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Peter Baker and Stephen Pond

Abstract

Many aspects Of the low-frequency response of a stratified inlet have not been previously observed because of the lack of simultaneous observations of runoff, wind, currents, and density over the entire body of water. Month-long observations throughout the water column of Knight Inlet, British Columbia, both outside and inside the sill, during the spring (1988) and summer (1989) runoff regimes are presented. These data are detided with harmonic analysis and used to investigate the subdiurnal residual response with respect to the wind, runoff, and deep water renewal. Near the surface, response to alongchannel winds was found to dominate with a coherence squared of greater than 0.8. The coherence was therefore used to directly estimate the wind influence, and dewinded residuals were formed by subtracting these estimates from the detided records. Structures were found in the dewinded residuals that correspond to a near-surface estuarine circulation vertically nested with deep water renewal.

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Kirk Baker and Peter Scheff

Abstract

Many counties are required to submit an emissions control plan to the U.S. Environmental Protection Agency to reduce concentrations of particulate matter of less than 2.5 μm in diameter (PM2.5), which are dominated by ammonium sulfate and ammonium nitrate in the central United States. These control scenarios are simulated with photochemical models, which use emissions and meteorological variables to simulate PM2.5 formation, transport, and deposition. A monitor study was established in the central United States to measure simultaneously the PM2.5 sulfate ion, nitrate ion, ammonium ion, and chemical precursor species sulfur dioxide, nitric acid, and ammonia during 2004. These data, combined with nearby meteorological observations, provide an opportunity to assess whether meteorological variables or deposition processes may introduce systematic biases in PM2.5 ammonium sulfate and ammonium nitrate predictions. Skill in estimating total wet deposition is assessed by comparing model output with National Atmospheric Deposition Program monitors in the region. Meteorological variables that are important for mass transport (wind vector) and thermodynamic chemistry (temperature and relative humidity) compare well to observations. A model sensitivity, in which the temperatures in the inorganic chemistry module are adjusted to compensate for an underprediction bias, shows a minimal model response in predicted PM2.5 ammonium nitrate. The dry deposition of sulfur dioxide seems to have a systematic impact on ambient estimates of sulfur dioxide in the photochemical model. An attempt to correlate bias and error in meteorological variables to bias and error in PM2.5 species showed the most relationship between relative humidity and temperature and ammonium nitraite. Wet deposition of total sulfate, nitrate, and ammonium tend to be underpredicted in the winter months.

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Peter W. Baker and M. C. Hodson

Abstract

In 1964, R.R. Rogers proposed a method for estimating vertical air velocity in rainfall which is based on the Marshall-Palmer raindrop size distribution. Many investigators have shown that there are deviations from the Marshall-Palmer distribution such that the intercept N0, and the shape may vary. Essential to Rogers' method is the relationship between reflectivity Z and Doppler fall velocity . A numerical method has been used to obtain versus Z relationships for raindrop size distributions different from those of Marshall and Palmer and to compare these with results obtained using Rogers's method. Thus errors that are likely to occur in estimating vertical air motions have been evaluated and are shown to be a maximum of 1.4 m s−1.

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Barry H. Lynn, David R. Stauffer, Peter J. Wetzel, Wei-Kuo Tao, Pinhas Alpert, Nataly Perlin, R. David Baker, Ricardo Muñoz, Aaron Boone, and Yiqin Jia

Abstract

Three major modifications to the treatment of land surface processes in the Pennsylvania State University–National Center for Atmospheric Research mesoscale model MM5, are tested in a matrix of eight model experiments. Paired together in each dimension of the matrix are versions of the code with and without one of the changes. The three changes involve 1) a sophisticated land surface model [the Parameterization for Land–Atmosphere Convective Exchange (PLACE)], 2) the soil moisture and temperature initial conditions derived from running PLACE offline, and 3) a 1.5-order turbulent kinetic energy (TKE) turbulence boundary layer. The code without changes, defined as the control code, uses the most widely applied land surface, soil initialization, and boundary layer options found in the current MM5 community code. As an initial test of these modifications, a case was chosen in which they should have their greatest effect: conditions where heterogeneous surface forcing dominates over dynamic processes. The case chosen is one with widespread summertime moist convection, during the Convection and Precipitation Electrification Experiment (CaPE) in the middle of the Florida peninsula. Of the eight runs, the code with all three changes (labeled TKE-PLACE) demonstrates the best overall skill in terms of biases of the surface variables, rainfall, and percent and root-mean-square error of cloud cover fraction for this case. An early, isolated convective storm that formed near the east coast, at the downwind edge of a region of anomalous wet soil, and within the dense cluster of CaPE mesoscale observation stations, is correctly simulated only by TKE-PLACE. It does not develop in any of the other seven runs. A factor separation analysis shows that a successful simulation requires the inclusion of the more sophisticated land surface model, realistic initial soil moisture and temperature, and the higher-order closure of the planetary boundary layer (PBL) in order to better represent the effect of joint and synergistic (nonlinear) contributions from the land surface and PBL on the moist convection.

