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Eleonora M. C. Demaria, David C. Goodrich, and Kenneth E. Kunkel

Abstract

The detection and attribution of changes in precipitation characteristics relies on dense networks of rain gauges. In the United States, the COOP network is widely used for such studies even though there are reported inconsistencies due to changes in instruments and location, inadequate maintenance, dissimilar observation time, and the fact that measurements are made by a group of dedicated volunteers. Alternately, the Long-Term Agroecosystem Research (LTAR) network has been consistently and professionally measuring precipitation since the early 1930s. The purpose of this study is to compare changes in extreme daily precipitation characteristics during the warm season using paired rain gauges from the LTAR and COOP networks. The comparison, done at 12 LTAR sites located across the United States, shows underestimation and overestimation of daily precipitation totals at the COOP sites compared to the reference LTAR observations. However, the magnitude and direction of the differences are not linked to the underlying precipitation climatology of the sites. Precipitation indices that focus on extreme precipitation characteristics match closely between the two networks at most of the sites. Our results show consistency between the COOP and LTAR networks with precipitation extremes. It also indicates that despite the discrepancies at the daily time steps, the extreme precipitation observed by COOP rain gauges can be reliably used to characterize changes in the hydrologic cycle due to natural and human causes.

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Xiaochun Wang, Yi Chao, C. K. Shum, Yuchan Yi, and Hok Sum Fok

Abstract

Two methods to assess ocean tide models, the current method and the total discrepancy method, are compared from the perspective of their relationship to the root-mean-square difference of tidal sea surface height (total discrepancy). These two methods are identically the same when there is only one spatial location involved. When there is more than one spatial location involved, the current method is the root-mean-square difference of total discrepancy at each location, and the total discrepancy method is the averaged total discrepancy. The result from the current method is always larger than or equal to that from the total discrepancy method. Monte Carlo simulation indicates that the difference between their results increases with increasing spatial variability of total discrepancy. Both of these two methods are then used to compare the two tide models of the Ocean Surface Topography Mission (OSTM)/Jason-2. The discrepancy of these two models as measured by the total discrepancy method decreases monotonically from around 11.4 to 2.2 cm with depth increasing from 50 to 1000 m. In contrast, the discrepancy measured by the current method varies from 21.6 to 2.9 cm. Though the discrepancy measured by the current method decreases with increasing depth in general, there are abrupt increases at several depth ranges. These increases are associated with large spatial variability of total discrepancy and their physical explanation is elusive. Because the total discrepancy method is consistent with the root-mean-square difference of tidal sea surface height and its interpretation is straightforward, its usage is suggested.

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James D. Means and Daniel Cayan

), examine the accuracy of the estimated pressure by comparison with observations and determine the resultant error in precipitable water estimation ( section 3 ), compare our observations with other methods of determining precipitable water ( sections 4 and 5 ), and highlight some particular applications that may benefit from having a database of precipitable water ( section 6 ). 2. Methodology We have used the North American Regional Reanalysis (NARR) to estimate historical pressure and temperature

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Y. Quilfen, B. Chapron, and D. Vandemark

for the buoy, about 0.5 and 1 m s −1 , respectively. Following such a statistical description for the random error, we can then look for systematic evidence of seasonal and regional effects not accounted for in the operational wind vector model functions. That is, systematic errors with possible relation to nonwind geophysical parameters. 4. Seasonal and regional effects on the ERS scatterometer signal It is widely recognized that a radar backscatter power measurement (nadir or off-nadir) is

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Sylvia J. Murphy and Timothy R. Keen

are several approaches to the problem of supplying this boundary condition data. Regional models can reduce open boundary effects through the utilization of large model domains, whereas LAMs can avoid complex boundary conditions by limiting the model domain. For example, the grid may be designed to follow the natural boundaries of a bay or estuary, resulting in one or two small open boundaries near the mouth (e.g., Wolff and Konop 1984 ; Brown et al. 1996 ). If the LAM contains an open ocean

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Xi Liang, Qinghua Yang, Lars Nerger, Svetlana N. Losa, Biao Zhao, Fei Zheng, Lin Zhang, and Lixin Wu

temperature in the Fram Strait, but the paper did not examine the effects of ocean data assimilation on the simulation of sea ice. To forecast the Arctic Ocean environment, aiming at an operational implementation of a reliable ice–ocean forecasting system, a pan-Arctic ice–ocean coupled model system was established two years ago at the National Marine Environmental Forecasting Center. The system is based on the Regional Ocean Modeling System (ROMS; Shchepetkin and McWilliams 2003 , 2005 ; Moore et al

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P. F. J. Lermusiaux

from January to April 1995 in the eastern Mediterranean. To introduce this region and its contemporary upper-thermocline features, a multivariate 3D estimate of the potential density at 105 m and velocity at 5 m is plotted in Fig. 1 . Because of thermal-wind effects, several surface circulation structures are easily distinguished along the steepest slopes of isopycnals. To date, most investigations in the Levantine that combine data and dynamics have mainly resolved the subbasin scales and are

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Lihua Li, Gerald M. Heymsfield, Paul E. Racette, Lin Tian, and Ed Zenker

and design of its radio frequency (RF), intermediate frequency (IF), and digital receiver subsystems. Section 4 discusses CRS calibration, which is essential for quantitative use of the reflectivity data, and minimum detectable reflectivity (MDR), which is important for cloud detectibility. Section 5 presents reflectivity and Doppler observations from the first CRS operational deployment during NASA's Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment

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J. M. White, J. F. Bowers, S. R. Hanna, and J. K. Lundquist

variable. Most of the methods for estimating mixing depth will have difficulties during stable nights when the mixing depth may be less than 10 or 20 m and the radiosonde or remote sounder cannot measure that close to the ground. In this case, an instrumented tower is useful, with temperature sensors at multiple heights through 30–40 m. An alternate source of mixing depth estimates is a regional or mesoscale meteorological model. Mesoscale meteorological models such as the fifth-generation Pennsylvania

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K. R. Ridgway, J. R. Dunn, and J. L. Wilkin

address many of these data shortcomings and regional complexities. When applied to an ocean region it provides mean fields that resolve both the large-scale structure and narrow coastal fronts and currents. Our system is built around the weighted least squares quadratic or loess smoother of Cleveland and Devlin (1988) . The computational demands of the method are less than other popular approaches such as Gauss–Markov estimation, yet the filtering characteristics are nearly as good ( Chelton and

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