16 Data Assimilation for the Earth System (Posters)

AUTOMATIC WEATHER STATIONS AND ARTIFICIAL NEURAL NETWORKS: IMPROVING THE INSTRUMENTAL RECORD IN WEST ANTARCTICA

Paper number: 16-12
Program: THEME SESSIONS
Time: 4:30 PM – 4:30 PM

Authors

Abstract

Greater assimilation of modern meteorological data into analytical methods is needed to improve our understanding of West Antarctic paleoclimate. Progress in interpretation of the ever growing body of ice-core-based paleoclimate records requires a deeper understanding of regional meteorology. But the Antarctic instrumental record is both limited and interrupted. Automatic weather stations (AWS) currently provide the only year-round, continuous direct measurements of weather on the ice sheet. As the spatial coverage of the network has expanded year to year, so has our meteorological database. Unfortunately, many of the records are relatively short (less than 10 years) and/or incomplete (to varying degrees) due to the vagaries of the harsh environment. Recent developments in climate downscaling in temperate latitudes suggest it is possible to use GCM-scale meteorological data sets (e.g., ECMWF reanalysis products) to both fill gaps in the AWS records and extend them back in time to 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 data sets.

Artificial neural network (ANN) techniques are used to predict AWS data (e.g., temperature, pressure) using large-scale features of the atmosphere (e.g., 500 mb geopotential height) from a region around the AWS. ANN training uses observed AWS data and the corresponding GCM-scale data. One year of AWS data, possibly incomplete, has proven sufficient both for high-quality predictions within the training set and for AWS observations outside the training set (e.g., r2 > 0.95 for pressure). These results support high confidence in the ANN-based predictions from GCM-scale data for periods where AWS data are unavailable, e.g., before installation. ANNs thus provide a simple means to improve our surface meteorological records significantly in West Antarctica.