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Anant Parekh, Rashmi Sharma, and Abhijit Sarkar

). This leads us to believe that the different measurements can play both complimentary and supplementary roles. Their integration can be achieved after evaluating their interconsistencies and comparisons individually and with the analysis of known numerical models. Intercomparison and validation of satellite retrievals with in situ observations is also rendered complicated by several important differences between satellite and in situ measurements, for example, spatiotemporal inhomogeneity between in

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Shin-Hoo Kang, Tae-Young Goo, and Mi-Lim Ou

) monthly mean temperature was −1.2°C (26.8°C) in January (August). Annual precipitation for 2010 was 1444 mm, and the amount of rainfall in the summer was 765.5 mm ( KMA 2010 ). Field observations (26–27 May 2010, 3–4 November 2010, 23–24 March 2011, 17–18 May 2011, and 6–7 October 2011) were conducted 5 times by the National Institute of Meteorological Research (NIMR). The primary goal of the field observations was to evaluate the T / q retrieval performance of the AERI. In situ measurements of

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S.-G. Park, Hae-Lim Kim, Young-Woong Ham, and Sung-Hwa Jung

from splashing and improvement of the sampling rate from 10 to 25 kHz ( Battaglia et al. 2010 ). After further improvements, OTT recently (2011) developed the second-generation model of PARSIVEL (PARSIVEL 2 ); its main improvement is the more homogenous beams generated by the better laser device ( Tokay et al. 2014 ). This upgraded model of OTT PARSIVEL 2 was evaluated by Tokay et al. (2014) through a comparison with a collocated JWD, the first model of OTT PARSIVEL, and rain gauges. They

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David Farmer, Li Ding, Donald Booth, and Martin Lohmann

formidable challenge that is unlikely to be met with in situ measurement schemes. Accordingly, we selected a remote sensing approach using a bistatic configuration of sonars on the seafloor. The system was deployed in the North Sea close to a production platform in the Tyra field of the Danish sector in water of depth ∼38 m. The present discussion describes the measurement concept and illustrates aspects of its operation with a mathematical model. The model calculations are then compared with

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Jessica L. Proud, Kelvin K. Droegemeier, Vincent T. Wood, and Rodger A. Brown

are available for other phenomena or use by other neighboring radars). As a first step toward addressing this and related challenges, the goal of the present study is to use pseudo-observations of an idealized vertical vortex to evaluate a variety of sampling strategies for CASA radars in order to determine which might be most effective for real tornadoes and mesocyclones. Here, effectiveness is defined as the best fit of the pseudo-observations to an analytic model of tornadoes and mesocyclones

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Hyun Mee Kim and Dae-Hui Kim


In this study, the effect of boundary condition configurations in the regional Weather Research and Forecasting (WRF) model on the adjoint-based forecast sensitivity observation impact (FSOI) for 24 h forecast error reduction was evaluated. The FSOI has been used to diagnose the impact of observations on the forecast performance in several global and regional models. Different from the global model, in the regional model, the lateral boundaries affect forecasts and FSOI results. Several experiments with different lateral boundary conditions were conducted. The experimental period was from 1 to 14 June 2015. With or without data assimilation, the larger the buffer size in lateral boundary conditions, the smaller the forecast error. The nonlinear and linear forecast error reduction (i.e., observation impact) decreased as the buffer size increased, implying larger impact of lateral boundaries and smaller observation impact on the forecast error. In all experiments, in terms of observation types (variables), upper-air radiosonde observations (brightness temperature) exhibited the greatest observation impact. The ranking of observation impacts was consistent for observation types and variables among experiments with a constraint in the response function at the upper boundary. The fractions of beneficial observations were approximately 60%, and did not considerably vary depending on the boundary conditions specified when calculating the FSOI in the regional modeling framework.

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David McKee, Jacek Piskozub, Rüdiger Röttgers, and Rick A. Reynolds

: Approximation of the size distribution of marine particles by a sum of log-normal functions . Limnol. Oceanogr. , 41 , 744 – 754 . Kirk, J. T. O. , 1992 : Monte Carlo modeling of the performance of a reflective tube absorption meter . Appl. Opt. , 31 , 6463 – 6468 . Leymarie, E. , Doxaran D. , and Babin M. , 2010 : Uncertainties associated to measurements of inherent optical properties in natural waters . Appl. Opt. , 49 , 5415 – 5436 . Maffione, R. A. , and Dana D. R. , 1997

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Julian A. Simeonov, K. Todd Holland, and Steven P. Anderson

approaches were motivated by an ongoing NASA effort to deploy a new Surface Water and Ocean Topography (SWOT) altimeter ( Desai 2018 ) for global measurements of water surface elevation with an estimated 100-m resolution and 1-cm vertical accuracy. An early 2D depth inversion approach was based on variational optimization and the adjoint method to efficiently minimize an error function between observations and predictions from a model based on the depth-averaged shallow-water equations ( Honnorat et al

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Christoph K. Thomas and Alexander R. Smoot

to construct an inexpensive radiation shield that performs equally or better than commercially available models, and not to comprehensively evaluate the heat budget of the sensor. To account for any systematic biases in air temperature measurements between the sensors in the different radiation shields, a linear regression model was applied to all observations in the CAS. Model coefficients were determined using all nighttime data with wind speeds U ≥ 1 m s −1 when radiation errors and

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Vandoir Bourscheidt, Kenneth L. Cummins, Osmar Pinto Jr., and Kleber P. Naccarato

Vaisala) with different technology and sensitivity (lightning sensor models LPATS, IMPACT, LS7000, and LS8000, as shown in Table 1 ). All of these upgrades, associated with some gaps in the detection for different sensors, lead to variations in the network performance along the years. Special care must be taken to address these effects. Table . 1. BLDN network variations over the years (by amount and type of sensors). These variations are usually evaluated through the system detection efficiency

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