Episodic Performance and Sensitivity of the Urbanized MM5 (uMM5) to Perturbations in Surface Properties in Houston Texas

We present an application of a fine-resolution, meso-urban meteorological model (urbanized MM5; uMM5) to a multi-day episode in August 2000 in the Houston-Galveston Texas, USA region. The model’s episodic performance and its response to small changes in land-cover and surface physical properties in the area, e.g., scenarios of urban heat island mitigation, are evaluated. The model formulation is reviewed along with its parameterizations, data needs, and fine-resolution geometrical input. Development of scenarios of increased urban albedo and vegetative cover is also discussed. This initial application of the uMM5 to the Houston-Galveston region serves as a basis for future model improvements, evaluation of newer data and parameterization applications, testing more aggressive surface modification scenarios, and performing fine-resolution photochemical modelling. It also provides data for comparison of model results with those from previous studies of this region.     

Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada

Summary Uncertainty analysis is used to make a quantitative evaluation of the reliability of statistically downscaled climate data representing local climate conditions in the northern coastlines of Canada. In this region, most global climate models (GCMs) have inherent weaknesses to adequately simulate the climate regime due to difficulty in resolving strong land/sea discontinuities or heterogeneous land cover. The performance of the multiple regression-based statistical downscaling model in reproducing the observed daily minimum/maximum temperature, and precipitation for a reference period (1961–1990) is evaluated using climate predictors derived from NCEP reanalysis data and those simulated by two coupled GCMs (the Canadian CGCM2 and the British HadCM3). The Wilcoxon Signed Rank test and bootstrap confidence-interval estimation techniques are used to perform uncertainty analysis on the downscaled meteorological variables. The results show that the NCEP-driven downscaling results mostly reproduced the mean and variability of the observed climate very well. Temperatures are satisfactorily downscaled from HadCM3 predictors while some of the temperatures downscaled from CGCM2 predictors are statistically significantly different from the observed. The uncertainty in precipitation downscaled with CGCM2 predictors is comparable to the ones downscaled from HadCM3. In general, all downscaling results reveal that the regression-based statistical downscaling method driven by accurate GCM predictors is able to reproduce the climate regime over these highly heterogeneous coastline areas of northern Canada. The study also shows the applicability of uncertainty analysis techniques in evaluating the reliability of the downscaled data for climate scenarios development. Authors’ addresses: Dr. Yonas B. Dibike, NSERC Research Fellow, OURANOS Consortium, 550 Sherbrooke Street West, 19^th Floor, Montreal (QC) H3A 1B9, Canada; Philippe Gachon, Adaptation and Impact Research Division (AIRD), Atmospheric Science and Technology Directorate, Environment Canada at Ouranos, Montreal (QC), Canada; André St-Hilaire and Taha B. M. J. Ouarda, Institut National de la Recherche Scientifique Centre Eau, Terre & Environnement (INRS-ETE), University of Québec, 490 Rue de La Couronne, Québec (QC) G1K 9A9, Canada; Van T.-V. Nguyen, Department of Civil Engineering and Applied Mechanics, McGill University, 817 Sherbrooke Street West, Montreal (QC) H3A 2K6, Canada.     

Chance-constrained optimal control for multireservoir system optimization and risk analysis

 A vast array of techniques have been developed and applied to optimal operation of large-scale multireservoir systems. Researchers continue to be challenged by the highly complex, stochastic, nonlinear, and high dimensional nature of this dynamic optimization problem. An optimal control model is presented which incorporates chance-constraints on system state variables that assure satisfaction of operational restrictions under specified levels of reliability. The chance-constrained optimal control (CCOC) model is tested on a four-reservoir case study, and its performance assessed based on various quantitative and qualitative criteria, including maintenance of acceptable levels of risk and provision of risk-benefit trade-off information. The concepts of reliability, resiliency and vulnerability are utilized to characterize operating policies generated by the algorithm. CCOC is recommended for operational guidance of large-scale multireservoir systems due to its robustness, flexibility, modest computational requirements, and ability to include risk considerations directly impacting the choice of operational schemes.     

