Zhibin Sun

Evaluation of CALIPSO aerosol optical depth using AERONET and MODIS data over China

Aerosol optical depth (AOD) data from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were inter-compared and validated against ground-based measurements from Aerosol Robotic Network (AERONET) as well as Moderate Resolution Imaging Spectroradiometer (MODIS) over China during June 2006 to December 2012. We have compared the AOD between CALIOP and AERONET site by site using quality control flags to screen the AOD data. In general, CALIOP AOD is lower than AERONET due to cloud effect detected algorithm and retrieval uncertanty. Better agreement is apparent for these sites: XiangHe, Beijing, Xinglong, and SACOL. Low correlations were observed between CALIPSO and ground-based sunphotometer data in in south or east China. Comparison results show that the overall spatio-temporal distribution of CALIPSO AOD and MODIS AOD are basically consistent. As for the spatial distribution, both of the data show several high-value regions and low-value regions in China. CALIPSO is systematically lower than MODIS over China, especially over high AOD value regions for all seasons. As for the temporal variation, both data show a significant seasonal variation: AOD is largest in spring, then less in summer, and smallest in winter and autumn. Statistical frequency analysis show that CALIPSO AOD and MODIS AOD was separated at the cut-off points around 0.2 and 0.8, the frequency distribution curves were almost the same with AOD between 0.2 and 0.8, while AOD was smaller than 0.4, CALIPSO AOD gathered at the low-value region (0-0.2) and the frequency of MODIS AOD was higher than CALIPSO AOD with AOD greater than 0.8. CALIOP AOD values show good correlation with MODIS AOD for all time scales, particularly for yearly AOD with higher correlation coefficient of 0.691. Seasonal scatterplot comparisons suggest the highest correlation coefficient of 0.749 in autumn, followed by winter of 0.665, summer of 0.566, and spring of 0.442. Evaluation of CALIOP AOD retrievals provides prospect application for CALIPSO data.

Effects of forest regeneration practices on the flux of soil CO 2 after clear-cutting in subtropical China

Reforestation after clear-cutting is used to facilitate rapid establishment of new stands. However, reforestation may cause additional soil disturbance by affecting soil temperature and moisture, thus potentially influencing soil respiration. Our aim was to compare the effects of different reforestation methods on soil CO2 flux after clear-cutting in a Chinese fir plantation in subtropical China: uncut (UC), clear-cut followed by coppicing regeneration without soil preparation (CC), clear-cut followed by coppicing regeneration and reforestation with soil preparation, tending in pits and replanting (CCRP), and clear-cut followed by coppicing regeneration and reforestation with overall soil preparation, tending and replanting (CCRO). Clear-cutting significantly increased the mean soil temperature and decreased the mean soil moisture. Compared to UC, CO2 fluxes were 19.19, 37.49 and 55.93 mg m-2 h-1 higher in CC, CCRP and CCRO, respectively (P < 0.05). Differences in CO2 fluxes were mainly attributed to changes in soil temperature, litter mass and the mixing of organic matter with mineral soil. The results suggest that, when compared to coppicing regeneration, reforestation practices result in additional CO2 released, and that regarding the CO2 emissions, soil preparation and tending in pits is a better choice than overall soil preparation and tending.

Quality assurance of the UV irradiances of the UV-B Monitoring and Research Program: the Mauna Loa test case

The USDA UV-B Monitoring and Research Program (UVMRP) is an ongoing effort aiming to establish a valuable, longstanding database of ground-based ultraviolet (UV) solar radiation measurements over the US. Furthermore, the program aims to achieve a better understanding of UV variations through time, and develop a UV climatology for the Northern American section. By providing high quality radiometric measurements of UV solar radiation, UVMRP is also focusing on advancing science for agricultural, forest, and range systems in order to mitigate climate impacts. Within these foci, the goal of the present study is to investigate, analyze, and validate the accuracy of the measurements of the UV multi-filter rotating shadowband radiometer (UV-MFRSR) and Yankee (YES) UVB-1 sensor at the high altitude, pristine site at Mauna Loa, Hawaii. The response-weighted irradiances at 7 UV channels of the UV-MFRSR along with the erythemal dose rates from the UVB-1 radiometer are discussed, and evaluated for the period 2006-2015. Uncertainties during the calibration procedures are also analyzed, while collocated groundbased measurements from a Brewer spectrophotometer along with model simulations are used as a baseline for the validation of the data. Besides this quantitative research, the limitations and merits of the existing UVMRP methods are considered and further improvements are introduced.

