Aerosol Retrieval And Dust Detection Over Land From Himawari-8/AHI Data

Atmospheric aerosol has significant impacts on Earth's radiation balance,hydrological cycle and biogeochemical cycles.As one of the most important type of aerosol in mass and aerosol optical depth(AOD),dust also exert severe impacts on humans and environment.The new generation geostationary satellite Himawari-8 carring an Advanced Himawari Imager(AHI),which has high frequency of observation and multiple channels,makes it unique advantages to monitor atmospheric aerosol distribution and dust transport.In this dissertation,we presented an aerosol retrieval algorithm based on multiple AHI observations and optimal estimation technique(AROE).In addition,a dust detection and intensity estimation methods were presented based on the brightness temperature(BT)of thermal channels of AHI observation.The aerosol retrieval algorithm was based on a non-Lambertian forward model coupled with a surface bidirectional reflectance factor(BRF)model.Our retrieval is based on the assumption that the surface bidirectional reflective properties are invariable during a short time period,while aerosol properties change.The BRDF shape were obtained from MODIS BRDF/Albedo products and employed as prior knowledge.Using multiple AHI observations,the AOD and BRDF coefficients were jointly retrieved using an optimal estimation method.The AOD retrieval results were validated using ground-based measurements(AErosol RObotic NETwork(AERONET)sites)and were cross-compared with satellite product(MODIS Collection 6.0 AODs).A total of one year AHI data were used for this validation and comparison.The validation of the AOD with AERONET measurements shows a high correlation coefficient: R=0.88,RMSE=0.17,the liner regression function is = 0.97 * + 0.02,and approximately 69.6% AOD retrieval results within the expected error(EE)of(±0.2 * ± 0.05).In addition,Level 2 AOD product provided by Japan Meteorological Agency(JMAAOD)were also validated against with AERONET measurements.The liner regression function between JMA-AOD and AERONET-AOD is = 0.60 *+ 0.08,and the correlation coefficient is 0.82,and 69.6% AOD retrieval results within the EE.JMA-AOD shows lower accuracy than OE-AOD according to the validation against AERONET-AOD.In addition,OE-AOD were also compared with MODIS Collection 6.0 AODs,and it shows a high consistency.As there are huge areas of desert in China,which emit huge amount of mineral dust every year,causing abundant dust storms,especially during spring seasons.It is significant important to monitor dust aerosol with high frequency.In this paper,simple dust detection and intensity estimation methods using AHI data are developed.Based on the differences of thermal radiation characteristics between dust and other typical objects,brightness temperature difference(BTD)among four channels are used together for dust detection.Considering thermal radiation variation of dust particles over different land cover types,a dynamic threshold scheme for dust detection is adopted.An enhanced dust intensity index(EDII)is developed based on the reflectance of visible/near-infrared bands,BT of thermal-infrared bands,and retrieved AOD and is applied to the detected dust area.The dust detection results were compared quantitatively with dust identification results from the AERONET AOD and ?ngstr?m exponent,achieving a total dust detection accuracy of 84%.A good agreement is obtained between EDII and the visibility data from National Climatic Data Center ground measurements,with a correlation coefficient of 0.81,indicating the effectiveness of EDII in dust monitoring.