Air Temperature Comparison between the MMTS and the USCRN Temperature Systems

A new U.S. Climate Reference Network (USCRN) was officially and nationally commissioned by the Department of Commerce and the National Oceanic and Atmospheric Administration in 2004. During a 1-yr side-by-side field comparison of USCRN temperatures and temperatures measured by a maximum-minimum temperature system (MMTS), analyses of hourly data show that the MMTS temperature performed with biases: 1) a systematic bias–ambient-temperature-dependent bias and 2) an ambient-solar-radiation- and ambient-wind- speed-dependent bias. Magnitudes of these two biases ranged from a few tenths of a degree to over 1C compared to the USCRN temperatures. The hourly average temperatures for the USCRN were the dependent variables in the development of two statistical models that remove the biases due to ambient temperature, ambient solar radiation, and ambient wind speed in the MMTS. The model performance was examined, and the results show that the adjusted MMTS data were substantially improved with respect to both systematic bias and the bias associated with ambient solar radiation and ambient wind speed. In addition, the results indicate that the historical temperature datasets prior to the MMTS era need to be further investigated to produce long-term homogenous times series of area-average temperature

Effect of Particle-Hole Asymmetry on the Mott-Hubbard Metal-Insulator Transition

The Mott-Hubbard metal-insulator transition is one of the most important problems in correlated electron systems. In the past decade, much progress has been made on examining a particle-hole symmetric form of the transition in the Hubbard model with dynamical mean field theory where it was found that the electronic self energy develops a pole at the transition. We examine the particle-hole asymmetric metal-insulator transition in the Falicov-Kimball model, and find that a number of features change when the noninteracting density of states has a finite bandwidth. Since, generically particle-hole symmetry is broken in real materials, our results have an impact on understanding the metal-insulator transition in real materials.Comment: 5 pages, 3 figure

Estimation of the Water Balance Using Observed Soil Water in the Nebraska Sandhills

Analyzing the dynamic hydrologic conditions of the Sandhills is critical for water and range management, sustainability of the Sandhills ecosystem as well as for dune stability. There are complex models available to quantify both surface and subsurface hydrological processes. However, we present in this study an application of a relatively simple model to arrive at best estimates of the water balance components. Using the Thornthwaite-Mather (TM) model, water balance components were estimated for 4 Automated Weather Data Network (AWDN) weather monitoring stations. Estimated averages of the water balance components suggested that mean annual precipitation of these four sites was only about 420 mm but water loss through plant evapotranspiration (ET) was 861 mm, with PET of about 1214 mm. Our investigation shows that there was surplus of water between December and March and a deficit occurs at the start of the growing season in May and extends through senescence in September-October. This study also suggests that the High Plains aquifer possibly met the plant water requirement during this deficit period as well as during the soil water extraction period, from May through September

Closure to Estimation of the Water Balance Using Observed Soil Water in the Nebraska Sandhills

We are thankful to Szilagyi [2010] for providing us an opportunity to discuss the important points of our paper [Sridhar and Hubbard, 2010]. We demonstrated a seasonal water balance assessment using the Modified Thornthwaite-Mather (TM) model in the Nebraska Sandhills. We computed the water budget for a few representative weather monitoring stations located in the Sandhills using the high resolution soil moisture data to assess the storage. In our water balance analysis, soil moisture storage is determined based on observed soil moisture and actual evapotranspiration, ETact was computed for each month using the change in storage in soil water and precipitation. If the change in storage is positive based on our observed soil moisture, we considered two scenarios and the least of the two is considered for computing actual ET

Air Temperature Comparison between the MMTS and the USCRN Temperature Systems

A new U.S. Climate Reference Network (USCRN) was officially and nationally commissioned by the Department of Commerce and the National Oceanic and Atmospheric Administration in 2004. During a 1-yr side-by-side field comparison of USCRN temperatures and temperatures measured by a maximum-minimum temperature system (MMTS), analyses of hourly data show that the MMTS temperature performed with biases: 1) a systematic bias–ambient-temperature-dependent bias and 2) an ambient-solar-radiation- and ambient-wind- speed-dependent bias. Magnitudes of these two biases ranged from a few tenths of a degree to over 1C compared to the USCRN temperatures. The hourly average temperatures for the USCRN were the dependent variables in the development of two statistical models that remove the biases due to ambient temperature, ambient solar radiation, and ambient wind speed in the MMTS. The model performance was examined, and the results show that the adjusted MMTS data were substantially improved with respect to both systematic bias and the bias associated with ambient solar radiation and ambient wind speed. In addition, the results indicate that the historical temperature datasets prior to the MMTS era need to be further investigated to produce long-term homogenous times series of area-average temperature

Air Temperature Errors Caused by Air Filter and Construction Effects on HMP45C Temperature Sensors in Weather Stations

