Estimating daily pan evaporation using adaptive neural-based fuzzy inference system

Estimation of evaporation is important for water planning, management, and hydrological practices. There are many available methods to estimate evaporation from a water surface, comprising both direct and indirect methods. All the evaporation models are based on crisp conceptions with no uncertainty element coupled into the model structure although in daily evaporation variations there are uncontrollable effects to a certain extent. The probabilistic, statistical, and stochastic approaches require large amounts of data for the modeling purposes and therefore are not practical in local evaporation studies. It is therefore necessary to adopt a better approach for evaporation modeling, which is the fuzzy sets and adaptive neural-based fuzzy inference system (ANFIS) as used in this paper. ANFIS and fuzzy sets have been evaluated for its applicability to estimate evaporation from meteorological data which is including air and water temperatures, solar radiation, and air pressure obtained from Automated GroWheather meteorological station located near Lake E?irdir and daily pan evaporation values measured by XVIII. District Directorate of State Hydraulic Works. Results of ANFIS and fuzzy logic approaches were analyzed and compared with measured daily pan evaporation values. ANFIS approach could be employed more successfully in modeling the evaporation process than fuzzy sets.     

Models for obtaining daily global solar irradiation from air temperature data

The study presents a critical assessment of the possibility of global solar irradiation computation by using air temperature instead of sunshine duration with the classical Ångström equations. The reason for this approach comes from the fact that, although the air temperature is a worldwide measured meteorological parameter, this is rarely used in solar radiation estimation techniques. More than that, the literature is very silent concerning the testing of such models in Eastern Europe. Two new global solar irradiation models (to be called AEAT) related to solar irradiation under clear sky conditions and having the minimum and maximum daily air temperature as input parameters were tested and compared with others from the literature against data measured at five stations in Romania in the year 2000. The accuracy of AEAT is acceptable and comparable to that of the models which use sunshine duration or cloud amount as input parameters. Since temperature-based Ångström correlations are strongly sensitive to origin, the approach for AEAT as a tool for potential users is presented in detail. Additionally reported is a new method to increase the generality of AEAT concerning the extension of the geographical application area. Based on overall results it was concluded that air temperature successfully substitutes sunshine duration in the estimation of the available solar energy.     

New approach to measure the stability of the solar radiative regime

This paper focuses on different ways of characterizing the solar radiative regime of a day and the stability of this regime. The days may be stratified in classes of cloud shade, observed total cloud cover amount, daily averaged clearness index, and fractal dimension of the solar global irradiance signal. A new Boolean parameter related to solar irradiance fluctuation is defined, namely the sunshine stability number. The time averaged value of the sunshine stability number is used for the characterization of the radiative regime stability during a given time interval. Ranking the days from the view-point of the stability of their radiative regime is performed by using the daily average value of the sunshine stability number and appropriately defined values of disorder and complexity, respectively. Measurements performed in the Romanian town of Timisoara (latitude 45°46?? N, longitude 21°25?? E and 85?m altitude above mean sea level) are used here. They refer to time series of global and diffuse solar irradiance recorded at 15-s time interval between sunrise and sunset during all the days in 2009.     

A simplified but accurate spectral solar irradiance model

Summary ¶The paper deals with the computation of solar energy available in a specific location. First, a new formula describing the relation between global solar irradiation and the duration of bright sunshine is established. The analysis of its use shows that global solar clear sky irradiance models are essential tools for daily computation of global irradiation. An integrated spectral atmospheric transmittance model is presented, it can be used to compute beam and diffuse clear sky irradiance for all applications where broadband solar energy input is needed. Since it is desirable to use simplified estimation methods for many applications, a parametric global solar irradiance model, derived from the spectral model, is also presented. This model needs only surface meteorological data as input. The influence of the averaging method used for the input parameters on the model accuracy is evaluated. Comparison of model results with the measurements shows an acceptable level of accuracy with the new model. Finally, an application of daily global solar energy computation is presented.Received May 17, 2002; revised October 14, 2002; accepted February 11, 2003 Published online September 10, 2003     

Theoretical meteorology and the operational meteorologist

Abstract

Values of incoming solar and long‐wave radiation measured at the vessel Quadra during the three phases of GATE are used to assess the daily performance of three models, one for solar and two for long‐wave radiation. The solar radiation model, which uses data on precipitable water and cloud amount at three levels in the atmosphere performed satisfactorily during the first phase but gave poor results in the other two phases when cumulonimbus became more dominant. Both the flux‐emissivity approach using measured and interpolated Upper air data and Paltridge's empirical procedure produced estimates of long‐wave radiation which compared very closely with the measurements.     

