Third THORPEX International Science Symposium

Description:

This collection consists of contributions that were related to the Third THORPEX International Science Symposium, held in Monterey, California, in September 2009. THORPEX is a 10-yr international research and development program to accelerate improvements in the accuracy of one-day to two-week high impact weather forecasts for the benefit of society, the economy, and the environment.

THORPEX establishes an organizational framework that addresses weather research and forecast problems whose solutions will be accelerated through international collaboration among academic institutions, operational forecast centers, and users of forecast products.The articles will be presented below as they are published.

Collection editor:
Tom Hamill

Third THORPEX International Science Symposium chair:
Jim Hansen

Program chair:
Istvan Szunyogh

TIGGE User Workshop:
Richard Swinbank

WMO contact:
David Burridge

Third THORPEX International Science Symposium

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William A. Komaromi, Sharanya J. Majumdar, and Eric D. Rappin

Abstract

The response of Weather Research and Forecasting (WRF) model predictions of two tropical cyclones to perturbations in the initial conditions is investigated. Local perturbations to the vorticity field in the synoptic environment are created in features considered subjectively to be of importance to the track forecast. The rebalanced analysis is then integrated forward and compared with an unperturbed “control” simulation possessing similar errors to those in the corresponding operational model forecasts. In the first case, Typhoon Sinlaku (2008), the premature recurvature in the control simulation is found to be corrected by a variety of initial perturbations; in particular, the weakening of an upper-level low directly to its north, and the weakening of a remote short-wave trough in the midlatitude storm track. It is suggested that one or both of the short waves may have been initialized too strongly. In the second case, the forecasts for Hurricane Ike (2008) initialized 4 days prior to its landfall in Texas were not sensitive to most remote perturbations. The primary corrections to the track of Ike arose from a weakening of a midlevel ridge directly to its north, and the strengthening of a short-wave trough in the midlatitudes. For both storms, the targets selected by the ensemble transform Kalman filter (ETKF) were often, but not always, consistent with the most sensitive regions found in this study. Overall, the results can be used to retrospectively diagnose features in which the initial conditions require improvement, in order to improve forecasts of tropical cyclone track.

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Mio Matsueda, Masayuki Kyouda, Zoltan Toth, H. L. Tanaka, and Tadashi Tsuyuki

Abstract

Atmospheric blocking occurred over the Rocky Mountains at 1200 UTC 15 December 2005. The operational medium-range ensemble forecasts of the Canadian Meteorological Center (CMC), the Japan Meteorological Agency (JMA), and the National Centers for Environmental Prediction (NCEP), as initialized at 1200 UTC 10 December 2005, showed remarkable differences regarding this event. All of the NCEP members failed to predict the correct location of the blocking, whereas almost all of the JMA members and most of the CMC members were successful in predicting the correct location. The present study investigated the factors that caused NCEP to incorrectly predict the blocking location, based on an ensemble-based sensitivity analysis and the JMA global spectral model (GSM) multianalysis ensemble forecasts with NCEP, regionally amplified NCEP, and globally amplified NCEP analyses.

A sensitive area for the blocking formation was detected over the central North Pacific. In this area, the NCEP control analysis experienced problems in the handling of a cutoff cyclone, and the NCEP initial perturbations were ineffective in reducing uncertainties in the NCEP control analysis. The JMA GSM multianalysis ensemble forecasts revealed that regional amplification of initial perturbations over the sensitive area could lead to further improvements in forecasts over the blocking region without degradation of forecasts over the Northern Hemisphere (NH), whereas the global amplification of initial perturbations could lead to improved forecasts over the blocking region and degraded forecasts over the NH. This finding may suggest that excessive amplification of initial perturbations over nonsensitive areas is undesirable, and that case-dependent rescaling of initial perturbations may be of value compared with climatology-based rescaling, which is widely used in current operational ensemble prediction systems.

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Elizabeth Satterfield and Istvan Szunyogh

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The ability of an ensemble to capture the magnitude and spectrum of uncertainty in a local linear space spanned by the ensemble perturbations is assessed. Numerical experiments are carried out with a reduced resolution 2004 version of the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The local ensemble transform Kalman filter (LETKF) data assimilation system is used to assimilate observations in three steps, gradually adding more realistic features to the observing network. In the first experiment, randomly placed, noisy, simulated vertical soundings, which provide 10%% coverage of horizontal model grid points, are assimilated. Next, the impact of an inhomogeneous observing system is introduced by assimilating simulated observations in the locations of real observations of the atmosphere. Finally, observations of the real atmosphere are assimilated.

The most important findings of this study are the following: predicting the magnitude of the forecast uncertainty and the relative importance of the different patterns of uncertainty is, in general, a more difficult task than predicting the patterns of uncertainty; the ensemble, which is tuned to provide near-optimal performance at analysis time, underestimates not only the total magnitude of the uncertainty, but also the magnitude of the uncertainty that projects onto the space spanned by the ensemble perturbations; and finally, a strong predictive linear relationship is found between the local ensemble spread and the upper bound of the local forecast uncertainty.

