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1.
Rapid industrialization and haze episodes in Malaysia ensure pollution remains a public health challenge. Atmospheric pollutants such as PM10 are typically variable in space and time. The increased vigilance of policy makers in monitoring pollutant levels has led to vast amounts of spatiotemporal data available for modelling and inference. The aim of this study is to model and predict the spatiotemporal daily PM10 levels across Peninsular Malaysia. A hierarchical autoregressive spatiotemporal model is applied to daily PM10 concentration levels from thirty-four monitoring stations in Peninsular Malaysia during January to December 2011. The model set in a three stage Bayesian hierarchical structure comprises data, process and parameter levels. The posterior estimates suggest moderate spatial correlation with effective range 157 km and a short term persistence of PM10 in atmosphere with temporal correlation parameter 0.78. Spatial predictions and temporal forecasts of the PM10 concentrations follow from the posterior and predictive distributions of the model parameters. Spatial predictions at the hold-out sites and one-step ahead PM10 forecasts are obtained. The predictions and forecasts are validated by computing the RMSE, MAE, R2 and MASE. For the spatial predictions and temporal forecasting, our results indicate a reasonable RMSE of 10.71 and 7.56, respectively for the spatiotemporal model compared to RMSE of 15.18 and 12.96, respectively from a simple linear regression model. Furthermore, the coverage probability of the 95% forecast intervals is 92.4% implying reasonable forecast results. We also present prediction maps of the one-step ahead forecasts for selected day at fine spatial scale.  相似文献   

2.
A lightweight unmanned aerial vehicle (UAV) and a tethered balloon platform were jointly used to investigate three-dimensional distributions of ozone and PM2.5 concentrations within the lower troposphere (1000 m) at a localized coastal area in Shanghai, China. Eight tethered balloon soundings and three UAV flights were conducted on May 25, 2016. Generalized additive models (GAMs) were used to quantitatively describe the relationships between air pollutants and other obtained parameters. Field observations showed that large variations were captured both in the vertical and horizontal distributions of ozone and PM2.5 concentrations. Significant stratified layers of ozone and PM2.5 concentrations as well as wind directions were observed throughout the day. Estimated bulk Richardson numbers indicate that the vertical mixing of air masses within the lower troposphere were heavily suppressed throughout the day, leading to much higher concentrations of ozone and PM2.5 in the planetary boundary layer (PBL). The NO and NO2 concentrations in the experimental field were much lower than that in the urban area of Shanghai and demonstrated totally different vertical distribution patterns from that of ozone and PM2.5. This indicates that aged air masses of different sources were transported to the experimental field at different heights. Results derived from the GAMs showed that the aggregate impact of the selected variables for the vertical variations can explain 94.3% of the variance in ozone and 94.5% in PM2.5. Air temperature, relative humidity and atmospheric pressure had the strongest effects on the variations of ozone and PM2.5. As for the horizontal variations, the GAMs can explain 56.3% of the variance in ozone and 57.6% in PM2.5. The strongest effect on ozone was related to air temperature, while PM2.5 was related to relative humidity. The output of GAMs also implied that fine aerosol particles were in the stage of growth in the experimental field, which is different from ozone (aged air parcels of ozone). Geographical parameters influenced the horizontal variations of ozone and PM2.5 concentrations by changing underlying surface types. The differences of thermodynamic properties between land and sea resulted in quick changes of PBL height, air temperature and dew point over the coastal area, which was linked to the extent of vertical mixing at different locations. The results of GAMs can be used to analyze the sources and formation mechanisms of ozone and PM2.5 pollutions at a localized area.  相似文献   

3.
In this study, seven types of first‐order and one‐variable grey differential equation model (abbreviated as GM (1, 1) model) were used to forecast hourly roadside particulate matter (PM) including PM10 and PM2.5 concentrations in Taipei County of Taiwan. Their forecasting performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient (R) was 11.70%, 60.06, 7.75, and 0.90%, respectively when forecasting PM10. When forecasting PM2.5, the minimum MAPE, MSE, RMSE, and maximum R‐value of 16.33%, 29.78, 5.46, and 0.90, respectively could be achieved. All statistical values revealed that the forecasting performance of GM (1, 1, x(0)), GM (1, 1, a), and GM (1, 1, b) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) was an efficiently early warning tool for providing PM information to the roadside inhabitants.  相似文献   

