Long-term variation of Arctic Sudden Stratospheric Warmings(SSW)and potential causes

Utilizing the European Centre for Medium-Range Weather Forecast(ECMWF)Reanalysis v5(ERA5),for the first time,we have confirmed close links among Sudden Stratospheric Warmings(SSWs)in the Northern Hemisphere(NH),the polar vortices,and stratospheric Planetary Waves(PWs)by analyzing and comparing their trends.Interestingly,within overall increasing trends,the duration and strength of SSWs exhibit increasing and decreasing trends before and after the winter of 2002,respectively.To reveal possible physical mechanisms driving these trends,we analyzed the long-term trends of the winter(from December to February)polar vortices and of stratospheric PWs with zonal wave number 1.Notably,our results show that in all three time periods(the entire period of 41winters,1980 to 2020,and the two subperiods—1980-2002 and 2002-2020)enhancing SSWs were always accompanied by weakening winter polar vortices and strengthening polar PWs like Stationary Planetary Waves(SPWs)and 16-day waves,and vice versa.This is the first proof,based on ERA5 long-term trend data,that weakening polar vortices and enhancing stratospheric PWs(especially SPWs)could cause an increase in SSWs.

Land Degradation Due to Salinization in Arid and Semi-arid Regions with the Aid of Geo-information Techniques

This study applied a computerized parametric methodology to monitor, map, and quantify land degradation by salinization risk detection techniques at a 1：250 000 mapping scale using geo-information technology. The northern part of the Shaanxi province in China was taken as a case. Multi-temporal remotely sensed materials of both Landsat TM and thematic maps （ETM＋） were used as the bases to provide comprehensive views of surface conditions such as vegetation cover and salinization detection. With ERDAS ver. 9.1 software, the Normalized Differential Salinity Index （NDSl） and Salinity Index （S.I.） were computed and then evaluated for land degradation by salinization. Arc/Info ver. 9.2 software was used along with field observation data （GPS） for analysis. Using spatial analysis methods, results showed that 19 973.1 km^2 （72%） of land had no risk of land degradation by salinization, 3 684.7 km^2 （13%） had slight land degradation by salinization risk, 2 797.9 km^2 （10%） had moderate land degradation by salinization risk, and 1 218.9 km^2 （4%） of the total land area was at a high risk of land degradation by salinization. The study area, in general, is exposed to a high risk of soil salinization.

Leaf pigment retrieval using the PROSAIL model: Influence of uncertainty in prior canopy-structure information

Leaf pigments are critical indicators of plant photosynthesis,stress,and physiological conditions.Inversion of radiative transfer models(RTMs)is a promising method for robustly retrieving leaf biochem-ical traits from canopy observations,and adding prior information has been effective in alleviating the“ill-posed”problem,a major challenge in model inversion.Canopy structure parameters,such as leaf area index(LAI)and average leaf inclination angle(ALA),can serve as prior information for leaf pigment retrie-val.Using canopy spectra simulated from the PROSAIL model,we estimated the effects of uncertainty in LAI and ALA used as prior information for lookup table-based inversions of leaf chlorophyll(C _(ab))and car-otenoid(C_(ar)).The retrieval accuracies of the two pigments were increased by use of the priors of LAI(RMSE of C_(ab) from 7.67 to 6.32μg cm^(-2),C_(ar) from 2.41 to 2.28μg cm^(-2))and ALA(RMSE of C_(ab) from 7.67 to 5.72μg cm^(-2),C_(ar) from 2.41 to 2.23μg cm^(-2)).However,this improvement deteriorated with an increase of additive and multiplicative uncertainties,and when 40% and 20% noise was added to LAI and ALA respectively,these priors ceased to increase retrieval accuracy.Validation using an experimental winter wheat dataset also showed that compared with C_(ar),the estimation accuracy of C_(ab) increased more or deteriorated less with uncertainty in prior canopy structure.This study demonstrates possible limita-tions of using prior information in RTM inversions for retrieval of leaf biochemistry,when large uncer-tainties are present.

