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A. Priestley

Abstract

The semi-Lagrangian method is now, perhaps, the most widely researched algorithm in connection with numerical weather prediction (NWP) codes. Monotonicity has been added to the basic method by the use of shape-preserving interpolation, and, more recently, by using ideas from flux corrected transport (FCT). In this paper, the authors describe how to make the scheme quasi-conservative. Although the lack of conservation of the semi-Lagrangian method is not widely regarded as a serious problem, for climate studies, where many tens of thousands of time steps are needed, it could become so. The method proposed here is very cheap, and hence is a viable proposition for addition to existing semi-Lagrangian codes. Making the scheme conservative as well as monotone gives the scheme shock-capturing properties, thus making the method much more useful in application areas outside of meteorology.

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A. Priestley

Abstract

In this paper numerical solutions to the shallow-water equations on the sphere are obtained using a method that has already proved its worth in other areas of computational fluid dynamics (CFD), but has yet to make an impact in environmental- or meteorological-type flows. This is the Taylor–Galerkin finite-element method. This method offers the flexibility in mesh refinement associated with the finite-element method in general, together with the accuracy of the Lax-Wendroff method (although with fewer of the well-known problems of that method). Here the method is formulated in a form suitable for solving advection problems on the sphere, and its potential is explored on a well-known test problem. The problems are solved in Cartesian geometry, avoiding the singularities associated with the poles in the usual spherical polar transformation.

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C. H. B. Priestley
and
A. J. Troup

Abstract

No abstract available.

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Norman G. Loeb
,
Bruce A. Wielicki
,
Wenying Su
,
Konstantin Loukachine
,
Wenbo Sun
,
Takmeng Wong
,
Kory J. Priestley
,
Grant Matthews
,
Walter F. Miller
, and
R. Davies

Abstract

Observations from the Clouds and the Earth’s Radiant Energy System (CERES), Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Sea-Viewing Wide-Field-of-View Sensor (SeaWiFS) between 2000 and 2005 are analyzed in order to determine if these data are meeting climate accuracy goals recently established by the climate community. The focus is primarily on top-of-atmosphere (TOA) reflected solar radiances and radiative fluxes. Direct comparisons of nadir radiances from CERES, MODIS, and MISR aboard the Terra satellite reveal that the measurements from these instruments exhibit a year-to-year relative stability of better than 1%, with no systematic change with time. By comparison, the climate requirement for the stability of visible radiometer measurements is 1% decade−1. When tropical ocean monthly anomalies in shortwave (SW) TOA radiative fluxes from CERES on Terra are compared with anomalies in Photosynthetically Active Radiation (PAR) from SeaWiFS—an instrument whose radiance stability is better than 0.07% during its first six years in orbit—the two are strongly anticorrelated. After scaling the SeaWiFS anomalies by a constant factor given by the slope of the regression line fit between CERES and SeaWiFS anomalies, the standard deviation in the difference between monthly anomalies from the two records is only 0.2 W m−2, and the difference in their trend lines is only 0.02 ± 0.3 W m−2 decade−1, approximately within the 0.3 W m−2 decade−1 stability requirement for climate accuracy. For both the Tropics and globe, CERES Terra SW TOA fluxes show no trend between March 2000 and June 2005. Significant differences are found between SW TOA flux trends from CERES Terra and CERES Aqua between August 2002 and March 2005. This discrepancy is due to uncertainties in the adjustment factors used to account for degradation of the CERES Aqua optics during hemispheric scan mode operations. Comparisons of SW TOA flux between CERES Terra and the International Satellite Cloud Climatology Project (ISCCP) radiative flux profile dataset (FD) RadFlux product show good agreement in monthly anomalies between January 2002 and December 2004, and poor agreement prior to this period. Commonly used statistical tools applied to the CERES Terra data reveal that in order to detect a statistically significant trend of magnitude 0.3 W m−2 decade−1 in global SW TOA flux, approximately 10 to 15 yr of data are needed. This assumes that CERES Terra instrument calibration remains highly stable, long-term climate variability remains constant, and the Terra spacecraft has enough fuel to last 15 yr.

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Norman G. Loeb
,
Kory J. Priestley
,
David P. Kratz
,
Erika B. Geier
,
Richard N. Green
,
Bruce A. Wielicki
,
Patricia O’Rawe Hinton
, and
Sandra K. Nolan

Abstract

A new method for determining unfiltered shortwave (SW), longwave (LW), and window radiances from filtered radiances measured by the Clouds and the Earth’s Radiant Energy System (CERES) satellite instrument is presented. The method uses theoretically derived regression coefficients between filtered and unfiltered radiances that are a function of viewing geometry, geotype, and whether cloud is present. Relative errors in instantaneous unfiltered radiances from this method are generally well below 1% for SW radiances (std dev ≈0.4% or ≈1 W m−2 equivalent flux), less than 0.2% for LW radiances (std dev ≈0.1% or ≈0.3 W m−2 equivalent flux), and less than 0.2% (std dev ≈0.1%) for window channel radiances.

When three months (June, July, and August of 1998) of CERES Earth Radiation Budget Experiment (ERBE)-like unfiltered radiances from the Tropical Rainfall Measuring Mission satellite between 20°S and 20°N are compared with archived Earth Radiation Budget Satellite (ERBS) scanner measurements for the same months over a 5-yr period (1985–89), significant scene-type dependent differences are observed in the SW channel. Full-resolution CERES SW unfiltered radiances are ≈7.5% (≈3 W m−2 equivalent diurnal average flux) lower than ERBS over clear ocean, as compared with ≈1.7% (≈4 W m−2 equivalent diurnal average flux) for deep convective clouds and ≈6% (≈4–6 W m−2 equivalent diurnal average flux) for clear land and desert. This dependence on scene type is shown to be partly caused by differences in spatial resolution between CERES and ERBS and by errors in the unfiltering method used in ERBS. When the CERES measurements are spatially averaged to match the ERBS spatial resolution and the unfiltering scheme proposed in this study is applied to both CERES and ERBS, the ERBS all-sky SW radiances increase by ≈1.7%, and the CERES radiances are now consistently ≈3.5%–5% lower than the modified ERBS values for all scene types. Further study is needed to determine the cause for this remaining difference, and even calibration errors cannot be ruled out. CERES LW radiances are closer to ERBS values for individual scene types—CERES radiances are within ≈0.1% (≈0.3 W m−2) of ERBS over clear ocean and ≈0.5% (≈1.5 W m−2) over clear land and desert.

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L. Ruby Leung
,
William R. Boos
,
Jennifer L. Catto
,
Charlotte A. DeMott
,
Gill M. Martin
,
J. David Neelin
,
Travis A. O’Brien
,
Shaocheng Xie
,
Zhe Feng
,
Nicholas P. Klingaman
,
Yi-Hung Kuo
,
Robert W. Lee
,
Cristian Martinez-Villalobos
,
S. Vishnu
,
Matthew D. K. Priestley
,
Cheng Tao
, and
Yang Zhou

Abstract

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.

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