MEASUREMENT AND CALCULATION OF SPONTANEOUS EMISSION BRANCHING RATIOS, RELATIVE OSCILLATOR-STRENGTHS, TRANSITION-PROBABILITIES AND LEVEL LIFETIMES FOR ATOMIC URANIUM

We propose a laser induced sensitized fluorescence spectrometry for measuring the spontaneous emission branching ratios o?the transitions from the ten levels 5f36d7s7p-7M7, 5f36d7s7p-7L6, 5f37s27p-5K6, 5f26d27s2 - 5L7, 5f46d7s - 7L6, (17,070cm-1)-5L6, 5f26d27s2-5K6, 6d7s7p-7L5, 5f36d7s7p-7K5 and 5f26d27s2-5I5 to the ground state of atomic uranium (UI) for the first time. Their relative oscillator strengths have been measured by means of hollow cathode discharge (HCD) emission spectrometry. The radiative...

Spatial and temporal relationships between precipitation and ANPP of four types of grasslands in northern China

Precipitation is considered to be the primary resource limiting terrestrial biological activity in water-limited regions. Its overriding effect on the production of grassland is complex. In this paper, field data of 48 sites (including temperate meadow steppe,temperate steppe, temperate desert steppe and alpine meadow) were gathered from 31 published papers and monographs to analyze the relationship between above-ground net primary productivity (ANPP) and precipitation by the method of regression analysis. The results indicated that there was a great difference between spatial pattern and temporal pattern by which precipitation influenced grassland ANPP. Mean annual precipitation (MAP) was the main factor determining spatial distribution of grassland ANPP (r~2 = 0.61,P ＜ 0.01); while temporally, no significant relationship was found between the variance of AN PP and inter-annual precipitation for the four types of grassland. However, after dividing annual precipitation into monthly value and taking time lag effect into account, the study found significant relationships between ANPP and precipitation. For the temperate meadow steppe, the key variable determining inter-annual change of ANPP was last August-May precipitation (r~2= 0.47, P = 0.01); for the temperate steppe, the key variable was July precipitation (r~2 = 0.36, P = 0.02); for the temperate desert steppe, the key variable was April-June precipitation (r~2 = 0.51, P ＜0.01); for the alpine meadow, the key variable was last September-May precipitation (r~2 = 0.29, P ＜ 0.05). In comparison with analogous research, the study demonstrated that the key factor determining inter-annual changes of grassland ANPP was the cumulative precipitation in certain periods of that year or the previous year.

Calculation of Blocking Probabilities in Multistage Interconnection Networks with Redundant Paths

The blocking probability of a network is a common measure of its performance. There exist means of quickly calculating the blocking probabilities of Banyan networks; however, because Banyan networks have no redundant paths, they are not inherently fault-tolerant, and so their use in large-scale multiprocessors is problematic. Unfortunately, the addition of multiple paths between message sources and sinks in a network complicates the calculation of blocking probabilities. A methodology for exact calculation of blocking probabilities for small networks with redundant paths is presented here, with some discussion of its potential use in approximating blocking probabilities for large networks with redundant paths.

Using fractal downscaling of satellite precipitation products for hydrometeorological applications

The objective of spatial downscaling strategies is to increase the information content of coarse datasets at smaller scales. In the case of quantitative precipitation estimation (QPE) for hydrological applications, the goal is to close the scale gap between the spatial resolution of coarse datasets (e.g., gridded satellite precipitation products at resolution L × L) and the high resolution (l × l; L»l) necessary to capture the spatial features that determine spatial variability of water flows and water stores in the landscape. In essence, the downscaling process consists of weaving subgrid-scale heterogeneity over a desired range of wavelengths in the original field. The defining question is, which properties, statistical and otherwise, of the target field (the known observable at the desired spatial resolution) should be matched, with the caveat that downscaling methods be as a general as possible and therefore ideally without case-specific constraints and/or calibration requirements? Here, the attention is focused on two simple fractal downscaling methods using iterated functions systems (IFS) and fractal Brownian surfaces (FBS) that meet this requirement. The two methods were applied to disaggregate spatially 27 summertime convective storms in the central United States during 2007 at three consecutive times (1800, 2100, and 0000 UTC, thus 81 fields overall) from the Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 precipitation product (~25-km grid spacing) to the same resolution as the NCEP stage IV products (~4-km grid spacing). Results from bilinear interpolation are used as the control. A fundamental distinction between IFS and FBS is that the latter implies a distribution of downscaled fields and thus an ensemble solution, whereas the former provides a single solution. The downscaling effectiveness is assessed using fractal measures (the spectral exponent β, fractal dimension D, Hurst coefficient H, and roughness amplitude R) and traditional operational scores statistics scores [false alarm rate (FR), probability of detection (PD), threat score (TS), and Heidke skill score (HSS)], as well as bias and the root-mean-square error (RMSE). The results show that both IFS and FBS fractal interpolation perform well with regard to operational skill scores, and they meet the additional requirement of generating structurally consistent fields. Furthermore, confidence intervals can be directly generated from the FBS ensemble. The results were used to diagnose errors relevant for hydrometeorological applications, in particular a spatial displacement with characteristic length of at least 50 km (2500 km2) in the location of peak rainfall intensities for the cases studied. © 2010 American Meteorological Society.

