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Altuğ Aksoy

Abstract

A storm-relative data assimilation method for tropical cyclones is introduced for the ensemble Kalman filter, using the Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS) developed at the Hurricane Research Division of the Atlantic Oceanographic and Meteorological Laboratory at the National Oceanic and Atmospheric Administration. The method entails translating tropical cyclone observations to storm-relative coordinates and requires the assumption of simultaneity of all observations. The observations are then randomly redistributed to assimilation cycles to achieve a more homogeneous spatial distribution. A proof-of-concept study is carried out in an observing system simulation experiment in which airborne Doppler radar radial wind observations are simulated from a higher-resolution (4.5/1.5 km) version of the same model. The results here are compared to the earth-relative version of HEDAS. When storm-relative observations are assimilated using the original HEDAS configuration, improvements are observed in the kinematic representation of the tropical cyclone vortex in analyses. The use of the storm-relative observations with a more homogeneous spatial distribution also reveals that a reduction of the covariance localization horizontal length scale by ½ to ~120 km provides the greatest incremental improvements. Potential positive impact is also seen in the slower cycle-to-cycle error growth. Spatially smoother analyses are obtained in the horizontal, and the evolution of the azimuthally averaged wind structure during short-range forecasts demonstrates better consistency with the nature run.

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Sylvie Lorsolo
and
Altuğ Aksoy

Abstract

Performing wavenumber decomposition on azimuthally distributed data such as those in tropical cyclones can be challenging when data gaps exist in the signal. In the literature, ad hoc approaches are found to determine maximum gap size beyond which not to perform Fourier decomposition. The goal of the present study is to provide a more objective and systematic method to choose the maximum gap size allowed to perform a Fourier analysis on observational data. A Monte Carlo–type experiment is conducted where signals of various wavenumber configurations are generated with gaps of varying size, then a simple interpolation scheme is applied and Fourier decomposition is performed. The wavenumber decomposition is evaluated in a way that requires retrieval of at least 80% of the original amplitude with less than 20° phase shift. Maximum allowable gap size is then retrieved for wavenumbers 0–2. When prior assessment of signal configuration is available, the authors believe that the present study can provide valuable guidance for gap size beyond which Fourier decomposition is not advisable.

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Fuqing Zhang
,
Zhiyong Meng
, and
Altug Aksoy

Abstract

Through observing system simulation experiments, this two-part study exploits the potential of using the ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation. Part I focuses on the performance of the EnKF under the perfect model assumption in which the truth simulation is produced with the same model and same initial uncertainties as those of the ensemble, while Part II explores the impacts of model error and ensemble initiation on the filter performance. In this first part, the EnKF is implemented in a nonhydrostatic mesoscale model [the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)] to assimilate simulated sounding and surface observations derived from simulations of the “surprise” snowstorm of January 2000. This is an explosive East Coast cyclogenesis event with strong error growth at all scales as a result of interactions between convective-, meso-, and subsynoptic-scale dynamics.

It is found that the EnKF is very effective in keeping the analysis close to the truth simulation under the perfect model assumption. The EnKF is most effective in reducing larger-scale errors but less effective in reducing errors at smaller, marginally resolvable scales. In the control experiment, in which the truth simulation was produced with the same model and same initial uncertainties as those of the ensemble, a 24-h continuous EnKF assimilation of sounding and surface observations of typical temporal and spatial resolutions is found to reduce the error by as much as 80% (compared to a 24-h forecast without data assimilation) for both observed and unobserved variables including zonal and meridional winds, temperature, and pressure. However, it is observed to be relatively less efficient in correcting errors in the vertical velocity and moisture fields, which have stronger smaller-scale components. The analysis domain-averaged root-mean-square error after 24-h assimilation is ∼1.0–1.5 m s−1 for winds and ∼1.0 K for temperature, which is comparable to or less than typical observational errors. Various sensitivity experiments demonstrated that the EnKF is quite successful in all realistic observational scenarios tested. However, as will be presented in Part II, the EnKF performance may be significantly degraded if an imperfect forecast model is used, as is likely the case when real observations are assimilated.

