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Temple R. Lee and Sandip Pal


Rawinsonde observations have long been used to estimate the atmospheric boundary layer depth (BLD), which is an important parameter for monitoring air quality, dispersion studies, weather forecast models, and inversion systems for estimating regional surface–atmosphere fluxes of tracers. Although many approaches exist for deriving the BLDs from rawinsonde observations, the bulk Richardson approach has been found to be most appropriate. However, the impact of errors in the measured thermodynamic and kinematic fields on the estimated BLDs remains unexplored. We argue that quantifying BLD error (δBLD) estimates is equally as important as the BLDs themselves. Here we quantified δBLD by applying the bulk Richardson method to 35 years of rawinsonde data obtained from three stations in the United States: Sterling, Virginia; Amarillo, Texas; and Salt Lake City, Utah. Results revealed similar features in terms of their respective errors. A −2°C bias in temperature yielded a mean δBLD ranging from −15 to 200 m. A +2°C bias in temperature yielded a mean δBLD ranging from −214 to +18 m. For a −5% relative humidity bias, the mean δBLD ranged from −302 to +7 m. For a +5% relative humidity bias, the mean δBLD ranged from +2 to +249 m. Differences of ±2 m s−1 in the winds yielded BLD errors of ~±300 m. The δBLD increased as a function of BLD when introducing errors to the thermodynamic fields and decreased as a function of BLD when introducing errors to the kinematic fields. These findings expand upon previous work evaluating rawinsonde-derived δBLD by quantifying δBLD arising from rawinsonde-derived thermodynamic and kinematic measurements. Knowledge of δBLD is critical in, for example, intercomparison studies where rawinsonde-derived BLDs are used as references.

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Pieter Groenemeijer, Christian Barthlott, Ulrich Corsmeier, Jan Handwerker, Martin Kohler, Christoph Kottmeier, Holger Mahlke, Andreas Wieser, Andreas Behrendt, Sandip Pal, Marcus Radlach, Volker Wulfmeyer, and Jörg Trentmann


Measurements of a convective storm cluster in the northern Black Forest in southwest Germany have revealed the development of a warm and dry downdraft under its anvil cloud that had an inhibiting effect on the subsequent development of convection. These measurements were made on 12 July 2006 as part of the field campaign Prediction, Identification and Tracking of Convective Cells (PRINCE) during which a number of new measurement strategies were deployed. These included the collocation of a rotational Raman lidar and a Doppler lidar on the summit of the highest mountain in the region (1164 m MSL) as well as the deployment of teams carrying radiosondes to be released in the vicinity of convective storms. In addition, an aircraft equipped with sensors for meteorological variables and dropsondes was in operation and determined that the downdraft air was approximately 1.5 K warmer, 4 g kg−1 drier, and therefore 3 g m−3 less dense than the air at the same altitude in the storm’s surroundings. The Raman lidar detected undulating aerosol-rich layers in the preconvective environment and a gradual warming trend of the lower troposphere as the nearby storm system evolved. The Doppler lidar both detected a pattern of convergent radial winds under a developing convective updraft and an outflow emerging under the storm’s anvil cloud. The dryness of the downdraft air indicates that it had subsided from higher altitudes. Its low density reveals that its development was not caused by negative thermal buoyancy, but was rather due to the vertical mass flux balance accompanying the storm’s updrafts.

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Kenneth J. Davis, Edward V. Browell, Sha Feng, Thomas Lauvaux, Michael D. Obland, Sandip Pal, Bianca C. Baier, David F. Baker, Ian T. Baker, Zachary R. Barkley, Kevin W. Bowman, Yu Yan Cui, A. Scott Denning, Joshua P. DiGangi, Jeremy T. Dobler, Alan Fried, Tobias Gerken, Klaus Keller, Bing Lin, Amin R. Nehrir, Caroline P. Normile, Christopher W. O’Dell, Lesley E. Ott, Anke Roiger, Andrew E. Schuh, Colm Sweeney, Yaxing Wei, Brad Weir, Ming Xue, and Christopher A. Williams


Midlatitude weather systems contain large gradients in greenhouse gases (GHG), reflecting regional fluxes and continental inflow. ACT-America carbon weather observations provide a synoptic-scale benchmark for GHG flux and transport models.

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