Spatial and temporal change patterns of net primary productivity and its response to climate change in the Qinghai–Tibet Plateau of China from 2000 to 2015

The vegetation ecosystem of the Qinghai–Tibet Plateau in China,considered to be the′′natural laboratory′′of climate change in the world,has undergone profound changes under the stress of global change.Herein,we analyzed and discussed the spatial-temporal change patterns and the driving mechanisms of net primary productivity(NPP)in the Qinghai–Tibet Plateau from 2000 to 2015 based on the gravity center and correlation coefficient models.Subsequently,we quantitatively distinguished the relative effects of climate change(such as precipitation,temperature and evapotranspiration)and human activities(such as grazing and ecological construction)on the NPP changes using scenario analysis and Miami model based on the MOD17A3 and meteorological data.The average annual NPP in the Qinghai–Tibet Plateau showed a decreasing trend from the southeast to the northwest during 2000–2015.With respect to the inter-annual changes,the average annual NPP exhibited a fluctuating upward trend from 2000 to 2015,with a steep increase observed in 2005 and a high fluctuation observed from 2005 to 2015.In the Qinghai–Tibet Plateau,the regions with the increase in NPP(change rate higher than 10%)were mainly concentrated in the Three-River Source Region,the northern Hengduan Mountains,the middle and lower reaches of the Yarlung Zangbo River,and the eastern parts of the North Tibet Plateau,whereas the regions with the decrease in NPP(change rate lower than–10%)were mainly concentrated in the upper reaches of the Yarlung Zangbo River and the Ali Plateau.The gravity center of NPP in the Qinghai–Tibet Plateau has moved southwestward during 2000–2015,indicating that the increment and growth rate of NPP in the southwestern part is greater than those of NPP in the northeastern part.Further,a significant correlation was observed between NPP and climate factors in the Qinghai–Tibet Plateau.The regions exhibiting a significant correlation between NPP and precipitation were mainly located in the central and eastern Qinghai–Tibet Plateau,and the regions exhibiting a significant correlation between NPP and temperature were mainly located in the southern and eastern Qinghai–Tibet Plateau.Furthermore,the relative effects of climate change and human activities on the NPP changes in the Qinghai–Tibet Plateau exhibited significant spatial differences in three types of zones,i.e.,the climate change-dominant zone,the human activity-dominant zone,and the climate change and human activity interaction zone.These research results can provide theoretical and methodological supports to reveal the driving mechanisms of the regional ecosystems to the global change in the Qinghai–Tibet Plateau.

Assessment of total suspended sediment concentrations in Poyang Lake using HJ-1A/1B CCD imagery

We explored the potential of the environment and disaster monitoring and forecasting small satellite constellations (HJ-1A/1B satellites) charge-coupled device (CCD) imagery (spatial resolution of 30 m, revisit time of 2 days) in the monitoring of total suspended sediment (TSS) concentrations in dynamic water bodies using Poyang Lake, the largest freshwater lake in China, as an example. Field surveys conducted during October 17-26, 2009 showed a wide range of TSS concentration (3-524 mg/L). Atmospheric correction was implemented using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module in ENVI with the aid of aerosol information retrieved from concurrent Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) surveys, which worked well at the CCD bands with relatively high reflectance. A practical exponential retrieval algorithm was created between satellite remote sensing reflectance and in-situ measured TSS concentration. The retrieved results for the whole water area matched the in-situ data well at most stations. The retrieval errors may be related to the problem of scale matching and mixed pixel. In three selected subregions of Poyang Lake, the distribution trend of retrieved TSS was consistent with that of the field investigation. It was shown that HJ-1A/1B CCD imagery can be used to estimate TSS concentrations in Poyang Lake over synoptic scales after applying an appropriate atmospheric correction method and retrieval algorithm.

Monitoring Zenithal Total Delays over the three different climatic zones from IGS GPS final products:A comparison between the use of the VMF1 and GMF mapping functions

