Modeling daily soil temperature using data-driven models and spatial distribution

The objective of this study is to develop data-driven models, including multilayer perceptron (MLP) and adaptive neuro–fuzzy inference system (ANFIS), for estimating daily soil temperature at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using MLP. The ANFIS is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs). From the performance evaluation and scatter diagrams of MLP and ANFIS models, MLP 3 produces the best results for both stations at different depths (10 and 20 cm), and ANFIS 3 produces the best results for both stations at two different depths except for Champaign station at the 20 cm depth. Results of MLP are better than those of ANFIS for both stations at different depths. The MLP-based spatial distribution is used to estimate daily soil temperature using the best input combinations (one, two, and three inputs) at different depths below the ground. The MLP-based spatial distribution estimates daily soil temperature with high accuracy, but the results of MLP and ANFIS are better than those of the MLP-based spatial distribution for both stations at different depths. Data-driven models can estimate daily soil temperature successfully in this study.     

A three-dimensional satellite retrieval method for atmospheric temperature and moisture profiles

A three-dimensional variational method is proposed to simultaneously retrieve the 3-D atmospheric temperature and moisture profiles from satellite radiance measurements. To include both vertical structure and the horizontal patterns of the atmospheric temperature and moisture, an EOF technique is used to decompose the temperature and moisture field in a 3-D space. A number of numerical simulations are conducted and they demonstrate that the 3-D method is less sensitive to the observation errors compared to the 1-D method. When the observation error is more than 2.0 K, to get the best results, the truncation number for the EOF＇s expansion have to be restricted to 2 in the 1-D method, while it can be set as large as 40 in a 3-D method. This results in the truncation error being reduced and the retrieval accuracy being improved in the 3-D method. Compared to the 1-D method, the rms errors of the 3-D method are reduced by 48% and 36% for the temperature and moisture retrievals, respectively. Using the real satellite measured brightness temperatures at 0557 UTC 31 July 2002, the temperature and moisture profiles are retrieved over a region （20°-45°N, 100°- 125°E） and compared with 37 collocated radiosonde observations. The results show that the retrieval accuracy with a 3-D method is significantly higher than those with the 1-D method.     

Land-surface scheme validation using the Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) and Oklahoma Mesonet data: Preliminary results

Summary The Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) is a recently-developed observational system that collects, archives, and quality controls atmospheric, surface, and soil data in real-time from 90 stations across Oklahoma. Ten of the 90 sites, termed “super sites”, are equipped with additional sonic anemometry and four-component net radiometers to provide complete observations of the surface energy balance. Oklahoma Mesonet and OASIS data are used in this study to validate the sensitivity and accuracy of a land-surface scheme within a numerical prediction model. The Advanced Regional Prediction System (ARPS) is a three-dimensional, nonhydrostatic mesoscale model developed by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The land-surface model (LSM) used within ARPS is the Interactions Soil Biosphere Atmosphere (ISBA) scheme. Mesonet and OASIS data collected from the super site located in Norman, Oklahoma, are used as verification for the ISBA. Research presented in this study outlines the challenges in developing, maintaining, and using in-situ data for model validation. Such problems as instrument error, surface heterogeneity, and non-closure of the surface energy budget limit data accuracy. Preliminary results of model validation focus on the sensitivity of the soil physics within the ISBA scheme. Model sensitivity to vegetation cover, surface roughness, and soil type are investigated. Furthermore, several recent improvements to ISBA are evaluated and compared to observations. This study concludes that the sensitivity of the ISBA to a priori soil and vegetation type is detrimental for this scheme to be used in a mesoscale model without improved treatment of surface heterogeneity. Received November 18, 2001 Revised December 28, 2001     

Validation of the Atmospheric Infrared Sounder Retrieval Products over China and Their Application in Numerical Mode

