1. Summary Human Dimensions Panel John Gaynor NOAA August 12, 2009 1

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 Wx obs impact organizational decision making? – CASA has user integration group, working with forecasters, EMs, using public response surveys (indication that large portion of public doesnt respond to tornado warnings) 2

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** 3

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** 4

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 doesnt pass automated QC? **Does higher quality, more frequent, higher resolution data result in better policy?** What is the effectiveness of images? 5

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 6