The International GNSS Service(IGS) final products(ephemeris and clocks-correction) have made the GNSS an indispensable low-cost tool for scientific research, for example sub-daily atmospheric water vapor monitoring. In this study, we investigate if there is a systematic difference coming from the choice between the Vienna Mapping Function 1(VMF1) and the Global Mapping Function(GMF) for the modeling of Zenith Total Delay(ZTD) estimates, as well as the Integrated Precipitable Water Vapor(IPWV) estimates that are deduced from them. As ZTD estimates cannot be fully separated from coordinate estimates, we also investigated the coordinate repeatability between subsequent measurements.For this purpose, we monitored twelve GNSS stations on a global scale, for each of the three climatic zones(polar, mid-latitudes and tropical), with four stations on each zone. We used an automated processing based on the Bernese GNSS Software Version 5.2 by applying the Precise Point Positioning(PPP)approach, L3 Ionosphere-free linear combination, 7 cutoff elevation angle and 2 h sampling. We noticed an excellent agreement with the ZTD estimates and coordinate repeatability for all the stations w.r.t to CODE(the Center for Orbit Determination in Europe) and USNO(US Naval Observatory) products, except for the Antarctic station(Davis) which shows systematic biases for the GMF related results. As a final step, we investigated the effect of using two mapping functions(VMF1 and GMF) to estimate the IPWV,w.r.t the IPWV estimates provided by the Integrated Global Radiosonde Archive(IGRA). The GPS-derived IPWV estimates are very close to the radiosonde-derived IPWV estimates, except for one station in the tropics(Tahiti).

Detection of different-time-scale signals in the length of day variation based on EEMD analysis technique

Scientists pay great attention to different-time-scale signals in the lengllh of day （LOD） variations △LOD, which provide signatures of the Earth＇s interior structure, couplings among different layers, and potential excitations of ocean and atmosphere. In this study, based on the ensemble empirical mode decomposition （EEMD）, we analyzed the latest time series of △LOD data spanning from January 1962 to March 2015. We observed the signals with periods and amplitudes of about 0.5 month and 0.19 ms, 1.0 month and 0.19 ms, 0.5 yr and 0.22 ms, 1.0 yr and 0.18 ms, 2.28 yr and 0.03 ms, 5.48 yr and 0.05 ms, respectively, in coincidence with the results of predecessors. In addition, some signals that were previously not definitely observed by predecessors were detected in this study, with periods and amplitudes of 9.13 d and 0.12 ms, 13.69 yr and 0.10 ms, respectively. The mechanisms of the LOD fluctuations of these two signals are still open.

A Fast Algorithm of Cartographic Sounding Selection

An effective strategy and framework that adequately integrate the automated and manual processes for fast cartographic sounding selection is presented. The important submarine topographic features are extracted for important soundings selection, and an improved "influence circle" algorithm is introduced for sounding selection. For automatic configuration of soundings distribution pattern, a special algorithm considering multi factors is employed. A semi automatic method for solving the ambiguous conflicts is described. On the basis of the algorithms and strategies a system named HGIS for fast cartographic sounding selection is developed and applied in Chinese Marine Safety Administration Bureau (CMSAB). The application experiments show that the system is effective and reliable. At last some conclusions and the future work are given.

A terrain openness index for the extraction of karst Fenglin and Fengcong landform units from DEMs

The Fenglin and Fengcong landform units are considered to be an important representation for defining the degree of development of Karst landforms. However, these terrain features have been proven difficult to delineate and extract automatically because of their complex morphology. In this paper, a new method for identifying the Fenglin and Fengcong landform units is proposed. This method consists of two steps:(1) terrain openness calculation and(2) toe line extraction. The proposed method is applied and validated in the Karst case area of Guilin by using ASTER GDEM with one arc-second resolution. The openness of both the positive and negative terrain and a threshold were used to extract toe lines for segmenting depressions and pinnacles in Fenglin and Fengcong landforms. A comparison between the extracted Fenglin and Fengcong landform units and their real units from high resolution images wascarried out to evaluate the capability of the proposed method. Results show the proposed method can effectively extract the Fenglin and Fengcong landform units, and has an overall accuracy of 93.28%. The proposed method is simple and easy to implement and is expected to play an important role in the automatic extraction of similar landform units in the Karst area.

