Designing science web sites

From a scientist’s viewpoint a web site is one tool used to conduct research. From an artist’s viewpoint web sites are a form of visual composition. From a developer’s point of view a web site is a type of application. While web sites are a relatively new medium with a particular set of constraints, they do adhere to the same basic design principles that apply to other art forms. These design principles are the basic assumptions that affect the arrangement of elements within a composition. A successful design uses the principles and elements to achieve a visual goal in the composition. A web site designed for scientists has unique properties which are not shared by many other types of web sites. These properties influence the overall visual design of the web sites. Recently at the Institute of Geophysics and Planetary Physics at UCLA undertook a re-design of a number of its websites. In the effort, the use of visual design principles combined with the properties of a science web site were put to the test. In all, six different web sites were designed each with a difference science focus. We describe the process used to design the web sites which involve forming teams of designers, scientists and developers. We present example pages from each design and conclude with a discussion of what was learned during the process.     

Near real-time ocean circulation assimilation and prediction in the Intra-Americas Sea with ROMS

We present the feasibility of a prototype, near real-time assimilation and ensemble prediction system for the Intra-Americas Sea run autonomously aboard a ship of opportunity based on the Regional Ocean Modeling System (ROMS). Predicting an ocean state depends upon numerical models that contain uncertainties in their modeled physics, initial conditions, and model state. An advanced model, four-dimensional variational assimilation, and ensemble forecasting techniques are used to account for each of these uncertainties. Every 3 days, data from the previous 7 days period were assimilated to generate an estimate of the circulation and to create an ensemble of 2 weeks forecasts of the ocean state. This paper presents the methods and results for a multi-resolution assimilation system and ensemble forecasts of surface fields and dominant surface circulation features. When compared to post-processed science quality observations, the state estimates suffer from our reliance on real-time, quick-look satellite observations of the ocean surface. Despite a number of issues, the ensemble forecast estimate is often superior to observational persistence. This proof-of-concept prototype performed well enough to reveal deficiencies, provide useful insights, valuable lessons, and guidance for future improvements in real-time ocean forecasting.     

High pressure crystallization of the Ewarara,Kalka and Gosse Pile intrusions,Giles Complex,central Australia

Evidence is presented for the primary high pressure crystallization of the Ewarara, Kalka and Gosse Pile layered intrusions which form part of the Giles Complex in central Australia. These pressures are estimated at 10 to 12 kb. The high pressure characteristics include subsolidus reactions between olivine and plagioclase, orthopyroxene and plagioclase, and orthopyroxene and spinel; spinel and rutile exsolution in both ortho- and clino-pyroxene; spinel exsolution in plagioclase; high Al₂O₃ and Cr₂O₃ contents of both ortho- and clinopyroxene; high Al^VI in clinopyroxene; dominance of orthopyroxene as an early crystallizing phase; high distribution coefficients for co-existing pyroxene pairs; and thin chilled margins. Such phenomena are rare in documented layered basic intrusions.     

Modelling snowpack bulk density using snow depth,cumulative degree-days,and climatological predictor variables

Snowpack water equivalent (SWE) is a key variable for water resource management in snow-dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long-term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra- and inter-year variability in meteorological conditions on densification through a cumulative degree-day index derived from North American Regional Reanalysis products. A non-linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross-validation, the predicted densities had a root-mean-square error of 47.1 kg m⁻³, a mean bias of −0.039 kg m⁻³, and a Nash-Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression-based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure-from-Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.