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T-W. Yu, M. Iredell, and D. Keyser

Abstract

A neural network algorithm used in this study to derive Special Sensor Microwave/Imager (SSM/I) wind speeds from the Defense Meteorological Satellite Program satellite-observed brightness temperatures is briefly reviewed. The SSM/I winds derived from the neural network algorithm are not only of better quality, but also cover a larger area when compared to those generated from the currently operational Goodberlet algorithm. The areas of increased coverage occur mainly over the regions of active weather developments where the operational Goodberlet algorithm fails to produce good quality wind data due to high moisture contents of the atmosphere. These two main characteristics associated with the SSM/I winds derived from the neural network algorithm are discussed.

SSM/I wind speed data derived from both the neural network algorithm and the operational Goodberlet algorithm are tested in parallel global data assimilation and forecast experiments for a period of about three weeks. The results show that the use of neural-network-derived SSM/I wind speed data leads to a greater improvement in the first-guess wind fields than use of wind data generated by the operational algorithm. Similarly, comparison of the forecast results shows that use of the neural-network-derived SSM/I wind speed data in the data assimilation and forecast experiment gives better forecasts when compared to those from the operational run that uses the SSM/I winds from the Goodberlet algorithm. These results of comparison between the two parallel analyses and forecasts from the global data assimilation experiments are discussed.

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W. H. Gemmill, T. W. Yu, and D. M. Feit

Abstract

The performance of various techniques which determine ocean surface winds using information from large-scale analyses and forecast models is discussed. The techniques evaluated are the geostrophic relation, a simple empirical law, National Meteorological Center (NMC) 1000-mb winds, a two-region analytically matched boundary layer, a two-region boundary layer based on Rossby number similarity theory, and the Fleet Numerical Oceanography Center (FNOC) marine winds. Statistical comparisons of the model winds were made with observed buoy and ship winds for wind speed, wind direction, and the vector wind. This study is based on analyses and 24-h forecasts made once a day at 0000 UTC from 3 December 1985 through 6 January 1986 on a 2.5 × 2.5 degree latitude, longitude, grid.

The statistical results indicate that no one Model was clearly the best. The absolute wind speed difference between all the models and observations is, on the average, about 3 m s−1, and the RMS difference is about 4.O m s−1. However, the geostrophic relation was definitely the poorest, as would be expected. Model wind speeds and directions compared better with buoy data (lower RMS differences) than ship data. Furthermore, the study indicated that comparisons with buoys for wind speed were better over the northwest Atlantic than over the northwest Pacific, but the reverse was true for direction. For high wind speed reported by ships (> 22.5 m s−1) all model winds were comparatively lower.

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R. Atlas, R. N. Hoffman, S. M. Leidner, J. Sienkiewicz, T.-W. Yu, S. C. Bloom, E. Brin, J. Ardizzone, J. Terry, D. Bungato, and J. C. Jusem

Satellite scatterometer observations of the ocean surface wind speed and direction improve the depiction of storms at sea. Over the ocean, scatterometer surface winds are deduced from multiple measurements of reflected radar power made from several directions. In the nominal situation, the scattering mechanism is Bragg scattering from centimeter-scale waves, which are in equilibrium with the local wind. These data are especially valuable where observations are otherwise sparse—mostly in the Southern Hemisphere extratropics and Tropics, but also on occasion in the North Atlantic and North Pacific. The history of scatterometer winds research and its application to weather analysis and forecasting is reviewed here. Two types of data impact studies have been conducted to evaluate the effect of satellite data, including satellite scatterometer data, for NWP. These are simulation experiments (or observing system simulation experiments or OSSEs) designed primarily to assess the potential impact of planned satellite observing systems, and real data impact experiments (or observing system experiments or OSEs) to evaluate the actual impact of available space-based data. Both types of experiments have been applied to the series of satellite scatterometers carried on the Seasat, European Remote Sensing-1 and -2, and the Advanced Earth Observing System-1 satellites, and the NASA Quick Scatterometer. Several trends are evident: The amount of scatterometer data has been increasing. The ability of data assimilation systems and marine forecasters to use the data has improved substantially. The ability of simulation experiments to predict the utility of new sensors has also improved significantly.

