1. SummaryHuman Dimensions Panel John Gaynor NOAA August 12, 2009

2. The Social Science Dimension • CASA example – “User information when and where needed” • Integration of meteorology, engineering, and social science • **Main issue – risk and vulnerability** • How data contribute to response, mitigation & behavior? • How improved Wxobs impact organizational decision making? • CASA has user integration group, working with forecasters, EMs, using public response surveys (indication that large portion of public doesn’t respond to tornado warnings)

3. The Social Dimension (continued) • SSWIM example • Much to learn about the value of information • Periodic assessments needed to monitor changes • Need to meet multiple needs requiring diverse disciplines, various methods (tools) – must be appropriate to needs • Need to include demographic data and trends (e.g., geography) • **To change nature of R2O so that all players play equal roles**

4. End User Dimension • Wind energy • Resource assessment • Annual prediction of production potential • Need consistent measurements (0.1 m/s error = $14m/yr for Iberdrola) • Forecasting • From real-time to many days • Large drop in cost/hr with improved forecasts • Need to perform maintenance when winds are low • Need long-term obs • Better climate referencing • Need dense networks with more 3-D measurements • Data needed frequently and rapidly for forecasts and statistical models • Wind industry can provide significant number of obs to the NoN • **Need to address intellectual property issues** • Wind energy very intermittent • Need very short time scale forecasts (say 30 min, updated every 5 min) • **Standardize metadata, quality assurance/control**

5. End User Dimension(continued) • General power generation issues • Load forecasting (temp., humidity, winds, cloud cover) – done hourly • Precip. projections for hydropower & load shedding • Wind and temps. for transmission line ratings • **Need to look to future needs and technologies** • State climatologist • “Honest broker” – bringing communities together • Value in partnering with social scientists • Many questions to answer, e.g.: • What uncertainty thresholds is the public willing to accept? • How do people learn to understand and use new types of obs? • Does the public lose trust in obs if there is too much of a time lag? • Or, if some data withheld because it doesn’t pass automated QC? • **Does higher quality, more frequent, higher resolution data result in better policy?** • What is the effectiveness of images?

6. Discussion • End users decisions are deterministic, but internal decisions use probabilistic information • DSS development is an iterative process between developers and end users • NWS needs help on business case for its capacity building to improve services • **Need better forecasts of initiation of convection** • For first hr, need better measurement systems, especially in the vertical • For longer term, need models • How can propriety data be made available to commercial sectors? • Radar data should help in wind energy/utility forecasting • Mesonets will be even more valuable as more energy is generated at the individual home level • **Need to be more innovative – many ways to value data that can take us beyond propriety issues** • Need to look at marketing • Concerning social science integrated with meteorology, co-location is very important