An update of the progress achieved as part of the NOAA Intensity Forecasting Experiment (IFEX) is provided. Included is a brief summary of the noteworthy aircraft missions flown in the years since 2005, the first year IFEX flights occurred, as well as a description of the research and development activities that directly address the three primary IFEX goals: 1) collect observations that span the tropical cyclone (TC) life cycle in a variety of environments for model initialization and evaluation; 2) develop and refine measurement strategies and technologies that provide improved real-time monitoring of TC intensity, structure, and environment; and 3) improve the understanding of physical processes important in intensity change for a TC at all stages of its life cycle. Such activities include the real-time analysis and transmission of Doppler radar measurements; numerical model and data assimilation advancements; characterization of tropical cyclone composite structure across multiple scales, from vortex scale to turbulence scale; improvements in statistical prediction of rapid intensification; and studies specifically targeting tropical cyclogenesis, extratropical transition, and the impact of environmental humidity on TC structure and evolution. While progress in TC intensity forecasting remains challenging, the activities described here provide some hope for improvement.

The Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS) is developed to assimilate tropical cyclone inner-core observations for high-resolution vortex initialization. It is based on a serial implementation of the square root ensemble Kalman filter (EnKF). In this study, HWRF is used in an experimental configuration with horizontal grid spacing of 9 (3) km on the outer (inner) domain. HEDAS is applied to 83 cases from years 2008 to 2011. With the exception of two Hurricane Hilary (2011) cases in the eastern North Pacific basin, all cases are observed in the Atlantic basin. Observed storm intensity for these cases ranges from tropical depression to category-4 hurricane.Overall, it is found that high-resolution tropical cyclone observations, when assimilated with an advanced data assimilation technique such as the EnKF, result in analyses of the primary circulation that are realistic in terms of intensity, wavenumber-0 radial structure, as well as wavenumber-1 azimuthal structure. Representing the secondary circulation in the analyses is found to be more challenging with systematic errors in the magnitude and depth of the low-level radial inflow. This is believed to result from a model bias in the experimental HWRF caused by the overdiffusive nature of the planetary boundary layer parameterization utilized. Thermodynamic deviations from the observed structure are believed to be caused by both an imbalance between the number of the kinematic and thermodynamic observations in general and the suboptimal ensemble covariances between kinematic and thermodynamic fields. Future plans are discussed to address these challenges.

Within the National Oceanic and Atmospheric Administration, the Hurricane Research Division of the Atlantic Oceanographic and Meteorological Laboratory has developed the Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS) to assimilate hurricane inner-core observations for high-resolution vortex initialization. HEDAS is based on a serial implementation of the square root ensemble Kalman filter. HWRF is configured with a horizontal grid spacing of 9/3 km on the outer/inner domains. In this preliminary study, airborne Doppler radar radial wind observations are simulated from a higher-resolution (4.5/1.5 km) version of the same model with other modifications that resulted in appreciable model error.A 24-h nature run simulation of Hurricane Paloma was initialized at 1200 UTC 7 November 2008 and produced a realistic, category-2-strength hurricane vortex. The impact of assimilating Doppler wind observations is assessed in observation space as well as in model space. It is observed that while the assimilation of Doppler wind observations results in significant improvements in the overall vortex structure, a general bias in the average error statistics persists because of the underestimation of overall intensity. A general deficiency in ensemble spread is also evident. While covariance inflation/relaxation and observation thinning result in improved ensemble spread, these do not translate into improvements in overall error statistics. These results strongly suggest a need to include in the ensemble a representation of forecast error growth from other sources such as model error.

ABSTRACT:In recent years, the Ensemble Transform Kalman Filter (ETKF) has been demonstrated to be useful for identifying a priori dynamically important locations for the placement of supplementary dropwindsonde observations aimed at improving short-range (1-3 day) forecasts of high-impact winter weather. In this paper, the ability of this strategy to predict the influence (or 'signal') of assimilating observations into the NCEP Global Forecast System for forecasts of 200 hPa wind up to 6 days is evaluated. Using a 50-member ECMWF ensemble, the ETKF was found to exhibit significantly higher skill than a seasonal 'climatology' of the important locations in predicting both (1) the spatial structure of signals within the storm track on a case-by-case basis; and (2) the variance of these signals over a 2-month period, within objectively chosen verification regions on synoptic scales. The verification region was selected by extracting the zonal envelope of the Rossby wave packet associated with the propagating ETKF signal variance. It is recommended that larger verification regions be used for longer lead times, due to the eastward expansion of the wave packet.The capability of the ETKF to predict signal variance out to 6 days was found to be dependent on the flow regime. The ETKF was most capable when the background flow was predominantly zonal, and least capable in instances where the observations were placed upstream of a blocking high over the north-eastern Pacific. Therefore, the ETKF is sometimes (but not always) able to predict when there exists significant potential for a particular group of observations to improve medium-range forecasts.

NOAA has been gathering high-resolution, flight-level dropwindsonde and airborne Doppler radar data in tropical cyclones for almost three decades; the U.S. Air Force routinely obtained the same type and quality of data, excepting Doppler radar, for most of that time. The data have been used for operational diagnosis and for research, and, starting in 2013, have been assimilated into operational regional tropical cyclone models. This study is an effort to quantify the impact of assimilating these data into a version of the operational Hurricane Weather Research and Forecasting model using an ensemble Kalman filter. A total of 83 cases during 2008-11 were investigated. The aircraft whose data were used in the study all provide high-density flight-level wind and thermodynamic observations as well as surface wind speed data. Forecasts initialized with these data assimilated are compared to those using the model standard initialization. Since only NOAA aircraft provide airborne Doppler radar data, these data are also tested to see their impact above the standard aircraft data. The aircraft data alone are shown to provide some statistically significant improvement to track and intensity forecasts during the critical watch and warning period before projected landfall (through 60 h), with the Doppler radar data providing some further improvement. This study shows the potential for improved forecasts with regular tropical cyclone aircraft reconnaissance and the assimilation of data obtained from them, especially airborne Doppler radar data, into the numerical guidance.