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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Jeremy D. Berman
and
Ryan D. Torn

Abstract

One potential way to improve the skill of medium-range weather forecasts is to improve the evolution of Rossby waves, which largely modulate extratropical weather. Recent research has hypothesized that the predictability of downstream Rossby waves may be limited by forecast uncertainty linked to upstream diabatic processes such as latent heat release within the warm conveyor belt (WCB) of extratropical cyclones. This hypothesis is evaluated using Model for Prediction Across Scales (MPAS) ensemble forecasts for two events characterized by highly amplified flow over the North Atlantic associated with cyclogenesis. The source of variability in ridge forecasts is diagnosed using the ensemble-sensitivity technique and a potential vorticity (PV) tendency budget, which quantifies the contribution from individual physical processes toward subsequent ridge amplification. Before the onset of ridge amplitude differences for both events, ensemble forecasts with a more amplified ridge are associated with greater negative PV advection by the irrotational wind. The importance of PV advection by the irrotational wind suggests that PV changes are modulated by diabatic heating, which is confirmed by the sensitivity of ridge amplitude to earlier diabatic heating and lower-tropospheric moisture within an upstream WCB. After the onset of ridge amplitude differences, PV advection by the nondivergent wind becomes the primary driver of downstream forecast differences. Initial condition perturbations within the sensitive areas of the WCB confirm that increasing the initial lower-tropospheric moisture results in a more amplified ridge. This suggests that more accurate initial conditions near the WCB could lead to better downstream forecasts.

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Ryan D. Torn
and
Gregory J. Hakim

Abstract

An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.

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Ryan D. Torn
and
Gregory J. Hakim

Abstract

An ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting model is applied to generate ensemble analyses and forecasts of Hurricane Katrina (2005) and the surrounding area every 6 h over the lifetime of the storm on a nested domain. Analyses are derived from assimilating conventional in situ observations, reconnaissance dropsondes, including data taken during the Hurricane Rainband and Intensity Exchange Experiment (RAINEX), and tropical cyclone position estimates. Observation assimilation at individual times consistently reduces errors in tropical cyclone position, but not necessarily in intensity; however, withholding observations leads to significantly larger errors in both quantities. Analysis increments for observations near the tropical cyclone are dominated by changes in vortex position, and these increments increase the asymmetric structure of the storm. Data denial experiments indicate that dropsondes deployed in the synoptic environment provide minimal benefit to the outer domain; however, dropsondes deployed within the tropical cyclone lead to significant reductions in position and intensity errors on the inner domain. Specifically, errors in the inner domain ensemble-mean 6-h forecasts of minimum pressure are 70% larger when dropsonde data is not assimilated. Precipitation fields are qualitatively similar to Tropical Rainfall Measuring Mission (TRMM) satellite estimates, although model values are double the values of the satellite estimate. Moreover, the spinup period and initial imbalance in EnKF-initialized WRF forecasts is less than starting the model from a GFS analysis. Ensemble-mean 48-h forecasts initialized with EnKF analyses have track and intensity errors that are 50% smaller than GFS and NHC official forecasts.

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