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1 ANNE M. THOMPSON Professor, Department of Meteorology College of Earth & Mineral Sciences, Penn State Penn State, Open Forum: “Nobel Knowledge on Climate.

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Presentation on theme: "1 ANNE M. THOMPSON Professor, Department of Meteorology College of Earth & Mineral Sciences, Penn State Penn State, Open Forum: “Nobel Knowledge on Climate."— Presentation transcript:

1 1 ANNE M. THOMPSON Professor, Department of Meteorology College of Earth & Mineral Sciences, Penn State Penn State, Open Forum: “Nobel Knowledge on Climate Change”, February 19, 2008 AQAST TIGER TEAM PROGRESS PSU (A M Thompson, G Garner, A Reed, H Halliday, D Kollonige, R Stauffer, W Ryan) NCAR (S E Haupt, J A Lee) Univ Maryland (R R Dickerson) Presentation to AQAST Team, Madison, 12 June 12 “Ozone Violations in Maryland (July 2011): Roles of Temperature, Chemical Reactions, Dynamical Processes”

2 2 Tiger Team Project – Applications from DISCOVER-AQ, 2011, Maryland PROBABILISTIC FORECASTING TESTED IN D-AQ –* G Garner: Tested for MDE, 7/11; Paper in preparation –2012 forecasts on Webpage! Customer: MDE COMPARISON OF PANDORA NO 2 & OMI (A R EED ) MAIN THRUST OF TIGER TEAM EFFORT: O 3 non-attainment at Edgewood, MD. July 2011 D-AQ period exemplary in ozone violations. WHY?! –Examine role of meteorology, sea breeze. (R Stauffer et al., in prep, 2012). AQ violation days = Sea Breeze –VOC samples (H Halliday) from Edgewood, (cf Beltsville, J Fuentes) over diurnal cycle for 4-5 days. Note: Beltsville more biogenic influence (DC), Edgewood more anthropogenic (Balto) –Chemical influence of actual VOC, Nox/NO constrains ozone formation rates in photochemical box model. Compare mechanistic uncertainties CB05, RACM (D Kollonige) –Ensemble WRF forecasting for 1-2 days in D-AQ (J Lee) Data Models

3 Thanks to AQAST & PSU Gator Research Team! Dr. Anne Thompson - Professor, Dept. of Meteorology Fulbright Scholar, South Africa (2010 – 2011); SHADOZ (1998 - present) (2005) Jared Lee NCAR & PSU, PhD, 6/12 (2008) Greg Garner DISCOVER-AQ / NASA AQAST (2011); EPA STAR (2011) Evaluation of Air Quality Model Performance during the 2011 DISCOVER-AQ Campaign Andra Reed (2011) Comparison of Satellite and Ground-based Measurements of Total Column Ozone and Nitrogen Dioxide from Edgewood, MD in July 2011 Ryan Stauffer (2010) CAPABLE (2010); DISCOVER-AQ (2011) Bay Breeze Impact on Surface Ozone at Edgewood, MD (2010) Hannah Halliday CAPABLE (2010); DISCOVER-AQ (2011) VOC Types for Beltsville and Edgewood MD on Select Days in July 2011 Dr. Debra Kollonige (2012) NASA AQAST (2012) Newest Gator! PhD Atmospheric Physics, UMBC 12/2011

4 G Garner’s 2012 AQ Forecast Website: DC- MD (2 regions) –VA (5 regions) 4

5 A Reed’s Pandora NO 2 Analysis: Focus on “Poor” Agreement Days to Determine Meteorological Influences (Clouds, Aerosol Issues) 5 29 July 15 July Edgewood -Pandora NO 2 – x 10 (15) cm 2 OMI

6 H Halliday’s VOC Edgewood Data Summary

7 D. Kollonige’s Pre-Analysis of Edgewood Chemistry based on DISCOVER-AQ Field Data July 10-11, 2011 CO, O 3, and Isoprene * PSU’s NATIVE augmented MDE data w/ inorganic species (O 3, CO, NO, NO 2, etc.) and VOCs (isoprene, acetone, methanol, etc.) at Edgewood, MD for four days during D-AQ. * Uncertainties of measured model constraints can be determined by corresponding measurements from this dataset. July 21-22, 2011 CO, O 3, and Isoprene

8 D. Kollonige’s Sensitivity Analysis based on DISCOVER-AQ Field Data Based on Chen et al. (2010) and Chen (2011), a novel method of quantifying uncertainty sources from model parameters of chemical mechanisms can be applied to D-AQ measurements. Sensitivity analysis (SA) is: –Based on real conditions (Edgewood measurements). –Comprised of 100s of model parameters subject to variations w/in their uncertainties. –Using recently developed global SA method called Random Sampling – High Dimensional Model Representation (RS- HDMR), which maps input-output relations with only one set of random samples, greatly reducing the computational cost. SA currently being performed on several chemical mechanisms (MCMv3.2, CB05, RACM, etc.) to understand O 3 formation in MD. Met fields, inorganic species VOCs (lumped or explicit) Kinetic rate coefficients, product yields Random sampling (N values) of model inputs based on given PDFs Run model (chem. mechanism) N times to obtain N simulated outputs Assess sensitivity based on input/output relationships using RS- HDMR

9 NCAR- J Lee & S Haupt Uncertainty Analysis for DISCOVER-AQ-July 2011 Ozone violations during DISCOVER are related to a combination of physics, dynamics, and chemistry Uncertainty in a forecast can be quantified using an ensemble approach. Ensemble members can be constructed to span variability in physics and chemistry Constructing the ensemble pdf of 1-2 July 2011 high ozone events will illuminate –the probability of such events –the relative roles of dynamic and chemistry uncertainty –Highlight seabreeze conditions(?) Example six members of SREF ensemble


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