Automatic weather stations and artificial neural networks: Improving the instrumental record in West Antarctica

Automatic weather stations (AWSs) currently provide the only year-round, continuous direct measurements of near-surface weather on the West Antarctic ice sheet away from the coastal manned stations. Improved interpretation of the ever-growing body of ice-core-based paleoclimate records from this region requires a deeper understanding of Antarctic meteorology. As the spatial coverage of the AWS network has expanded year to year, so has the meteorological database. Unfortunately, many of the records are relatively short (less than 10 yr) and/or incomplete (to varying degrees) due to the vagaries of the harsh environment. Climate downscaling work in temperate latitudes suggests that it is possible to use GCM-scale meteorological datasets (e.g., ECMWF reanalysis products) to address these problems in the AWS record and create a uniform and complete database of West Antarctic surface meteorology (at AWS sites). Such records are highly relevant to the improved interpretation of the expanding library of snow-pit and ice-core datasets. Artificial neural network (ANN) techniques are used to predict 6-hourly AWS surface data (temperature, pressure) using large-scale features of the atmosphere (e.g., 500-mb geopotential height) from a region around the AWS. ANNs are trained with a calendar year of observed AWS data (possibly incomplete) and corresponding GCM-scale data. This methodology is sufficient both for high quality predictions within the training set and for predictions outside the training set that are at least comparable to the state of the art. For example, the results presented herein for temperature prediction are approximately equal to those from a satellite-based methodology but with no exposure to problems from surface melt events or sensor changes. Similarly, the significant biases seen in ECMWF surface temperatures are absent from the predictions here, resulting in an rms error that is half as large with respect to the original AWS observations. These results support high confidence in the ANN-based predictions from the GCM-scale data for periods when AWS data are unavailable, for example, before installation. ANNs thus provide a means to expand the surface meteorological records significantly in West Antarctica.