A new satellite-based rainfall monitoring algorithm that integrates the strengths of both low Earth-orbiting (LEO) and geostationary Earth-orbiting (GEO) satellite information has been developed. The Lagrangian Model (LMODEL) algorithm combines a 2D cloud-advection tracking system and a GEO data–driven cloud development and rainfall generation model with procedures to update model parameters an...

Rainfall data are often collected at coarser spatial scales than required for input into hydrology and agricultural models. We therefore describe a spatio-temporal model which allows multiple imputation of rainfall at fine spatial resolutions, with a realistic dependence structure in both space and time and with the total rainfall at the coarse scale consistent with that observed. The method in...

The present study developed an artificial neural network (ANN) model to overcome the difficulties in training the ANN models with continuous data consisting of rainy and non-rainy days. Among the six models analyzed the ANN model which used generalized feedforward type network and a hyperbolic tangent function and a combination of 5 meteorological parameters (relative humidity, air pressure, we...

We study spatial scaling and complexity properties of Amazonian radar rainfall fields using the Beta-Lognormal Model (BL-Model) with the aim to characterize and model the process at a broad range of spatial scales. The Generalized Space q-Entropy Function (GSEF), an entropic measure defined as a continuous set of power laws covering a broad range of spatial scales, Sq(λ) ∼ λΩ(q), is used as a t...

Despite the significant role of precipitation in the hydrological cycle, few studies have been conducted to evaluate the impacts of the temporal resolution of rainfall inputs on the performance of SWAT (soil and water assessment tool) models in large-sized river basins. In this study, both daily and hourly rainfall observations at 28 rainfall stations were used as inputs to SWAT for daily strea...

Taiwan’s special climate and landforms are affected by summer typhoons, with 78% of its rainfall occurring during the summer and autumn months. The range and the severity of disasters has increased in recent years, thanks in part to climate change, which has caused an unstable rainfall. Accurate rainfall predictions help to forecast rivers’ water levels. This study proposes a new rainfall predi...

The importance of considering the spatial distribution of rainfall for process-oriented hydrological modelling is well-known. However, the application of rainfall radar data to provide such detailed spatial resolution is still under debate. In this study the process-oriented TAC (Tracer Aided Catchment model, Distributed) model had been used to investigate the effects of different spatially dis...

Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. [1] We develop a methodology for the frequency of extreme rainfall intensities caused by tropical cyclones (TCs) in...

This study analyses the relationship between meteorological phenomena and outbreaks of waterborne-transmitted vero cytotoxin-producing Escherichia coli (VTEC) in the Republic of Ireland over an 8-year period (2005-2012). Data pertaining to the notification of waterborne VTEC outbreaks were extracted from the Computerised Infectious Disease Reporting system, which is administered through the nat...