Balaji Rajagopalan

Analyses of global sea surface temperature 1856–1991

Global analyses of monthly sea surface temperature (SST) anomalies from 1856 to 1991 are produced using three statistically based methods: optimal smoothing (OS), the Kalrnan filter (KF) and optimal interpolation (OI). Each of these is accompanied by estimates of the error covariance of the analyzed fields. The spatial covariance function these methods require is estimated from the available data; the time-marching model is a first-order autoregressive model again estimated from data. The data input for the analyses are monthly anomalies from the United Kingdom Meteorological Office historical sea surface temperature data set (MOHSST5) (Parker et al., 1994) of the Global Ocean Surface Temperature Atlas (COSTA) (Bottoraley et al., 1990). These analyses are compared with each other, with COSTA, and with an analy- sis generated by projection (P) onto a set of empirical orthogonal functions (as in Smith et al. (1996)). In theory, the quality of the analyses should rank in the order OS, KF, OI, P, and COSTA. It is found that the first four give comparable results in the data-rich periods (1951-1991), but at times when data is sparse the first three differ significantly from P and COSTA. At these times the latter two often have extreme and fluctuating values, prima facie evidence of error. The statistical schemes are also verified against data not used in any of the analyses (proxy records derived from corals and air temperature records from coastal and island stations). We also present evidence that the analysis error estimates are indeed indicative of the quality of the products. At most times the OS and KF products are close to the OI product, but at times of especially poor coverage their use of information from other times is advantageous. The methods appear to reconstruct the major features of the global SST field from very sparse data. Comparison with other indications of the E1 Nifio - Southern Oscillation cycle show that the analyses provide usable information on interannual variability as far back as the 1860s.

Seasonal Cycle Shifts in Hydroclimatology over the Western United States

Abstract Analyses of streamflow, snow mass temperature, and precipitation in snowmelt-dominated river basins in the western United States indicate an advance in the timing of peak spring season flows over the past 50 years. Warm temperature spells in spring have occurred much earlier in recent years, which explains in part the trend in the timing of the spring peak flow. In addition, a decrease in snow water equivalent and a general increase in winter precipitation are evident for many stations in the western United States. It appears that in recent decades more of the precipitation is coming as rain rather than snow. The trends are strongest at lower elevations and in the Pacific Northwest region, where winter temperatures are closer to the melting point; it appears that in this region in particular, modest shifts in temperature are capable of forcing large shifts in basin hydrologic response. It is speculated that these trends could be potentially a manifestation of the general global warming trend in r...

Knowledge-sharing and influence in online social networks via viral marketing

Online social networks are increasingly being recognized as an important source of information influencing the adoption and use of products and services. Viral marketing—the tactic of creating a process where interested people can market to each other—is therefore emerging as an important means to spread-the-word and stimulate the trial, adoption, and use of products and services. Consider the case of Hotmail, one of the earliest firms to tap the potential of viral marketing. Based predominantly on publicity from word-of-mouse [4], the Web-based email service provider garnered one million registered subscribers in its first six months, hit two million subscribers two months later, and passed the eleven million mark in eighteen months [7]. Wired magazine put this growth in perspective in its December 1998 issue: “The Hotmail user base grew faster than [that of ] any media company in history—faster than CNN, faster than AOL, even faster than Seinfeld’s audience. By mid-2000, Hotmail had over 66 million users with 270,000 new accounts being established each day.” While the potential of viral marketing to efficiently reach out to a broad set of potential users is attracting considerable attention, the value of this approach is also being questioned [5]. There needs to be a greater understanding of the contexts in which this strategy works and the characteristics of products and services for which it is most effective. This is particularly important because the inappropriate use of viral marketing can be counterproductive by creating unfavorable attitudes towards products. Work examining this phenomenon currently provides either descriptive accounts of particular initiatives [8] or advice based on anecdotal evidence [2]. What is missing is an analysis of viral marketing that highlights systematic patterns in the nature of knowledge-sharing and persuasion by influencers and responses by recipients in online social networks. To this end, we propose an organizing framework for viral marketing that draws on prior theory and highlights different behavioral mechanisms underlying knowledge-sharing, influence, and compliance in online social networks. Though the framework is descrip-

The Schaake Shuffle: A Method for Reconstructing Space–Time Variability in Forecasted Precipitation and Temperature Fields

Abstract A number of statistical methods that are used to provide local-scale ensemble forecasts of precipitation and temperature do not contain realistic spatial covariability between neighboring stations or realistic temporal persistence for subsequent forecast lead times. To demonstrate this point, output from a global-scale numerical weather prediction model is used in a stepwise multiple linear regression approach to downscale precipitation and temperature to individual stations located in and around four study basins in the United States. Output from the forecast model is downscaled for lead times up to 14 days. Residuals in the regression equation are modeled stochastically to provide 100 ensemble forecasts. The precipitation and temperature ensembles from this approach have a poor representation of the spatial variability and temporal persistence. The spatial correlations for downscaled output are considerably lower than observed spatial correlations at short forecast lead times (e.g., less than 5...

