Search Results

You are looking at 1 - 9 of 9 items for

  • Author or Editor: Kathryn J. Sellwood x
  • Refine by Access: All Content x
Clear All Modify Search
Kathryn J. Sellwood
,
Jason A. Sippel
, and
Altŭg Aksoy

Abstract

This study presents an initial demonstration of assimilating small uncrewed aircraft system (sUAS) data into an operational model with a goal to ultimately improve tropical cyclone (TC) analyses and forecasts. The observations, obtained using the Coyote sUAS in Hurricane Maria (2017), were assimilated into the operational Hurricane Weather Research and Forecast (HWRF) system as they could be in operations. Results suggest that the Coyote data can benefit HWRF forecasts. A single-cycle case study produced the best results when the Coyote observations were assimilated at greater horizontal resolution with more relaxed quality control (QC) than comparable flight-level high-density observations currently used in operations. The case study results guided experiments that cycled HWRF for a roughly 4-day period that covered all Coyote flights into Maria. The cycled experiment that assimilated the most data improved initial inner-core structure in the analyses and better agreed with other aircraft observations. The average errors in track and intensity decreased in the subsequent forecasts. Intensity forecasts were too weak when no Coyote data were assimilated, and assimilating the Coyote data made the forecasts stronger. Results also suggest that a symmetric distribution of Coyote data around the TC center is necessary to maximize its benefits in the current configuration of operational HWRF. Although the sample size was limited, these experiments provide insight for potential operational use of data from newer sUAS platforms in future TC applications.

Significance Statement

This study represents the first time that observations from a small uncrewed aircraft system (sUAS) have been assimilated into an operational numerical model. Including these data was shown to have potential for improving forecasts of tropical cyclone track and intensity. The data were obtained using the Coyote sUAS, but these results are expected to be applicable to newer platforms that will be operational soon.

Restricted access
Sim D. Aberson
,
Kathryn J. Sellwood
, and
Paul A. Leighton

Abstract

Current practice is to transmit dropwindsonde data from aircraft using the TEMP-DROP format, which provides only the release location and time with 0.1° latitude × 0.1° longitude (about 11 km) and 1-h resolutions, respectively. The current dropwindsonde has a fall speed of approximately 15 m s−1, so the instrument will be advected faster horizontally than it will descend if the wind speed exceeds this value. Where wind speeds are greatest, such as in tropical cyclones, this will introduce large errors in the location of the observations, especially near the surface. A technique to calculate the correct time and location of each observation in the TEMP-DROP message is introduced. The mean differences between the calculated and reported locations are about 0.5 km for distance and 15 s for time, or <1% of the error size for distance and <10% for time.

Full access
Sharanya J. Majumdar
,
Kathryn J. Sellwood
,
Daniel Hodyss
,
Zoltan Toth
, and
Yucheng Song

Abstract

The characteristics of “target” locations of tropospheric wind and temperature identified by a modified version of the ensemble transform Kalman filter (ETKF), in order to reduce 0–7-day forecast errors over North America, are explored from the perspective of a field program planner. Twenty cases of potential high-impact weather over the continent were investigated, using a 145-member ensemble comprising perturbations from NCEP, ECMWF, and the Canadian Meteorological Centre (CMC).

Multiple targets were found to exist in the midlatitude storm track. In half of the cases, distinctive targets could be traced upstream near Japan at lead times of 4–7 days. In these cases, the flow was predominantly zonal and a coherent Rossby wave packet was present over the northern Pacific Ocean. The targets at the longest lead times were often located within propagating areas of baroclinic energy conversion far upstream. As the lead time was reduced, these targets were found to diminish in importance, with downstream targets corresponding to a separate synoptic system gaining in prominence. This shift in optimal targets is sometimes consistent with the radiation of ageostrophic geopotential fluxes and transfer of eddy kinetic energy downstream, associated with downstream baroclinic development. Concurrently, multiple targets arise due to spurious long-distance correlations in the ETKF. The targets were least coherent in blocked flows, in which the ETKF is known to be least reliable. The effectiveness of targeting in the medium range requires evaluation, using data such as those collected during the winter phase of The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Field Campaign (T-PARC) in 2009.

Full access
Sim D. Aberson
,
Jun A. Zhang
,
Jonathan Zawislak
,
Kathryn Sellwood
,
Robert Rogers
, and
Joseph J. Cione

Abstract

The global positioning system dropwindsonde has provided thousands of high-resolution kinematic and thermodynamic soundings in and around tropical cyclones (TCs) since 1997. These data have revolutionized the understanding of TC structure, improved forecasts, and validated observations from remote sensing platforms. About 400 peer-reviewed studies on TCs using these data have been published to date. This paper reviews the history of dropwindsonde observations, changes to dropwindsonde technology since it was first used in TCs in 1982, and how the data have improved forecasting and changed our understanding of TCs.

