Satellite remote sensing and conceptual cloud modeling for convective rainfall simulation

Motivated by the need for rainfall prediction models in data scarce areas, we adapted a simple storage based cloud model to use routinely available thermal infrared (TIR) data. The data is obtained from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the Meteosat Second Generation (MSG-2) satellite. Model inputs are TIR cloud top temperatures at 15-min intervals and observations of pressure, temperature, and dew point temperatures from ground-based stations at 30-min intervals. The sensitivity of the parsimonious cloud model to its parameters is evaluated by a regional sensitivity analysis (RSA) which suggests that model performance is sensitive to few parameters. The model was calibrated and tested for four convective events that were observed during the wet season in the source basin of the Upper Blue Nile River. The difference between the simulated and the observed depth of the selected rain events varies between 0.2 and 1.8 mm with a root mean square error of smaller than 0.5 mm for each event. It is shown that the updraft velocity characteristic can provide relevant information for rainfall forecasting. The simulation results suggest the effectiveness of the model approach as evaluated by selected performance measures. The various characteristics of the rainfall events as simulated generally match to observed counter parts when ground-based and remote sensing observations are combined.

Research highlights► In the present study, the effectiveness of conceptual cloud modeling at predicting convective rainfall in a data-sparse area is evaluated where input data must be through remote sensing and routinely recorded ground observations. We adapted a simple storage based cloud model to use routinely available thermal infrared (TIR) data which is obtained from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the Meteosat Second Generation (MSG-2). The present study is a first attempt to simulate convective cloud systems by two layers of reservoir. ► We assessed sensitivity of the parsimonious cloud model to its parameters through Regional sensitivity analysis (RSA) which suggested that model performance is sensitive to few parameters. ► When using the calibrated parameter values to simulate rainfall of independent events, the simulated and observed rainfall depths showed a good agreement. The main limitation has been with the rain initiation time where the model produced rainfall too early or with a delay for some events as compared to the observed hyetographs. The difference between the simulated and the observed rain initiation time is in the order of 30 min which, in hydrology, is considered small for medium to large catchments (>1000 km2). ► It is shown that integrating remotely sensed TIR data with ground based meteorological observations through a conceptual cloud model results has good potential considering the (very) low model input data requirement and the many sources of uncertainty in modeling. We also showed that the updraft velocity characteristics can provide relevant information for rainfall forecasting.