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B. L. Cheong, R. D. Palmer, T-Y. Yu, K-F. Yang, M. W. Hoffman, S. J. Frasier, and F. J. Lopez-Dekker

Abstract

In this work, the accuracy of the Doppler beam-swinging (DBS) technique for wind measurements is studied using an imaging radar—the turbulent eddy profiler (TEP) developed by the University of Massachusetts, with data collected in summer 2003. With up to 64 independent receivers, and using coherent radar imaging (CRI), several hundred partially independent beams can be formed simultaneously within the volume defined by the transmit beam. By selecting a subset of these beams, an unprecedented number of DBS configurations with varying zenith angle, azimuth angle, and number of beams can be investigated. The angular distributions of echo power and radial velocity obtained by CRI provide a unique opportunity to validate the inherent assumption in the DBS method of homogeneity across the region defined by the beam directions. Through comparison with a reference wind field, calculated as the optimal uniform wind field derived from all CRI beams with sufficient signal-to-noise ratio (SNR), the accuracy of the wind estimates for various DBS configurations is statistically analyzed. It is shown that for a three-beam DBS configuration, although the validity of the homogeneity assumption is enhanced at smaller zenith angles, the root-mean-square (RMS) error increases because of the ill-conditioned matrix in the DBS algorithm. As expected, inhomogeneities in the wind field produce large bias for the three-beam DBS configuration for large zenith angles. An optimal zenith angle, in terms of RMS error, of approximately 9°–10° was estimated. It is further shown that RMS error can be significantly reduced by increasing the number of off-vertical beams used for the DBS processing.

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J. Boutin, Y. Chao, W. E. Asher, T. Delcroix, R. Drucker, K. Drushka, N. Kolodziejczyk, T. Lee, N. Reul, G. Reverdin, J. Schanze, A. Soloviev, L. Yu, J. Anderson, L. Brucker, E. Dinnat, A. Santos-Garcia, W. L. Jones, C. Maes, T. Meissner, W. Tang, N. Vinogradova, and B. Ward

Abstract

Remote sensing of salinity using satellite-mounted microwave radiometers provides new perspectives for studying ocean dynamics and the global hydrological cycle. Calibration and validation of these measurements is challenging because satellite and in situ methods measure salinity differently. Microwave radiometers measure the salinity in the top few centimeters of the ocean, whereas most in situ observations are reported below a depth of a few meters. Additionally, satellites measure salinity as a spatial average over an area of about 100 × 100 km2. In contrast, in situ sensors provide pointwise measurements at the location of the sensor. Thus, the presence of vertical gradients in, and horizontal variability of, sea surface salinity complicates comparison of satellite and in situ measurements. This paper synthesizes present knowledge of the magnitude and the processes that contribute to the formation and evolution of vertical and horizontal variability in near-surface salinity. Rainfall, freshwater plumes, and evaporation can generate vertical gradients of salinity, and in some cases these gradients can be large enough to affect validation of satellite measurements. Similarly, mesoscale to submesoscale processes can lead to horizontal variability that can also affect comparisons of satellite data to in situ data. Comparisons between satellite and in situ salinity measurements must take into account both vertical stratification and horizontal variability.

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L. M. Beal, J. Vialard, M. K. Roxy, J. Li, M. Andres, H. Annamalai, M. Feng, W. Han, R. Hood, T. Lee, M. Lengaigne, R. Lumpkin, Y. Masumoto, M. J. McPhaden, M. Ravichandran, T. Shinoda, B. M. Sloyan, P. G. Strutton, A. C. Subramanian, T. Tozuka, C. C. Ummenhofer, A. S. Unnikrishnan, J. Wiggert, L. Yu, L. Cheng, D. G. Desbruyères, and V. Parvathi

Abstract

The Indian Ocean Observing System (IndOOS), established in 2006, is a multinational network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more frequent extremes in monsoon rainfall, and decreasing oceanic productivity. In view of these new scientific challenges, a 3-yr international review of the IndOOS by more than 60 scientific experts now highlights the need for an enhanced observing network that can better meet societal challenges, and provide more reliable forecasts. Here we present core findings from this review, including the need for 1) chemical, biological, and ecosystem measurements alongside physical parameters; 2) expansion into the western tropics to improve understanding of the monsoon circulation; 3) better-resolved upper ocean processes to improve understanding of air–sea coupling and yield better subseasonal to seasonal predictions; and 4) expansion into key coastal regions and the deep ocean to better constrain the basinwide energy budget. These goals will require new agreements and partnerships with and among Indian Ocean rim countries, creating opportunities for them to enhance their monitoring and forecasting capacity as part of IndOOS-2.

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L. M. Beal, J. Vialard, M.K. Roxy, J. Li, M. Andres, H. Annamalai, M. Feng, W. Han, R. Hood, T. Lee, M. Lengaigne, R. Lumpkin, Y. Masumoto, M.J. McPhaden, M. Ravichandran, T. Shinoda, B.M. Sloyan, P.G. Strutton, A.C. Subramanian, T. Tozuka, C.C. Ummenhofer, A.S. Unnikrishnan, J. Wiggert, L. Yu, L. Cheng, D.G. Desbruyères, and V. Parvathi

Capsule

An internationally-coordinated plan to consolidate and enhance the Indian Ocean Observing System (IndOOS) to better address scientific priorities and meet future societal needs for climate information and prediction.

