Imagine an event generating multiple innovations in medical research in one weekend. Imagine an event giving spreadsheet-limited laboratory researchers a taste of what computers can _really_ do. Imagine an event where the powers of coding and analysis are combined with powers of biology, genetics and medicine to convert dry or unobtainable data into ground-breaking medical insight. And imagine it all done in a spirit of frenzied teamwork and friendly competition. This is HealthHack, a 48 hour hackfest in which medical researchers nucleate teams of brilliant, creative people to solve the data-processing problems faced by real researchers and real clinicians. The weekend opens on Friday evening with the problem owners pitching their ideas, problem and proposed solution, to the assembled hackers, followed by an hour in which hackers can approach problem owners with their questions and seek additional details. Hackers then choose which problem they are going to work on, and the assembled teams plan their assault and commence work on their application. Apart from going home (presumably) to sleep, the event runs all weekend. Sunday 4pm is _down tools_, and the teams then present their prototypes to everyone, with prizes for first place, second place, and _spirit of HealthHack_. Below is a sampling of the teams and their creations:

DATE Friday September 19, 2014 VENUE Talks in securing space within the Unimelb/VLSCI Science Gallery. As plan B we have secured space for workshops and the talks for this date: FOR TALKS IN THE AM 207 Bouverie Street - Theatre 1 FOR WORKSHOPS IN THE PM VLSCI space 207 Bouverie Street - B118 207 Bouverie Street - B113 TARGET PARTICIPANTS All science practitioners in open data open publishing (and those participating at ISBC2014)

My goal is to: - - identify ETS from observations at many Australian sites (soundings which aren’t outflow/rain contaminated) - - apply identification methods to Era Int to see how many ETS Era Int identifies - - Use Era Int to answer the following questions: 1. Where and when (monthly, diurnally, seasonally) do ES occur in Australia? 2. What are the typical synoptic situations associated with Australian ES (monthly, diurnally, seasonally)? 3. (Time permitting) Which types of instabilities (moist gravitational, moist symmetric, inertial instabilities) are associated with Australian ES? Colman 1990 - climatology EL TS identified by: Surface reports of thunderstorms, and 00Z and 12Z sfc, 850hPa, 700 hPa, 50hPa pressure and θe, and the following criteria: 1. Report lies on the cold side of an analysed front, 2. station temp, pressure, dew point consistent with surroundings (reports in mountainous terrain are excluded by this criteria). 3. surface air in warm sector must have higher θe than air on the surface cold side. 4. surface air lifted psuedoadiabatically to 50hPa must have temperatures LESS than 850hPa lifted air temperatures lifted to 50hPa (eliminate poorly analysed surface fronts). Associated with each event were observations of 850 and 50 hPa horizontal wind shear, confluence or diffluence. Reports=1093 Events=497 events (2 rep/event).

THE FIRST ARTICLE ON THE FINAL DAY This implies that the YMCs must have gone through at least one full crossing time before their presently observed age. Using the crossing time equation, tcross=(GMr−3vir)−1/2, and fixing the crossing time to 1 Myr we can solve for t, which is only dependent on the mass.

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ABSTRACT MOJITO-30 MOnash Julian-day Index of Temperature Over 30

_Objective_ 1. Compare Bureau of Meteorology Temperature soundings to ECMWF Era Interim Temperatures, by calculating the MEAN T differences (Tdiff = Tbom - Tera), at all Era Int pressure levels 2. Compare Bureau of Meteorology Temperature soundings to ECMWF Era Interim Temperatures, by calculating the MEAN TD differences (TDdiff = TDbom - TDera), at all Era Int pressure levels 3. Calculate standard deviation of all T differences at all Era Int pressure levels 4. Calculate standard deviation of all TD differences at all Era Int pressure levels 5. identify stable layers in BoM sounding data 6. identify stable layers in Era Int data 7. Calculate percentage of BoM stable layers NOT captured by Era Int

Introduction 1. GPATS - - what it is and the network - - how gpats is detected - - strengths and limitations 2. LIS - - what it is and the network - - how lis is detected - - strengths and limitations 1. GPATS Verification with LIS - - Extract and process LIS dates and times, accounting for leap seconds - - 8 days (4 summer 4 winter), of LIS subjectively chosen for spatially large amounts of lightning activity - - gpats restricted to LIS viewbox space/time windows resulting in #X boxes total A list of determined ES events is crucial to developing an ES climatology and synoptic classification. To discriminate U.S. ES events from SS, Colman (1990a), Moore et. al. 2003, Horgan et. al. 2007 and others used National Weather Service observer reports of thunderstorms to identify thunderstorm locations. Unfortunately, Australia does not have a dense spatial and temporal surface and upper air observing network. Using thunderstorm reports at upper air sounding locations and times may result in too few ES to create an Australian ES climatology and classify synoptic environments. In fact, a search of Melbourne Airport “Thunderstorm” Present Weather Observations at 00 and 12UTC between 2008-2015 yielded only 6 reports. An effective alternative to Bureau of Meteorology observer thunderstorms reports could be the location and timing of observed lightning strokes from the Global Position and Tracking System (GPATS), as a proxy for thunderstorm locations provided the network’s stroke location accuracy is within the typical thunderstorm spatial length scale of 4 to 40km (meso-beta scale, Fujita 1981). The European ADTNET sensor network with similar density and location calculation methods to GPATS, has published location accuracies of 5-6 km (Cummins and Murphy 2009). In addition, Kumar et. al. 2013 found almost all (94%) of GPATS strokes near Darwin over two wet seasons (October 2005–April 2006 and October 2006–April 2007), were within 10km of radar-derived convective cells. These results, although pertaining to the Australian tropical wet seasons, gives confidence to GPATS stroke location accuracies being within typical thunderstorm length scales. A limitation of the GPATS ground-based network is its ability to detect all types of lightning strokes (intra-cloud strokes (IC) and cloud to ground (CG) or ground to cloud (GC) strokes constituting “total lightning”). In a comparison of GPATS stroke types to stroke types from a high-quality research lightning flash counter (CGR4) in Brisbane, Kuleshov (2012) showed GPATS grossly under reported IC strokes, with the majority of detected strokes being either CG or GC types. Given IC to CG (or IC to GC) ratios range from 2-10 in most thunderstorms (Cummins and Murphy 2009), GPATS may be under-detecting much of the total lightning spatial coverage. A spatial comparison between GPATS strokes and stroke data from reliable total lightning sensors could quantify how much spatial coverage GPATS is missing. The Lightning Image Sensor (LIS) on the TRMM polar orbiting satellite is one such total lightning sensor, considered to be very accurate in detecting total lightning (Cummins and Murphy 2009). LIS stroke data, and spatio-temporal observation window data of the TRMM satellite swath (0.5 degree,  2 second each window respectively) is readily available. The spatial coverage of GPATS restricted to the same spatio-temporal observation windows of LIS, can be compared to the spatial coverage of LIS over Australia and , if comparable, GPATS can replace observed thunderstorm reports with superior spatial and temporal coverage. Given the vast amount of daily GPATS and LIS lightning stroke data (typically 100,000 to 300,000 strokes per day), only four summer and four winter days will be analysed. Lastly a GPATS dataset covering Australian and ranging from March 2008 to December 2014 is readily available from the Bureau of Meteorology. Any ES and SS climatology using this GPATS dataset to identify thunderstorm locations will similarly range from March 2008 to December 2014.