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Behnjamin J. Zib, Xiquan Dong, Baike Xi, and Aaron Kennedy

capturing the observed surface radiation fluxes than the earlier reanalyses seen in Walsh et al. (2009) . 2. Datasets and methodology a. BSRN and ARM observations The variables explored in this study are total CF, surface downwelling shortwave (SW-down) and longwave (LW-down) radiation fluxes, and 2-m air temperatures. The broadband SW-down (0.3–3 μ m) and LW-down (4–50 μ m) fluxes at BAR were obtained by Eppley Precision Spectral Pyranometers (PSPs) and Precision Infrared Pyrgeometers (PIRs

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Michael G. Bosilovich, Franklin R. Robertson, and Junye Chen

al. 2011 ). In reviewing the observed global energy budget, TFK09 also compared the reanalysis energy budgets (specifically ERA-40, NCEP–DOE R2, and JRA-25), and some similar biases are evident. First, the net TOA energy did not balance well, with too much upward flux. However, JRA-25 bias is related to too much outgoing longwave radiation (OLR), while NCEP–DOE R2 is due to too much reflected shortwave radiation, but both imbalances were on the order of 10 W m −2 . Also, all reanalyses had

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Aaron D. Kennedy, Xiquan Dong, Baike Xi, Shaocheng Xie, Yunyan Zhang, and Junye Chen

has been done to derive forcing using constrained variational analysis from observations during intensive observation periods (IOPs) at the Department of Energy (DOE) Atmospheric Radiation Measurement Program (ARM) sites ( Zhang and Lin 1997 ; Zhang et al. 2001 ). More recently, Xie et al. (2003) evaluated the forcing datasets derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) during three IOPs at the ARM Southern Great Plains (SGP) site. They found that although the

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Michele M. Rienecker, Max J. Suarez, Ronald Gelaro, Ricardo Todling, Julio Bacmeister, Emily Liu, Michael G. Bosilovich, Siegfried D. Schubert, Lawrence Takacs, Gi-Kong Kim, Stephen Bloom, Junye Chen, Douglas Collins, Austin Conaty, Arlindo da Silva, Wei Gu, Joanna Joiner, Randal D. Koster, Robert Lucchesi, Andrea Molod, Tommy Owens, Steven Pawson, Philip Pegion, Christopher R. Redder, Rolf Reichle, Franklin R. Robertson, Albert G. Ruddick, Meta Sienkiewicz, and Jack Woollen

, Radiation, and Transport GOES Geostationary Operational Environmental Satellite GPCP Global Precipitation Climatology Project GPROF Goddard profiling algorithm GPS-RO Global Positioning System Radio Occultation GSFC Goddard Space Flight Center GSI Gridpoint statistical interpolation GTS Global Telecommunication System HALOE Halogen Occultation Experiment HIRS High Resolution Infrared Radiation Sounder IAU Incremental Analysis Update ICOADS International Comprehensive Ocean–Atmosphere Dataset IR Infrared

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Yonghong Yi, John S. Kimball, Lucas A. Jones, Rolf H. Reichle, and Kyle C. McDonald

information from satellite visible and infrared radiances and likely provide better cloud estimates than GEOS-4. The cloud modeling and observation systems in GCMs have great impact on modeled land surface parameters, including air temperatures and VPD presented in this study. MERRA generally shows warmer and drier conditions (high air temperatures and VPD) relative to GEOS-4 and WMO observations especially in the SH and northern tropics, which is consistent with generally greater solar radiation and

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Rolf H. Reichle, Randal D. Koster, Gabriëlle J. M. De Lannoy, Barton A. Forman, Qing Liu, Sarith P. P. Mahanama, and Ally Touré

conditions, reanalysis products also provide estimates of land surface fields, including surface meteorological forcing data (such as precipitation, radiation, air temperature, and humidity) as well as land surface states and fluxes (such as soil moisture, snow, and runoff). Reanalysis estimates can be used for a large variety of research and applications, for example, the generation of enhanced land surface meteorological datasets ( Berg et al. 2005 ; Guo et al. 2006 ; Sheffield et al. 2006 ), the

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Derek J. Posselt, Andrew R. Jongeward, Chuan-Yuan Hsu, and Gerald L. Potter

1. Introduction In recent decades, reanalysis has emerged as an effective way to combine numerical model output with information from a diverse suite of atmospheric state measurements such that the proper relationships between dynamics, thermodynamics, radiation, and surface fluxes are preserved ( Kalnay et al. 1996 ; Uppala et al. 2005 ; Onogi et al. 2005 ; Rienecker et al. 2011 ). In large part because these datasets provide a long-term dynamically consistent and multivariate

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Michael A. Brunke, Zhuo Wang, Xubin Zeng, Michael Bosilovich, and Chung-Lin Shie

to the parameterizations of deep convection and radiation and a new representation of sea ice ( Uppala et al. 2005 ). Data assimilation was done by a 3DVar system. Inputs included conventional sources (surface land and ship observations, rawinsondes, dropsondes, PIBALs, wind profilers, and aircraft) and satellite measurements [upper-level winds from geostationary satellites, radiances from the Vertical Temperature Profile Radiometer (VTPR), the High Resolution Infrared Sounder (HIRS), the

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Franklin R. Robertson and Jason B. Roberts

sensible heat fluxes (SHF) as well as 2-m specific humidity and temperature, 10-m wind speeds, and SST. TOA and surface radiative fluxes produced as part of the National Aeronautics and Space Administration (NASA) Global Energy and Water Cycle Experiment, Surface Radiation Budget Project (GEWEX SRB) were obtained from the NASA Langley Research Center Atmospheric Sciences Data Center. Daily all-sky and clear-sky products available at 1.0° latitude × longitude resolution from the SRB_REL3.0_LW_DAILY and

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Franklin R. Robertson, Michael G. Bosilovich, Junye Chen, and Timothy L. Miller

prognostic budgets due to the observational analysis: res H is a small value that includes the gravity wave drag and a residual that results from maintaining energy balance in the presence of numerical dissipation, each of which is also included in the output diagnostics ( Suarez et al. 2010 ). The vertically integrated radiation heating term, RAD, can be expanded further into the difference of the top-of-atmosphere (TOA) and surface shortwave fluxes (SW T and SW S , respectively) minus the sum of

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