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Steven L. Mullen and David P. Baumhefner

Abstract

The impact of initial condition uncertainty on short-range (0–48 h) simulations of explosive surface cyclogenesis is examined within the context of a perfect model environment. Eleven Monte Carlo simulations are performed on 10 cases of rapid oceanic cyclogenesis that occurred in a long-term, perpetual January integration of a global spectral model. The perturbations used to represent the initial condition error have a magnitude and spatial decomposition that closely matches estimates of global analysis error.

Large variability characterizes the error growth rates, both among the individual Monte Carlo simulations and among the case-average values. Some individual simulations display error growth doubling times as fast as approximately 12 h during the 24-h period of most rapid intensification, while others exhibit virtually no error growth. The variability is also reflected in the wide 90% confidence bounds for many surface weather elements such as the cyclone position and central pressure. However, no statistically significant differences are found between the initial states leading to large simulation errors and those leading to negligible errors. These results attest to the importance of initial condition uncertainty as the major cause of forecast variability and indicate a strong sensitivity to subtle differences in initial perturbation location and structure.

The effect that simple ensemble averaging has on reducing uncertainty is discussed. Averaging a 16-member ensemble decreases the random component of the initial data error by 80%–90% and the 90% confidence bounds by 70%–80% for cyclone position, central pressure, and 12-h pressure change. It is hypothesized that ensemble forecasting could benefit the utility of short-range forecasts for many weather elements of operational interest and conclude that research efforts should be directed at examining its effectiveness in an operational setting.

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Byron A. Boville and David P. Baumhefner

Abstract

The error growth associated with the usual upper boundary formulation (and location) in numerical weather prediction (NWP) and general circulation models (GCMs) is studied. The experimental strategy is to look at the growth of differences between the equilibrated climate simulation of a control simulation and three ensembles of 30 day simulations branching off of the control. The control simulation is a seasonal integration of a medium horizontal-resolution GCM with 30 levels extending from the surface to the upper mesosphere. Each ensemble consists of nine cases with initial conditions taken at 10-day intervals from the control. The main experiment uses a model identical to that of the control except that only the bottom 15 levels (below 10 mb) are retained. The additional experiments either perturb the initial conditions or alter the physical parameterizations (horizontal diffusion) to obtain information on the significance of the results of the main experiment.

It is found that random error growth rate in the troposphere for the case of the altered upper boundary is slightly faster than that for initial-condition uncertainty alone. However, this is not likely to make a significant impact in operational forecast models at present because the uncertainty in the initial conditions is so large.

Systematic errors in the troposphere due to the upper boundary are relatively small prior to day 20. However, the ten day mean errors from days 20–30 are about the same magnitude as the anomalies that extended range forecasts are attempting to predict. The upper boundary treatment is likely to cause significant systematic errors in such forecasts. In the lower stratosphere, the errors are substantial within the first few days, particularly in the winter hemisphere.

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Joseph J. Tribbia and David P. Baumhefner

Abstract

This paper presents the results of an ensemble of 20 predictability experiments derived from the NCAR Community Climate Model (CCM). Particular emphasis is placed on the question of the predictability of dynamically driven low-frequency components of the model atmosphere. The conclusion drawn, using time averaging alone as a means of isolating low-frequency variability, is that in the ensemble mean there is little skill in a 30-day mean forecast. Examination of the variability of skill among the ensemble members indicates that approximately 40 percent of the perturbed monthly mean forecasts would be useful. Examples of skillful and poor monthly mean predictions am shown and conclusions are drawn as to the implications of the results with regard to the likelihood of success of extended range deterministic forecasts.

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Steven L. Mullen and David P. Baumhefner

Abstract

The relative importance of different parameterized physical process and baroclinic dynamics in numerical simulations of explosive oceanic cyclogenesis is examined. The numerical simulations are derived from a global spectral model having nine vertical levels and a rhomboidal 31 truncation. Eleven cases of rapid cyclogenesis over the North Pacific Ocean that occurred in a 150-day simulation of perpetual January conditions are used as initial conditions for model sensitivity experiments. Statistical techniques based on predictability theory are employed to estimate the relative importance of the sensitivity experiments.

