Ground Observation Stations Research Articles

Determination of the Correlation between the Air Temperature Measured <i>in Situ</i> and Remotely Sensed Data from MODIS and SEVIRI in Congo-Brazzaville

This study compared data from the MODerate-resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Terra satellite and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on EUMETSAT’s Meterosat Second Generation (MSG) satellite with in situ data obtained from ground observation stations in Congo-Brazzaville. Remote sensing instruments can be used to estimate air temperature, which has an important role in monitoring the effects of climate change. Congo-Brazzaville is located in equatorial forest, which is difficult to access, and has a limited number of ground meteorological stations measuring air temperature. This study used MODIS and MSG data for the period 2009-2014 to assess the performance of land surface temperature data from satellites against in situ data from ground-based stations in Congo-Brazzaville using a linear regression model. This work has allowed us to determine which satellite is best adapted for use in Central Africa.

Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China

Abstract. Precipitation and shortwave radiation play important roles in climatic, hydrological and biogeochemical cycles. Several global and regional forcing data sets currently provide historical estimates of these two variables over China, including the Global Land Data Assimilation System (GLDAS), the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the China Meteorological Forcing Dataset (CMFD). The CN05.1 precipitation data set, a gridded analysis based on CMA gauge observations, also provides high-resolution historical precipitation data for China. In this study, we present an intercomparison of precipitation and shortwave radiation data from CN05.1, CMFD, CLDAS and GLDAS during 2008–2014. We also validate all four data sets against independent ground station observations. All four forcing data sets capture the spatial distribution of precipitation over major land areas of China, although CLDAS indicates smaller annual-mean precipitation amounts than CN05.1, CMFD or GLDAS. Time series of precipitation anomalies are largely consistent among the data sets, except for a sudden decrease in CMFD after August 2014. All forcing data indicate greater temporal variations relative to the mean in dry regions than in wet regions. Validation against independent precipitation observations provided by the Ministry of Water Resources (MWR) in the middle and lower reaches of the Yangtze River indicates that CLDAS provides the most realistic estimates of spatiotemporal variability in precipitation in this region. CMFD also performs well with respect to annual mean precipitation, while GLDAS fails to accurately capture much of the spatiotemporal variability and CN05.1 contains significant high biases relative to the MWR observations. Estimates of shortwave radiation from CMFD are largely consistent with station observations, while CLDAS and GLDAS greatly overestimate shortwave radiation. All three forcing data sets capture the key features of the spatial distribution, but estimates from CLDAS and GLDAS are systematically higher than those from CMFD over most of mainland China. Based on our evaluation metrics, CLDAS slightly outperforms GLDAS. CLDAS is also closer than GLDAS to CMFD with respect to temporal variations in shortwave radiation anomalies, with substantial differences among the time series. Differences in temporal variations are especially pronounced south of 34° N. Our findings provide valuable guidance for a variety of stakeholders, including land-surface modelers and data providers.

Data Integration and Analysis System (DIAS) Contributing to Climate Change Analysis and Disaster Risk Reduction

In 2015, global attempts were made to reconcile the relationship between development and environmental issues. This led to the adoption of key agreements such as the Sustainable Development Goals. In this regard, it is important to identify and evaluate under-recognized disaster risks that hinder sustainable development: measures to mitigate climate change are the same as those that build resilience against climate-related disasters. To do this we need to advance scientific and technical knowledge, build data infrastructure that allows us to predict events with greater accuracy, and develop data archives. For this reason we have developed the Data Integration and Analysis System (DIAS). DIAS incorporates analysis, data and models from many fields and disciplines. It collects and stores data from satellites, ground observation stations and numerical weather prediction models; integrates this data with geographical and socio-economic information; then generates results for crisis management of global environmental issues. This article gives an overview of DIAS and summarizes its application to climate change analysis and disaster risk reduction. As the article shows, DIAS aims to initiate cooperation between different stakeholders, and contribute to the creation of scientific knowledge. DIAS provides a model for sharing transdisciplinary research data that is essential for achieving the goal of sustainable development.

