Estimating the uncertainty of areal precipitation using data assimilation

Abstract

We present a method to estimate spatially and temporally variable uncertainty of areal precipitation data. The aim of the method is to merge measurements from different sources, remote sensing and in situ, into a combined precipitation product and to provide an associated dynamic uncertainty estimate. This estimate should provide an accurate representation of uncertainty both in time and space, an adjustment to additional observations merged into the product through data assimilation, and flow dependency. Such a detailed uncertainty description is important for example to generate precipitation ensembles for probabilistic hydrological modelling or to specify accurate error covariances when using precipitation observations for data assimilation into numerical weather prediction models. The presented method uses the Local Ensemble Transform Kalman Filter and an ensemble nowcasting model. The model provides information about the precipitation displacement over time and is continuously updated by assimilation of observations. In this way, the precipitation product and its uncertainty estimate provided by the nowcasting ensemble evolve consistently in time and become flow-dependent. The method is evaluated in a proof of concept study focusing on weather radar data of four precipitation events. The study demonstrates that the dynamic areal uncertainty estimate outperforms a constant benchmark uncertainty value in all cases for one of the evaluated scores, and in half the number of cases for the other score. Thus, the flow dependency introduced by the coupling of data assimilation and nowcasting enables a more accurate spatial and temporal distribution of uncertainty. The mixed results achieved in the second score point out the importance of a good probabilistic nowcasting scheme for the performance of the method.

Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

“…The climatic variables were positively correlated with the tea yield [6,[24][25][26]. Among the climate factors, temperature variability was determined as a stronger positive effect than precipitation in the predicted tea yield [43,51,54]. In our study, NDVI and mean temperature played a more important role than the other variables for estimating yield (Figure 8), which can be attributed to the different order as follows: mean temperature > NDVI > Tmin > precipitation > Tmax > solar radiation.…”

“…Van Leeuwen et al [52] confirmed that MODIS NDVI data were affected by atmospheric water vapor, even though this effect was minimal. In addition, the uncertainty, particularly in precipitation, and in general, meteorological have been addressed by Savino Curci et al [53] and C.Merker et al [54]. These uncertainties of data can develop in the near future when considering the error of various variable input in the prediction yield.…”