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.     

我国上空的水汽含量及其气候学估算

该文根据中国高空气候标准值 (1971—2000年) 逐月数据集124个探空站资料,计算出各站的整层大气水汽含量,并绘制出年水汽含量分布,除青藏高原地区外,其余地区基本上呈纬向分布。继而配合我国地面气候标准值逐月数据集的水汽压和地面气压数据,在对水汽压进行相应的订正后,将其与整层水汽含量进行相关分析,拟合出全国普遍适用的、统一的或分月的线性经验表达式。拟合结果与实测值之间的均方根误差为0.25 cm。文中还详细讨论了多项式不同次数对拟合结果的影响,结果表明:与数据点走向拟合较好的多项式,次数高其结果并非误差最小。利用经地面气压订正的地面水汽压 (x) 与整层水汽含量 (y) 的拟合公式为y=0.185x+0.093,其最大优点是站点无论高低、不分地域普遍适用。     

Estimation of Hourly Solar Radiation at the Surface under Cloudless Conditions on the Tibetan Plateau Using a Simple Radiation Model

In this study,the clear sky hourly global and net solar irradiances at the surface determined using SUNFLUX,a simple parameterization scheme,for three stations(Gaize,Naqu,and Lhasa) on the Tibetan Plateau were evaluated against observation data.Our modeled results agree well with observations.The correlation coefficients between modeled and observed values were > 0.99 for all three stations.The relative error of modeled results,in average was < 7%,and the root-mean-square variance was < 27 W m 2.The solar irradiances in the radiation model were slightly overestimated compared with observation data;there were at least two likely causes.First,the radiative effects of aerosols were not included in the radiation model.Second,solar irradiances determined by thermopile pyranometers include a thermal offset error that causes solar radiation to be slightly underestimated.The solar radiation absorbed by the ozone and water vapor was estimated.The results show that monthly mean solar radiation absorbed by the ozone is < 2% of the global solar radiation(< 14 W m 2).Solar radiation absorbed by water vapor is stronger in summer than in winter.The maximum amount of monthly mean solar radiation absorbed by water vapor can be up to 13% of the global solar radiation(95 W m 2).This indicates that water vapor measurements with high precision are very important for precise determination of solar radiation.     

中国西部干旱地区太阳直接辐射通量密度与大气质量的关系

本文根据中国西部干旱地区大气物理状态(大气可降水量和大气浑浊度)的变化范围,利用太阳直接辐射通量密度的理论计算公式,计算了各种大气透明系数状况下的太阳直接辐射通量密度。计算结果表明,大气质量m=2时的透明系数P_2和m相同时,由于水汽和气溶胶的含量配置不同而引起太阳直接辐射通量密度的差异,在辐射日变程的主要时段(m=1-3)内并不大。于是得到各种透明系数P_2状况下太阳直接辐射通量密度随大气质量变化的平均关系。不同海拔高度比较,当透明系数P_2相同时,各自的大气物理状态虽然不同,但m相同时的太阳直接辐射通量密度基本相同。现有的表征太阳直接辐射通量密度值和大气质量联系的关系式都不能很好地描述计算结果。根据太阳直接辐射通量密度随大气质量变化的特性,我们提出了新的关系式。最后,整理了不同拔海高度和不同地理景观的四个日射站(西藏那曲、青海格尔木、甘肃敦煌和民勤)的多年观测资料。经验计算结果与观测值比较,一致性良好。     

