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Sybille Y. Schoger, Dmitri Moisseev, Annakaisa von Lerber, Susanne Crewell, and Kerstin Ebell

the first place and using additional correction equations for Pluvio to account for precipitation losses in high wind conditions. In addition to the application of the newly developed Z e –S relationships to the measurements at Ny-Ålesund, also the MRR measurements as part of the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE; December 2019–May 2020) on Bear Island, Svalbard, will be analyzed in more detail and snowfall rates estimated. In this way, we will gain further

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Minda Le and V. Chandrasekar

Polarimetric Radar (NPOL), and CSU–CHILL radar in the last three years of 2014–18. Fourteen validation cases are selected under different geographical conditions, different seasons of the year, and different surface types. For those cases occurred during the OLYMPEX campaign, we enhanced the validation results ( Chandrasekar and Le 2017 ) together with site ground reports and Precipitation Imaging Package (PIP) images. A match ratio is calculated between surface snowfall product and ground radar retrievals

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Sara Q. Zhang, T. Matsui, S. Cheung, M. Zupanski, and C. Peters-Lidard

modeling system that represents cloud, precipitation, aerosol, and land process ( Peters-Lidard et al. 2015 ). It is based on the Advanced Research WRF ( Skamarock et al. 2008 ) with additional coupling to advanced Goddard physics packages, satellite simulators, and high-resolution satellite/reanalysis data to initialize boundary conditions. In this work the NU-WRF model simulations are carried out at storm scale with lateral boundary forcing from the Modern-Era Retrospective Analysis for Research and

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Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

, soil moisture, and 2-m air temperature and specific humidity. In section 4 , we discuss the overall forecast skills and present the conclusions. 2. Datasets and methodology a. Datasets This study uses three datasets in the data assimilation experiments, namely, the NCEP Final Analysis (FNL) to provide the boundary and initial conditions to our WRF experiments, the TRMM 3B42 precipitation to be assimilated into the WRF Model, and the SMOS soil moisture to be assimilated into the Noah land surface

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W.-K. Tao, T. Iguchi, and S. Lang

outer domain and 1129 × 703 × 61 for the inner domain; the CalWater 2015 domains ( Figs. 1b,c ) employ 671 × 582 × 61 and 685 × 604 × 61 grid points. Time steps of 18 and 6 s were used in the outer and inner nested domains, respectively. The initial and lateral boundary conditions of the outer domains and the initial conditions for the inner domains come from NCEP final (FNL) operational global analysis data ( Kalnay et al. 1996 ) with a grid spacing of 1° in both latitude and longitude with 6

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Dalia B. Kirschbaum, George J. Huffman, Robert F. Adler, Scott Braun, Kevin Garrett, Erin Jones, Amy McNally, Gail Skofronick-Jackson, Erich Stocker, Huan Wu, and Benjamin F. Zaitchik

depth and streamflow at 3-hourly, 0.125° resolution. The 95th percentile was selected to represent water depth conditions above which flooding is likely ( Wu et al. 2012 , 2014 ). GFMS identifies potential flood areas in real time and provides temporal histories of flooding conditions at each pixel, including estimates of flood detection/intensity (water depth above the threshold) and streamflow (and its flood threshold). The resulting products are designed to provide users with situational

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Zeinab Takbiri, Ardeshir Ebtehaj, Efi Foufoula-Georgiou, Pierre-Emmanuel Kirstetter, and F. Joseph Turk

Johnson 2011 ). These characteristics are difficult to accurately parameterize as of today. Second, the already weak snowfall scattering signal tends to be masked by the increased atmospheric emissivity and liquid water content in precipitating conditions ( Liu and Seo 2013 ; Wang et al. 2013 ; Panegrossi et al. 2017 ). Third, changes in surface emissivity due to snow accumulation on the ground can significantly alter the snowfall microwave signal. Dry snow cover scatters the upwelling surface

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Jiaying Zhang, Liao-Fan Lin, and Rafael L. Bras

), and the Kain–Fritsch (new Eta) cumulus scheme ( Kain and Fritsch 1990 ). The initial and lateral boundary conditions for the simulations are from the National Centers for Environmental Prediction (NCEP) final analysis (FNL) with a 0.25° × 0.25° spatial resolution every 6 hours ( NCEP 2015 ). We initialize the WRF experiments at 1800 UTC every day from 1 August 2015 to 31 July 2017. Each experiment has a lead time of 30 hours, and the simulations of the first 6 hours are for model spinup. The

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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

; Casella et al. 2013 ), and surface conditions ( You et al. 2015 ) successfully reduce errors of the solution in precipitation algorithms. Specifically, Casella et al. (2013) demonstrate the validity of using meteorological parameters in guiding the retrieval scheme in recovering microphysical profiles and surface rain rates from PMW satellite measurements. Using a simulation framework, they found that retrieval ambiguity can be reduced by employing cross combinations of meteorological variables that

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Daniel J. Cecil and Themis Chronis

to variability in the underlying surface. Fig . 1. Example convective outbreak in Texas at 2225 UTC 26 May 2015. (a) Ground-based radar reflectivity mosaic. GMI (b) 37-, (c) 19-, and (d) 10-GHz vertically polarized brightness temperatures. Contour interval in (b)–(d) is 25 K, with thick contours every 50 K, and the minimum brightness temperature in the domain is printed in the panel title. An example of the ambiguity in discriminating storms from surface conditions is shown in Fig. 1 . Strong

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