Model Planetary Boundary Layer Heights and GroundBased Airborne
Model Planetary Boundary Layer Heights and Ground-Based, Airborne, and Satellite Lidar Data Provide New Insight on Air Quality Jasper Lewis (Code 612, NASA/GSFC and UMBC/JCET); Ellsworth Welton (Code 612, NASA/GSFC); Erica Mc. Grath-Spangler (Code 610. 1, NASA/GSFC and USRA); Jennifer Hegarty (AER) High-resolution Weather Research and Forecasting (WRF) modeled planetary boundary layer heights (PBLHs) are evaluated against ground-based micropulse lidar (MPL), the NASA Langley airborne High Spectral Resolution Lidar-1 (HSRL-1), and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) on the CALIPSO satellite during the DISCOVER-AQ Baltimore-Washington, D. C. field campaign. WRF–lidar differences were dependent on model configuration, PBLH calculation method, and synoptic conditions. Contrasting synoptic conditions show poor agreement between WRF and lidar-derived PBLHs on a day with poor air quality (July 11) and good agreement on a day with moderate air quality (July 14).
Name: Jasper Lewis, NASA/GSFC, Code 612, UMBC JCET E-mail: jasper. r. lewis@nasa. gov Phone: 301 -614 -6721 References: Hegarty, J. D. , J. Lewis, E. L. Mc. Grath-Spangler, J. Henderson, A. J. Scarino, P. De. Cola, R. Ferrare, M. Hicks, R. D. Adams-Selin, and E. J. Welton, 2018: Analysis of the Planetary Boundary Layer Height during DISCOVER-AQ Baltimore–Washington, D. C. , with Lidar and High-Resolution WRF Modeling. J. Appl. Meteor. Climatol. , 57, 2679– 2696, https: //doi. org/10. 1175/JAMC-D-18 -0014. 1 Data Sources: NASA Micro Pulse Lidar Network (MPLNET). NASA Langley airborne HSRL-1. CALIPSO data were obtained from the NASA Langley Research Center Atmospheric Science Data Center. NOAA/NWS Sterling, VA MPL. The other field-deployed MPL data for DISCOVER-AQ were produced by Tim Berkoff, and the ozonesonde launches and data processing were coordinated by Everette Joseph at Beltsville and Anne Thompson at Edgewood. This work was supported under the NASA Atmospheric Composition Campaign Data Analysis and Modeling program under Grant NNH 14 CM 13 C. Technical Description of Figures: High-resolution WRF simulations with horizontal grid spacing of 1 km and eight different combinations of PBL schemes, urban parameterization, and sea surface temperature inputs were evaluated against ground-based, airborne, and satellite lidars. Two days with contrasting weather conditions are shown for July 11 (left figure) and July 14 (right figure). In each figure, WRF simulations using the Mellor–Yamada– Janjic scheme coupled with the multilayer Building Environment Parameterization Building Energy Model are shown with the HSRL-1 PBLHs along the flight track (overlapping colored circles) and MPL PBLHs (colored squares). On July 11, the WRF PBLHs are much higher than those from HSRL-1 and the MPLs and generally show poor correlation (R < 0. 20). However, the PBLH derived from a collocated ozonesonde profile at the Beltsville, MD MPL site is in better agreement with WRF than the MPL. For July 14, the WRF–lidar PBLH correlations are much better (R > 0. 80) and there is good agreement between all the MPL, ozonesonde, and WRF PBLHs. CALIPSO PBLHs are not available for July 11, but the orbital track nearest to the Baltimore. Washington, D. C. corridor on July 14 (not shown; to the NW) had PBLH retrievals of ~1. 5 km. Analysis of data for the month of the July, segregated by wind direction, generally shows the WRF–lidar PBLH correlations are lower and the biases are higher on days with SW–W winds. Scientific significance, societal relevance, and relationships to future missions: An additional finding from July 11 is the method used to determine the PBLH from WRF and ozonesonde profiles (gradient, bulk Richardson, or parcel) produced results that varied by as much as 1 km. The fact that there were such large differences between the PBLHs attributable to the calculation method suggests that the meteorological PBL structure is less distinct and a potentially quantifiable degree of uncertainty in the mixing-height estimates could be used in conjunction with retrospective air quality simulations to better understand the causes of pollution