scholarly journals Applications of the MVWG Multivariable Stochastic Weather Generator

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Nándor Fodor ◽  
Ildikó Dobi ◽  
János Mika ◽  
László Szeidl
Keyword(s):  
Weather Generator ◽  
Weather Data ◽  
Data Series ◽  
Series Length ◽  
Stochastic Weather Generator ◽  
Crop Models ◽  
Weather Generators ◽  
Interpolation Technique ◽  
Meteorological Observations ◽  
Homogeneous Regions

Weather generators (WG) became significant modules of crop models and decision support systems in the past decade. Using a large meteorological database from North America; two basic problems, related to the applicability of WGs in case of short or lacking data series, were investigated in the framework of the Multivariable weather generator (MVWG). First, the minimum data series length, required for adequate parameterization of the WG, was determined. Our results suggest that 15 years of observed data are enough for adequate parameterization of the MVWG. We then investigated a possibility of spatial interpolation of WG parameters using the outputs of the WG for sites with no meteorological observations. Coupled with the presented interpolation technique, MVWG was able to generate realistic weather data for sites with no measurements situated in climatically and geographically homogeneous regions.

scholarly journals Estimation of extreme flooding based on stochastic weather generators supported by the use of non-systematic flood data

2020 ◽  
Author(s):  
Carles Beneyto ◽  
José Ángel Aranda ◽  
Gerardo Benito ◽  
Félix Francés
Keyword(s):  
Distribution Function ◽  
Hydrological Model ◽  
Initial Conditions ◽  
Weather Generator ◽  
Data Series ◽  
Stochastic Weather Generator ◽  
Systematic Information ◽  
Continuous Simulation ◽  
Extra Information ◽  
Weather Generators

<p>An adequate characterization of extreme floods is key for the correct design of the infrastructures and for the flood risk estimation. Traditionally, these studies have been carried out based on the design storm. However, we now know that this approach is uncertain since peak discharges and hydrographs are strongly dependant on the initial conditions of the basin and on the spatio-temporal distribution of the precipitation.<br>One of the possible solutions that has recently been better welcomed between the scientific community is the continuous simulation. This combination of statistical and deterministic methods consist of the generation of extended synthetic data series of discharges by combining the use of a stochastic weather generator and a hydrological model. Nevertheless, weather generators still need robust data series of observed precipitation in order to perform adequately, especially when trying to capture extremes. To date, however, the length of both available precipitation and discharge records are still not sufficient to guarantee an adequate estimation of extreme discharges, presenting these high uncertainty.<br>In the present study, the same approach is taken (i.e. continuous simulation). However, in order to deal with the short length of the data records and to improve the estimations of extreme discharges, non-systematic information (i.e. historical and Palaeoflood) is integrated in the methodology, extending the length of the flow records and giving extra information of the higher tail of the distribution function, thus reducing the uncertainty of these estimations.<br>This methodology was implemented in a Spanish Mediterranean ephemeral catchment, Rambla de la Viuda (Castello, Valencia). The study area comprises an approximate area of 1,500 km2 and presents a mean rainfall of 615 mm, most of them falling within the autumn months (SON) as a consequence of medicanes. The weather generator used was GWEX, which was designed to focus on extremes, and the hydrological model implemented was TETIS, which is a conceptual model and spatially distributed. Both of them were implemented at a daily scale. Non-systematic information was obtained from previous studies, having information at two locations and, therefore, being able to validate the results in more than one point.<br>The results, in terms of precipitation, showed that weather generators using heavy-tailed marginal distribution functions outperform those using light-tailed distributions (e.g. Exponential or Gamma), especially when extra information is incorporated, as in this study, where regional maxima precipitation studies were integrated for the parametrisation of the weather generator.<br>With regards to discharges, the incorporation of non-systematic information clearly gave extra information of the higher tail of the distribution function (up to approx. T=600 years in this study), allowing to validate the generated discharges up to larger return periods and, therefore, reducing the uncertainty of the extreme discharge estimations</p>

scholarly journals Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogs

2019 ◽  
Author(s):  
Damien Raynaud ◽  
Benoit Hingray ◽  
Guillaume Evin ◽  
Anne-Catherine Favre ◽  
Jérémy Chardon
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Weather Generator ◽  
Time Step ◽  
Stochastic Weather Generator ◽  
Natural Risk ◽  
Synoptic Weather ◽  
Weather Generators ◽  
Long Time ◽  
Precipitation And Temperature

Abstract. Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorology records used to characterize the large-scale atmospheric configuration of the generation day. To overcome those limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days in the 20th century to generate a 1000-year sequence of new atmospheric trajectories and (2) a stochastic downscaling model in a second step, applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analog-based weather generators.

