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.
Highlights
Numerical weather prediction (NWP) models are critically reliant on a large number of appropriate quality in situ and remotely sensed observations
In of of in in situ observations on 24-h weather forecast error reduction using the adjoint-based FSO approach developed by the Met Office run in conjunction with the operational ACCESS global NWP system
The impact is measured by the reduction in the 24-h forecast error expressed as a moist energy norm calculated in the Australian domain
Summary
Numerical weather prediction (NWP) models are critically reliant on a large number of appropriate quality in situ and remotely sensed observations. These observations provide the data that are needed to accurately define the forecast initial conditions, via the process of data assimilation. The forecast skill of NWP is substantially influenced by the observations To both improve the analysis and increase the forecast skill of NWP systems such as ACCESS [1], the national observing network needs to be regularly evaluated in terms of design and observational types. NWP is one of the major mechanisms for converting observed data into information and services, so an objective measure of the impact of each observation on the quality of short-term forecasts can potentially guide decisions related to network efficiency and effectiveness
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