Justin McLay

Comparison of Hybrid Ensemble/4DVar and 4DVar within the NAVDAS-AR Data Assimilation Framework

AbstractThe effect on weather forecast performance of incorporating ensemble covariances into the initial covariance model of the four-dimensional variational data assimilation (4D-Var) Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) is investigated. This NAVDAS-AR-hybrid scheme linearly combines the static NAVDAS-AR initial background error covariance with a covariance derived from an 80-member flow-dependent ensemble. The ensemble members are generated using the ensemble transform technique with a (three-dimensional variational data assimilation) 3D-Var-based estimate of analysis error variance. The ensemble covariances are localized using an efficient algorithm enabled via a separable formulation of the localization matrix. The authors describe the development and testing of this scheme, which allows for assimilation experiments using differing linear combinations of the static and flow-dependent background error covariances. The tests are ...

Evaluation of the Ensemble Transform Analysis Perturbation Scheme at NRL

Abstract The ensemble transform (ET) scheme changes forecast perturbations into analysis perturbations whose amplitudes and directions are consistent with a user-provided estimate of analysis error covariance. A practical demonstration of the ET scheme was undertaken using Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) analysis error variance estimates and the Navy Operational Global Atmospheric Prediction System (NOGAPS) numerical weather prediction (NWP) model. It was found that the ET scheme produced forecast ensembles that were comparable to or better in a variety of measures than those produced by the Fleet Numerical and Oceanography Center (FNMOC) bred-growing modes (BGM) scheme. Also, the demonstration showed that the introduction of stochastic perturbations into the ET forecast ensembles led to a substantial improvement in the agreement between the ET and NAVDAS analysis error variances. This finding is strong evidence that even a small-sized ET ensemble ...

A Local Formulation of the Ensemble Transform (ET) Analysis Perturbation Scheme

Abstract Following ideas from the local ensemble transform Kalman filter, a local formulation of the ensemble transform (ET) analysis perturbation scheme is developed by partitioning the numerical weather prediction model domain into latitude bands or latitude–longitude blocks. In comparison with analysis perturbations from the original “global” ET formulation, analysis perturbations from the “banded” or “block” ET formulations are much more consistent with estimates of analysis error variance. Banded or block ET forecast ensembles also perform better under a variety of verification metrics than do global ET forecast ensembles. Substantial performance gains are observed for both the midlatitudes and the tropics. A local ET is scheduled to be made operational at the Fleet Numerical Meteorology and Oceanography Center.

Impact of Resolution and Design on the U.S. Navy Global Ensemble Performance in the Tropics

The performance of the U.S. Navy globalatmosphericensemble prediction systemis examinedwith a focus on tropical winds and tropical cyclone tracks. Ensembles are run at a triangular truncation of T119, T159, and T239, with 33, 17, and 9 ensemble members, respectively, to evaluate the impact of resolution versus the number of ensemble member tradeoffs on ensemble performance. Results indicate that the T159 and T239 ensemble mean tropical cyclone track errors are significantly smaller than those of the T119 ensemble out to 4days.For ensemble forecastsofupper-andlower-tropospherictropicalwinds,increasingresolutionhasonly a small impact on ensemble mean root-mean-square error for wind speed, but does improve Brier scores for 10-m wind speed at the 5 m s 21 threshold. In addition to the resolution tests, modifications to the ensemble transform initial perturbation methodology and inclusion of stochastic kinetic energy backscatter are also evaluated. Stochastic kinetic energy backscatter substantially increases the ensemble spread and improves Brier scores in the tropics, but for the most part does not significantly reduce ensemble mean tropical cyclone track error.

Impact of Stochastic Convection on the Ensemble Transform

Abstract The impact of stochastic convection on ensembles produced using the ensemble transform (ET) initial perturbation scheme is examined. This note compares the behavior of ensemble forecasts based only on initial ET perturbations with the behavior of ensemble forecasts based on the ET initial perturbations and forecasts that include stochastic convection. It is illustrated that despite the fact that stochastic convection occurs only after the forecast integrations have started, it induces changes in the initial perturbations as well. This is because the ET is a “cycling” scheme, in which previous short-term forecasts are used to produce the initial perturbations for the current forecast. The stochastic convection scheme induces rapid perturbation growth in regions where convection is active, primarily in the tropics. When combined with the ET scheme, this results in larger initial perturbation variance in the tropics, and, because of a global constraint on total initial perturbation variance, smaller...

Regional Ensemble Forecasts Using the Ensemble Transform Technique

Abstract A computationally inexpensive ensemble transform (ET) method for generating high-resolution initial perturbations for regional ensemble forecasts is introduced. The method provides initial perturbations that (i) have an initial variance consistent with the best available estimates of initial condition error variance, (ii) are dynamically conditioned by a process similar to that used in the breeding technique, (iii) add to zero at the initial time, (iv) are quasi-orthogonal and equally likely, and (v) partially respect mesoscale balance constraints by ensuring that each initial perturbation is a linear sum of forecast perturbations from the preceding forecast. The technique is tested using estimates of analysis error variance from the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) and the Navy’s regional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) over a 3-week period during the summer of 2005. Lateral boundary conditions are provided by...