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Howard J. Diamond, Thomas R. Karl, Michael A. Palecki, C. Bruce Baker, Jesse E. Bell, Ronald D. Leeper, David R. Easterling, Jay H. Lawrimore, Tilden P. Meyers, Michael R. Helfert, Grant Goodge, and Peter W. Thorne

The year 2012 marks a decade of observations undertaken by the U.S. Climate Reference Network (USCRN) under the auspices of NOAA's National Climatic Data Center and Atmospheric Turbulence and Diffusion Division. The network consists of 114 sites across the conterminous 48 states, with additional sites in Alaska and Hawaii. Stations are installed in open (where possible), rural sites very likely to have stable land-cover/use conditions for several decades to come. At each site a suite of meteorological parameters are monitored, including triple redundancy for the primary air temperature and precipitation variables and for soil moisture/temperature. Instrumentation is regularly calibrated to National Institute for Standards and Technology (NIST) standards and maintained by a staff of expert engineers. This attention to detail in USCRN is intended to ensure the creation of an unimpeachable record of changes in surface climate over the United States for decades to come. Data are made available without restriction for all public, private, and government use. This article describes the rationale for the USCRN, its implementation, and some of the highlights of the first decade of operations. One critical use of these observations is as an independent data source to verify the existing U.S. temperature record derived from networks corrected for nonhomogenous histories. Future directions for the network are also discussed, including the applicability of USCRN approaches for networks monitoring climate at scales from regional to global. Constructive feedback from end users will allow for continued improvement of USCRN in the future and ensure that it continues to meet stakeholder requirements for precise climate measurements.

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Stephen D. Eckermann, Jun Ma, Karl W. Hoppel, David D. Kuhl, Douglas R. Allen, James A. Doyle, Kevin C. Viner, Benjamin C. Ruston, Nancy L. Baker, Steven D. Swadley, Timothy R. Whitcomb, Carolyn A. Reynolds, Liang Xu, N. Kaifler, B. Kaifler, Iain M. Reid, Damian J. Murphy, and Peter T. Love

Abstract

A data assimilation system (DAS) is described for global atmospheric reanalysis from 0- to 100-km altitude. We apply it to the 2014 austral winter of the Deep Propagating Gravity Wave Experiment (DEEPWAVE), an international field campaign focused on gravity wave dynamics from 0 to 100 km, where an absence of reanalysis above 60 km inhibits research. Four experiments were performed from April to September 2014 and assessed for reanalysis skill above 50 km. A four-dimensional variational (4DVAR) run specified initial background error covariances statically. A hybrid-4DVAR (HYBRID) run formed background error covariances from an 80-member forecast ensemble blended with a static estimate. Each configuration was run at low and high horizontal resolution. In addition to operational observations below 50 km, each experiment assimilated 105 observations of the mesosphere and lower thermosphere (MLT) every 6 h. While all MLT reanalyses show skill relative to independent wind and temperature measurements, HYBRID outperforms 4DVAR. MLT fields at 1-h resolution (6-h analysis and 1–5-h forecasts) outperform 6-h analysis alone due to a migrating semidiurnal (SW2) tide that dominates MLT dynamics and is temporally aliased in 6-h time series. MLT reanalyses reproduce observed SW2 winds and temperatures, including phase structures and 10–15-day amplitude vacillations. The 0–100-km reanalyses reveal quasi-stationary planetary waves splitting the stratopause jet in July over New Zealand, decaying from 50 to 80 km then reintensifying above 80 km, most likely via MLT forcing due to zonal asymmetries in stratospheric gravity wave filtering.

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Wayman E. Baker, Robert Atlas, Carla Cardinali, Amy Clement, George D. Emmitt, Bruce M. Gentry, R. Michael Hardesty, Erland Källén, Michael J. Kavaya, Rolf Langland, Zaizhong Ma, Michiko Masutani, Will McCarty, R. Bradley Pierce, Zhaoxia Pu, Lars Peter Riishojgaard, James Ryan, Sara Tucker, Martin Weissmann, and James G. Yoe

The three-dimensional global wind field is the most important remaining measurement needed to accurately assess the dynamics of the atmosphere. Wind information in the tropics, high latitudes, and stratosphere is particularly deficient. Furthermore, only a small fraction of the atmosphere is sampled in terms of wind profiles. This limits our ability to optimally specify initial conditions for numerical weather prediction (NWP) models and our understanding of several key climate change issues.

Because of its extensive wind measurement heritage (since 1968) and especially the rapid recent technology advances, Doppler lidar has reached a level of maturity required for a space-based mission. The European Space Agency (ESA)'s Atmospheric Dynamics Mission Aeolus (ADM-Aeolus) Doppler wind lidar (DWL), now scheduled for launch in 2015, will be a major milestone.

This paper reviews the expected impact of DWL measurements on NWP and climate research, measurement concepts, and the recent advances in technology that will set the stage for space-based deployment. Forecast impact experiments with actual airborne DWL measurements collected over the North Atlantic in 2003 and assimilated into the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model are a clear indication of the value of lidar-measured wind profiles. Airborne DWL measurements collected over the western Pacific in 2008 and assimilated into both the ECMWF and U.S. Navy operational models support the earlier findings.

These forecast impact experiments confirm observing system simulation experiments (OSSEs) conducted over the past 25–30 years. The addition of simulated DWL wind observations in recent OSSEs performed at the Joint Center for Satellite Data Assimilation (JCSDA) leads to a statistically significant increase in forecast skill.

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