A statistical comparison of the stochastic weather generators WGEN and SIMMETEO

Soltani, Afshin; Hoogenboom, Gerrit

doi:10.3354/cr024215

Cited by 49 publications

(32 citation statements)

References 37 publications

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“…The percentage significant differences (t * and F *) between observed and generated weather data when the length of input data is equal to the entire length of observed data (30 yr here; D 30 in Table 2) indicates the adequacy and validity of the generators. Details on the performance of the generators for the selected locations of this study can be found in Soltani & Hoogenboom (2003).The response of absolute difference between observed and generated weather variables (expressed in their physical unit) to the length of input data was similar to those of t* (Table 2). We prefer to use t*, because it is impossible to apply the segmented model (Eq.…”

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confidence: 72%

“…Daily values are computed internally, using linear interpolation. More details can be found in Richardson (1981), Richardson & Wright (1984), Semenov et al (1998), and Soltani & Hoogenboom (2003).The SIMMETEO generator of WeatherMan is embodied in the WGEN generator, but it uses different input data compared to WGEN. The modified SIMME-TEO model uses monthly climatic means of the number of wet days, precipitation, solar radiation, and maximum temperature and minimum temperature as well as regression equations to compute conditional means, standard deviations and precipitation parameters (Geng et al 1986, 1988, Pickering et al 1994.…”

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confidence: 99%

“…The poorer performance of both generators for extreme temperatures compared to other variables might be expected, as the extremes are not used as input parameters of the generators. Additional details and a full discussion on the efficiency of the generators can be found in Soltani & Hoogenboom (2003).The main objective of this study was to determine the minimum number of years required for parameter estimation of the WGEN and SIMMETEO weather generators. The results from this study showed this was a function of both the weather generator as well as the weather variable that was generated.…”

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confidence: 99%

“…These stations cover a wide geographical area and represent several climatic zones in Iran. Climatic 110 Soltani & Hoogenboom: Parameter estimation of stochastic weather generators characteristics of the selected weather stations can be found in Soltani & Hoogenboom (2003). For each location, precipitation and maximum and minimum temperatures were available for 30 yr (1966−1995).…”

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confidence: 99%

“…The modified SIMME-TEO model uses monthly climatic means of the number of wet days, precipitation, solar radiation, and maximum temperature and minimum temperature as well as regression equations to compute conditional means, standard deviations and precipitation parameters (Geng et al 1986, 1988, Pickering et al 1994. Details on the parameter estimation methods used in SIMMETEO can be found in Soltani & Hoogenboom (2003). …”

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Minimum data requirements for parameter estimation of stochastic weather generators

Soltani¹,

Hoogenboom²

2003

Clim. Res.

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Long-term daily weather data are commonly required for the application of simulation models in agricultural systems. The objective of this study was to evaluate the impact of the length of input weather data on the quality of generated weather data and to determine the minimum data required for an accurate estimation of the climate coefficients for a weather generator. Five locations in Iran with contrasting climates were selected for this study. The WGEN and SIMMETEO weather generators were parameterized using 5, 10, 15, 20 and 30 yr of weather data as the base period (BP), and then 30 yr daily weather data sets were generated corresponding to each BP. While WGEN requires daily weather data for parameter estimation, SIMMETEO uses monthly summaries. The quality of the generated weather was measured as percentage significant differences between observed and generated data. Statistical tests were conducted, including t-and F-tests, to compare differences between generated weather data versus 30 yr historical weather data. The results showed that the number of years required to obtain generated data that were similar to observed data was a function of the weather generator and the weather variable. WGEN was more sensitive to the amount of input data than SIMMETEO. In 8 of the 14 cases WGEN required a specific minimum amount of data to be efficient in weather generation versus 6 of the 14 cases for SIMMETEO. There was a correlation between generator performance and its minimum data requirement. For the cases where the generators showed a good performance, i.e. low-percentage significant differences between observed and generated data, a specific minimum number of years was required. For generating precipitation, a smaller number of years was required than for generating temperature. Based on the results from this study, it can be concluded that at least 15 yr of input data are needed for WGEN and SIMMETEO to obtain generated sequences with statistical characteristics similar to the observed data. However, a greater length of input data may lead to an improved set of generated data as shown through more stable generated data. KEY WORDS: Weather generation · Weather data · Climate · Simulation models · Decision support systems Resale or republication not permitted without written consent of the publisherClim Res 25: [109][110][111][112][113][114][115][116][117][118][119] 2003 years (> 20 yr) are required to extend the data, because the probability distribution of simulated outcomes is important for understanding simulation results or decision making (Thornton & Hoogenboom 1994).• Periods of daily records are missing within long-term historical data sets.• Only monthly averages and totals are available.• Only a few variables are recorded, such as precipitation and maximum and minimum temperatures, or in the worst case, only daily precipitation. These problems have been recognized by many scientists, who have developed weather generators to address some of these problems.The most commonly used weather gener...

