简介：Foghasrecentlybecomeafrequenthigh-impactweatherphenomenonalongthecoastalregionsofNorthChina.Accuratefogforecastingremainschallengingduetolimitedunderstandingofthepredictabilityandmechanismoffogformationassociatedwithsynoptic-scalecirculation.Onefrequentsynopticpatternoffogformationinthisareaisassociatedwithcoldfrontpassage(cold-frontsynopticpattern,CFSP).ThispaperexploredthepredictabilityofatypicalCFSPfogeventfromtheperspectiveofanalyzingkeycharacteristicsofsynoptic-scalecirculationdeterminingfogforecastingperformanceandthepossiblemechanism.TheeventwasensembleforecastedwiththeWeatherResearchandForecastingmodel.Twogroupsofensemblememberswithgoodandbadforecastingperformancewereselectedandcomposited.Resultsshowedthatthepredictabilityofthiscasewaslargelydeterminedbythesimulatedstrengthsofthecold-frontcirculation(i.e.,troughandridgeandtheassociatedsurfacehigh).Thebad-performingmemberstendedtohaveaweakerridgebehindastrongertrough,andassociatedhigherpressureoverlandandaweakersurfacehighoverthesea,leadingtoanadverseimpactonstrengthanddirectionofsteeringflowsthatinhibitwarmmoistadvectionandenhancecolddryadvectiontransportedtothefocusregion.Associatedwiththiscolddryadvection,adversesynopticconditionsofstratificationandmoistureforfogformationwereproduced,consequentlycausingfailureoffogforecastinginthefocusregion.Thisstudyhighlightstheimportanceofaccuratesynoptic-scaleinformationforimprovedCFSPfogforecasting,andenhancesunderstandingoffogpredictabilityfromperspectiveofsynoptic-scalecirculation.

简介：Thequantitativeinterpretationofgravityanomaliesduetoshallowstructuresneedsseparationbetweenlongwavelengthanomalies(regionalanomalies)andshortwavelengthanomalies(residualanomalies).Theregional-residualfieldseparationcanbecarriedoutusingthepolynomialmethod.Inthiscase,theso-calledregionalfieldofordernistreatedasapolynomialofdegreen.Thepresentstudyshowsthatthedegreenmustvarybetweenasmallestvaluenminandamaximumvaluenmax.Thisarticlepresentsamethodtoprocessgravitydatathatallowsdeterminationofnminandnmaxforagivenstudyarea.WeapplythemethodtogravitydataoftheSouth-WestCameroonzone.Inthischosenstudyarea,wefindthatregionalanomalymapsofordersrangingfrom1to9andresidualanomalymapsofordersrangingfrom1to8canbeusedforsuitableinterpretation.Theanalysesshowthatonemayneedresidualanomalymapsofseveralorderstoperformsatisfactoryquantitativeinterpretationofthedifferentintrusivebodiesfoundinagivenarea.

简介：Earthquakeengineershavemadealotofeffortstoderiveacomprehensivesetofclosedformexpressionsforperformanceevaluationofframes,whicharealreadypresentedinguidelinessuchasSAC/FEMA.Theseanalyticalexpressionshavebeendevelopedtoestimatetheannualprobabilityofexceedingalimitstate.Intheprocessofsuchseismicassessments,someessentialassumptionsareadoptedtosimplifytheprocess.Oneofthesefundamentalassumptionsdeclaresthatdriftdemandatanyseismicintensitylevelfollowsalognormaldistributionarounditsmedian.Toinvestigatethevalidityofthisassumption,thispaperdescribesacasestudyofthetypesoferrorsthatcouldbeproducedbyusingthesamplemedianasthecentraltendency.BasedontheMaximumLikelihoodEstimationmethodaswellasotherstatisticalevidence,thispaperproposestheuseofthesamplegeometricmeaninsteadofthesamplemedianforthecentraltendency.Further,theresultsofseismicreliabilityevaluationsof4sampleframesarecomparedbasedonutilizingboththegeometricmeanandthesamplemedian.Inthisprocess,bothfirstandsecondorderpowerlawfitsofthehazardcurveareimplementedtocomparetheeffectsofhazardestimationandtheselectionofthecentraltendencyonthefinalresults.Itisobservedintheapplicationexamplethatthesamplegeometricmeancouldleadtomoreaccurateresults.

简介：MostofthestudiesonArtificialNeuralNetwork(ANN)modelsremainrestrictedtosmallerriversandcatchments.Inthispaper,anattempthasbeenmadetocorrelatevariabilityofsedimentloadswithrainfallandrunoffthroughtheapplicationoftheBackPropagationNeuralNetwork(BPNN)algorithmforalargetropicalriver.ThealgorithmandsimulationaredonethroughMATLABenvironment.Themethodologycomprisedofacollectionofdataonrainfall,waterdischarge,andsedimentdischargefortheNarmadaRiveratvariouslocations(alongwithtimevariables)andapplicationtodevelopathreelayerBPNNmodelforthepredictionofsedimentdischarges.Fortrainingandvalidationpurposesasetof549datapointsforthemonsoon(16June-15November)periodofthreeconsecutiveyears(1996–1998)wasused.Fortestingpurposes,theBPNNmodelwasfurthertrainedusingasetof732datapointsofmonsoonseasonoffouryears(2006–07to2009–10)atninestations.Themodelwastestedbypredictingdailysedimentloadforthemonsoonseasonoftheyear2010–11.ToevaluatetheperformanceoftheBPNNmodel,errorswerecalculatedbycomparingtheactualandpredictedloads.Thevalidationandtestingresultsobtainedatalltheselocationsaretabulatedanddiscussed.Resultsobtainedfromthemodelapplicationarerobustandencouragingnotonlyforthesub-basinsbutalsofortheentirebasin.Theseresultssuggestthattheproposedmodeliscapableofpredictingthedailysedimentloadevenatdownstreamlocations,whichshownonlinearityinthetransportationprocess.Overall,theproposedmodelwithfurthertrainingmightbeusefulinthepredictionofsedimentdischargesforlargeriverbasins.