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1.
高时空分辨率的气温栅格数据是多种地学模型和气候模型的重要输入。山区地形复杂,气温空间异质性强,如何获取高时空分辨率的山区地表气温数据一直是研究热点与难点。本文选择地形复杂的河北省张家口市作为试验区,基于局部薄盘样条函数对ERA5再分析日均近地表气温（2 m高度）进行空间插值,并利用随机森林算法,结合少量气象站观测气温数据、地形地表参数数据构建日均气温订正模型和气温逐时化模型,实现空间分辨率由0.1 °（约11 km）到30 m的逐时气温降尺度,最后将该模型拓展应用于其他时间与区域,检验本文发展的降尺度方法在没有站点观测数据条件下的时空移植性。结果显示,本文降尺度方法得到的高时空分辨率山区气温数据精度较高,1月均方根误差（RMSE）平均值为2.4 ℃,明显优于气象站点插值结果,且气温相对高低的空间分布更为合理、纹理更加丰富;将该方法应用到其他时间与区域的RMSE平均值分别为2.9 ℃与2.5 ℃,均小于再分析资料直接插值所产生的误差。研究结果总体表明,在气象站点较少甚至没有时,可利用本文方法通过ERA5再分析气温准确获取复杂地形条件下的山区高时空分辨率气温数据。 相似文献
2.
陆地表面温度是描述区域或者全球范围内陆地表面与大气的相互作用和能量平衡最重要的环境参数之一。针对目前尚未有遥感卫星能够同时提供具有高时间和高空间分辨率的地表温度产品的问题,国内外学者发展了多种对低空间分辨率的地表温度进行降尺度的算法。然而,由于对地表温度解释变量和降尺度模型的选择往往具有区域局限性,导致了降尺度模型的泛化能力受到了一定的限制。本文首先评估了地表反射率、遥感光谱指数、地形因子、地表覆盖、经纬度以及基本状态变量6类环境参量与地表温度之间的相关关系,并在此基础上筛选出最佳解释变量;同时,结合在非线性回归问题上表现比较优秀的随机森林算法,建立了一种鲁棒性的基于随机森林算法地表温度降尺度模型（RRF）。本文选取了中国范围内具有代表性的11个地区作为主要研究区,将空间分辨率为1 km的MODIS地表温度产品降尺度至90 m。以北京市2个典型地表类型的子区域为代表研究区,通过与传统的基于归一化植被指数与地表温度相关关系的TsHARP模型,以及基于红波段和近红外波段以及地表高程作为尺度因子建立的简单Basic-RF模型的对比分析可得,RRF模型在2个子研究区降尺度结果均优于TsHARP模型和Basic-RF模型,其均方根误差分别为2.39 K和2.27 K。通过进一步对2个子研究区训练的RRF进行交叉验证,证明在一个研究区训练的RRF应用至另一研究区的降尺度时,RRF模型表现出了较好的鲁棒性,降尺度结果的均方根误差分别为2.56 K和2.44 K,精度误差相差仅为0.17 K。通过将RRF应用于中国范围内的多个研究区,结果表明利用少量训练数据构建的RRF模型适用于大范围的区域,地表温度降尺度结果都能取得较好的精度。 相似文献
3.
基于TRMM降水数据的空间降尺度模拟 总被引：1，自引：0，他引：1
本研究以黑河流域为例,通过在不同空间尺度上建立降水与影响因素的关系并选择最优尺度以此进行降尺度,建立了基于地理加权回归（GWR）与高精度曲面建模方法（HASM）相结合的跨尺度统计降尺度方法,对TRMM降水数据进行了降尺度模拟。最后,结合站点观测数据采用交叉验证法对降尺度结果进行了验证。结果表明,与传统的降尺度方法相比,考虑最优尺度的降尺度方法在一定程度上提高了降尺度结果的精度,同时表明对跨尺度过程中产生的误差进行修正可进一步提高结果精度。本研究所提出的方法可用于粗分辨率降水数据的降尺度模拟,可为站点稀疏地区或地形复杂地区高分辨率高精度降水数据的获取提供方法上的支持。 相似文献
4.
