Evaluation of WRF and HadRM Mesoscale Climate Simulations over the U.S. Pacific Northwest

This work compares the Weather Research and Forecasting (WRF) and Hadley Centre Regional Model (HadRM) simulations with the observed daily maximum and minimum temperature (Tmax and Tmin) and precipitation at Historical Climatology Network (HCN) stations over the U.S. Pacific Northwest for 2003–07. The WRF and HadRM runs were driven by the NCEP/Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis (R-2) data. The simulated Tmax in WRF and HadRM as well as in R-2 compares well with the observations. Predominantly cold biases of Tmax are noted in WRF and HadRM in spring and summer, while in winter and fall more stations show warm biases, especially in HadRM. Large cold biases of Tmax are noted in R-2 at all times. The simulated Tmin compares reasonably well with the observations, although not as well as Tmax in both models and in the reanalysis R-2. Warm biases of Tmin prevail in both model simulations, while R-2 shows mainly cold biases. The R-2 data play a role in the model biases of Tmax, although there are also clear indications of resolution dependency. The model biases of Tmin originate mainly from the regional models. The temporal correlation between the simulated and observed daily precipitation is relatively low in both models and in the reanalysis; however, the correlation increases steadily for longer averaging times. The high-resolution models perform better than R-2, although the nested WRF domains do have the largest biases in precipitation during the winter and spring seasonsKeywords: Mesoscale models, Temperature, North America, Climatolog

The Effects of Global Change Upon United States Air Quality

To understand more fully the effects of global changes on ambient concentrations of ozone and particulate matter with aerodynamic diameter smaller than 2.5 μm (PM2.5) in the United States (US), we conducted a comprehensive modeling effort to evaluate explicitly the effects of changes in climate, biogenic emissions, land use and global/regional anthropogenic emissions on ozone and PM2.5 concentrations and composition. Results from the ECHAM5 global climate model driven with the A1B emission scenario from the Intergovernmental Panel on Climate Change (IPCC) were downscaled using the Weather Research and Forecasting (WRF) model to provide regional meteorological fields. We developed air quality simulations using the Community Multiscale Air Quality Model (CMAQ) chemical transport model for two nested domains with 220 and 36 km horizontal grid cell resolution for a semi-hemispheric domain and a continental United States (US) domain, respectively. The semi-hemispheric domain was used to evaluate the impact of projected global emissions changes on US air quality. WRF meteorological fields were used to calculate current (2000s) and future (2050s) biogenic emissions using the Model of Emissions of Gases and Aerosols from Nature (MEGAN). For the semi-hemispheric domain CMAQ simulations, present-day global emissions inventories were used and projected to the 2050s based on the IPCC A1B scenario. Regional anthropogenic emissions were obtained from the US Environmental Protection Agency National Emission Inventory 2002 (EPA NEI2002) and projected to the future using the MARKet ALlocation (MARKAL) energy system model assuming a business as usual scenario that extends current decade emission regulations through 2050. Our results suggest that daily maximum 8 h average ozone (DM8O) concentrations will increase in a range between 2 to 12 parts per billion (ppb) across most of the continental US. The highest increase occurs in the South, Central and Midwest regions of the US due to increases in temperature, enhanced biogenic emissions and changes in land use. The model predicts an average increase of 1–6 ppb in DM8O due to projected increase in global emissions of ozone precursors. The effects of these factors are only partially offset by reductions in DM8O associated with decreasing US anthropogenic emissions. Increases in PM2.5 levels between 4 and 10 μg m−3 in the Northeast, Southeast, Midwest and South regions are mostly a result of increase in primary anthropogenic particulate matter (PM), enhanced biogenic emissions and land use changes. Changes in boundary conditions shift the composition but do not alter overall simulated PM2.5 mass concentrations

Dynamically and Statistically Downscaled Seasonal Simulations of Maximum Surface Air Temperature Over the Southeastern United States

Coarsely resolved surface air temperature (2 m height) seasonal integrations from the Florida State University/Center for Ocean-Atmospheric Prediction Studies Global Spectral Model (FSU/COAPS GSM) (~1.8º lon.-lat. (T63)) for the period of 1994 to 2002 (March through September each year) are downscaled to a fine spatial scale of ~20 km. Dynamical and statistical downscaling methods are applied for the southeastern United States region, covering Florida, Georgia, and Alabama. Dynamical downscaling is conducted by running the FSU/COAPS Nested Regional Spectral Model (NRSM), which is nested into the domain of the FSU/COAPS GSM. We additionally present a new statistical downscaling method. The rationale for the statistical approach is that clearer separation of prominent climate signals (e.g., seasonal cycle, intraseasonal, or interannual oscillations) in observation and GSM, respectively, over the training period can facilitate the identification of the statistical relationship in climate variability between two data sets. Cyclostationary Empirical Orthogonal Function (CSEOF) analysis and multiple regressions are trained with those data sets to extract their statistical relationship, which eventually leads to better prediction of regional climate from the large-scale simulations. Downscaled temperatures are compared with the FSU/COAPS GSM fields and observations. Downscaled seasonal anomalies exhibit strong agreement with observations and a reduction in bias relative to the direct GSM simulations. Interannual temperature change is also reasonably simulated at local grid points. A series of evaluations including mean absolute errors, anomaly correlations, frequency of extreme events, and categorical predictability reveal that both downscaling techniques can be reliably used for numerous seasonal climate applications

A Biomechanical Model of The Foot.

The foot is modeled as a statically indeterminate structure supporting its load at the heads of the five metatarsals and the tuberosity of the calcaneous. The distribution of support is determined through an analysis of the deformations caused in the structure as a result of the forces at these locations. The analysis includes the effect of the plantar aponeurosis and takes into account the deformation of the metatarsals and bending of the joints. A parametric study is presented to illustrate the behavior of the solution under a broad range of conditions

The Foot as a Shock Absorber.

A mathematical analysis of the deformation of the foot is developed to determine the role that stretch of ligaments and tendons plays in absorbing shock following impact. Our analysis is based on an anatomical biomechanical model that includes each of the bones of the foot. We calculate the time course of the deflection of the joints and the elongation of the ligaments and tendons and determine the ground reaction force acting on the heel. Quasi-linear viscoelastic theory is used for soft tissue constitutive relationships. With biomechanical data selected from the literature, we obtain a vertical force impact peak of 8000 N, occurring at 16 ms following heel strike. This is of higher magnitude and shorter duration than is found experimentally, as is to be expected, since we did not include the heel pad in our model and we assumed that the impact surface was ideally rigid

An Application of Beam Theory to Determine the Stress and Deformation of Long Bones.

We present a generalized beam theory in which deformation and load are determined simultaneously, in order to analyze statically indeterminant problems involving long bones. We regard a long bone as a beam curved in three dimensions for which the cross-sectional properties vary continuously along its length. The theory is used to determine the force, moment, deflection and twist along the fifth metatarsal when it is subjected to both a pointwise and a distributed load