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Jan Paegle, Julia E. Paegle, Fred P. Lewis, and Allan J. McGlasson

Abstract

Conventional explanations of the strong jet streams and active storm tracks off the east coasts of Asia and North America invoke the ambient winter baroclinicity that is generated by land-sea contrasts and the tendency of the continental relief to generate long-wave troughs over these regions (Charney and Eliassen, 1949; Smagorinsky, 1953; Bates, 1977; and many others). These arguments do not extend in an obvious manner into the rest of the globe. In particular, they do not seem to account for the significant global-scale variability that exists in the tropics and in most of the Southern Hemisphere. Here, the planetary-scale flow apparently responds less to topography, and the land-sea contrasts are very much smaller than those found around 40°N, but significant large-scale variability persists. The present analysis of a preliminary DST data set suggests that much of this variability is due to the presence of large-scale monsoons. Some model studies suggest that strong transient disturbances of the monsoon may propagate energy toward subtropical jet streams on short time scales.

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Julio Buchmann, Lawrence E. Buja, Jan Paegle, Chi-Dong Zhang, and David P. Baumhefner

Abstract

We investigate the pattern of Amazon Basin rainfall forecasts of ten Global Weather Experiment (GWE) cases. Although the computations are based upon a rather crude wavenumber 15 resolution, the control forecasts exhibit a rather fine structure of the rainfall over tropical South America, including enhancements over the interior of the Amazon Basin and suppression on the northeast coast of Brazil. The forecasts appear to be in rather good agreement with climatology. The sensitivity of this model forecast to the presence of anomalous east Pacific heating is investigated through experiments in which a nonadiabatic term is added to the thermodynamic equation. These experiments suggest significant suppression of rainfall over the central Amazon Basin, and especially over the northeast portion of Brazil. This suppression is associated with the downward branch of a Walker circulation whose development is determined by a region of subsidence which propagates eastward from the eastern Pacific at a rate of about 30 m s−1. This evolution, which is consistent with the Kelvin wave contribution to the Walker cell, affects Brazil within about two days of the heating onset.

The evolution of upper-level convergence, implied sinking motion, and suppression of rainfall over tropical South America in the forecasts does not depend sensitively upon the placement of the anomalous tropical Pacific heating. In particular, enhancements of the North Pacific are approximately as effective as those of the South Pacific.

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Jennifer C. Roman, Gonzalo Miguez-Macho, Lee A. Byerle, and Jan Paegle

Abstract

Current limitations of atmospheric predictive skill are investigated through comparison of correlation and error statistics of operational and research global models for two winter seasons. In 1993, bias-corrected models produced anomaly correlations of 0.6 after 6.5–7 days, with relatively little forecast skill beyond that point. In 2003, the forecast skill of a more developed, higher-resolution operational model has been extended 36 h, while the skill of the unchanged, low-resolution research model has been extended 6 h. This implies more predictable patterns in 2003 or model and initial state improvements made since 1993. The relative importance of improved model resolution/physics and improved initial state to the lengthening of forecast skill is diagnosed through the evaluation of rms evolution of analyzed and forecast differences of 500-mb height and meridional wind. Results indicate that forecast sensitivity to initial data is less important than is the sensitivity to the model used. However, the sensitivity to model used (rms of model forecast differences) is smaller than the rms forecast error of either model, indicating model forecasts are more similar to each other than to reality. In 1993, anomaly correlations of model forecasts to each other reach 0.6 by roughly 8 days; that is, the models predict each other's behavior 1.5 days longer than they predict that of the real atmosphere. Correlations of model errors to each other quantify this similarity, with correlations exceeding the asymptotic value of 0.5 through the 14-day forecasts. Investigations of initial state error evolution by wavenumber show long waves (0–15) account for 50% more of the total uncertainty growth in 14-day research model integrations than do short waves (16–42). Results indicate current predictive skill may be impacted by model sophistication, but error pattern similarities suggest a common deficiency of models, perhaps in the initial state uncertainty.

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Bryan G. White, Jan Paegle, W. James Steenburgh, John D. Horel, Robert T. Swanson, Louis K. Cook, Daryl J. Onton, and John G. Miles

Abstract

The short-term forecast accuracy of six different forecast models over the western United States is described for January, February, and March 1996. Four of the models are operational products from the National Centers for Environmental Prediction (NCEP) and the other two are research models with initial and boundary conditions obtained from NCEP models. Model resolutions vary from global wavenumber 126 (∼100 km equivalent horizontal resolution) for the Medium Range Forecast model (MRF) to about 30 km for the Meso Eta, Utah Local Area Model (Utah LAM), and Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model Version 5 (MM5). Forecast errors are described in terms of bias error and mean square error (mse) as computed relative to (i) gridded objective analyses and (ii) rawinsonde observations. Bias error and mse fields computed relative to gridded analyses show considerable variation from model to model, with the largest errors produced by the most highly resolved models. Using this approach, it is impossible to separate real forecast errors from possibly correct, highly detailed forecast information because the forecast grids are of higher resolution than the observations used to generate the gridded analyses. Bias error and mse calculated relative to rawinsonde observations suggest that the Meso Eta, which is the most highly resolved and best developed operational model, produces the most accurate forecasts at 12 and 24 h, while the MM5 produces superior forecasts relative to the Utah LAM. At 36 h, the MRF appears to produce superior mass and wind field forecasts. Nevertheless, a preliminary validation of precipitation performance for fall 1997 suggests the more highly resolved models exhibit superior skill in predicting larger precipitation events. Although such results are valid when skill is averaged over many simulations, forecast errors at individual rawinsonde locations, averaged over subsets of the total forecast period, suggest more variability in forecast accuracy. Time series of local forecast errors show large variability from time to time and generally similar maximum error magnitudes among the different models.

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Wayman E. Baker, George D. Emmitt, Franklin Robertson, Robert M. Atlas, John E. Molinari, David A. Bowdle, Jan Paegle, R. Michael Hardesty, Robert T. Menzies, T. N. Krishnamurti, Robert A. Brown, Madison J. Post, John R. Anderson, Andrew C. Lorenc, and James McElroy

The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate.

This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds.

Observing system simulation experiments, conducted using two different general circulation models, have shown 1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and 2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substantially improve tropical cyclone forecasts. Similarly, to improve water vapor flux divergence calculations, a direct measure of the ageostrophic wind is needed since the present level of uncertainty cannot be reduced with better temperature and moisture soundings alone.

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