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Robert S. Davis

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Courtenay Strong and Robert E. Davis

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Numerous teleconnections have been identified based upon spatial variability in sea level pressure or lower-tropospheric geopotential height fields. These teleconnections, which are commonly strongest in winter when the mean meridional temperature gradient is large, typically are neither derived from nor linked to changes in the jet stream. Here, winter tropospheric jet stream cores over the Northern Hemisphere (NH) are recovered from 6-hourly gridded data and interannual variability in winter jet core position, speed, and pressure are investigated in the context of NH teleconnections. Common methods for researching jet stream speed and position variability may yield unrepresentative results because jet core pressure variability is ignored (only one isobaric surface is evaluated) or pressure variability effects are smoothed (values are vertically averaged across several isobaric surfaces). In this analysis, data are extracted at the surface of maximum wind, thus controlling for jet core pressure variability and allowing for a more representative tracking of three-dimensional jet core variations.

In the extratropics, the leading pattern of variability in jet core frequency is correlated with the Arctic Oscillation index (AOI) and appears as an oscillation about the spiral-shaped mean configuration of the winter jet stream. In contrast to previous research, the authors find no evidence of Pacific jet deceleration during positive AOI. The second leading mode of variability appears as a split (merged) winter-mean jet stream in the east Pacific together with a merged (split) winter-mean jet stream over North America, a pattern of change that correlates with the Pacific–North American pattern and is reflected in the amplitude of the long-wave ridge over western North America.

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David R. Walker and Robert E. Davis

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A climatology of the once-daily (0000 UTC) 1000-hPa error fields of the National Meteorological Center's 80-wave Medium-Range Forecast (MRF) model is studied. An analysis of the error field has been conducted over the contiguous United States and over the Northern Hemisphere from 20° to 80°N for three warm and four cool seasons (9 September 1987 to 6 March 1991). Temporal and spatial mean error fields over various integration lengths are presented.

The skill, as measured by the anomaly correlation, has not significantly changed over the lifetime of the 80-wave MRF model. Anomaly correlation values at 1000 hPa and 500 hPa show that the model is retaining useful information about the anomalies in the height field out to about one week. A reduction in the model biases may reflect an improvement in model physics (longwave radiational calculations, etc). The cool and warm seasons have distinctly different spatial error patterns. The 1000-hPa warm season shows spurious height falls over the southwestern United States that grow with increasing integration length. The 1000-hPa cool season underestimates the intensity of low pressure systems over and east of Hudson Bay and overestimates their strength over the Pacific Northwest.

Principal components analysis of the 429-variable error covariance matrices for the cool and warm seasons identifies 6 orthogonal variables that explain over 60% of the original error variance. MRF model problems appear to be related to problems the model has with simulating the atmosphere's interaction with orographic features (Alberta and Colorado Rockies), storm tracks and baroclinic zones (Gulf Stream region and United States-Canadian border), and persistent atmospheric features (Hudson Bay low, eastern Pacific subtropical high, and desert Southwest heat low).

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Robert E. Davis and David R. Walker

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An automated, year-round synoptic climatology is developed for the western United States from rawinsonde observations from 1979 to 1988. The classification uses thermal, moisture, and flow parameters to characterize seasonal and interannual synoptic-scale variations in hydrodynamic and thermodynamic conditions. Based on twice daily observations for a network of 21 stations from the Pacific coast to the Rockies, a synoptic climatology is developed using air temperature, dewpoint temperature, geopotential height, and the east-west and north-south components of the wind vector at 800-, 700-, 500-, and 250-mb constant pressure surfaces. The 798 variable by 3620 day matrix is reduced to six orthogonal principal components, and the resulting component scores are grouped using a two-stage clustering technique. The 13 synoptic situations represent days experiencing homogeneous weather conditions. These synoptic situations exhibit marked seasonality and interannual variability and depict features observed in the general circulation of the atmosphere. Examples include both summer monsoonal and dry situations, zonal flow situations with a strong polar or subtropical jet, or both and strong ridging or troughing, and meridional flow.