高精度曲面建模的中国气候降尺度模型 总被引：2，自引：0，他引：2
与站点统计降尺度插值和动力降尺度相比,高精度曲面建模(HASM)降尺度,具有不需大尺度预报因子,直接从GCM结果构建区域上高空间分辨率的未来气候模拟曲面的优势。HASM降尺度将未来气候,分为历史观测拟合的气候基准值和GCM未来气候变化值进行模拟,精度明显高于传统方法,但常系数全局拟合的气候基准值忽略了降水分布的空间非平稳性,导致降水模拟受到较大影响。为增强降水降尺度的气候背景值的描述能力,通过分析全国尺度降水的非线性非平稳性特点,提出耦合空间变系数气候基准值的HASM空间变系数降尺度模型(HASM-SVDM)以改进HASM对非平稳要素的降尺度能力,并以1961-2010年全国气温降水观测数据结合地形特征信息,利用HASM降尺度方法对HadCM3的A1Fi、A2a和B2a 3种情景的1961-1990、2010-2039、2040-2069和2070-2099时段的全国未来气温与降水进行降尺度模拟。分析表明,耦合全局线性模型的HASM常系数降尺度模型适合全国气温的降尺度模拟,而耦合空间变系数拟合的HASM-SVDM增强了空间非平稳背景值的描述能力,模拟的空间分布更能体现降水总体的非均匀分布趋势,适合全国降水的降尺度模拟。 相似文献
5.
WRF模式作为一个中尺度气候模式,其分辨率从几米到几千公里,其自身的双向嵌套特征也为进行动力尺度下推提供了有力条件。本文利用WRF模式和传统的统计方法对研究区的气温进行尺度下推。首先,通过动力下推得到不同分辨率下的气温空间分布,并选取15个气象站点进行点对点验证,为了更明显观察不同尺度间的差异,对不同尺度的输出与ANUSPLIN插值结果进行比对,结果显示动力尺度下推中,分辨率越高模拟效果越好。其次,我们采用传统的统计下推方法,从27km下推到3km分辨率,并与WRF和ANUSPLIN插值在该尺度的结果进行对比分析,结果显示统计下推结果的趋势与动力下推的插值结果是一致的,但具有明显的马赛克效果,通过分析认为,这与统计方法的尺度下推只考虑高程信息的变化对气温的影响,而未考虑其他因素有关,如若在下推时加入更多的变量,如对温度有较大影响的坡度、坡向、土地覆被类型等因素,综合分析不同尺度之间的关系,会使下推结果有所改善。 相似文献
6.
气象变量常作为重要的影响因子出现在环境污染、疾病健康和农业等领域,而高分辨率的气象资料可作为众多研究的基础数据,对推进相关研究的发展意义重大。本文以中国大陆为研究区域,利用2015年824个气象站点的气温、相对湿度和风速3套数据,结合不同的解释变量组合,分别构建了各自的GAM和残差自编码器神经网络（简称残差网络）模型,以10倍交叉验证判断模型是否过拟合。研究结果表明：① GAM和残差网络方法都不存在过拟合问题,同GAM相比,残差网络显著提高了模型预测的精度（3个气象因素的交叉验证CV R2平均提高了0.21,CV RMSE平均降低了37%）,其中相对湿度模型的提升幅度最大（CV R2:0.85 vs. 0.52,CV RMSE:7.53% vs. 13.59%）;② 残差模型的结果较普通克里格插值结果和再分析资料更接近站点观测数据,表明残差网络可作为高分辨率气象数据研制的可靠方法。此外,研究还发现在相对湿度模型中加入臭氧浓度和气温、在风速模型中加入GLDAS风速再分析资料,可提升模型的性能。 相似文献
7.
