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A. Gangopadhyay
and
A. R. Robinson

Abstract

The multiscale feature models (MSFMs) developed for the circulation of the western North Atlantic (Part I) have been used for initialization in this study to forecast the Gulf Stream meanders and rings. The Harvard primitive equation model, which was calibrated and verified for the statistics of the synoptical dynamics in this region (Part II), provides the basis for these simulations. Three 2-week-long synoptical dynamical hindcasts are presented. These hindcasts are carried out in a forecast mode without assimilating any future information. In general, when compared against SST-derived frontal location and ring–stream interactions, 2-week-long forecasts are found to be statistically superior than persistence and dynamically consistent. These forecasts are also compared against similar forecasts based on the U.S. Navy’s Optimum Thermal Interpolation System (OTIS) initializations. It is found that the MSFM-initialized simulations provide a better predictive capability than OTIS-initialized simulations over 2 weeks. Quantitatively, in terms of a statistical measure called the average offset of the axis of the stream, the former did better than persistence in all three cases over the 2-week periods. However, OTIS has two inherent characteristics, namely, a longitudinal distribution of temperature–salinity from climatology, and a warm pool at the core of the stream, which should be incorporated in the MSFM scheme to further enhance its predictive capability. The usefulness of multiscale kinematic synthesis in the initialization of the calibrated primitive equation dynamical model for forecasting the region over a 2-week period brings a closure to this three-part study of circulation and dynamics of the western North Atlantic.

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A. R. Robinson
and
A. Gangopadhyay

Abstract

A multiparameter kinematic synthesis of multiscale feature models for the circulation of the western North Atlantic was developed in Part I of this study. The dispersion characteristics and dynamical behavior of this circulation model are presented here in detail. Insertion of the kinematically synthesized features into a numerical dynamical model dynamically adjust the features and provide the basis for a multiparameter sensitivity study with respect to the reasonable range of parameter variation consistent with observations. Two primary characteristics of the Gulf Stream meandering behavior, namely, the wave-growth characteristics and the ring formation and absorption statistics are studied via both quasigeostrophic and primitive equation dynamics. In achieving realistic dispersion characteristics, a comprehensive methodology for dynamical model tuning and validation in this limited region ocean is developed. The realistic regimes of parameter variation are identified on the basis of observational growth rate and phase speed of the Gulf Stream meanders. Long-term simulations within these realistic regimes provide statistics of ring production and interaction behavior. The observed range of transport variability of the Gulf Stream system further constrains the parameter selection. Final tuning of parameters is obtained through an extensive study of the response from the dynamical models to the changes of parameters of the circulation model. Usefulness of the circulation model for initializing, forecasting, and updating in real synoptic cases will be the focus of Part III of this series of studies.

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E. F. Carter
and
A. R. Robinson

Abstract

A general model for statistically optimal estimates is presented for dealing with scalar, vector and multivariale datasets. The method deals with anisotropic fields and treats space and time dependence equivalently. Problems addressed include the analysis, or the production of synoptic lime series of regularly gridded fields from irregular and gappy datasets, and the estimate of fields by compositing observations from several different instruments and sampling schemes. Technical issues are discussed, including the convergence of statistical estimates, the choice of representation of the correlations, the influential domain of an observation, and the efficiency of numerical computations.

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Jerome A. Smith
,
Christopher N. K. Mooers
, and
Allan R. Robinson

Abstract

The skill with which amplitudes of quasi-geostrophic modes can be estimated is important in the analysis and modeling of data from mixed CTD/XBT surveys. Here, several methods for estimation of quasi-geostrophic vertical mode amplitudes (QGMs) are compared, both in the context of idealized estimation and (especially) in application to some recent CTD and XBT data from the California Current Systems (CCS). The methods compared are: 1) direct least-squares fitting by QGMs (LSF); 2) projection of “empirical orthogonal function” amplitudes onto QGM amplitudes at each station (EOF); 3) ridge regression (RR); 4) an “optimal estimate” using covariances between QGM amplitudes (OE); and 5) another optimal estimate using covariances between EOF amplitudes and QGM amplitudes (CEOF). For deep CTD casts (>1500 m), all methods perform well. For shallow CTD and XBT casts (<750 m), method five (CEOF) is recommended, using EOFs and amplitude covariances derived from just the deeper CTD casts. Since low-frequency internal waves have the same modal structure for density as the QGMS, they are not distinguishable from the QGMs in the present analysis. The analysis is applied to a recent survey to produce amplitude maps for the first few baroclinic modes. Comparisons with another survey indicate that the density analysis is transportable, but the T-S characteristics are so variable that the temperature analysis is not (the surveys are approximately three months apart).