0-10 KM TEMPERATURE AND HUMIDITY PROFILES RETRIEVAL FROM GROUND-BASED MICROWAVE RADIOMETER

Deviation exists between measured and simulated microwave radiometer sounding data. The bias results in low-accuracy atmospheric temperature and humidity profiles simulated by Back Propagation artificial neural network models. This paper evaluated a retrieving atmospheric temperature and humidity profiles method by adopting an input data adjustment-based Back Propagation artificial neural networks model. First, the sounding data acquired at a Nanjing meteorological site in June 2014 are inputted into the MonoRTM Radiative transfer model to simulate atmospheric downwelling radiance at the 22 spectral channels from 22.234GHz to 58.8GHz, and we performed a comparison and analysis of the real observed data; an adjustment model for the measured microwave radiometer sounding data was built. Second, we simulated the sounding data of the 22 channels using the sounding data acquired at the site from 2011 to 2013. Based on the simulated rightness temperature data and the sounding data, BP neural network-based models were trained for the retrieval of atmospheric temperature, water vapor density and relative humidity profiles. Finally, we applied the adjustment model to the microwave radiometer sounding data collected in July 2014, generating the corrected data. After that, we inputted the corrected data into the BP neural network regression model to predict the atmospheric temperature, vapor density and relative humidity profile at 58 high levels from 0 to 10 km. We evaluated our model’s effect by comparing its output with the real measured data and the microwave radiometer’s own second-level product. The experiments showed that the inversion model improves atmospheric temperature and humidity profile retrieval accuracy; the atmospheric temperature RMS error is between 1K and 2.0K; the water vapor density’s RMS error is between 0.2 g/m3 and 1.93g/m3; and the relative humidity’s RMS error is between 2.5% and 18.6%.     

基于径向基神经网络的地基微波辐射计反演算法研究

利用兰州大学半干旱气候与环境观测站(SACOL站)2009—2010年的地基微波辐射计亮温资料和榆中站探空资料,建立了应用于地基微波辐射计温度、相对湿度和水汽密度反演的径向基神经网络,并将反演结果与地基微波辐射计自带反演产品进行了对比,探究了径向基神经网络在地基微波辐射计气象要素反演算法本地化的应用效果。结果表明:径向基神经网络反演的温度、相对湿度和水汽密度的均方根误差最大值分别为2.72 K、22.32%和0.73 g·m^-3,在所有高度层上径向基神经网络的反演结果均优于微波辐射计,反演产品对2—10 km、1—7 km、0—3 km的大气温度、相对湿度和水汽密度廓线的反演均有明显改善,径向基神经网络能够应用于地基微波辐射计气象要素的反演算法的本地化。     

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T _mean and R _H variables.     

Role of Solar Radiation and Water Vapour Pressure Deficit in Controlling Latent Heat Flux Density in a Scots Pine Forest

We assessed in absolute and relative terms how solar radiation and water vapour pressure deficit control the latent heat flux density in a Scots pine (Pinus sylvestris L.) forest growing under boreal conditions. The absolute and relative total control can be expressed as sums of the physical and biological forms of control. Physical control is based on the direct effects of solar radiation and water vapour pressure deficit on latent heat flux density, and biological control on the effects of solar radiation and water vapour pressure deficit on latent heat flux density through surface resistance. Measurements based on the eddy covariance method were used in the assessment, which defined the scale adopted in the study. Relative physical control over latent heat flux density was mainly exercised by water vapour pressure deficit, and the role of solar radiation was only marginal. The relative biological control exercised by solar radiation over latent heat flux density was higher in the morning and afternoon, unlike the control exercised by water vapour pressure deficit, which was strongest around noon. The values for the relative total control exercised by solar radiation over latent heat flux density were in general higher than those for relative total control exercised by water vapour pressure deficit. This is inconsistent with expectations based on Omega theory, where a decoupling coefficient () indicates the relative control exercised by surface resistance over latent heat flux density. Solar radiation and water vapour pressure deficit do not necessarily act in opposite directions in the absolute or relative total control that they maintain over latent heat flux density.     

青藏高原两类下垫面地表能量分配对气候要素的响应研究

利用青藏高原北麓河观测站（退化高寒草甸）和玛曲观测站（高寒草原）2014年地面观测资料,通过组合分类法,对比分析了两类下垫面生长季土壤含水量、水汽压差和净辐射对地表能量分配的直接影响和间接影响,并且利用路径分析法研究了影响地表能量分配的关键气候因子。结果表明:北麓河站和玛曲站潜热占比（潜热通量与地表可利用能量的比值）对土壤含水量的响应分别处于土壤水分抑制阶段和能量抑制阶段。其中,北麓河站潜热占比在水汽压差较大时随土壤含水量增长较快,受净辐射的影响较小;而玛曲站潜热占比随土壤含水量的变化趋势受水汽压差和净辐射的影响均较小。北麓河站潜热占比随水汽压差的增大先减小后趋于不变,并且潜热占比对水汽压差的敏感性随土壤含水量的增大而减小;而玛曲站潜热占比随水汽压差的增大先增大后趋于不变,几乎不受土壤含水量和净辐射的影响。北麓河站和玛曲站潜热占比均随净辐射的增大趋于稳定,其稳定值分别与土壤含水量和水汽压差有关。路径分析结果显示,降水是影响北麓河站潜热占比的主要气候因子,而气温是影响玛曲站潜热占比的主要气候因子。     