选用2012~2017年Kings Park 站探空资料,基于迭代最小二乘方法构建2种香港地区顾及高度改正的加权平均温度模型--Tm_hk1和Tm_hk2,并利用2018年探空资料对模型在香港地区的精度和适用性进行评估。结果表明,在香港地区,依赖测站温度的Tm_hk1模型具有较高的精度,年均偏差优于0.3 K,均方根误差优于1.8 K,与Bevis公式和GPT2w模型相比,Tm_hk1模型的精度分别提升35.4%和29.7%;而不依赖气象参数的Tm_hk2模型与GPT2w模型的精度相当,年均方根误差均优于2.5 K,Bevis公式的精度最差(RMS为2.7 K),且具有较大负偏差(bias为－1.8 K)。从季节性分析可知,Bevis公式、Tm_hk2 和GPT2w模型精度具有明显的季节性变化,总体为夏季精度较高(RMSE为1.3~2.2 K),冬季精度较低(RMSE为3.0~4.4 K);Tm_hk1模型在各季节均具有最高精度（RMSE为1.4~2.4 K）和适用性。 相似文献
8.
以中国西南地区2015~2017年探空数据为实验数据,使用多层感知器（MLP）神经网络回归方法建立西南地区的加权平均温度(Tm)模型。将气象参数（地表温度、水汽压）和非气象参数（高程、纬度和年积日）作为模型输入因子,由数值积分法计算得到的Tm作为学习目标,通过神经网络模型进行迭代训练从而得到中国西南地区的Tm。以2018年探空站Tm数据为参考值,对MLP模型精度进行验证,并与Bevis模型和GPT3模型进行对比分析。结果表明,MLP模型的年均RMSE和年均bias分别为1.99 K和0.15 K,比Bevis模型、GPT3模型年均RMSE分别降低1.36 K(40.6%)和1.51 K(43.1%),年均bias分别下降0.70 K（82.4%）和1.04 K（87.4%）,且该模型在中国西南区域不同高程、纬度和季节的精度与稳定性优于Bevis模型和GPT3模型。 相似文献
9.
Recently, study in past trends of climate variables gained significant consideration because of its contribution in adaptions and mitigation strategies for potential future changes in climate, primarily in the area of water resource management. Future interannual and inter-seasonal variations in maximum and minimum temperature may bring significant changes in hydrological systems and affect regional water resources. The present study has been performed to observe past(1970-2010) as well as future(2011-2100)spatial and temporal variability in temperature(maximum and minimum) over selected stations of Sutlej basin located in North-Western Himalayan region in India. The generation of future time series of temperature data at different stations is done using statistical downscaling technique. The nonparametric test methods, modified Mann-Kendall test and Cumulative Sum chart are used for detecting monotonic trend and sequential shift in time series of maximum and minimum temperature. Sen’s slope estimator test is used to detect the magnitude of change over a period of time on annual and seasonal basis. The cooling experienced in annual TMax and TMin at Kasol in past(1970-2010) would be replaced by warming in future as increasing trends are detected in TMax during 2020 s and 2050 s and in TMin during 2020 s, 2050 s and 2080 s under A1 B and A2 scenarios. Similar results of warming are also predicted at Sunnifor annual TMin in future under both scenarios which witnessed cooling during 1970-2010. The rise in TMin at Rampur is predicted to be continued in future as increasing trends are obtained under both the scenarios. Seasonal trend analysis reveals large variability in trends of TMax and TMin over these stations for the future periods. 相似文献
10.
利用中国区域2015~2017年探空数据,建立一种顾及地表温度、地表水汽压、高程和纬度的中国区域大气加权平均温度Tm模型（BET模型）。以2018年探空站Tm数据为参考值,分析BET模型精度,并与Bevis模型和GPT3模型进行对比。结果表明,BET模型年均RMSE与bias分别为3.15 K和0.04 K,相比于Bevis模型、1°×1°分辨率的GPT3模型和5°×5°分辨率的GPT3模型,年均RMSE分别降低29.2%、32.8%和39.1%,年均bias分别降低96.4%、96.7%和97.4%,且该模型在中国区域不同高程和纬度上的精度与稳定性优于Bevis模型和GPT3模型。 相似文献
11.