Toward Improved Solar Irradiance Forecasts: Comparison of the Global Horizontal Irradiances Derived from the COMS Satellite Imagery Over the Korean Peninsula

This study introduces the University of Arizona Solar Irradiance Based on Satellite/Korea Institute of Energy Research, which is usually called UASIBS/KIER model. Then the evaluation of modeling performance is done against the ground observations for the instantaneous, hourly, and daily time scales over the Korean Peninsula in this study. The relative root mean square error for the instantaneous time scale is 7.4 and 16.7% for the clear and cloudy skies, respectively. The hourly mean estimates are compared with the in situ measurements from 35 ground observation stations, resulting in a relative root mean square error ranging from 9.1 to 15.5%. The daily aggregates are proven as the most reliable estimates. The UASIBS/KIER estimates are also compared with the routine solar insolation product from the Korea Meteorological Administration. Finally, the solar energy resource map has been built by the daily solar irradiance derived from the UASIBS/KIER model, followed by its comparison with the other gridded datasets.

Fusion of multi-source near-surface CO2 concentration data based on high accuracy surface modeling

Under the background of growing greenhouse gas emissions and the resulting global warming, researches about the spatial-temporal variation analysis of the concentration of carbon dioxide in the regional and global scale has become one of the most important topics in the scientific community. Simulating and analyzing the spatial-temporal variation of the carbon dioxide concentration on a global scale under limited observation data has become one of the key problems to be solved in the research field of spatial analysis technology. A new research approach based on high accuracy surface modeling data fusion (HASM-DF) method was proposed in this paper, in which the output of the CO2 concentration of the GEOS-Chem model were taken as driving field, and the observation values of CO2 concentration at ground observation station were taken as accuracy control conditions. The new approach's objective is to fulfill the fusion of the two kinds of CO2 data, and obtain the distribution of CO2 on a global scale with a higher accuracy than the results of GEOS-Chem. Root mean square error (RMSE) was chosen as the basic accuracy index, and the experimental analysis shows that the RMSE of the result of the proposed approach is 1.886 ppm, which is significantly lower than that of the GEOS-Chem's 2.239 ppm. Furthermore, compared with the results created by the interpolation methods used the observation values at stations; the fusion results keep a good spatial heterogeneity similar to the results of GEOS-Chem. This research analyzed the spatial distribution and time series variation of the near-surface CO2 based on the fusion result on a global scale. And it can found that areas such as East Asia, Western North American, Central South America and Central Africa and other region show a relatively high value of the near-surface CO2 concentration. And we also found that the near-surface CO2 concentration changes with season, especially in North America and Eurasia, the near-surface CO2 in summer was significantly lower than winter in these areas.

Water vapor pressure trends in south and southwest Iran

Recognitionanddetectionofclimaticparameters inhave animportant role inclimate change monitoring. In this study, the analysis of oneofthe most importantparameters, water vapor pressure (WVP), was investigated. For this purpose, two non-parametric techniques, Mann-Kendall and Sen's Slope Estimator, were used to analyze the WVP trend and to determine the magnitude of the trends, respectively. To analyze these tests, ground station observations [10 stations for period of 44 years (1967-2010)] and gridded data [pixels with the dimension of 9 × 9 km over a 30-year period (1981-2010)] in South and SouthwestofIran were used. By programming in MATLAB software, the monthly, seasonal and annual WVP time series were extracted and MK and Sen's slope estimator tests were done. The results of monthly MK test on ground station observations showed that the significant downward trends are more considerable than significant upward trends. It also showed that the WVP highest frequency was more in warm months, April to September and the highest frequency of significant trends slope was in February and May. The spatial distribution of MK test of monthly gridded WVP time series showed that the upward trends were detected mostly in western zone and near the Persian Gulf in August. On the other hand, the downward trends through months. The maximum and minimum values of positive trends slope occurred in warm months and cold months, respectively. The analysis of the MK test of the annual WVP time series indicated the upward significant trends in the southeast and southwest zones of study area.

Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil

Satellite-based rainfall is an alternative source of information for regions such as the Northeast Brazil (NEB) where there are large areas that do not have ground observation stations. In this study, the monthly rainfall derived from the satellite-based rainfall product, Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS v.2), is compared with observation from 21 ground stations in the NEB, for the period 1981–2013. Various metrics based on pairwise comparison were applied to evaluate its performance in estimating rainfall amount and rain detection capability. Results show that the CHIRPS data correlate well with observations for all stations (r = 0.94), but tend to overestimate low and underestimate high rainfall values (>100 mm/month). Although CHIRPS achieves better results during the wet season (March to May; bias = −4.60%), its ability for the rain detection is poor (probability = 0.44). The best global performance was noted in the Cerrado biome (r = 0.93; rain detection = 0.53), but fails to detect rain in other biomes (r = 0.92; rain detection = 0.16). The study concluded that the CHIRPS v.2 dataset can be a useful substitute for rain-gauge precipitation data outside the semiarid NEB.

Toward Improved Solar Irradiance Forecasts: Derivation of Downwelling Surface Shortwave Radiation in Arizona from Satellite

Over the past few decades, substantial progress has been made in the retrieval of surface shortwave radiation from satellite measurements for the Earth’s energy budget as well as solar energy applications. We present a new algorithm to derive the downwelling surface shortwave radiation for the Southwestern US using geostationary satellite products. A look-up table generated by the Goddard Space Flight Center Radiative Transfer Model is employed to derive the shortwave radiation at the ground by comparing observed and modeled top of atmosphere shortwave albedo. The algorithm was compared to ground observation stations at three locations, such as the University of Arizona, the University of Nevada, Las Vegas, and Desert Rock, NV. For all sky conditions, the average values of root-mean-square error between the instantaneous estimates and in situ measurements ranged from 84.2 to 89.4 W m−2 and were 30 W m−2 when evaluated on daily time scales. The error statistics were considerably better for clear sky than for cloudy sky. The average values of instantaneous root-mean-square error for the clear-sky conditions range from 39.4 to 43.7 W m−2, while average root-mean-square error for the cloudy-sky conditions is between 137.0 and 141.2 W m−2.

Preprocessing WRF initial conditions for coastal stratocumulus forecasting

The impact of atmospheric liquid water content at model initialization in the Weather Research and Forecasting (WRF) model is explored through the application of two preprocessing schemes. The first scheme, the Well-mixed Preprocessor (WEMPP), was designed and developed based on a well-mixed boundary layer to provide an initial guess at liquid water content when initializing with data from the North American Mesoscale (NAM) model, as liquid water content is not present in NAM output. The second scheme was adapted from a satellite Cloud Data Assimilation (CLDDA) package intended to make the initial model cloud field consistent with observations, using input data from the CIMSS GOES sounder cloud product.Preprocessed simulations were compared against baseline WRF simulations initialized with NAM and the Rapid Refresh (RAP) model (which contains liquid water output), as well as the raw parent model outputs. These intra-day forecasts were validated against both 5-min and 10-min averaged ground station and 30-min (hourly averaged) satellite irradiance observations over the course of a month.Due to their extensive spatial coverage, optical thickness, and reflectivity, stratocumulus (Sc) clouds are responsible for much of the variability in available solar resource at the surface in coastal California, where most rooftop photovoltaic systems are located. Currently, the trend of numerical weather prediction models is to underpredict both the presence and thickness of Sc. Therefore, the validation is conducted for a summer month in southern California, when Sc are most prevalent.Ground station validation showed average improvements by WEMPP in predicting surface irradiance over the baseline NAM (RAP) WRF initializations of 33% (−3%) MBE and 16% (9%) MAE, and by CLDDA of 47% (18%) MBE and 26% (20%) MAE. Additionally, simulations preprocessed by CLDDA were consistently able to outperform 24-h persistence forecast at 3 out of 4 ground stations.Validation against SolarAnywhere® satellite irradiance observations showed that the combination of both preprocessors provided the most improvement in the prediction of Sc spatial coverage, thickness, and lifetime in coastal regions where marine layer stratocumulus is most frequently observed, but cloud cover over the ocean was overestimated by all preprocessors.

