NCAR Research Applications Laboratory Completing the Forecast—Bridging Weather Predictions to User Applications Matthias Steiner NCAR Research Applications Laboratory Email: msteiner@ucar.edu Everybody stole thunder by now – so maybe a summary of various discussion points so far WMO WWRP Workshop on Use of NWP for Nowcasting NCAR Center Green in Boulder, Colorado Wednesday, 26 October 2011

Weather Forecasting & its Uncertainty NRC Report 2006 Uncertainty is a fundamental characteristic of weather, seasonal climate, & hydrological prediction, & no forecast is complete without a description of its uncertainty. WMO Report 2008 Effective communication of forecast uncertainty manages user expectations, builds confidence, & enables better decision making. AMS Report 2011 Plan to define a vision, strategic goals, roles & responsibilities, & an implementation roadmap that will guide the weather & climate enterprise toward routinely providing the Nation with comprehensive, skillful, reliable, & useful information about the uncertainty of weather, water, & climate forecasts. Uncertainty information probably as important as forecast in first place for decision makers 2

Wide Range of Decisions based on Weather Forecasts Air Traffic Manager: How many airplanes can airport handle? Risk of delays & diversions high It’s about sensitivity of a user or sector to aspects of weather, assessment of risks, & readiness to cope with weather impacts! Public User: Do I take umbrella today – yes/no? Risk of getting soaked Consequences of Bad Decision medium Broad range of weather information users, with various degrees of sensitivity to weather, requirements regarding space & time scales, what weather aspects matter, forecast lead time need to be able to respond, etc. Even for similar users, readiness to cope with impacts vary significantly (e.g., 1 inch of snow for DIA is no big deal but would be shutting airport down for ATL) Other factors that affect aviation may be runway construction, president flying into that location, etc. CAUTION: It’s not just about weather, there are many other factors that influence a user’s decision! low low medium high Degree of User Requirements & Sophistication 3

Collaboration instead of Throwing over Fence Weather User Public Safety Recreation Transportation Utilities Construction Agriculture Emergency etc. Forecast Products Decision to be made Effective forecasts have to be tailored to specific user needs One solution fits all doesn’t exist, need to tailor forecasts for each user application (i.e., extract what matters to decision making process) Throwing forecast over fence (or putting it on website) isn’t solution 4

Tailoring of Forecasts – Translation & Integration Weather Information Weather analyses & forecast data Weather Translation Extraction of relevant information Impact Estimation Placing into situational context Response Scenarios Mitigation strategies Weather Information Provider Weather-impacted User It is not about doing each other’s job, but understanding enough to help . . . Building effective bridges between providers & users of weather information requires: understanding information needs as well as communicating capabilities & limitations (it’s a two-way street) providing weather information relevant to user’s decision making training in products & building of trust 5

Example #1: Air Traffic Management Weather Information Weather analyses & forecast data Storm intensity & echo tops Weather Translation Extraction of relevant information Aviation constraints or threshold events Sector capacity & workload impact Impact Estimation Placing into situational context Strategic traffic flow management initiatives & tactical programs Response Scenarios Mitigation strategies 6

Example #2: Coping with Hurricanes Weather Information Weather analyses & forecast data Hurricane track, size, & intensity Storm surge, flooding, inundated areas Weather Translation Extraction of relevant information Affected population & infrastructure, disruption of services, damages due to wind & water, etc. Impact Estimation Placing into situational context Implementation of evacuation & recovery plans Response Scenarios Mitigation strategies 7

Example #3: Water Resources Management Weather Information Weather analyses & forecast data Rainfall (or lack thereof) Runoff & flow into reservoir, water levels behind dam Weather Translation Extraction of relevant information Dam overflow, water rights, or minimal streamflow for fish Impact Estimation Placing into situational context Controlled release of water & timing thereof Response Scenarios Mitigation strategies 8

Example #4: Wind Energy Harvesting Weather Information Weather analyses & forecast data Wind & variability Wind at hub height, min/max thresholds, & ramp events Weather Translation Extraction of relevant information Energy generated by windfarms Impact Estimation Placing into situational context Balancing power grid using different energy sources Response Scenarios Mitigation strategies 9

Probabilistic Forecasting using Ensembles Look at every ensemble member from a user perspective & ensemble “user relevant information” instead of weather 10

Example #5: Aviation Capacity Prediction User: Air Traffic Planners Example #5: Aviation Capacity Prediction Probability of losing fraction of capacity due to weather? Translation Impacting weather reduces usable air space Extraction of capacity reduction based on each member of ensemble forecast Focus on storm hazard & its organization (permeability of pattern) Observed traffic reduction compared to clear weather Predicted chance of 30% capacity loss in E-W direction 9 h ahead 11

Completing the Forecast – Take Home Message Making forecasts most valuable to users requires . . . close collaboration between weather forecast providers & end users / decision makers understanding of information needs, but also communicating capabilities & limitations translation of weather into user-relevant information (extraction of relevant information from each ensemble member) integration of weather into user’s decision making process (impact estimation & response scenarios utilizing decision support tools) calibration of probabilities & including some measure of confidence training for understanding & utilizing probabilistic forecasts development of trust in translated forecasts & decision support tools embracing change & possibly adjusting operational procedures 12

Food for additional Thought role of national weather services versus private sector - private sector’s role may be tailoring forecasts to commercial users/sectors, while weather services focus on public role of human in increasingly automated work environment - human over loop rather than in loop (let automation take care of repetitive tasks) - focus on what matters (e.g., areas of high sensitivity or impact) human factors aspects in communicating weather & impact information - carefully choose words, graphics & colors (e.g., avoid “met speak”) assessment of forecast performance - not only look at skill in forecasting weather aspects, but also assess how much value was added to user’s decision making process - how close is performance to predictability limit integration of weather into decision making process - enables important feedback on how good forecasts have to be in order to be meaningful to user application 13

References American Meteorological Society (AMS), 2011: A Weather and Climate Enterprise strategic Implementation Plan for generating and communicating Forecast Uncertainty Information. Commission on Weather and Climate Enterprise Board on Enterprise Communication, 99 pp. Lazo et al., 2011: U.S. economic Sensitivity to Weather Variability. Bull. Amer. Meteor. Soc., 92, 709 – 720. National Research Council (NRC), 2006: Completing the Forecast: Characterizing and communicating Uncertainty for better Decisions using Weather and Climate Forecasts. Committee on Estimating and Communicating Uncertainty in Weather and Climate Forecasts, 124 pp. Sharman et al., 2008: The operational mesogamma-scale analysis and forecast system of the U.S. Army Test and Evaluation Command. Part III: Forecasting with secondary-applications models. J. Appl. Meteor. Clim., 47, 1105 – 1122. Steiner et al., 2010: Translation of ensemble weather forecasts into probabilistic air traffic capacity impact. Air Traffic Control Quarterly, 18, 229 – 254. World Meteorological Organization (WMO), 2008: Guidelines on Communicating Forecast Uncertainty. WMO/TD 1422, 25 pp. 14