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

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