Sea breeze: Induced mesoscale systems and severe weather

Sea-breeze-deep convective interactions over the Florida peninsula were investigated using a cloud/mesoscale numerical model. The objective was to gain a better understanding of sea-breeze and deep convective interactions over the Florida peninsula using a high resolution convectively explicit model and to use these results to evaluate convective parameterization schemes. A 3-D numerical investigation of Florida convection was completed. The Kuo and Fritsch-Chappell parameterization schemes are summarized and evaluated

Making energy access meaningful

The world's poor need more than a token supply of electricity. The goal should be to provide the power necessary to boost productivity and raise living standards

Numerical simulation of the influence of the large-scale monsoon flow on the diurnal weather patterns over Kenya

August 1992.Bibliography: pages 204-211.Sponsored by NSF ATM-8915265

Adaptive constraints for feature tracking

In this paper extensions to an existing tracking algorithm are described. These extensions implement adaptive tracking constraints in the form of regional upper-bound displacements and an adaptive track smoothness constraint. Together, these constraints make the tracking algorithm more flexible than the original algorithm (which used fixed tracking parameters) and provide greater confidence in the tracking results. The result of applying the new algorithm to high-resolution ECMWF reanalysis data is shown as an example of its effectiveness

Observational evidence of temperature trends at two levels in the surface layer

Citation: Lin, X., Pielke, R. A., Mahmood, R., Fiebrich, C. A., & Aiken, R. (2016). Observational evidence of temperature trends at two levels in the surface layer. Atmospheric Chemistry and Physics, 16(2), 827-841. doi:10.5194/acp-16-827-2016Long-term surface air temperatures at 1.5m screen level over land are used in calculating a global average surface temperature trend. This global trend is used by the IPCC and others to monitor, assess, and describe global warming or warming hiatus. Current knowledge of near-surface temperature trends with respect to height, however, is limited and inadequately understood because surface temperature observations at different heights in the surface layer of the world are rare especially from a high-quality and long-term climate monitoring network. Here we use high-quality two-height Oklahoma Mesonet observations, synchronized in time, fixed in height, and situated in relatively flat terrain, to assess temperature trends and differentiating temperature trends with respect to heights (i.e., near-surface lapse rate trend) over the period 1997 to 2013. We show that the near-surface lapse rate has significantly decreased with a trend of -0.18 +/- 0.03 degrees C (10 m)(-1) per decade indicating that the 9m height temperatures increased faster than temperatures at the 1.5m screen level and/or conditions at the 1.5m height cooled faster than at the 9m height. However, neither of the two individual height temperature trends by themselves were statistically significant. The magnitude of lapse rate trend is greatest under lighter winds at night. Nighttime lapse rate trends were significantly more negative than daytime lapse rate trends and the average lapse rate trend was three times more negative under calm conditions than under windy conditions. Our results provide the first observational evidence of near-surface temperature changes with respect to height that could enhance the assessment of climate model predictions

Global vegetation cover changes from coarse resolution satellite data

Land cover plays a key role in various biophysical processes related to global climate and terrestrial biogeochemistry. Although global land cover has dramatically changed over the last few centuries, until now there has been no consistent way of quantifying the changes globally. In this study we used long-term climate and soils data along with coarse resolution satellite observations to quantify the magnitude and spatial extent of large-scale land cover changes attributable to anthropogenic processes. Differences between potential leaf area index (LAI), derived from climate-soil-leaf area equilibrium, and actual leaf area index obtained from satellite data are used to estimate changes in land cover. Further, changes in LAI between potential and actual conditions are linked to climate by expressing them as possible changes in radiometric surface temperatures (Tr) resulting from changes in surface energy partitioning. As expected, areas with high population densities, such as India, China, and western Europe showed large reductions in LAI. Changes in global land cover expressed as summer, midafternoon Tr, ranged from −8° to +16°C. Deforestation resulted in an increase in Tr, while irrigated agriculture reduced the Tr. Many of the current general circulation models (GCMs) use potential vegetation maps to represent global vegetation. Our results indicate that there are widespread changes in global land cover due to deforestation and agriculture below the resolution of many GCMs, and these changes could have a significant impact on climate. Potential and actual LAI data sets are available for climate modelers at 0.5° × 0.5° resolution to study the possible impacts of land cover changes on global temperatures and circulation patterns