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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

Abstract

The Atmospheric Carbon and Transport (ACT) – America NASA Earth Venture Suborbital Mission set out to improve regional atmospheric greenhouse gas (GHG) inversions by exploring the intersection of the strong GHG fluxes and vigorous atmospheric transport that occurs within the midlatitudes. Two research aircraft instrumented with remote and in situ sensors to measure GHG mole fractions, associated trace gases, and atmospheric state variables collected 1140.7 flight hours of research data, distributed across 305 individual aircraft sorties, coordinated within 121 research flight days, and spanning five, six-week seasonal flight campaigns in the central and eastern United States. Flights sampled 31 synoptic sequences, including fair weather and frontal conditions, at altitudes ranging from the atmospheric boundary layer to the upper free troposphere. The observations were complemented with global and regional GHG flux and transport model ensembles. We found that midlatitude weather systems contain large spatial gradients in GHG mole fractions, in patterns that were consistent as a function of season and altitude. We attribute these patterns to a combination of regional terrestrial fluxes and inflow from the continental boundaries. These observations, when segregated according to altitude and air mass, provide a variety of quantitative insights into the realism of regional CO2 and CH4 fluxes and atmospheric GHG transport realizations. The ACT-America data set and ensemble modeling methods provide benchmarks for the development of atmospheric inversion systems. As global and regional atmospheric inversions incorporate ACT-America’s findings and methods, we anticipate these systems will produce increasingly accurate and precise sub-continental GHG flux estimates.

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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

Abstract

The Atmospheric Carbon and Transport (ACT)-America NASA Earth Venture Suborbital Mission set out to improve regional atmospheric greenhouse gas (GHG) inversions by exploring the intersection of the strong GHG fluxes and vigorous atmospheric transport that occurs within the midlatitudes. Two research aircraft instrumented with remote and in situ sensors to measure GHG mole fractions, associated trace gases, and atmospheric state variables collected 1,140.7 flight hours of research data, distributed across 305 individual aircraft sorties, coordinated within 121 research flight days, and spanning five 6-week seasonal flight campaigns in the central and eastern United States. Flights sampled 31 synoptic sequences, including fair-weather and frontal conditions, at altitudes ranging from the atmospheric boundary layer to the upper free troposphere. The observations were complemented with global and regional GHG flux and transport model ensembles. We found that midlatitude weather systems contain large spatial gradients in GHG mole fractions, in patterns that were consistent as a function of season and altitude. We attribute these patterns to a combination of regional terrestrial fluxes and inflow from the continental boundaries. These observations, when segregated according to altitude and air mass, provide a variety of quantitative insights into the realism of regional CO2 and CH4 fluxes and atmospheric GHG transport realizations. The ACT-America dataset and ensemble modeling methods provide benchmarks for the development of atmospheric inversion systems. As global and regional atmospheric inversions incorporate ACT-America’s findings and methods, we anticipate these systems will produce increasingly accurate and precise subcontinental GHG flux estimates.

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Gregory C. Johnson, Rick Lumpkin, Tim Boyer, Francis Bringas, Ivona Cetinić, Don P. Chambers, Lijing Cheng, Shenfu Dong, Richard A. Feely, Baylor Fox-Kemper, Eleanor Frajka-Williams, Bryan A. Franz, Yao Fu, Meng Gao, Jay Garg, John Gilson, Gustavo Goni, Benjamin D. Hamlington, Helene T. Hewitt, William R. Hobbs, Zeng-Zhen Hu, Boyin Huang, Svetlana Jevrejeva, William E. Johns, Sato Katsunari, John J. Kennedy, Marion Kersalé, Rachel E. Killick, Eric Leuliette, Ricardo Locarnini, M. Susan Lozier, John M. Lyman, Mark A. Merrifield, Alexey Mishonov, Gary T. Mitchum, Ben I. Moat, R. Steven Nerem, Dirk Notz, Renellys C. Perez, Sarah G. Purkey, Darren Rayner, James Reagan, Claudia Schmid, David A. Siegel, David A. Smeed, Paul W. Stackhouse, William Sweet, Philip R. Thompson, Denis L. Volkov, Rik Wanninkhof, Robert A. Weller, Caihong Wen, Toby K. Westberry, Matthew J. Widlansky, Josh K. Willis, Lisan Yu, and Huai-Min Zhang
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