Search Results

You are looking at 1 - 10 of 45 items for

  • Author or Editor: Ryan D. Torn x
  • All content x
Clear All Modify Search
Ryan D. Torn

Abstract

The dynamical mechanisms that led to downstream ridging during the extratropical transition (ET) of Typhoons Tokage and Nabi are evaluated using data drawn from a cycling ensemble Kalman filter coupled with the Weather Research and Forecasting Model (WRF). During both transitions, the ensemble covariances indicate that the 350-K potential vorticity (PV) at the apex of the ridge, which is used to define the ridge structure, is proportional to the amount of precipitation along the baroclinic zone to the northeast of the tropical cyclone (TC), and at some times to the upper-tropospheric divergence above the tropical cyclone. Multivariate regression calculations indicate that the frontal precipitation has the largest impact on the ridge amplitude and area during Tokage’s transition, while the TC divergence has roughly equal impact during some times of Nabi’s transition. The amount of precipitation along the baroclinic zone is modulated by the lower-tropospheric frontogenesis and moisture flux on the east side of the tropical cyclone, both of which are related to the TC winds. Although both of these metrics covary with the PV at the ridge apex, a one standard deviation perturbation to the moisture flux is associated with a larger change in the ridge PV.

Diagnostic perturbations to the initial conditions confirm that increasing (decreasing) the initial moisture flux leads to comparatively lower (higher) PV at the ridge apex 12 h later. Assimilating a single hypothetical wind or moisture observation within the large moisture flux region leads to a 0.3 standard deviation change in the 12-h PV forecast when the observation innovation is comparable to the observation error. Overall, these results suggest that better wind and moisture analyses at the periphery of the TC could improve forecasts of the downstream ridging during ET.

Full access
Ryan D. Torn

Abstract

The impact of the extratropical transition (ET) of tropical cyclones and baroclinic cyclogenesis in the western North Pacific (WNP), Atlantic, and southern Indian Ocean (SIO) basins on the predictability of the downstream midlatitude flow is assessed using 30 years of cases from the Global Ensemble Forecast System (GEFS) Reforecast, version 2. In all three basins, ET is associated with statistically larger 500-hPa geopotential height forecast standard deviation (SD) compared to the forecast climatology. The higher SD values originate from where the TC enters the midlatitudes and spread downstream at the group velocity of the associated wave packet. Of the three basins, WNP ET is associated with the largest amplitude and longest-lasting SD anomalies. Forecasts initialized 2–4 days prior to the onset of ET have larger SD anomalies compared to forecasts initialized during or after the onset of ET. By contrast, the region of positive SD anomaly associated with winter baroclinic cyclones is confined to the upstream trough, with fall cyclones exhibiting some downstream propagation characteristics similar to ET. The ET cases with the larger downstream SD anomaly are characterized by a more amplified ridge downstream of the TC as it enters the midlatitudes. By contrast, ET cases with an upstream trough, large TC position variability at the onset of ET, latent heat release, or upper-tropospheric PV advection by the irrotational wind are not characterized by significantly larger downstream SD compared to cases without an upstream trough or smaller values of these quantities.

Full access
Ryan D. Torn

Abstract

An ensemble Kalman filter (EnKF) combined with the Advanced Research Weather Research and Forecasting model (ARW-WRF; hereafter WRF) on a 36-km Atlantic basin domain is cycled over six different time periods that include the 10 tropical cyclones (TCs) selected for the NOAA High-Resolution Hurricane (HRH) test. The analysis ensemble is generated every 6 h by assimilating conventional in situ observations, synoptic dropsondes, and TC advisory position and minimum sea level pressure (SLP) data. On average, observation assimilation leads to smaller TC position errors in the analysis compared to the 6-h forecast; however, the same is true for TC minimum SLP only for tropical depressions and storms. Over the 69 HRH initialization times, TC track forecasts from a single member of the WRF EnKF ensemble has 12 h less skill compared to other operational models; the increased track error partially results from the WRF EnKF analysis having a stronger Atlantic subtropical ridge. For nonmajor TCs, the WRF EnKF forecast has lower TC minimum SLP and maximum wind speed errors compared to some operational models, particularly the GFDL model, while category-3, -4, and -5 TCs are characterized by large biases due to horizontal resolution. WRF forecasts initialized from an EnKF analysis have similar or smaller TC track, intensity, and 34-kt wind radii errors relative to those initialized from two other operational analyses, which suggests that EnKF assimilation produces the best TC forecasts for this domain. Both TC track and intensity forecasts are deficient in ensemble variance, which is at least partially due to the lack of error growth in dynamical fields and model biases.

