NexGenFWS – past, present and future Gary Weymouth, Beth Ebert, Tennessee Leeuwenburg, Michael Foley, Phil Riley, Nathan Faggian, Brianna Laugher, Ben Hu, James Sofra, Tim Hume, Ying Zhao, John Bally and others The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology NexGenFWS value chain & outcomes Probabilities Observations Forecaster intervention Graphical and mobile forecasts every 6 km Post-processor – corrected guidance and uncertainties 650 automated location based 7 day forecasts Graphical Forecast Editor Derive grids – automation with support for expert intervention Local and overseas NWP Gridded downstream applications NexGenFWS history 2014 2012 2013 2010 2011 Pilot 2008 GFE is hybrid gridded / point / area system • Gridded • • • • Very flexible Naturally supports production of derived fields and downstream use of data Natural match with gridded guidance Natural support of probabilistic forecasts • Point • Site forecasts match obs well – important for credibility of Bureau service • Area • District forecasts utilise areal means, with automated 'local effects' to describe significant variations across a region. Similarly for hazards like areas of storms • Area QNH forecasts for aviation • Some complexity in graphical representation of areal forecasts • System-relative • Aspects of TC forecasts • Consistency • Some complexity in sampling and maintaining area – grid consistency in text • Combination of gridded and point forecasts works but has some complexity Handling TCs in GFE – TC Iggy: TCModule official track GFE TC grids Wind grids Marine grids de Maria Text Formatters hurricane possible storm possible Wind prob grids 34/48/64 kt gales possible North West Cape to Carnarvon Winds: Above 48 knots possible depending on movement and development of Tropical Cyclone Iggy. First cut forecast Automated routine forecast in minutes, baseline for verification Ensemble and probabilistic guidance Mean and spread information (OCF), PDFs Forecaster tuning? Ensemble members – may include ensemble means Public (probability) forecasts through GFE Bias correction, downscaling Chance of events Fields derived from ensemble members Operational Consensus Forecasts (OCF) • Weighted bias-corrected mean of several NWP models • Point and grid versions • Mainly surface level variables (T, Td, wind, precip, POP, cloud) Error in hourly T forecasts Benefit of guidance (NWP) post-processing Adds considerable value at low relative cost Bureau of Meteorology's OCF performance Novak et al. (2013) found QPF calibration gave equivalent skill gain to ~13 years of GFS development Foundations of forecast process streamlining • Focus forecaster effort • Focus developer effort • Demonstrate value Verify Optimal forecaster interventions & efficiency Alert Improve guidance & automate • Baseline for verification – ‘first cut forecast’ • Efficiency • Allow focus on highimpact value add • Focus on highimpact weather • Fewer misses in less time • Safer automation Using hi-res Rapid Update Cycle NWP • Combine high resolution structure of RUC with largerscale bias-corrected multi-model consensus • Stitch into GFE Gridded Operational Consensus Forecast Bias-corrected RUC • Forecast Demonstration Project in Sydney Oct-Dec 2014 Automated fire indices, difference grids, alerts Grass Fire Danger Index NexGenFWS summary NexGenFWS vision achieved: Timely and accurate forecasts where you are, when you need them. • Massive gains to services, efficiency, automation, standard processes • Effective path to operations • Modern dissemination channels • Downstream systems support • Operational probabilistic forecasts and guidance • High-resolution forecasts • Foundation for future Future focus • Automation, verification supporting efficiency, particularly for routine forecasts • Surge capacity where a forecast office needs help due to severe weather • More ensemble guidance and probabilities requiring less editing, supporting ensemble-based services • Hazard forecasts including probabilistic forecasts, alerts Merci! The end Bureau and international context, trends and developments – Bureau and community resilience Routine forecast automation NWP – post-processing adds great value to NWP investments. Continuous guidance improvement. RUC and ensembles. Forecasts and warnings more timely, more often GFE & VW: core parts of NWP value chain Severe wx focus, surge capacity Impact forecasts Expanded forecaster communicati on role New channels and products GFE is a solid platform to support forecast automation • Current GFE forecaster workload would be impossible without the automation already used every shift • automation has already enabled massive service increase. • More automation possible and needed • GFE as automation platform: • • • • • developers and forecasters use the same system forecaster input where automation doesn’t stack up brilliant way to capture business knowledge and rules good development platform including visualisation Forecaster intervention support is a small part of total NexGen cost • already does model post-processing and product generation US GFE & Australian GFE US GFE - Original foundation for Australian GFE - >100 WFOs each with SOO to undertake developments Australian GFE - 7 RFCs covering huge domain - Significant capability, infrastructure and automation improvements - Centrally-managed innovation using regional contributions - Encapsulates Bureau business logic Australian NexGen and UK VW Feature Australian GFE UK VW Grid/point Grid (and 650 points) Point text forecasts, gridded NWP processing Domain Huge Small Automation Auto + forecaster edits Fully auto/ fully manual tendency Handles TC Yes No ? Graphics GPU acceleration CPU rendering Forecast database Yes No Grid editing Strong Limited (metmorph being further developed) Visualisation OK Advanced Layers Simple Advanced Alerting Limited Strong NWP postproc Limited Strong