The atmospheric infrared sounder (AIRS) instrument onboard Aqua Satellite is a high spectral resolution infrared sounder. In recent years, AIRS has gradually become the primary method of atmospheric vertical observations. To examine the validation of AIRS retrieval products (V3.0) over China, the AIRS surface air temperature retrievals were compared with the ground observations obtained from 540 meteorological stations in July 2004 and January 2005, respectively. The sources of errors were considerably discussed. Based on the error analysis, the AIRS retrieved surface air temperature products were systemically corrected. Moreover, the AIRS temperature and humidity profile retrievals were compared with T213 numerical forecasting products. Because T213 forecasting products are not the actual atmospheric states,to further verify the validation, the AIRS temperature and humidity profile products were assimilated into the MM5 model through the analysis nudging. In this paper, the case on February 14, 2005 in North China was simulated in detail. Then, we investigated the effects of AIRS retrievals on snowfall, humidity field,vertical velocity field, divergence field, and cloud microphysical processes. The major results are: (1) the errors of AIRS retrieved surface air temperature products are largely systematic deviations, for which the influences of terrain altitude and surface types are the major reasons; (2) the differences between the AIRS atmospheric profile retrievals and T213 numerical prediction products in temperature are generally less than 2 K, the differences in relative humidity are generally less than 25%; and (3) the AIRS temperature and humidity retrieval products can adjust the model initial field, and thus can improve the capacity of snowfall simulation to some extent.     

Validation of the Atmospheric Infrared Sounder Retrieval Products over China and Their Application in Numerical Model

The atmospheric infrared sounder (AIRS) instrument onboard Aqua Satellite is a high spectral resolution infrared sounder. In recent years, AIRS has gradually become the primary method of atmospheric vertical observations. To examine the validation of AIRS retrieval products (V3.0) over China, the AIRS surface air temperature retrievals were compared with the ground observations obtained from 540 meteorological stations in July 2004 and January 2005, respectively. The sources of errors were considerably discussed. Based on the error analysis, the AIRS retrieved surface air temperature products were systemi-cally corrected. Moreover, the AIRS temperature and humidity profile retrievals were compared with T213 numerical forecasting products. Because T213 forecasting products are not the actual atmospheric states, to further verify the validation, the AIRS temperature and humidity profile products were assimilated into the MM5 model through the analysis nudging. In this paper, the case on February 14, 2005 in North China was simulated in detail. Then, we investigated the effects of AIRS retrievals on snowfall, humidity field, vertical velocity field, divergence field, and cloud microphysical processes. The major results are: (1) the errors of AIRS retrieved surface air temperature products are largely systematic deviations, for which the influences of terrain altitude and surface types are the major reasons; (2) the differences between the AIRS atmospheric profile retrievals and T213 numerical prediction products in temperature are generally less than 2 K, the differences in relative humidity are generally less than 25%; and (3) the AIRS temperature and humidity retrieval products can adjust the model initial field, and thus can improve the capacity of snowfall simulation to some extent.     

Use of total precipitable water classification of a priori error and quality control in atmospheric temperature and water vapor sounding retrieval

This study investigates the use of dynamic a priori error information according to atmospheric moistness and the use of quality controls in temperature and water vapor profile retrievals from hyperspectral infrared (IR) sounders. Temperature and water vapor profiles are retrieved from Atmospheric InfraRed Sounder (AIRS) radiance measurements by applying a physical iterative method using regression retrieval as the first guess. Based on the dependency of first-guess errors on the degree of atmospheric moistness, the a priori first-guess errors classified by total precipitable water (TPW) are applied in the AIRS physical retrieval procedure. Compared to the retrieval results from a fixed a priori error, boundary layer moisture retrievals appear to be improved via TPW classification of a priori first-guess errors. Six quality control (QC) tests, which check non-converged or bad retrievals, large residuals, high terrain and desert areas, and large temperature and moisture deviations from the first guess regression retrieval, are also applied in the AIRS physical retrievals. Significantly large errors are found for the retrievals rejected by these six QCs, and the retrieval errors are substantially reduced via QC over land, which suggest the usefulness and high impact of the QCs, especially over land. In conclusion, the use of dynamic a priori error information according to atmospheric moistness, and the use of appropriate QCs dealing with the geographical information and the deviation from the first-guess as well as the conventional inverse performance are suggested to improve temperature and moisture retrievals and their applications.     