Contribution of the North Atlantic subtropical high to regional climate model (RCM) skill in simulating southeastern United States summer precipitation

© 2014, Springer-Verlag Berlin Heidelberg.This study assesses the skill of advanced regional climate models (RCMs) in simulating southeastern United States (SE US) summer precipitation and explores the physical mechanisms responsible for the simulation skill at a process level. Analysis of the RCM output for the North American Regional Climate Change Assessment Program indicates that the RCM simulations of summer precipitation show the largest biases and a remarkable spread over the SE US compared to other regions in the contiguous US. The causes of such a spread are investigated by performing simulations using the Weather Research and Forecasting (WRF) model, a next-generation RCM developed by the US National Center for Atmospheric Research. The results show that the simulated biases in SE US summer precipitation are due mainly to the misrepresentation of the modeled North Atlantic subtropical high (NASH) western ridge. In the WRF simulations, the NASH western ridge shifts 7° northwestward when compared to that in the reanalysis ensemble, leading to a dry bias in the simulated summer precipitation according to the relationship between the NASH western ridge and summer precipitation over the southeast. Experiments utilizing the four dimensional data assimilation technique further suggest that the improved representation of the circulation patterns (i.e., wind fields) associated with the NASH western ridge substantially reduces the bias in the simulated SE US summer precipitation. Our analysis of circulation dynamics indicates that the NASH western ridge in the WRF simulations is significantly influenced by the simulated planetary boundary layer (PBL) processes over the Gulf of Mexico. Specifically, a decrease (increase) in the simulated PBL height tends to stabilize (destabilize) the lower troposphere over the Gulf of Mexico, and thus inhibits (favors) the onset and/or development of convection. Such changes in tropical convection induce a tropical–extratropical teleconnection pattern, which modulates the circulation along the NASH western ridge in the WRF simulations and contributes to the modeled precipitation biases over the SE US. In conclusion, our study demonstrates that the NASH western ridge is an important factor responsible for the RCM skill in simulating SE US summer precipitation. Furthermore, the improvements in the PBL parameterizations for the Gulf of Mexico might help advance RCM skill in representing the NASH western ridge circulation and summer precipitation over the SE US.

Thermodynamic and dynamic contributions to future changes in regional precipitation variance: focus on the Southeastern United States

© 2014, Springer-Verlag Berlin Heidelberg.The frequency and severity of extreme events are tightly associated with the variance of precipitation. As climate warms, the acceleration in hydrological cycle is likely to enhance the variance of precipitation across the globe. However, due to the lack of an effective analysis method, the mechanisms responsible for the changes of precipitation variance are poorly understood, especially on regional scales. Our study fills this gap by formulating a variance partition algorithm, which explicitly quantifies the contributions of atmospheric thermodynamics (specific humidity) and dynamics (wind) to the changes in regional-scale precipitation variance. Taking Southeastern (SE) United States (US) summer precipitation as an example, the algorithm is applied to the simulations of current and future climate by phase 5 of Coupled Model Intercomparison Project (CMIP5) models. The analysis suggests that compared to observations, most CMIP5 models (~60 %) tend to underestimate the summer precipitation variance over the SE US during the 1950–1999, primarily due to the errors in the modeled dynamic processes (i.e. large-scale circulation). Among the 18 CMIP5 models analyzed in this study, six of them reasonably simulate SE US summer precipitation variance in the twentieth century and the underlying physical processes; these models are thus applied for mechanistic study of future changes in SE US summer precipitation variance. In the future, the six models collectively project an intensification of SE US summer precipitation variance, resulting from the combined effects of atmospheric thermodynamics and dynamics. Between them, the latter plays a more important role. Specifically, thermodynamics results in more frequent and intensified wet summers, but does not contribute to the projected increase in the frequency and intensity of dry summers. In contrast, atmospheric dynamics explains the projected enhancement in both wet and dry summers, indicating its importance in understanding future climate change over the SE US. The results suggest that the intensified SE US summer precipitation variance is not a purely thermodynamic response to greenhouse gases forcing, and cannot be explained without the contribution of atmospheric dynamics. Our analysis provides important insights to understand the mechanisms of SE US summer precipitation variance change. The algorithm formulated in this study can be easily applied to other regions and seasons to systematically explore the mechanisms responsible for the changes in precipitation extremes in a warming climate.