“一带一路”倡议是新时期中国为加强对外开放提出的全球化合作倡议,资源环境的优化配置对全球化发展意义重大。气温作为重要的基础数据和输入要素,对其进行空间化处理是实现大尺度区域资源环境优化配置的前提。本文基于地理信息技术（GIS）,运用距离平方反比法（IDS）、协同克里格法（CK）、回归距离平方反比法（RIDS）和回归协同克里格法（RCK）,对“一带一路”地区1980—2017年的2679个气象站点的月平均气温和年平均气温数据进行插值,获得了“一带一路”地区10 km分辨率的气温空间分布数据。交叉验证结果表明：① IDS、CK、RIDS和RCK插值法在整体上均较好地展示了“一带一路”地区气温的地理空间分布规律,4种插值方法的月均气温的均方根误差分别在1.93~2.43、1.78~2.14、1.31~2.23和1.23~1.92 ℃之间;年均气温的均方根误差分别为1.94、1.83、1.37和1.27 ℃;② 在“一带一路”地区,加入协变量分析的CK插值精度整体优于IDS,并且削弱了IDS的极值现象;③ RIDS和RCK对年均气温的插值精度分别较IDS和CK提高了29.4%和30.6%,表明加入地理要素并进行残差修正的插值精度得到了进一步提高。总体来看,RCK插值法对气温数据的插值精度最高,可以考虑将此方法作为“一带一路”地区温度等气象要素的插值方法。 相似文献
12.
The performance of six statistical approaches,which can be used for selection of the best model to describe the growth of individual fish,was analyzed using simulated and real length-at-age data.The six approaches include coefficient of determination(R2),adjusted coefficient of determination(adj.-R2),root mean squared error(RMSE),Akaike's information criterion(AIC),bias correction of AIC(AICc) and Bayesian information criterion(BIC).The simulation data were generated by five growth models with different num... 相似文献
13.
利用东北地区2000-2010年93个气象站点观测数据作为“真实值”,对TRMM降水数据进行精度验证,发现研究区TRMM降水数据与观测数据之间具有明显的线性相关性,且TRMM降水数据数值偏大于观测值,表明TRMM降水数据在东北地区具有一定的可信度。对东北地区多年平均、2001、2010年的TRMM数据,进行GWR模型降尺度研究,得到1 km的新降水数据,并与全局OLS回归模型进行对比。结果表明：（1）相比全局OLS回归模型,GWR模型的降尺度结果可获得更好的R与RMSE,说明GWR模型更适用于东北地区TRMM数据的降尺度研究;（2）东北地区GWR模型的降尺度分析结果与观测数据之间的相关系数在0.44-0.97之间,且分布较分散;（3）经过降尺度的TRMM降水数据,在空间分辨率上有较大提高,能更真实地反映研究区的降水特征,为该数据小尺度的应用研究奠定基础。 相似文献
14.
针对目前的技术手段下难以直接获得大范围高精度精细化降水空间分布的问题,本文以闽浙赣地区为研究范围,选用GPM IMERG降水产品,综合应用地面实测降水数据以及水汽与植被指数数据,基于地理加权回归（GWR）法构建了基于水汽因子的降尺度模型,同时基于最小二乘（OLS）法构建了基于水汽因子与植被指数的对比模型,将降水产品的分辨率从0.1°提升至1 km,最终获得2015年闽浙赣地区各月精细化降水空间分布,使用验证站点实测数据进行验证。结果表明：① 构建的 3个降尺度模型中,GWR模型与2种OLS模型相比,拟合优度分别提升了102.9%和93.9%,模型降尺度结果整体优于2种OLS模型,且月际差异小,稳定性更高;2种OLS模型中,采用了水汽因子的模型拟合效果有8个月份更优;② 融合多源数据的GWR降尺度模型获得的结果在研究区内是可靠的,与GPM降水产品相比,在提升空间分辨率的同时,平均相对误差与均方根误差月均分别下降了42%和32%,精度明显改善。 相似文献
15.