基于组网观测的那曲土壤湿度不同时间尺度的变化特征

利用第三次青藏高原大气科学试验的土壤湿度观测数据,分析了那曲多空间尺度组网观测的28个站2、5、10、20和30 cm 5个不同深度土壤湿度的季节变化和日变化特征,并对比讨论了土壤湿度站点间的差异。分析表明,各层土壤湿度均存在显著的季节变化。冬春季节,20 cm以上土壤湿度随深度变浅而减小。夏秋季节土壤湿度随深度增加而减小,并分别在7月上、中旬和9月出现两个峰值。10月以后进入土壤湿度衰减期。土壤温度和土壤湿度存在协同变化关系。在一定的温度范围内,土壤发生冻结-融化过程,引起土壤湿度变化。在太阳辐射加热下,土壤表层水分蒸发,进而影响土壤温度。不同观测站间土壤湿度差异较大,夏秋季离散性大于冬春季。不同季节土壤湿度的日变化存在差异。春季10 cm以上土壤湿度日变化明显,08-10时（北京时）达到最低,19-20时达到最高。夏季土壤湿度日变化较为平缓。秋季2 cm深度土壤湿度日变化明显。线性拟合结果表明,1、4、10月土壤湿度和土壤温度为正相关关系。但是在夏季,土壤湿度与土壤温度为负相关。站点间土壤湿度变化的离散性表明,多测站才能全面体现青藏高原某区域的陆面状态。文中结果为青藏高原地区土壤湿度卫星参数验证和数值模式参数化提供了多角度的观测依据。      

Atmospheric profile retrieval with AIRS data and validation at the ARM CART site

The physical retrieval algorithm of atmospheric temperature and moisture distribution from the Atmospheric InfraRed Sounder （AIRS） radiances is presented. The retrieval algorithm is applied to AIRS clear-sky radiance measurements. The algorithm employs a statistical retrieval followed by a subsequent nonlinear physical retrieval. The regression coefficients for the statistical retrieval are derived from a dataset of global radiosonde observations （RAOBs） comprising atmospheric temperature, moisture, and ozone profiles. Evaluation of the retrieved profiles is performed by a comparison with RAOBs from the Atmospheric Radiation Measurement （ARM） Program Cloud And Radiation Testbed （CART） in Oklahoma, U. S. A.. Comparisons show that the physically-based AIRS retrievals agree with the RAOBs from the ARM CART site with a Root Mean Square Error （RMSE） of 1K on average for temperature profiles above 850 hPa, and approximately 10% on average for relative humidity profiles. With its improved spectral resolution, AIRS depicts more detailed structure than the current Geostationary Operational Environmental Satellite （GOES） sounder when comparing AIRS sounding retrievals with the operational GOES sounding products.     

Local indications of climate changes in Turkey: Bursa as a case example

This study aims to put out on what ratio Bursa province, one of the important heavy industry regions of Turkey, has been affected climatic process called “Global Warming” or “Climate Change”. For this intend climatic measurement results from Bursa center, top of Uludağ Mount, Yenişehir and Keles meteorological stations were used. These measurements were taken as minimum temperature at night-time, maximum temperature at day-time, and mean temperature, mean pressure, insolation intensity, insolation duration, mean wind speed, minimum temperature above soil, soil temperatures at depths of 5, 10, and 20 cm rainfall. Overall, our statistical results showed that there was a considerable warming at statistically 1% and 5% levels in summer months, particularly in July Almost all performed measurements confirm this result. According to climatic data for thirty years (1975–2005), in the last twelve years contrary to previous 18 years, mean temperature values were higher than long-term mean value nine times (years) repetitively. Temperatures did not deviated higher than 0.5°C in six of these. At the temperatures below mean, The maximum deviation was −0.4°C.     