A framework for creating hybrid‐open source software communities

Abstract The open source software (OSS) model is a fundamentally new and revolutionary way to develop software. The success of the OSS model is also setting the stage for a structural change in the software industry; it is beginning to transform software industry from manufacturing to a service industry. Despite the success of the OSS model, for‐profit organizations are having difficulty building a business model around the open source paradigm. Whereas there are some isolated empirical studies, little rigorous research has been done on how traditional organizations can implement and benefit from OSS practices. This research explores how organizations can foster an environment similar to OSS to manage their software development efforts to reap its numerous advantages. Drawing on organizational theory, we develop a framework that guides the creation and management of a hybrid‐OSS community within an organization. We discuss the implications of this framework and suggest areas for future research.

Advancing dynamical prediction of Indian monsoon rainfall

Despite advances in seasonal climate forecasting using dynamical models, skill in predicting the Indian monsoon by such methods has proven poor. Our analysis identifies a flaw in the hitherto popular design of prediction systems in which atmospheric models are driven with a projected ocean surface temperature. Such a configuration presupposes Indian monsoon variability to be a consequence solely of the atmosphere reacting to the ocean. It is becoming increasingly evident that the Indian monsoon is suitably described as a fully coupled ocean-land-atmospheric system, though implications for skill have not been demonstrated. We discover significant improvements in the skill of Indian monsoon predictions when atmospheric models are not constrained by specified observed SSTs in the Indian Ocean warm pool region. Evidence comes from intercomparing 50-years of monsoon skill in atmospheric models using specified SSTs with skill in coupled ocean atmosphere models.

Categorical Climate Forecasts through Regularization and Optimal Combination of Multiple GCM Ensembles

Abstract A Bayesian methodology is used to assess the information content of categorical, probabilistic forecasts of specific variables derived from a general circulation model (GCM) forecast ensemble, and to combine a “prior” forecast (climatological probabilities of each category) with a categorical probabilistic forecast derived from a GCM ensemble to develop posterior, or “regularized” categorical probabilities. The combination algorithm assigns a weight to a particular model forecast and to climatology. The ratio of the sample likelihood of the model based on the posterior categorical probabilities, to that based on climatological probabilities, computed over the period of record of historical forecasts, provides a measure of the skill or information content of a candidate model. The weight given to a GCM forecast serves as a secondary indicator of its information content. Model weights are determined by maximizing the likelihood ratio. Results using the so-called ranked probability skill score as an...

Dominant Patterns of Climate Variability in the Atlantic Ocean during the Last 136 Years

Abstract Dominant spatiotemporal patterns of joint sea surface temperature (SST) and sea level pressure (SLP) variability in the Atlantic Ocean are identified using a multivariate frequency domain analysis. Five significant frequency bands are isolated ranging from the quasi biennial to the quasi decadal. Two quasi-biennial bands are centered around 2.2- and 2.7-yr periods; two interannual bands are centered around 3.5- and 4.4-yr periods; the fifth band at the quasi-decadal frequency is centered around 11.4-yr period. Between 1920 and 1955, the quasi-decadal band is less prominent compared to the quasi-biennial bands. This happens to be the period when SLP gradually increased over the Greenland–Iceland regions. The spatial pattern at the quasi-decadal frequency displays an out-of-phase relationship in the SLP in the vicinity of the subtropical anticyclones in both hemispheres (indicative of an out-of-phase quasi-decadal variability in the North and South Atlantic Hadley circulation). The quasi-decadal fr...

Spatiotemporal variability of ENSO and SST teleconnections to summer drought over the United States during the twentieth century

Presented are investigations into the spatial structure of teleconnections between both the winter El Nino- Southern Oscillation (ENSO) and global sea surface temperatures (SSTs), and a measure of continental U.S. summer drought during the twentieth century. Potential nonlinearities and nonstationarities in the relationships are noted. During the first three decades of this century, summer drought teleconnections in response to SST patterns linked to ENSO are found to be strongest in the southern regions of Texas, with extensions into regions of the Midwest. From the 1930s through the 1950s, the drought teleconnection pattern is found to extend into southern Arizona. The most recent three decades show weak teleconnections between summer drought over southern Texas and Arizona, and winter SSTs, which is consistent with previous findings. Instead, the response to Pacific SSTs shows a clear shift to the western United States and southern regions of California. These epochal variations are consistent with epochal variations observed in ENSO and other low-frequency climate indicators. This changing teleconnection response complicates statistical forecasting of drought.

Competition Among Virtual Communities and User Valuation: The Case of Investing-Related Communities

Virtual communities are a significant source of information for consumers and businesses. This research examines how users value virtual communities and how virtual communities differ in their value propositions. In particular, this research examines the nature of trade-offs between information quantity and quality, and explores the sources of positive and negative externalities in virtual communities. The analyses are based on more than 500,000 postings collected from three large virtual investing-related communities (VICs) for 14 different stocks over a period of four years. The findings suggest that the VICs engage in differentiated competition as they face trade-offs between information quantity and quality. This differentiation among VICs, in turn, attracts users with different characteristics. We find both positive and negative externalities at work in virtual communities. We propose and validate that the key factor that determines the direction of network externalities is posting quality. The contributions of the study include the extension of our understanding of the virtual community evaluation by users, the exposition of competition between virtual communities, the role of network externalities in virtual communities, and the development of an algorithmic methodology to evaluate the quality (noise or signal) of textual data. The insights from the study provide useful guidance for design and management of VICs.