, and Physics , 2012 .

[4]  Robert Atlas,et al.  Atmospheric Observations and Experiments to Assess Their Usefulness in Data Assimilation , 1997 .

[5]  Randall Rose,et al.  New Ocean Winds Satellite Mission to Probe Hurricanes and Tropical Convection , 2016 .

[6]  Robert Atlas,et al.  Development and validation of a hurricane nature run using the joint OSSE nature run and the WRF model , 2013 .

[7]  Ying-Hwa Kuo,et al.  Bridging research to operations transitions: Status and plans of community GSI , 2016 .

[8]  Deborah K. Smith,et al.  A Cross-calibrated, Multiplatform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications , 2011 .

[9]  Penina Axelrad,et al.  Retrieval of Ocean Surface Wind Speed and Wind Direction Using Reflected GPS Signals , 2004 .

[10]  Oreste Reale,et al.  Preliminary evaluation of the European Centre for Medium‐Range Weather Forecasts' (ECMWF) Nature Run over the tropical Atlantic and African monsoon region , 2007 .

[11]  Robert Atlas,et al.  Advances in Tropical Cyclone Intensity Forecasts , 2015 .

[12]  Z. Pu,et al.  An Observing System Simulation Experiment (OSSE) to Assess the Impact of Doppler Wind Lidar (DWL) Measurements on the Numerical Simulation of a Tropical Cyclone , 2010 .

[13]  David D. Parrish,et al.  GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments , 2013 .

[14]  Robert Atlas,et al.  Observing System Simulation Experiments (OSSEs) to Evaluate the Potential Impact of an Optical Autocovariance Wind Lidar (OAWL) on Numerical Weather Prediction , 2015 .

[15]  Robert Atlas,et al.  Observing System Simulation Experiments to Assess the Potential Impact of New Observing Systems on Hurricane Forecasting , 2015 .

[16]  Vijay Tallapragada,et al.  Community Support and Transition of Research to Operations for the Hurricane Weather Research and Forecasting Model , 2015 .

[17]  Z. Pu,et al.  LIDAR-MEASURED WIND PROFILES The Missing Link in the Global Observing System , 2014 .

[18]  Eric A. Hendricks,et al.  Evaluation of Multiple Dynamic Initialization Schemes for Tropical Cyclone Prediction , 2013 .

[19]  Robert Atlas,et al.  The Effects of Marine Winds from Scatterometer Data on Weather Analysis and Forecasting , 2001 .

[20]  J. Garrison,et al.  Effect of sea roughness on bistatically scattered range coded signals from the Global Positioning System , 1998 .