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Graeme L. Stephens and Christian D. Kummerow

that govern such distributions point to the elementary importance of the synoptic-scale controls of the atmospheric circulations that shape our weather systems ( Rossow and Cairns 1995 ). The vast range of scales that influence cloud and precipitation properties and the effects of these properties on weather and climate dictate a sampling strategy that inevitably requires the use of data collected from sensors flown on earth-orbiting satellites. A number of methods for determining various cloud and

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S. Lakshmivarahan, John M. Lewis, and Junjun Hu

these areas may be broadly divided into two groups: either based on the adjoint approach rooted in the classical variational analysis or using one of the many ensemble implementations of Kalman filtering approach to data assimilation. While much of the literature concentrates on the choice of the suitable type of observations to improve the forecast quality, a few also consider distribution of observation to answer a similar goal. Specifically, Berliner et al. (1999) examine the application of

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Tianfeng Chai, Ching-Long Lin, and Rob K. Newsom

. The above experiments recommend use of a big model domain covering sector-shaped HRDL data for real lidar data retrieval. d. Observational errors In contrast with the radial velocity provided for the previous ITEs, real lidar observations always contain errors. Lin et al. (2001) conducted sensitivity tests on observational errors of various amplitude and spatial correlation. With introduction of buffer zones, this issue must be reexamined. As quality control is routinely applied in

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Quanxin Xia, Ching-Long Lin, Ronald Calhoun, and Rob K. Newsom

3804 m to the south and 1090 m to the east of the ARL lidar. b. Observational error The original lidar dataset contains a variety of data, such as radial velocity, SNR, time, azimuth angle, and elevation angle. The SNR is a performance measure for coherent lidar observational samples. Low SNR data usually correspond to weak return signals. The SNR threshold technique is used for quality control. The lidar data whose SNR are lower than a certain value are tagged as “bad” points. Those “bad” radial

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H. J. Eskes, A. J. M. Piters, P. F. Levelt, M. A. F. Allaart, and H. M. Kelder

corresponding uncertainties in the quality of the model profiles. Unfortunately there is not much experimental data from which to obtain the required vertical error statistics. Nadir satellite instruments are theoretically limited to a vertical resolution of 5–8 km. Limb instruments reach a resolution of about 1 km, but the horizontal resolution is much worse. Ozone sondes are very sparse and the quality is often difficult to judge. Different strategies to model the vertical ozone distribution have been

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Jean-Christopher Lambert, Michel Van Roozendael, Martine De Mazière, Paul C. Simon, Jean-Pierre Pommereau, Florence Goutail, Alain Sarkissian, and James F. Gleason

that the data provided by the satellite experiment do respond to spatial, temporal, and quality requirements specific to the application for which the experiment has been designed. In particular, the accuracy and precision of these data must be assessed over the whole relevant spatial domain and vertical range during the entire mission. In addition, satellite observations are not continuous since they depend strongly on space policies of the various nations. The link needed between sensors

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Oreste Reale, William K. Lau, Kyu-Myong Kim, and Eugenia Brin

AIRS data only from channels completely unaffected by clouds, we use quality-controlled temperature retrievals, also obtained under partly cloudy conditions, following Susskind et al. (2006) and Susskind (2007) . Despite the widespread general assumption that clear-sky radiances are the best way to assimilate AIRS data, Reale et al. (2009) have shown retrievals under partly cloudy conditions to provide better analyses of a tropical cyclone in the Indian Ocean than the clear-sky radiance

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Donald E. Strebel, David R. Landis, K. Fred Huemmrich, Jeffrey A. Newcomer, and Blanche W. Meeson

dataset into the FIS, running quality control checks, preparing documentation, conducting reviews, and publishing it through PLDS, was essentially the same as described in Table 5.2 of Strebel et al. (1994b) . That process entailed two dozen steps that took, on average, a half of a person-year per dataset. Many of these steps were completed for immediate use of the data by the FIFE investigators and hence were not repeated for the publication experiment. The goal of creating a high-quality published

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Daewon W. Byun

differences must be overcome before a fully coupled meteorological–chemical–emissions modeling system is routinely used for air quality management. Customarily, air quality models are run many times to understand the effects of emission control strategies on the pollutant concentrations using the same meteorological data. A noncoupled prognostic model with FDDA provides adequate meteorological data needed for such operational use. Also, instead of running meteorological models prognostically, it is

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Ching-Long Lin, Tianfeng Chai, and Juanzhen Sun

study attempts to assess the applicability of the model to retrieve microscale turbulent structures in the CBL; determine the value of model control parameters for optimal retrieval, including observational frequency, number of iterations, and temporal and spatial smoothness coefficients; and evaluate the relationship between retrieval quality and data error. We have carried out a number of identical twin experiments whose observational data are generated by the 4DVAR itself. The results demonstrate

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