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11.
Thermoluminescence, electron paramagnetic resonance and optical absorption properties of rhodonite, a natural silicate mineral,
have been investigated and compared to those of synthetic crystal, pure and doped. The TL peaks grow linearly for radiation
dose up to 4 kGy, and then saturate. In all the synthetic samples, 140 and 340°C TL peaks are observed; the difference occurs
in their relative intensities, but only 340°C peak grows strongly for high doses. Al2O3 and Al2O3 + CaO-doped synthetic samples presented several decades intenser TL compared to that of synthetic samples doped with other
impurities. A heating rate of 4°C/s has been used in all the TL readings. The EPR spectrum of natural rhodonite mineral has
only one huge signal around g = 2.0 with width extending from 1,000 to 6,000 G. This is due to Mn dipolar interaction, a fact proved by numerical calculation
based on Van Vleck dipolar broadening expression. The optical absorption spectrum is rich in absorption bands in near-UV,
visible and near-IR intervals. Several bands in the region from 540 to 340 nm are interpreted as being due to Mn3+ in distorted octahedral environment. A broad and intense band around 1,040 nm is due to Fe2+. It decays under heating up to 900°C. At this temperature it is reduced by 80% of its original intensity. The pink, natural
rhodonite, heated in air starts becoming black at approximately 600°C. 相似文献
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基于1980—2014年的哈德莱中心海冰及海温的月平均SST资料,美国联合台风警报中心(JTWC)的best-track资料以及NCEP/NCAR再分析月平均资料,利用广义平衡反馈方法(GEFA)研究南印度洋热带气旋(TC)生成频数对海表温度异常的响应特征。研究表明:(1)南印度TC生成频数对北太平洋第一模态(NP1)和热带大西洋第二模态(TA2)有显著响应,分别通过了置信度为99%和96%的Monte-Carlo检验,对应的响应振幅分别为0.67和0.49。(2)局地环境要素对关键SSTA模的GEFA响应结果显示:当NP1出现类似于太平洋年代际振荡(PDO)的正位相时,850 h Pa相对涡度在15°S附近的印度洋海域上都有一个自西向东的显著正响应带,垂直风切变在马达加斯加以东的大部分海域都表现为显著的负响应,600 h Pa相对湿度在马达加斯加以东的部分海域表现为显著的正响应;当TA2对应的时间系数为正异常时,850 h Pa相对涡度和600 h Pa相对湿度在澳大利亚的西北部印度洋海域表现为显著的正响应,垂直风切变在澳大利亚的西北部印度洋海域表现为显著的负响应。 相似文献
13.
The South China Sea,a <Emphasis Type="Italic">cul-de-sac</Emphasis> of North Pacific Intermediate Water 总被引：1，自引：0，他引：1
Yuzhu?YouEmail author Ching-Sheng?Chern Yih?Yang Cho-Teng?Liu Kon-Kee?Liu Su-Cheng?Pai 《Journal of Oceanography》2005,61(3):509-527
This study discusses branching of the Kuroshio Current including North Pacific Intermediate Water (NPIW) into the South China Sea (SCS). The spreading path of the subtropical salinity minimum of NPIW is southwestward pointing to the Luzon Strait between Taiwan and Luzon islands. Using a large collection of updated hydrography, results show that the SCS is a cul-de-sac for the subtropical NPIW because even the NPIW’s upper boundary neutral density surface σ N = 26.5 is completely blocked by the Palawan sill and partly blocked by the southern Mindoro Strait. In autumn, NPIW is driven out of the Luzon Strait by the preceding anticyclonic summer monsoon due to an intraseasonal variation and seasonal phase lag response to the weaker summer monsoon. Stronger inflow under winter monsoon than outflow under summer monsoon results in a net annual transport of NPIW of about 1.1 ± 0.2 Sv (1 Sv = 106 m3s−1) into the SCS. This net transport accounts for the anomaly in NPIW transport across the World Ocean Circulation Experiment section P8 (130° E). An earlier study estimated a large westward NPIW transport of about 3.9 ± 0.2 Sv, resulting in a difference of 1.2 ± 0.2 Sv from the basin-wide mean of 2.7 ± 0.2 Sv. Observations are generally in agreement with numerical results although the intraseasonal signal seems to cause a slight bias and remains to be simulated by future model experiments. 相似文献
14.
Summary The main objective of this study was to develop empirical models with different seasonal lead time periods for the long range
prediction of seasonal (June to September) Indian summer monsoon rainfall (ISMR). For this purpose, 13 predictors having significant
and stable relationships with ISMR were derived by the correlation analysis of global grid point seasonal Sea-Surface Temperature
(SST) anomalies and the tendency in the SST anomalies. The time lags of the seasonal SST anomalies were varied from 1 season
to 4 years behind the reference monsoon season. The basic SST data set used was the monthly NOAA Extended Reconstructed Global
SST (ERSST) data at 2° × 2° spatial grid for the period 1951–2003. The time lags of the 13 predictors derived from various
areas of all three tropical ocean basins (Indian, Pacific and Atlantic Oceans) varied from 1 season to 3 years. Based on these
inter-correlated predictors, 3 predictor sub sets A, B and C were formed with prediction lead time periods of 0, 1 and 2 seasons,
respectively, from the beginning of the monsoon season. The selected principal components (PCs) of these predictor sets were
used as the input parameters for the models A, B and C, respectively. The model development period was 1955–1984. The correct
model size was derived using all-possible regressions procedure and Mallow’s “Cp” statistics.
