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T. J. Bellerby
and
E. C. Barrett

Abstract

The progressive refinement approach is a strategy for generating new shorter-term (less than 10 days) satellite rainfall estimation algorithms from structural combinations of an easy-to-calibrate algorithm generating longer- term rainfall totals only, and a shorter-term algorithm that efficiently evaluates individual precipitation events. It is designed to address the problem of local calibration of the latter type of algorithm without recourse to high-quality ground data, which are frequently not available. A PR daily rainfall estimation method based upon the Bristol polar-orbiter effective rainfall monitoring integrative technique algorithm is described and evaluated.

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C. Kidd
,
D. Kniveton
, and
E. C. Barrett

Abstract

This paper reviews the basis of passive microwave algorithms that derive rainfall rates directly from relationships between brightness temperatures and rainfall rates established by statistical relationships and empirical calibration. The performance of these algorithms and their present and future roles are assessed in comparison with the increasing number of modeling techniques used for passive microwave rainfall retrievals.

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R. L. Schwiesow
,
R. E. Cupp
,
V. E. Derr
,
E. W. Barrett
,
R. F. Pueschel
, and
P. C. Sinclair

Abstract

Using an airborne lidar, we have measured atmospheric aerosol backscatter coefficients (differential backscatter cross section per unit volume) for 10.6 μm wavelength laser radiation as a function of height to 5200 m for a number of meteorological conditions over the United States high plains. Airborne in situ samplers measured the particle size distribution at the same time and altitude as the lidar measured backscatter. One backscatter coefficient profile at 10.6 μm was compared with a 0.694 μm lidar backscatter profile as well as with the particle size distribution profile. The average infrared backscatter coefficient ranged from ∼8 × 10−9 m−1 sr−1 at the surface to 1 × 10−10 sr−1 at 5200 m altitude.

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E. A. Smith
,
J. E. Lamm
,
R. Adler
,
J. Alishouse
,
K. Aonashi
,
E. Barrett
,
P. Bauer
,
W. Berg
,
A. Chang
,
R. Ferraro
,
J. Ferriday
,
S. Goodman
,
N. Grody
,
C. Kidd
,
D. Kniveton
,
C. Kummerow
,
G. Liu
,
F. Marzano
,
A. Mugnai
,
W. Olson
,
G. Petty
,
A. Shibata
,
R. Spencer
,
F. Wentz
,
T. Wilheit
, and
E. Zipser

Abstract

The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 satellite precipitation retrieval algorithms, implemented for application with Special Sensor Microwave/Imager (SSM/I) passive microwave (PMW) measurements and run for a set of rainfall case studies at full resolution–instantaneous space–timescales. The cases are drawn from over the globe during all seasons, for a period of 7 yr, over a 60°N–17°S latitude range. Ground-based data were used for the intercomparisons, principally based on radar measurements but also including rain gauge measurements. The goals of PIP-2 are 1) to improve performance and accuracy of different SSM/I algorithms at full resolution–instantaneous scales by seeking a better understanding of the relationship between microphysical signatures in the PMW measurements and physical laws employed in the algorithms; 2) to evaluate the pros and cons of individual algorithms and their subsystems in order to seek optimal “front-end” combined algorithms; and 3) to demonstrate that PMW algorithms generate acceptable instantaneous rain estimates.

It is found that the bias uncertainty of many current PMW algorithms is on the order of ±30%. This level is below that of the radar and rain gauge data specially collected for the study, so that it is not possible to objectively select a best algorithm based on the ground data validation approach. By decomposing the intercomparisons into effects due to rain detection (screening) and effects due to brightness temperature–rain rate conversion, differences among the algorithms are partitioned by rain area and rain intensity. For ocean, the screening differences mainly affect the light rain rates, which do not contribute significantly to area-averaged rain rates. The major sources of differences in mean rain rates between individual algorithms stem from differences in how intense rain rates are calculated and the maximum rain rate allowed by a given algorithm. The general method of solution is not necessarily the determining factor in creating systematic rain-rate differences among groups of algorithms, as we find that the severity of the screen is the dominant factor in producing systematic group differences among land algorithms, while the input channel selection is the dominant factor in producing systematic group differences among ocean algorithms. The significance of these issues are examined through what is called “fan map” analysis.