Spatio-temporal analysis of forest fire events in the Margalla Hills,Islamabad,Pakistan using socio-economic and environmental variable data with machine learning methods

Most forest fires in the Margalla Hills are related to human activities and socioeconomic factors are essential to assess their likelihood of occurrence.This study considers both environmental(altitude,precipitation,forest type,terrain and humidity index)and socioeconomic(population density,distance from roads and urban areas)factors to analyze how human behavior affects the risk of forest fires.Maximum entropy(Maxent)modelling and random forest(RF)machine learning methods were used to predict the probability and spatial diffusion patterns of forest fires in the Margalla Hills.The receiver operating characteristic(ROC)curve and the area under the ROC curve(AUC)were used to compare the models.We studied the fire history from 1990 to 2019 to establish the relationship between the probability of forest fire and environmental and socioeconomic changes.Using Maxent,the AUC fire probability values for the 1999 s,2009 s,and 2019 s were 0.532,0.569,and 0.518,respectively;using RF,they were 0.782,0.825,and 0.789,respectively.Fires were mainly distributed in urban areas and their probability of occurrence was related to accessibility and human behaviour/activity.AUC principles for validation were greater in the random forest models than in the Maxent models.Our results can be used to establish preventive measures to reduce risks of forest fires by considering socio-economic and environmental conditions.

Comparison of stratospheric evolution during the major sudden stratospheric warming events in 2018 and 2019

Using Modern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2)data in the northern hemisphere at the 10 hPa level,we compared the stratospheric evolution of temperature and geopotential height during two major sudden stratosphere warming events(SSWs)that occurred in the Arctic winter of 2018 and 2019.In the prewarming period,poleward temperature-enhanced regions were mainly located around 120°E with a displaced vortex and around 120°E and 60°W with splitting vortices.The evolution of geopotential height indicated that these temperature-enhanced regions were both on the western side of high-latitude anticyclones.In the postwarming period,the polar vortex turned from splitting to displacement in the 2018 SSW but from displacement to splitting in the 2019 SSW.Both transitions were observed over the Atlantic region,which may have been caused by anticyclones moving through the polar region.Our findings revealed that the evolution of the anticyclone is important during SSWs and is closely related to temperature-enhanced regions in the prewarming periods and to transitions of the polar vortices in postwarming periods.

Estimation of Fractional Vegetation Cover Based on Digital Camera Survey Data and a Remote Sensing Model

The objective of this paper is to improve the monitoring speed and precision of fractional vegetation cover (fc). It mainly focuses on fc estimation when fcmax and fcmin are not approximately equal to 100% and 0%, respectively due to using remote sensing image with medium or low spatial resolution. Meanwhile, we present a new method of fc estimation based on a random set of fc maximum and minimum values from digital camera (DC) survey data and a di- midiate pixel model. The results show that this is a convenient, efficient and accurate method for fc monitoring, with the maximum error -0.172 and correlation coefficient of 0.974 between DC survey data and the estimated value of the remote sensing model. The remaining DC survey data can be used as verification data for the precision of the fc estimation. In general, the estimation of fc based on DC survey data and a remote sensing model is a brand-new development trend and deserves further extensive utilization.

Analysis of the impact on the gravity field determination from the data with the ununiform noise distribution using block-diagonal least squares method

The block-diagonal least squares method, which theoretically has specific requirements for the observation data and the spatial distribution of its precision, plays an important role in ultra-high degree gravity field determination. On the basis of block-diagonal least squares method, three data processing strategies are employed to determine the gravity field models using three kinds of simulated global grid data with different noise spatial distri- bution in this paper. The numerical results show that when we employed the weight matrix corresponding to the noise of the observation data, the model computed by the least squares using the full normal matrix has much higher precision than the one estimated only using the block part of the normal matrix. The model computed by the block-diagonal least squares method without the weight matrix has slightly lower precision than the model computed using the rigorous least squares with the weight matrix. The result offers valuable reference to the using of block-diagonal least squares method in ultra-high gravity model determination.

Characteristics of the quasi-16-day wave in the mesosphere and lower thermosphere region as revealed by meteor radar,Aura satellite,and MERRA2 reanalysis data from 2008 to 2017

