Abstract

The presence of melting snow in a radar beam produces a highly enhanced return which can lead to large errors in estimates of areal surface rainfall made with radar. This paper describes an algorithm for use in real time to detect the presence of bright bands in conventional (non-Doppler) weather rádar data. The algorithm derives values for the characteristic parameters of height and intensity of the bright band which can then be used to calculate a correction. Detection relies on the fact that the bright band causes a peak in the apparent rainfall rate measured by the radar at a range dependent on its height above the radar and the elevation of the radar beam.

Analysis of about 270 hours of data collected over a period of five months shows that the algorithm is reliable. It detects the bright-band effect when it occurs and there is sufficient precipitation present and does not raise any false alarms. When the precipitation is patchy it is able to identify the bright band correctly but not with enough confidence to apply a correction. When a correction is applied the “error” is reduced by approximately 50% on average.

Abstract

The climatological large-scale patterns of diurnal and semidiurnal near-surface wind variations over the tropical Pacific Ocean are documented using 3 yr of hourly measurements from the Tropical Atmosphere–Ocean moored buoy array. Semidiurnal variations account for 68% of the mean daily variance of the zonal wind component, while diurnal variations account for 82% of the mean daily variance of the meridional wind component. The spatially uniform amplitude (0.15 m s⁻¹) and phase (0300 LT) of the semidiurnal zonal wind variations are shown to be consistent with atmospheric thermal tidal theory.

The diurnal meridional wind variations on either side of the equator are approximately out of phase. This pattern results in a diurnal variation of wind divergence along the equator, with maximum divergence in the early morning (∼0800 LT). The average amplitude of the diurnal cycle in zonal mean divergence is 0.45 × 10⁻⁶ s⁻¹, which corresponds to a day–night change of 45% relative to the daily mean. The relative day–night changes in near-surface equatorial wind divergence are larger in the western Pacific (78%) than in the eastern Pacific (31%) due mainly to differences in the daily mean divergence. The diurnal amplitude of equatorial wind divergence changes seasonally and interannually in proportion to the strength of the mean divergence.

It is suggested that diurnal heating of the sea surface may contribute to the zonally symmetric diurnal cycle of equatorial wind divergence.

Abstract

The variability of monthly mean sea-surface temperature (SST) anomalies over the extratropical North Atlantic and Pacific and its relation to atmospheric circulation anomalies over the Northern Hemisphere during wintertime is investigated, by applying eigenvector analysis to a 39-year dataset and correlating the time series of the resulting expansion coefficients with the hemispheric 500 mb height and sea-level pressure fields.

In agreement with previous studies, the simultaneous correlation between the tone series of the expansion coefficient of the first empirical orthogonal function (EOF 1) of North Pacific sea surface temperature (SST) and the hemispheric 500 mb height field resembles the Pacific/North American pattern, and the corresponding pattern for Atlantic SST resembles the North Atlantic Oscillation. In the vicinity of the conters of action of thew patterns, the SST fluctuations associated with these modes explain on the order of half the variance of 500 mb height and slightly less of the variance of the associated ma-level pressure fluctuations.

Analogous calculations were performed on the anomalous midwinter SST tendency, defined as the SST anomalies averaged from February through April minus the SST anomalies averaged from October through December of the previous calendar year. The leading EOF's of SST tendency in both oceans exhibit distinctive “sandwich” patterns, with bands anomalies of one polarity over the western ocean centered near 30°–35°N, flanked by anomalies of the opposite polarity over the northern oceans near 50°N and in the subtropics, new 15°N. EOF 1 of North Atlantic SST tendency is more robust than its counterpart for SST itself. The correlation between the time series of the expansion coefficient of EOF 1 of North Pacific SST tendency and the hemispheric 500 mb height field resembles the western Pacific pattern, and the corresponding pattern for Atlantic SST resembles the western Atlantic pattern, as defined by Wallace and Gutzler. “High zonal index” atmospheric circulation patterns in winter [i.e., strong anticyclones new 30°N, strong westerlies along 50°N, and strong tradewinds] are observed in association with anomalous warming of the ocean in the anticyclone belt and cooling in the westerly and tradewind belts. It is suggested that these patterns are particularly effective in modulating the fluxes of latent and sensible heat at the air-sea interface and wind-driven vertical mixing and entrainment through their influence upon surface wind speed.

