Most, if not all, researchers attend conferences as a part of their practice, and yet it is an under-researched activity. Little attention has been paid either to developing a theoretically informed understanding of conference practice as knowledge building, or to assessing the extent to which conferences are successful. This paper addresses these issues in the context of a small empirical study of the introduction of mobile, interactive ('back-channel') technologies into a conference setting. Science studies and learning theories literatures are used to develop an eight-point statement describing the aims of an idealised conference. This is then used as a framework through which to make sense of what happened when 'back-channel' technologies such as internet relay chat (IRC) and blogging were introduced into the 2004 Colston Symposium 'The Evolution of Learning and Web Technologies: Survival of the Fittest?'. Focusing on sequential issues and the conference as a forum for knowledge building, the analysis shows that conference order is disrupted by the introduction of the back-channel technologies. Nevertheless, other pressures on academic and professional practice (the governance agenda, calls for greater collaboration and a more consensual approach, and so on) suggest that the potential of the new technologies to help open up the black box of scientific and professional practice will be seen as increasingly important. If these tools are to be used effectively in the future, conferences will need to be supported by new skills and practices.

Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observations are becoming a major data source for numerical weather prediction (NWP) because of the advantages of their high spatiotemporal resolution and humidity measurements. In this study, the estimation of TAMDAR observational errors, and the impacts of TAMDAR observations with new error statistics on short-term forecasts are presented. The observational errors are estimated by a three-way collocated statistical comparison. This method employs collocated meteorological reports from three data sources: TAMDAR, radiosondes, and the 6-h forecast from a Weather Research and Forecasting Model (WRF). The performance of TAMDAR observations with the new error statistics was then evaluated based on this model, and the WRF Data Assimilation (WRFDA) three-dimensional variational data assimilation (3DVAR) system. The analysis was conducted for both January and June of 2010. The experiments assimilate TAMDAR, as well as other conventional data with the exception of non-TAMDAR aircraft observations, every 6 h, and a 24-h forecast is produced. The standard deviation of the observational error of TAMDAR, which has relatively stable values regardless of season, is comparable to radiosondes for temperature, and slightly smaller than that of a radiosonde for relative humidity. The observational errors in wind direction significantly depend on wind speeds. In general, at low wind speeds, the error in TAMDAR is greater than that of radiosondes; however, the opposite is true for higher wind speeds. The impact of TAMDAR observations on both the 6-and 24-h WRF forecasts during the studied period is positive when using the default observational aircraft weather report (AIREP) error statistics. The new TAMDAR error statistics presented here bring additional improvement over the default error.

Investigating the characteristics of modelforecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to endusers and model developers as well. To this end, the random and systematic errors (i.e., biases) of the 2-m temperature and 10-m wind predictions of the NCAR-AirDat weather research and forecasting (WRF)-based real-time four-dimensional data assimilation (RTFDDA) and forecasting system are analyzed. This system has been running operationally over a contiguous United States (CONUS) domain at a 4-km grid spacing with four forecast cycles daily from June 2009 to September 2010. In the result an exceptionally useful forecast dataset was generated and used for studying the error properties of the model forecasts, in terms of both a longer time period and a broader coverage of geographic regions than previously studied. Spatiotemporal characteristics of the errors are investigated based on the 24-h forecasts between June 2009 and April 2010, and the 72-h forecasts between May and September 2010. It was found that the biases of both wind and temperature forecasts vary greatly seasonally and diurnally, with dependency on the forecast length, station elevation, geographical location, and meteorological conditions. The temperature showed systematic cold biases during the daytime at all station elevations and warm biases during the nighttime above 1,000 m above sea level (ASL), while below 600 m ASL cold biases occurred during the nighttime. The forecasts of surface wind speed exhibited strong positive biases during the nighttime, while the negative biases were observed in the spring and summer afternoons. The surface wind speed was mostly over-predicted except for the stations located between 1,000 and 2,100 m ASL, for which negative biases were identified for most forecast cycles. The highest wind-speed errors were found over the high terrain and near sea-level stations. The wind-direction errors were relatively large at the high-terrain elevation in the Rocky and Appalachian mountain ranges and the western coastal areas and the error structure exhibited notable diurnal variability.

