Publications

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Journal Article
Somerville, RCJ.  1977.  Pattern Recognition Techniques for Weather Forecast Verification. Contributions to Atmospheric Physics [Beitraege zur Physik der Atmosphaere.], Wiesbaden, Germany. 50:403-410. Abstract
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Roads, JO, Somerville RCJ.  1982.  Predictability of Ultralong Waves in Global and Hemispheric Quasi-Geostrophic Barotropic Models. Journal of the Atmospheric Sciences. 39:745-755.   10.1175/1520-0469(1982)039<0745:pouwig>2.0.co;2   AbstractWebsite

A global quasi-geostrophic barotropic model, including orography, zonal forcing and frictional dissipation, is compared to two hemispheric models, one with antisymmetric equatorial boundary conditions and one with symmetric boundary conditions. The stationary solutions in the global model and the hemispheric models are found to be different, because the hemispheric models lack either the symmetric or antisymmetric waves, and because the nonlinear feedbacks are much larger in the hemispheric models. Time-dependent calculations show that the hemispheric models can excite anomalous Rossby waves and can produce erroneous short-range forecasts in middle latitudes. We conclude that global models are preferred for making both short-range and long-range forecasts for middle latitudes.

Somerville, RCJ.  1987.  The predictability of weather and climate. Climatic Change. 11:239-246.: Kluwer Academic Publishers   10.1007/bf00138802   AbstractWebsite

The last thirty years have seen the development of comprehensive numerical models of the large-scale circulation of the atmosphere, based on physical principles. Such models are quite skillful at describing the evolving weather up to a few days ahead, despite imperfect theory and inadequate observational data. Yet even a hypothetical perfect model, which exactly represented the dynamics of the real atmosphere, and which used data from the best conceivable observing system, could not produce an accurate forecast of indefinitely long range. Any forecast must eventually lose skill because of the intrinsic instability of the atmosphere itself.This limitation on the predictability of the detailed evolution of the atmosphere (“weather”) does not preclude the possibility of seasonal and longer-range forecasts of means and other statistical properties (“climate”). However, we are only beginning to learn what aspects of climate may be predictable, and what theoretical tools and observational data will be required to predict them.

Waliser, DE, Somerville RCJ.  1994.  Preferred Latitudes of the Intertropical Convergence Zone. Journal of the Atmospheric Sciences. 51:1619-1639.   10.1175/1520-0469(1994)051<1619:plotic>2.0.co;2   AbstractWebsite

The latitude preference of the intertropical convergence zone (ITCZ) is examined on the basis of observations, theory, and a modeling analysis. Observations show that convection is enhanced at latitudes of about 4-degrees to 10-degrees relative to the equator, even in regions where the sea surface temperature (SST) is maximum on the equator. Both linear shallow-water theory and a moist primitive equation model suggest a new explanation for the off-equatorial latitude preference of the ITCZ that requires neither the existence of zonally propagating disturbances nor an off-equatorial maximum in SST. The shallow-water theory indicates that a finite-width, zonally oriented, midtropospheric heat source (i.e., an ITCZ) produces the greatest local low-level convergence when placed a finite distance away from the equator. This result suggests that an ITCZ is most likely to be supported via low-level convergence of moist energy when located at these ''preferred'' latitudes away from die equator. For a plausible range of heating widths and damping parameters, the theoretically predicted latitude is approximately equal to the observed position(s) of the ITCZ(s). Analysis with an axially symmetric, moist, primitive equation model indicates that when the latent heating field is allowed to be determined internally, a positive feedback develops between the midtropospheric latent heating and the low-level convergence, with the effect of enhancing the organization of convection at latitudes of about 4-degrees to 12-degrees. Numerical experiments show that 1) two peaks in convective precipitation develop straddling the equator when the SST maximum is located on the equator; 2) steady ITCZ-like structures form only when the SST maximum is located away from the equator; and 3) peaks in convection can develop away from the maximum in SST, with a particular preference for latitudes of about 4-degrees to 12-degrees-, even in the (''cold'') hemisphere without the SST maximum. The relationship between this mechanism and earlier theories is discussed, as are implications for the coupled ocean-atmosphere system and the roles played by midlevel latent heating and SST gradients in forcing the low-level atmospheric circulation in the tropics.

Shen, SSP, Velado M, Somerville RCJ, Kooperman GJ.  2013.  Probabilistic assessment of cloud fraction using Bayesian blending of independent datasets: Feasibility study of a new method. Journal of Geophysical Research: Atmospheres.   10.1002/jgrd.50408   AbstractWebsite

We describe and evaluate a novel method to blend two observed cloud fraction (CF) datasets through Bayesian posterior estimation. The research reported here is a feasibility study designed to explore the method. In this proof-of-concept study, we illustrate the approach using specific observational datasets from the U. S. Department of Energy Atmospheric Radiation Measurement Program's Southern Great Plains site in the central United States, but the method is quite general and is readily applicable to other datasets. The total sky image (TSI) camera observations are used to determine the prior distribution. A regression model and the active remote sensing of clouds (ARSCL) radar/lidar observations are used to determine the likelihood function. The posterior estimate is a probability density function (pdf) of the CF whose mean is taken to be the optimal blend of the two observations. The data at hourly, daily, 5-day, monthly, and annual time scales are considered. Some physical and probabilistic properties of the CFs are explored from radar/lidar, camera, and satellite observations and from simulations using the Community Atmosphere Model (CAM5). Our results imply that (a) the Beta distribution is a reasonable model for CF for both short- and long-time means, the 5-day data are skewed right, and the annual data are almost normally distributed, and (b) the Bayesian method developed successfully yields a pdf of CF, rather than a deterministic CF value, and it is feasible to blend the TSI and ARSCL data with a capability for bias correction.