events such as high-ozone episodes. Since high-ozone days in the northeastern United States often occur with synoptic surface winds from the southwest direction, this uncertainty characterization of the mixing height estimates could be of significant importance. Furthermore, this research can provide guidance towards future studies of PBL processes such as bay breezes and the PBLH gradient that exists between urban and rural areas. In addition to using CALIPSO data, this work will facilitate formulation of two Decadal Survey missions: the Aerosol, Cloud, Convection, and Precipitation Designated Observable and the Planetary Boundary Layer Incubator. Earth Sciences Division - Atmospheres
Satellite Observations Suggest Too Efficient Dust Removal in Models Hongbin Yu 1, Qian Tan 2, Mian Chin 3, Lorraine Remer 4, Ralph Kahn 1, and others 1 NASA GSFC Code 613, 2 BAERI, 3 GSFC Code 614, 4 UMBC-JCET Decade-long (2007 -2016) records of CALIOP, MODIS, MISR, and IASI observations are used to quantify dust deposition flux and loss frequency (LF) over the tropical Atlantic Ocean. Deposition flux and LF show distinct variations in season and space. The satellite observations suggest that models remove dust too fast (i. e. , LF being 2 -5 times higher) along the trans-Atlantic transit, which has led to an ongoing analysis of models to better define model deficiencies.
Name: Hongbin Yu, NASA/GSFC, Code 613 E-mail: Hongbin. Yu@nasa. gov Phone: 301 -614 -6209 References: Yu, H. , Q. Tan, M. Chin, L. A. Remer, R. A. Kahn, H. Bian, D. Kim, Z. Zhang, T. Yuan, A. H. Omar, D. M. Winker, R. C. Levy, O. Kalashnikova, L. Crepeau, V. Capelle, A. Chedin, Estimates of African dust deposition along the trans‐Atlantic transit using the decade-long record of aerosol measurements from CALIOP, MODIS, MISR, and IASI. Journal of Geophysical Research – Atmospheres, 124, 7975 -7996, 2019. https: //doi. org/10. 1029/2019 JD 030574. Data Sources: Aerosol measurements over 2007 -2016 from CALIOP, MODIS, MISR and IASI; wind profiles from MERRA-2; rainfall rate from the Global Precipitation Climatology Project (GPCP). We are grateful to all team members of the datasets for their dedicated efforts of producing the high-quality data. The work was supported by the NASA CALIPSO/Cloud. Sat Science Team program administered by Dr. David Considine. Technical Description of Figures: Left Graphic: Climatology of dust deposition flux (1 nd column), and dust loss frequency or LF (2 nd column) derived from the 2007 -2016 CALIOP/CALIPSO observations in (a) December-January-February (DJF), (b) March-April-May (MAM), (c) June-July-August (JJA), and (d) September-October-November (SON). Dust deposition flux is calculated following the mass balance of meridional and zonal dust mass fluxes in a 5°x 2° grid cell, on a monthly basis. LF is defined as a ratio of dust deposition flux to dust mass loading and has higher accuracy than the deposition itself. It measures how efficient the dust is removed from the atmosphere (e. g. , larger LF indicates more efficient removal). Pattern differences exist between dust optical depth (DOD) and the deposition flux, suggesting DOD should not be used directly as a proxy for the dust deposition. Similarly, DOD from MODIS, MISR, and IASI are also used, in combination with CALIOP vertical profile information, to estimate the dust deposition and LF (figures not shown). Depending on satellite sensors, dust is distinguished from other types of aerosol using satellite observables of particle size and shape information (e. g. , CALIOP depolarization ratio, MODIS finemode fraction, MISR non-spherical fraction, and IASI thermal-infrared aerosol detection), with details documented in. Yu et al. (2009, 2013, 2015). Right Graphic: A comparison of the satellite-based estimates of dust deposition (upper panel) and dust LF (lower panel) with five AEROCOM models(Kim et al. , JGR, 2014) in tropical Atlantic Ocean. The models consistently overestimate the dust LF by factors of 2 -5 (suggesting that model parameterizations yield too efficient dust removals), although the simulated dust deposition amount can be higher or lower than the satellite-based estimates. LF is a useful diagnostic