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Sylvie Lorsolo
,
John Gamache
, and
Altug Aksoy

Abstract

The Hurricane Research Division Doppler radar analysis software provides three-dimensional analyses of the three wind components in tropical cyclones. Although this software has been used for over a decade, there has never been a complete and in-depth evaluation of the resulting analyses. The goal here is to provide an evaluation that will permit the best use of the analyses, but also to improve the software. To evaluate the software, analyses are produced from simulated radar data acquired from an output of a Hurricane Weather Research and Forecasting (HWRF) model nature run and are compared against the model “truth” wind fields. Comparisons of the three components of the wind show that the software provides analyses of good quality. The tangential wind is best retrieved, exhibiting an overall small mean error of 0.5 m s−1 at most levels and a root-mean-square error less than 2 m s−1. The retrieval of the radial wind is also quite accurate, exhibiting comparable errors, although the accuracy of the tangential wind is generally better. Some degradation of the retrieval quality is observed at higher altitude, mainly due to sparser distribution of data in the model. The vertical component of the wind appears to be the most challenging to retrieve, but the software still provides acceptable results. The tropical cyclone mean azimuthal structure and wavenumber structure are found to be very well captured. Sources of errors inherent to airborne Doppler measurements and the effects of some of the simplifications used in the simulation methodology are also discussed.

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Altuğ Aksoy
,
David C. Dowell
, and
Chris Snyder

Abstract

The effectiveness of the ensemble Kalman filter (EnKF) for assimilating radar observations at convective scales is investigated for cases whose behaviors span supercellular, linear, and multicellular organization. The parallel EnKF algorithm of the Data Assimilation Research Testbed (DART) is used for data assimilation, while the Weather Research and Forecasting (WRF) Model is employed as a simplified cloud model at 2-km horizontal grid spacing. In each case, reflectivity and radial velocity measurements are utilized from a single Weather Surveillance Radar-1988 Doppler (WSR-88D) within the U.S. operational network. Observations are assimilated every 2 min for a duration of 60 min and correction of folded radial velocities occurs within the EnKF. Initial ensemble uncertainty includes random perturbations to the horizontal wind components of the initial environmental sounding. The EnKF performs effectively and with robust results across all the cases. Over the first 18–30 min of assimilation, the rms and domain-averaged prior fits to observations in each case improve significantly from their initial levels, reaching comparable values of 3–6 m s−1 and 7–10 dBZ. Representation of mesoscale uncertainty, albeit in the simplest form of initial sounding perturbations, is a critical part of the assimilation system, as it increases ensemble spread and improves filter performance. In addition, assimilation of “no precipitation” observations (i.e., reflectivity observations with values small enough to indicate the absence of precipitation) serves to suppress spurious convection in ensemble members. At the same time, it is clear that the assimilation is far from optimal, as the ensemble spread is consistently smaller than what would be expected from the innovation statistics and the assumed observation-error variance.

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Hui Christophersen
,
Altug Aksoy
,
Jason Dunion
, and
Sim Aberson

Abstract

The impacts of Global Hawk (GH) dropwindsondes on tropical cyclone (TC) analyses and forecasts are examined over a composite sample of missions flown during the NASA Hurricane and Severe Storm Sentinel (HS3) and the NOAA Sensing Hazards with Operational Unmanned Technology (SHOUT) field campaigns. An ensemble Kalman filter is employed to assimilate the dropwindsonde observations at the vortex scale. With the assimilation of GH dropwindsondes, TCs generally exhibit fewer position and intensity errors, a better wind–pressure relationship, and improved representation of integrated kinetic energy in the analyses. The resulting track and intensity forecasts with all the cases generally show a positive impact when GH dropwindsondes are assimilated. The impact of GH dropwindsondes is further explored with cases stratified by intensity change and presence of crewed aircraft data. GH dropwindsondes demonstrate a larger impact for nonsteady-state TCs [non-SS; 24-h intensity change larger than 20 kt (~10 m s−1)] than for steady-state (SS) TCs. The relative skill from assimilating GH dropwindsondes ranges between 25% and 35% for either the position or intensity improvement in the final analyses overall, but only ~5%–10% for SS cases alone. The resulting forecasts for non-SS cases show higher skill for both track and intensity than SS cases. In addition, the GH dropwindsonde impact on TC forecasts varies in the presence of crewed aircraft data. An increased intensity improvement at long lead times is seen when crewed aircraft data are absent. This demonstrates the importance of strategically designing flight patterns to exploit the sampling strengths of the GH and crewed aircraft in order to maximize data impacts on TC prediction.