4.
Tropospheric (ground‐level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1‐h average ozone concentrations in Istanbul were predicted using multi‐layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross‐validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d2) for the testing data were 0.90, 8.78 µg/m3, 11.15 µg/m3, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM10, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non‐linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul.  相似文献   

5.
The objective of the present study is the assessment of Jeddah ambient air quality in terms of PM2.5, and the associated lead 7 years after phasing out leaded gasoline in Saudi Arabia. Twenty‐four air samples were collected at four locations throughout Jeddah during the period from December 23, 2008 to April 6, 2009. The collected PM2.5‐samples were analyzed by ICP‐MS for determination of lead. The average atmospheric PM2.5 concentration was 50.8 µg/m3. Atmospheric PM2.5‐concentrations were higher than the 24‐h U.S. National Ambient Air Quality Standards (NAAQS) in 14 sample events. The average lead concentration for all samples was 0.07326 µg/m3. Atmospheric lead concentration was dependent on the sampling location. Concentrations at the two southern locations were higher than at the two northern locations. Southern locations had higher lead concentrations due to very high traffic density, in addition to their proximity to industrial zone. In general, the results of this study show a considerable decrease in atmospheric lead concentration 7 years after phasing out leaded gasoline. The study recommends further studies to accurately determine the current sources of atmospheric lead.  相似文献   

6.
In this study, three approaches namely parallel, sequential, and multiple linear regression are applied to analyze the local air quality improvements during the COVID-19 lockdowns. In the present work, the authors have analyzed the monitoring data of the following primary air pollutants: particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). During the lockdown period, the first phase has most noticeable impact on airquality evidenced by the parallel approach, and it has reflected a significant reduction in concentration levels of PM10 (27%), PM2.5 (19%), NO2 (74%), SO2 (36%), and CO (47%), respectively. In the sequential approach, a reduction in pollution levels is also observed for different pollutants, however, these results are biased due to rainfall in that period. In the multiple linear regression approach, the concentrations of primary air pollutants are selected, and set as target variables to predict their expected values during the city's lockdown period.The obtained results suggest that if a 21-days lockdown is implemented, then a reduction of 42 µg m−3 in PM10, 23 µg m−3 in PM2.5, 14 µg m−3 in NO2, 2 µg m−3 in SO2, and 0.7 mg m−3 in CO can be achieved.  相似文献   

7.
Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R2 = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R2 = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R2 = 0.47 and RMSE 9.34 VWC%, and for the latter R2 = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m2 digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley & Sons, Ltd.  相似文献   

8.
In this study, particulate matters (TSP, PM10, PM2.5 and PM10–2.5) which are hazardous for environment and human health were investigated in Erzurum urban atmosphere at a sampling point from February 2005 to February 2006. During sampling, two low volume samplers were used and each sampling period lasted approximately 24 h. In order for detection of representative sampling region and point of Erzurum, Kriging method was applied to the black smoke concentration data for winter seasons. Mass concentrations of TSP, PM10 and PM2.5 of Erzurum urban atmosphere were measured on average, as 129, 31 and 13 μg/m3, respectively, in the sampling period. Meteorological factors, such as temperature, wind speed, wind direction and rainfall were typically found to be affecting PMs, especially PM2.5. Air temperature did not seem to be significantly affecting TSP and PM10 mass concentrations, but had a considerably negative induction on PM2.5 mass concentrations. However, combustion sourced PM2.5 was usually diluted from the urban atmosphere by the speed of wind, soil sourced coarse mode particle concentrations (TSP, PM10) were slightly affected by the speed of wind. Rainfall was found to be decreasing concentrations to 48% in all fractions (TSP, PM10, PM10–2.5, PM2.5) and played an important role on dilution of the atmosphere. Fine mode fraction of PM (PM2.5) showed significant daily and seasonal variations on mass concentrations. On the other hand, coarse mode fractions (TSP, PM10 and PM10–2.5) revealed more steady variations. It was observed that fine mode fraction variations were affected by the heating in residences during winter seasons.  相似文献   