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Jan R. Rogers and Robert H. Davis

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The inclusion of van der Waals attractions in the interaction between cloud droplets has been recently shown to significantly increase the collision efficiencies of the smaller droplets. In the current work, these larger values for the collision efficiencies are used in a population dynamics model of the droplet size distribution evolution with time, in hopes of at least partially resolving the long-standing paradox in cloud microphysics that predicted rates of the onset of precipitation are generally much lower than those which are observed. Evolutions of several initial cloud droplet spectra have been tracked in time. Size evolutions are compared as predicted from the use of collision efficiencies computed using two different models to allow for droplet–droplet contact: One which considers slip flow effects only, and one which considers the combined effects of van der Waals forces and slip flow. The rate at which the droplet mass density function shifts to larger droplet sizes is increased by typically 20–25% when collision efficiencies which include van der Waals forces are used. The overall result is the more rapid formation of larger, rain-sized droplets, particularly from initial distributions with small values of the average radii and narrow initial distributions.

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Adam Winstral, Kelly Elder, and Robert E. Davis

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Wind is widely recognized as one of the dominant controls of snow accumulation and distribution in exposed alpine regions. Complex and highly variable wind fields in rugged terrain lead to similarly complex snow distribution fields with areas of no snow adjacent to areas of deep accumulation. Unfortunately, these complexities have limited inclusion of wind redistribution effects in spatial snow distribution models. In this study the difficulties associated with physically exhaustive wind field modeling are avoided and terrain-based parameters are developed to characterize wind effects. One parameter, , was based on maximum upwind slopes relative to seasonally averaged winds to characterize the wind scalar at each pixel location in an alpine basin. A second parameter, , measured upwind breaks in slope from a given location and was combined with an upwind application of to create a drift delineator parameter, D 0, which was used to delineate sites of intense redeposition on lee slopes. Based on 504 snow depth samples from a May 1999 survey of the upper Green Lakes Valley, Colorado, the correlation of the developed parameters to the observed snow distribution and the effect of their inclusion in a spatial snow distribution model were quantified. The parameter was found to be a significant predictor, accounting for more of the variance in the observed snow depth than could be explained by elevation, solar radiation, or slope. Samples located in D 0-delineated drift zones were shown to have significantly greater depths than samples located in nondrift zones. A regression tree model of snow distribution based on a predictor variable set of , D 0, elevation, solar radiation, and slope explained 8%–23% more variance in the observed snow distribution, and performed noticeably better in unsampled areas of the basin, compared to a regression tree model based on only the latter three predictors.

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Robert C. Mcarthur, James R. Davis, and David Reynolds

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The purpose of this paper is to illustrate how the construction of a knowledge-based system (KBS) to support nowcasting, can be used to guide and facilitate the development of objective pattern recognition algorithms for use with meteorological data. We believe that a KBS based on the semantic interpretation of weather data, using the concept of weather scenarios, can assist the development and use of objective algorithms for pattern recognition in two ways:

1) it focuses the development of pattern recognition algorithms on only those phenomena which are most useful to operational forecasters;

2) its top-down logic constrains when, where, and how objective algorithms should be applied.

We first describe our understanding of nowcasting expertise and the use of pattern recognition (“manual”) by human forecasters. We then briefly review the current use of automatic pattern recognition in nowcasting, present the elements within a scenario and discuss a KBS architecture for using scenarios. Finally, we close by discussing the practical benefits of merging a qualitative KBS with algorithmic pattern recognition techniques.

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Anthony Davis, Alexander Marshak, Warren Wiscombe, and Robert Cahalan

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This study investigates the internal structure of marine stratocumulus (Sc) using the spatial fluctuations of liquid water content (LWC) measured along horizontal flights off the coast of southern California during the First ISCCP Regional Experiment (FIRE) in summer of 1987. The results of FIRE 87 data analyses are compared to similar ones for marine Sc probed during the Atlantic Stratocumulus Transition Experiment (ASTEX) in summer 1992 near the Azores. In this first of two parts, the authors use spectral analysis to determine the main scale-invariant regimes, defined by the ranges of scales where wavenumber spectra follow power laws; from there, they discuss stationary issues. Although crucial for obtaining meaningful spatial statistics (e.g., in climate diagnostics), the importance of establishing stationarity—statistical invariance under translation—is often overlooked. The sequel uses multifractal analysis techniques and addresses intermittency issues. By improving our understanding of both nonstationarity and intermittency in atmospheric data, we are in a better position to formulate successful sampling strategies.

