Ground-based measurements of particle size and fall speed distributions using a Particle Size and Velocity (PARSIVEL) disdrometer are compared among samples obtained in mixed precipitation (rain and wet snow) and rain in the Oregon Cascade Mountains and in dry snow in the Rocky Mountains of Colorado. Coexisting rain and snow particles are distinguished using a classification method based on their size and fall speed properties. The bimodal distribution of the particles' joint fall speed-size characteristics at air temperatures from 0.5°to 0°C suggests that wet-snow particles quickly make a transition to rain once melting has progressed sufficiently. As air temperatures increase to 1.5°C, the reduction in the number of very large aggregates with a diameter Ͼ 10 mm coincides with the appearance of rain particles larger than 6 mm. In this setting, very large raindrops appear to be the result of aggregrates melting with minimal breakup rather than formation by coalescence. In contrast to dry snow and rain, the fall speed for wet snow has a much weaker correlation between increasing size and increasing fall speed. Wet snow has a larger standard deviation of fall speed (120%-230% relative to dry snow) for a given particle size. The average fall speed for observed wet-snow particles with a diameter Ն 2.4 mm is 2 m s Ϫ1 with a standard deviation of 0.8 m s Ϫ1 . The large standard deviation is likely related to the coexistence of particles of similar physical size with different percentages of melting. These results suggest that different particle sizes are not required for aggregation since wet-snow particles of the same size can have different fall speeds. Given the large standard deviation of fall speeds in wet snow, the collision efficiency for wet snow is likely larger than that of dry snow. For particle sizes between 1 and 10 mm in diameter within mixed precipitation, rain constituted 1% of the particles by volume within the isothermal layer at 0°C and 4% of the particles by volume for the region just below the isothermal layer where air temperatures rise from 0°to 0.5°C. As air temperatures increased above 0.5°C, the relative proportions of rain versus snow particles shift dramatically and raindrops become dominant. The value of 0.5°C for the sharp transition in volume fraction from snow to rain is slightly lower than the range from 1.1°to 1.7°C often used in hydrological models.

SUMMARYThis paper examines the three-dimensional structure and dynamics of southerly hybrid gap/mountain ow through the Wipp valley (Wipptal), Austria, observed on 30 October 1999 using high-resolution observations and model simulations. The observations were obtained during a shallow south föhn event documented in the framework of the Mesoscale Alpine Programme (MAP). Three important data sources were used: the airborne differential-absorption lidar LEANDRE 2, the ground-based Doppler lidar TEACO2 and in situ measurements from the National Oceanic and Atmospheric Administration P-3 aircraft. This event was simulated down to 2 km horizontal resolution using the non-hydrostatic mesoscale model Meso-NH. The structure and dynamics of the ow were realistically simulated. The combination of high-resolution observations and numerical simulations provided a comprehensive three-dimensional picture of the ow through the Wipptal: in the gap entrance region (Brenner Pass, Austria), the low-level jet was not solely due to the channelling of the southerly synoptic ow through the elevated gap. Part of the Wipptal ow originated as a mountain wave at the valley head wall of the Brenner Pass. Downstream of the pass, the shallow föhn ow had the characteristics of a downslope windstorm as it rushed down towards the Inn valley (Inntal) and the City of Innsbruck, Austria. Downhill of the Brenner Pass, the strongest ow was observed over a small obstacle along the western side wall (the Nösslachjoch), rather than channelled in the deeper part of the valley just to the east. Further north, the low-level jet was observed in the centre of the valley. Approximately halfway between Brenner Pass and Innsbruck, where the along-axis direction of the valley changes from north to north-north-west, the low-level jet was observed to be de ected to the eastern side wall of the Wipptal. Interaction between the Stubaier Alpen (the largest and highest topographic feature to the west of the Wipptal) and the south-westerly synoptic ow was found to be the primary mechanism responsible for the de ection. The along-and cross-valley structure and dynamics of the ow were observed to be highly variable due to the in uence of surrounding mountains, localized steep slopes within the valley and out ows from tributaries (the Gschnitztal and the Stubaital) to the west of the Wipptal.For that shallow föhn case, observations and simulations provided a large body of evidence that downslope ow created thinning/ thickening uid and accelerations/ decelerations reminiscent of mountain wave/hydraulic theory. Along the Wipptal, two hydraulic-jump-like transitions were observed and simulated, (i) on the lee slope of the Nösslachjoch and (ii) in the Gschnitztal exit region. A hydraulic solution of the ow was calculated in the framework of reduced-gravity shallow-water theory. The down-valley evolution of the Froude number computed using LEANDRE 2, P-3 ight level and TEACO2 measurements con rmed that these transitions were associated with super-to subcritical transitions.

With a goal of improving operational numerical weather prediction (NWP), the Developmental Testbed Center (DTC) has been working with operational centers, including, among others, the National Centers for Environmental Prediction (NCEP), National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and the U.S. Air Force, to support numerical models/systems and their research, perform objective testing and evaluation of NWP methods, and facilitate research-to-operations transitions. This article introduces the first attempt of the DTC in the data assimilation area to help achieve this goal. Since 2009, the DTC, NCEP’s Environmental Modeling Center (EMC), and other developers have made significant progress in transitioning the operational Gridpoint Statistical Interpolation (GSI) data assimilation system into a community-based code management framework. Currently, GSI is provided to the public with user support and is open for contributions from internal developers as well as the broader research community, following the same code transition procedures. This article introduces measures and steps taken during this community GSI effort followed by discussions of encountered challenges and issues. The purpose of this article is to promote contributions from the research community to operational data assimilation capabilities and, furthermore, to seek potential solutions to stimulate such a transition and, eventually, improve the NWP capabilities in the United States.

While traditional verification methods are commonly used to assess numerical model quantitative precipitation forecasts (QPFs) using a grid-to-grid approach, they generally offer little diagnostic information or reasoning behind the computed statistic. On the other hand, advanced spatial verification techniques, such as neighborhood and object-based methods, can provide more meaningful insight into differences between forecast and observed features in terms of skill with spatial scale, coverage area, displacement, orientation, and intensity. To demonstrate the utility of applying advanced verification techniques to mid-and coarseresolution models, the Developmental Testbed Center (DTC) applied several traditional metrics and spatial verification techniques to QPFs provided by the Global Forecast System (GFS) and operational North American Mesoscale Model (NAM). Along with frequency bias and Gilbert skill score (GSS) adjusted for bias, both the fractions skill score (FSS) and Method for Object-Based Diagnostic Evaluation (MODE) were utilized for this study with careful consideration given to how these methods were applied and how the results were interpreted. By illustrating the types of forecast attributes appropriate to assess with the spatial verification techniques, this paper provides examples of how to obtain advanced diagnostic information to help identify what aspects of the forecast are or are not performing well.