Open access
Altuğ Aksoy
,
Sim D. Aberson
,
Tomislava Vukicevic
,
Kathryn J. Sellwood
,
Sylvie Lorsolo
, and
Xuejin Zhang

Abstract

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.

Full access
Tomislava Vukicevic
,
Altuğ Aksoy
,
Paul Reasor
,
Sim D. Aberson
,
Kathryn J. Sellwood
, and
Frank Marks

Abstract

In this study the properties and causes of systematic errors in high-resolution data assimilation of inner-core tropical cyclone (TC) observations were investigated using the Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS). Although a recent study by Aksoy et al. demonstrated overall good performance of HEDAS for 83 cases from 2008 to 2011 using airborne observations from research and operational aircraft, some systematic errors were identified in the analyses with respect to independent observation-based estimates. The axisymmetric primary circulation intensity was underestimated for hurricane cases and the secondary circulation was systematically weaker for all cases. The diagnostic analysis in this study shows that the underestimate of primary circulation was caused by the systematic spindown of the vortex core in the short-term forecasts during the cycling with observations. This tendency bias was associated with the systematic errors in the secondary circulation, temperature, and humidity. The biases were reoccurring in each cycle during the assimilation because of the inconsistency between the strength of primary and secondary circulation during the short-term forecasts, the impact of model error in planetary boundary layer dynamics, and the effect of forecast tendency bias on the background error correlations. Although limited to the current analysis the findings in this study point to a generic problem of mutual dependence of short-term forecast tendency and state estimate errors in the data assimilation of TC core observations. The results indicate that such coupling of errors in the assimilation would also lead to short-term intensity forecast bias after the assimilation for the same reasons.

Full access
Sim D. Aberson
,
Altuğ Aksoy
,
Kathryn J. Sellwood
,
Tomislava Vukicevic
, and
Xuejin Zhang

Abstract

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.

Full access
Altuğ Aksoy
,
Sylvie Lorsolo
,
Tomislava Vukicevic
,
Kathryn J. Sellwood
,
Sim D. Aberson
, and
Fuqing Zhang

Abstract

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 km on the outer/inner domains. In this preliminary study, airborne Doppler radar radial wind observations are simulated from a higher-resolution 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.

Full access
Jonathan Zawislak
,
Robert F. Rogers
,
Sim D. Aberson
,
Ghassan J. Alaka Jr.
,
George R. Alvey III
,
Altug Aksoy
,
Lisa Bucci
,
Joseph Cione
,
Neal Dorst
,
Jason Dunion
,
Michael Fischer
,
John Gamache
,
Sundararaman Gopalakrishnan
,
Andrew Hazelton
,
Heather M. Holbach
,
John Kaplan
,
Hua Leighton
,
Frank Marks
,
Shirley T. Murillo
,
Paul Reasor
,
Kelly Ryan
,
Kathryn Sellwood
,
Jason A. Sippel
, and
Jun A. Zhang

Abstract

Since 2005, NOAA has conducted the annual Intensity Forecasting Experiment (IFEX), led by scientists from the Hurricane Research Division at NOAA’s Atlantic Oceanographic and Meteorological Laboratory. They partner with NOAA’s Aircraft Operations Center, who maintain and operate the WP-3D and Gulfstream IV-SP (G-IV) Hurricane Hunter aircraft, and NCEP’s National Hurricane Center and Environmental Modeling Center, who task airborne missions to gather data used by forecasters for analysis and forecasting and for ingest into operational numerical weather prediction models. The goal of IFEX is to improve tropical cyclone (TC) forecasts using an integrated approach of analyzing observations from aircraft, initializing and evaluating forecast models with those observations, and developing new airborne instrumentation and observing strategies targeted at filling observing gaps and maximizing the data’s impact in model forecasts. This summary article not only highlights recent IFEX contributions toward improved TC understanding and prediction, but also reflects more broadly on the accomplishments of the program during the 16 years of its existence. It describes how IFEX addresses high-priority forecast challenges, summarizes recent collaborations, describes advancements in observing systems monitoring structure and intensity, as well as in assimilation of aircraft data into operational models, and emphasizes key advances in understanding of TC processes, particularly those that lead to rapid intensification. The article concludes by laying the foundation for the next generation of IFEX as it broadens its scope to all TC hazards, particularly rainfall, storm-surge inundation, and tornadoes, that have gained notoriety during the last few years after several devastating landfalling TCs.

Full access