Generated Weather Data Research Articles

EVALUATION OFCLIGENANDWXGENWEATHER DATA GENERATORS UNDER CANADIAN CONDITIONS

In this study, we evaluate for five selected Canadian climatological stations, the weather data generator, CLIGEN, which was developed for water erosion prediction (WEPP) as well as the similar generator, WXGEN, developed for the erosion/productivity impact calculator (EPIC). An important feature of WXGEN and CLIGEN is the small amount of input data required to generate long periods of record. The input parameters include: monthly relative frequency of wet- and dry-day sequences, monthly mean, standard deviation and skewness of precipitation, monthly mean and standard deviation of daily maximum and minimum air temperature, and solar radiation. CLIGEN-generated weather data were found to correspond well with observed mean values, but had consistently lower variation than observed values. The correlation between wet-days and temperature and radiation was not modelled accurately. In contrast, WXGEN was found to more accurately simulate the observed variability and preserve the relationship between temperatur...

Evaluation of radiation and temperature data generators in the Australian tropics and sub-tropics using crop simulation models

Long-term historical weather data are needed to conduct crop simulation analyses. However, the network of weather recording stations which collect all necessary daily weather data (commonly rainfall, solar radiation, maximum and minimum temperature) for such analyses is sparse. Frequently only rainfall is recorded. Thus, weather data generation techniques are required for three situations: (i) where only rainfall data are available, (ii) where both rainfall and temperature data are available, but radiation is missing, and (iii) where records are otherwise complete, but techniques are cequired to fill short periods of missing data. Three weather generation techniques are compared, termed here (i) Bristow and Campbell's method, (ii) TAMSIM and (iii) WGEN. Methods (ii) and (iii) were used to generate temperature and radiation data to accompany recorded rainfall records, and methods (i)–(iii) to generate a solar radiation record to accompany recorded temperature and rainfall records. Data from four stations in tropical and subtropical Australia with long-term complete weather records were used to compare actual with generated data sets. Results were evaluated firstly by comparing the cumulative distribution function (CDF) of generated and actual values, and secondly by comparing CDFs calculated from the output of three crop simulation models used with the generated and actual data sets. Generally the distributions of radiation and temperature differed significantly. However, when the weather data sets were used by simulation models to estimate biomass, only 10 of the 50 CDFs differed significantly. When both temperature and radiation were generated, 30% of CDFs from TAMSIM and 20% of WGEN differed significantly. When only radiation was generated, 40% of CDFs generated by the Bristow and Campbell's method, 10% of WGEN and none of TAMSIM differed significantly. All methods simulated the more temperate sites with higher precision than the wet, tropical site. Simulated yields showed a similar pattern. It was concluded that where both temperature and radiation data have to be generated, WGEN is appropriate because it contains a stochastic element and thus simulates catastrophic events such as frosts. Where only radiation generation is required, both WGEN and TAMSIM performed adequately. Where temperature or radiation data sets are complete except for occasional missing days, TAMSIM was considered to be the most appropriate. In cases were the objective is not to conduct long-term simulation analyses, Bristow and Campbell's method appeared more appropriate because of its ability to better simulate the day to day variation in solar radiation.

BEANGRO: A Process‐Oriented Dry Bean Model with a Versatile User Interface

AbstractMicrocomputer‐based crop simulation models are increasingly used in agricultural research. Although most models have been developed for specific applications, there is a trend toward developing more versatile models, with user interfaces that permit a wide range of applications without the user having to modify the model structure. BEANGRO is such a versatile, user‐friendly simulation model for dry bean (Phaseolus vulgaris L.). It is written in the FORTRAN computer language and operates on personal computers. The flexible user interface allows researchers to specify diverse model inputs and outputs, easily compare simulation results with observed data, and conduct simulation experiments interactively through menu‐driven options for sensitivity analysis. BEANGRO follows the standard file formats of the Decision Support System for Agrotechnology Transfer (DSSAT) software system, and may be used within DSSAT to assess effects of different agricultural management practices for runs over multiple years using either historical or generated weather data. The model responds to environmental variables (including air temperature, solar radiation, precipitation, and soil moisture retention characteristics), as well as to crop management conditions. Cultivar traits are specified through an input file, and traits may be modified interactively. BEANGRO operates at daily time steps and simulates bean growth and development from planting until harvest maturity. This traditional process‐oriented dry bean model and its user interface has created an interest among nontraditional modeler users and has been distributed to researchers for application in various projects, ranging from landuse planning to plant breeding and plant physiology.