DisTrad（Disaggregation procedure for radiometric surface temperature）模型是常用于遥感地表温度空间分辨率提升的主要模型之一。DisTrad模型常面向空间范围有限、地形相对平坦的研究区域,且常选用植被参数（如植被指数或植被覆盖度等）作为关键参数。然而在空间范围较大、地形起伏地区,地表温度的空间变异可能无法完全通过植被参数解释。本研究选取四川盆地及毗邻地区为研究区,通过模拟数据研究DisTrad模型在地形起伏区地表温度空间分辨率提升中的适用性。数字高程模型（Digital Elevation Model,DEM）、归一化差值植被指数（Normalized Difference Vegetation Index,NDVI）等参数,采用滑动窗口逐步回归,将空间分辨率为6km的地表温度提升至空间分辨率为1km。研究结果表明,改进的模型在平原及海拔较低的高原地区提升获得的地表温度空间分辨率具有较高精度,均方根误差（Root Mean Square Error,RMSE）为0.5K左右;在地形起伏较大的地区,RMSE为4K,验证了改进的模型提升的可行性。 相似文献
16.
Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening(Ts HARP) method and a binary linear model are compared to downscale the original daytime FengY un 2 F(FY-2 F) land surface temperature(LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land(SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error(RMSE) of the LST downscaled from the binary linear model is 1.30℃ compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78℃. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37℃ in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37℃ and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data. 相似文献
17.
以中国区域89个探空站2017年资料为参考值,对ERA5和MERRA-2再分析资料积分计算的Tm的精度进行评估,并分析2种资料计算的Tm的bias和RMSE的时空变化特性。结果表明：1）以探空站资料为参考值,ERA5和MERRA-2再分析资料计算的Tm的年均bias分别为0.41 K和0.10 K,年均RMSE分别为1.26 K和1.34 K。2）2种资料计算的Tm的bias和RMSE具有相似的时空变化特性,时间上总体表现为夏季精度高、冬季精度稍低,但ERA5再分析资料计算的Tm的bias在全年均表现为正值,而MERRA-2再分析资料计算的Tm的bias在夏季表现为负值,其余时间表现为正值;在空间上,2种资料计算的Tm的bias和RMSE在高程上无明显变化特性,但在纬度上RMSE均表现出随纬度增加而逐渐变大的趋势,总体保持在2.5 K以内。 相似文献
18.
《山地科学学报》2015,(6)
This study was conducted to evaluate the performance of the four stem taper models on Camellia japonica in Jeju Island, Korea using fit statistics and lack-of-fit statistics. The five statistical criteria that were used in this study were standard error of estimate(SEE), mean bias( E), absolute mean difference(AMD), coefficient of determination(R2), and root mean square error(RMSE). Results showed that the Kozak model 02 stem taper had the best performance in all fit statistics(SEE: 3.4708, E : 0.0040 cm, AMD : 0.9060 cm, R2 : 0.9870, and RMSE : 1.2545). On the other hand, Max and Burkhart stem taper model had the poorest performance in each statistical criterion(SEE: 4.2121, E : 0.2520 cm, AMD : 1.1300 cm, R2 : 0.9805, and RMSE: 1.5317). For the lack-of-fit statistics, the Kozak model 02 also provided the best performance having the best AMD in most of the relative height classes for diameter outside bark prediction and in most of the DBH classes for total volume prediction while Max and Burkhart had the poorest performance. These stem taper equations could help forest managers to better estimate the diameter outside bark at any given height, merchantable stem volumes and total stem volumes of the standing trees of Camellia japonica in the forests of Jeju Island, Korea. 相似文献
19.
