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Assessing the Impact of Surface and Upper-Air Observations on the Forecast Skill of the ACCESS Numerical Weather Prediction Model over Australia

Australian Bureau of Meteorology, 700 Collins Str., Melbourne, VIC 3008, Australia
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Atmosphere 2018, 9(1), 23; https://doi.org/10.3390/atmos9010023
Received: 15 December 2017 / Revised: 11 January 2018 / Accepted: 12 January 2018 / Published: 16 January 2018
(This article belongs to the Special Issue Efficient Formulation and Implementation of Data Assimilation Methods)
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Abstract

The impact of the Australian Bureau of Meteorology’s in situ observations (land and sea surface observations, upper air observations by radiosondes, pilot balloons, wind profilers, and aircraft observations) on the short-term forecast skill provided by the ACCESS (Australian Community Climate and Earth-System Simulator) global numerical weather prediction (NWP) system is evaluated using an adjoint-based method. This technique makes use of the adjoint perturbation forecast model utilized within the 4D-Var assimilation system, and is able to calculate the individual impact of each assimilated observation in a cycling NWP system. The results obtained show that synoptic observations account for about 60% of the 24-h forecast error reduction, with the remainder accounted for by aircraft (12.8%), radiosondes (10.5%), wind profilers (3.9%), pilot balloons (2.8%), buoys (1.7%) and ships (1.2%). In contrast, the largest impact per observation is from buoys and aircraft. Overall, all observation types have a positive impact on the 24-h forecast skill. Such results help to support the decision-making process regarding the evolution of the observing network, particularly at the national level. Consequently, this 4D-Var-based approach has great potential as a tool to assist the design and running of an efficient and effective observing network. View Full-Text
Keywords: numerical weather prediction; variational data assimilation; adjoint model; sensitivity analysis; forecast sensitivity; observations numerical weather prediction; variational data assimilation; adjoint model; sensitivity analysis; forecast sensitivity; observations
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Soldatenko, S.; Tingwell, C.; Steinle, P.; Kelly-Gerreyn, B.A. Assessing the Impact of Surface and Upper-Air Observations on the Forecast Skill of the ACCESS Numerical Weather Prediction Model over Australia. Atmosphere 2018, 9, 23.

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