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

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snow transition of individual storms has a strong influenceon the difference between snowdepth in the open and underforest canopy.Kosuth, PascalA method for river discharge estimate from satelliteobservation alone, without any in situmeasurementKosuth, Pascal 1 ; Négrel, Jean 1 ; Strauss, Olivier 2 ; Baume, Jean-Pierre 3 ; Faure, Jean-Baptiste 4 ; Litrico, Xavier 3, 5 ; Malaterre,Pierre-Olivier 31. TETIS, IRSTEA, Montpellier, France2. LIRMM, Université Montpellier 2, Montpellier, France3. GEAU, IRSTEA, Montpellier, France4. Hydrologie-Hydraulique, IRSTEA, Lyon, France5. LyRE, R&D Center, Lyonnaise des Eaux, Bordeaux,FranceIs it possible to estimate river discharge from satellitemeasurement of river surface variables (width L, water levelZ, surface slope Is, surface velocity Vs) without any in situmeasurement ? Current or planned satellite observationtechniques in the hydrology-hydraulic domain are limited tothe measurement of surface variables such as river width L(optical and SAR imagery), water level Z (radar and Lidaraltimetry), river surface longitudinal slope Is (cross-trackinterferometry) and surface velocity Vs (along-trackinterferometry). On the opposite, river bottom parameterssuch as river bottom height (Zb), river bottom longitudinalslope (Ib), Manning coefficient (n), vertical velocity profilecoefficient ( the ratio between mean water velocity andsurface velocity), that are key data for discharge estimate andmodeling, cannot be measured by satellite. They require insitu measurement (Zb, Ib, ) and model calibration (n). Wepresent here a method to derive river bottom parametersfrom satellite measured river surface variables in the absenceof any in situ measurement. This will then allow us toestimate river discharge for any set of surface variables. Themethod relies on a set of hydraulic hypothesis (for instancerectangular section, constant Manning coefficient n andconstant velocity profile coefficient ). It consists of solvingan equality constraint between two formulations linking theriver discharge Q to the surface variables and the unknownparameters (Q1 and Q2 obtained from the massconservation equation and the energy conservationequation): Q1=L..Vs.h Q2=L.h 5/3 .Is 1/2 .[n 2 +g -1 .h 1/3 .(Is-Ib)]where h=Z-Zb Given a river section, estimating the riverbottom parameters (, Zb, Ib, K) is achieved by using a set ofsurface variables (L, Z, Is, Vs) i=1 to N, measured on this sectionat various times ti throughout the hydrological cycle, and bydetermining the set of river bottom parameters thatminimizes a deviation criteria between (Q1)i and (Q2)i. Thisminimization is achieved by iterative or direct methods,depending on the type of criteria and on additionalhypothesis (ex. a uniform regime hypothesis leads to ananalytical solution). Several simulations have shown theefficiency of theses methods on exact simulated data set (i.e.for which (Q1)i=(Q2)i). We have assessed the robustness ofthese methods to measurement noise on river surfacevariables. We proposed several modifications to increase thisrobustness and the ability to provide acceptable riverdischarge estimates (error
in the flow over the floodplain arising from the scouring atthe O’Bryan Ridge in the Floodway. The presentation willshow the initial results from the study of these data andhighlight the value of using high resolution remote-sensingdata for the study of flood impacts.kumar A, JayaRole of El Niño in Modulating the Period ofPrecipitation Variability of Asian Summer MonsoonUsing Satellite Observationskumar A, Jaya 11. EOAS-Meteorology, Florida State University, Tallahassee,FL, USAActive-Break (AB) Cycle of the Asian Summer Monsoon(ASM) is one of the crucial factors in deciding the amount ofprecipitation received during ASM. During the AB Cycle, theatmosphere and the underlying ocean closely interact in thetime scale of about 40 days. Net heat flux at the oceansurface which is controlled by convective clouds (radiativeheating) and low-level winds (evaporative cooling) play animportant role in this interaction. This study addresseslength of monsoon rainfall variability between dry and wetperiods (AB cycle) especially in El Niño developing phaseusing remotely sensed wind data from QuikSCATscatterometer, Microwave SST image from Tropical RainfallMeasurement Microwave Image (TMI) and available Argodata of MLD. These data set aids in defining the moisturesupply to the monsoon environment especially from theoceanic areas. North Bay of Bengal and the Oceans to its eastand west have large amplitude Sea Surface Temperature(SST)variation in the AB cycle in response to the net heat fluxvariations as the Mixed layer Depth (MLD) there is shallow(typically 20 meters) during the months June to September,forced by the cyclonic wind stress curl north of the seasonalmonsoon westerlies. In an El Niño situation, the low levelmonsoon winds extend eastward beyond the date linecreating an area of shallow MLD between longitudes 120°Eand 160°W. This area then warms rapidly and causes thecycle of convection to shift there from North Indian Oceanbefore moving to the Equatorial Indian Ocean. This causes alengthening of the AB cycle from one month in a La Niña to2 months in an El Niño. Hence, propose that the long ABcycle in El Niño years (typically of 50-60 day period) is themain cause of the El Niño produced droughts in the Indianmonsoon. Key words: El Niño, Mixed layer Depth, Active-Break CycleKuo, Kwo-SenPrecipitation Characteristics of Tornado-ProducingMesoscale Convective Systems in the ContinentalUnited StatesKuo, Kwo-Sen 1 ; Hong, Yang 2 ; Clune, Thomas L. 31. GEST/Caelum, NASA Goddard SFC, Greenbelt, MD,USA2. School of Meteorology, University of Oklahoma,Norman, OK, USA3. Software Systems Support Office, NASA Goddard SpaceFlight, Greenbelt, MD, USAIn this study we report precipitation statistics, relevantto the terrestrial water cycle, obtained for tornado-producingmesoscale convective systems (MCSs) in the continentalUnited States (CONUS) . In a technology-demonstrationeffort, we have synergistically combined several datasets andbuilt an innovative system to automatically track tornadoproducingMCSs in CONUS and, at the same time, collectand record physical and morphological parameters for eachMCS. We