Index of Atmospheric Research Areas
The purpose of the Atmospheric Research ORS Lab is to monitor and investigate the optical properties of the atmosphere. This is accomplished with remotely-sensed data, received from operational and research satellites, and stationary instruments deployed at observation sites.
Some of our active development efforts to augment and expand our capabilities and understanding are outlined herein:
Eye-safe Heterodyne Detection Coherent Doppler Lidar
Sameh Abdelazim, David Santoro, Mark Arend, Fred Moshary, Sam Ahmed
Coherent pulsed LIDAR is receiving increasing attention as a method for detecting aerosol concentration in the air and detecting wind speed. Wind speed detection, in particular within urban areas, is essential to modeling air flow patterns to analyze pollution transmission and determine, for example, optimal locations for wind turbines. City College of New York is currently developing a mobile coherent Doppler LIDAR station to detect wind speed. In Doppler sounding with coherent LIDAR, pulses are transmitted into the atmosphere. These pulses reflect off aerosol particles in the sky and return to the system. The motion of these aerosols can be measured based on the Doppler shift of the wavelengths transmitted. With a mobile system, it is possible to point at the same location from three or more different directions, and thus to calculate an accurate vector wind speed for the area.
The station in development at City College of New York uses a 1.54 µm near-infrared pulsed beam and polarization maintaining fiber optics. This wavelength is safe to the eye and provides an efficient balance of back-scattering and absorption. Signal analysis and detection is accomplished using heterodyne detection to supress RIN noise. In this process, two signals of slightly different frequency are created. Shifting can then be detected by comparing the initial modified frequency to the return frequency. A/D conversion is performed by using a data acquisition board with FPGA on-board. Finally, a basic wedge prism i0073c is used to control the zenith angle of the beam as it is transmitted to the atmosphere. This will enable data to be taken from a variety of directions. Real-time scanning will be accomplished by a computer-controlled dual-axis knuckle assembly to provide both azimuth rotation and elevation beam positioning.
The system consists of the following components: laser source, modulator, fiber amplifier, optical antenna, detector, and signal processor. These components and their relationships are shown in the second image.
Laser Power Analysis
System Parameter Tests
In future work, we will compare our results at various locations around the New York City area, specifically at or near sites where we have other wind measurement vertical profilers by expanding the system to be mobile and multi-directional and using our mobile Lidar van. We can then fully analyze wind speed and aerosol density in a 3-D spacial manner.
Mid-IR Quantum Cascade Laser for LIDAR Application
Morann Dagan1, Ihor Sydoryk1, Fred Moshary1, Barry Gross1
LIght Detection and Ranging (LIDAR) instruments have proved to be powerful for atmospheric research and environmental monitoring . LIDAR systems are used to build atmospheric profiles by collecting the backscatter signal from molecules and particles in the atmosphere. A LIDAR system consists of a transmitter which generates a pulsed signal at a specific wavelength, a receiver to collect the backscatter signal, and a computer system which digitizes the signal as a function of time or range . The wavelength chosen for the transmitter depends on what you want to see in the atmosphere. To see the backscatter signal from the larger particles, a longer wavelength should be used and vice versa. Choosing a wavelength in the mid infrared (MIR) band would be beneficial for detecting aerosols and characterizing cloud drop size below the cloud .
The drawback of a MIR LIDAR system is the low signal to noise ratio (SNR). This is due to a low backscatter in the MIR band (3 µm – 8 µm) compared to the visible (380 nm – 750 nm) since SNR is inversely proportional to wavelength. Based on SNR estimates we explored the viability of a Mid-IR QCL LIDAR, including calibration issues. The measurement sensitivity typically depends on the amount of light that is backscattered, collected, and focused onto the detector. This is dictated primarily by the backscatter coefficient of the scattering source. Typical LIDAR systems detect elastic backscattering from atmospheric molecules (Rayleigh scattering) and aerosol particles (particle or Mie scattering). In the mid-IR elastic backscattering is dominated by coarse mode particulate scattering because of the 1/λ4 wavelength dependence of Rayleigh scattering. Particle backscatter efficiencies depend strongly on the atmospheric density of aerosols (1/ λ2); however, in the MIR, backscatter efficiencies tend to be very small. In order to improve the low SNR, averaging pulses over time helps reduce the noise and therefore increases our SNR. Figure 3 shows the SNR values of the potential LIDAR system, where an SNR of approximately .4 (should be 1) at 1km can be achieved for MIR channel (4.55 um) on a clear day after 30 minute averaging. Figure 4 shows the SNR of the system when looking at clouds, where the SNR is improved by more than a factor of ten for one minute averaging.
At CCNY, we are building a MIR LIDAR to improve and better understand the atmospheric profile. By using the Mid-IR LIDAR data along with the visible (VIS) LIDAR data, it would make it possible to separate the fine from the coarse aerosols which is critical in interpreting anthropogenic aerosol sources. Further, using both data sets will help in characterizing aerosol-cloud interaction and cloud drop size below the cloud. Backscatter data in the VIS, NIR (near IR), and MIR (mid IR) is able to distinguish these modes much better than backscatter measurements in the VIS and NIR only.
The MIR LIDAR is being designed to be mobile; therefore our system is small and compact to make it portable. Figure 2, below, shows our current system configuration. This LIDAR is capable of running anywhere with power available since the entire system is situated on a cart for easy transportation. We are able to take data at any angle, making our LIDAR functional for many applications. Our transmitter is a Quantum-Cascade laser (QCL) with a wavelength of 4.55 microns, a pulse width of 202 ns and a peak power of 4.5 W. We chose a QCL at that wavelength since it falls within an atmospheric window (wavelengths at which electromagnetic radiation can transmit through the atmosphere to the surface), see figure 1. Quantum-Cascade lasers are particularly suitable in this wavelength range, as they offer several Watts of pulsed optical power, while retaining a good far field pattern as required for laser remote sensing techniques in atmospheric research. Our receiver is an f/5 Newtonian telescope with a 10 inch primary mirror. Our data is collected using a detector with a spectral range of approximately 2.5 – 5 µm and a spectral response, D*, of 9.25x1010. Once signal is collected, it is digitized using a 12 bit Gage digitizer and then analyzed using LabVIEW and MATLAB. At 100 KHz repetition rate, our system range is 1.5 km with 60 m resolution.
Currently, we are working on the final calibrations of our LIDAR system. We have so far collected hard target signal from distances up to 800 meters away. Figure 4 shows examples of backscatter signal off buildings 90 - 250 meters away. These figures average the signal 256 times to increase the SNR. In the top plot of figure 4 a clear signal can be seen from a building 250 meters away. The lower plot shows a signal coming from a building 90 meters and another 150 meters away. However, based on the SNR calculations above, we will probably need to average over 30 minutes to see a signal from aerosol particles.
Our next crucial step to complete our system is to improve detector alignment. To do this, we plan on redesigning the layout of our detector by replacing our relay lens with a folding mirror and adding a xyz stage for the detector. Once we have better alignment we hope to start picking aerosol and cloud signal. With a working system, we can then consider revising it with a remote controlled Newtonian telescope for easy angle adjustments or coming up with a design to thermo-manage our system giving us the possibilities of running our system at lower temperatures.