Ti separation was achieved by ion-exchange chromatography using Bio-Rad AG 1-X8 anion-exchange and DGA resins. For high-Fe/Ti and high-Mg/Ti igneous samples, a three-column procedure was required, whereas a two-column procedure was used for low-Fe/Ti and low-Mg/Ti igneous samples. The Ti isotopes were analysed by MC-ICP-MS, and instrumental mass bias was corrected using a 47Ti-49Ti double-spike technique. The 47Ti-49Ti double-spike and SRM 3162a were calibrated using SRM 979-Cr, certificated value 53Cr/52Crtrue = 0.11339. Isobaric interference was evaluated by analysing Alfa-Ti doped with Na, Mg, Ca, and Mo, and results indicate that high concentrations of Na and Mg have no significant effect on Ti isotope analyses; however, Ca and Mo interferences lead to erroneous δ49/47Ti values when Ca/Ti and Mo/Ti ratios exceed 0.01 and 0.1, respectively. Titanium isotopic compositions were determined for 12 igneous reference materials, BCR-2, BHVO-2, GBW07105, AGV-1, AGV-2, W-2, GBW07123, GBW07126, GBW07127, GBW07101, JP-1, and DTS-2b. Samples yield δ49/47Ti (‰) of ?0.035 ± 0.022, ?0.038 ± 0.031, 0.031 ± 0.022, 0.059 ± 0.038, 0.044 ± 0.037, 0.000 ± 0.015, 0.154 ± 0.044, ?0.044 ± 0.018, 0.010 ± 0.022, 0.064 ± 0.043, 0.169 ± 0.034, and ?0.047 ± 0.025 (relative to OL-Ti, ±2SD), respectively; of which isotopic compositions of DTS-2b, JP-1, GBW07101, GBW07105, GBW07123, GBW07126, and GBW07127 are reported for the first time. Standard Alfa-Ti was analysed repeatedly over a ten-month period, indicating a reproducibility of ±0.047 (2SD) for δ49/47Ti, similar to the precisions obtained for geochemical reference materials. 相似文献
In this paper, the numerical simulation bias of the non-hydrostatic version GRAPES-Meso (Mesoscale of the Global and Regional Assimilation and Prediction System) at the resolution of 0.18o for a torrential rain case, which happened in May 31st to June 1st 2005 over Hunan province, are diagnosed and investigated by using the radiosondes, intensive surface observation, and the operational global analysis data, and the sensitivity experimental results as well. It is shown in the result that the GRAPES-Meso could reproduce quite well the main features of large-cale circulation and the distribution of the accumulated 24h precipitation and the key locations of the torrential rainfall are captured reasonably well by the model. However, bias exist in the simulation of the mesoscale features of the torrential rain and details of the relevant systems, for example, the simulated rainfall that is too earlier in model integration and remarkable underprediction of the peak value of rainfall rates over the heaviest rainfall region, the weakness of the upper jet simulation and the overprediction of the south-west wind in the lower troposphere etc. The investigation reveals that the sources of the simulation bias are different. The erroneous model rainfall in the earlier integration stage over the heaviest rainfall region is induced by the model initial condition bias of the wind field at about 925hPa over the torrential rainfall region, where the bias grow rapidly and spread upward to about 600hPa level within the few hours into the integration and result in abnormal convergence of the wind and moisture, and thus the unreal rainfall over that region. The large bias on the simulated rainfall intensity over the heaviest rainfall region might be imputed to the following combined factors of (1) the simulation bias on the strength and detailed structures of the upper-level jet core which bring about significant underpredictions of the dynamic conditions (including upper-level divergence and the upward motion) for heavy rainfall due to unfavorable mesoscale vertical coupling between the strong upper-level divergence and lower-level convergence; and (2) the inefficient coupling of the cumulous parameterization scheme and the explicit moisture in the integration, which causes the failure of the explicit moisture scheme in generating grid-scale rainfall in a certain extent through inadequate convective adjustment and feedback to the grid-scale. In addition, the interaction of the combined two factors could form a negative feedback to the rainfall intensity simulation, and eventually lead to the obvious underprediction of the rainfall rate. 相似文献
General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions. 相似文献
Sea surface temperature SST obtained from the initial version of the Korea Operational Oceanographic System(KOOS) SST satellite have low accuracy during summer and daytime. This is attributed to the diurnal warming effect. Error estimation of SST data must be carried out to use the real-time forecasting numerical model of the KOOS. This study suggests two quality control methods for the KOOS SST system. To minimize the diurnal warming effect, SSTs of areas where wind speed is higher than 5 m/s were used. Depending on the wind threshold value, KOOS SST data for August 2014 were reduced by 0.15°C. Errors in SST data are considered to be a combination of random, sampling, and bias errors. To estimate bias error, the standard deviation of bias between KOOS SSTs and climatology SSTs were used. KOOS SST data yielded an analysis error standard deviation value similar to OSTIA and NOAA NCDC(OISST) data. The KOOS SST shows lower random and sampling errors with increasing number of observations using six satellite datasets. In further studies, the proposed quality control methods for the KOOS SST system will be applied through more long-term case studies and comparisons with other SST systems. 相似文献