Examining the Predictability of the Stratospheric Sudden Warming of January 2013 Using Multiple NWP Systems

AbstractThe first multimodel study to estimate the predictability of a boreal sudden stratospheric warming (SSW) is performed using five NWP systems. During the 2012/13 boreal winter, anomalous upward propagating planetary wave activity was observed toward the end of December, which was followed by a rapid deceleration of the westerly circulation around 2 January 2013, and on 7 January 2013 the zonal-mean zonal wind at 60°N and 10 hPa reversed to easterly. This stratospheric dynamical activity was followed by an equatorward shift of the tropospheric jet stream and by a high pressure anomaly over the North Atlantic, which resulted in severe cold conditions in the United Kingdom and northern Europe. In most of the five models, the SSW event was predicted 10 days in advance. However, only some ensemble members in most of the models predicted weakening of westerly wind when the models were initialized 15 days in advance of the SSW. Further dynamical analysis of the SSW shows that this event was characterized ...

Surface Cyclolysis in the North Pacific Ocean. Part III: Composite Local Energetics of Tropospheric-Deep Cyclone Decay Associated with Rapid Surface Cyclolysis

Two regional local energetics composites of tropospheric-deep cyclone decay were constructed based upon 49 cyclones in the Gulf of Alaska region and 18 cyclones in the Bering Sea region whose decay was marked by rapid surface cyclolysis. Both composites indicate that surface drag is only a secondary sink of eddy kinetic energy (EKE) during the decay. This result holds even when a generous accounting is made for uncertainty in the surface drag calculation. The subordinate role of surface drag in the Gulf of Alaska region composite is particularly interesting, given that the cyclones in this composite decay in close proximity to rugged and extensive high-elevation terrain. Both composites also display two of the fundamental characteristics of the downstream development model of cyclone decay: the role of radiative dispersion as the chief sink of EKE during decay, and the occurrence of prominent downstream EKE dispersion. Furthermore, the two composites illustrate that an unusually pronounced decline in baroclinic conversion occurs simultaneously with the intense radiative dispersion. Taken together, these results suggest that the energetic decay of cyclones marked by rapid surface cyclolysis is driven from the upper troposphere, not from the surface. Some notable differences also emerge from the two composites. Considerable downstream development occurs in the immediate vicinity of the decaying cyclone in the Bering Sea region composite, but not in the Gulf of Alaska region composite. Meanwhile, the areal extent of the downstream dispersion is greater in the Gulf of Alaska region composite. The latter circumstance suggests that decay events in the Gulf of Alaska region, while not producing significant downstream development in their near vicinity, may have important energetic implications for subsequent development farther downstream over North America. The composites also indicate that the decline of EKE in the vicinity of the decaying cyclone is more pronounced in the Gulf of Alaska region. In the Bering Sea region composite, this EKE is maintained via a persistent convergence of ageostrophic geopotential flux (AGF) that emanates from regions well south of the primary cyclone. Similar evidence for the influence of upstream disturbances on the cyclone decay does not appear in the Gulf of Alaska region composite.

Inaccuracies with Multimodel Postprocessing Methods Involving Weighted, Regression-Corrected Forecasts

AbstractEnsemble postprocessing is frequently applied to correct biases and deficiencies in the spread of ensemble forecasts. Methods involving weighted, regression-corrected forecasts address the typical biases and underdispersion of ensembles through a regression correction of ensemble members followed by the generation of a probability density function (PDF) from the weighted sum of kernels fit around each corrected member. The weighting step accounts for the situation where the ensemble is constructed from different model forecasts or generated in some way that creates ensemble members that do not represent equally likely states. In the present work, it is shown that an overweighting of climatology in weighted, regression-corrected forecasts can occur when one first performs a regression-based correction before weighting each member. This overweighting of climatology results in an increase in the mean-squared error of the mean of the predicted PDF. The overweighting of climatology is illustrated in a ...

Inducing Tropical Cyclones to Undergo Brownian Motion: A Comparison between Itô and Stratonovich in a Numerical Weather Prediction Model

AbstractStochastic parameterization has become commonplace in numerical weather prediction (NWP) models used for probabilistic prediction. Here a specific stochastic parameterization will be related to the theory of stochastic differential equations and shown to be affected strongly by the choice of stochastic calculus. From an NWP perspective the focus will be on ameliorating a common trait of the ensemble distributions of tropical cyclone (TC) tracks (or position); namely, that they generally contain a bias and an underestimate of the variance. With this trait in mind the authors present a stochastic track variance inflation parameterization. This parameterization makes use of a properly constructed stochastic advection term that follows a TC and induces its position to undergo Brownian motion. A central characteristic of Brownian motion is that its variance increases with time, which allows for an effective inflation of an ensemble’s TC track variance. Using this stochastic parameterization the authors...