Full access
Ryan D. Torn

Abstract

The value of assimilating targeted dropwindsonde observations meant to improve tropical cyclone intensity forecasts is evaluated using data collected during the Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) field project and a cycling ensemble Kalman filter. For each of the four initialization times studied, four different sets of Weather Research and Forecasting Model (WRF) ensemble forecasts are produced: one without any dropwindsonde data, one with all dropwindsonde data assimilated, one where a small subset of “targeted” dropwindsondes are identified using the ensemble-based sensitivity method, and a set of randomly selected dropwindsondes. For all four cases, the assimilation of dropwindsondes leads to an improved intensity forecast, with the targeted dropwindsonde experiment recovering at least 80% of the difference between the experiment where all dropwindsondes and no dropwindsondes are assimilated. By contrast, assimilating randomly selected dropwindsondes leads to a smaller impact in three of the four cases. In general, zonal and meridional wind observations at or below 700 hPa have the largest impact on the forecast due to the large sensitivity of the intensity forecast to the horizontal wind components at these levels and relatively large ensemble standard deviation relative to the assumed observation errors.

Full access
Ryan D. Torn

Abstract

Tropical cyclone (TC) intensity forecasts are impacted by errors in atmosphere and ocean initial conditions and the model formulation, which motivates using an ensemble approach. This study evaluates the impact of uncertainty in atmospheric and oceanic initial conditions, as well as stochastic representations of the drag C d and enthalphy C k exchange coefficients on ensemble Advanced Hurricane WRF (AHW) TC intensity forecasts of multiple Atlantic TCs from 2008 to 2011. Each ensemble experiment is characterized by different combinations of either deterministic or ensemble atmospheric and/or oceanic initial conditions, as well as fixed or stochastic representations of C d or C k. Among those experiments with a single uncertainty source, atmospheric uncertainty produces the largest standard deviation in TC intensity. While ocean uncertainty leads to continuous growth in ensemble standard deviation, the ensemble standard deviation in the experiments with C d and C k uncertainty levels off by 48 h. Combining atmospheric and oceanic uncertainty leads to larger intensity standard deviation than atmosphere or ocean uncertainty alone and preferentially adds variability outside of the TC core. By contrast, combining C d or C k uncertainty with any other source leads to negligible increases in standard deviation, which is mainly due to the lack of spatial correlation in the exchange coefficient perturbations. All of the ensemble experiments are deficient in ensemble standard deviation; however, the experiments with combinations of uncertainty sources generally have an ensemble standard deviation closer to the ensemble-mean errors.

Full access
Ryan D. Torn

Abstract

An ensemble Kalman filter (EnKF) coupled to the Advanced Research version of the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts of a strong African easterly wave (AEW) during the African Monsoon Multidisciplinary Analysis field campaign. Ensemble sensitivity analysis is then used to evaluate the impacts of initial condition errors on AEW amplitude and position forecasts at two different initialization times.

WRF forecasts initialized at 0000 UTC 8 September 2006, prior to the amplification of the AEW, are characterized by large variability in evolution as compared to forecasts initialized 48 h later when the AEW is within a denser observation network. Short-lead-time amplitude forecasts are most sensitive to the midtropospheric meridional winds, while at longer lead times, midtropospheric θe errors have equal or larger impacts. For AEW longitude forecasts, the largest sensitivities are associated with the θe downstream of the AEW and, to a lesser extent, the meridional winds. Ensemble predictions of how initial condition errors impact the AEW amplitude and position compare qualitatively well with perturbed integrations of the WRF model.

Much of the precipitation associated with the AEW is generated by the Kain–Fritsch cumulus parameterization, thus the initial-condition sensitivities are also computed for ensemble forecasts that employ the Betts–Miller–Janjić and Grell cumulus parameterization schemes, and for a high-resolution nested domain with explicit convection, but with the same initial conditions. While the 12-h AEW amplitude forecast is characterized by consistent initial-condition sensitivity among the different schemes, there is greater variability among methods beyond 24 h. In contrast, the AEW longitude forecast is sensitive to the downstream thermodynamic profile with all cumulus schemes.