9.
Assessing the long-term benefits of marginal improvements in air quality from regulatory intervention is methodologically challenging. In this study, we explore how the relative risks (RRs) of mortality from air pollution exposure change over time and whether patterns in the RRs can be attributed to air quality improvements. We employed two-stage multilevel Cox models to describe the association between air pollution and mortality for 51 cities with data from the American Cancer Society (ACS) cohort (N = 264,299, deaths = 69,819). New pollution data were computed through models that predict yearly average fine particle (PM2.5) concentrations throughout the follow-up (1982–2000). Average PM2.5 concentrations from 1999 to 2000 and sulfate concentrations from 1980 were also examined. We estimated the RRs of mortality associated with air pollution separately for five time periods (1982–1986, 1987–1990, 1991–1994, 1995–1998, and 1999–2000). Mobility models were implemented with a sub-sample of 100,557 subjects to assist with interpreting the RR estimates. Sulfate RRs exhibit a large decline from the 1980s to the 1990s. In contrast, PM2.5 RRs follow the opposite pattern, with larger RRs later in the 1990s. The reduction in sulfate RR may have resulted from air quality improvements that occurred through the 1980s and 1990s in response to the acid rain control program. PM2.5 concentrations also declined in many places, but toxic mobile sources are now the largest contributors to PM in urban areas. This may account for the heightened RR of mortality associated with PM2.5 in the 1990s. The paper concludes with a three alternative explanations for the temporal pattern of RRs, each emphasizing the uncertainty in ascribing health benefits to air quality improvements.  相似文献   

10.
Particle hygroscopicity plays a key role in understanding the mechanisms of haze formation and particle optical properties. The present study developed a method for predicting the effective hygroscopic parameter k and the water content of PM_(2.5) on the basis of the k-K?hler theory and bulk chemical components of PM_(2.5). Our study demonstrated that the effective hygroscopic parameter can be estimated using the PM_(2.5) mass concentration, water-soluble ions, and total water-soluble carbon. By combining the estimated k and ambient relative humidity, the water content of PM_(2.5) can be further estimated. As an example, the k and water content of PM_(2.5) in Beijing were estimated utilizing the method proposed in this study. The annual average value of k of PM_(2.5) in Beijing was 0.25±0.09, the maximum k value 0.26±0.08 appeared in summer, and the seasonal variation is insignificant. The PM_(2.5) water content was determined by both the PM_(2.5) hygroscopicity and the ambient relative humidity(RH). The annual average mass ratio of water content and PM_(2.5) was 0.18±0.20, and the maximum value 0.31±0.25 appeared in summer. Based on the estimated water content of PM_(2.5) in Beijing, the relationship between the PM_(2.5) water content and RH was parameterized as: m(%)=0.03+(5.73×10~(-8)) ×RH~(3.72).This parametric formula helps to characterize the relationship between the PM_(2.5) mass concentration and atmospheric visibility.  相似文献   

11.
Urban populations are exposed to a high level of fine and ultrafine particles from motor vehicle emissions which affect human health. To assess the hourly variation of fine particle (PM2.5) concentration and the influence of temperature and relative humidity (RH) on the ambient air of Lucknow city, monitoring of PM2.5 along with temperature and RH was carried out at two residential locations, namely Vikas Nagar and Alambagh, during November 2005. The 24 h mean PM2.5 concentration at Alambagh was 131.74 μg/m3 and showed an increase of 13.74%, which was significantly higher (p < 0.05) than the Vikas Nagar level. The 24 h mean PM2.5 on weekdays for both locations was found to be 142.74 μg/m3 (an increase of 66.23%) which was significantly higher (p < 0.01) than the weekend value, indicating that vehicular pollution is one of the important sources of PM2.5. The mean PM2.5 at night for all the monitoring days was 157.69 μg/m3 and was significantly higher (p < 0.01) than the daytime concentration (89.87 μg/m3). Correlation and multiple regressions showed that the independent variables, i. e., time, temperature, and RH together accounted for 54%, whereas RH alone accounted for 53% of total variations of PM2.5, suggesting that RH is the best influencing variable to predict the PM2.5 concentration in the urban area of Lucknow city. The 24 h mean PM2.5 for all the monitoring days was found to be higher than the NAAQS recommended by the US‐EPA (65 μg/m3) and can be considered to be an alarming indicator of adverse health effects for city dwellers.  相似文献   