Analysis of Ice Water Path Retrieval Errors Over Tropical Ocean

Retrieval of multi-layered cloud properties, especially ice water path (IWP), is one of the most perplexing problems in satellite cloud remote sensing. This paper develops a method for improving the IWP retrievals for ice-over-water overlapped cloud systems using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Visible and Infrared Scanner (VIRS) data. A combined microwave, visible and infrared algorithm is used to identify overlapped clouds and estimate IWP separately from liquid water path. The retrieval error of IWP is then evaluated by comparing the IWP to that retrieved from single-layer ice clouds surrounding the observed overlapping systems. The major IWP retrieval errors of overlapped clouds are primarily controlled by the errors in estimating the visible optical depth. Optical depths are overestimated by about 10–40% due to the influence of the underlying cloud. For the ice-over-warm-water cloud systems (cloud water temperature Tw > 273 K), the globally averaged IWP retrieval error is about 10%. This cloud type accounts for about 15% of all high-cloud overlapping cases. Ice-over-super-cooled water clouds are the predominant overlapped cloud system, accounting for 55% of the cases. Their global averaged error is 17.2%. The largest IWP retrieval error results when ice clouds occur over extremely super-cooled water clouds (Tw 6 255 K). Overall, roughly 33% of the VIRS IWP retrievals are overestimated due to the effects of the liquid water clouds beneath the cirrus clouds. To improve the accuracy of the IWP retrievals, correction models are developed and applied to all three types of overlapped clouds. The preliminary results indicate that the correction models reduce part of the retrieval error.     

北京地区冻土时空分布特征

基于1981—2021年北京地区6个气象站的逐日最大冻土深度、平均气温、平均地表温度及5、10、15、20、40、80 cm地温等资料，分析了近40年北京地区最大冻土深度的时空分布特征及其与气温和地温的关系。结果表明：北京地区最大冻土深度总体呈变浅趋势，气候倾向率为-2.3 cm/10 a，各站点最大冻土深度变浅趋势从西到东呈逐渐减弱趋势。北京地区最大冻土深度与40、80 cm地温相关性最好，与地表温度相关性较差。选取2021至2022年北京地区冻土对比试验数据，评估测温式冻土自动观测仪观测精度，发现仪器安装至少一个冻融周期后与冻土人工观测吻合度更好，测温式冻土自动观测仪的观测精度与仪器安装位置的地下岩层、土质分布密切相关，需要在仪器稳定运行后根据当地实际优化算法和冻融阈值。     

人工冻土观测的冻点对应的土壤温度研究

基于2018年12月至2020年3月喀左、沈阳、辽阳、满洲里4个国家级地面气象站人工冻土器与测温式冻土自动观测仪观测的资料,对人工冻土观测获得的冻点与测温式冻土自动观测仪获得的相应深度的温度进行对比分析。结果表明：人工冻土器获取的冻点对应的土壤温度与0℃总体一致,又不完全重合;0—35 cm深度范围,冻点对应的温度变化范围为-2~6℃,呈现跳跃性变化。35 cm以下深度范围,冻土冻点对应的温度变化范围为-0.5~1.0℃;融化过程冻点对应的平均温度高于冻结过程冻点对应的平均温度。从完全融化时间上来看,人工冻土器观测到的完全融化时间晚于测温式冻土仪0℃线完全消失的时间。人工冻土观测的实质是获得土壤温度0℃点所在位置。灌注不同台站水的冻土器内管在相同的温度环境下,冻结与融化状态无明显区别;人工冻土器内管冻结过程是温度和持续时间双重作用的结果,深层土壤温度变化缓慢,使得内管中的水冻结和融化需要的时间长。另外,作为接触式测温设备,减小外因产生的时滞是提高其灵敏度的重要环节,建议测温式冻土仪的外管壁使用温度滞后效应更小的金属外管。     

