The trend analysis of the meteorological parameters has an important role in climate change studies. One of the most important factors which affects the trend analysis is the existence of the autocorrelation in meteorological parameters’ time series. Therefore, in this research, the effect of autocorrelation on trend of some meteorological parameters such as precipitation, relative humidity, solar radiation, wind speed and mean temperature was analyzed. For this purpose, the annual values of mentioned parameters were calculated using daily recorded data in 30 synoptic stations of Iran during 1960–2014. Then the trend of calculated annual time series was analyzed using the Mann Kendall (M–K) and modified Mann–Kendall (MM-K) tests (with considering all significant autocorrelation coefficients). The comparison of two mentioned tests showed that the autocorrelation affects the trend of the studied parameters. On the basis of the Root Mean Square Error (RMSE) results, in the trend analysis of temperature and precipitation, there were maximum and minimum differences between the statistic (Z) of the M–K and MM-K tests, respectively. The most linear relationship between results of two used trend tests was observed for precipitation and other parameters including wind speed, solar radiation, temperature and relative humidity were placed in the next steps, respectively. The trend results of the MM-K test showed that the precipitation, relative humidity and wind speed parameters had decreasing trend, while the trend of the solar radiation and temperature parameters was positive in more than 50% of studied stations. Also, the precipitation, relative humidity and wind speed parameters had negative trend slope in 63.3%, 80% and 60%, of stations, while, the solar radiation and temperature parameters had positive slope in 63.3% and 90% of studied stations, respectively.

中文翻译：

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自相关对气象参数趋势的影响

气象参数的趋势分析在气候变化研究中具有重要作用。影响趋势分析的最重要因素之一是气象参数时间序列中自相关的存在。因此，本研究分析了自相关对降水、相对湿度、太阳辐射、风速、平均温度等气象参数变化趋势的影响。为此，使用 1960-2014 年伊朗 30 个天气站的每日记录数据计算了上述参数的年值。然后使用 Mann Kendall (M-K) 和修正的 Mann-Kendall (MM-K) 检验（考虑所有显着的自相关系数）分析计算的年度时间序列的趋势。两个提到的测试的比较表明自相关影响研究参数的趋势。根据均方根误差 (RMSE) 结果，在温度和降水趋势分析中，M-K 和 MM-K 检验的统计量 (Z) 之间分别存在最大和最小差异。观察到降水量和其他参数（包括风速、太阳辐射、温度和相对湿度）的两个使用趋势测试结果之间的最线性关系，分别放在接下来的步骤中。MM-K试验趋势结果表明，50%以上的研究站降水、相对湿度和风速参数呈下降趋势，而太阳辐射和温度参数呈正趋势。还有降水，