Based on eddy covariance(EC) measurements during 2016–20, the effects of sky conditions on the net ecosystem productivity(NEP) over a subtropical “floating blanket ” wetland were investigated. Sky conditions were d...Based on eddy covariance(EC) measurements during 2016–20, the effects of sky conditions on the net ecosystem productivity(NEP) over a subtropical “floating blanket ” wetland were investigated. Sky conditions were divided into overcast, cloudy, and sunny conditions. On the half-hourly timescale, the daytime NEP responded more rapidly to the changes in the total photosynthetic active radiation(PARt) under overcast and cloudy skies than that under sunny skies. The increase in the apparent quantum yield under overcast and cloudy conditions was the greatest in spring and the least in summer. Additionally, lower atmospheric vapor pressure deficit(VPD) and moderate air temperature were more conducive to enhancing the apparent quantum yield under cloudy skies. On the daily timescale, NEP and the gross primary production(GPP) were higher under cloudy or sunny conditions than those under overcast conditions across seasons. The daily NEP and GPP during the wet season peaked under cloudy skies. The daily ecosystem light use efficiency(LUE) and water use efficiency(WUE) during the wet season also changed with sky conditions and reached their maximum under overcast and cloudy skies, respectively. The diffuse photosynthetic active radiation(PAR_d) and air temperature were primarily responsible for the variation of daily NEP from half-hourly to monthly timescales, and the direct photosynthetic active radiation(PAR_b) had a secondary effect on NEP. Under sunny conditions, PAR_b and air temperature were the dominant factors controlling daily NEP. While daily NEP was mainly controlled by PAR_d under cloudy and overcast conditions.展开更多
Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interp...Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.展开更多
文摘目前还没有基于国产卫星的1 km分辨率的全天候陆表温度(LST)产品,FY-3D卫星提供了中分辨率成像仪(MERSI)Ⅱ型1 km分辨率晴空LST产品与微波成像仪(MWRI)25 km全天候LST产品,因此可结合两者优势开展全天候1 km分辨率LST的融合研究。基于地理加权回归(GWR)方法,选择海拔、FY-3D归一化植被指数和归一化建筑指数等建立GWR模型对FY-3D/MWRI 25 km LST降尺度到1 km,并与MERSI 1 km LST进行融合;同时针对MWRI轨道间隙,利用前后1天融合后的云覆盖像元1 km LST进行补值,可以得到接近全天候下的1 km LST。基于以上融合算法,选择了中国区域多个典型日期FY-3D/MERSI和MWRI LST官网产品进行了融合试验,并利用公开发布的全天候1 km LST产品(TPDC LST)对FY-3D 1 km LST融合结果进行了评估。研究结果表明,基于GWR法的LST降尺度方法,可以有效避免传统微波LST降尺度方法中存在的“斑块”效应和局地温度偏低等问题;LST融合结果有值率从融合前的22.4%~36.9%可提高到融合后69.3%~80.7%,融合结果与TPDC LST的空间决定系数为0.503~0.787,均方根误差为3.6~5.8 K,其中晴空为2.6~4.9 K,云下为4.1~6.1 K;分析还表明目前官网产品FY-3D/MERSI和MWRI LST均存在缺值较多与精度偏低等问题,显示其存在较大改进潜力,这有利于进一步改进FY-3D LST融合质量。
基金funded by the National Natural Science Foundation of China (Grant No. 91937301)the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant No. 2019QZKK0105)the National Natural Science Foundation of China (Grant Nos. 41975017, 41905010)。
文摘Based on eddy covariance(EC) measurements during 2016–20, the effects of sky conditions on the net ecosystem productivity(NEP) over a subtropical “floating blanket ” wetland were investigated. Sky conditions were divided into overcast, cloudy, and sunny conditions. On the half-hourly timescale, the daytime NEP responded more rapidly to the changes in the total photosynthetic active radiation(PARt) under overcast and cloudy skies than that under sunny skies. The increase in the apparent quantum yield under overcast and cloudy conditions was the greatest in spring and the least in summer. Additionally, lower atmospheric vapor pressure deficit(VPD) and moderate air temperature were more conducive to enhancing the apparent quantum yield under cloudy skies. On the daily timescale, NEP and the gross primary production(GPP) were higher under cloudy or sunny conditions than those under overcast conditions across seasons. The daily NEP and GPP during the wet season peaked under cloudy skies. The daily ecosystem light use efficiency(LUE) and water use efficiency(WUE) during the wet season also changed with sky conditions and reached their maximum under overcast and cloudy skies, respectively. The diffuse photosynthetic active radiation(PAR_d) and air temperature were primarily responsible for the variation of daily NEP from half-hourly to monthly timescales, and the direct photosynthetic active radiation(PAR_b) had a secondary effect on NEP. Under sunny conditions, PAR_b and air temperature were the dominant factors controlling daily NEP. While daily NEP was mainly controlled by PAR_d under cloudy and overcast conditions.
基金supported by NOAA JTTI award via Grant #NA21OAR4590165, NOAA GOESR Program funding via Grant #NA16OAR4320115provided by NOAA/Office of Oceanic and Atmospheric Research under NOAA-University of Oklahoma Cooperative Agreement #NA11OAR4320072, U.S. Department of Commercesupported by the National Oceanic and Atmospheric Administration (NOAA) of the U.S. Department of Commerce via Grant #NA18NWS4680063。
文摘Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.