scholarly journals AnaWEGE: a weather generator based on analogues of atmospheric circulation

2013 ◽  
Vol 6 (3) ◽  
pp. 4745-4774
Author(s):  
P. Yiou
Keyword(s):  
Time Series ◽  
North Atlantic ◽  
Weather Generator ◽  
Climate Variable ◽  
Stochastic Weather Generator ◽  
Sea Level Pressure ◽  
The North ◽  
The North Atlantic ◽  
Meteorological Observations ◽  
Stochastic Rules

Abstract. This paper presents a stochastic weather generator based on analogues of circulation (AnaWEGE). Analogues of circulation have been a promising paradigm to analyse climate variability and its extremes. The weather generator uses precomputed analogues of sea-level pressure over the North Atlantic. The stochastic rules of the generator constrain the continuity in time of the simulations. The generator then simulates spatially coherent time series of a climate variable, drawn from meteorological observations. The weather generator is tested for European temperatures, and for winter and summer seasons. The biases in temperature quantiles and autocorrelation are rather small compared to observed variability. The ability of simulating extremely hot summers and cold winters is also assessed.

scholarly journals Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues

2020 ◽  
Vol 24 (9) ◽  
pp. 4339-4352
Author(s):  
Damien Raynaud ◽  
Benoit Hingray ◽  
Guillaume Evin ◽  
Anne-Catherine Favre ◽  
Jérémy Chardon
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Weather Generator ◽  
Weather Variables ◽  
Time Step ◽  
Stochastic Weather Generator ◽  
Natural Risk ◽  
Synoptic Weather ◽  
Weather Generators ◽  
Precipitation And Temperature

Abstract. Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local weather variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorological records used to characterize the large-scale atmospheric configuration of the generation day. To overcome these limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days of the 20th century to generate a 1000-year sequence of new atmospheric trajectories, and (2) a stochastic downscaling model in a second step applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analogue-based weather generators.

scholarly journals AnaWEGE: a weather generator based on analogues of atmospheric circulation

2014 ◽  
Vol 7 (2) ◽  
pp. 531-543 ◽  
Author(s):  
P. Yiou
Keyword(s):  
Time Series ◽  
North Atlantic ◽  
Weather Generator ◽  
Climate Variable ◽  
Stochastic Weather Generator ◽  
Sea Level Pressure ◽  
The North ◽  
The North Atlantic ◽  
Meteorological Observations ◽  
Stochastic Rules

Abstract. This paper presents a stochastic weather generator based on analogues of circulation (AnaWEGE). Analogues of circulation have been a promising paradigm to analyse climate variability and its extremes. The weather generator uses precomputed analogues of sea-level pressure over the North Atlantic. The stochastic rules of the generator constrain the continuity in time of the simulations. The generator then simulates spatially coherent time series of a climate variable, drawn from meteorological observations. The weather generator is tested for European temperatures, and for winter and summer seasons. The biases in temperature quantiles and autocorrelation are rather small compared to observed variability. The ability of simulating extremely hot summers and cold winters is also assessed.

Exploring the uncertainty of Weather Generators’ extreme estimates associated with the length of the input data series

2021 ◽  
Author(s):  
Carles Beneyto ◽  
José Ángel Aranda ◽  
Félix Francés
Keyword(s):  
Distribution Function ◽  
Input Data ◽  
Climate Scenario ◽  
Cumulative Distribution ◽  
Statistical Characteristics ◽  
Weather Data ◽  
Data Series ◽  
Sample Length ◽  
Weather Generators ◽  
Quantile Estimates