Projecting impacts of climate change on surface water temperatures of a large subalpine lake: Lake Tahoe, USA

Predicted increases in atmospheric CO₂ concentration are expected to cause increases in air temperatures in many regions around the world, and this will likely lead to increases in the surface water temperatures of aquatic ecosystems in these regions. Using daily air and littoral water temperature data collected from Lake Tahoe, a large sub-alpine lake located in the Sierra Nevada mountains (USA), we developed and tested an empirical approach for constructing models designed to estimate site-specific daily surface water temperatures from daily air temperature projections generated from a regional climate model. We used cluster analysis to identify thermally distinct groups among sampled sites within the lake and then developed and independently validated a set of linked regression models designed to estimate daily water temperatures for each spatially distinct thermal group using daily air temperature data. When daily air temperatures projections, generated for 2080–2099 by a regional climate model, were used as input to these group models, projected increases in summer surface water temperatures of as much as 3 °C were projected. This study demonstrates an empirical approach for generating models capable of using daily air temperature projections from established climate models to project site specific impacts on littoral surface waters within large limnetic ecosystems.     

Estimation of Hourly and Daily Global Solar Radiation at Clear Days Using an Approach Based on Modified Version of Gaussian Distribution

The performance of two models,Jam and Baig,based on the modified version of Gaussian distribution function in estimating the daily total of global solar radiation and its distribution through the hours of the day from sunrise to sunset al any clear day is evaluated with our own measured data in the period from June 1992 to May 1993 in Qena Egypt The results show a high relative deviation of calculated values from measured ones,especially for Jain model,in the most hours of the day,except for those near to local noon.This misfit behavior is quite obvious in the early morning and late afternoon A new approach has been proposed in this paper to estimate the daily and hourly global solar radiation This model performs with very high accuracy on the recorded data in our region.The validity of this approach was verified with new measurements in some clear days in June and August 1994.The resultant very low relative deviation of the calculated values of global solar radiation from the measured ones confirms the     

沪宁高速公路路面温度变化特征及统计模型

使用2006年7月-2007年6月沪宁高速公路沿线梅村和仙人山站附近的逐分钟路面温度、气温、湿度、风向、风速、降水气象资料, 分析了梅村和仙人山不同季节和不同天气状况下路面温度的日变化特征。结果表明:不同季节路面温度和气温具有明显的日变化;日出至日落时段,路面温度与气温有较大差异。在此基础上,应用逐步回归方法建立了梅村和仙人山最高和最低路面温度统计模型, 得出最低路面温度模型模拟结果与实况的变化趋势接近,误差绝对值不超过2℃, 具有很好的实际应用价值; 而最高路面温度模型在一定程度上模拟结果偏差较大,实际应用中需进行适当修订。     

Daily soil temperature modeling using neuro-fuzzy approach

Soil temperature is an important meteorological parameter which influences a number of processes in agriculture, hydrology, and environment. However, soil temperature records are not routinely available from meteorological stations. This work aimed to estimate daily soil temperature using the coactive neuro-fuzzy inference system (CANFIS) in arid and semiarid regions. For this purpose, daily soil temperatures were recorded at six depths of 5, 10, 20, 30, 50, and 100 cm below the surface at two synoptic stations in Iran. According to correlation analysis, mean, maximum, and minimum air temperatures, relative humidity, sunshine hours, and solar radiation were selected as the inputs of the CANFIS models. It was concluded that, in most cases, the best soil temperature estimates with a CANFIS model can be provided with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Gaussian membership function. Comparison of the models’ performances at arid and semiarid locations showed that the CANFIS models’ performances in arid site were slightly better than those in semiarid site. Overall, the obtained results indicated the capabilities of the CANFIS model in estimating soil temperature in arid and semiarid regions.     