12.
Different satellite-based radiation (Makkink) and temperature (Hargreaves-Samani, Penman-Monteith temperature, PMT) reference evapotranspiration (ETo) models were compared with the FAO56-PM method over the Cauvery basin, India. Maximum air temperature (Tmax) required in the ETo models was estimated using the temperature–vegetation index (TVX) and an advanced statistical approach (ASA), and evaluated with observed Tmax obtained from automatic weather stations. Minimum air temperature (Tmin) was estimated using ASA. Land surface temperature was employed in the ETo models in place of air temperature (Ta) to check the potency of its applicability. The results suggest that the PMT model with Ta as input performed better than the other ETo models, with correlation coefficient (r), averaged root mean square error (RMSE) and mean bias error (MBE) of 0.77, 0.80 mm d?1 and ?0.69 for all land cover classes. The ASA yielded better Tmax and Tmin values (r and RMSE of 0.87 and 2.17°C, and 0.87 and 2.27°C, respectively).  相似文献   

13.
Two sensitivity simulations were performed and compared by model in order to understand how high-rise buildings influence meteorology and air quality in the Lujiazui Central Business District (CBD) of Shanghai, China. The coupled meteorological-photochemical model, Metphomod, was used, with a 500-m horizontal resolution and the observations and the simulated results generally agreed well. The scheme considering buildings within roughness could reduce uncertainties in the simulated meteorological conditions and concentrations of air pollutants. The high-rise buildings decreased wind speeds by 0.5–4 m/s, increased temperatures by up to 1 °C and turbulent kinetic energy by 1–2 J/m3 in the Lujiazui CBD. The changes in meteorological conditions also increased NO by about 2–5 %. However, the complex meteorological changes of higher temperatures and stronger turbulent kinetic energy, coupled with changes of precursors’ concentrations in the Lujiazui CBD, decreased O3 concentrations by up to 6 % somewhere, while increasing O3 formation by up to 2 % in downwind areas. The results suggested that it was necessary to include high-rise building parameters in models when estimating the meteorology and diagnosing air pollution of highly urbanized regions.  相似文献   

14.
Fish habitat and aquatic life in rivers are highly dependent on water temperature. Therefore, it is important to understand andto be able to predict river water temperatures using models. Such models can increase our knowledge of river thermal regimes as well as provide tools for environmental impact assessments. In this study, artificial neural networks (ANNs) will be used to develop models for predicting both the mean and maximum daily water temperature. The study was conducted within Catamaran Brook, a small drainage basin tributary to the Miramichi River (New Brunswick, Canada). In total, eight ANN models were investigated using a variety of input parameters. Of these models, four predicted mean daily water temperature and four predicted maximum daily water temperature. The best model for mean daily temperature had eight input parameters: minimum, maximum and mean air temperatures of the current day and those of the preceding day, the day of year and the water level. This model had an overall root‐mean‐square error (RMSE) of 0·96 °C, a bias of 0·26 °C and a coefficient of determination R2 = 0·971. The model that best predicted maximum daily water temperature was similar to the first model but excluded mean daily air temperature. Good results were obtained for maximum water temperatures with an overall RMSE of 1·18 °C, a bias of 0·15 °C and R2 = 0·961. The results of ANN models were similar to and/or better than those observed from the literature. The advantages of artificial neural networks models in modelling river water temperature lie in their simplicity of use, their low data requirement and their good performance, as well as their flexibility in allowing many input and output parameters. Copyright © 2008 Crown in the right of Canada and John Wiley & Sons, Ltd.  相似文献   