Results from the simulation comparisons indicate that the total diabatic heating accounts for about one-half of the cyclone's deepening rate, with baroclinic dynamics accounting for the remaining part. The absence of diabatic heating also leads to a systematic error in the position of the cyclone. Surface fluxes of sensible heat are responsible for about one-half of the deepening rate due to diabatic processes, while latent heating due to grid-scale resolvable precipitation in conjunction with surface latent heat flux accounts for most of the remaining half. An increase in the surface drag over the ocean to its larger land value was found to be of comparable importance to both surface sensible heat flux and latent beat release, but was only half as important as the total diabatic heating. These changes in the model were judged to produce highly significant response especially at low levels. The addition of radiative heating, the substitution of a Kuo-type cumulus parameterization scheme for the model's default moist convective adjustment scheme, and a factor of 4 increase in the horizontal diffusion did not produce significant responses.

The case-to-case variability exhibited by the 11-member ensemble is examined. The potential danger in attempting to generalize results from a single case of explosive cyclogenesis as being representative of those for the ensemble average is illustrated.

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David P. Baumhefner and Richard C. J. Somerville

Abstract

No abstract available.

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David P. Baumhefner and Paul R. Julian

Abstract

The error produced by an observational system of remotely sensed temperature profiles is partially simulated with aid of an operational retrieval scheme. A given temperature distribution is converted to radiances and then back to temperatures. A comparison is made between the retrieved temperatures and the original values.

The sensitivity of the retrieval scheme to various input parameters, such as guess profiles, the statistical coefficients used in the retrieval scheme, and cloudiness, is examined. Experiments with the placement of a reference level from which to integrate the geopotential field are performed.

The relative growth rate of the simulated initial error is examined by forecasting with two initial states, one case with the observational error and one central case. Error growths are calculated for different reference levels, clear, and cloudy cases.

The results show that the generated error fields do not seriously contaminate forecasts of the large-scale baroclinic waves for periods up to one week, providing the retrievals are free of the effects of clouds and an accurate guess is used. In general, the results support the need for reference-level information, and if forecast error in the low troposphere is to be a minimum, placing of the reference level at sea level.

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Stephen J. Colucci and David P. Baumhefner

Abstract

A set of thirty 30-day mean 500-mb height anomaly forecasts run from National Meteorological Center initial analyses by the NCAR Community Climate Model is examined in order to learn if the forecast accuracy can be estimated with the initial conditions. Defining initial weather regimes by a 500-mb geostrophic zonal index anomaly difference between 50°W and 10°E discriminates between the best and worst 30-day mean forecasts in the sample. Initial regimes characterized by anomalously high zonal index (500-mb geostrophic westerlies) at 50°W and low index at 10°E yield on average lower 30-day mean forecast-observed anomaly correlation than initial regimes with opposite conditions (anomalously low zonal index at 50°N and high index at 10°E). It is suggested that initial regimes with abnormally fast geostrophic 500-mb westerlies at 50°W are followed in time by intense and poorly forecast synoptic-scale cyclones over the Atlantic Ocean. It is shown in a case study that the local synoptic- to planetary-scale interaction, as measured by its contribution to quasigeo-strophic 500-mb height tendencies, is misforecast early in the forecast cycle following these initial conditions. Early rapid synoptic-scale error growth, in this case, is followed by rapid planetary-scale error growth (incorrectly forecast demise of a blocking pattern), deterioration of forecast accuracy, and an unskillful 30-day mean forecast. It is further suggested that initial regimes with abnormally weak 500-mb geostrophic westerlies at 50°W are followed in time by less intense, but better forecast, cyclone waves over the Atlantic Ocean. In a representative case study, the local synoptic- to planetary-scale interaction is well forecast early in the forecast cycle. The local synoptic- and planetary-scale error growths are more restrained (a blocking pattern is correctly maintained), deterioration of forecast accuracy is postponed, and a skillful 30-day mean forecast results. It is hypothesized on the basis of this work that the accuracy of numerical 30-day mean forecasts may depend upon the accuracy with which the cyclones and their interactions with the planetary scale are predicted early in the forecast cycle, and that this accuracy in turn may depend upon the initial conditions.