This article presents a new methodology for assimilating wind observations in their observed form of speed and direction, while taking into account both speed and direction error. It ensures the analysed speed and direction will be consistent with their background and observed values. The new formulation is implemented in the Weather Research and Forecasting Data Assimilation system, and idealised experiments are used to demonstrate the potential benefit. The results suggest that analyses from the new formulation are more reasonable when compared to the conventional methodology. The forecasts generated in these idealised experiments also demonstrate the value of this new formulation. Preliminary results from real data experiments are in general agreement with results presented here, and they will be reported in a following article

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A method for correcting the magnetic deviation error from planes using a flux valve heading sensor is presented. This error can significantly degrade the quality of the wind data reported from certain commercial airlines. A database is constructed on a per-plane basis and compared to multiple model analyses and observations. A unique filtering method is applied using coefficients derived from this comparison. Three regional airline fleets hosting the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor were analyzed and binned by error statistics. The correction method is applied to the outliers with the largest deviation, and the wind observational error was reduced by 22% (2.4 kt; 1 kt 5 0.51 m s 21 ), 50% (8.2 kt), and 68% (20.5 kt) for each group. AUGUST 2014 J A C O B S E T A L . FIG. 4. Examples of raw data with sinusoidal fits for two different planes for magnetic deviation relative to the (a) RAP and RTFDDA models and (b) RAP and RTFDDA models and ACARS observations vs heading (degrees from north). AUGUST 2014 J A C O B S E T A L . FIG. 6. Vertical profiles (pressure altitude in thousands of feet) of total (square), longitudinal (circle), and transverse (triangle) wind RMSE (kt) for phase 1 (solid) and phase 2 (corrected; dashed) of group B (PenAir Saab 340) planes.

Many and varied information sources are used by researchers and managers across sectors relevant to public policy development. When aggregated, these sources can be described in terms of sector-specific information landscapes. This paper describes results from a survey that investigated such landscapes and relates them to the working practices of those for whom they were relevant. This is achieved through the use of co-word or co-term analysis, a technique derived from actor-network theory. This technique allows for the production of graphic plots of normalised free text term pairs, which take into account inclusiveness. The results suggest that knowledge communities can be identified by this technique.

The Atlantic Surface Cyclone Intensification Index (ASCII) is a forecast index that quantifies the strength of low-level baroclinicity in the coastal region of the Carolinas. It is based on the gradient between the coldest 24-h average air temperature at Cape Hatteras and Wilmington, North Carolina, and the temperature at the western boundary of the Gulf Stream. The resulting prestorm baroclinic index (PSBI) is used to forecast the probability that a cyclone in the domain will exhibit rapid cyclogenesis. The initial ASCII study covered the years 1982-90. This dataset was recently expanded to cover the years 1991-2002, which doubled the number of cyclone events in the sample. These additional data provide similar position and slope of the linear regression fits to the previous values, and explain as much as 30% of the variance in cyclone deepening rate.Despite operational value, the neglect of upper-tropospheric forcing as a predictor in the original ASCII formulation precludes explanation of a large fraction of the deepening rate variance. Here, a modified index is derived in which an approximate measure of upper-level forcing is included. The 1991-2002 cyclone events were separated into bins of "strongly forced," "moderately forced," and "weakly forced" based on the strength of the nearest upstream maximum of 500-mb absolute vorticity associated with the surface low. This separation method reduced the scatter and further isolated the contributions of surface forcing versus upper-level forcing on extratropical cyclogenesis. Results of the combined upper-level index and surface PSBI demonstrate that as much as 74% of the deepening rate variance can be explained for cases with stronger upper-level forcing.