太阳直接辐射的分光测量

利用热电式相对日射表和八种有色玻璃滤光片,于1975—1978年先后在我国七个地区(共十个测点)进行了太阳分光辐射的测量,获得了2698组资料。根据这些资料,分析了太阳高度、季节、地理纬度和海拔高度以及气溶胶和水汽等大气成份对各波段的太阳分光辐射的影响。最后,用大气质量、浑浊度系数和地面绝对湿度三个物理量作参数,以表格的形式列出了各波段分光辐射的相对通量值。     

利用FY-2C卫星资料估算太阳辐射研究

我国FY-2C气象卫星为估算到达地面的太阳辐射提供可靠的数据源。文章以华中3省（河声、湖北和湖南）为试验区,采用神经网络方法进行FY-2C资料太阳辐射的估算,并与统计方法结果进行比较。结果表明：①FY-2C资料可以用于太阳辐射估算,它的5个波段对太阳辐射的反演均有贡献;②用FY2C资料估算太阳辐射时,神经网络方法精度优于统计方法;③FY-2C估算太阳辐射的误差主要由卫星观测与地面观测的空间和时间范围的差异引起。     

国产MWP967KV型地基微波辐射计探测精度

MWP967KV型地基微波辐射计是我国自主研发,拥有完整自主知识产权的新型大气微波遥感探测设备,为了实现国产设备在气象业务中的应用,需对设备的探测精度进行对比分析。利用2015年8月—2018年3月四川盆地南部山区的无线电探空数据和地基微波辐射计数据,分析晴空和有云天气条件下温度廓线、相对湿度廓线和水汽密度廓线及相关物理参数的精度。结果表明:晴空、层积云和高积云的微波辐射计与探空仪的温度、水汽密度和相对湿度相关系数整体上分别在0.9890,0.9665,0.5868以上,均达到0.01显著性水平。3种参数廓线的相关系数整体均呈地面大于高空,仅温度廓线相关系数达到0.01显著性水平,相对湿度廓线和水汽密度廓线在高空的相关系数未达到0.01显著性水平。3种参数的相关性整体上温度最高,水汽密度次之,相对湿度最低。温度、相对湿度和水汽密度的均方根误差平均值分别为2.8℃,22%和1.38 g·m-3,温度廓线和相对湿度廓线在层积云和高积云的云中及云上的精度明显降低,均方根误差较云层下温度升高1℃~2℃,相对湿度增大10%~20%。逆温层会影响廓线及物理参数的精度。晴天或云天等大范围相似天气条件下,探空气球飘移距离与温度廓线、相对湿度廓线和水汽密度廓线偏差的相关性较弱。     

利用简单生物圈模式SiB2模拟锡林浩特草原地表湍流通量

利用简单生物圈模式SiB2模拟了2007年7月1日至9月30日期间锡林浩特草原的地表能量分配、CO2通量、地表有效辐射温度和土壤湿度.采用锡林浩特国家气候观象台野外试验基地实地测量资料确定SiB2所需要的参数和初始值后,由该资料中30 min一次的太阳短波辐射、大气长波辐射、水汽压、气温、水平风速和降水驱动SiB2,最...     