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Altuğ Aksoy
,
David C. Dowell
, and
Chris Snyder

Abstract

The quality of convective-scale ensemble forecasts, initialized from analysis ensembles obtained through the assimilation of radar observations using an ensemble Kalman filter (EnKF), is investigated for cases whose behaviors span supercellular, linear, and multicellular organization. This work is the companion to , which focused on the quality of analyses during the 60-min analysis period. Here, the focus is on 30-min ensemble forecasts initialized at the end of that period. As in , the Weather Research and Forecasting (WRF) model is employed as a simplified cloud model at 2-km horizontal grid spacing. Various observation-space and state-space verification metrics, computed both for ensemble means and individual ensemble members, are employed to assess the quality of ensemble forecasts comparatively across cases. While the cases exhibit noticeable differences in predictability, the forecast skill in each case, as measured by various metrics, decays on a time scale of tens of minutes. The ensemble spread also increases rapidly but significant outlier members or clustering among members are not encountered. Forecast quality is seen to be influenced to varying degrees by the respective initial soundings. While radar data assimilation is able to partially mitigate some of the negative effects in some situations, the supercell case, in particular, remains difficult to predict even after 60 min of data assimilation.

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Altuğ Aksoy
,
Fuqing Zhang
, and
John W. Nielsen-Gammon

Abstract

The performance of the ensemble Kalman filter (EnKF) in forced, dissipative flow under imperfect-model conditions is investigated through simultaneous state and parameter estimation where the source of model error is the uncertainty in the model parameters. A two-dimensional, nonlinear, hydrostatic, nonrotating, and incompressible sea-breeze model is used for this purpose with buoyancy and vorticity as the prognostic variables and a square root filter with covariance localization is employed. To control filter divergence caused by the narrowing of parameter variance, a “conditional covariance inflation” method is devised. Up to six model parameters are subjected to estimation attempts in various experiments. While the estimation of single imperfect parameters results in error of model variables that is indistinguishable from the respective perfect-parameter cases, increasing the number of estimated parameters to six inevitably leads to a decline in the level of improvement achieved by parameter estimation. However, the overall EnKF performance in terms of the error statistics is still superior to the situation where there is parameter error but no parameter estimation is performed. In fact, compared with that situation, the simultaneous estimation of six parameters reduces the average error in buoyancy and vorticity by 40% and 46%, respectively.

Several aspects of the filter configuration (e.g., observation location, ensemble size, radius of influence, and parameter variance limit) are found to considerably influence the identifiability of the parameters. The parameter-dependent response to such factors implies strong nonlinearity between the parameters and the state of the model and suggests that a straightforward spatial covariance localization does not necessarily produce optimality.

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Hui Christophersen
,
Altug Aksoy
,
Jason Dunion
, and
Kathryn Sellwood

Abstract

The impact of Global Hawk (GH) dropwindsondes on tropical cyclone analyses and forecasts is evaluated in an ensemble-based vortex-scale data assimilation system. Two cases from Hurricane Edouard (2014) are presented. In the first case, inner-core observations were exclusively provided by GH dropwindsondes, while in the second case, GH dropwindsondes were concentrated in the storm’s near environment and were complemented by an extensive number of inner-core observations from other aircraft. It is found that when GH dropwindsondes are assimilated, a positive impact on the minimum sea level pressure (MSLP) forecast persists for most lead times in the first case, conceivably due to the better representation of the initial vortex structure, such as the warm-core anomaly and primary and secondary circulations. The verification of the storm’s kinematic and thermodynamic structure in the forecasts of the first case is carried out relative to the time of the appearance of a secondary wind maximum (SWM) using the tail Doppler radar and dropwindsonde composite analyses. A closer-to-observed wavenumber-0 wind field in the experiment with GH dropwindsondes is seen before the SWM is developed, which likely contributes to the superior intensity forecast up to 36 h. The improvement in the warm-core anomaly in the forecasts from the experiment with GH dropwindsondes is believed to have also contributed to the consistent improvement in the MSLP forecast. For the latter case, a persistent improvement in the track forecast is seen, which is consistent with a better representation of the near-environmental flow obtained from GH data in the same region.

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Hui Christophersen
,
Robert Atlas
,
Altug Aksoy
, and
Jason Dunion

Abstract

This study demonstrates that Global Hawk unmanned aircraft system dropwindsondes and Atmospheric Infrared Sounder (AIRS) observations can be complementary in sampling a tropical cyclone (TC). The assimilation of both datasets in a regional ensemble data assimilation system shows that the cumulative impact of both datasets is greater than either one alone because of the presence of mutually independent information content. The experiment that assimilates both datasets has smaller position and intensity errors in the mean analysis than those with individual datasets. The improvements in track and intensity forecasts that result from combining both datasets also indicate synergistic benefits. Overall, superior track and intensity forecasts are evident. This study suggests that polar-orbiting satellite spatial coverage should be considered in operational reconnaissance mission planning in order to achieve further improvements in TC analyses and forecasts.

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