Combined UV irradiance from TOMS-OMI satellite and UVMRP ground measurements across the continental U.S.

Surface ultraviolet (UV) observations can be obtained from satellite or ground observations. This study uses data fusion to combine the advantages from both sources of observations, aiming at achieving a better estimate of surface UV. In this study, ensemble methods were used to estimate the covariances, which are the most important components in data fusion. The combined UV observations not only have the same coverage as satellite data, but also improve their regional accuracy around the ground observatories.

Correction and prediction of ultraviolet (UV-MFRSR) radiation value based on GARCH model

The reliability of the measurement of ultraviolet radiation has always been a hot spot of research. The observation of ultraviolet radiation is not only affected by the solar elevation angle, aerosol thickness, ozone, dioxide, there is also a great connection with the systematic error of the measuring instrument. In fact, in the ultraviolet radiation observation, due to the lack of routine maintenance and periodic calibration, the radiation meter will obviously decline after a period of time, and the longer the use time, the more obvious the attenuation. Therefore, in order to obtained the consistent time series of the stable observational values, some reasonable methods must be adopted to correct the measured values. The data source of this research was part of the UV-MFRSR type ultraviolet radiometer observations from 2003 to 2010. These data were obtained by these daily time series calibration method. In theory, these time series points represent the response time of the instrument, and they should be stable for several months or even years. However, the performance of the in-situ calibration method was influenced by the aerosol / ozone loading mode in practice. The purpose of this study was to get a smooth observation curve by eliminating some observational anomalies. In addition, the actual data in the observation process, some date data is missing, so the reasonable prediction model is used to estimate the value of these data. In this paper, the ARIMA and GARCH models were used to predict the missing data and compared between the predicted value and the true value, it is found that the fitting degree of the predicted value and the true value based on the AR-GARCH model is higher.

Ensemble learning of satellite remote sensing images via integrating deep and fast learning algorithms for water quality monitoring (Conference Presentation)

Previous remote sensing studies of intelligent feature extraction led to the successful image fusion, merging, and cloudy pixel reconstruction destined for the spatiotemporal change detection. Based on fused satellite images with better spatial and temporal resolution, this study explores a thorough comparative analysis in terms of feature extraction capability of deep learning, regular learning, fast learning, and ensemble learning relative to some traditional feature extraction algorithms (2-band and linear regression models). In specific, this study aims to evaluate the systematic influences of fast and deep learning models with potential to create a new ensemble learning tool for better feature extraction based on fused remote sensing images. In ensemble learning step, the whole ground-truth dataset is fed into the selected ensemble learning algorithm (i.e., a classifier fusion algorithm) with the aid of singular value decomposition to create an integrative tool. Practical implementation was assessed by a case study of water quality monitoring over dry and wet seasons in Lake Nicaragua, Central America. Both deep and fast learning algorithms outperform the regular learning algorithm with a single layer forward network and ensemble learning may take advantage of merits of deep, fast, and regular learning algorithms. Final water quality assessment was generated based on the integrative algorithm of the two with bio-optical models for eutrophication assessment in Lake Nicaragua. Although deep learning has better results in validation and the ensemble learning model aggregates different types of strength from all models based on all limited ground-truth samples.