Multivariate homogeneity testing in a northern case study in the province of Quebec,Canada

In regional frequency analysis, the examination of the regional homogeneity represents an important step of the procedure. Flood events possess multivariate characteristics which can not be handled by classical univariate regional procedures. For instance, classical procedures do not allow to assess regional homogeneity while taking into consideration flood peak, volume and duration. Chebana and Ouarda proposed multivariate discordancy and homogeneity tests. They carried out a simulation study to evaluate the performance of these tests. In the present paper, practical aspects are investigated jointly on flood peak and flood volume of a data set from the Côte‐Nord region in the province of Quebec, Canada. It is shown that, after removing the discordant sites, the remaining ones constitute a homogeneous region for the volumes and heterogeneous region for the peaks. However, if both variables are jointly considered, the obtained region is possibly homogeneous. Furthermore, the results demonstrate the usefulness of the bivariate test to take into account the dependence structure between the variables representing the event, and to take advantage of more information from the hydrograph. Copyright © 2009 John Wiley & Sons, Ltd.     

Interpretation case study of the Sahl El Qaa area, southern Sinai Peninsula, Egypt

The study area is located in the Sinai Peninsula, which is considered one of the most promising regions for oil resources. Three different tectonic forces affect the area in the triple junction structures associated with the opening of the Gulf of Suez and the strike slip movement along the Gulf of Aqaba. The main goal of this work is to model the structure of the basement rocks in the study area using magnetic methods. To achieve this, a high-resolution land magnetic survey was acquired and the results were combined with existing seismic reflection data. The magnetic interpretation was carried out using the analytical signal, horizontal gradient, Euler and Werner deconvolution and 3D magnetic modelling methods. We concluded that most of the deduced structures are trending in N–S, N35°–N45° west and E–W directions. The Aqaba trend (N15°–N25° east) is barely noticeable. The depth to the basement rocks ranges from 1 km to more than 2 km below sea level and these results are in agreement with the available well log data. In addition, interpretation of seismic reflection sections was carried out and compared with overlapping magnetic profiles interpreted using Euler deconvolution. They show that the sedimentary section was affected by the basement tectonics, with faults extending from the basement upwards through the sedimentary cover. These faults constitute good potential structural traps for oil accumulation.     

Diagnostic study and modeling of the annual positive water temperature onset

A data-driven model is designed using artificial neural networks (ANN) to predict the average onset for the annual water temperature cycle of North-American streams. The data base is composed of daily water temperature time series recorded at 48 hydrometric stations in Québec (Canada) and northern US, as well as the geographic and physiographic variables extracted from the 48 associated drainage basins. The impact of individual and combined drainage area characteristics on the stream annual temperature cycle starting date is investigated by testing different combinations of input variables. The best model allows to predict the average temperature onset for a site, given its geographical coordinates and vegetation and lake coverage characteristics, with a root mean square error (RMSE) of 5.6 days. The best ANN model was compared favourably with parametric approaches.     

Derivation of streamflow statistics based on a filtered point process

This work presents the derivation of general streamflow cumulants from daily rainfall time series. The general streamflow cumulants can be used to compute basic streamflow statistics such as mean, variance, coefficient of skewness, and correlation coefficient. Streamflow is considered as a filtered point process where the input is a daily rainfall time series assumed to be a marked point process. The marks of the process are the daily rainfall amounts which are assumed independent and identically distributed. The number of rainfall occurrences is a counting process represented by either the binomial, the Poisson, or the negative binomial probability distribution depending on its ratio of mean to variance. The first three cumulants and the covariance function of J-day averaged streamflows are deduced based on the characteristic function of a filtered point process. These cumulants are functions of the stochastic properties of the daily rainfall process and the basin-response function representing the causal relationship between rainfall and runoff.     