perform the tracking in two datasets with highspatial (two horizontal dimensions) and temporalresolutions: the 1-km 5-minute Q2 surface precipitationdataset and the 4-km 30-minute GOES 10.8-µm brightnesstemperature dataset. First, we use a threshold to delineatepotential MCS regions in a temporal snapshot in each of thetwo datasets. We then apply segmentation and subsequentlyneighbor-enclosed area tracking (NEAT) method, bothforward and backward in time, to identify distinct tornadoproducingMCSs from their inception to eventualdissipation. Next, we use historical National Weather Service(NWS) tornado watches to locate the general area (countyand/or state) of a tornado and its associated MCS. Thisinformation is further complemented with the coarseresolution track data in the database of the Tornado HistoryProject (http://www.tornadohistoryproject.com/). Followingour automated tracking, an episode of a tornado-producingMCS becomes a three-dimensional entity, two in space andone in time. We can thus automatically obtain, from the Q2surface precipitation dataset, various precipitation andmorphological characteristics for each episode, such as thenumber of tornados produced by a system, the evolution ofits area coverage, its maximum rain rate, the time-integratedprecipitation volume of a system’s lifetime, etc. Finally,statistics are gathered for all the tornado-producing MCSsfor the years 2008(partial)-2011. These are correlated andcompared to those, i.e. per-system characteristics as well asannual and all-system statistics, obtained form GOES 10.8-µm brightness temperature datasets.85
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SCIENTIFIC PROGRAMSUNDAY, 19 FEBRUA
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1600h - 1900hMM-1MM-2MM-3MM-4MM-5MM
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GM-7GM-8GM-9GM-10GM-11GM-12GM-13160
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EM-25EM-26EM-27EM-28EM-29EM-301600h
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SMM-8SMM-9SMM-10SMM-11SMM-12SMM-13S
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SCM-24SCM-251600h - 1900hPM-1PM-2PM
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1030h - 1200h1030h - 1200h1030h - 1
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ET-13ET-14ET-15ET-16ET-17ET-18ET-19
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SWT-19SWT-201600h - 1900hSMT-1SMT-2
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SCT-14SCT-15SCT-16SCT-17SCT-18SCT-1
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MT-2MT-3MT-4MT-5MT-6MT-7MT-8MT-9MT-
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esilience to hydrological hazards a
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Alfieri, Joseph G.The Factors Influ
Page 33 and 34: Montana and Oregon. Other applicati
Page 35 and 36: accuracy of snow derivation from si
Page 37 and 38: seasonal trends, and integrate clou
Page 40 and 41: a single mission, the phrase “nea
Page 42 and 43: climate and land surface unaccounte
Page 44 and 45: esolution lidar-derived DEM was com
Page 46 and 47: further verified that even for conv
Page 48 and 49: underway and its utility can be ass
Page 50 and 51: Courault, DominiqueAssessment of mo
Page 52 and 53: used three Landsat-5 TM images (05/
Page 55: storage change solutions in the for
Page 59 and 60: Famiglietti, James S.Getting Real A
Page 61 and 62: can be thought of as operating in t
Page 63 and 64: mission and will address the follow
Page 65 and 66: Gan, Thian Y.Soil Moisture Retrieva
Page 67 and 68: match the two sets of estimates. Th
Page 69 and 70: producing CGF snow cover products.
Page 71 and 72: performance of the AWRA-L model for
Page 73 and 74: oth local and regional hydrology. T
Page 75 and 76: Euphorbia heterandena, and Echinops
Page 77 and 78: the effectiveness of this calibrati
Page 79 and 80: presents challenges to the validati
Page 81 and 82: long period time (1976-2010) was co
Page 83: has more improved resolution ( ) to
Page 87 and 88: fraction of the fresh water resourc
Page 89 and 90: to determine the source of the wate
Page 91 and 92: hydrologists, was initially assigne
Page 93 and 94: Sturm et al. (1995) introduced a se
Page 95 and 96: calendar day are then truncated and
Page 97 and 98: climate associated with hydrologica
Page 99 and 100: California Institute of Technology
Page 101 and 102: egion in Northern California that i
Page 103 and 104: Moller, DelwynTopographic Mapping o
Page 105 and 106: obtained from the Fifth Microwave W
Page 107 and 108: a constraint that is observed spati
Page 109 and 110: groundwater degradation, seawater i
Page 111 and 112: approach to estimate soil water con
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
Page 119 and 120: interferometric synthetic aperture
Page 121 and 122: elevant satellite missions, such as
Page 123 and 124: support decision-making related to
Page 125 and 126: oth the quantification of human wat
Page 127 and 128: parameter inversion of the time inv
Page 129 and 130: ground-based observational forcing
Page 131 and 132: Selkowitz, DavidExploring Landsat-d
Page 133 and 134: Shahroudi, NargesMicrowave Emissivi
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well as subsurface hydrological con
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Sturm, MatthewRemote Sensing and Gr
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Sutanudjaja, Edwin H.Using space-bo
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which can be monitored as an indica
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tools and methods to address one of
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Vanderjagt, Benjamin J.How sub-pixe
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Vila, Daniel A.Satellite Rainfall R
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and landuse sustainability. In this
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e very significant as seepage occur
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Wood, Eric F.Challenges in Developi
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Xie, PingpingGauge - Satellite Merg
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Yebra, MartaRemote sensing canopy c
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used. PIHM has ability to simulate
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