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Dong Wang and Tobias Kukulka

Abstract

This study investigates the dynamics of velocity shear and Reynolds stress in the ocean surface boundary layer for idealized misaligned wind and wave fields using a large-eddy simulation (LES) model based on the Craik–Leibovich equations, which captures Langmuir turbulence (LT). To focus on the role of LT, the LES experiments omit the Coriolis force, which obscures a stress–current-relation analysis. Furthermore, a vertically uniform body force is imposed so that the volume-averaged Eulerian flow does not accelerate but is steady. All simulations are first spun-up without wind-wave misalignment to reach a fully developed stationary turbulent state. Then, a crosswind Stokes drift profile is abruptly imposed, which drives crosswind stresses and associated crosswind currents without generating volume-averaged crosswind currents. The flow evolves to a new stationary state, in which the crosswind Reynolds stress vanishes while the crosswind Eulerian shear and Stokes drift shear are still present, yielding a misalignment between Reynolds stress and Lagrangian shear (sum of Eulerian current and Stokes drift). A Reynolds stress budgets analysis reveals a balance between stress production and velocity–pressure gradient terms (VPG) that encloses crosswind Eulerian shear, demonstrating a complex relation between shear and stress. In addition, the misalignment between Reynolds stress and Eulerian shear generates a horizontal turbulent momentum flux (due to correlations of along-wind and crosswind turbulent velocities) that can be important in producing Reynolds stress (due to correlations of horizontal and vertical turbulent velocities). Thus, details of the Reynolds stress production by Eulerian and Stokes drift shear may be critical for driving upper-ocean currents and for accurate turbulence parameterizations in misaligned wind-wave conditions.

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R. T. Sutton, P-P. Mathieu, M. Collins, and B. Dong
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Alexander Vasilkov, Alexei Lyapustin, B. Greg Mitchell, and Dong Huang

Abstract

Ultraviolet (UV) data collected over the ocean by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) are used. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm adapted for EPIC processing performs cloud detection, aerosol retrievals, and atmospheric correction providing the water-leaving reflectance of the ocean at 340 and 388 nm. The water-leaving reflectance is an indicator of the presence of absorbing and scattering constituents in seawater. The retrieved water-leaving reflectance is compared with full radiative transfer calculations based on a model of inherent optical properties (IOP) of ocean water in UV. The model is verified with data collected on the Aerosol Characterization Experiments (ACE) Asia cruise supported by the NASA Sensor Intercomparison for Marine Biological and Interdisciplinary Ocean Studies (SIMBIOS) project. The model assumes that the ocean water IOPs are parameterized through a chlorophyll concentration. The radiative transfer simulations were carried out using the climatological chlorophyll concentration from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite. The EPIC-derived water-leaving reflectance is also compared with climatological Lambertian-equivalent reflectivity (LER) of the ocean derived from measurements of the Ozone Monitoring Instrument (OMI) on board the NASA polar-orbiting Aura satellite. The EPIC reflectance agrees well (within 0.01) with the model reflectance except for oligotrophic oceanic areas. For those areas, the model reflectance is biased low by about 0.01 at 340 nm and up to 0.03 at 388 nm. The OMI-derived climatological LER is significantly higher than the EPIC water-leaving reflectance, largely due to the surface glint contribution. The globally averaged difference is about 0.04.

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Dong L. Wu, Paul B. Hays, and Wilbert R. Skinner

Abstract

Common methods in spectral analyses of satellite data are the discrete Fourier transform (DFT) type of approaches, which generally require regular sampling and uniform spacing. These conditions sometimes cannot be met in the satellite applications, for example, such as one made by the High Resolution Doppler Imager (HRDI) on board the Upper Atmosphere Research Satellite (UARS). To be able to handle irregular sampling cases, a least squares fitting method is established here for a space-time Fourier analysis and has been applied to the HRDI sampling as well as other regular sampling cases. This method can resolve space-time spectra as robustly and accurately as DFT-type methods for the regular cases. In the same fashion, given an appropriate sampling pattern, it can also handle the irregular cases in which there exist large data gaps, frequent mode changes, and varying weight samples. Various sampling schemes and the associated aliasing spectra are examined. A better sampling plan than those currently used by the UARS instruments to reduce spectral aliasing is proposed, which leads to the question of how to optimize satellite sampling in the future.