<p>Stochastic Weather Generators (WG) have been extensively used in recent years for hydrologic modeling, among others. Compared to traditional approaches, the main advantage of using WGs is that they can produce synthetic continuous time series of weather data of unlimited length preserving their spatiotemporal distribution. Synthetic simulations are based on the statistical characteristics of the observed weather, thus, relying upon the length and spatial distribution of the input data series. In most cases, and especially in arid/semiarid regions, these are scarce, which makes it difficult for WGs to obtain reliable quantile estimates, particularly those associated with low-frequency events. The present study aims to explore the importance of the input weather data length in the performance of WGs, focusing on the adequate estimation of the higher quantiles, and quantifying their uncertainty.</p><p>An experimental case study consisting of nine rain gauges from the Spain02-v5 network in a 0.11º resolution covering an approximate area of 180 km<sup>2</sup> was implemented. The WG used for the experiment was GWEX, which includes a three-parameter (σ, κ, and ξ) cumulative distribution function (E-GPD) to model de precipitation amounts, being the shape parameter ξ the one directly governing the upper tail of the distribution function. A fictitious climate scenario of 15,000 was simulated fixing the ξ value to 0.11.  From this scenario, 50 realizations of 5,000 years with a different sample length (i.e. 30, 60, 90, 120, 150, 200, 300 years) were simulated for four different particular cases: (1) leaving the ξ value as default (i.e. 0.05); (2) estimating the ξ value from the observations; (3) calibrating the ξ value with the T = 100 years quantile from the 15,000 years; and (4) fixing the ξ value to the fictitious scenario value. Relative root mean square error (RRMSE) and coefficient of variation (CV) were calculated for each set of realizations and compared with the obtained from the fictitious climate scenario.</p><p>Preliminary results showed a clear reduction in the value of both the CV and the RRMSE with the increase of the sample length for the four particular cases, being this reduction more evident for the higher order quantiles and as we move from particular case (1) to (4). Furthermore, it was observed that there was not any significant improvement in the higher quantile estimates between the 200-yrs and the 300-yrs samples, concluding that there is a sample length threshold from which the estimates do not improve. Finally, even observing a clear improvement in all estimates when increasing the sample length, a systematic underestimation of the higher quantiles in all cases was still observed, which remarks the importance of seeking extra sources of information (e.g. regional max. Pd. studies) for a better parameterization of the WG, especially for arid/semiarid climates.</p>

Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan

2012 ◽  
Vol 92 (3) ◽  
pp. 421-425 ◽  
Author(s):  
Hong Wang ◽  
Yong He ◽  
Budong Qian ◽  
Brian McConkey ◽  
Herb Cutforth ◽  
...  
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Short Communication ◽  
Weather Data ◽  
Yield Reduction ◽  
Climate Change Scenarios ◽  
Communication Climate ◽  
Stochastic Weather Generator ◽  
Ipcc Sres

Wang, H., He, Y., Qian, B., McConkey, B., Cutforth, H., McCaig, T., McLeod, G., Zentner, R., DePauw, R., Lemke, R., Brandt, K., Liu, T., Qin, X., White, J., Hunt, T. and Hoogenboom, G. 2012. Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan. Can. J. Plant Sci. 92: 421–425. This study assessed potential impacts of climate change on wheat production as a biofuel crop in southern Saskatchewan, Canada. The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) was used to simulate biomass and grain yield under three climate change scenarios (CGCM3 with the forcing scenarios of IPCC SRES A1B, A2 and B1) in the 2050s. Synthetic 300-yr weather data were generated by the AAFC stochastic weather generator for the baseline period (1961–1990) and each scenario. Compared with the baseline, precipitation is projected to increase in every month under all three scenarios except in July and August and in June for A2, when it is projected to decrease. Annual mean air temperature is projected to increase by 3.2, 3.6 and 2.7°C for A1B, A2 and B1, respectively. The model predicted increases in biomass by 28, 12 and 16% without the direct effect of CO2 and 74, 55 and 41% with combined effects (climate and CO2) for A1B, A2 and B1, respectively. Similar increases were found for grain yield. However, the occurrence of heat shock (>32°C) will increase during grain filling under the projected climate conditions and could cause severe yield reduction, which was not simulated by DSSAT-CSM. This implies that the future yield under climate scenarios might have been overestimated by DSSAT-CSM; therefore, model modification is required. Several measures, such as early seeding, must be taken to avoid heat damages and take the advantage of projected increases in temperature and precipitation in the early season.

scholarly journals PENENTUAN NILAI PREMI ASURANSI PERTANIAN BERBASIS INDEKS CURAH HUJAN PADA KOMODITAS KEDELAI YANG DISIMULASI MENGGUNAKAN DISTRIBUSI WEIBULL

2020 ◽  
Vol 9 (4) ◽  
pp. 257
Author(s):  
NI PUTU AYUNDA SURYA DEWI ◽  
KOMANG DHARMAWAN ◽  
KARTIKA SARI
Keyword(s):  
Insurance Premium ◽  
Weather Generator ◽  
Rainfall Data ◽  
Simulated Rainfall ◽  
Crop Failure ◽  
Agricultural Insurance ◽  
Stochastic Weather Generator ◽  
Insurance Premiums ◽  
Rainfall Index ◽  
Insurance Costs

Agricultural insurance protects farmers who experience crop failure. This study aims to calculate the value of agricultural insurance premium by applying simulated rainfall index-based using stochastic weather generator on soybean commodities in Negara sub-district. This study are used rainfall data to determine the probability of the transition, then perform rainfall simulations using the Stochastic Weather Generator method to obtain trigger values and continued with the calculation of agricultural insurance premiums. Results of this study provide the value that higher trigger is taken, the greater the insurance premium that must be paid. The value of insurance premiums to be paid is 4,18% - 5,66% of insurance costs Rp2.605.000,00.

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