Comparing the spectral responses of different instruments to natural LWC variability, the authors find scale breaks (characteristic scales separating two distinct power law regimes) that are spurious, being traceable to well-documented idiosyncrasies of the Johnson–Williams probe and forward scattering spectrometer probes. In data from the King probe, the authors find no such artifacts; all spectra are of the scale-invariant form k −β with exponents β in the range 1.1–1.7, depending on the flight. Using the whole FIRE 87 King LWC database, the authors find power-law behavior with β = 1.56 ± 0.06 from 20 m to 20 km. From a spectral vantage point, the ASTEX cloud system behaves statistically like a scaled-up version of FIRE 87: a similar exponent β = 1.43 ± 0.08 is obtained, but the scaling range is shifted to [60 m, 60 km], possibly due to the 2–3 times greater boundary layer thickness.

Finally, the authors reassess the usefulness of spectral analysis:

  1. • Its main shortcoming is ambiguity: very different looking stochastic processes can yield similar, even identical, spectra. This problem impedes accurate modeling of the LWC data and, ultimately, is why multifractal methods are required.
  2. • Its main asset is applicability in stationary and nonstationary situations alike and, in conjunction with scaling, it can be used to detect nonstationary behavior in data.

Having β > 1, LWC fields in marine Sc are nonstationary within the scaling range and stationary only at larger scales. Nonstationarity implies long-range correlations, and we demonstrate the damage these cause when tying to estimate means and standard deviations with limited amounts of LWC data.

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Robert G. Nystrom, Richard Rotunno, Chris A. Davis, and Fuqing Zhang

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Several previous studies have demonstrated the significant sensitivity of simulated tropical cyclone structure and intensity to variations in surface-exchange coefficients for enthalpy (C k) and momentum (C d), respectively. In this study we investigate the consistency of the estimated peak intensity, intensification rate, and steady-state structure between an analytical model and idealized axisymmetric numerical simulations for both constant C k and C d values and various wind speed–dependent representations of C k and C d. The present analysis with constant C k and C d values demonstrates that the maximum wind speed is similar for identical C k/C d values less than 1, regardless of whether changes were made to C k or C d. However, for a given C k/C d greater than 1, the simulated and theoretical maximum wind speed are both greater if C d is decreased compared to C k increased. This behavior results because of a smaller enthalpy disequilibrium at the radius of maximum winds for larger C k. Additionally, the intensification rate is shown to increase with C k and C d and the steady-state normalized wind speed beyond the radius of maximum winds is shown to increase with increasing C d. Experiments with wind speed–dependent C k and C d were found to be generally consistent, in terms of the intensification rate and the simulated and analytical-model-estimated maximum wind speed, with the experiments with constant C k and C d.

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Oliver W. Frauenfeld, Robert E. Davis, and Michael E. Mann

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A new and distinctly interdecadal signal in the climate of the Pacific Ocean has been uncovered by examining the coupled behavior of sea surface temperatures (SSTs) and Northern Hemisphere atmospheric circulation. This interdecadal Pacific signal (IPS) of ocean–atmosphere interaction exhibits a highly statistically significant interdecadal component yet contains little to no interannual (El Niño scale) variability common to other Pacific climate anomaly patterns. The IPS thus represents the only empirically derived, distinctly interdecadal signal of Pacific Ocean SST variability that likely also represents the true interdecadal behavior of the Pacific Ocean–atmosphere system. The residual variability of the Pacific’s leading SST pattern, after removal of the IPS, is highly correlated with El Niño anomalies. This indicates that by simply including an atmospheric component, the leading mode of Pacific SST variability has been decomposed into its interdecadal and interannual patterns. Although the interdecadal signal is unrelated to interannual El Niño variability, the interdecadal ocean–atmosphere variability still seems closely linked to tropical Pacific SSTs. Because prior abrupt changes in Pacific SSTs have been related to anomalies in a variety of physical and biotic parameters throughout the Northern Hemisphere, and because of the persistence of these changes over several decades, isolation of this interdecadal signal in the Pacific Ocean–atmosphere system has potentially important and widespread implications to climate forecasting and climate impact assessment.

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