For non–ventilated air temperature measurements at weather stations, both ambient wind speed and solar radiation are known to affect the magnitude of air temperature measurement errors. The objective of this study is to explore the effect of the sensor’s housing and to quantify any stagnation or conduction errors. The HMP45C temperature and relative humidity sensor with a Gill radiation shield is widely used in remote monitoring sites. The use of a filter in the HMP45C leads to loss of ventilation, while the protrusion of the sensor housing below the Gill shield exposes it to radiation loading and potentially increased conduction of heat to the sensor. The HMP45C sensors were deployed with and without an air filter in both standard Gill shields and in a Gill shield modified with extra plates to completely cover the base of the sensor housing. The data collected were examined using spectral analysis and statistical methods. Results show that both average air temperature errors and variations of air temperature errors were smaller in the HMP45C sensors when the manufacturer–supplied air filter was removed. The ranges of the three–sigma errors can be decreased by 0.4°C to 0.7°C and the accuracy of monthly average air temperature can be improved at least 0.1°C by employing an HMP45C without the air filter. Results suggest that the maximum air temperature taken with the filter may reach more than 1.0°C higher than that taken without the filter. The major frequency component contributing to air temperature errors using the HMP45C sensor in the Gill shield is the frequency of one day per cycle, which is expected. Partial radiation blocking combined with aspiration significantly reduces the contribution of the one–day cycle. In field tests, the R. M. Young aspirated temperature system proved very accurate compared to an aspirated precision industrial platinum resistance thermometer (PRT)

Guide to the evaluation of human exposure to noise from large wind turbines

Guidance for evaluating human exposure to wind turbine noise is provided and includes consideration of the source characteristics, the propagation to the receiver location, and the exposure of the receiver to the noise. The criteria for evaluation of human exposure are based on comparisons of the noise at the receiver location with the human perception thresholds for wind turbine noise and noise-induced building vibrations in the presence of background noise

Relating United States Crop Land Use to Natural Resources and Climate Change

Crop production depends not only on the yield but also on the area harvested. The yield response to climate change has been widely examined, but the sensitivity of crop land use to hypothetical climate change has not been examined directly. Crop land-use regression models for estimating crop area indices (CAIs) - the percent of land used for corn, soybean, wheat, and sorghum production - are presented. Inputs to the models include available water-holding capacity of the soil, percent of land available for rain-fed agricultural production, annual precipitation, and annual temperature. The total variance of CAI explained by the models ranged from 78% for wheat to 87% for sorghum, and the root-mean-square errors ranged from 1.74% for sorghum to 4.24% for corn. The introduction of additional climatic variables to the models did not significantly improve their performance. The crop land-use models were used to predict the CAI for every crop reporting district in the United States for the current climatic condition and for possible future climate change scenarios (various combinations of temperature and precipitation changes over a range of -3° to +6°C and -20% to +20%, respectively). The magnitude of climatic warning suggested by GCMs (GISS and GFDL) is from 3.5° to 5.9°C for regions of the United States. For this magnitude of warming, the model suggests corn and soybean production areas may decline while wheat and sorghum production areas may expand. If the warming is accompanied by a decrease in annual precipitation from 1% to 10%, then the areas used for corn and soybean production could decrease by as much as 20% and 40%, respectively. The area for sorghum and wheat under these conditions would increase by as much as 80% and 70%, respectively; the exact amount depending strongly on the change in precipitation. In general, small changes in temperature or precipitation produced larger corresponding changes (on a percentage basis) in soybean, wheat, and sorghum area than in corn area

Relating United States Crop Land Use to Natural Resources and Climate Change

Crop production depends not only on the yield but also on the area harvested. The yield response to climate change has been widely examined, but the sensitivity of crop land use to hypothetical climate change has not been examined directly. Crop land-use regression models for estimating crop area indices (CAIs) - the percent of land used for corn, soybean, wheat, and sorghum production - are presented. Inputs to the models include available water-holding capacity of the soil, percent of land available for rain-fed agricultural production, annual precipitation, and annual temperature. The total variance of CAI explained by the models ranged from 78% for wheat to 87% for sorghum, and the root-mean-square errors ranged from 1.74% for sorghum to 4.24% for corn. The introduction of additional climatic variables to the models did not significantly improve their performance. The crop land-use models were used to predict the CAI for every crop reporting district in the United States for the current climatic condition and for possible future climate change scenarios (various combinations of temperature and precipitation changes over a range of -3° to +6°C and -20% to +20%, respectively). The magnitude of climatic warning suggested by GCMs (GISS and GFDL) is from 3.5° to 5.9°C for regions of the United States. For this magnitude of warming, the model suggests corn and soybean production areas may decline while wheat and sorghum production areas may expand. If the warming is accompanied by a decrease in annual precipitation from 1% to 10%, then the areas used for corn and soybean production could decrease by as much as 20% and 40%, respectively. The area for sorghum and wheat under these conditions would increase by as much as 80% and 70%, respectively; the exact amount depending strongly on the change in precipitation. In general, small changes in temperature or precipitation produced larger corresponding changes (on a percentage basis) in soybean, wheat, and sorghum area than in corn area

Strong-coupling expansions for the anharmonic Holstein model and for the Holstein-Hubbard model

A strong-coupling expansion is applied to the anharmonic Holstein model and to the Holstein-Hubbard model through fourth order in the hopping matrix element. Mean-field theory is then employed to determine transition temperatures of the effective (pseudospin) Hamiltonian. We find that anharmonic effects are not easily mimicked by an on-site Coulomb repulsion, and that anharmonicity strongly favors superconductivity relative to charge-density-wave order. Surprisingly, the phase diagram is strongly modified by relatively small values of the anharmonicity.Comment: 34 pages, typeset in ReVTeX, 11 encapsulated postscript files include