The concept and the use of the forecast error sensitivity to observations for diagnostic purposes are illustrated in this paper. The tool computes the contribution of all observations to the forecast error: a positive contribution is associated with forecast error increase and a negative contribution with forecast error decrease. The forecast range investigated is 24 hour. It can be seen that globally, the assimilated observations decrease the forecast error; locally however also poor performance can be found. The forecast deterioration can be related either to the data quality or to the data assimilation and forecast system. The data impact on the forecast is spatially and also temporally variable. It depends on atmospheric regimes, which may be well or not well represented by the model or by the data. An example of a routine diagnostic assessment of observational impact on the short-range forecast performance is shown. The example also illustrates the tools flexibility to represent different degrees of detail of forecast improvement or deterioration.
C1 - Learning DA - 2013 LA - eng N2 -
The concept and the use of the forecast error sensitivity to observations for diagnostic purposes are illustrated in this paper. The tool computes the contribution of all observations to the forecast error: a positive contribution is associated with forecast error increase and a negative contribution with forecast error decrease. The forecast range investigated is 24 hour. It can be seen that globally, the assimilated observations decrease the forecast error; locally however also poor performance can be found. The forecast deterioration can be related either to the data quality or to the data assimilation and forecast system. The data impact on the forecast is spatially and also temporally variable. It depends on atmospheric regimes, which may be well or not well represented by the model or by the data. An example of a routine diagnostic assessment of observational impact on the short-range forecast performance is shown. The example also illustrates the tools flexibility to represent different degrees of detail of forecast improvement or deterioration.
PY - 2013 TI - Observation Impact on the Short Range Forecast ER -