Abstract
This paper presents a simple method for estimating the impact of assimilating individual or group of observations on forecast accuracy improvement. This method is derived from the nsemble-based observation impact analysis method of Liu and Kalnay (Q J R Meteorol Soc 134:1327–1335, 2008). The method described here is different in two ways from their method. Firstly, it uses a quadratic function of model-minus-observation residuals as a measure of forecast accuracy, instead of model-minus-analysis. Secondly, it simply makes use of time series of observations and the corresponding model output generated without data assimilation. These time series are usually available in an operational database. Hence, it is simple to implement. It can be used before any data assimilation is implemented. Therefore, it is useful as a design tool of a data assimilation system, namely for selecting which observations to assimilate. The method can also be used as a diagnostic tool, for example, to assess if all observation contributes positively to the accuracy improvement. The method is applicable for systems with stationary error process and fixed observing network. Using twin experiments with a simple one-dimensional advection model, the method is shown to work perfectly in an idealized situation. The method is used to evaluate the observation impact in the operational storm surge forecasting system based on the Dutch Continental Shelf Model version 5 (DCSMv5).
Highlights
Accurate forecasts of storm surges are important in the Netherlands, since a large part of its land lies below sea level
Unlike the methods of Langland and Baker (2004) and Liu and Kalnay (2008) that are applicable for an existing data assimilation system, this method can be used for the estimation of observation impact even prior to the actual implementation of a Kalman filter
To gain insight about the spatial distribution of the observation impact, we present in Fig. 12 the forecast accuracy improvement at each station as the result of assimilating observed data from the eight operational assimilation stations
Summary
We implement the timeseries-based method for analyzing the operational storm surge forecasting system in the Netherlands. Observed water level data are available from these stations regularly with a time step of 10 min. One is generated by forcing the DCSMv5 with HIRLAM wind and the other with the meteorological forecasts of the UK Met Office (UKMO) The latter runs regularly in the operational system as a benchmark and fall back option if for some reason HIRLAM data is not available. It has been shown that modeling the model error based on the HIRLAM and UKMO differences leads to a better performing Kalman filter (Sumihar 2009). All these data are already available in the database. The evaluation period is from July 1st, 2009 00:00 until July 1st, 2010 00:00
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