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Stephen J. Colucci and David P. Baumhefner

Abstract

The forecastability of a blocking episode during January 1985 over Europe and the eastern Atlantic Ocean is studied with forecast ensembles. Ten-member ensembles from version CCM2 (at T42 resolution) of the Community Climate Model of the National Center for Atmospheric Research are initialized at various lead times prior to the analyzed block onset and run out to 14 days. Particular attention is focused on the ensemble initialized five days prior to block onset since, of all the ensembles, this one was characterized by the greatest variability concerning the block-onset prediction. Two of the 10 members of this particular ensemble predicted a transition from unblocked to blocked flow over the Atlantic–Europe half of the Northern Hemisphere during the 14-day forecast range, but not without error; details regarding the timing and/or location of the block were misforecast. A comparison of these ensemble members, plus one other that did not predict a transition to blocking, with the corresponding analyses revealed that the block forecast errors could be traced to a model failure to predict the anomalously weakened midtropospheric planetary-scale geostrophic westerlies analyzed upstream of and prior to the block onset. The forecast error appeared to be attributable to a model bias toward an erroneously southward displacement of the midtropospheric zonal jet over Europe and the eastern Atlantic Ocean. On the other hand, the interaction between the planetary-scale flow and synoptic-scale activity, as measured by the midtropospheric advection of synoptic-scale quasigeostrophic potential vorticity by the planetary-scale geostrophic wind, was well predicted by the ensemble members, but perhaps fortuitously. The results demonstrate that the forecast ensemble was able to overcome the influence of the systematic error by indicating the possibility of a transition to blocked flow over the domain and within the forecast range.

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Richard A. Anthes and David P. Baumhefner

In operational numerical weather prediction systems, both observations and numerical models contribute to the skill of the forecast. A simple diagram representing the relative contributions of observations and models to the current level of forecast skill and to the ultimate predictability of atmospheric phenomena is interpreted in this note. The forecast skill of 500 mb heights and an estimate of the ultimate predictability of this variable are used in a quantitative illustration of the diagram.

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Steven L. Mullen and David P. Baumhefner

Abstract

The impact of initial condition uncertainty on short-range (up to 48 h) forecasts of large-scale explosive cyclogenesis is examined. Predictability experiments are conducted on 11 cases of rapid oceanic cyclogenesis that occurred in a long-term, perpetual January integration of a global, high-resolution, spectral model. Results are derived from the 11-case ensemble average. The perturbation used to represent the initial condition error in this study has a magnitude and spatial distribution that closely matches estimates of global analysis error. Results from the predictability experiments are compared to a set of physics sensitivity experiments which are used to represent an estimate of a “typical” modeling, error.

Compared to the control simulations, the inclusion of initial error produces a composite cyclone with maximum deepening rate that is slightly reduced and a 24 h period of most rapid deepening that is somewhat delayed. The absolute position error in the surface cyclone is approximately 100 km the first +36 h of the forecast then abruptly increases to 300 km by +48 h. We estimate that, on the average, the forecast error due to initial condition uncertainty is as large as that due to the modeling error associated with today's best operational models, whereas five years ago modeling error was much more important.

The relative importance of initial condition uncertainty for explosive cyclogenesis is compared to that for the entire midlatitude flow in general. Error growth rates in an explosive cyclogenetic environment are 50% greater in the upper troposphere (500 mb and above) and two times faster near the surface (850 mb and below). The rapid growth rates indicate that short-range forecasts of explosive cyclogenesis are much wore sensitive to initial error than those for ordinary flows.

The case-to-case variability exhibited by the 11-member ensemble is examined. Noteworthy departure from the aggregate results are evident In individual cases, initial condition error can lead to short-range forecast differences which can be either greater than those due to a typical modeling error or much less. This variability implies a strong sensitivity to initial condition perturbation location and structure.

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