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Avijit Gangopadhyay
,
A. R. Robinson
, and
H. G. Arango

Abstract

This is the first part of a three-part study on the circulation, dynamics, and mesoscale forecasting of the western North Atlantic. The overall objective of this series of studies is threefold: 1) to present a methodology for deriving a dynamically balanced regional climatology that maintains the synoptic structure of the permanent fronts embedded in a mean background circulation, 2) to present a methodology for using such a regional climatology for calibrating and validating dynamical models, and 3) to use similarly derived synoptic realizations as initialization and assimilation fields for mesoscale nowcasting and forecasting.

In this paper, a data-based, kinematically balanced circulation model for the western North Atlantic is developed and described. The various multiscale synoptic and general circulation structures in this region are represented by analytical and analytical/empirical functions based on dynamical considerations and using observational datasets. These include the jet-scale currents, namely, the Gulf Stream and the deep western boundary current, the subbasin-scale recirculating gyres called the southern and the northern recirculation gyres, and the slope water gyre. The inclusion of subbasin-scale gyres as the background circulation for the energetic jet and mesoscale activity in any limited oceanic region is a new paradigm of this multiscale regional modeling study. A generalized kinematical constraint that links the multiscale structures is derived in terms of their interaction scales. For synoptic realizations, the currents and gyres are distorted from their mean state with mass conserving constraints, and mesoscale structures are added thereon. The kinematically balanced linked system is then adjusted via quasigeostrophic dynamics and a regional water-mass model to obtain three-dimensional circulation fields to be used for initialization and assimilation in primitive equation models.

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Daria Kluver
,
Tom Mote
,
Daniel Leathers
,
Gina R. Henderson
,
Weihan Chan
, and
David A. Robinson

Abstract

This study details the creation of a gridded snowfall dataset for North America, with focus on the quality of the interpolated product. Daily snowfall amounts from National Weather Service Cooperative Observer Program stations and Meteorological Service of Canada surface stations are interpolated to 1° by 1° grids from 1900 to 2009 and examined for data record length and quality. The interpolation is validated spatially and temporally through the use of stratified sampling and k-fold cross-validation analyses. Interpolation errors average around 0.5 cm and range from less than 0.01 to greater than 2.5 cm. For most locations, this is within the measurement sensitivity. Grid cells with large variations in elevation experience higher errors and should be used with caution. A new gridded snowfall climatology is presented based on in situ observations that capture seasonal and interannual variability in monthly snowfall over most of the North American land area from 1949 to 2009. The Community Collaborative Rain, Hail and Snow Network is used as an independent set of point data that is compared to the gridded product. Errors are mainly in the form of the gridded data underestimating snowfall compared to the point data. The spatial extent, temporal length, and resolution of the dataset are unprecedented with regard to observational snowfall products and will present new opportunities for examining snowfall across North America.

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Kenneth E. Kunkel
,
Michael A. Palecki
,
Kenneth G. Hubbard
,
David A. Robinson
,
Kelly T. Redmond
, and
David R. Easterling

Abstract

There is an increasing interest in examining long-term trends in measures of snow climatology. An examination of the U.S. daily snowfall records for 1900–2004 revealed numerous apparent inconsistencies. For example, long-term snowfall trends among neighboring lake-effect stations differ greatly from insignificant to +100% century−1. Internal inconsistencies in the snow records, such as a lack of upward trends in maximum seasonal snow depth at stations with large upward trends in snowfall, point to inhomogeneities. Nationwide, the frequency of daily observations with a 10:1 snowfall-to-liquid-equivalent ratio declined from 30% in the 1930s to a current value of around 10%, a change that is clearly due to observational practice. There then must be biases in cold-season liquid-equivalent precipitation, or snowfall, or both. An empirical adjustment of snow-event, liquid-equivalent precipitation indicates that the potential biases can be statistically significant.

Examples from this study show that there are nonclimatic issues that complicate the identification of and significantly change the trends in snow variables. Thus, great care should be taken in interpretation of time series of snow-related variables from the Cooperative Observer Program (COOP) network. Furthermore, full documentation of optional practices should be required of network observers so that future users of these data can properly account for such practices.

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