Muhammad MASOOD Ghulam NABI Muhammad BABUR Aftab Hussain AZHAR Muhammad KALEEM ULLAH 《山地科学学报》2021,(3):716-734
Satellite-based Precipitation Estimates(SPEs)have gained importance due to enhanced spatial and temporal resolution,particularly in Indus basin,where raingauge network has fewer observation stations and drainage area is laying in many countries.Formulation of SPEs is based on indirect mechanism,therefore,assessment and correction of associated uncertainties is required.In the present study,disintegration of uncertainties associated with four prominent real time SPEs,IMERG,TMPA,CMORPH and PERSIANN has been conducted at grid level,regional scale,and summarized in terms of regions as well as whole study area basis.The bias has been disintegrated into hit,missed,false biases,and Root Mean Square Error(RMSE)into systematic and random errors.A comparison among gauge-and satellite-based precipitation estimates at annual scale,showed promising result,encouraging use of real time SPEs in the study area.On grid basis,at daily scale,from box plots,the median values of total bias(-0.5 to 0.5 mm)of the used SPEs were also encouraging although some under/over estimations were noted in terms of hit bias(-0.15 to 0.05 mm/day).Relatively higher values of missed(0.3 to 0.5 mm/day)and false(0.5 to 0.7 mm/day)biases were observed.The detected average daily RMSE,systematic errors,and random errors were also comparatively higher.Regional-scale spatial distribution of uncertainties revealed lower values of uncertainties in plain areas,depicting the better performance of satellite-based products in these areas.However,in areas of high altitude(>4000 m),due to complex topography and climatic conditions(orographic precipitation and glaciated peaks)higher values of biases and errors were observed.Topographic barriers and point scale gauge data could also be a cause of poor performance of SPEs in these areas,where precipitation is more on ridges and less in valleys where gauge stations are usually located.Precipitation system’s size and intensity can also be a reason of higher biases,because Microwave Imager underestimate precipitation in small systems(<200 km2)and overestimate in large systems(>2000 km2).At present,use of bias correction techniques at daily time scale is compulsory to utilize real time SPEs in estimation of floods in the study area.Inter comparison of satellite products indicated that IMERG gave better results than the others with the lowest values of systematic errors,missed and false biases. 相似文献
20.
Impacts of Climatic Factors on Runoff Coefficients in Source Regions of the Huanghe River 总被引：4，自引：0，他引：4
CHEN Liqun LIU Changming LI Yanping WANG Guoqiang 《中国地理科学(英文版)》2007,17(1):047-055
Runoff coefficients of the source regions of the Huanghe River in 1956–2000 were analyzed in this paper. In the 1990s runoff of Tangnaihai Hydrologic Station of the Huanghe River experienced a serious decrease, which had at- tracted considerable attention. Climate changes have important impact on the water resources availability. From the view of water cycling, runoff coefficients are important indexes of water resources in a particular catchment. Kalinin baseflow separation technique was improved based on the characteristics of precipitation and streamflow. After the separation of runoff coefficient (R/P), baseflow coefficient (Br/P) and direct runoff coefficient (Dr/P) were estimated. Statistic analyses were applied to assessing the impact of precipitation and temperature on runoff coefficients (including Dr/P, Br/P and R/P). The results show that in the source regions of the Huanghe River, mean annual baseflow coefficient was higher than mean annual direct runoff coefficient. Annual runoff coefficients were in direct proportion to annual pre- cipitation and in inverse proportion to annual mean temperature. The decrease of runoff coefficients in the 1990s was closely related to the decrease in precipitation and increase in temperature in the same period. Over different sub-basins of the source regions of the Huanghe River, runoff coefficients responded differently to precipitation and temperature. In the area above Jimai Hydrologic Station where annual mean temperature is –3.9oC, temperature is the main factor in- fluencing the runoff coefficients. Runoff coefficients were in inverse relation to temperature, and precipitation had nearly no impact on runoff coefficients. In subbasin between Jimai and Maqu Hydrologic Station Dr/P was mainly affected by precipitation while R/P and Br/P were both significantly influenced by precipitation and temperature. In the area be-tween Maqu and Tangnaihai hydrologic stations all the three runoff coefficients increased with the rising of annual precipitation, while direct runoff coefficient was inversely proportional to temperature. In the source regions of the Huanghe River with the increase of average annual temperature, the impacts of temperature on runoff coefficients be-come insignificant. 相似文献