两种陆面模式对中国区域土壤温度模拟的对比分析

为研究不同陆面模式对中国区域土壤温度的模拟效果,基于中国气象局陆面数据同化系统（CMA Land Data Assimilation System,CLDAS）大气驱动数据分别驱动Noah和Noah-MP陆面模式进行中国区域土壤温度的模拟（简称:CLDAS_Noah和CLDAS_Noah-MP试验）,使用2010—2018年中国气象局2380个土壤温度观测站点10和40 cm观测数据以及美国全球陆面数据同化系统（The Global Land Data Assimilation System,GLDAS）驱动的Noah模式（GLDAS_Noah试验）模拟的土壤温度结果,从空间分布、季节、分区等角度进行了评估,实现了不同驱动数据相同陆面模式和相同驱动数据不同陆面模式的对比分析。结果表明: GLDAS_Noah、CLDAS_Noah和CLDAS_Noah-MP试验均能合理模拟出中国区域土壤温度空间分布,但在量级上有一定差异,主要表现在中国东北、新疆、青藏高原等积雪区。对于相同陆面模式不同驱动数据,均方根误差显示CLDAS_Noah试验在季节与分区上均优于GLDAS_Noah试验,间接表明CLDAS大气驱动数据优于GLDAS大气驱动数据,且大气驱动数据是提高土壤温度模拟精度的重要因素之一;对于相同驱动数据不同陆面模式,总体上CLDAS_Noah-MP试验棋拟效果优于CLDAS_Noah试验,其中CLDAS_Noah试验模拟的10和40 cm深度土壤温度在冬季积雪区误差明显大于CLDAS_Noah-MP试验,可能与Noah-MP模式改进了积雪方案有关,但10和40 cm深度下CLDAS_Noah-MP试验在东北、华北、青藏高原地区对春季土壤温度模拟误差明显大于CLDAS_Noah试验,可能与Noah-MP模式融雪方案有关。总之,本研究对于后续开展土壤温度多模式集成、土壤温度站点资料同化,最终研制中国区域高质量土壤温度数据集具有一定的参考意义。      

COSMIC反演大气温湿廓线与模式客观分析场的比较

利用2009年不同季节COSMIC湿反演的大气温度和相对湿度廓线数据,分别与时、空相匹配的ECMWF(European Centre for Medium-Range Weather Forecasts,欧洲中尺度天气预报中心)、NCEP(National Centers for Environmental Prediction,美国环境预报中心)模式客观分析场和无线电探空观测数据,进行全球范围的比较分析.初步研究表明,无论夏季还是冬季,各种资料源之间相互比较的偏差和标准差分布相似,与季节无关.就温度而言,三种资料源的温度水平、垂直分布都很接近,ECMWF模式数据比NCEP不论是温度廓线还是湿度廓线都更接近COSMIC反演值.模式的水汽客观分析场在对流层基本上都比无线电探空观测值偏湿,对流层中高层在大部分海洋地区也比COSMIC反演场偏湿.COSMIC反演的相对湿度相对于无线电探空整层偏大,具有明显正偏差,在300 hPa偏差达最大值(约30%).     

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.     

Soil thermal dynamics of terrestrial ecosystems of the conterminous United States from 1948 to 2008: an analysis with a process-based soil physical model and AmeriFlux data

The spatiotemporal distribution characteristics of soil temperature are a significant, but seldom described signal of climate warming. This study examines the spatiotemporal trends in soil temperature at depths of 10, 20, and 50 cm in the conterminous US during 1948–2008. We find a warming trend of between 0.2 and 0.4 °C at all depths from 1948 to 2008. The lowest soil temperatures are in Colorado and the area where Wyoming, Idaho, and Montana meet. The coastal areas, such as Texas, Florida, and California, experienced the highest soil temperature. In addition, areas that experienced weak cooling in summer soil temperature include Texas, Oklahoma, and Arkansas. Warming was recorded in Arizona, Nevada, and Oregon. In winter, Mississippi, Alabama, and Georgia show a cooling trend, and Montana, North Dakota, and South Dakota have been warming over the 61-year period. Additionally, mix-forest areas experience slightly cooler soil temperature in comparison with air temperature. Shrubland areas experience slightly warmer soil temperature in comparison with air temperature. This study is among the first to analyze the spatiotemporal distribution characteristics of soil temperature in the conterminous US by using multiple site observational data. Improved understanding of the spatially complex responses of soil temperature shall have significant implications for future studies in climate change over the region.     

