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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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Vijay Kumar, Antony Joseph, R. G. Prabhudesai, S. Prabhudesai, Surekha Nagvekar, and Vimala Damodaran

above model PPTR, based on static calibration at differing temperatures. 2. Static calibration Performance of the PPTR has been evaluated based on static calibration, in which the value of the input pressure was kept constant during a given measurement. The relationship between the input x and the output y of the PPTR was established as a first-order polynomial, of the well-known form y = mx ± c , and was determined by a least squares curve-fitting technique. The results of calibration were

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Bertrand Bessagnet, Laurent Menut, Florian Couvidat, Frédérik Meleux, Guillaume Siour, and Sylvain Mailler

, . 10.1007/s10874-009-9129-2 Bessagnet , B. , and Coauthors , 2016 : Presentation of the EURODELTA III intercomparison exercise—Evaluation of the chemistry transport modelsperformance on criteria pollutants and joint analysis with meteorology . Atmos. Chem. Phys. , 16 , 12 667 – 12 701 , . 10.5194/acp-16-12667-2016 Faustini , A. , R. Rapp , and F. Forastiere , 2014 : Nitrogen dioxide and mortality

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An-Zhou Cao, Hui Chen, Wei Fan, Hai-Lun He, Jin-Bao Song, and Ji-Cai Zhang

the three schemes in YE15 . The paper is organized as follows. The estimation scheme, which is composed of the bottom Ekman boundary layer model and its adjoint model, and a minimization algorithm, is described in section 2 . In section 3 , a series of numerical experiments based on currents simulated with a prescribed EVP are performed to test the feasibility and effectiveness of the proposed scheme. The performance of the proposed scheme is compared with the three schemes in YE15 . Section

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Marouan Bouali and Alexander Ignatov

provide a detailed technical description of the destriping methodology. We also discuss the evaluation of image quality improvement and define two quantitative metrics to assess the performance of the stripe reduction algorithm. Section 3 provides information on the data used for this study and reports all the necessary results that demonstrate the benefits of destriping BTs on level 1B/SDR data prior to SST production. Section 4 concludes this paper. 2. Algorithm a. Noise model In the following

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Peiyang Cheng, Arastoo Pour-Biazar, Richard T. McNider, and John R. Mecikalski

Program and the Texas Commission on Environmental Quality (TCEQ). Note the results in this study do not necessarily reflect policy or science positions by the funding agencies. REFERENCES Ali , M. H. , and I. Abustan , 2014 : A new novel index for evaluating model performance . J. Nat. Resour. Dev. , 4 , 1 – 9 , . Anderson , M. C. , and Coauthors , 2011 : Mapping daily evapotranspiration at field to continental scales using geostationary and polar

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Qingtao Song, Tetsu Hara, Peter Cornillon, and Carl A. Friehe

surface wind stress at the meso- γ scale (2–20 km) in the vicinity of an SST front and to evaluate the performance of the model using the selected parameterization. This selection and evaluation was done through comparison of MM5 simulations with in situ observations made during the Frontal Air–Sea Interaction Experiment (FASINEX) ( Friehe et al. 1991 ). Admittedly, the response of the MM5 boundary layer has been extensively tested over land ( Pleim and Xiu 1995 ; Davis et al. 1999 ; Bright and

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Alain Protat and Isztar Zawadzki

). The next subsection evaluates the performance of this basic constraining model (5) . b. Performance of the basic constraining model The basic retrieval procedure (described in section 2a ) and computer code were verified using a simulated data set described in Protat et al. (1998) . A relative error of less than 1% was found for the dynamic perturbations. These errors are attributed to the discretization of the momentum and continuity equations involved in the retrieval procedure. In a

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