哈密地区空中水汽及增水潜力分析

采用哈密地区6站1975—2014年逐日地面水汽压和降水量资料,计算了哈密各站的大气可降水量、有效空中水资源量、自然降水产出率和人工增水潜力值,并分析了各量的时空分布特征。结果表明:哈密地区年平均整层大气可降水量为2560~4327 mm,年均有效空中水资源量约为232~828 mm,占整层大气可降水量的1/4~1/10;年均自然降水产出率在9%~28%,自然降水产出率与降水量成正比关系。哈密地区的年人工增水潜力理论计算值在844~2399 mm之间,潜力值在夏季最大,巴里坤和伊吾明显多于其它区域。     

The total solar radiation time series simulation in Athens, using neural networks

Summary  The present study describes a neural network approach for modeling and making short-term predictions on the total solar radiation time series. The future hourly values of total solar radiation for several years are predicted, by extracting knowledge from their past values, using feedforward backpropagation neural networks. The results are tested using various sets of non training measurements, the findings are very encouraging and the model is found able to simulate the future values of total solar radiation time series based on their past values. “Multi-lag” output predictions are performed using the predicted values to the input database in order to model future total solar radiation values with sufficient accuracy. Furthermore, an autoregressive model is developed for analysing and representing the total solar radiation time series. The predicted values of solar radiation are compared with the observed data series and it was found that the neural network approach leads to better predictions than the AR model. Received November 22, 1999 Revised February 17, 2000     

高原地区基于微波辐射计反演大气廓线的神经网络算法研究

以2007～2018年西宁二十里铺气象站探空资料为模拟样本,利用MonoRTM模式模拟中心频率21.985～58.759GHz的35通道亮温,应用BP神经网络对模拟数据进行反复训练,构建最优反演模型,并以2019年探空资料为测试样本,对比分析了不同季节和不同天气条件下BP神经网络与微波辐射计的反演效果.结果表明:晴空条...     

北京等7个气象台站太阳总辐射观测资料的准确度评估

基于地面太阳短波总辐射对气溶胶光学特性和地表反照率的敏感性, 该文提出了一个评估我国气象台站总辐射资料准确度的方法。该方法选用气溶胶光学厚度和太阳天顶角较小情形下的晴天辐射资料, 从太阳直射辐射反演气溶胶光学厚度, 用于计算宽带透过率, 再从该透过率和总辐射资料反演太阳常数E_{0, P}, 并采用E_{0, P}对世界辐射基准 (WRR) 的偏差表示总辐射资料的不确定性。模拟结果表明:气溶胶折射率虚部和大气柱水汽含量的输入误差是两个主要的评估不确定因子。用于准确度评估的资料越多, 越有利于平滑气溶胶、水汽含量等输入参数随机误差的效应, 评估结果越合理。应用这一方法, 该文评估了2000— 2004年我国沈阳、额济纳旗、北京、乌鲁木齐、格尔木、上海和广州7个气象台站总辐射资料的准确度。7个站共有1161个太阳常数反演值, 都满足太阳天顶角余弦 (μ₀) 大于0.7的条件。这些E_{0, P}值对WRR的最大偏差为7.33%, 97.78%的E_{0, P}值对WRR的偏差小于5%, 总平均E_{0, P}值对WRR偏差只有-1.15 %。依据这些结果, 当μ₀≥0.7时, 这些台站的晴天总辐射资料的不确定度估计为5%。     

利用太阳光度计反演郑州地区水汽含量

利用CE-318型太阳光度计936 nm波段的太阳辐射观测资料,采用改进的Langley法反演了郑州地区大气柱水汽含量,并分析了大气柱水汽含量与地面水汽压之间的关系。结果表明:郑州地区大气柱水汽含量的季节变化:夏季>秋季>春季>冬季,日变化为早晚低,中午高;郑州地区大气柱水汽含量与地面水汽压之间存在着良好的线性关系。     

Estimation methods for monthly humidity from dynamical downscaling data for quantitative assessments of climate change impacts

Methods are proposed to estimate the monthly relative humidity and wet bulb temperature based on observations from a dynamical downscaling coupled general circulation model with a regional climate model (RCM) for a quantitative assessment of climate change impacts. The water vapor pressure estimation model developed was a regression model with a monthly saturated water vapor pressure that used minimum air temperature as a variable. The monthly minimum air temperature correction model for RCM bias was developed by stepwise multiple regression analysis using the difference in monthly minimum air temperatures between observations and RCM output as a dependent variable and geographic factors as independent variables. The wet bulb temperature was estimated using the estimated water vapor pressure, air temperature, and atmospheric pressure at ground level both corrected for RCM bias. Root mean square errors of the data decreased considerably in August.