During boreal summer, much of the water vapor and CO entering the global tropical stratosphere is transported over the Asian monsoon͞Tibetan Plateau (TP) region. Studies have suggested that most of this transport is carried out either by tropical convection over the South Asian monsoon region or by extratropical convection over southern China. By using measurements from the newly available National Aeronautics and Space Administration Aura Microwave Limb Sounder, along with observations from the Aqua and Tropical Rainfall-Measuring Mission satellites, we establish that the TP provides the main pathway for cross-tropopause transport in this region. Tropospheric moist convection driven by elevated surface heating over the TP is deeper and detrains more water vapor, CO, and ice at the tropopause than over the monsoon area. Warmer tropopause temperatures and slower-falling, smaller cirrus cloud particles in less saturated ambient air at the tropopause also allow more water vapor to travel into the lower stratosphere over the TP, effectively short-circuiting the slower ascent of water vapor across the cold tropical tropopause over the monsoon area. Air that is high in water vapor and CO over the Asian monsoon͞TP region enters the lower stratosphere primarily over the TP, and it is then transported toward the Asian monsoon area and disperses into the large-scale upward motion of the global stratospheric circulation. Thus, hydration of the global stratosphere could be especially sensitive to changes of convection over the TP.climate ͉ CO ͉ stratosphere water vapor W ater vapor concentrations in the tropical lower stratosphere (LS) are 60% greater in boreal summer than in winter. This seasonal variation not only influences the radiation budget near the local tropopause but also propagates upward and toward the pole with the global stratospheric circulation (1, 2). Numerical simulations suggest that Ϸ75% of the total summer water vapor transport into the global tropical stratosphere may occur over the South Asian monsoon and Tibetan Plateau (TP) regions (3), contributing to Ͼ25% of the water vapor in the middle stratosphere (4).Studies have hypothesized that an increase in crosstropopause transport in the Asian monsoon͞TP region may have contributed to an increasing trend in stratospheric water vapor (5) during the 1980s and 1990s (6, 7). This trend probably increased the global greenhouse forcing (8) and enhanced ozone depletion in the Arctic (9). Any explanation of this trend or future trends would likely need to address how source regions for stratospheric water have changed. Recent studies have revealed high CO in the upper troposphere (UT) over the South Asian monsoon region (10). This CO is produced by biomass or fossil fuel burning, suggesting a human influence on transport of combustion pollutants and, perhaps, water vapor into the LS (11). Thus, a clarification of the mechanisms of water vapor and CO transport into the LS in this region is an important step toward understanding tropospheric influences on hydrati...

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For uplink large-scale MIMO systems, linear minimum mean square error (MMSE) signal detection algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose a low-complexity signal detection algorithm based on the successive overrelaxation (SOR) method to avoid the complicated matrix inversion. We first prove a special property that the MMSE filtering matrix is symmetric positive definite for uplink large-scale MIMO systems, which is the premise for the SOR method. Then a low-complexity iterative signal detection algorithm based on the SOR method as well as the convergence proof is proposed. The analysis shows that the proposed scheme can reduce the computational complexity from O(K 3 ) to O(K 2 ), where K is the number of users. Finally, we verify through simulation results that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.

[1] Large-scale measurements of ozone (O 3 ) and aerosol distributions were made from the NASA DC-8 aircraft during the Transport and Chemical Evolution over the Pacific (TRACE-P) field experiment conducted in February-April 2001. Remote measurements were made with an airborne lidar to provide O 3 and multiple-wavelength aerosol backscatter profiles from near the surface to above the tropopause along the flight track. In situ measurements of O 3 , aerosols, and a wide range of trace gases were made onboard the DC-8. Five-day backward trajectories were used in conjunction with the O 3 and aerosol distributions on each flight to indicate the possible origin of observed air masses, such as from biomass burning regions, continental pollution, desert regions, and oceanic regions. Average latitudinal O 3 and aerosol scattering ratio distributions were derived from all flights west of 150°E, and these distributions showed the average latitude and altitude dependence of different dynamical and chemical processes in determining the atmospheric composition over the western Pacific. TRACE-P (TP) showed an increase in the average latitudinal distributions of both O 3 and aerosols compared to PEM-West B (PWB), which was conducted in February-March 1994. O 3 , aerosol, and potential vorticity levels were used to identify nine air mass types and quantify their frequency of occurrence as a function of altitude. This paper discusses the characteristics of the different air mass types encountered during TP and compares them to PWB. These results confirmed that most of the O 3 increase in TP was due to photochemistry. The average latitudinal eastward O 3 flux in the western Pacific during TP was found to peak near 32°N with a total average O 3 flux between 14 and 46°N of 5.2 Tg/day. The eastward total CO flux was calculated to be 2.2 Tg-C/day with $6% estimated from Asia. The Asian flux of CO 2 and CH 4 was estimated at 4.9 and 0.06 Tg-C/day.

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Large-scale multiple-input multiple-output (LS-MIMO) is considered as a promising key technology for future 5G wireless communications due to its very high spectrum and energy efficiency. However, one challenging problem to achieve these benefits is a practical signal detection algorithm in the uplink. In this paper, we propose a low-complexity near-optimal signal detection algorithm using the conjugate gradient (CG) method for uplink multi-user large-scale MIMO systems, which can avoid the complicated matrix inversion required by linear minimum mean square error (MMSE) signal detection algorithm. We also provide the convergence proof of the proposed scheme to guarantee its usability in practice. The analysis indicates that the proposed scheme can reduce the computational complexity by about one order of magnitude. The simulation results of the bit error rate performance verify that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm by using only a small number of iterations.

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Optical wireless communication (OWC) has been a rapidly growing research area in recent years. Applying multiple-input multiple-output (MIMO), particularly large-scale MIMO, into OWC is very promising to substantially increase spectrum efficiency. However, one challenging problem to realize such an attractive goal is the practical signal detection algorithm for optical MIMO systems, whereby the linear signal detection algorithm like minimum mean square error (MMSE) can achieve satisfying performance but involves complicated matrix inversion of large size. In this paper, we first prove a special property that the filtering matrix of the linear MMSE algorithm is symmetric positive definite for indoor optical MIMO systems. Based on this property, a low-complexity signal detection algorithm based on the successive overrelaxation (SOR) method is proposed to reduce the overall complexity by one order of magnitude with a negligible performance loss. The performance guarantee of the proposed SOR-based algorithm is analyzed from the following three aspects. First, we prove that the SOR-based algorithm is convergent for indoor large-scale optical MIMO systems. Second, we prove that the SOR-based algorithm with the optimal relaxation parameter can achieve a faster convergence rate than the recently proposed Neumann-based algorithm. Finally, a simple quantified relaxation parameter, which is independent of the receiver location and signal-to-noise ratio, is proposed to guarantee the performance of the SOR-based algorithm in practice. Simulation results verify that the proposed SOR-based algorithm can achieve the exact performance of the classical MMSE algorithm with a small number of iterations.Index Terms-Optical wireless communications, large-scale MIMO, signal detection, minimum mean square error (MMSE), low complexity.