Nd:YAG 532, 1064 nm PMT A PD Atmosphere Smoke, Haze, Dust, Clouds, Aerosols 14” Schmidt- Cassegrain Telescope Transmitter Receiver Determination of Planetary Boundary Layer for Air Quality Forecasting Jaime C. Compton 1,2 , Nikisa S. Jordan 2 , Raymond M. Hoff 1,2 ,Ruben Delgado 2 1 Physics Department, 2 Joint Center for Earth Systems Technology University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 ABSTRACT A growing environmental concern is the pollution due to particulate matter (PM) in the atmosphere. Exposure to high amounts of PM in the air is harmful to human health and contributes to respiratory diseases. The Environmental Protection Agency (EPA) and the National Oceanic and Atmospheric Administration (NOAA) have committed to forecasting PM by 2011. The current forecasting models from the National Weather Service indicate strong uncertainties in a key input of these models, the planetary boundary layer height (PBL in meters). Measuring the evolution and structure of the PBL enables the understanding of the formation, transportation and forecasting of PM. Remote sensing of atmospheric aerosols in the lower troposphere that affect air quality is carried out at the University of Maryland, Baltimore County (UMBC), by the Atmospheric Lidar Group. The Elastic Lidar Facility measures the intensity of 532 nm backscattered photons as a function of height, providing a temporal evolution of the vertical distribution of aerosols. The covariance wavelet technique (CWT) was used to objectively determine the PBL height from the elastic lidar measurements. The results of the CWT method are compared to meteorological data from radiosondes, launched at Howard University-Beltsville Campus, and GEOS-5 to demonstrate the viability of lidar aerosol profiles to monitor the real-time variation of the PBL. Accurate determination of the PBL using the CWT will allow improved air quality forecasting, understanding of regional pollution dynamics, and validation of satellite aerosol optical depth into PM forecasting. The Planetary Boundary Layer Surface Layer • Turbulent fluxes and stress vary by less than 10% of their magnitude • Bottom 10% of PBL Stable (Nocturnal) Boundary Layer • The BL from sunset to sunrise • Forms near the ground in response to the cooling of the air by the radiatively cooled surface Residual Layer • Contains decaying or zero turbulence • Contains residual heat, moisture, and pollutants mixed during the previous day. Capping Inversion (Night) / Entrainment Zone (Day) • Characterized by high static stability • Suppresses turbulence within it • Turbulence from below has difficulty penetrating • Acts as lid to rising thermals • Temperature Inversion • Absolute temperature increases with height Mixed Layer (ML) • Characterized by intense mixing of heat, moisture, and momentum in a statically unstable situation where thermal plumes of warm air rise from the ground • ML Growth – Solar heating of ground – Entrainment • Process of less or non-turbulent air from the above free atmosphere mixing down into the turbulent ML At the top of the PBL in the Entrainment Zone/Capping Inversion, the temperature begins to increase with height (pictured left). This is known as temperature inversion. Radiosondes (pictured right) are used to measure the temperatures throughout the atmosphere.  Li ght D etection a nd R anging  Measures intensity of backscattered light as function of distance  General idea of how it works: 1. Emits laser beam pulses into atmosphere 2. Light backscatters off of the particles 3. Telescope collects backscattered light 4. Analyzed by a computer Temperature Inversion and Radiosondes Elastic Lidar Results • Covariance transform defined by Gamage and Hagelberg (1993): • z t and z b are the top and bottom altitudes in the lidar backscatter profile • f(z)is the lidar backscatter profile as a function of altitude, z • a -1 is the normalization factor Covariance Wavelet Technique • Haar function • z=vertical distance in this application • a=spatial extent dilation of the function • b=center of Haar function- translation of the function  To determine the PBL the following algorithm is used: o Compute convolution of the backscatter profile, f(z), and the Haar function, o Taking a lidar backscattering profile, calculate a local minimum in W f (a,b) using a scale of a located at z = b o This process is repeated for all individual lidar backscatter profiles to create a time series of boundary locations. The PBL is the source of nearly all energy, water vapor, and trace chemical species that are transported into the atmosphere. Much of the atmospheric chemistry occurs here. Daily Evolution and Composition of the PBL Date Time [UTC] Radiosonde (RS) [km] CWT [km] CWT MEAN [km] CWT STD DEV [km] %Difference between RS and CWT %Difference between RS and CWT MEAN 7/27/2005 18:18 1.74 1.81 1.79 0.11 4.24 3.29 6/30/2006 18:27 1.80 1.83 1.70 0.32 1.65 5.54 7/16/2006 18:28 1.65 1.77 1.31 0.25 7.39 20.4 8/02/2006 19:18 1.30 1.36 1.43 0.08 5.25 10.4 7/09/2007 18:13 1.87 1.87 1.73 0.17 0.09 7.84 7/14/2007 18:06 1.71 1.38 1.77 0.42 19.4 3.45 8/02/2007 18:32 1.94 1.54 1.80 0.31 20.3 7.24 8/03/2007 06:40 1.10 1.14 1.37 0.09 3.18 24.4 3/02/2008 01:08 1.91 1.84 1.72 0.09 3.62 10.3 3/10/2008 02:00 1.47 1.39 1.37 0.02 4.91 6.48 GEOS-5 [km] %Difference between GEOS-5 and CWT 8/2/2006 18:30 1.65 1.32 20.2 19:30 1.78 1.42 19.9 Lidar vs. Radiosonde/Lidar vs. GEOS-5 Table below shows and compares the PBL heights as determined by the CWT using lidar against radiosondes launched from Howard University and GEOS-5 modeling data. CWT MEAN is a mean average of PBL heights determined for the time period of five minutes before and after the radiosondes launch to account for any uncertainty in the radiosondes’ altitude ascent time. Conclusions CWT and the radiosondes measurements range from 0.09% to 10% in most cases, and therefore are in agreement with each other. The distance between Beltsville and UMBC (51.2 km) is likely the cause of disagreement between the two. The large disagreement between GEOS-5 and the CWT is likely due to flaws in the spatial resolution of the physics in the GEOS-5 modeling algorithm. Lidar Profiles With PBL Calculated By CWT, GEOS-5 Image (lower right): Future Work Implement the CWT to the UMBC Atmospheric Lidar Group real time lidar live feed. Provide lidar PBL heights to near real time NOAA and EPA PM forecasting. Acknowledgements: JCET Summer Intern Program References 1. Brooks, I.M., 2003. Finding Boundary Layer Top: Application of a Wavelet Covariance Transform to Lidar Backscatter Profiles. Journal of Atmospheric and Oceanic Technology, 20: 1092-1105 2. Davis, K. J., N. Gamage, C.R. Hagelberg, C. Kiemle, D.H. Lenschow, and P.P. Sullivan, 2000. An objective method for deriving atmospheric structures from airborne lidar observations, J. Atmos. Oceanic Technol., 17, 1455-1468 3. Stull R.B., 1988. An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, 666pp.