Evaluation of NOx emissions for Turkey using satellite and ground-based observations

The aim of this study was to evaluate NOx emissions from available inventories using satellite NO2 retrievals for Turkey. NOx emissions from two available inventories (EMEP and TNO) were compared with each other, differences and similarities were determined. NOx emission inventories were also compared with OMI NO2 satellite retrievals. Correlation coefficients (R2) between parameters and emissions were found in the range of 0.29–0.69 for selected high populated cities. OMI NO2 satellite retrievals overpass NO2 ground observation stations were also processed and the correlation between these observations for selected cities were found highest in summer and lowest in fall and winter. According to the comparison for individual cities, good correlations were observed for Ankara (R2 = 0.62), Izmir (R2 = 0.59) and Istanbul (R2 = 0.90). Weekly profiles and weekend/weekday ratios for ground-based and OMI NO2 observations were calculated and weekly profiles used for EMEP were also compared with observed weekly profiles. Findings showed that OMI is able to detect polluted regions in Turkey, but there are discrepancies between OMI and ground-based NO2 observations. Results also indicated issues on NOx emissions of large scale power plants in EMEP emission inventory for Turkey.

A multi-scale hybrid neural network retrieval model for dust storm detection, a study in Asia

Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the large uncertainties in global climate modeling, due to its significant impact on the radiation budget and atmospheric stability. Observations of dust storms in humid tropical south China (e.g. Hong Kong), are challenging due to high industrial pollution from the nearby Pearl River Delta region. This study develops a method for dust storm detection by combining ground station observations (PM10 concentration, AERONET data), geostationary satellite images (MTSAT), and numerical weather and climatic forecasting products (WRF/Chem). The method is based on a hybrid neural network (NN) retrieval model for two scales: (i) a NN model for near real-time detection of dust storms at broader regional scale; (ii) a NN model for detailed dust storm mapping for Hong Kong and Taiwan. A feed-forward multilayer perceptron (MLP) NN, trained using back propagation (BP) algorithm, was developed and validated by the k-fold cross validation approach. The accuracy of the near real-time detection MLP-BP network is 96.6%, and the accuracies for the detailed MLP-BP neural network for Hong Kong and Taiwan is 74.8%. This newly automated multi-scale hybrid method can be used to give advance near real-time mapping of dust storms for environmental authorities and the public. It is also beneficial for identifying spatial locations of adverse air quality conditions, and estimates of low visibility associated with dust events for port and airport authorities.

Assessing the Capabilities of Three Regional Climate Models over CORDEX Africa in Simulating West African Summer Monsoon Precipitation

This study evaluates the ability of three Regional Climate Models (RCMs) used in Coordinated Regional Climate Downscaling Experiment (CORDEX) to simulate the characteristics of rainfall pattern during the West Africa Summer Monsoon from 1998 to 2008. The seasonal climatology, annual rainfall cycles, and wind fields of the RCMs output were assessed over three homogenous subregions and validated using precipitation data from eighty-one (81) ground observation stations and TRMM satellite data. Furthermore, the ability of the RCMs to simulate response to El Nino and La Nina events was assessed. Results show that two of the RCMs (RCA and REMO) simulated the main features of the rainfall climatology and associated dynamics over the three subregions (Guinea Coast, Savannah, and Sahel) of West Africa. The RCMs also capture the African Easterly Jet (AEJ) and Tropical Easterly Jet (TEJ) with little variations in position and intensity. Analysis shows significant biases in individual models depending on subregion and season under consideration which may be attributed to strong cyclonic circulation observed at 850 mb pressure level. In general, the study shows RCA and REMO fairly simulate West Africa rainfall adequately and can therefore be used for the assessment of West African Summer Monsoon and future climate projections.

Estimation of potential source regions of PM2.5 in Beijing using backward trajectories

Possible source regions of the PM2.5 mass concentrations in Beijing, China have been identified using the Potential Source Contribution Function (PSCF) analysis based on the backward trajectory modeling. Five–day backward trajectories for the arrival of air masses to the ground observation station at Chaoyang Olympic Sports Center were calculated, using the Hybrid Single–Particle Lagrangian Integrated Trajectory (HYSPLIT) model in 2013. The PSCF results demonstrated that regional sources in different seasons could be one of the crucial contributors to PM2.5 mass concentrations in Beijing, especially in the winter season. Compared PSCF results with the MODIS fine–mode aerosol optical thickness (AOTfine), the regions showing high AOTfine especially the Southern Hebei, Northern Henan and Southwest Shandong are the significant potential PM2.5 contributors to Beijing. Environmental authorities may use the derived models and results for pinpointing the source areas, for improving air quality in Beijing City.

