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A. Bizard, K. Caillault, C. Lavigne, A. Roblin, and P. Chervet

is well suited to give information about thick cloud-top and cloud-base heights. As an example of our climatology, Fig. 1 shows occurrences of clouds with optical depth higher than 0.3, in the range of cloud top altitudes 11–13 km, in spring, at nighttime. Fig . 1. Occurrences of clouds (%) with optical depth > 0.3 with top heights between 11 and 13 km in spring during nighttime from CALIOP. b. Transmittance probabilities Statistical evaluation of airborne or satellite sensors' performances

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Cuong M. Nguyen and V. Chandrasekar

. In section 5 , Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar data in both uniform and staggered PRT transmission scheme are used to illustrate the performance of GMAP-TD and are compared against GMAP. The last section summarizes the important results of this paper. 2. GMAP-TD a. Signal model For meteorological targets, the returned signal is the sum of the backscatter from individual hydrometeors in a radar pulse volume. Precipitation particles

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Emma Cotter and Brian Polagye

existing model. Finally, classification is implemented in real time at second location, approximately 100 m away. Real-time performance is evaluated, classification models are refined, and recommendations are given for implementing machine learning classification at new marine energy sites. 2. Methods a. Data 1) Test site We collected data during two deployments in Sequim Bay, Washington, in 2017 and 2019, at the locations shown in Fig. 1 . The site is a tidal channel at the mouth of Sequim Bay that

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Jackson Tan, George J. Huffman, David T. Bolvin, and Eric J. Nelkin

software improvements may reduce this latency in the future. In this study, we will evaluate the morphing scheme using vectors computed from six model-based variables: total (surface) precipitation from atmospheric model physics (PRECTOT), total precipitable water vapor (TQV; also known as total column water vapor), total precipitable liquid water (TQL), total precipitable ice water (TQI), specific humidity at 500 hPa (Q500), and specific humidity at 850 hPa (Q850). TQV, TQL, and TQI are the vertically

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Mark D. Orzech, Jayaram Veeramony, and Hans Ngodock

performance is qualitatively evaluated by comparing how well they reproduce the observed spectra at the offshore boundary and all four instrument locations, including nonassimilated spectra as well as the selected innovation spectrum. For a more quantitative comparison, overall model accuracy is also evaluated using an RMS skill score computed from spectral densities as shown: In Eq. (8) , S mod is the model spectrum and S obs is the observed spectrum (from nonlinear forward SWAN). Spectral energy

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C. Amerault and X. Zou

model grid space, as is done in the RTM. This can lead to differences on the order of tens of kelvins ( Kummerow et al. 1996 ). More discussion on these errors is provided in section 5b(3) . In future work, T b observations will be used to evaluate the performance of different explicit moisture schemes on smaller grid spacings. 4. Adjoint sensitivity of the radiative transfer model a. Formulation There are a number of ways to determine the sensitivity of a model. Traditional sensitivity analyses

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Zhengzheng Li, Yan Zhang, and Scott E. Giangrande

Gaussian mixture model (GMM) ensures convergence to the prior distribution of dual-polarization variables, the GMPE (a minimum variance unbiased estimator) was shown to outperform PLRs in both rainfall-rate estimation and attenuation correction using simulated polarimetric radar measurements. However, the performance of the GMPE has not been tested for real-world rainfall applications. Rainfall-rate estimators can be developed either through measurements or simulations. Rainfall-rate estimators

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Hyun Mee Kim and Dae-Hui Kim

1. Introduction The initial conditions obtained by assimilating the model background and observations are used to predict weather in numerical weather prediction (NWP). Because individual observations assimilated to produce the initial conditions for the prediction contribute differently to the performance of forecasts, the impact of individual observations on the forecasts needs to be evaluated quantitatively to improve the performance of the NWP. The impact of real observations on forecasts

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Peyman Rahnama, Yves J. Rochon, Ian C. McDade, Gordon G. Shepherd, William A. Gault, and Alan Scott

determined for each pixel without inversion that represents some weighted average wind along the line of sight of each pixel. The random noise standard deviations of these winds are derived through propagation of the measurement noise variances. Wind differences can serve in evaluating the sensitivity to different instrument characteristics. These standard deviations and differences are useful in conducting evaluations on instrument performance and conditions. The calculated wind random error standard

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Michel Chong and Stéphanie Cosma

in MUSCAT properly takes into account the orography-induced air circulation. More significant is the resemblance between the horizontal components deduced from MUSCAT ( Figs. 4c, 4e , 5c, 5e ) and the model ( Figs. 4d, 4f , 5d, 5f ), which were marked by strong shear zones. These figures clearly show the performances of MUSCAT in providing highly reliable horizontal wind components. An overall comparison can be summarized in Fig. 6 , which shows the statistical distributions of the wind

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