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Yanting Wang and V. Chandrasekar

et al. 2005 ). The fine-tuning of this relation is not the focus of this paper. Instead, the performance of rainfall estimation from the DCAS network observations is of essential interest here. As a baseline of comparison, one of KOUN’s R ( K dp ) relations presented by Ryzhkov et al. (2005) is chosen and adapted to X band, which is repeated here as This relation is based on the measured DSD from the same coverage area and a drop shape model as specified by Brandes et al. (2002) . It has

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Senliang Bao, Ren Zhang, Huizan Wang, Hengqian Yan, Yang Yu, and Jian Chen

salinity profile reconstruction model. The profile data from 2014 are then used for testing the model. 4. The performance of the FOAGRNN in estimating salinity profiles a. Comparison with the linear method To evaluate the performance of the FOAGRNN method, the multiple linear regression and FOAGRNN methods are used to retrieve salinity profiles for the tropical Pacific Ocean and the Kuroshio Extension region during 2014. Then, the RMSE of estimated profiles from the two methods against in situ

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Rod Frehlich and Robert Sharman

. Summary and discussion We provide here a technique to evaluate lidar performance statistics (bias, average, and standard deviation) based on coupled numerical simulations of pulsed Doppler lidar and a prescription of a wake vortex pair to provide “truth.” In particular, the performance of the ML estimation algorithms of wake vortex parameters was evaluated to establish the optimal performance achievable by a pulsed Doppler lidar scanning perpendicular to the flight path. Using the known

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R. Pinkel and J. A. Smith

velocity estimates from incoherentDoppler sounders. The method involves transmitting a number of repeats of a broadband "subcode." TheDoppler shift is estimated from the complex autocovariance of the return, evaluated at a time lag equal to thesubcode duration. The repeat-sequence code is an extension of the simple pulse-train concept developed in theearly days of radar. By transmitting codes, rather than discrete pulses, the average transmitted power is increased.A model is developed here to predict

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Simone Tanelli, Eastwood Im, Satoru Kobayashi, Roberto Mascelloni, and Luca Facheris

frequency components of the pointing error below 1 Hz, and that overall accuracy of 1 m s −1 or better can be achieved even when extremely strong convective cells are present. The results obtained through CFT were compared to those obtainable through standard spectral moments estimators once a simplified model to correct for NUBF-induced biases is implemented. This second approach guarantees acceptable results in moderate NUBF conditions, but its performances are more sensitive to the accurate

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Malgorzata Szczodrak, Peter J. Minnett, Nicholas R. Nalli, and Wayne F. Feltz

M-AERI. The performance of the NCEP and ECMWF model-based retrievals of the atmospheric profiles is evaluated by comparison with profiles obtained by coincident radiosonde measurements and with M-AERI retrievals obtained using the radiosonde profiles as a first guess. This study is based on data collected during the 2004 Aerosol and Ocean Science Expedition (AEROSE; Nalli et al. 2006 ). 2. Instruments and data AEROSE was a multidisciplinary oceanographic field campaign conducted in the tropical

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Alexandria G. McCombs, April L. Hiscox, Cuizhen Wang, Ankur R. Desai, Andrew E. Suyker, and Sebastien C. Biraud

satellite remote sensing–derived phenology metrics to estimate crop-based CFP metrics to improve models of energy and gas exchange. Multiple methods exist to remotely estimate CFP, but they have rarely been compared. The work presented here evaluates which VIs best identify CFP metrics derived from downscaled MODIS and Landsat satellite observations. This was done by comparing ground-observed CFP transition periods from eddy covariance flux tower observations of NEE to satellite-derived phenology

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Charlie N. Barron, A. Birol Kara, Harley E. Hurlburt, C. Rowley, and Lucy F. Smedstad

series are available from tide gauges in the Atlantic, Pacific, and Indian Oceans (e.g., Caldwell and Merrifield 2000 ). With the availability of these observations, model–data comparisons can be made using a variety of statistical metrics. In this paper, these data are applied in a detailed set of statistical metrics to evaluate NCOM performance in predicting SSH variability over the global ocean. In particular, daily averaged SSH from 612 yearlong daily time series from 282 tide gauges are used

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C. W. Wright, E. J. Walsh, W. B. Krabill, W. A. Shaffer, S. R. Baig, M. Peng, L. J. Pietrafesa, A. W. Garcia, F. D. Marks Jr., P. G. Black, J. Sonntag, and B. D. Beckley

airborne measurements using GPS dropwindsondes and stepped frequency microwave radiometers ( Powell et al. 1998 , 2003 ; Uhlhorn and Black 2003 ; Uhlhorn et al. 2007 ) as well as wind data gathered from temporary towers set up along the coast in the hurricane’s projected path ( Schroeder and Smith 2003 ). The area of least improvement has been in obtaining detailed data on the temporal/spatial evolution of the water level to evaluate the performance of the numerical models. Tide gauges in the

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Seppo Pulkkinen, V. Chandrasekar, and Ari-Matti Harri

, and PhaSt methods is given in Table 1 . Table 1. Summary of the DARTS, SF-DARTS, S-DARTS, STEPS and PhaSt methods. The symbols R and Z denote precipitation intensity and reflectivity, respectively, and the function G denotes transformation to the normal distribution. This paper is organized as follows. The proposed methodology is described in section 2 . Performance evaluation and demonstration of using S-DARTS in the CASA DFW domain is given in section 3 . Concluding remarks and

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