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  • Author or Editor: Kenneth Mitchell x
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Jennifer M. Comstock
,
Robert d'Entremont
,
Daniel DeSlover
,
Gerald G. Mace
,
Sergey Y. Matrosov
,
Sally A . McFarlane
,
Patrick Minnis
,
David Mitchell
,
Kenneth Sassen
,
Matthew D. Shupe
,
David D. Turner
, and
Zhien Wang

The large horizontal extent, with its location in the cold upper troposphere, and ice composition make cirrus clouds important modulators of the Earth's radiation budget and climate. Cirrus cloud microphysical properties are difficult to measure and model because they are inhomogeneous in nature and their ice crystal size distribution and habit are not well characterized. Accurate retrievals of cloud properties are crucial for improving the representation of cloud-scale processes in largescale models and for accurately predicting the Earth's future climate. A number of passive and active remote sensing retrieval algorithms exist for estimating the microphysical properties of upper-tropospheric clouds. We believe significant progress has been made in the evolution of these retrieval algorithms in the last decade; however, there is room for improvement. Members of the Atmospheric Radiation Measurement (ARM) program Cloud Properties Working Group are involved in an intercomparison of optical depth τ and ice water path in ice clouds retrieved using ground-based instruments. The goals of this intercomparison are to evaluate the accuracy of state-of-the-art algorithms, quantify the uncertainties, and make recommendations for their improvement.

Currently, there are significant discrepancies among the algorithms for ice clouds with very small optical depths (τ < 0.3) and those with 1 < τ < 5. The good news is that for thin clouds (0.3 < τ < 1), the algorithms tend to converge. In this first stage of the intercomparison, we present results from a representative case study, compare the retrieved cloud properties with aircraft and satellite measurements, and perform a radiative closure experiment to begin gauging the accuracy of these retrieval algorithms.

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Fedor Mesinger
,
Geoff DiMego
,
Eugenia Kalnay
,
Kenneth Mitchell
,
Perry C. Shafran
,
Wesley Ebisuzaki
,
Dušan Jović
,
Jack Woollen
,
Eric Rogers
,
Ernesto H. Berbery
,
Michael B. Ek
,
Yun Fan
,
Robert Grumbine
,
Wayne Higgins
,
Hong Li
,
Ying Lin
,
Geoff Manikin
,
David Parrish
, and
Wei Shi

In 1997, during the late stages of production of NCEP–NCAR Global Reanalysis (GR), exploration of a regional reanalysis project was suggested by the GR project's Advisory Committee, “particularly if the RDAS [Regional Data Assimilation System] is significantly better than the global reanalysis at capturing the regional hydrological cycle, the diurnal cycle and other important features of weather and climate variability.” Following a 6-yr development and production effort, NCEP's North American Regional Reanalysis (NARR) project was completed in 2004, and data are now available to the scientific community. Along with the use of the NCEP Eta model and its Data Assimilation System (at 32-km–45-layer resolution with 3-hourly output), the hallmarks of the NARR are the incorporation of hourly assimilation of precipitation, which leverages a comprehensive precipitation analysis effort, the use of a recent version of the Noah land surface model, and the use of numerous other datasets that are additional or improved compared to the GR. Following the practice applied to NCEP's GR, the 25-yr NARR retrospective production period (1979–2003) is augmented by the construction and daily execution of a system for near-real-time continuation of the NARR, known as the Regional Climate Data Assimilation System (R-CDAS). Highlights of the NARR results are presented: precipitation over the continental United States (CONUS), which is seen to be very near the ingested analyzed precipitation; fits of tropospheric temperatures and winds to rawinsonde observations; and fits of 2-m temperatures and 10-m winds to surface station observations. The aforementioned fits are compared to those of the NCEP–Department of Energy (DOE) Global Reanalysis (GR2). Not only have the expectations cited above been fully met, but very substantial improvements in the accuracy of temperatures and winds compared to that of GR2 are achieved throughout the troposphere. Finally, the numerous datasets produced are outlined and information is provided on the data archiving and present data availability.

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RICHARD LAWFORD
,
MIKE BOSILOVICH
,
SUSANNA EDEN
,
SAM BENEDICT
,
CONSTANCE BROWN
,
ARNOLD GRUBER
,
PAUL HOUSER
,
KUOLIN HSU
,
JIN HUANG
,
WILLIAM LAU
,
TILDEN MEYERS
,
KENNETH MITCHELL
,
CHRISTA PETERS-LIDARD
,
JOHN ROADS
,
MATT RODELL
,
SOROOSH SOROOSHIAN
,
DAN TARPLEY
, and
STEVE WILLIAMS

The Coordinated Enhanced Observing Period (CEOP) is an international project that was first proposed by the Global Energy and Water Cycle Experiment (GEWEX) in 1997 and was formally launched in 2001. Since that time it has been adopted by the World Climate Research Programme (WCRP), which views it as an essential part of its strategy for developing global datasets to evaluate global climate models, and by the Integrated Global Observing Strategy Partnership (IGOS-P), which views it as the first element of its global water cycle theme. The United States has been an active partner in all phases of CEOP. In particular, the United States has taken the lead in contributing data from a number of reference sites, providing data processing, and archiving capabilities and related research activities through the GEWEX Americas Prediction Project (GAPP). Other U.S. programs and agencies are providing components including model and data assimilation output, satellite data, and other services. The U.S. science community has also been using the CEOP database in model evaluation and phenomenological studies. This article summarizes the U.S. contributions during the first phase of CEOP and outlines opportunities for readers to become involved in the data analysis phase of the project.

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