Abstract

This paper introduces a conceptual framework for comparing methods that isolate important coupled modes of variability between time series of two fields. Four specific methods are compared: principal component analysis with the fields combined (CPCA), canonical correlation analysis (CCA) and a variant of CCA proposed by Barnett and Preisendorfer (BP), principal component analysis of one single field followed by correlation of its component amplitudes with the second field (SFPCA), and singular value decomposition of the covariance matrix between the two fields (SVD). SVD and CPCA are easier to implement than BP, and do not involve user-chosen parameters. All methods are applied to a simple but geophysically relevant model system and their ability to detect a coupled signal is compared as parameters such as the number of points in each field, the number of samples in the time domain, and the signal-to-noise ratio are varied.

In datasets involving geophysical fields, the number of sampling times may not be much larger than the number of observing locations or grid points for each field. In a model system with these characteristics, CPCA usually extracted the coupled pattern somewhat more accurately than SVD, BP, and SFPCA, since the patterns it yielded exhibit smaller sampling variability; SVD and BP gave quite similar results; and CCA was uncompetitive due to a high sampling variability unless the coupled signal was highly localized. The coupled modes derived from CPCA and SFPCA exhibit an undesirable mean bias toward the leading EOFs of the individual fields; in fact, for small signal-to-noise ratios these methods may identify a coupled signal that is similar to a dominant EOF of one of the fields but is completely orthogonal to the true coupled signal within that field. For longer time series, or in situations where the coupled signal does not resemble the EOFs of the individual fields, these biases can make SVD and BP substantially superior to CPCA.

Abstract

Single field principal component analysis (PCA), direct singular value decomposition (SVD), canonical correlation analysis (CCA), and combined principal component analysis (CPCA) of two fields are applied to a 39-winter dataset consisting of normalized seasonal mean sea surface temperature anomalies over the North Pacific and concurrent 500-mb height anomalies over the same region. The CCA solutions are obtained by linear transformations of the SVD solutions. Spatial patterns and various measures of the variances and covariances explained by the modes derived from the different types of expansions are compared, with emphasis on the relative merits of SVD versus CCA. Results for two different analysis domains (i.e., the Pacific sector versus a full hemispheric domain for the 500-mb height field) are also compared in order to assess the domain dependence of the two techniques. The SVD solution is also compared with the results of 28 Monte Carlo simulations in which the temporal order of the SST grids is randomized and found to be highly significant.

As expected, the leading SVD modes explain substantially more of the squared covariance between the two fields than any of the CCA modes, while the paired expansion coefficients of the leading CCA modes are more strongly correlated than any of the SVD modes. The expansion coefficient for the leading SVD mode is almost identical to the leading principal component of the SST field, regardless of whether the 500-mb height field is hemispheric or restricted to the Pacific sector. SST patterns strongly resembling the second and third EOFs are also recovered among the three leading SVD modes.

The leading CCA mode in the expansion based on the three leading singular value vectors for the Pacific sector resembles the pattern of anomalies observed in association with ENSO. The other modes more closely resemble the patterns derived from PCA of the 500-mb height field than those for the SVD modes on which they are based. The SVD and CPCA solutions for the first three modes proved to be quite similar.

The SVD and CCA solutions based on the hemispheric 500-mb height field are indicative of a coupling between the interannual variability of North Pacific and North Atlantic SST by virtue of their mutual relationship to one of the atmosphere's most prominent planetary wave patterns.

Abstract

This study documents statistical relationships between the El Niño–Southern Oscillation (ENSO) phenomenon and extreme seasonal temperature anomalies over the continental United States. Relationships are examined for El Niño and La Niña conditions for each of the four standard seasons. Two complementary approaches are used. In the first approach, seasonal temperature anomalies are ranked from coldest to warmest over a 100-yr climate division dataset. Mean Southern Oscillation index (SOI) values are then computed for times preceding or concurrent with extreme seasonal temperature anomalies to define regions where relationships between the SOI and seasonal temperature extremes are statistically significant. In the second approach, seasonal extremes in the SOI, which are generally related to El Niño or La Niña, are first identified, and then the numbers of extreme temperature seasons occurring in association with these events are determined. Comparison of the observed number of extreme seasons with the climatologically expected values provides quantitative estimates of how ENSO alters the conditional probability, or risk, of large seasonal temperature anomalies in a given region.

The results show that the greatest geographical coverage of statistically significant relationships between ENSO and seasonal temperature extremes occurs in winter and spring, especially with the SOI leading by one season. Certain well-recognized relationships for seasonal temperature anomalies are also confirmed for extreme seasons, such as the association of El Niño conditions with very warm winters over the Pacific Northwest and very cold winters along the Gulf Coast. Other less-discussed relationships also appear, including possible nonlinearities in relationships between El Niño and La Niña events and extremes in autumn temperatures. Some relationships show evidence of secular changes, especially in summer.

