Hamed Parsiani’s research while affiliated with University of Puerto Rico System and other places

Modulated by global, continental, regional, and local scale processes, convective precipitation in coastal tropical regions is paramount in maintaining the ecological balance and socioeconomic health within them. The western coast of the Caribbean island of Puerto Rico is ideal for observing local convective dynamics as interactions between complex processes involving orography, surface heating, land cover, and sea-breeze trade-wind convergence influence different rainfall climatologies across the island. A multi-season observational effort entitled the Convection, Aerosol, and Synoptic-Effects in the Tropics (CAST) experiment was undertaken using Puerto Rico as a test case, to improve the understanding of island-scale processes and their effects on precipitation. Puerto Rico has a wide network of observational instruments, including ground weather stations, soil moisture sensors, a Next Generation Radar (NEXRAD), twice-daily radiosonde launches, and Aerosol Robotic Network (AERONET) sunphotometer...

This paper presents coordinated ground-based observations by the NOAA-CREST Lidar Network (CLN) for profiling of aerosols, cloud, water vapor, and wind along the US east coast including Caribbean region at Puerto Rico. The instrumentation, methodology and observation capability are reviewed. The applications to continental and intercontinental-scale transport of smoke and dust plumes, and their large scale regional impact are discussed.

Saharan dust (SD) heavily impacts convective precipitation in the Caribbean. To better understand the role of SD in precipitation development during the midsummer drought (MSD), an observational campaign, centered at the city of Mayagüez, Puerto Rico (18.21 N, 67.13 W), between 3 June and 15 July 2014, was conducted in order to select a range of atmospheric conditions to be simulated using the Regional Atmospheric Modeling System (RAMS) cloud resolving model under “no SD” and “SD” conditions. The events included one dry day with moderate-heavy SD, one localized moderate rainfall event with moderate SD, one island-wide light precipitation event with heavy SD, and one island-wide heavy precipitation event with light-moderate SD. Model results show that (1) precipitation results are improved when compared with observation with the presence of SD, (2) precipitation, cloud fraction, dew point temperatures, and humidity are significantly reduced under SD conditions, (3) precipitation can occur when SD is removed for a dry day, (4) there is evidence of rain being delayed due to the presence of SD without rainfall intensity or accumulation increases, (5) liquid mixing ratio increases of up to 1.4 g kg −1 occur in the absence of SD, and (6) vertical wind increases of up to 0.8 m s −1 occur in the absence of SD.

At the UPRM Atmospheric Research Laboratory, the backscatter-power data received fro m the Lidar equip ment is processed and used to construct a power profile. The Lidar has a trans mit/receive capability for the basic wavelengths of 355, 532, and 1064 n m. Recently, it has been expanded to provide Raman scattering power data at 387n m, and 407n m, the later being improved for signal to noise ratio enhancement. The power profiles are then used to determine various atmospheric parameters, including the fundamental aerosol backscatter coefficient. The overlap factor in a co-axial Lidar system is responsible for the inability of the telescope to focus the backscatter data originated at the lower alt itudes, hence, it will influence any product derived fro m this data. The method applied (Ansmann 2002) to determine the corrected-overlap profile of the UPRM Lidar equip ment is based on the received Raman inelastic (387n m) and elastic (355n m) backscatter signals. It is an iterative approach that makes use of the fact that the deviation between the Klett's solution for the backscatter coefficient, which is calculated fro m the elastic backscatter signal, and the Raman Lidar solution, contains the information about the overlap factor. As a result, the determination of the overlap factor will improve backscatter power data at the lower alt itudes and provide a more accurate boundary layer detection. In this paper, the backscatter power correction has been applied to the Lidar three received wavelengths. KEY WORDS: Boundary layer, elastic-inelastic, backscatter coefficient improvement, overlap factor.