Full access
Ryan D. Torn and Chris Snyder

Abstract

With the growing use of tropical cyclone (TC) best-track information for weather and climate applications, it is important to understand the uncertainties that are contained in the TC position and intensity information. Here, an attempt is made to quantify the position uncertainty using National Hurricane Center (NHC) advisory information, as well as intensity uncertainty during times without aircraft data, by verifying Dvorak minimum sea level pressure (SLP) and maximum wind speed estimates during times with aircraft reconnaissance information during 2000–09. In a climatological sense, TC position uncertainty decreases for more intense TCs, while the uncertainty of intensity, measured by minimum SLP or maximum wind speed, increases with intensity. The standard deviation of satellite-based TC intensity estimates can be used as a predictor of the consensus intensity error when that consensus includes both Dvorak and microwave-based estimates, but not when it contains only Dvorak-based values. Whereas there has been a steady decrease in seasonal TC position uncertainty over the past 10 yr, which is likely due to additional data available to NHC forecasters, the seasonal TC minimum SLP and maximum wind speed values are fairly constant, with year-to-year variability due to the mean intensity of all TCs during that season and the frequency of aircraft reconnaissance.

Full access
Ryan D. Torn and David Cook

Abstract

An ensemble of Weather Research and Forecasting Model (WRF) forecasts initialized from a cycling ensemble Kalman filter (EnKF) system is used to evaluate the sensitivity of Hurricanes Danielle and Karl’s (2010) genesis forecasts to vortex and environmental initial conditions via ensemble sensitivity analysis. Both the Danielle and Karl forecasts are sensitive to the 0-h circulation associated with the pregenesis system over a deep layer and to the temperature and water vapor mixing ratio within the vortex over a comparatively shallow layer. Empirical orthogonal functions (EOFs) of the 0-h ensemble kinematic and thermodynamic fields within the vortex indicate that the 0-h circulation and moisture fields covary with one another, such that a stronger vortex is associated with higher moisture through the column. Forecasts of the pregenesis system intensity are only sensitive to the leading mode of variability in the vortex fields, suggesting that only specific initial condition perturbations associated with the vortex will amplify with time. Multivariate regressions of the vortex EOFs and environmental parameters believed to impact genesis suggest that the Karl forecast is most sensitive to the vortex structure, with smaller sensitivity to the upwind integrated water vapor and 200–850-hPa vertical wind shear magnitude. By contrast, the Danielle forecast is most sensitive to the vortex structure during the first 24 h, but is more sensitive to the 200-hPa divergence and vertical wind shear magnitude at longer forecast hours.

Full access
Jeremy D. Berman and Ryan D. Torn

Abstract

Perturbations to the potential vorticity (PV) waveguide, which can result from latent heat release within the warm conveyor belt (WCB) of midlatitude cyclones, can lead to the downstream radiation of Rossby waves, and in turn high-impact weather events. Previous studies have hypothesized that forecast uncertainty associated with diabatic heating in WCBs can result in large downstream forecast variability; however, these studies have not established a direct connection between the two. This study evaluates the potential impact of latent heating variability in the WCB on subsequent downstream forecasts by applying the ensemble-based sensitivity method to European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts of a cyclogenesis event over the North Atlantic. For this case, ensemble members with a more amplified ridge are associated with greater negative PV advection by the irrotational wind, which is associated with stronger lower-tropospheric southerly moisture transport east of the upstream cyclone in the WCB. This transport is sensitive to the pressure trough to the south of the cyclone along the cold front, which in turn is modulated by earlier differences in the motion of the air masses on either side of the front. The position of the cold air behind the front is modulated by upstream tropopause-based PV anomalies, such that a deeper pressure trough is associated with a more progressive flow pattern, originating from Rossby wave breaking over the North Pacific. Overall, these results suggest that more accurate forecasts of upstream PV anomalies and WCBs may reduce forecast uncertainty in the downstream waveguide.

Free access
Ryan D. Torn and Gregory J. Hakim

Abstract

The 2-yr performance of a pseudo-operational (real time) limited-area ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting Model is described. This system assimilates conventional observations from surface stations, rawinsondes, the Aircraft Communications Addressing and Reporting System (ACARS), and cloud motion vectors every 6 h on a domain that includes the eastern North Pacific Ocean and western North America. Ensemble forecasts from this system and deterministic output from operational numerical weather prediction models during this same period are verified against rawinsonde and surface observation data. Relative to operational forecasts, the forecast from the ensemble-mean analysis has slightly larger errors in wind and temperature but smaller errors in moisture, even though satellite radiances are not assimilated by the EnKF. Time-averaged correlations indicate that assimilating ACARS and cloud wind data with flow-dependent error statistics provides corrections to the moisture field in the absence of direct observations of that field. Comparison with a control experiment in which a deterministic forecast is cycled without observation assimilation indicates that the skill in the EnKF’s forecasts results from assimilating observations and not from lateral boundary conditions or the model formulation. Furthermore, the ensemble variance is generally in good agreement with the ensemble-mean error and the spread increases monotonically with forecast hour.

Full access