Assimilation of atmospheric infrared sounder radiances with WRF-GSI for improving typhoon forecast

The Atmospheric Infrared Sounder (AIRS) can provide the profile information on atmospheric temperature and humidity in high vertical resolution. The assimilation of its radiances has been proven to improve the Numerical Weather Prediction (NWP) in global models. In this study, regional assimilation of AIRS radiances was carried out in a community assimilation system, using Gridpoint Statistical Interpolation (GSI) coupled with the Weather Research and Forecasting (WRF) model. The AIRS channel selection, quality control, and radiances bias correction were examined and illustrated for optimized assimilation. The bias correction scheme in the regional model showed that corrections on most of the channels produce satisfactory results except for several land surface channels. The assimilation and forecast experiments were carried out for three typhoon cases (Saola, Damrey, and Haikui in 2012) with and without including AIRS radiances. Results show that the assimilation of AIRS radiances into the WRF/GSI model improves both the typhoon track and intensity in a 72-hour forecast.

Developing a prototype satellite-based cyber-physical system for smart wastewater treatment

Frequent adjustment of operating strategies in a wastewater treatment plant as a situational response to water quality in the water body for effluent disposal has been facing a grand opportunity. This opportunity is emanated from transitioning the sporadic water quality monitoring in the water body for effluent disposal to the satellite-based situation-awareness, self-adaptive and fast response system. To achieve this goal, the cyber-physical system (CPS) is developed in this study to respond to the needs of smart wastewater infrastructure management. This prototype CPS is able to gather the massive volumes of water quality information via advanced remote sensing technologies to timely detect water pollution, exchange information through cyber interfaces, provide early-warning awareness with the aid of different feature extraction models, and support actionable intelligence for tuning the effluent disposal and recycling strategies for wastewater treatment. Integrated feature extraction techniques using extreme learning machine algorithms within the CPS architecture is emphasized in this study for smart wastewater infrastructure management through the interactions between the treatment facilities and satellites.

Total ozone column retrieval from UV-MFRSR irradiance measurements: evaluation at Mauna Loa station

The USDA UV-B Monitoring and Research Program (UVMRP) comprises of 36 climatological sites along with 4 long-duration research sites, in 27 states, one Canadian province, and the south island of New Zealand. Each station is equipped with an Ultraviolet multi-filter rotating shadowband radiometer (UV-MFRSR) which can provide response-weighted irradiances at 7 wavelengths (300, 305.5, 311.4, 317.6, 325.4, and 368 nm) with a nominal full width at half maximun of 2 nm. These UV irradiance data from the long term monitoring station at Mauna Loa, Hawaii, are used as input to a retrieval algorithm in order to derive high time frequency total ozone columns. The sensitivity of the algorithm to the different wavelength inputs is tested and the uncertainty of the retrievals is assessed based on error propagation methods. For the validation of the method, collocated hourly ozone data from the Dobson Network of the Global Monitoring Division (GMD) of the Earth System Radiation Laboratory (ESRL) under the jurisdiction of the US National Oceanic & Atmospheric Administration (NOAA) for the period 2010-2015 were used.

Trends of tropospheric NO2 over Yangtze River Delta region and the possible linkage to rapid urbanization

Over the past decade, China has experienced a rapid increase in urbanization. The urban built-up areas (population) of Shanghai increased by 16.1% (22.9%) from 2006 to 2015. This study aims to analyze the variations of tropospheric NO2 over Yangtze River Delta region and the impacts of rapid urbanization during 2006-2015. The results indicate that tropospheric NO2 vertical column density (VCD) of all cities in the study area showed an increasing trend during 2006-2011 whereas a decreasing trend during 2011-2015. Most cities showed a lower tropospheric NO2 VCD value in 2015 compared to that in 2006, except for Changzhou and Nantong. Shanghai and Ningbo are two hotspots where the tropospheric NO2 VCD decreased most significantly, at a rate of 22% and 19%, respectively. This effect could be ascribed to the implementation of harsh emission control policies therein. Similar seasonal variability was observed over all cities, with larger values observed in the summer and smaller values shown in the winter. Further investigations show that the observed increasing trend of tropospheric NO2 during 2006-2011 could be largely explained by rapid urbanization linked to car ownership, GDP, power consumption, population and total industrial output. Such effect was not prominent after 2011, mainly due to the implementation of emission control strategies.