The paper concludes with a discussion on the role of intercomparison projects in seeking improvements to algorithms, and a suggestion on why moving beyond the “ground truth” validation approach by use of a calibration-quality forward model would be a step forward in seeking objective evaluation of individual algorithm performance and optimal algorithm design.

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Guy P. Brasseur
,
Mohan Gupta
,
Bruce E. Anderson
,
Sathya Balasubramanian
,
Steven Barrett
,
David Duda
,
Gregg Fleming
,
Piers M. Forster
,
Jan Fuglestvedt
,
Andrew Gettelman
,
Rangasayi N. Halthore
,
S. Daniel Jacob
,
Mark Z. Jacobson
,
Arezoo Khodayari
,
Kuo-Nan Liou
,
Marianne T. Lund
,
Richard C. Miake-Lye
,
Patrick Minnis
,
Seth Olsen
,
Joyce E. Penner
,
Ronald Prinn
,
Ulrich Schumann
,
Henry B. Selkirk
,
Andrei Sokolov
,
Nadine Unger
,
Philip Wolfe
,
Hsi-Wu Wong
,
Donald W. Wuebbles
,
Bingqi Yi
,
Ping Yang
, and
Cheng Zhou

Abstract

Under the Federal Aviation Administration’s (FAA) Aviation Climate Change Research Initiative (ACCRI), non-CO2 climatic impacts of commercial aviation are assessed for current (2006) and for future (2050) baseline and mitigation scenarios. The effects of the non-CO2 aircraft emissions are examined using a number of advanced climate and atmospheric chemistry transport models. Radiative forcing (RF) estimates for individual forcing effects are provided as a range for comparison against those published in the literature. Preliminary results for selected RF components for 2050 scenarios indicate that a 2% increase in fuel efficiency and a decrease in NOx emissions due to advanced aircraft technologies and operational procedures, as well as the introduction of renewable alternative fuels, will significantly decrease future aviation climate impacts. In particular, the use of renewable fuels will further decrease RF associated with sulfate aerosol and black carbon. While this focused ACCRI program effort has yielded significant new knowledge, fundamental uncertainties remain in our understanding of aviation climate impacts. These include several chemical and physical processes associated with NOx–O3–CH4 interactions and the formation of aviation-produced contrails and the effects of aviation soot aerosols on cirrus clouds as well as on deriving a measure of change in temperature from RF for aviation non-CO2 climate impacts—an important metric that informs decision-making.

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Justin Sheffield
,
Andrew P. Barrett
,
Brian Colle
,
D. Nelun Fernando
,
Rong Fu
,
Kerrie L. Geil
,
Qi Hu
,
Jim Kinter
,
Sanjiv Kumar
,
Baird Langenbrunner
,
Kelly Lombardo
,
Lindsey N. Long
,
Eric Maloney
,
Annarita Mariotti
,
Joyce E. Meyerson
,
Kingtse C. Mo
,
J. David Neelin
,
Sumant Nigam
,
Zaitao Pan
,
Tong Ren
,
Alfredo Ruiz-Barradas
,
Yolande L. Serra
,
Anji Seth
,
Jeanne M. Thibeault
,
Julienne C. Stroeve
,
Ze Yang
, and
Lei Yin

Abstract

This is the first part of a three-part paper on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that evaluates the historical simulations of continental and regional climatology with a focus on a core set of 17 models. The authors evaluate the models for a set of basic surface climate and hydrological variables and their extremes for the continent. This is supplemented by evaluations for selected regional climate processes relevant to North American climate, including cool season western Atlantic cyclones, the North American monsoon, the U.S. Great Plains low-level jet, and Arctic sea ice. In general, the multimodel ensemble mean represents the observed spatial patterns of basic climate and hydrological variables but with large variability across models and regions in the magnitude and sign of errors. No single model stands out as being particularly better or worse across all analyses, although some models consistently outperform the others for certain variables across most regions and seasons and higher-resolution models tend to perform better for regional processes. The CMIP5 multimodel ensemble shows a slight improvement relative to CMIP3 models in representing basic climate variables, in terms of the mean and spread, although performance has decreased for some models. Improvements in CMIP5 model performance are noticeable for some regional climate processes analyzed, such as the timing of the North American monsoon. The results of this paper have implications for the robustness of future projections of climate and its associated impacts, which are examined in the third part of the paper.

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