2012 AGU Chapman Conference on Remote Sensing of the ...

hosting facility for in situ soil moisture observations.Available in situ soil moisture measurements from networksover the whole globe are collected, harmonized and stored inthe data base after an advanced flagging scheme is appliedto indicate the quality of the measurements. This databecomes accessible for users through a web interface anddownloaded files are provided in various file formats inaccordance with international data and metadata standards.Currently, data from 25 networks in total covering morethan 700 stations in Europe, North America, Australia, Asiaand Africa is hosted by the ISMN, including historical andoperational datasets with near-real time availablemeasurements. Apart from soil moisture measurements indifferent depths, also meteorological observations, e.g. soiltemperature, air temperature and precipitation, andimportant metadata are stored in the database. As the ISMNis growing continuously a fully automated process chainincluding harmonization and quality control for thecollected data has been developed. Incoming data isautomatically converted into volumetric soil moisture unitsand harmonized in terms of temporal scale. The quality of insitu soil moisture measurements is crucial for the validationof satellite- and model-based soil moisture retrievals.Therefore quality flags are added to each measurement aftera check for plausibility and geophysical limits. Recently,novel quality indicators were defined to detect for examplespurious spikes and jumps in the measurement time series.In addition, new methods for the characterization of thequality of single stations and networks were introduced.With the improved quality control system and continuouslygrowing data content the ISMN will become an increasinglyimportant source for evaluating satellite-based soil moistureproducts and land surface models. The presentation will givea general overview of the ISMN and its recent updates, anddiscuss the methods and potential impact of the new qualitycharacterization system.http://www.ipf.tuwien.ac.at/insitu/Figure. Overview of the locations of soil moisture stations indicatedby pins.Xi, BaikeAN EVALUATION AND INTERCOMPARISON OFCLOUD FRACTION AND RADIATIVE FLUXES INRECENT ATMOSPHERIC REANALYSES OVERARCTIC CYCLE BY USING SATELLITEOBSERVATIONSXi, Baike 1 ; Dong, Xiquan 1 ; Zib, Behn 11. University of North Dakota, Grand Forks, ND, USAWith continual advancements in data assimilationsystems, new observing systems, and improvements in modelparameterizations, several new atmospheric reanalysisdatasets have recently become available. This study is aimedin providing insight into the advantages and disadvantagesof several recently available and widely used atmosphericreanalysis datasets over the Arctic with respect to cloudfraction and TOA radiative fluxes. Reanalyzed cloudfractions (CFs) and TOA radiative fluxes in several of theselatest reanalyses are evaluated and compared to CERES-CRSsatellite-derived radiation products over the entire Arctic.The five reanalyses being evaluated in this study are (i)NASA’s Modern-Era Retrospective analysis for Research andApplications (MERRA), (ii) NCEP’s Climate Forecast SystemReanalysis (CFSR), (iii) NOAA’s Twentieth CenturyReanalysis Project (20CR), (iv) ECMWF’s Reanalysis Interim(ERA-I), and (v) NCEP-DOE’s Reanalysis II (R2). Thesimulated monthly biases in TOA radiation fluxes wereexamined over the entire Arctic region [70o-90o N] ascompared with CERES-CRS radiation products. In the TOAevaluation, MERRA had the lowest annual mean biases inboth reflected SW and outgoing LW fluxes at TOA over theentire Arctic region (+1.0 Wm-2 and +0.2 Wm-2,respectively). However, from a spatial distribution analysis ofthe biases it is frequently seen where large positive biases andlarge negative biases canceled out resulting in small netbiases across the region. Therefore, absolute biases weredetermined for each season and CFSR was shown to have thelowest mean absolute bias for both TOA SW and LWupwelling fluxes. R2 contained the largest positive bias inTOA SWup flux of +10.3 Wm-2 for the annual average withsummertime biases as large as +26 Wm-2. On the otherhand, 20CR was the only reanalysis to have an annual meannegative bias (-6.0 Wm-2) in TOA SWup flux over the Arcticwith biases as large as -14.3 Wm-2 during springtime. Thedifferences between satellite and reanalyses TOA LWupfluxes were much less than the SWup fluxes ranging from -1.2 Wm-2 (20CR) to +1.8 Wm-2 (ERA-I) on the annualaverage. Lastly, Arctic-wide CFs were examined in each of thereanalyses along with CERES-MODIS-derived cloudamounts. It was determined that the reanalyses have adifficult time representing the observed seasonal variation ofclouds over the Arctic, especially during the winter seasons.These errors/biases in CFs in turn have significantimplications on TOA upwelling radiation fluxes.154