A space-time geostatistical approach for ensemble rainfall nowcasting

Abstract

Nowcasting systems are essential to prevent extreme events and reduce their socio-economic impacts. The major challenge of these systems is to capture high-risk situations in advance, with good accuracy, location and time. Uncertainties associated with the precipitation events have an impact on the hydrological forecasts, especially when it concerns localized flash flood events. Radar monitoring can help to detect the space-time evolution of rain fields, but nowcasting techniques are needed to go beyond the observation and provide scenarios of rainfall for the next hours of the event. In this study, we investigate a space-time geostatistical framework to generate multiple scenarios of future rainfall. The rainfall ensemble is generated based on space-time properties of precipitation fields given by radar measurements and rainfall data from rain gauges. The aim of this study is to investigate the potential of a framework that applies a geostatistical conditional simulation method to generate an ensemble nowcasting of rainfall fields. The Var region (south eastern France) and 14 events are used to validate the approach. Results show that the proposed method can be a solution to combine information from radar fields and rain gauges to generate nowcasting rainfall fields adapted for flash flood alert.

Using the Weather Research and Forecasting (WRF) Model for Precipitation Forecasting in an Andean Region with Complex Topography

“…Finally, it would be of interest to improve rainfall forecasts by combining NWP tools with geostatistical methods, which incorporate spatial statistics and local topography to describe, enhance or predict rainfall in basins with complex topography [75][76][77]. Such geostatistical approach would eventually offset to a certain extent WRF forecast problems over the Andean complex topography.…”

Statistical Variability of Severe Rainfall Events in Southeastern Brazil

“…In this model, the correlation distance is defined as the point where the variogram reaches 95% of its plateau (Mälicke et al, 2018). In summary, the characteristics studied here are (Caseri et al, 2016):…”

“…Brazil is among the ten countries most affected by floods in the world (Guha-Sapir et al, 2016). These severe events have high spatial and temporal variability, and thus the prediction of these events is an important challenge for the scientific community (Machado et al, 2014;Caseri et al, 2016).…”

Response to Short Comment 1, Carlos Velasco-Forero

“…The residual radar measurement uncertainty can be effectively modelled by the non-stationary generator to obtain QPE ensembles, which reproduce the local statistical characteristics and anisotropy of the observed rainfall fields (Jordan et al, 2003;Ciach et al, 2007;Villarini et al, 2009;Germann et al, 2009;Cecinati et al, 2017). In addition, we believe that current radar rain gauge merging and adjustment techniques (e.g.…”