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P-P. Mathieu, R. T. Sutton, B. Dong, and M. Collins

Abstract

The predictability of winter climate over the North Atlantic–European (NAE) region during ENSO events is investigated. Rather than employing traditional composite analyses, the authors focus on the impacts of six individual events: three El Niño events and three La Niña events. The investigation is based on the analysis of ensemble simulations with an atmospheric GCM forced with prescribed sea surface temperatures (SST) for the period December 1985–May 2001, and on observations. Model experiments are used to separate the respective roles of SST anomalies in the Indo-Pacific basin and in the Atlantic basin.

A significant (potentially predictable) climate signal is found in the NAE region for all six ENSO events. However, there are notable differences in the impacts of individual El Niño and La Niña events. These differences arise not simply from atmospheric internal variability but also because the atmosphere is sensitive to specific features of the SST anomaly fields that characterize the individual events. The different impacts arise partly from differences in Indo-Pacific SST and partly from differences in Atlantic SST. SST anomalies in both ocean basins can influence tropical convection and excite a Rossby wave response over the North Atlantic. The evidence presented here for the importance of Atlantic Ocean conditions argues that, in the development of systems for seasonal forecasting, attention should not be focused too narrowly on the tropical Pacific Ocean.

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Ji-Young Gu, A. Ryzhkov, P. Zhang, P. Neilley, M. Knight, B. Wolf, and Dong-In Lee

Abstract

The ability of C-band polarimetric radar to account for strong attenuation/differential attenuation is demonstrated in two cases of heavy rain that occurred in the Chicago, Illinois, metropolitan area on 5 August 2008 and in central Oklahoma on 10 March 2009. The performance of the polarimetric attenuation correction scheme that separates relative contributions of “hot spots” (i.e., strong convective cells) and the rest of the storm to the path-integrated total and differential attenuation has been explored. It is shown that reliable attenuation correction is possible if the radar signal is attenuated by as much as 40 dB. Examination of the experimentally derived statistics of the ratios of specific attenuation Ah and differential attenuation A DP to specific differential phase K DP in hot spots is included in this study. It is shown that these ratios at C band are highly variable within the hot spots. Validation of the attenuation correction algorithm at C band has been performed through cross-checking with S-band radar measurements that were much less affected by attenuation. In the case of the Oklahoma storm, a comparison was made between the data collected by closely located C-band and S-band polarimetric radars.

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L. J. Wilcox, B. Dong, R. T. Sutton, and E. J. Highwood
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Nam-Young Kang, Myeong-Soon Lim, James B. Elsner, and Dong-Hyun Shin

Abstract

The accuracy of track forecasts for tropical cyclones (TCs) is well studied, but less attention has been paid to the representation of track-forecast uncertainty. Here, Bayesian updating is employed on the radius of the 70% probability circle using 72-h operational forecasts with comparisons made to the classical approach based on the empirical cumulative density (ECD). Despite an intuitive and efficient way of treating track errors, the ECD approach is statistically less informative than Bayesian updating. Built on a solid statistical foundation, Bayesian updating is shown to be a useful technique that can serve as a substitute for the classical approach in representing operational TC track-forecast uncertainty.

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Ali Jozaghi, Mohammad Nabatian, Seongjin Noh, Dong-Jun Seo, Lin Tang, and Jian Zhang

Abstract

We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

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Wolfgang Buermann, Jiarui Dong, Xubin Zeng, Ranga B. Myneni, and Robert E. Dickinson

Abstract

In this study the utility of satellite-based leaf area index (LAI) data in improving the simulation of near-surface climate with the NCAR Community Climate Model, version 3 (CCM3), GCM is evaluated. The use of mean LAI values, obtained from the Advanced Very High Resolution Radiometer Pathfinder data for the 1980s, leads to notable warming and decreased precipitation over large parts of the Northern Hemisphere lands during the boreal summer. Such warming and decreased rainfall reduces discrepancies between the simulated and observed near-surface temperature and precipitation fields. The impact of interannual vegetation extremes observed during the 1980s on near-surface climate is also investigated by utilizing the maximum and minimum LAI values from the 10-yr LAI record. Surface energy budget analysis indicates that the dominant impact of interannual LAI variations is modification of the partitioning of net radiant energy between latent and sensible heat fluxes brought about through changes in the proportion of energy absorbed by the vegetation canopy and the underlying ground and not from surface albedo changes. The enhanced latent heat activity in the greener scenario leads to an annual cooling of the earth land surface of about 0.3°C, accompanied by an increase in precipitation of 0.04 mm day−1. The tropical evergreen forests and temperate grasslands contribute most to this cooling and increased rainfall. These results illustrate the importance and utility of satellite-based vegetation LAI data in simulations of near-surface climate variability.

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