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Warren J. Tennant, Glenn J. Shutts, Alberto Arribas, and Simon A. Thompson

Abstract

An improved stochastic kinetic energy backscatter scheme, version 2 (SKEB2) has been developed for the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Wind increments at each model time step are derived from a streamfunction forcing pattern that is modulated by a locally diagnosed field of likely energy loss due to numerical smoothing and unrepresented convective sources of kinetic energy near the grid scale. The scheme has a positive impact on the root-mean-square error of the ensemble mean and spread of the ensemble. An improved growth rate of spread results in a better match with ensemble-mean forecast error at all forecast lead times, with a corresponding improvement in probabilistic forecast skill from a more realistic representation of model error. Other examples of positive impact include improved forecast blocking frequency and reduced forecast jumpiness. The paper describes the formulation of the SKEB2 and its assessment in various experiments.

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Benoît Vié, Olivier Nuissier, and Véronique Ducrocq

Abstract

This study assesses the impact of uncertainty on convective-scale initial conditions (ICs) and the uncertainty on lateral boundary conditions (LBCs) in cloud-resolving simulations with the Application of Research to Operations at Mesoscale (AROME) model. Special attention is paid to Mediterranean heavy precipitating events (HPEs). The goal is achieved by comparing high-resolution ensembles generated by different methods. First, an ensemble data assimilation technique has been used for assimilation of perturbed observations to generate different convective-scale ICs. Second, three ensembles used LBCs prescribed by the members of a global short-range ensemble prediction system (EPS). All ensembles obtained were then evaluated over 31- and/or 18-day periods, and on 2 specific case studies of HPEs. The ensembles are underdispersive, but both the probabilistic evaluation of their overall performance and the two case studies confirm that they can provide useful probabilistic information for the HPEs considered.

The uncertainty on convective-scale ICs is shown to have an impact at short range (under 12 h), and it is strongly dependent on the synoptic-scale context. Specifically, given a marked circulation near the area of interest, the imposed LBCs rapidly overwhelm the initial differences, greatly reducing the spread of the ensemble. The uncertainty on LBCs shows an impact at longer range, as the spread in the coupling global ensemble increases, but it also depends on the synoptic-scale conditions and their predictability.

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Thomas M. Hamill, Jeffrey S. Whitaker, Michael Fiorino, and Stanley G. Benjamin

Abstract

Verification was performed on ensemble forecasts of 2009 Northern Hemisphere summer tropical cyclones (TCs) from two experimental global numerical weather prediction ensemble prediction systems (EPSs). The first model was a high-resolution version (T382L64) of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The second model was a 30-km version of the experimental NOAA/Earth System Research Laboratory’s Flow-following finite-volume Icosahedral Model (FIM). Both models were initialized with the first 20 members of a 60-member ensemble Kalman filter (EnKF) using the T382L64 GFS. The GFS–EnKF assimilated the full observational data stream that was normally assimilated into the NCEP operational Global Statistical Interpolation (GSI) data assimilation, plus human-synthesized “observations” of tropical cyclone central pressure and position produced at the National Hurricane Center and the Joint Typhoon Warning Center. The forecasts from the two experimental ensembles were compared against four operational EPSs from the European Centre for Medium-Range Weather Forecasts (ECMWF), NCEP, the Canadian Meteorological Centre (CMC), and the Met Office (UKMO).

The errors of GFS–EnKF ensemble track forecasts were competitive with those from the ECMWF ensemble system, and the overall spread of the ensemble tracks was consistent in magnitude with the track error. Both experimental EPSs had much lower errors than the operational NCEP, UKMO, and CMC EPSs, but the FIM–EnKF tracks were somewhat less accurate than the GFS–EnKF. The ensemble forecasts were often stretched in particular directions, and not necessarily along or across track. The better-performing EPSs provided useful information on potential track error anisotropy. While the GFS–EnKF initialized relatively deep vortices by assimilating the TC central pressure estimate, the model storms filled during the subsequent 24 h. Other forecast models also systematically underestimated TC intensity (e.g., maximum forecast surface wind speed). The higher-resolution models generally had less bias.

Analyses were conducted to try to understand whether the additional central pressure observation, the EnKF, or the extra resolution was most responsible for the decrease in track error of the experimental Global Ensemble Forecast System (GEFS)–EnKF over the operational NCEP. The assimilation of the additional TC observations produced only a small change in deterministic track forecasts initialized with the GSI. The T382L64 GFS–EnKF ensemble was used to initialize a T126L28 ensemble forecast to facilitate a comparison with the operational NCEP system. The T126L28 GFS–EnKF EPS track forecasts were dramatically better than the NCEP operational, suggesting the positive impact of the EnKF, perhaps through improved steering flow.