Inter-annual variability of precipitation constrains the production response of boreal Pinus sylvestris to nitrogen fertilization

© 2015 Published by Elsevier B.V.Tree growth resources and the efficiency of resource-use for biomass production determine the productivity of forest ecosystems. In nutrient-limited forests, nitrogen (N)-fertilization increases foliage [N], which may increase photosynthetic rates, leaf area index (L), and thus light interception (IC). The product of such changes is a higher gross primary production and higher net primary production (NPP). However, fertilization may also alter carbohydrate partitioning from below- to aboveground, increasing aboveground NPP (ANPP). We analyzed effects of long-term N-fertilization on NPP, and that of long-term carbon storing organs (NPPS) in a Pinus sylvestris forest on sandy soil, a wide-ranging forest type in the boreal region. We based our analyses on a combination of destructive harvesting, consecutive mensuration, and optical measurements of canopy openness. After eight-year fertilization with a total of 70gNm^-2, ANPP was 27±7% higher in the fertilized (F) relative to the reference (R) stand, but although L increased relative to its pre-fertilization values, IC was not greater than in R. On the seventh year after the treatment initiation, the increase of ANPP was matched by the decrease of belowground NPP (78 vs. 92gCm^-2yr^-1; ~17% of NPP) and, given the similarity of IC, suggests that the main effect of N-fertilization was changed carbon partitioning rather than increased canopy photosynthesis. Annual NPPS increased linearly with growing season temperature (T) in both treatments, with an upward shift of 70.2gCm^-2yr^-1 by fertilization, which also caused greater amount of unexplained variation (r²=0.53 in R, 0.21 in F). Residuals of the NPPS-T relationship of F were related to growing season precipitation (P, r²=0.48), indicating that T constrains productivity at this site regardless of fertility, while P is important in determining productivity where N-limitation is alleviated. We estimated that, in a growing season average T (11.5±1.0°C; 33-year-mean), NPPS response to N-fertilization will be nullified with P 31mm less than the mean (325±85mm), and would double with P 109mm greater than the mean. These results suggest that inter-annual variation in climate, particularly in P, may help explaining the reported large variability in growth responses to fertilization of pine stands on sandy soils. Furthermore, forest management of long-rotation systems, such as those of boreal and northern temperate forests, must consider the efficiency of fertilization in terms of wood production in the context of changes in climate predicted for the region.

Precipitation predictability during summer crop production in the Pampas Region of Argentina

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Precipitation predictability during summer crop production in the Pampas Region of Argentina

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Precipitation of heavy metals from wastewater using simulated flue gas: sequent additions of fly ash, lime and carbon dioxide

Lime is a preferred precipitant for the removal of heavy metals from industrial wastewater due to its relatively low cost. To reduce heavy metal concentration to an acceptable level for discharge, in this work, fly ash was added as a seed material to enhance lime precipitation and the suspension was exposed to CO2 gas. The fly ash-lime-carbonation treatment increased the particle size of the precipitate and significantly improved sedimentation of sludge and the efficiency of heavy metal removal. The residual concentrations of chromium, copper, lead and zinc in effluents can be reduced to (mg L-1) 0.08, 0.14, 0.03 and 0.45, respectively. Examination of the precipitates by XRD and thermal analysis techniques showed that calcium-heavy metal double hydroxides and carbonates were present. The precipitate agglomerated and hardened naturally, facilitating disposal without the need for additional solidification/stabilization measures prior to landfill. It is suggested that fly ash, lime and CO2, captured directly from flue gas, may have potential as a method for wastewater treatment. This method could allow the ex-situ sequestration of CO2, particularly where flue-gas derived CO2 is available near wastewater treatment facilities. (C) 2009 Elsevier Ltd. All rights reserved.

Sovereign rescheduling probabilities in emerging markets: a comparison with credit rating agencies' ratings

This study estimates default probabilities of 124 emerging countries from 1981 to 2002 as a function of a set of macroeconomic and political variables. The estimated probabilities are then compared with the default rates implied by sovereign credit ratings of three major international credit rating agencies (CRAs) – Moody's Investor's Service, Standard & Poor's and Fitch Ratings. Sovereign debt default probabilities are used by investors in pricing sovereign bonds and loans as well as in determining country risk exposure. The study finds that CRAs usually underestimate the risk of sovereign debt as the sovereign credit ratings from rating agencies are usually too optimistic.