Analysis of the performance of direct gain systems in Alexandria, Egypt

The present paper presents an analysis of the performance of the direct gain systems using the weather data of Alexandria, Egypt (latitude 31.2°). The weather data file of Alexandria contains monthly average values of daily radiation on a horizontal surface, clearness index, ambient temperature, wind speed and relative humidity. Since TRNSYS needs hourly values of the previous parameters, the weather data generator developed at the Solar Energy Laboratory at Madison-Wisconsin is used to generate hourly data from the monthly average data available for Alexandria. TRNSYS is a transient simulation program developed at the University of Wisconsin-Madison. A numerical model is used to calculate the energy flows across the window to the building. The window model is then incorporated into TRNSYS to determine the thermal performance of the direct gain systems. The effect of the number of glazing, the building capacitance, and the night insulation resistance on the direct gain systems performance is studied. Finally, a comparison between the performance of the direct gain system and the collector-storage wall composed of concrete is carried out.

A methodology for the synthesis of hourly weather data

The ability to generate weather data from limited inputs and location independent correlations would allow simulations of environmentally driven systems to be run at locations for which detailed weather records do not exist. A further improvement would be to generate just one year of data which would yield the same simulation results as those of long-term data, thereby minimizing the computation needed to assess long-term system performance. In this paper, a methodology to calculate such a year of data is described. It is a combinationand modification of several previously published generation methods. Statistics obtained from long-term (22 years), Typical Meteorological Year, and synthetically generated weather data are compared at three locations (Albuquerque, NM, Madison, WI, and New York, NY).

Seasonal changes in interrelationships between climatic variables

The correlation patterns between air temperature and insolation were investigated for four sites ranging from northern to southern Australia and one site in Venezuela. This investigation was considered as a prior important step for the stochastic generation of these climatic variables as input data for crop models. The use of sunshine hours when insolation data were not available to estimate these correlation patterns was investigated. Data from one location, having simultaneously measured records of both solar radiation and sunshine hours, were used in this study. The correlation coefficients between fractional cloudiness estimated from solar radiation or sunshine hours and the maximum temperature on a given day ( TX) and between fractional cloudiness and the difference between the maximum temperature on the same day and the minimum temperature on the next day ( TD), were calculated for each 14-day period in the year and for each location. Most of these correlation coefficients displayed seasonal variations specific to each location. A methodology is described whereby these correlations can be preserved whilst synthetically generating weather data for input to models of plant growth. It has been suggested that the preservation of these intercorrelations is important for realism in predictions of crop production.

Investigation of optimum parameters for solar domestic hot water system in Alexandria, Egypt

The present paper presents a detailed study for solar domestic hot water (SDHW) system using the weather data of Alexandria, Egypt. The weather data generator subroutine is used to generate hourly data from the average monthly data available for Alexandria. A comparison was made between the values of the system performance predicted by both the transient simulation program (TRNSYS) and the f -chart design method. The results indicate the reliability of the f -chart method in Egypt's climate taking advantage of its huge reduction in computer cost in comparison with detailed simulation analysis using TRNSYS. The effect of varying the system parameters on system performance is also studied. Finally an optimum value, within the practical limit, for each parameter is recommended.

Weather Input for Nonpoint‐Source Pollution Models

Three stochastic weather models are developed for use with nonpoint‐source pollution simulation models that require sequences of daily precipitation and mean daily temperature. Two simple models have parameters that can be estimated from available secondary data. Both generate temperature and precipitation independently. A third model represents a more sophisticated approach and requires primary data for parameter estimation. A model validation analysis was attempted using three U.S. weather stations. The first two models did not reproduce the higher‐order moments or the tail of the wet‐dry precipitation distribution. All models used normal temperature distributions which did not reproduce the higher‐order moments of the observed temperature distributions. Using historical and generated weather data as input to the Cornell Nutrient Simulation model, annual runoff and percolation, and dissolved nitrogen and phosphorus losses were compared. The third model gave output that was not statistically different from that obtained with the historical weather data; the first two models occasionally gave statistically different values. Using the mean annual losses given by historical weather data as a basis, model errors were less than 20%.