24A.2 Toward an Ensemble Radar Precipitation Estimation Based on a Dynamic Description of the Measurement Errors

Friday, 1 September 2017: 8:45 AM
St. Gallen (Swissotel Chicago)
Maud Martet, Meteo France, Toulouse, France; and G. Thomas and N. Gaussiat

Radar based Quantitative Precipitation Estimation (QPE) is increasingly being used to initialise hydrological models. Radars can provide higher temporal and spatial resolution observations of rainfall than rain-gauges and are critical instruments for predicting flash flood events (Vincendon et al, 2010). However, radar based QPE is affected by retrieval errors that are an important source of uncertainty for hydrological predictions.

Radar ensembles have been proposed by numerous authors to represent the precipitation estimate as well as the uncertainty. Each member of a radar ensemble is an estimate of the unknown and true precipitation field and the spread of the ensemble a measure of the uncertainty.

Radar ensembles can be generated either by modelling individual sources of error, (Lee et al., (2007), Lee and Zawadzki (2005a, 2005b, 2006), Jordan et al (2003), Berenguer and Zawadzki (2008)) or by describing statistically the residual errors in radar based QPE (Germann et al (2009), Ciach et al (2007), Llort et al (2008)). As explained by Seed et al, (2013), the difficulty with the first approach is that error structure is complex and inter-dependent. The challenge with the later approach is the requirement for a reference field: this is usually a dense network of point observations from rain gauges.

In this study, we chose to statistically study the errors of the radar based QPE, as in Germann et al.(2009). The original method is based on singular value decomposition of the error covariance matrix, stochastic simulation using the LU decomposition algorithm, and autoregressive filtering. But, in order to take into account different sources of error (Z-R relationships, VPR correction, distance from the radar), we produced separate statistics and error covariance matrices for different classes of precipitation intensity, freezing level height and quality index, since the quality index decreases with both the distance from the radar and the height of the beam above the ground, as explained in Tabary et al. (2007). For each parameter, three classes have been considered and therefore 27 error covariance matrices are used in the ensemble generator to produce perturbations dynamically linked to the radar measurements.

The results presented at the conference will show to which extend the new method can be used to both improve QPE estimates and provide and provide a more realistic estimation of the uncertainty.

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