Numerical Simulation of the 9-10 June 1972 Black Hills Storm Using CSU RAMS

Strong easterly flow of low-level moist air over the eastern slopes of the Black Hills on 9-10 June 1972 generated a storm system that produced a flash flood, devastating the area. Based on observations from this storm event, and also from the similar Big Thompson 1976 storm event, conceptual models have been developed to explain the unusually high precipitation efficiency. In this study, the Black Hills storm is simulated using the Colorado State University Regional Atmospheric Modeling System. Simulations with homogeneous and inhomogeneous initializations and different grid structures are presented. The conceptual models of storm structure proposed by previous studies are examined in light of the present simulations. Both homogeneous and inhomogeneous initialization results capture the intense nature of the storm, but the inhomogeneous simulation produced a precipitation pattern closer to the observed pattern. The simulations point to stationary tilted updrafts, with precipitation falling out to the rear as the preferred storm structure. Experiments with different grid structures point to the importance of removing the lateral boundaries far from the region of activity. Overall, simulation performance in capturing the observed behavior of the storm system was enhanced by use of inhomogeneous initialization

Estimating the lyapunov-exponent spectrum from short time series of low precision

We propose a new method to compute Lyapunov exponents from limited experimental data. The method is tested on a variety of known model systems, and it is found that the algorithm can be used to obtain a reasonable Lyapunov-exponent spectrum from only 5000 data points with a precision of 10 -lor 10 -2 in three-or four-dimensional phase space, or 10000 data points in five-dimensional phase space. We also apply our algorithm to the daily-averaged data of surface temperature observed at two locations in the United States to quantitatively evaluate atmospheric predictability. PACS numbers: 05.45.+b, 02.60.+y, 47.20.Tg, 92.60.Wc Nonlinear phenomena occur in nature in a wide range of apparently different contexts, yet they often display common features, or can be understood using similar concepts. Deterministic chaos and fractal structure in dissipative dynamical systems are among the most important nonlinear paradigms. The spectrum of Lyapunov exponents provides a quantitative measure of the sensitivity to initial conditions (i.e., the divergence of neighboring trajectories exponentially in time) and is the most useful dynamical diagnostic for chaotic systems. In fact, any system containing at least one positive Lyapunov exponent is defined to be chaotic, with the magnitude of the exponent determining the time scale for predictability. In any well-behaved dissipative dynamical system, one of the Lyapunov exponents must be strictly negative. I If the Lyapunov-exponent spectrum can be determined, the Kolmogorov entropy2 can be computed by summing all of the positive exponents, and the fractal dimension may be estimated using the Kaplan-Yorke conjecture. 3 The Lyapunov-exponent spectrum can be computed relatively easily for known model systems.4 However, it is difficult to estimate Lyapunov exponents from experimental data for a complex system (e.g., the atmosphere). Wolf et al.5 proposed a method to estimate one or two positive exponents. Sano and Sawada 6 and Eckmann et al.7 developed similar procedures to determine several of the Lyapunov exponents (including positive, zero, and even negative values). This is now a very active research area, and several authors8 have introduced further improvements. However, all of these methods require rela-@ 1991 The Americ tively long time series and/or data of high precision (for example, Eckmann et at. used 64000 data points with a precision of 10-4 for the Lorenz equations9), but such high-quality data cannot be obtained in many real-world situations. The infinitesimal length scales used to define Lyapunov exponents are inaccessible in experimental data. 5 The presence of noise or limited precision leads to a length scale Ln below which the structure of the underlying strange attractor is obscured. Also, for a finite data set of N points, there is a minimum length scale Lo-L/N1/D, where L is the horizontal extent of the attractor and D is its information dimension,lo below which structure cannot be resolved. When Lo~ Ln, increasing N is not likely to provide any further information on the structure of the attractor, so that a relatively small data set can be sufficient for computing Lyapunov exponents. Furthermore, if the length scales Lo and Ln are small enough for the chaotic dynamics to be the same as at infinitesimal length scales, then the computation of Lyapunov exponents using these length scales should yield reasonable results. Abraham et at. II have demonstrated that it is possible to calculate the dimensions of attractors from small, noisy data sets. The purpose of this paper is to develop a procedure by which one can evaluate the Lyapunovexponent spectrum from relatively small data sets of low precision. We test the method on a variety of known model systems, and we also use the method to study the predictability of the atmosphere from observational meteorological data. It should be noted, as pointed ou