A rainfall analysis and forecasting tool. (November 2017)

• Record Type: Journal Article
• Authors: Diez-Sierra, Javier; del Jesus, Manuel
• Is Part Of: Environmental modelling & software. Volume 97(2017)
• Page Start: 243
• Page End: 258
• Publication Date: 2017-11
• Subjects: Rainfall modeling -- Weather types -- Spatial analysis -- Kriging -- Rainfall extremes; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.08.011
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: MENSEI-L is a stand-alone software tool for the automatic analysis of pluviometric networks, that also provides three-day rainfall forecasts based on weather types. The software tool, implemented in Python and R, is able to fill missing values in original daily data series and to generate synthetic pluviometers in ungauged locations, by means of kriging techniques. MENSEI-L also characterizes punctual and spatial, average and extreme distributions of precipitation for the complete pluviometric network. Tenerife (Canary Islands, Spain) is used as study site to evaluate the capabilities of MENSEI-L and the implicit rainfall analysis methodology that it implements. MENSEI-L proves to be a useful tool to extract information from dense observation networks where manual analysis is not practical. Highlights: A rainfall analysis methodology is implemented around three procedures: weather types generation, point statistics and a spatio-temporal interpolation. The methodology, implemented in a software called MENSEI-L, uses of the latest Python and R libraries for the analysis. MENSEI-L can be used to reconstruct a pluviometric network by filling missing values and generating synthetic pluviometers. MENSEI-L uses the reconstructed network and the weather types to generate rainfall characterizations of average and 3-day rainfall forecast. MENSEI-L constitutes a powerful tool that simplifies the process to inform engineering design, decision-making and risk assessments.