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Annie L. Putman, Xiahong Feng, Eric S. Posmentier, Anthony M. Faiia, and Leslie J. Sonder

final departure from the PBL, 2) reanalysis data reliably describe actual meteorological conditions, and 3) appropriate altitudes are selected to begin back trajectories. This article tests the sensitivity of a new method for addressing the third requirement. Finding the proper altitude to initiate a back trajectory is important for at least two reasons. First, wind direction and velocity change with altitude, and therefore air parcels released at different heights may indicate very different vapor

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Axel von Engeln and João Teixeira

( Wood and Bretherton 2004 ). Other authors (e.g., Karlsson et al. 2010 ) have used highly accurate estimates of cloud-top height from the Multiangle Imaging SpectroRadiometer (MISR) to investigate the subtropical PBL height evolution. A global climatology for PBL height can be derived from reanalysis data, which combines information from observations and models; it can provide important insight into the physics of the boundary layer from a global perspective and can be used to evaluate weather and

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Will McCarty, Mohar Chattopadhyay, and Austin Conaty

global surface wind fields that are regularly spaced both temporally and spatially. The scatterometer-derived ocean vector winds are complementary to the conventional observing network, and the utility of these observations in data assimilation is applicable both in terms of forecasting ( Yu and Mcpherson 1984 ; Atlas et al. 2001 ; Bi et al. 2011 ; Liu et al. 2018 ) and reanalysis ( Goswami and Sengupta 2003 ; Dee et al. 2011a , b ). A scatterometer determines surface roughness from a measured

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Fuhong Liu, Jeremy R. Krieger, and Jing Zhang

in such simulations due to inaccurate initial and boundary conditions. With the development of various satellite retrievals, as well as improvements in the in situ observational network, the accuracy of model initial conditions and forecast performance can be improved through the application of data assimilation techniques. Various reanalysis projects are taking advantage of such improved initial conditions to generate high quality data. We adopt the same approach and are applying WRF and its

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R. D. Koster, S. D. Schubert, H. Wang, S. P. Mahanama, and Anthony M. DeAngelis

may still be relevant. b. Quantifying and analyzing flash drought with reanalysis data An atmospheric reanalysis ( CCSP 2008 ) is, in essence, a mathematically optimal merging of observations and Earth system model physics that results in spatially and temporally comprehensive quantitative estimates of atmospheric and land surface variables across the globe. The input observations are generally extensive, and the modeled physical formulations impart appropriate physical behaviors to the variables

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Benjamin A. Schenkel, Ning Lin, Daniel Chavas, Michael Oppenheimer, and Alan Brammer

data sources to compile TC size data for ROCI and several 10-m wind radii (e.g., Demuth et al. 2006 ; Landsea and Franklin 2013 ). These studies, however, may also have been negatively influenced by the heterogeneous sampling of the TC wind field. More recently, atmospheric reanalysis data has been used to compute TC size using the radius of the azimuthal-mean environmental pressure ( Knaff and Zehr 2007 ). Finally, Knaff et al. (2014) constructed estimates of the radius in which the azimuthal

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Sebastian A. Krogh, John W. Pomeroy, and James McPhee

reanalysis (ERA-Interim). ERA-Interim is the latest global atmospheric reanalysis produced by the ECMWF, and it covers the period from 1 January 1989 to the present. The gridded data product includes a large variety of 3-hourly surface parameters and 6-hourly upper-air parameters ( Dee et al. 2011 ). This reanalysis has a spatial resolution of 1.5° and 37 pressure levels [increasing by 14 levels from the preceding version 40-yr ECMWF Re-Analysis (ERA-40)]. On the other hand, CFSR spans the 31-yr period

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Xinghua Bao and Fuqing Zhang

atmospheric reanalysis systems may give considerably different results for the same diagnostic quantities due to difference in technical details (model characteristics, horizontal and vertical resolution, the top level, physical parameterizations, boundary conditions, and assimilation scheme, etc.) or observation data assimilated in the reanalysis systems ( Bao and Zhang 2013 ; Fujiwara et al. 2017 ). In particular, the input observation data assimilated in reanalyses, including conventional and

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H. Lauri, T. A. Räsänen, and M. Kummu

regional or global gridded meteorological datasets based on remote sensing or reanalysis data provide promising, convenient, and relatively easy-to-use input data for modeling in remote areas where no measurements exist or where measurement network data is scarce ( Su et al. 2008 ). However, the suitability and accuracy of such data for hydrological modeling of the Mekong catchment has not been extensively tested. The Mekong catchment is also interesting from a precipitation modeling point of view, as

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Zhiyong Huang, Jiu Jimmy Jiao, Xin Luo, Yun Pan, and Taoyong Jin

al. (2015) used GRACE-based TWSA to study the 2006 summer drought and 2011 spring drought in the Yangtze River basin in China and found a connection between drought and ENSO. Ndehedehe et al. (2017) examined the connection between TWS and three global climate indices—the Atlantic multidecadal oscillation (AMO), IOD, and ENSO over West Africa. In addition to GRACE data, the development of the data-assimilation-based reanalysis system offers an alternative way to monitor and characterize drought

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