Light Detection and Ranging (LIDAR) is a recent remote sensing system which has been gradually expanding as a network among the countries actively concerned about the atmospheric contaminants, earth radiation budget, rain variations, clean air index, etc. In this work, the design of a typical Lidar, ground based system for three wavelengths is explained. Essential parameters for the atmospheric characterizations such as Aerosol Optical Depth (AOD), Angstrom coefficient (Å), and Aerosol Size Distribution (ASD) are explored and the obtained results are discussed. These parameters have been calculated and plotted based on three Lidar system wavelengths of 355, 532, and 1064 nm using the data obtained from Lidar in the Western part of Puerto Rico (location of UPRM Lidar system). The relationship between the essential parameters presented by the plots and the atmospheric behavior is explained.

Algorithms have been developed for the determination of essential parameters such as Aerosol Size Distribution, Angstrom coefficient, and Single Scattering Albedo necessary in the determination of regional climatological model and weather prediction capabilities. The atmospheric power profile data for the calculations of these parameters have been obtained from Light Detection And Ranging (LIDAR) which is operational at City University of New York (CUNY). A similar system is near completion at the University of Puerto Rico at Mayaguez (UPRM). Also, the data from the Lidar in the orbit (CALIPSO satellite) has been used to calculate the Aerosol Optical Depth (AOD) for the western part of Puerto Rico and compare it to the AOD obtained from an AERONET network which is also located in the western region of Puerto Rico. The Calipso Lidar satellite transmits laser and collects backscattered light at two standard wavelengths (one wavelength at two polarizations). The AERONET network is a complementary method of determining the Optical Depth of the aerosol. The three aforementioned systems operate based on multiple wavelength laser light transmission and reception. Each system is explored in this paper, and full emphasis is given to the Lidar system which presently is near operation at the UPRM, explaining the functionality of the Laser, optical telescope, optics, sensors, signal processing systems, the power profile reflected from the aerosols are obtained at standard wavelengths of 355, 532, and 1064nms, both at CUNY and UPRM. The plots of aerosol distribution in the column of atmosphere in terms of the essential aerosol parameters have been produced using the Lidar data over New York urban area.

The establishment of the first Lidar (Light Detection and Ranging) laboratory for Atmospheric Research in the University of Puerto Rico at Mayagüez (UPRM), and aerosol characterization algorithm which relies on Lidar data are presented in this paper. The Lidar system consists of several major parts which are the laser, expander, telescope, optics, and signal processing components. The Lidar which is elaborated upon in this paper allows the study of atmospheric profile by transmitting and receiving three fundamental wavelengths of 355, 532, and 1064nm. And the research work presented in this paper uses the underlying mathematics to arrive at the determination of aerosol backscatter and extinction coefficients, optical depth, and size distribution, using the three wavelengths Lidar data obtained from City University of New York (CUNY) Lidar, while the UPRM Lidar system is being installed.

Aerosol size distribution (ASD) is an integral parameter in regional atmospheric models [1]. The LIDAR laboratory at UPRM will provide Puerto Rico a means of measuring ASD, and therefore improve these models. This project intends to develop a method of obtaining ASD with the use of a Sun-photometer data, local CIMEL data obtained from AERONET (Aerosol Robotic Network) [2], and an artificial neural network (ANN). The Sun-photometer used is an instrument that measures Aerosol Optical Depth (AOD) at five wavelengths of 380, 440, 500, 675, and 870 nms . A feed-forward, back-propagation artificial neural network was used to map the underlying pattern between AOD and ASD in southwestern Puerto Rico. After training, the network will produce ASD outputs based on new AOD inputs measured locally at UPRM with the Sun-photometer. It was determined that accurate predictions of ASD (MSE of 10 -4) could be made depending on the size of the AOD data pool selected.

An algorithm is developed and detailed in this paper which determines atmospheric aerosol parameters such as backscatter and extinction coefficients, aerosol optical thickness, and the aerosol size distribution. The algorithm uses the power profile data obtained from Lidar at the City University of New York (CUNY). The aerosol optical thickness at 20 km range has been validated using a Sun-photometer. The assembly of a typical Lidar system capable of providing the power profile at three standard wavelengths of 355, 532, and 1064 nm is elaborated upon, by actually detailing the Lidar system under construction at the University of Puerto Rico at Mayaguez, as one of several Lidars to be established in the Caribbean.