Accuracy and sensitivity analysis for 54 models of computing hourly diffuse solar irradiation on clear sky

Fifty-four broadband models for computation of solar diffuse irradiation on horizontal surface were tested in Romania (South-Eastern Europe). The input data consist of surface meteorological data, column integrated data, and data derived from satellite measurements. The testing procedure is performed in 21 stages intended to provide information about the sensitivity of the models to various sets of input data. There is no model to be ranked “the best” for all sets of input data. However, some of the models performed better than others, in the sense that they were ranked among the best for most of the testing stages. The best models for solar diffuse radiation computation are, on equal footing, ASHRAE 2005 model (ASHRAE 2005) and King model (King and Buckius, Solar Energy 22:297–301, 1979). The second best model is MAC model (Davies, Bound Layer Meteor 9:33–52, 1975). Details about the performance of each model in the 21 testing stages are found in the Electronic Supplementary Material.     

几种水平面太阳总辐射量计算模型的对比分析

利用中国区域1961-1999年39 a间98个常规气象观测数据,建立6个模型分别以天文辐射、干洁大气总辐射和湿洁大气总辐射为起始数据,进行太阳辐射日总量的模拟,对比分析了6个水平面太阳总辐射量计算模型的性能.结果表明:在三种起始数据中,干洁大气总辐射和湿洁大气总辐射均能较好地体现宏观地势对太阳辐射空间分布的影响,以湿洁大气总辐射为起始数据的计算模型拟合精度相对较高.对6个水平面太阳总辐射量计算模型的对比分析发现:2个以日照百分率为主导因子,气温日较差为修正项的综合模型拟合误差最小,精度最高;经典的日照百分率模型次之,但其模型系数最稳定可靠;3个气温日较差模型拟合效果最差.最终选用经验系数稳定、拟合精度较高的日照百分率模型,制作了2001年中国水平面太阳辐射日总量空间分布图.     

Simulation and Regionalization of Daily Global Solar Radiation: A Case Study in Quebec,Canada

Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54?MJ?m^?2?d^?1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46?MJ?m^?2?d^?1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area.     

Estimating daily global solar radiation during the growing season in Northeast China using the ?ngstr?m–Prescott model

Daily global solar radiation is an important input required in most crop models. In the present study, a sunshine-based model, the ?ngstr?m–Prescott model, is employed to estimate daily global solar radiation on a horizontal surface during the growing season in Northeast China. Data from six control groups are used. The controls include the entire sequence, precipitation days, and non-precipitation days both during the growing season and year-round. Estimations are validated by comparing the calculated values with the corresponding measured values. The results indicate that estimating daily global solar radiation during the growing season using data only from the growing season is better than using year-round data. Classifying days with respect to precipitation and non-precipitation is also unnecessary. The performance on estimating daily global solar radiation during the growing season using the entire data in growing season performs best. A sunshine-based equation is obtained using our method to estimate growing season daily radiation for all meteorological stations in Northeast China. The approved approach is expected to be beneficial to crop models and other agricultural purposes.     

Retrieving daily global solar radiation from routine climate variables

Solar radiation is an important variable for studies related to solar energy applications, meteorology, climatology, hydrology, and agricultural meteorology. However, solar radiation is not routinely measured at meteorological stations; therefore, it is often required to estimate it using other techniques such as retrieving from satellite data or estimating using other geophysical variables. Over the years, many models have been developed to estimate solar radiation from other geophysical variables such as temperature, rainfall, and sunshine duration. The aim of this study was to evaluate six of these models using data measured at four independent worldwide networks. The dataset included 13 stations from Australia, 25 stations from Germany, 12 stations from Saudi Arabia, and 48 stations from the USA. The models require either sunshine duration hours (Ångstrom) or daily range of air temperature (Bristow and Campbell, Donatelli and Bellocchi, Donatelli and Campbell, Hargreaves, and Hargreaves and Samani) as input. According to the statistical parameters, Ångstrom and Bristow and Campbell indicated a better performance than the other models. The bias and root mean square error for the Ångstrom model were less than 0.25 MJ m² day^?1 and 2.25 MJ m² day^?1, respectively, and the correlation coefficient was always greater than 95 %. Statistical analysis using Student’s t test indicated that the residuals for Ångstrom, Bristow and Campbell, Hargreaves, and Hargreaves and Samani are not statistically significant at the 5 % level. In other words, the estimated values by these models are statistically consistent with the measured data. Overall, given the simplicity and performance, the Ångstrom model is the best choice for estimating solar radiation when sunshine duration measurements are available; otherwise, Bristow and Campbell can be used to estimate solar radiation using daily range of air temperature.     