One-Dimensional Variational Retrieval of Temperature and Humidity Profiles from the FY4A GIIRS

A physical retrieval approach based on the one-dimensional variational(1 D-Var) algorithm is applied in this paper to simultaneously retrieve atmospheric temperature and humidity profiles under both clear-sky and partly cloudy conditions from FY-4 A GIIRS(geostationary interferometric infrared sounder) observations. Radiosonde observations from upper-air stations in China and level-2 operational products from the Chinese National Satellite Meteorological Center(NSMC)during the periods from December 2019 to January 2020(winter) and from July 2020 to August 2020(summer) are used to validate the accuracies of the retrieved temperature and humidity profiles. Comparing the 1 D-Var-retrieved profiles to radiosonde data, the accuracy of the temperature retrievals at each vertical level of the troposphere is characterized by a root mean square error(RMSE) within 2 K, except for at the bottom level of the atmosphere under clear conditions. The RMSE increases slightly for the higher atmospheric layers, owing to the lack of temperature sounding channels there.Under partly cloudy conditions, the temperature at each vertical level can be obtained, while the level-2 operational products obtain values only at altitudes above the cloud top. In addition, the accuracy of the retrieved temperature profiles is greatly improved compared with the accuracies of the operational products. For the humidity retrievals, the mean RMSEs in the troposphere in winter and summer are both within 2 g kg^–1. Moreover, the retrievals performed better compared with the ERA5 reanalysis data between 800 h Pa and 300 h Pa both in summer and winter in terms of RMSE.     

2006-2010年下辽河平原地温和土壤热通量变化特征

依据国家沈阳农田生态系统野外研究站2006-2010年监测数据,分析0-100cm 土层8个层次的地温、0-100 cm地温、地温极值、0-20 cm地温与气温的关系和土壤热通量的变化趋势。结果表明：从年际变化看,8个层次地温和地温极值呈下降趋势;0 cm层次地温变化受外界影响较大。研究区域年尺度0-20 cm地温与气温有比较一致的变化规律。作物生长季节,可分为4-7月气温上升和8-10月气温下降两阶段;这两个不同阶段的0-20 cm地温与气温分别做线性拟合,与整个生长季4-10月线性拟合相比,线性相关性可信度更高。土壤热通量受气温和土壤质量含水量影响年际变化较大,年尺度土壤热通量≥0 MJ/m²,该区域地表是热汇。     

Trends and scales of observed soil moisture variations in China

A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981-1998 are used to study the long-term and seasonal trends of soil moisture variations, as well as estimate the temporal and spatial scales of soil moisture for different soil layers. Additional datasets of precipitation and temperature difference between land surface and air (TDSA) are analyzed to gain further insight into the changes of soil moisture. There are increasing trends for the top 10 cm, but decreasing trends for the top 50 cm of soil layers in most regions. Trends in precipitation appear to dominantly influence trends in soil moisture in both cases. Seasonal variation of soil moisture is mainly controlled by precipitation and evaporation, and in some regions can be affected by snow cover in winter. Timescales of soil moisture variation are roughly 1-3 months and increase with soil depth. Further influences of TDSA and precipitation on soil moisture in surface layers, rather than in deeper layers, cause this phenomenon. Seasonal variations of temporal scales for soil moisture are region-dependent and consistent in both layer depths. Spatial scales of soil moisture range from 200-600 km, with topography also having an affect on these. Spatial scales of soil moisture in plains are larger than in mountainous areas. In the former, the spatial scale of soil moisture follows the spatial patterns of precipitation and evaporation, whereas in the latter, the spatial scale is controlled by topography.     

河西走廊东部冬季浅层地温与春季沙尘天气的关系

利用河西走廊东部民勤、凉州、永昌3个气象站1960~2010年冬季0、5、10、15、20 cm地温和1961~2011年春季沙尘暴和扬沙天气的常规观测资料,分析了河西走廊东部冬季浅层地温和春季沙尘天气日数的时空特征,进而探讨了春季沙尘天气与冬季浅层地温的关系。结果表明:受海拔高度、地理位置等影响,河西走廊东部冬季浅层地温有明显地域差异,其中高海拔的永昌最低,低海拔的民勤次之,而海拔介于民勤和永昌之间的凉州最高;春季沙尘天气日数自低海拔地区向高海拔地区逐渐减少,即民勤最多、凉州区次之、永昌最少;河西走廊东部的沙尘天气日数与浅层地温在空间上呈一定的负相关,二者的年变化趋势明显相反,即冬季浅层地温总体呈逐年升高的趋势,而春季沙尘日数呈逐年减少的趋势,且都存在6~7 a和9~10 a的周期;相关分析表明,河西走廊东部春季沙尘日数与冬季浅层地温呈负相关,其中与0 cm地温的相关性最显著。