This paper presents rainfall-induced landslide thresholds and predicts landslide hazard in Kuala Lumpur metropolitan city and surrounding areas. Landslide events from 2000 to 2012 were collected. The long and short antecedent rainfall days were prepared for landslide and non-landslide days simultaneously. First, threshold analysis was conducted by using data obtained from rainfall stations loca...

The majority of studies assessing aerosol effects on rainfall use coarse spatial scale (1° latitude/longitude or more) and multi-seasonal or decadal data sets. Here, we present results from a spatial correlation of aerosol size distribution and rain rate for selected stratiform and cumuliform precipitation events. The chemistry transport version of the Weather Research and Forecasting model was...

A scale-invariance analysis of space and time rainfall events monitored by meteorological radar over the area of Rome (Italy) is proposed. The study of the scale-invariance properties of intense precipitation storms, particularly important in flood forecast and risk mitigation, allows to transfer rainfall information from the large scale predictive meteorological models to the small scale hydro...

Extreme rainfall events, specifically in urban areas, have dramatic impacts on society and can lead to loss of life and property.Despite these hazards, little is known about the city-scale variability of heavy rainfall events. In the current study, gridded stage IV radar data from 2002 to 2015 are employed to investigate the clustering and the spatial variability of simultaneous rainfall exceed...

[1] Rainfall variability occurs over a wide range of time scales owing to processes initiated by cloud microphysics and sustained by atmospheric circulation. A central topic in rainfall research is to determine whether rainfall variability at a given scale is caused by dynamics acting at some other scales. Random multiplicative cascades (RMCs) are standard approaches for describing rainfall var...

The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from sa...

The daily rainfall data of 37 years were collected from the IMD approved Meteorological Observatory situated at GB Pant University of Agriculture and Technology, Pantnagar, India. The data were then processed to identify the maximum rainfall received on any one day (24hrs duration), in any week (7 days), in a month (4 weeks), in a monsoon season (4 months) and in a year (365 days period). The d...

This paper presents a study of neural network model for prediction of Indian rainfall. The purpose of this paper is to evaluate the applicability of ANN. In this paper the performance of different networks have been evaluated and tested.The multilayered artificial neural network with learning by backpropagation algorithm is used .The paper implements weather prediction by building training and ...

A mesoscale objective analysis scheme for producing daily rainfall analysis on a regular latitude/longitude grid over the Indian monsoon region is described. The Barnes scheme is applied to interpolate irregularly distributed daily rainfall data on to a regular grid. The spatial resolution of the interpolated arrays is 0.25 degrees of latitude by 0.25 degrees of longitude. Daily rainfall derive...

Studies of rainfall variation generally focus on large areas. For example, in India, the area average monsoon rainfall series of the whole country or meteorological subdivisions are used. This would be of no use for local agriculture, particularly in places where rainfall is very high or very low, especially for crops like small cardamom and vanilla which are very sensitive to soil moisture and...