Parameterization and mapping of solar radiation in data sparse regions

Knowledge of temporal and spatial variation of solar radiation is essential for many applications. In this work, a simple and feasible procedure is conducted to map the daily solar radiation for Liaoning province, one of the most important agricultural areas in China, but with sparsely measured solar radiation data. The daily sunshine duration are interpolated to the whole area, subsequently, solar radiation are calculated by ?ngstr?m-Prescott model, the generic parameters of which are determined by least square to minimize the overall fitting residual between the ratio of actual to potential sunshine duration and the ratio of actual to extra-terrestrial solar radiation of the sites where solar radiation are available. In other local regions with sparse data, mapping of the solar radiation could be done following the simple procedure. In the present study area, using the interpolated daily sunshine duration data by ANUSPLIN, ?ngstr?m-Prescott model with the generic parameters (a = 0.505, and b = 0.204) returns reasonable results, with the overall RMSE of 2.255 MJ m^?2, and RRMSE of 16.54%. The daily solar radiation varies between 5.26 in December and 22.74 MJ m^?2 in May, and shows an obviously spatial variation which is mainly contributed to the climate and topography. The substitution of solar radiation from nearby station is preferred to estimation by ?ngstr?m-Prescott model if the distance between the stations falls below the threshold of 135 ± 15 km. The RMSE of such substitution increases by approximately 0.157 MJ m^?2 per 10 km.     

Evaluation of support vector machine for estimation of solar radiation from measured meteorological variables

Solar radiation is an essential and important variable to many models. However, it is measured at a very limited number of meteorological stations in the world. Developing method for accurate estimation of solar radiation from measured meteorological variables has been a focus and challenging task. This paper presents the method of solar radiation estimation using support vector machine (SVM). The main objective of this work is to examine the feasibility of SVM and explore its potential in solar radiation estimation. A total of 20 SVM models using different combinations of sunshine ratio, maximum and minimum air temperature, relative humidity, and atmospheric water vapor pressure as input attributes are explored using meteorological data at 15 stations in China. These models significantly outperform the empirical models with an average 14 % higher accuracy. When sunshine duration data are available, model SVM2 using sunshine ratio and air temperature range is proposed. It significantly outperforms the empirical models with an average 26 % higher accuracy. When sunshine duration data are not available, model SVM19 using maximum temperature, minimum temperature and atmospheric water vapor pressure is proposed. It significantly outperforms the temperature-based empirical models with an average of 18 % higher accuracy. The remarkable improvement indicates that the SVM method would be a promising alternative over traditional approaches for estimation of solar radiation at any locations.     

Modeling land surface phenology in a mixed temperate forest using MODIS measurements of leaf area index and land surface temperature

The development of satellite-derived vegetation indices and metrics has enabled researchers to monitor land surface phenology (LSP). While the use of satellite data to monitor LSP is prevalent, there has been minimal effort to model LSP in temperate climates using satellite observations of the land surface. Satellite-derived LSP models are beneficial for studying past and future changes in phenology and related ecosystem processes (e.g., water, energy, and carbon fluxes). The purpose of this study was to model LSP during the spring in a mixed temperate forest using satellite-derived measurements of leaf area index (LAI) and land surface temperature (LST). As part of the model validation process, the use of LST as a proxy for air temperature to model LSP was also investigated. The results indicate that LST derived from the MODIS Terra sensor at 10:30?a.m. (local solar time) can be used to develop a LSP model that predicts the full profile of LAI from winter dormancy to maturity and the date when LAI reaches half of the annual maximum (LAI_50%) with relatively low error. In addition, the modeled LAI values closely tracked in situ observations of the phenological development of the dominant deciduous tree species located in the study area where the model was developed. A comparison of LST and daily maximum air temperature at two levels above the ground surface revealed distinct differences and nonlinearities in the relationship between these two variables. However, accumulated growing degree-days calculated from each of these variables were similar because the largest differences between LST and daily maximum air temperature occurred prior to the beginning of heat accumulation. Consequently, the model predictions of LAI_50% derived from the use of LST and daily maximum air temperature were similar. When the developed model was applied in two other mixed forests, the errors were larger due to substantial interannual variability in the relationship between LAI and heat accumulation and systematic differences in this relationship between sites. Although the model cannot be successfully applied in these other mixed forests, the ability of the model to capture a consistent relationship between satellite estimates of LAI and LST in the study area where it was developed demonstrates that satellite observations of the land surface can be used in certain locations to create LSP phenology models. When validated, the models can be used to examine past and future changes in phenology and related ecosystem processes.     