Various model statistics computed for the independent period (1985–2003) showed that model B had the best prediction skill
among the three models. The root mean square error (RMSE) of model B during the independent test period (6.03% of Long Period
Average (LPA)) was much less than that during the development period (7.49% of LPA). The performance of model B was reasonably
good during both ENSO and non-ENSO years particularly when the magnitudes of actual ISMR were large. In general, the predicted
ISMR during years following the El Ni?o (La Ni?a) years were above (below) LPA as were the actual ISMR. By including an NAO
related predictor (WEPR) derived from the surface pressure anomalies over West Europe as an additional input parameter into
model B, the skill of the predictions were found to be substantially improved (RMSE of 4.86% of LPA). 相似文献
15.
New statistical models for long-range forecasting of southwest monsoon rainfall over India 总被引：2，自引：0，他引：2
The India Meteorological Department (IMD) has been issuing long-range forecasts (LRF) based on statistical methods for the
southwest monsoon rainfall over India (ISMR) for more than 100 years. Many statistical and dynamical models including the
operational models of IMD failed to predict the recent deficient monsoon years of 2002 and 2004. In this paper, we report
the improved results of new experimental statistical models developed for LRF of southwest monsoon seasonal (June–September)
rainfall. These models were developed to facilitate the IMD’s present two-stage operational forecast strategy. Models based
on the ensemble multiple linear regression (EMR) and projection pursuit regression (PPR) techniques were developed to forecast
the ISMR. These models used new methods of predictor selection and model development. After carrying out a detailed analysis
of various global climate data sets; two predictor sets, each consisting of six predictors were selected. Our model performance
was evaluated for the period from 1981 to 2004 by sliding the model training period with a window length of 23 years. The
new models showed better performance in their hindcast, compared to the model based on climatology. The Heidke scores for
the three category forecasts during the verification period by the first stage models based on EMR and PPR methods were 0.5
and 0.44, respectively, and those of June models were 0.63 and 0.38, respectively. Root mean square error of these models
during the verification period (1981–2004) varied between 4.56 and 6.75% from long period average (LPA) as against 10.0% from
the LPA of the model based on climatology alone. These models were able to provide correct forecasts of the recent two deficient
monsoon rainfall events (2002 and 2004). The experimental forecasts for the 2005 southwest monsoon season based on these models
were also found to be accurate. 相似文献
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The summer monsoon rainfall over India exhibits strong intraseasonal variability. Earlier studies have identified Madden Julian Oscillation (MJO) as one of the most influencing factors of the intraseasonal variability of the monsoon rainfall. In this study, using India Meteorological Department (IMD) high resolution daily gridded rainfall data and Wheeler?CHendon MJO indices, the intra-seasonal variation of daily rainfall distribution over India associated with various Phases of eastward propagating MJO life cycle was examined to understand the mechanism linking the MJO to the intraseasonal variability. During MJO Phases of 1 and 2, formation of MJO associated positive convective anomaly over the equatorial Indian Ocean activated the oceanic tropical convergence zone (OTCZ) and the resultant changes in the monsoon circulation caused break monsoon type rainfall distribution. Associated with this, negative convective anomalies over monsoon trough zone region extended eastwards to date line indicating weaker than normal northern hemisphere inter tropical convergence zone (ITCZ). The positive convective anomalies over OTCZ and negative convective anomalies over ITCZ formed a dipole like pattern. Subsequently, as the MJO propagated eastwards to west equatorial Pacific through the maritime continent, a gradual northward shift of the OTCZ was observed and negative convective anomalies started appearing over equatorial Indian Ocean. During Phase 4, while the eastwards propagating MJO linked positive convective anomalies activated the eastern part of the ITCZ, the northward propagating OTCZ merged with monsoon trough (western part of the ITCZ) and induced positive convective anomalies over the region. During Phases 5 and 6, the dipole pattern in convective anomalies was reversed compared to that during Phases 1 and 2. This resulted active monsoon type rainfall distribution over India. During the subsequent Phases (7 and 8), the convective and lower tropospheric anomaly patterns were very similar to that during Phase 1 and 2 except for above normal convective anomalies over equatorial Indian Ocean. A general decrease in the rainfall was also observed over most parts of the country. The associated dry conditions extended up to northwest Pacific. Thus the impact of the MJO on the monsoon was not limited to the Indian region. The impact was rather felt over larger spatial scale extending up to Pacific. This study also revealed that the onset of break and active events over India and the duration of these events are strongly related to the Phase and strength of the MJO. The break events were relatively better associated with the strong MJO Phases than the active events. About 83% of the break events were found to be set in during the Phases 7, 8, 1 and 2 of MJO with maximum during Phase 1 (40%). On the other hand, about 70% of the active events were set in during the MJO Phases of 3 to 6 with maximum during Phase 4 (21%). The results of this study indicate an opportunity for using the real time information and skillful prediction of MJO Phases for the prediction of break and active conditions which are very crucial for agriculture decisions. 相似文献
20.
In this study, seven types of first‐order and one‐variable grey differential equation model (abbreviated as GM (1, 1) model) were used to forecast hourly roadside particulate matter (PM) including PM10 and PM2.5 concentrations in Taipei County of Taiwan. Their forecasting performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient (R) was 11.70%, 60.06, 7.75, and 0.90%, respectively when forecasting PM10. When forecasting PM2.5, the minimum MAPE, MSE, RMSE, and maximum R‐value of 16.33%, 29.78, 5.46, and 0.90, respectively could be achieved. All statistical values revealed that the forecasting performance of GM (1, 1, x(0)), GM (1, 1, a), and GM (1, 1, b) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) was an efficiently early warning tool for providing PM information to the roadside inhabitants. 相似文献