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E. A. Irvine, S. L. Gray, J. Methven, and I. A. Renfrew

Abstract

For a targeted observations case, the dependence of the size of the forecast impact on the targeted dropsonde observation error in the data assimilation is assessed. The targeted observations were made in the lee of Greenland; the dependence of the impact on the proximity of the observations to the Greenland coast is also investigated. Experiments were conducted using the Met Office Unified Model (MetUM), over a limited-area domain at 24-km grid spacing, with a four-dimensional variational data assimilation (4D-Var) scheme. Reducing the operational dropsonde observation errors by one-half increases the maximum forecast improvement from 5% to 7%–10%, measured in terms of total energy. However, the largest impact is seen by replacing two dropsondes on the Greenland coast with two farther from the steep orography; this increases the maximum forecast improvement from 5% to 18% for an 18-h forecast (using operational observation errors). Forecast degradation caused by two dropsonde observations on the Greenland coast is shown to arise from spreading of data by the background errors up the steep slope of Greenland. Removing boundary layer data from these dropsondes reduces the forecast degradation, but it is only a partial solution to this problem. Although only from one case study, these results suggest that observations positioned within a correlation length scale of steep orography may degrade the forecast through the anomalous upslope spreading of analysis increments along terrain-following model levels.

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John E. Janowiak, Peter Bauer, Wanqiu Wang, Phillip A. Arkin, and Jon Gottschalck

Abstract

In this paper, the results of an examination of precipitation forecasts for 1–30-day leads from global models run at the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) during November 2007–February 2008 are presented. The performance of the model precipitation forecasts are examined in global and regional contexts, and results of a case study of precipitation variations that are associated with a moderate to strong Madden–Julian oscillation (MJO) event are presented.

The precipitation forecasts from the ECMWF and NCEP operational prediction models have nearly identical temporal correlation with observed precipitation at forecast leads from 2 to 9 days over the Northern Hemisphere during the cool season, despite the higher resolution of the ECMWF operational model, while the ECMWF operational model forecasts are slightly better in the tropics and the Southern Hemisphere during the warm season. The ECMWF Re-Analysis Interim (ERA-Interim) precipitation forecasts perform only slightly worse than the NCEP operational model, while NCEP’s Climate Forecast System low-resolution coupled model forecasts perform the worst among the four models. In terms of bias, the ECMWF operational model performs the best among the four model forecasts that were examined, particularly with respect to the ITCZ regions in both the Atlantic and Pacific. Local temporal correlations that were computed on daily precipitation totals for day-2 forecasts against observations indicate that the operational models at ECMWF and NCEP perform the best during the 4-month study period, and that all of the models have low to insignificant correlations over land and over much of the tropics. They perform the best in subtropical and extratropical oceanic regions.

Also presented are results that show that striking improvements have been made over the past two decades in the ability of the models to represent precipitation variations that are associated with MJO. The model precipitation forecasts exhibit the ability to characterize the evolution of precipitation variations during a moderate–strong period of MJO conditions for forecast leads as long as 10 days.

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Ronald Gelaro, Rolf H. Langland, Simon Pellerin, and Ricardo Todling

Abstract

An experiment is being conducted to directly compare the impact of all assimilated observations on short-range forecast errors in different forecast systems using an adjoint-based technique. The technique allows detailed comparison of observation impacts in terms of data type, location, satellite sounding channel, or other relevant attributes. This paper describes results for a “baseline” set of observations assimilated by three forecast systems for the month of January 2007. Despite differences in the assimilation algorithms and forecast models, the impacts of the major observation types are similar in each forecast system in a global sense. However, regional details and other aspects of the results can differ substantially. Large forecast error reductions are provided by satellite radiances, geostationary satellite winds, radiosondes, and commercial aircraft. Other observation types provide smaller impacts individually, but their combined impact is significant. Only a small majority of the total number of observations assimilated actually improves the forecast, and most of the improvement comes from a large number of observations that have relatively small individual impacts. Accounting for this behavior may be especially important when considering strategies for deploying adaptive (or “targeted”) components of the observing system.

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Munehiko Yamaguchi and Sharanya J. Majumdar

Abstract

Ensemble initial perturbations around Typhoon Sinlaku (2008) produced by ECMWF, NCEP, and the Japan Meteorological Agency (JMA) ensembles are compared using The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) data, and the dynamical mechanisms of perturbation growth associated with the tropical cyclone (TC) motion are investigated for the ECMWF and NCEP ensembles. In the comparison, it is found that the vertical and horizontal distributions of initial perturbations as well as the amplitude are quite different among the three NWP centers before, during, and after the recurvature of Sinlaku. In addition, it turns out that those variations cause a difference in the TC motion not only at the initial time but also during the subsequent forecast period. The ECMWF ensemble exhibits relatively large perturbation growth, which results from 1) the baroclinic energy conversion in a vortex, 2) the baroclinic energy conversion associated with the midlatitude waves, and 3) the barotropic energy conversion in a vortex. Those features are less distinctive in the NCEP ensemble. A statistical verification shows that the ensemble spread of TC track predictions in NCEP (ECMWF) is larger than ECMWF (NCEP) for 1- (3-) day forecasts on average. It can be inferred that while the ECMWF ensemble starts from a relatively small amplitude of initial perturbations, the growth of the perturbations helps to amplify the ensemble spread of tracks. On the other hand, a relatively large amplitude of initial perturbations seems to play a role in producing the ensemble spread of tracks in the NCEP ensemble.

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