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Minimum data requirements for parameter estimation of stochastic weather generators

Soltani¹,

Hoogenboom²

2003

Clim. Res.

Self Cite

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Simulation of historical temperatures using a multi-site, multivariate block resampling algorithm with perturbation

2012

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Stochastic weather generators have evolved as tools for creating long time series of synthetic meteorological data at a site for risk assessments in hydrologic and agricultural applications. Recently, their use has been extended as downscaling tools for climate change impact assessments. Non‐parametric weather generators, which typically use a K‐nearest neighbour (K‐NN) resampling approach, require no statistical assumptions about probability distributions of variables and can be easily applied for multi‐site use. Two characteristics of traditional K‐NN models result from resampling daily values: (1) temporal correlation structure of daily temperatures may be lost, and (2) no values less than or exceeding historical observations can be simulated. Temporal correlation in simulated temperature data is important for hydrologic applications. Temperature is a major driver of many processes within the hydrologic cycle (for example, evaporation, snow melt, etc.) that may affect flood levels. As such, a new methodology for simulation of climate data using the K‐NN approach is presented (named KnnCAD Version 4). A block resampling scheme is introduced along with perturbation of the reshuffled daily temperature data to create 675 years of synthetic historical daily temperatures for the Upper Thames River basin in Ontario, Canada. The updated KnnCAD model is shown to adequately reproduce observed monthly temperature characteristics as well as temporal and spatial correlations while simulating reasonable values which can exceed the range of observations. Copyright © 2012 John Wiley & Sons, Ltd.

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Assessment of NASA/POWER satellite‐based weather system for Brazilian conditions and its impact on sugarcane yield simulation

Monteiro¹,

Sentelhas

Pedra³

2017

Intl Journal of Climatology

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The gridded database provided by National Aeronautics and Space Administration/Prediction of World Wide Energy Resources (NASA/POWER) presents a global coverage of complete weather data at horizontal resolution of 1° latitude–longitude, becoming a potential source for agrometeorological studies. Once Brazil is a country with continental dimensions and the major sugarcane world producer, and its density of ground weather stations suitable for an efficient agricultural planning is sparse, the objectives of this study were to test how robust is the NASA/POWER database through its comparison with the Brazilian ground weather stations network records (INMET) and to quantify the impacts on potential (Yp) and attainable (Yatt) sugarcane yield simulations when setting NASA/POWER as source of input weather data. The comparisons for weather data records and sugarcane yield simulations were carried out from 1997 to 2016. Statistical indices presented a satisfactory performance for average air temperature (R2 = 0.73; d = 0.91), minimum air temperature (R2 = 0.72; d = 0.91), maximum air temperature (R2 = 0.57; d = 0.84), solar radiation (SR) (R2 = 0.71; d = 0.92), sunshine hours (R2 = 0.68; d = 0.90) and reference evapotranspiration, when calculated through Priestley–Taylor (ETo‐PT) method (R2 = 0.76; d = 0.93). When the weather variables were aggregated and compared with a 10‐day time scale, a strong improvement of statistical indices was obtained. Yp presented root mean square error (RMSE) smaller than 10 t ha−1 while relative mean error (RME) ranged between ±10% for majority of grid cells, with exception for southern Brazil due to low and frost temperatures that satellite cannot capture accurately. Even NASA/POWER offering a relatively coarse grid size database and perhaps some regional data fitting would give better results at higher latitudes and elevation. The results found in this study proved that NASA/POWER products could be used as a source of climatic data for agricultural activities with a reasonable confidence for regional and national spatial scales.

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A statistical comparison of the stochastic weather generators WGEN and SIMMETEO

Cited by 49 publications

References 37 publications

Minimum data requirements for parameter estimation of stochastic weather generators

Minimum data requirements for parameter estimation of stochastic weather generators

Simulation of historical temperatures using a multi-site, multivariate block resampling algorithm with perturbation

Assessment of NASA/POWER satellite‐based weather system for Brazilian conditions and its impact on sugarcane yield simulation

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