WeaGETS – a Matlab-based daily scale weather generator for generating precipitation and temperature

Troin, Magali; Brissette, François; Leconte, Robert

doi:10.1016/j.proenv.2012.01.211

Cited by 49 publications

(46 citation statements)

References 14 publications

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“…Using an extended peak flow series allows greater confidence in the fitted generalized extreme value (GEV) parameters and provides a more robust estimate of flood magnitude. The present study employs the Weather Generator École de Technologie Supérieure (WeaGets) multivariable single‐site stochastic WGEN (Chen et al, ). The model is fitted (over 14‐day periods) to observed precipitation (1976–2005) and temperature in each catchment.…”

Section: Methods and Datamentioning

confidence: 99%

Using a Scenario‐Neutral Framework to Avoid Potential Maladaptation to Future Flood Risk

Broderick

Murphy

Wilby

et al. 2019

Water Resources Research

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This study develops a coherent framework to detect those catchment types associated with a high risk of maladaptation to future flood risk. Using the “scenario‐neutral” approach to impact assessment the sensitivity of Irish catchments to fluvial flooding is examined in the context of national climate change allowances. A predefined sensitivity domain is used to quantify flood responses to +2 °C mean annual temperature with incremental changes in the seasonality and mean of the annual precipitation cycle. The magnitude of the 20‐year flood is simulated at each increment using two rainfall‐runoff models (GR4J, NAM), then concatenated as response surfaces for 35 sample catchments. A typology of catchment sensitivity is developed using clustering and discriminant analysis of physical attributes. The same attributes are used to classify 215 ungauged/data‐sparse catchments. To address possible redundancies, the exposure of different catchment types to projected climate is established using an objectively selected subset of the Coupled Model Intercomparison Project Phase 5 ensemble. Hydrological model uncertainty is shown to significantly influence sensitivity and have a greater effect than ensemble bias. A national flood risk allowance of 20%, considering all 215 catchments is shown to afford protection against ~48% to 98% of the uncertainty in the Coupled Model Intercomparison Project Phase 5 subset (Representative Concentration Pathway 8.5; 2070–2099), irrespective of hydrological model and catchment type. However, results indicate that assuming a standard national or regional allowance could lead to local over/under adaptation. Herein, catchments with relatively less storage are sensitive to seasonal amplification in the annual cycle of precipitation and warrant special attention.

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Section: Methods and Datamentioning

confidence: 99%

Using a Scenario‐Neutral Framework to Avoid Potential Maladaptation to Future Flood Risk

Broderick

Murphy

Wilby

et al. 2019

Water Resources Research

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“…For each climatic variable v, a value of a climatic variable v i corresponding to the probability pi is calculated [16] [57] as in Equation (1): …”

Section: Modeling Precipitation Occurrencementioning

confidence: 99%

Statistical Downscaling of Precipitation and Temperature Using Long Ashton Research Station Weather Generator in Zambia: A Case of Mount Makulu Agriculture Research Station

Chisanga¹,

Phiri²,

Chinene³

2017

AJCC

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The Long Ashton Research Station Weather Generator (LARS-WG) is a stochastic weather generator used for the simulation of weather data at a single site under both current and future climate conditions using General Circulation Models (GCM). It was calibrated using the baseline and evaluated to determine its suitability in generating synthetic weather data for 2020 and 2055 according to the projections of HadCM3 and BCCR-BCM2 GCMs under SRB1 and SRA1B scenarios at Mount Makulu (Latitude: 15.550˚S, Longitude: 28.250˚E, Elevation: 1213 meter), Zambia. Three weather parameters-precipitation, minimum and maximum temperature were simulated using LARS-WG v5.5 for observed station and AgMERRA reanalysis data for Mount Makulu. Monthly means and variances of observed and generated daily precipitation, maximum temperature and minimum temperature were used to evaluate the suitability of LARS-WG. Other climatic conditions such as wet and dry spells, seasonal frost and heat spells distributions were also used to assess the performance of the model. The results showed that these variables were modeled with good accuracy and LARS-WG could be used with high confidence to reproduce the current and future climate scenarios. Mount Makulu did not experience any seasonal frost. The average temperatures for the baseline (Observed station

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“…This model was implemented as WGEN (Weather GENerator) by Richardson and Wright (1984) [1], which used a simple Markov Chain for precipitation occurrence, a gamma distribution for simulation of rainfall amounts, and an autoregressive model for the remaining variables. A number of subsequent WGs, such as WXGEN [8], CLIGEN [9,10], LARS-WG [11][12][13], ClimGen [14], WeaGETS [15,16], Met and Roll [17], MOFRBC [18,19], WeatherMan [20], MarkSim [21], AAFC-WG [22,23], WM2 [24], KnnCAD [25][26][27], and the WG used by the UK Met Office (UKCP09) [28,29], all share the basic principles of stochastic simulation presented in WGEN. These WGs are station-scale generators, with time scales that range from daily (or even hourly in the case of rainfall) to annual, daily resolution being the most common.…”

Section: Introductionmentioning

confidence: 99%

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

Urdiales

Meza

Gironás

et al. 2018

Water

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Stochastic weather simulation, or weather generators (WGs), have gained a wide acceptance and been used for a variety of purposes, including climate change studies and the evaluation of climate variability and uncertainty effects. The two major challenges in WGs are improving the estimation of interannual variability and reducing overdispersion in the synthetic series of simulated weather. The objective of this work is to develop a WG model of daily rainfall, incorporating a covariable that accounts for interannual variability, and apply it in three climate regions (arid, Mediterranean, and temperate) of Chile. Precipitation occurrence was modeled using a two-stage, first-order Markov chain, whose parameters are fitted with a generalized lineal model (GLM) using a logistic function. This function considers monthly values of the observed Sea Surface Temperature Anomalies of the Region 3.4 of El Niño-Southern Oscillation (ENSO index) as a covariable. Precipitation intensity was simulated with a mixed exponential distribution, fitted using a maximum likelihood approach. The stochastic simulation shows that the application of the approach to Mediterranean and arid climates largely eliminates the overdispersion problem, resulting in a much improved interannual variability in the simulated values.

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WeaGETS – a Matlab-based daily scale weather generator for generating precipitation and temperature

Cited by 49 publications

References 14 publications

Using a Scenario‐Neutral Framework to Avoid Potential Maladaptation to Future Flood Risk

Using a Scenario‐Neutral Framework to Avoid Potential Maladaptation to Future Flood Risk

Statistical Downscaling of Precipitation and Temperature Using Long Ashton Research Station Weather Generator in Zambia: A Case of Mount Makulu Agriculture Research Station

Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

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