Simulation of daily energy budget and mean soil temperatures at an arid site

Summary Soil temperature is often inadequately based upon relatively few measurements at widely dispersed locations. Within arid regions, such as the desert southwestern United States, soils, microclimates, and thus soil temperature may be markedly heterogeneous. Because extensive measurement of soil temperature is often not feasible, models are needed that simulate soil temperature based on readily available soil survey and above-ground weather information. This paper describes a simple energy-budget based model for simulating daily mean temperatures within a bare arid land soil. The model requires basic information on soil physical properties, and daily weather data including air temperature, windspeed, rainfall, and solar radiation to calculate daily surface energy budget components and surface temperature. One of two alternative numerical methods is then used to calculated subsurface temperatures. Tests of the model using 1987 daily temperature data from an arid site at Yuma, Arizona resulted in root mean square deviations within 1.4°C between daily modeled and measured temperatures at both 0.05 and 0.10 m depths. Sensitivity analysis showed modeled temperatures at 0.05 m depth to be most sensitive to parameters affecting the surface energy balance such as air temperature and solar radiation. Modeled temperatures at 1.0m depth were relatively more sensitive to initial temperature conditions and to parameters affecting distribution of energy within the profile such as thermal conductivity.With 3 Figures     

Effects of increasing surface reflectivity on aerosol,radiation, and cloud interactions in the urban atmosphere

The online Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is used to simulate the effects of albedo enhancement on aerosol, radiation, and cloud interactions in the Greater Montreal Area during the 2011 heat wave period. We used a 2-way nested approach to capture the full impacts of meteorological and photochemical reactions in the urban atmosphere. We conducted four sets of simulations with and without aerosol estimations and convective parameterizations to explore the aerosol interactions with radiation and cloud in the urban atmosphere. The direct, semi-direct, and indirect effects of aerosols are analyzed. The meteorological performance of the model indicates that the model slightly underpredicts air temperature, overpredicts wind speed, and underpredicts relative humidity. The chemical component of the model indicates that the model tends to underpredict fine particulate matters and overpredict ozone and nitrogen dioxide concentrations. The surface reflectivity of roofs, walls, and grounds is increased from 0.2 to 0.65, 0.60, and 0.45, respectively. Albedo enhancement led to a net decrease in radiative balance at solar noon by 25 W/m2, a decrease in daily air temperature by 0.5 °C, a reduction in water mixing ratio to 0.5 g/kg, and a decline in cloud coverage by 3% in the center part of the domain. Increasing urban albedo caused a decrease in planetary boundary layer height by 25 m. Albedo enhancement affords a decrease in temperature-sensitive photochemical reaction rates and thus reduces daily ozone concentrations by 3 ppb across the entire domain. The concentration of daily fine particulate matters decreased by 3 μg/m3 in the center part of the GMA during the 2011 heat wave period.     

基于空气污染指数的太阳日总辐射计算方法

通过对2001—2012年全国23个站实测资料的分析,利用非线性回归法建立了以气温日较差、天文日照百分率和空气污染指数为主导因子的太阳日总辐射模型,这里简称为DSRM-Y模型 (Daily Solar Radiation Model-Y),检验其效果并与已有的DSRM-C模型 (Daily Solar Radiation Model-C) 进行效果比对。结果表明:太阳日总辐射与空气污染指数呈显著负相关,DSRM-Y模型的太阳日总辐射估算值与实测值的散点图以及平均偏差、均方根误差、误差分析均表现出较好的拟合效果。将模型应用于西宁、上海、昆明3个代表站,空气污染指数上升后,3个站太阳日总辐射均呈减少趋势;23个站DSRM-Y模型的均方根误差均小于DSRM-C模型,即DSRM-Y模型的拟合效果好于DSRM-C模型。