This study presents an analysis of the quasi-16-day wave(Q16DW)at three stations in the middle latitudes by using a meteor radar chain in conjunction with Aura Microwave Limb Sounder temperature data and MERRA2(Modern-Era Retrospective Analysis for Research and Applications,Version 2)reanalysis data from 2008 to 2017.The radar chain consists of three meteor radar stations located at Mohe(MH,53.5°N,122.3°E),Beijing(BJ,40.3°N,116.2°E),and Wuhan(WH,30.5°N,114.6°E).The Q16DW wave exhibits similar seasonal variation in the neutral wind and temperature,and the Q16DW amplitude is generally strong during winter and weak around summer.The Q16DW at BJ was found to have secondary enhancement around September in the zonal wind,which is rarely reported at similar latitudes.The latitudinal variations of the Q16DW in the neutral wind and temperature are quite different.The Q16DW at BJ is the most prominent in both neutral wind components among the three stations and the Q16DW amplitudes at MH and WH are comparable,whereas the wave amplitude in temperature decreases with decreasing latitude.The quasi-geostrophic refractive index squared at the three stations in the period from 2008 to 2017 was revealed.The results indicate that the Q16DW in the mesosphere and lower thermosphere(MLT)at MH has a limited contribution from the lower atmosphere.Around March and October,the Q16DW in the troposphere at BJ can propagate upward into the MLT region,whereas at WH,the contribution to the Q16DW in the MLT region is largely from the mesosphere.

Spatiotemporal estimation of hourly 2-km ground-level ozone over China based on Himawari-8 using a self-adaptive geospatially local model

Ground-level ozone(O_(3))is a primary air pollutant,which can greatly harm human health and ecosystems.At present,data fusion frameworks only provided ground-level O_(3) concentrations at coarse spatial(e.g.,10 km)or temporal(e.g.,daily)resolutions.As photochemical pollution continues increasing over China in the last few years,a high-spatial–temporal-resolution product is required to enhance the comprehension of ground-level O_(3) formation mechanisms.To address this issue,our study creatively explores a brand-new framework for estimating hourly 2-km ground-level O_(3) concentrations across China(except Xinjiang and Tibet)using the brightness temperature at multiple thermal infrared bands.Considering the spatial heterogeneity of ground-level O_(3),a novel Self-adaptive Geospatially Local scheme based on Categorical boosting(SGLboost)is developed to train the estimation models.Validation results show that SGLboost performs well in the study area,with the R2 s/RMSEs of 0.85/19.041 lg/m^(3) and 0.72/25.112 lg/m^(3) for the space-based cross-validation(CV)(2017–2019)and historical space-based CV(2019),respectively.Meanwhile,SGLboost achieves distinctly better metrics than those of some widely used machine learning methods,such as e Xtreme Gradient boosting and Random Forest.Compared to recent related works over China,the performance of SGLboost is also more desired.Regarding the spatial distribution,the estimated results present continuous spatial patterns without a significantly partitioned boundary effect.In addition,accurate hourly and seasonal variations of ground-level O_(3) concentrations can be observed in the estimated results over the study area.It is believed that the hourly 2-km results estimated by SGLboost will help further understand the formation mechanisms of ground-level O_(3) in China.

Inversion of Venus internal structure based on geodetic data

Understanding the internal structure of Venus promotes the exploration of the evolutionary history of this planet.However,the existing research concerning the internal structure of Venus has not used any inversion methods.In this work we employed an inversion method to determine the internal structure of Venus using observational or hypothetical geodetic data;these data include mass,mean radius,mean moment of inertia and second degree tidal Love number k2.To determine the core state of Venus,we created two models of Venus,an isotropic 3-layer model with entire liquid core and an isotropic 4-layer model with liquid outer core and a solid inner core,assuming that the interior of Venus is spherically symmetric and in hydrostatic equilibrium.A series of the sensitivity analysis of interior structure parameters to the geodetic data considered in here shows that not all of the parameters can be constrained by the geodetic data from Venus.On this basis,a Markov Chain Monte Carlo algorithm was used to determine the posterior probability distribution and the optimal values of the internal structure parameters of Venus with the geodetic data.We found that the 3-layer model is more credible than the 4-layer model via currently geodetic data.For the assumption of the 3-layer model with the k2=0.295±0.066,I/MR^2=0.33±0.0165,andρ=5242.7±2.6 kg m^-3,the liquid iron-rich core of Venus has a radius of 3294+215-261km,which suggests a larger core than previous research has indicated.The average density of the mantle and liquid core of Venus are 4101+325-375and 11885+955-1242kg m~^-3,respectively.