In some regions and times of year, El Niño and La Niña conditions substantially alter the probabilities of very warm or very cold seasons. For example, over Texas, El Niño conditions in winter almost triple the risk that the subsequent spring will be very cold, while significantly reducing the risk of a very warm spring. In the same region, wintertime La Niña conditions double the risk that the following spring will be very warm, while significantly reducing the likelihood of a very cold spring. Therefore, given the proper ENSO phase, skillful forecasts of regional risks of seasonal temperature extremes appear feasible.

While atmospheric reanalysis datasets are widely used in climate science, many technical issues hinder comparing them to each other and to observations. The reanalysis fields are stored in diverse file architectures, data formats, and resolutions. Their metadata, such as variable name and units, can also differ. Individual users have to download the fields, convert them to a common format, store them locally, change variable names, regrid if needed, and convert units. Even if a dataset can be read via the Open-Source Project for a Network Data Access Protocol (commonly known as OPeNDAP) or a similar protocol, most of this work is still needed. All of these tasks take time, effort, and money. Our group at the Cooperative Institute for Research in the Environmental Sciences at the University of Colorado and affiliated colleagues at the NOAA's Earth System Research Laboratory Physical Sciences Division have expertise both in making reanalysis datasets available and in creating web-based climate analysis tools that have been widely used throughout the meteorological community. To overcome some of the obstacles in reanalysis intercomparison, we have created a set of web-based Reanalysis Intercomparison Tools (WRIT) at www.esrl.noaa.gov/psd/data/writ/. WRIT allows users to easily plot and compare reanalysis datasets, and to test hypotheses. For standard pressure-level and surface variables there are tools to plot trajectories, monthly mean maps and vertical cross sections, and monthly mean time series. Some observational datasets are also included. Users can refine date, statistics, and plotting options. WRIT also facilitates the mission of the Reanalyses.org website as a convenient toolkit for studying the reanalysis datasets.

Abstract

We use idealized large-eddy simulations (LES) and a simple analytical theory to study the influence of submesoscales on the concentration and export of sinking particles from the mixed layer. We find that restratification of the mixed layer following the development of submesoscales reduces the rate of vertical mixing which, in turn, enhances the export rate associated with gravitational settling. For a neutral tracer initially confined to the mixed layer, subinertial (submesoscale) motions enhance the downward tracer flux, consistent with previous studies. However, the sign of the advective flux associated with the concentration of sinking particles reverses, indicating reentrainment into the mixed layer. A new theory is developed to model the gravitational settling flux when the particle concentration is nonuniform. The theory broadly agrees with the LES results and allows us to extend the analysis to a wider range of parameters.

Abstract

Atmospheric transport and dispersion over complex terrain were investigated. Meteorological and sulfur hexafluoride (SF₆) concentration data were collected and used to evaluate the performance of a transport and diffusion model coupled with a mass consistency wind field model. Meteorological data were collected throughout April 1995. Both meteorological and plume location and concentration data were measured in December 1995. The meteorological data included measurements taken at 11–15 surface stations, one to three upper-air stations, and one mobile profiler. A range of conditions was encountered, including inversion and postinversion breakup, light to strong winds, and a broad distribution of wind directions.

The models used were the MINERVE mass consistency wind model and the SCIPUFF (Second-Order Closure Integrated Puff) transport and diffusion model. These models were expected to provide and use high-resolution three-dimensional wind fields. An objective of the experiment was to determine if these models could provide emergency personnel with high-resolution hazardous plume information for quick response operations.

Evaluation of the models focused primarily on their effectiveness as a short-term (1–4 h) predictive tool. These studies showed how they could be used to help direct emergency response following a hazardous material release. For purposes of the experiments, the models were used to direct the deployment of mobile sensors intended to intercept and measure tracer clouds.

The April test was conducted to evaluate the performance of the MINERVE wind field generation model. It was evaluated during the early morning radiation inversion, inversion dissipation, and afternoon mixed atmosphere. The average deviations in wind speed and wind direction as compared to observations were within 0.4 m s⁻¹ and less than 10° for up to 2 h after data time. These deviations increased as time from data time increased. It was also found that deviations were greatest during inversion dissipation.

The December test included the release and tracking of atmospheric tracers. The MINERVE–SCIPUFF modeling system was used to direct remote sensing equipment. Posttest analyses were performed to determine model reliability. It was found that plume centroid position as determined by the models was within 10% of the observed plume centroid.