15.
PM2.5 is the key pollutant in atmospheric pollution in China.With new national air quality standards taking effect,PM2.5 has become a major issue for future pollution control.To effectively prevent and control PM2.5,its emission sources must be precisely and thoroughly understood.However,there are few publications reporting comprehensive and systematic results of PM2.5 source apportionment in the country.Based on PM2.5 sampling during 2009 in Shenzhen and follow-up investigation,positive matrix factorization(PMF)analysis has been carried out to understand the major sources and their temporal and spatial variations.The results show that in urban Shenzhen(University Town site),annual mean PM2.5 concentration was 42.2μg m?3,with secondary sulfate,vehicular emission,biomass burning and secondary nitrate as major sources;these contributed30.0%,26.9%,9.8%and 9.3%to total PM2.5,respectively.Other sources included high chloride,heavy oil combustion,sea salt,dust and the metallurgical industry,with contributions between 2%–4%.Spatiotemporal variations of various sources show that vehicular emission was mainly a local source,whereas secondary sulfate and biomass burning were mostly regional.Secondary nitrate had both local and regional sources.Identification of secondary organic aerosol(SOA)has always been difficult in aerosol source apportionment.In this study,the PMF model and organic carbon/elemental carbon(OC/EC)ratio method were combined to estimate SOA in PM2.5.The results show that in urban Shenzhen,annual SOA mass concentration was 7.5μg m?3,accounting for 57%of total organic matter,with precursors emitted from vehicles as the major source.This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in China.  相似文献   

16.
The relationship between solute concentrations and discharge can inform an integrated understanding of hydrological and biogeochemical processes at watershed scales. Recent work from multiple catchments has shown that there is typically little variation in concentration relative to large variations in discharge. This pattern has been described as chemostatic behavior. Pond Branch, a forested headwater catchment in Maryland, has been monitored for stream nitrate (NO3?) concentrations at weekly intervals for 14 years. In the growing season and autumn of 2011 a high‐frequency optical NO3? sensor was used to supplement the long‐term weekly data. In this watershed, long‐term weekly data show that NO3? concentrations decrease with increasing discharge whereas 6 months of 15‐minute sensor observed concentrations reveal a more chemostatic behavior. High‐frequency NO3? concentrations from the sensor collected during different storm events reveal variable concentration–discharge patterns highlighting the importance of high resolution data and ecohydrological drivers in controlling solute export for biologically reactive solutes such as NO3?.  相似文献   

17.
This work proposes a space/time estimation method for atmospheric PM2.5 components by modelling the mass fraction at a selection of space/time locations where the component is measured and applying the model to the extensive PM2.5 monitoring network. The method we developed utilizes the nonlinear Bayesian maximum entropy framework to perform the geostatistical estimation. We implemented this approach using data from nine carcinogenic, particle-bound polycyclic aromatic hydrocarbons (PAHs) measured from archived PM2.5 samples collected at four locations around the World Trade Center (WTC) from September 22, 2001 to March 27, 2002. The mass fraction model developed at these four sites was used to estimate PAH concentrations at additional PM2.5 monitors. Even with limited PAH data, a spatial validation showed the application of the mass fraction model reduced the mean squared error (MSE) by 7–22%, while in the temporal validation there was an exponential improvement in MSE positively associated with the number of days of PAH data removed. Our results include space/time maps of atmospheric PAH concentrations in the New York area after 9/11.  相似文献   