A preliminary experiment of determining the geopotential difference using two hydrogen atomic clocks and TWSTFT technique

General relativity theory(GRT)concludes that a precise clock ticks at different running rates if it is under the influence of different geopotentials.Therefore,by comparing the running rates of clocks at arbitrary two stations,the geopotential difference between them can be determined.In this study,with the help of two hydrogen atomic clocks(noted as H-masers),using the two-way satellite time and frequency transfer(TWSTFT)technique,we carried out experiments of the geopotential difference determination at the China Aerospace Science&Industry Corporation(CASIC),Beijing.Here the ensemble empirical mode decomposition(EEMD)method is adopted to remove periodic signals included in the original observations.Finally,the clock-comparison-determined geopotential difference in the experiments is determined.Results show that the difference between the geopotential difference determined by GRT and that determined by measuring tape is about 1316.1±931.0 m2s-2,which is equivalent to 134.3±95.0 m in height,and in consistence with the stability of the H-masers applied in the experiments(at the level of10-15/day).With the rapid improvement of atomic clocks’accuracy,the geopotential determination by accurate clocks is prospective,and it is promising to realize the unification of the world vertical height system(WVHS).

Monitoring Multiple Cropping Index of Henan Province, China Based on MODIS-EVI Time Series Data and Savitzky-Golay Filtering Algorithm

Multiple cropping index(MCI)is a very important indicator in crop production and agricultural intensification,which represents the utilizing degree of agriculture resources at time scale and the effective utilization situation of arable land.The objective of this paper is monitoring multiple cropping index of Henan province of China according to the time series of MODIS(Moderate-Resolution Imaging Spectroradiometer)EVI(Enhanced Vegetation Index)after Savitzky-Golay filter processing from the year 2006 to 2011.The results revealed that this method could provide an effective way to monitor multiple cropping index,and the method of no additional authentication data is independent and reliable.The result was accurate and stable,the slope of linear regression of the multiple cropping index between the statistical results and the remote sensing results was 1.0136(R2=0.779).The precision of sample areas validation was 97.91%.Suggesting that the time series MODIS-EVI which after Savitzky-Golay filtering processed,could provide an effective way to extract spatial information of multiple cropping index for management department of agriculture.

Atmospheric correction of ocean color imagery over turbid coastal waters using active and passive remote sensing

This paper demonstrates an atmospheric correction method to process MODIS/Aqua (Moderate-resolution Imaging Spectroradiometer) ocean color imagery over turbid coastal waters with the aid of concurrent CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) aerosol data, assuming that there exists "nonturbid" water in the study area where MODIS aerosol optical properties can be retrieved accurately. Aerosol properties from CALIOP measurements were obtained and related to those from MODIS. This relationship, combined with CALIOP aerosol data, was extended to turbid water to derive MODIS aerosol properties, where atmospheric correction using MODIS data alone often fails. By combining MODIS and CALIOP data, aerosol signals were separated from the total signals at the satellite level, and water-leaving radiances in turbid waters were subsequently derived. This method was tested on several MODIS/Aqua ocean color images over South China turbid waters. Comparison with field data shows that this method was effective in reducing the errors in the retrieved water-leaving radiance values to some extent. In the Zhujiang (Pearl) River Estuary, this method did not overestimate the aerosol effects as severely, and provided far fewer negative water-leaving radiance values than the NASA (National Aeronautics and Space Administration) default methods that used MODIS data alone.

Spatiotemporal interpolation of precipitation across Xinjiang, China using space-time CoKriging

In various environmental studies, geoscience variables not only have the characteristics of time and space, but also are influenced by other variables. Multivariate spatiotemporal variables can improve the accuracy of spatiotemporal estimation. Taking the monthly mean ground observation data of the period 1960–2013 precipitation in the Xinjiang Uygur Autonomous Region, China, the spatiotemporal distribution from January to December in 2013 was respectively estimated by space-time Kriging and space-time CoKriging. Modeling spatiotemporal direct variograms and a cross variogram was a key step in space-time CoKriging. Taking the monthly mean air relative humidity of the same site at the same time as the covariates, the spatiotemporal direct variograms and the spatiotemporal cross variogram of the monthly mean precipitation for the period 1960–2013 were modeled. The experimental results show that the space-time CoKriging reduces the mean square error by 31.46% compared with the space-time ordinary Kriging. The correlation coefficient between the estimated values and the observed values of the space-time CoKriging is 5.07% higher than the one of the space-time ordinary Kriging. Therefore, a space-time CoKriging interpolation with air humidity as a covariate improves the interpolation accuracy.