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Your search keyword '"Qinbin Li"' showing total 97 results
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97 results on '"Qinbin Li"'

1. A Conical-Scanning Microwave Limb Sounder for Atmospheric Measurements

Author

Joe W. Waters, Nathaniel J. Livesey, Michelle L. Santee, Paul C. Stek, Richard E. Cofield, Peter H. Siegel, Jonathan H. Jiang, Qinbin Li, Gloria L. Manney, William G. Read, Duane E. Waliser, and Dong L. Wu

Subjects
Microwave limb sounding, microwave remote sensing, ozone monitoring, temperature profiling, cloud ice measurements, pollution measurements, Telecommunication, TK5101-6720, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4
Abstract

We describe a novel scanning microwave limb sounder (SMLS) instrument that performs rapid and broad azimuth conical scans of Earth's limb while simultaneously scanning the limb in the vertical. This azimuthal scanning capability gives dramatic improvement in temporal and spatial coverage over that of previous limb sounding instruments. In a 1500-kilometer altitude, 52°-inclination Earth orbit, SMLS provides 6–8 vertical profile measurements separated by 1.9 hours every 24 hours everywhere between ±65° latitude, and 2–4 such measurements everywhere between ±(65–82°). Horizontal resolution is ∼50×50 km. Vertical resolution is ∼2 km for water vapor and cloud ice and ∼1–3 km for chemical species. In an equatorial orbit, emphasizing the tropics and subtropics, SMLS produces profile measurements every 1.9 hours everywhere between ±35° latitude. SMLS measurements address scientific issues of relevance to the upper troposphere, stratosphere, mesosphere, and lower thermosphere regions of the atmosphere (heights from ∼10 km to ∼100 km).

Published
2024
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3. Enhanced PM2.5 pollution in China due to aerosol-cloud interactions

Author

Bin Zhao, Kuo-Nan Liou, Yu Gu, Qinbin Li, Jonathan H. Jiang, Hui Su, Cenlin He, Hsien-Liang R. Tseng, Shuxiao Wang, Run Liu, Ling Qi, Wei-Liang Lee, and Jiming Hao

Subjects
Medicine, Science
Abstract

Abstract Aerosol-cloud interactions (aerosol indirect effects) play an important role in regional meteorological variations, which could further induce feedback on regional air quality. While the impact of aerosol-cloud interactions on meteorology and climate has been extensively studied, their feedback on air quality remains unclear. Using a fully coupled meteorology-chemistry model, we find that increased aerosol loading due to anthropogenic activities in China substantially increases column cloud droplet number concentration and liquid water path (LWP), which further leads to a reduction in the downward shortwave radiation at surface, surface air temperature and planetary boundary layer (PBL) height. The shallower PBL and accelerated cloud chemistry due to larger LWP in turn enhance the concentrations of particulate matter with diameter less than 2.5 μm (PM2.5) by up to 33.2 μg m−3 (25.1%) and 11.0 μg m−3 (12.5%) in January and July, respectively. Such a positive feedback amplifies the changes in PM2.5 concentrations, indicating an additional air quality benefit under effective pollution control policies but a penalty for a region with a deterioration in PM2.5 pollution. Additionally, we show that the cloud processing of aerosols, including wet scavenging and cloud chemistry, could also have substantial effects on PM2.5 concentrations.

Published
2017
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50. Exploiting GPUs for Efficient Gradient Boosting Decision Tree Training

Author

Qinbin Li, Jian Chen, Kotagiri Ramamohanarao, Bingsheng He, Jiashuai Shi, and Zeyi Wen

Subjects
020203 distributed computing, Speedup, Computer science, Decision tree, Sorting, 02 engineering and technology, Parallel computing, Data set, Instruction set, Computational Theory and Mathematics, Hardware and Architecture, Histogram, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, sort, Gradient boosting
Abstract

In this paper, we present a novel parallel implementation for training Gradient Boosting Decision Trees (GBDTs) on Graphics Processing Units (GPUs). Thanks to the excellent results on classification/regression and the open sourced libraries such as XGBoost, GBDTs have become very popular in recent years and won many awards in machine learning and data mining competitions. Although GPUs have demonstrated their success in accelerating many machine learning applications, it is challenging to develop an efficient GPU-based GBDT algorithm. The key challenges include irregular memory accesses, many sorting operations with small inputs and varying data parallel granularities in tree construction. To tackle these challenges on GPUs, we propose various novel techniques including (i) Run-length Encoding compression and thread/block workload dynamic allocation, (ii) data partitioning based on stable sort, and fast and memory efficient attribute ID lookup in node splitting, (iii) finding approximate split points using two-stage histogram building, (iv) building histograms with the aware of sparsity and exploiting histogram subtraction to reduce histogram building workload, (v) reusing intermediate training results for efficient gradient computation, and (vi) exploiting multiple GPUs to handle larger data sets efficiently. Our experimental results show that our algorithm named ThunderGBM can be 10x times faster than the state-of-the-art libraries (i.e., XGBoost, LightGBM and CatBoost) running on a relatively high-end workstation of 20 CPU cores. In comparison with the libraries on GPUs, ThunderGBM can handle higher dimensional problems which the libraries become extremely slow or simply fail. For the data sets the existing libraries on GPUs can handle, ThunderGBM achieves up to 10 times speedup on the same hardware, which demonstrates the significance of our GPU optimizations. Moreover, the models trained by ThunderGBM are identical to those trained by XGBoost, and have similar quality as those trained by LightGBM and CatBoost.

Published
2019
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