Bennett Archambault Portrait

Portrait of Bennett Archambault who was a speaker at the evening school graduation of the New York Trade School in 1949. From a press release attached to the portrait: "Walter Weir, Inc., 250 West 57th Street, New York 19, N.Y., John Black, Public Relations & Publicity Department, Plaza 7-0140, May 23, 1949, For Release: Friday, May 27, 1949, BENNETT ARCHAMBAULT: Member of the Board of Trustees, New York Trade School, and Treasurer, The M.W. Kellogg Company, who reviewed the school's long history and drew an impressive picture of its future, in an address at the 68th Annual Evening School Graduation, held last night (May 26) in the school building on East 67th Street, New York." Black and white photograph.

Model Configuration And Data Management In The Short-Term Water Information Forecasting Tools

The Short-term Water Information and Forecasting Tools (SWIFT) is a suite of tools for flood and short-term streamflow forecasting, consisting of a collection of hydrologic model components and utilities. Catchments are modeled using conceptual subareas and a node-link structure for channel routing. The tools comprise modules for calibration, model state updating, output error correction, ensemble runs and data assimilation. Given the combinatorial nature of the modelling experiments and the sub-daily time steps typically used for simulations, the volume of model configurations and time series data is substantial and its management is not trivial. SWIFT is currently used mostly for research purposes but has also been used operationally, with intersecting but significantly different requirements. Early versions of SWIFT used mostly ad-hoc text files handled via Fortran code, with limited use of netCDF for time series data. The configuration and data handling modules have since been redesigned. The model configuration now follows a design where the data model is decoupled from the on-disk persistence mechanism. For research purposes the preferred on-disk format is JSON, to leverage numerous software libraries in a variety of languages, while retaining the legacy option of custom tab-separated text formats when it is a preferred access arrangement for the researcher. By decoupling data model and data persistence, it is much easier to interchangeably use for instance relational databases to provide stricter provenance and audit trail capabilities in an operational flood forecasting context. For the time series data, given the volume and required throughput, text based formats are usually inadequate. A schema derived from CF conventions has been designed to efficiently handle time series for SWIFT.