18.
We present an uncertainty analysis of ecological process parameters and CO2 flux components (R eco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance measurements of CO2 fluxes at subtropical evergreen coniferous plantation, Qianyanzhou of ChinaFlux. Daily-differencing approach was used to analyze the random error of CO2 fluxes measurements and bootstrapping method was used to quantify the uncertainties of three CO2 flux components. In addition, we evaluated different models and optimization methods in influencing estimation of key parameters and CO2 flux components. The results show that: (1) Random flux error more closely follows a double-exponential (Laplace), rather than a normal (Gaussian) distribution. (2) Different optimization methods result in different estimates of model parameters. Uncertainties of parameters estimated by the maximum likelihood estimation (MLE) are lower than those derived from ordinary least square method (OLS). (3) The differences between simulated Reco, NEE and GEE derived from MLE and those derived from OLS are 12.18% (176 g C·m−2·a−1), 34.33% (79 g C·m−2·a−1) and 5.4% (92 g C·m−2·a−1). However, for a given parameter optimization method, a temperature-dependent model (T_model) and the models derived from a temperature and water-dependent model (TW_model) are 1.31% (17.8 g C·m−2·a−1), 2.1% (5.7 g C·m−2·a−1), and 0.26% (4.3 g C·m−2·a−1), respectively, which suggested that the optimization methods are more important than the ecological models in influencing uncertainty in estimated carbon fluxes. (4) The relative uncertainty of CO2 flux derived from OLS is higher than that from MLE, and the uncertainty is related to timescale, that is, the larger the timescale, the smaller the uncertainty. The relative uncertainties of Reco, NEE and GEE are 4%−8%, 7%−22% and 2%−4% respectively at annual timescale. Supported by the National Natural Science Foundation of China (Grant No. 30570347), Innovative Research International Partnership Project of the Chinese Academy of Sciences (Grant No. CXTD-Z2005-1) and National Basic Research Program of China (Grant No. 2002CB412502)  相似文献   

19.
The active layer of frozen ground data assimilation system adopts the SHAW (Simulteneous Heat and Water) model as the model operator. It employs an ensemble kalman filter to fuse state variables predicted by the SHAW model with in situ observation and the SSM/I 19 GHz brightness temperature for the purpose of optimizing model hydrothermal state variables. When there is little water movement in the frozen soil during the winter season, the unfrozen water content depends primarily on soil temperature. Thus, soil temperature is the crucial state variable to be improved. In contrast, soil moisture is heavily influenced by precipitation during the summer season. The simulation accuracy of soil moisture has a strong and direct impact on the soil temperature. In this case, the crucial state variable to be improved is soil moisture. One-dimensional assimilation experiments that have been carried out at AMDO station show that land data assimilation method can improve the estimation of hydrothermal state variables in the soil by fusing model information and observation information. The reasonable model error covariance matrix plays a key role in transferring the optimized surface state information to the deep soil, and it provides improved estimations of whole soil state profiles. After assimilating the 4-cm soil temperature by in situ observation, the soil temperature RMSE (Root Mean Square Error) of each soil layer decreased by 0.96°C on average relative to the SHAW simulation. After assimilating the 4-cm soil moisture in situ observation, the soil moisture RMSE of each soil layer decreased by 0.020 m3·m−3. When assimilating the SSM/I 19 GHz brightness temperature, the soil temperature RMSE of each soil layer during the winter decreased by 0.76°C, while the soil moisture RMSE of each soil layer during the summer decreased by 0.018 m3·m−3.  相似文献   

20.
With the complex nature of land surfaces, more attention should be paid to the performance of remotely sensed models to estimate evapotranspiration from moderate and low spatial resolution data. Taking into account the characteristic of a stable evaporative fraction (EF) in the daytime, this paper uses the surface energy balance system (SEBS) to estimate the EF from MODIS data for a subtropical evergreen coniferous plantation in southern China and evaluates the stability of the SEBS model in estimating the EF under complex surface conditions. The results show that the SEBS‐estimated EF is larger than the measured EF partly because of the serious lack of energy‐balance closure. This difference can be largely reduced by the residual energy correction method. More evaporative land cover within the MODIS pixel is a main reason for the overestimated EF. SEBS underestimates sensible heat flux, and the underestimation of surface available energy also contributes to the overestimation of the EF. The EF estimated from MODIS/Terra data is in agreement with that from MODIS/Aqua data with a coefficient of determination (R2) of 0.552, a mean bias error (BIAS) of 0.028, and a root mean square error (RMSE) of 0.079, which is consistent with the result from in situ measurements. In addition, the estimation of surface available energy from remotely sensed data is evaluated on this complex underlying surface. Compared with in situ measurements, the available energy is underestimated by 28 W m?2 with an RMSE = 50 W m?2 and an R2 = 0.87. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

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