Impacts of Large-Scale Surface Modifications on Meteorological Conditions and Energy Use: A 10-Region Modeling Study

Summary  This paper summarizes results from a mesoscale modeling study to quantify the possible meteorological and energy-use impacts of large-scale increases in surface albedo and vegetative fraction. Ten regions in the U.S. were characterized and simulated in base- and modified-surface conditions. Time- and space-dependent meteorological variables were simulated for each region in four 3-day episodes to represent a range of seasonal variations. Using a simple interpolative procedure, a complete year of hourly weather data was created for each region (based on episodic meteorological simulation results) and input into energy-use models. The modified weather input was used to assess the effects of large-scale albedo and vegetative fraction changes on annual energy consumption in each of the ten areas targeted in this study. The simulations suggest annual electricity savings of between 1and 6.7 kWh m⁻² (of roof area) in residential neighborhoods and between 2 and 6.1 kWh m⁻² in office areas, depending on region. Annual gas penalties amount to up to 34.8 MJ m⁻² (of roof area) in residential neighborhoods and up to 21.1 MJ m⁻² in office areas. Received December 1, 1996 Revised May 11, 1998     

Evaluating the Effects of Radiative Forcing Feedback in Modelling Urban Ozone Air Quality in Portland,Oregon: Two-Way Coupled MM5–CMAQ Numerical Model Simulations

We summarize an on-line coupled meteorological–emissions–photochemical modelling system that allows feedback from air-quality/chemistry to meteorology via radiative forcing. We focus on the radiative-forcing impacts (direct effects) of ozone. We present an application of the coupled modelling system to the episode of 23–31 July 1998 in Portland, Oregon, U.S.A. Results suggest that the inclusion of radiative-forcing feedback produces small but accountable impacts. For this region and episode, stand-alone radiative transfer simulations, i.e., evaluating the effects of radiative forcing independently of changes in meteorology or emissions, suggest that a change of 1 ppb in ground-level ozone is approximately equivalent to a change of 0.017 W m⁻² in radiative forcing. In on-line, coupled, three-dimensional simulations, where the meteorological dependencies are accounted for, domain-wide peak ozone concentrations were higher by 2–4 ppb (relative to a simulated peak of 119.4 ppb) when including the effects of radiative-forcing feedback. A scenario of 10% reduction in anthropogenic emissions produced slightly larger decreases in ozone, an additional 1 ppb in local-peak reductions, relative to scenarios without feedback.     

CGCM3 predictors used for daily temperature and precipitation downscaling in Southern Québec, Canada

This study provides some guidance on the choice of predictor variables from both reanalysis products and the third version of the Canadian Coupled Global Climate Model (CGCM3) outputs for regression-based statistical downscaling models (SDMs) for climate change application in southern Québec (Canada). Twenty CGCM3 grid points and four surface observation sites in the study area were employed. Twenty-five deseasonalized predictors and four deseasonalized predictands (daily maximum and minimum temperatures, precipitation occurrence and wet day precipitation amount) were used to investigate correlation coefficients among predictors and to evaluate their predictive ability when used in a multiple linear regression (MLR) downscaling model. The basic statistical characteristics of vorticity at 1,000-, 850- and 500-hPa levels, U-component of velocity at 1,000-hPa level, temperature at 2?m (T ₂) and wind direction at 1,000- and 500-hPa level of CGCM3 showed a larger difference with those of the NCEP reanalysis data. Therefore, those seven variables require high caution to be included as predictors in statistical downscaling models. Specific humidity at 1,000-, 850- and 500-hPa levels, geopotential height at 850- and 500-hPa levels and T ₂ were the most sensitive predictors for future climate conditions (i.e. A1B and A2 emission scenarios). Specific humidity and geopotential height at different levels and T ₂ were important explainable predictors for the daily temperatures. Mean sea level pressure, specific humidity, U and V components and divergence showed potential as predictors for daily precipitation. Spatial explained variance of MLRs between predictors of every different CGCM3 grid points and the four predictands showed large values at the CGCM3 grid points located near the observation sites, whereas relatively small values were shown at the CGCM3 grid points located more than 400?km from the sites. The explained variance of the downscaled predictands by predictors of three or four CGCM3 grid points located near the observation site produced 2–5% larger R-squares than those by predictors of the nearest grid point. The results illustrated that the use of predictors from more than one AOGCM grid points located near the observation site can increase the skill of the MLR downscaling models.