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  • 1
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    NOAA’s Sensing Hazards with Operational Unmanned Technology (SHOUT) Experiment Observations and Forecast Impacts (2020)
    American Meteorological Society
    Details
    Publication Date: 2020-07-01
    Description: The National Oceanic and Atmospheric Administration’s (NOAA) Sensing Hazards with Operational Unmanned Technology (SHOUT) project evaluated the ability of observations from high-altitude unmanned aircraft to improve forecasts of high-impact weather events like tropical cyclones or mitigate potential degradation of forecasts in the event of a future gap in satellite coverage. During three field campaigns conducted in 2015 and 2016, the National Aeronautics and Space Administration (NASA) Global Hawk, instrumented with GPS dropwindsondes and remote sensors, flew 15 missions sampling 6 tropical cyclones and 3 winter storms. Missions were designed using novel techniques to target sampling regions where high model forecast uncertainty and a high sensitivity to additional observations existed. Data from the flights were examined in real time by operational forecasters, assimilated in operational weather forecast models, and applied postmission to a broad suite of data impact studies. Results from the analyses spanning different models and assimilation schemes, though limited in number, consistently demonstrate the potential for a positive forecast impact from the observations, both with and without a gap in satellite coverage. The analyses with the then-operational modeling system demonstrated large forecast improvements near 15% for tropical cyclone track at a 72-h lead time when the observations were added to the otherwise complete observing system. While future decisions regarding use of the Global Hawk platform will include budgetary considerations, and more observations are required to enhance statistical significance, the scientific results support the potential merit of the observations. This article provides an overview of the missions flown, observational approach, and highlights from the completed and ongoing data impact studies.
    Print ISSN: 0003-0007
    Electronic ISSN: 1520-0477
    Topics: Geography , Physics
    Published by American Meteorological Society
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  • 2
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    Parallel Direct Solution of the Covariance-Localized Ensemble Square Root Kalman Filter Equations with Matrix Functions (2018)
    American Meteorological Society
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    Publication Date: 2018-08-13
    Description: Recently, the serial approach to solving the square root ensemble Kalman filter (ESRF) equations in the presence of covariance localization was found to depend on the order of observations. As shown previously, correctly updating the localized posterior covariance in serial requires additional effort and computational expense. A recent work by Steward et al. details an all-at-once direct method to solve the ESRF equations in parallel. This method uses the eigenvectors and eigenvalues of the forward observation covariance matrix to solve the difficult portion of the ESRF equations. The remaining assimilation is easily parallelized, and the analysis does not depend on the order of observations. While this allows for long localization lengths that would render local analysis methods inefficient, in theory, an eigenpair-based method scales as the cube number of observations, making it infeasible for large numbers of observations. In this work, we extend this method to use the theory of matrix functions to avoid eigenpair computations. The Arnoldi process is used to evaluate the covariance-localized ESRF equations on the reduced-order Krylov subspace basis. This method is shown to converge quickly and apparently regains a linear scaling with the number of observations. The method scales similarly to the widely used serial approach of Anderson and Collins in wall time but not in memory usage. To improve the memory usage issue, this method potentially can be used without an explicit matrix. In addition, hybrid ensemble and climatological covariances can be incorporated.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 3
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    Composite Impact of Global Hawk Unmanned Aircraft Dropwindsondes on Tropical Cyclone Analyses and Forecasts (2018)
    American Meteorological Society
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    Publication Date: 2018-07-01
    Description: 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.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 4
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    Parallel Direct Solution of the Ensemble Square Root Kalman Filter Equations with Observation Principal Components (2017)
    American Meteorological Society
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    Publication Date: 2017-09-01
    Description: The ensemble square root Kalman filter (ESRF) is a variant of the ensemble Kalman filter used with deterministic observations that includes a matrix square root to account for the uncertainty of the unperturbed ensemble observations. Because of the difficulties in solving this equation, a serial approach is often used where observations are assimilated sequentially one after another. As previously demonstrated, in implementations to date the serial approach for the ESRF is suboptimal when used in conjunction with covariance localization, as the Schur product used in the localization does not commute with assimilation. In this work, a new algorithm is presented for the direct solution of the ESRF equations based on finding the eigenvalues and eigenvectors of a sparse, square, and symmetric positive semidefinite matrix with dimensions of the number of observations to be assimilated. This is amenable to direct computation using dedicated, massively parallel, and mature libraries. These libraries make it relatively simple to assemble and compute the observation principal components and to solve the ESRF without using the serial approach. They also provide the eigenspectrum of the forward observation covariance matrix. The parallel direct approach described in this paper neglects the near-zero eigenvalues, which regularizes the ESRF problem. Numerical results show this approach is a highly scalable parallel method.
    Print ISSN: 0739-0572
    Electronic ISSN: 1520-0426
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 5
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    The Impact of NASA Global Hawk Unmanned Aircraft Dropwindsonde Observations on Tropical Cyclone Track, Intensity, and Structure: Case Studies (2017)
    American Meteorological Society
    Details
    Publication Date: 2017-04-19
    Description: 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.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 6
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    Combined Use of Satellite Observations and Global Hawk Unmanned Aircraft Dropwindsondes for Improved Tropical Cyclone Analyses and Forecasts (2018)
    American Meteorological Society
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    Publication Date: 2018-07-26
    Description: 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.
    Print ISSN: 0882-8156
    Electronic ISSN: 1520-0434
    Topics: Geography , Physics
    Published by American Meteorological Society
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  • 7
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    Ensemble-based data assimilation and targeted observation of a chemical tracer in a sea breeze model (2007)
    Elsevier
    Details
    Publication Date: 2007-05-01
    Print ISSN: 1352-2310
    Electronic ISSN: 1873-2844
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Geosciences , Physics
    Published by Elsevier
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  • 8
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    Evaluation of the Hurricane Research Division Doppler Radar Analysis Software Using Synthetic Data (2013)
    American Meteorological Society
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    Publication Date: 2013-06-01
    Description: 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.
    Print ISSN: 0739-0572
    Electronic ISSN: 1520-0426
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 9
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    Ensemble-Based Simultaneous State and Parameter Estimation in a Two-Dimensional Sea-Breeze Model (2006)
    American Meteorological Society
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    Publication Date: 2006-10-01
    Description: 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.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 10
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    A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses (2009)
    American Meteorological Society
    Details
    Publication Date: 2009-06-01
    Description: 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.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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