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William D. Grant and Albert J. Williams III

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William D. Grant and Albert J. Williams III

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Hans C. Graber, Robert C. Beardsley, and William D. Grant

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Surface winds derived from atmospheric pressure fields are used as input to a finite-depth wind-wave model to predict the sea state during a cold air frontal passage over the Yellow and East China Seas, which occurred 15–18 November 1983. The predicted maximum wave-stress field near the bottom is used to examine the concept of turbulent wave intensities causing sediment resuspension. The temporal variability of the wave field at three sites is used to illustrate the dependence of the bottom response on depth within the Yellow Sea. Maps of the temporal and spatial distribution of index for initiation of sediment movement are computed for different noncohesive sediment materials during this storm period and compared to sedimentological results for this region.

This study demonstrates that wave action is a mechanism which can significantly influence the sediment transport pattern induced by the regional circulation existing in this marginal sea. The results also identify regions where winter storm-generated surface waves are too weak to affect bottom sediments. Although the spatial variability of sediment resuspension depends on sea state and sediment material, the predicted wave-induced bottom shear stresses during a characteristic winter storm show that fine-grained material can be re-suspended as far out as the 100 m isobath in the East China Sea. Temporal maps of the index of sediment movement further show that the critical shear stress is exceeded for silty sand over large regions of the East China Sea during the duration of the storm studied.

These numerical simulation results suggest that the present-day distribution of sediments in the Yellow and East China Seas is in part a direct consequence of storm-generated surface waves during the winter season. The numerical model results further suggest that erosion of sand along the Chinese and Korean coasts is largely determined by surface wave action. Furthermore, the present-day mud patch south of Cheju Island appears to be a consequence of the circulation pattern in the Yellow and East China Seas and the southeastward decrease in wave and tidal bottom stress.

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William D. Grant, Albert J. Williams III, and Scott M. Glenn

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High quality near-bottom boundary layer measurements obtained at a midshelf location (90 m water depth) in the CODE region off Northern California are described. Bottom tripod velocity measurements and supporting data obtained during typical spring and early summer conditions (June 1981 during CODE-1) are analyzed to obtain bath velocity profiles and mean bottom stress and bottom roughness estimates. During the time period described, the mean near-bottom (<2 m) velocity profile are highly logarithmic (R>0.997) approximately 30 percent of the time. Effects induced by unsteadiness from internal waves result in some degradation of the profiles (0.96≤R≤0.997) the rest of the time. Mean stress profiles indicate the logarithmic layer is approximately a constant-stress layer. The near-bottom flow field is Composed of mean currents and oscillatory currents due to well. Typing mean u * values estimated from measurements greater than 30 cm above the bottom have magnitudes of 0.5–1.0 cm s−1. Mean stress values are three to seven times larger than expected from predictions using a typical smooth-bottom drag coefficient and one-and-one-half to three-and-one-half times larger than expected for predictions using a drag coefficient based on the observed rough bottom. Corresponding z 0 values have magnitude of approximately 1 cm, an order of magnitude larger than the observed physical bottom roughness. These values are demonstrated to he consistent with those expected from theoretical models for combined wave and current flows. The u * values estimated from the CODE-1 data and predicted by the Grant and Madsen model typically agree within 10–15 percent.

The waves influencing the midshelf bottom-stress estimates are 12–20 second swell associated with distant Pacific storms. Them waves are present over most of the year. The results demonstrate that waves must be taken into account in predicting bottom stress over the Northern California Shelf and that these predictions can be made using existing theory.

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Albert J. Williams 3rd, John S. Tochko, Richard L. Koehler, William D. Grant, Thomas F. Gross, and Christopher V. R. Dunn

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A vertical array of acoustic current meters measures the vector flow field in the lowest 5 m of the oceanic boundary layer. By resolving the velocity to 0.03 cm s−1 over 15 cm paths, it samples the dominant turbulent eddies responsible for Reynolds stress to within 50 cm of the bottom. Profiles through the inner boundary layer, from six sensor pods, of velocity, turbulent kinetic energy, and Reynolds stress can be recorded for up 10 four months with a 2 Hz sample rate and 20 min averaging interval. We can study flow structure and spectra from as many as four event-triggered recordings of unaveraged samples, each lasting one hour, during periods of intense sediment transport. Acoustic transducer multiplexing permits 24 axes to be interfaced to a single receiving circuit. Electrical reversal of transducers in each axis eliminates zero drift. A deep-sea tripod supports the sensor array rigidly with minimum flow disturbance, yet releases on command for free vehicle recovery.

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William S. Olson, Christian D. Kummerow, Song Yang, Grant W. Petty, Wei-Kuo Tao, Thomas L. Bell, Scott A. Braun, Yansen Wang, Stephen E. Lang, Daniel E. Johnson, and Christine Chiu

Abstract

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.

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Brian J. Butterworth, Ankur R. Desai, Stefan Metzger, Philip A. Townsend, Mark D. Schwartz, Grant W. Petty, Matthias Mauder, Hannes Vogelmann, Christian G. Andresen, Travis J. Augustine, Timothy H. Bertram, William O. J. Brown, Michael Buban, Patricia Cleary, David J. Durden, Christopher R. Florian, Trevor J. Iglinski, Eric L. Kruger, Kathleen Lantz, Temple R. Lee, Tilden P. Meyers, James K. Mineau, Erik R. Olson, Steven P. Oncley, Sreenath Paleri, Rosalyn A. Pertzborn, Claire Pettersen, David M. Plummer, Laura D. Riihimaki, Eliceo Ruiz Guzman, Joseph Sedlar, Elizabeth N. Smith, Johannes Speidel, Paul C. Stoy, Matthias Sühring, Jonathan E. Thom, David D. Turner, Michael P. Vermeuel, Timothy J. Wagner, Zhien Wang, Luise Wanner, Loren D. White, James M. Wilczak, Daniel B. Wright, and Ting Zheng

Abstract

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

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