Monitoring particulate matter less than 10 μm (PM10) near the ground routinely is critical for Malaysia for emergency management because Malaysia receives considerable amount of pollutants from both local and trans-boundary sources. Nevertheless, aerosol data covering major cities over a large spatial extent and on a continuous manner are limited. Thus, in the present study we aimed to estimate PM10 at 5 km spatial scale using AOD derived from MERIS sensor at 3 metropolitan cities in Malaysia. MERIS level 2 AOD data covering 5 years (2007-2011) were used to develop an empirical model to estimate PM10 at 11 locations covering Klang valley, Penang and Johor Bahru metropolitan cities. This study is different from previous studies conducted in Malaysia because in the current study we estimated PM10 by considering meteorological parameters that affect aerosol properties, including atmospheric stability, surface temperature and relative humidity derived from MODIS data and our product will be at ~5 km spatial scale. Results of this study show that the direct correlation between monthly averaged AOD and PM10 yielded a low and insignificant relationship (R2= 0.04 and RMSE = 7.06μg m-3). However, when AOD, relative humidity, land surface temperature and k index (atmospheric stability) were combined in a multiple linear regression analysis the correlation coefficient increased to 0.34 and the RMSE decreased to 8.91μg m-3. Among the variables k- index showed highest correlation with PM 10 (R2=0.35) compared to other variables. We further improved the relationship among PM10 and the independent variables using Artificial Neural Network. Results show that the correlation coefficient of the calibration dataset increased to 0.65 with low RMSE of 6.72μg m-3. The results may change when we consider more data points covering 10 years (2002- 2011) and enable the construction of a local model to estimate PM10 in urban areas in Malaysia.
The Roque de los Muchachos Observatory, located on the island of La Palma in the Canary Islands, is home of many
astronomical facilities. In the context of the Extremely Large Telescope Design Study, an intensive lidar campaign was
performed in the ORM near the Jacobus Kapteyn Telescope (17°52'41.2" W, 28°45'40.1" N, 2395 m asl) between 26th
May and 14th June 2008. The goal of the campaign was to characterize the atmosphere in terms of planetary boundary
layer height and aerosol stratification vs. synoptic conditions. As a by-product an estimate of the aerosol optical
thickness was also obtained and compared to the total atmospheric extinction coefficient measured by the Carlsberg
Meridian Telescope.
KEYWORDS: Signal to noise ratio, LIDAR, Interference (communication), Raman spectroscopy, Linear filtering, Receivers, Fermium, Frequency modulation, Analog electronics, Photon counting
In this paper we estimate the signal-to-noise ratio (SNR) at the opto-electronic receiver output of both elastic and Raman lidar channels by means of parametric estimation of the total noise variance affecting the lidar system.
In the most general case, the total noise variance conveys contributions from photo-induced signal-shot, dark-shot and thermal noise components. While photo-inducted signal-shot variance is proportional to the received optical signal (lidar return signal plus background component), dark-shot and thermal noise variance components are not. This is the basis for parametric estimation, in which the equivalent noise variance in any receiving channel is characterized by means of a two-component vector modeling equivalent noise parameters.
The algorithm is based on simultaneous low-pass and high-pass filtering of the observable lidar returns and on weighted constrained optimization of the proposed variance noise model when fitting an estimate of the observation noise.
A noise simulator is used to compare different noisy lidar channels (i.e. with different pre-defined noise vectors or dominant noise regimes) with the two-component noise vectors estimate retrieved. Both shot-dominant and thermal-dominant noise regimes, as well as a hybrid case are studied. Finally, the algorithm is used to estimate the SNR from lidar returns from tropospheric elastic and Raman channels with satisfactory results.
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