A Method Based on Remote Sensing to Look for Grey Haze Emission Source Area

In this study, we try to find the possible emission source area of the grey haze through comparing the ground observation station weather data and remote sensing data for the grey haze process that Beijing-Tianjin-Hebei region experienced in November the 8-14, 2013 in which the degree of contamination range from severe to mild to severe. The result demonstrates that before the invasion of the cold air , the coverage of the grey haze reach its maximum where south central areas of Beijing-Tianjin-Hebei is more severer compared with the south central and peri-urban areas of Hebei province. For the greater intensity grey haze companied by the wind convergence in the surface boundary layer, the result of the MODIS satellite monitoring is consistent with the meteorological station observation. Whereas MODIS satellite monitoring method could make up the shortfall of the meteorological station for the mild grey haze in certain degree. After the cold air, haze primary development area is very obvious, and the starting position of haze in the role of diffusion by the wind is also here, based on this can lock the grey haze emission source area and make concrete origination by using the high resolution satellite. The precise positioning could provide the scientific basis for the pertinent management on pollution control for the local government and relevant authority.

Large-scale deformation monitoring in mining area by D-InSAR and 3D laser scanning technology integration

Large-scale deformation can not be detected by traditional D-InSAR technique because of the limit of its detectable deformation gradient, we propose a method that combines SAR data with point cloud data obtained by 3D laser scanning to improve the gradient of deformation detection. The proposed method takes advantage of high-density of 3D laser scanning point cloud data and its high precision of point positioning after 3D modeling. The specific process can be described as follows: first, large-scale deformation points in the interferogram are masked out based on interferometric coherence; second, the interferogram with holes is unwrapped to obtain a deformation map with holes, and last, the holes in the deformation map are filled with point cloud data using inverse distance weighting algorithm, which will achieve seamless connection of monitoring region. We took the embankment dam above working face of a certain mining area in Shandong province as an example to study large-scale deformation in mining area using the proposed method. The results show that the maximum absolute error is 64mm, relative error of maximum subsidence value is 4.95%, and they are consistent with leveling data of ground observation stations, which confirms the feasibility of this method. The method we presented provides new ways and means for achieving large-scale deformation monitoring by D-InSAR in mining area.

STUDY OF DUST POLLUTION DETECTING ABILITY IN URBAN AREAS BY REMOTE SENSING TECHNOLOGY TO SUPPORT AIR ENVIRONMENT OBSERVATION

This paper presents the approach towards application of remote sensing technology to monitor the air environemnt. Specific inital research is findings PM10 dust from SPOT 5 satellite image. The calculation based on reflectance value on remote sensing satellite images. The main method is to calculate statistical correlation regression between the PM10 concentration from ground station observations and reflectance value on each image band and the main components of satellite imagery in 2003 to find the best regression function, applied then to images 2011 where its radiance value was relatively normalized under atmospheric, geometric, environmental conditions of image 2003. The results showed the best correlation in nonlinear regression case. Spatial distribution of PM10 concentrations > 200μg/m3 found on most main roads, industrial parks and residential areas. This study is a first step test, but the results have demonstrated that satellite imagery can be used as a useful, effective tool, to monitor air environment in cities.

Improvement to an existing satellite data set in support of an Australia solar atlas

To assist in site identification for development of solar energy facilities in Australia, relevant spatial data were drawn from a range of sources. Along with infrastructure and development constraints, spatial solar irradiance estimates were a key requirement. Existing estimates, derived from satellite observations, were found to include significant bias when compared to ground station observations, likely due to the difference in measurement (spatial average for satellite and point location for ground station). Rather than revert to reprocessing the original data, post-processing corrections were developed to compensate for this bias.Investigations were also made into the influence of aerosols on the satellite estimates (aerosols were not included in the existing model), and whether any spatial bias existed in the satellite estimates. In both cases, no evidence was found to indicate that improvements in the data were possible by including these effects.

Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data

Carbon dioxide (CO2) is the most important greenhouse gas (GHG) in the atmosphere and is the greatest contributor to global warming. CO2 concentration data are usually obtained from ground observation stations or from a small number of satellites. Because of the limited number of observations and the short time series of satellite data, it is difficult to monitor CO2 concentrations on regional or global scales for a long time. The use of the remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) or Moderate Resolution Imaging Spectroradiometer (MODIS) data can overcome these problems, particularly in areas with low densities of CO2 concentration watch stations. A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS (which we have named the TVP model) was developed in the current study to assess CO2 concentrations on a global scale. We assumed that CO2 concentration from the Thermal And Near infrared Sensor for carbon Observation (TANSO) aboard the Greenhouse gases Observing SATellite (GOSAT) are the true values and we used these values to check the TVP model accuracy. The results indicate that the accuracy of the TVP model is different in different continents: the greatest Pearson’s correlation coefficient (R2) was 0.75 in Eurasia (RMSE = 1.16) and South America (RMSE = 1.17); the lowest R2 was 0.57 in Australia (RMSE = 0.73). Compared with the TANSO-observed CO2 concentration (XCO2), we found that the accuracy throughout the World is between −2.56∼3.14 ppm. Potential sources of TVP model uncertainties were also analyzed and identified.

Algorithm for daytime radiation fog detection based on MODIS/TERRA data over land

We present an algorithm for daytime radiation fog detection (ADRFD) to characterize the fog distribution over land areas through two main steps: (1) detection of areas with clouds and radiation fog based on edge pixels and (2) separation of radiation fog from clouds based on object properties. The algorithm is tested in southeast China over an area of 2,640,000 km2 (longitude extent: 105°E to 122°E, latitude extent: 23°N to 40°N), with 1 km resolution moderate resolution imaging spectroradiometer/TERRA images, digital elevation model of China (grid size is 1×1 km2 and accuracy of elevation is 25 m), and the fog detection accuracies are evaluated using the observation results from 3590 ground observation stations. Results show that ADRFD can detect areas with clouds and radiation fog and effectively differentiate radiation fog from clouds based on object properties with a Kappa coefficient of 0.89 and critical success index at 0.87. It is concluded that ADRFD is a promising approach for daytime radiation fog detection over large land surfaces based on the condition that fog objects do not intersect with cloud objects or are not located under cloud objects. Extensions of this current study will be on improving the parameters determination method of ADRFD. ADRFD can be used to detect other types of fog in different regions and different seasons theoretically, but it should be tested further. In addition, more improvements are needed to allow for the detection of smaller areas of fog and fog regions intersecting with or under clouds.

Non-parametric trend analysis of the aridity index for three large arid and semi-arid basins in Iran

Currently, an important scientific challenge that researchers are facing is to gain a better understanding of climate change at the regional scale, which can be especially challenging in an area with low and highly variable precipitation amounts such as Iran. Trend analysis of the medium-term change using ground station observations of meteorological variables can enhance our knowledge of the dominant processes in an area and contribute to the analysis of future climate projections. Generally, studies focus on the long-term variability of temperature and precipitation and to a lesser extent on other important parameters such as moisture indices. In this study the recent 50-year trends (1955–2005) of precipitation (P), potential evapotranspiration (PET), and aridity index (AI) in monthly time scale were studied over 14 synoptic stations in three large Iran basins using the Mann–Kendall non-parametric test. Additionally, an analysis of the monthly, seasonal and annual trend of each parameter was performed. Results showed no significant trends in the monthly time series. However, PET showed significant, mostly decreasing trends, for the seasonal values, which resulted in a significant negative trend in annual PET at five stations. Significant negative trends in seasonal P values were only found at a number of stations in spring and summer and no station showed significant negative trends in annual P. Due to the varied positive and negative trends in annual P and to a lesser extent PET, almost as many stations with negative as positive trends in annual AI were found, indicating that both drying and wetting trends occurred in Iran. Overall, the northern part of the study area showed an increasing trend in annual AI which meant that the region became wetter, while the south showed decreasing trends in AI.