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

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$A 10 year climatology of cloud fraction and vertical distribution ...$

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
Xie, PingpingGauge - Satellite Merged Analyses of LandPrecipitation: A Prototype Algorithm INVITEDXie, Pingping 1 ; Xiong, An-Yuan 21. NOAA/NCEP, Camp Springs, MD, USA2. CMA National Meteorological Information Center,Beijing, ChinaA prototype algorithm has been developed to createhigh-resolution precipitation analyses over land by merginggauge-based analysis and CMORPH satellite estimates. Atwo-step strategy is adopted to remove the bias inherent inthe CMORPH satellite precipitation estimates and tocombine the bias-corrected satellite estimates with the gaugeanalysis, respectively. First, bias correction is performed forthe CMORPH estimates by matching the cumulatedprobability density function (PDF) of the satellite data withthat of the gauge analysis using co-located data pairs over aspatial domain of 5olat/lon centering at the target grid boxand over a time period of 30-days ending at the target date.The spatial domain is expanded, wherever necessary overgauge sparse regions, to ensure the collection of sufficientnumber of gauge – satellite data pairs. The bias-correctedCMORPH precipitation estimates are then combined withthe gauge analysis through the optimal interpolation (OI)technique, in which the bias-corrected CMORPH is used asthe first guess while the gauge data is used as theobservations to modify the first guess over regions withstation coverage. Error statistics are computed for the inputgauge and satellite data to maximize the performance of thehigh-resolution merged analysis of daily precipitation. Crossvalidationtests and comparisons against independent gaugeobservations demonstrate feasibility and effectiveness of theconceptual algorithm in constructing merged precipitationanalysis with substantially removed bias and significantlyimproved pattern agreements compared to the input gaugeand satellite data. Details about the implementation strategyand global applications will be reported at the conference.Xu, BinHourly Gauge-satellite Merged PrecipitationAnalysis over ChinaXu, Bin 1 ; Yoo, Soo-Hyun 2 ; Xie, Pingping 2 ; Xiong, An-Yuan 11. CMA National Meteorological Information Centre,Beijing, China2. NOAA Climate Prediction Center, Washington, DC, USAAs part of the collaboration between ChinaMeteorological Administration (CMA) NationalMeteorological Information Centre (NMIC) and NOAAClimate Prediction Center (CPC), a new system is beingdeveloped to construct hourly precipitation analysis on a0.25olat/lon grid over China by merging informationderived from gauge observations and CMORPH satelliteprecipitation estimates. Foundation to the development ofthe gauge-satellite merging algorithm is the definition of thesystematic and random error inherent in the CMORPHsatellite precipitation estimates. In this study, we quantify155the CMORPH error structures through comparisons againsta gauge-based analysis of hourly precipitation derived fromstation reports from a dense network over China, andcombine the gauge analysis with the bias-correctedCMORPH through the optimal interpolation (OI) techniqueusing the error statistics defined in this study. First,systematic error (bias) of the CMORPH satellite estimatesare examined with co-located hourly gauge precipitationanalysis over 0.25olat/lon grid boxes with at least onereporting station. The CMORPH exhibits biases of regionalvariations showing over-estimates over eastern China, andseasonal changes with over-/under-estimates duringwarm/cold seasons. The CMORPH bias presents rangedependency.In general, the CMORPH tends toover-/under-estimate weak / strong rainfall. The bias, whenexpressed in the form of ratio between the gaugeobservations and the CMORPH satellite estimates, increaseswith the rainfall intensity but tends to saturate at a certainlevel for high rainfall. Based on the above results, a prototypealgorithm is developed to remove the CMORPH biasthrough matching the PDF of original CMORPH estimatesagainst that of the gauge analysis using data pairs co-locatedover grid boxes with at least one reporting gauge over a 30-day period ending at the target date. The spatial domain forcollecting the co-located data pairs is expanded so that atleast 5000 pairs of data are available to ensure statisticalavailability. The bias-corrected CMORPH is then comparedagainst the gauge data to quantify the remaining randomerror. The results showed that the random error in the biascorrectedCMORPH is proportional to the smoothness ofthe target precipitation fields, expressed as the standarddeviation of the CMORPH fields, and to the size of thespatial domain over which the data pairs to construct thePDF functions are collected. An empirical equation is thendefined to compute the random error in the bias-correctedCMORPH from the CMORPH spatial standard deviationand the size of the data collection domain. An algorithm isbeing developed to combine the gauge analysis with the biascorrectedCMORPH through the optimal interpolation (OI)technique using the error statistics defined in this study. Inthis process, the bias-corrected CMORPH will be used as thefirst guess, while the gauge data will be utilized asobservations to modify the first guess over regions withgauge network coverage. Detailed results will be reported atthe conference.
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esilience to hydrological hazards a
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Alfieri, Joseph G.The Factors Influ
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Montana and Oregon. Other applicati
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accuracy of snow derivation from si
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seasonal trends, and integrate clou
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a single mission, the phrase “nea
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climate and land surface unaccounte
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esolution lidar-derived DEM was com
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further verified that even for conv
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underway and its utility can be ass
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Courault, DominiqueAssessment of mo
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used three Landsat-5 TM images (05/
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storage change solutions in the for
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Famiglietti, James S.Getting Real A
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can be thought of as operating in t
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mission and will address the follow
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Gan, Thian Y.Soil Moisture Retrieva
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match the two sets of estimates. Th
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producing CGF snow cover products.
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performance of the AWRA-L model for
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oth local and regional hydrology. T
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Euphorbia heterandena, and Echinops
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the effectiveness of this calibrati
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presents challenges to the validati
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long period time (1976-2010) was co
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has more improved resolution ( ) to
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in the flow over the floodplain ari
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fraction of the fresh water resourc
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to determine the source of the wate
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hydrologists, was initially assigne
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Sturm et al. (1995) introduced a se
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calendar day are then truncated and
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climate associated with hydrologica
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California Institute of Technology
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egion in Northern California that i
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Page 113 and 114: Norouzi, HamidrezaLand Surface Char
Page 115 and 116: Painter, Thomas H.The JPL Airborne
Page 117 and 118: Pavelsky, Tamlin M.Continuous River
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Page 131 and 132: Selkowitz, DavidExploring Landsat-d
Page 133 and 134: Shahroudi, NargesMicrowave Emissivi
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Page 137 and 138: Sturm, MatthewRemote Sensing and Gr
Page 139 and 140: Sutanudjaja, Edwin H.Using space-bo
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Page 145 and 146: Vanderjagt, Benjamin J.How sub-pixe
Page 147 and 148: Vila, Daniel A.Satellite Rainfall R
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Page 151 and 152: e very significant as seepage occur
Page 153: Wood, Eric F.Challenges in Developi
Page 157 and 158: Yebra, MartaRemote sensing canopy c
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2012 AGU Chapman Conference on Remote Sensing of the ...

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