Efficient watershed modeling using a multi-site weather generator for meteorological data

Stochastic Modeling of Rainfall Series in Kelantan Using an Advanced Weather Generator

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.1709 ◽

2018 ◽

Vol 8 (1) ◽

pp. 2537-2541

Author(s):

A. H. Syafrina ◽

A. Norzaida ◽

O. Noor Shazwani

Keyword(s):

Extreme Values ◽

Probability Distributions ◽

Meteorological Data ◽

Extreme Rainfall ◽

Peninsular Malaysia ◽

Weather Generator ◽

Rainfall Series ◽

Weather Data ◽

Extreme Rainfall Events ◽

North East

Weather generator is a numerical tool that uses existing meteorological records to generate series of synthetic weather data. The AWE-GEN (Advanced Weather Generator) model has been successful in producing a broad range of temporal scale weather variables, ranging from the high-frequency hourly values to the low-frequency inter-annual variability. In Malaysia, AWE-GEN has produced reliable projections of extreme rainfall events for some parts of Peninsular Malaysia. This study focuses on the use of AWE-GEN model to assess rainfall distribution in Kelantan. Kelantan is situated on the north east of the Peninsular, a region which is highly susceptible to flood. Embedded within the AWE-GEN model is the Neyman Scott process which employs parameters to represent physical rainfall characteristics. The use of correct probability distributions to represent the parameters is imperative to allow reliable results to be produced. This study compares the performance of two probability distributions, Weibull and Gamma to represent rainfall intensity and the better distribution found was used subsequently to simulate hourly scaled rainfall series. Thirty years of hourly scaled meteorological data from two stations in Kelantan were used in model construction. Results indicate that both probability distributions are capable of replicating the rainfall series at both stations very well, however numerical evaluations suggested that Gamma performs better. Despite Gamma not being a heavy tailed distribution, it is able to replicate the key characteristics of rainfall series and particularly extreme values. The overall simulation results showed that the AWE-GEN model is capable of generating tropical rainfall series which could be beneficial in flood preparedness studies in areas vulnerable to flood.

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Challenges in constructing a high-resolution, multi-site, and multivariate weather generator and potential solutions

10.5194/egusphere-egu2020-12075 ◽

2020 ◽

Author(s):

Xin Li

Keyword(s):

High Resolution ◽

Meteorological Data ◽

Weather Generator ◽

Meteorological Field ◽

The Past ◽

Weather Generators ◽

The Future ◽

Change Context ◽

Future Climate Projection

<p>A reliable simulation of the spatiotemporal characteristics of the meteorological field is of great significance for hydrological impact studies. To approach this target, a number of weather generators (WGs) have been developed over the past few decades. However, a detailed literature review shows that currently developed WGs are subject to one or several aspects of the following limitations: (1) low spatial and temporal resolutions to describe the real spatiotemporal dynamics of meteorological processes; 2) incapability to simulate a spatially coherent, temporally consistent, and physically meaningful meteorological field; and 3) inability to extend into the future in a climate change context. To tackle these problems, this study proposes some potential solutions: (1) using the multi-site multivariate WGs (MMWGs) to simulate the spatial, temporal, and inter-variable dependencies in the meteorological field; (2) coupling the MMWGs with the resampling-based algorithms to generate high-resolution spatiotemporal meteorological data; and (3) perturbing the parameters of the distribution and dependency models based on the future climate projection. A case study is carried out and shows that the proposed solutions are effective in addressing the aforementioned challenges. These findings could assist in developing high-resolution MMWGs for weather simulation and impact assessment.</p>

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