for identifying uncertainties associated with dust transport and removal processes (separating from emissions) and hence provides additional insights to guide the model improvement. This comparison has led to an ongoing in-depth analysis of the GEOS model simulation to better define major model deficiencies contributing to such efficient dust removal. A multi-model analysis of Trans-Atlantic Dust Deposition (TADD) is also proposed under the AEROCOM Phase III experiments. Scientific significance, societal relevance, and relationships to future missions: The satellite-based estimates of dust deposition produced from this study fill the geographical gaps and extend time span of scarce in situ measurements of dust deposition, particularly in open oceans. The dataset can be used to study impacts of mineral dust on ocean biogeochemical cycles and climate change, as well as to guide the improvement of model parameterizations of dust processes. Significant differences among existing satellite measurements of dust manifest the necessity of developing advanced satellite sensors with enhanced capabilities of characterizing three-dimensional distribution of aerosol and deciphering aerosol properties to distinguish dust from other types of aerosol for the A-CCP mission recommended by the 2017 Decadal Survey. Earth Sciences Division - Atmospheres
Temporal characterization of dust activity in the Central Patagonia desert (years 1964– 2017) Santiago Gassó (Code 613, ESSIC/GSFC), Omar Torres (Code 614, NASA/GSFC ) Fig 1: Number of Dusty Days (NDD) per Month in the Patagonia desert Fig 2: Example of Dust Activity in Central Patagonia Fig 3: Annual NDD from Satellite and Surface Observations Satellite NDD (Black line) Dust Cloud Surface NDD (Green bars) Colhué Huapi Lake Site of Surf. Observations of dust South West Atlantic Local surface observations show a multi-decadal increasing trend in the number of dusty days (NDD) per month at the largest and most active source in South America. Independent satellite observations of the Aerosol Index (sensors N 7 -TOMS, EP-TOMS and Aura-OMI) confirm the trend.
Name: Santiago Gassó, NASA/GSFC, Code 613 & ESSIC/UMD E-mail: Santiago. Gasso@nasa. gov Phone: 301 -614 -6244 References: Gassó, S. , & Torres, O. (2019). Temporal characterization of dust activity in the Central Patagonia desert (years 1964– 2017). Journal of Geophysical Research: Atmospheres, 124. https: //doi. org/10. 1029/2018 JD 030209 Omar Torres et al. (2018), TOMS/N 7 Near UV Aerosol Index and LER 1 -Orbit L 2 Swath 50 x 50 km, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https: //doi. org/10. 5067/MEASURES/AER/DATA 201 Omar Torres et al. (2018), TOMS/EP Near UV Aerosol Index and LER 1 -Orbit L 2 40 x 40 km, Goddard Space Flight Center, Goddard Earth Sciences Data and Information Services Center (GES DISC), https: //doi. org/10. 5067/MEASURES/AER/DATA 202 Omar Torres (2006), OMI/Aura Near UV Aerosol Optical Depth and Single Scattering Albedo 1 -orbit L 2 Swath 13 x 24 km V 003, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), 10. 5067/Aura/OMI/DATA 2004 Data Sources: The surface observation data sets utilized in this work was obtained directly from the argentine weather service and available from http: //www. smn. gov. ar. Satellite data was obtained from https: //worldview. earthdata. nasa. gov (MODIS observations). Aura-OMI, EP-TOMS and Nimbus 7 -TOMS data are available from the Goddard Earth Science data center through the links provided in the references section. Technical Description of Figures: Figure 1: A 50 -year long data base of hourly observations was aggregated monthly to provide long-term view of dust activity in this region. These observations were airport weather reports with visual observations of haziness including dust identification. Three periods of increase dust activity are identified roughly lasting 7 to 12 years each. Figure 2: The Colhué Huapi Lake is a shallow lake located in Central Patagonia (insert left), 90 km west of the coastal city of Comodoro Rivadavia. The constant and strong westerlies along with the coastal sediments (exposed by the frequent large swings in lake’s water level) combine to generate dust blowouts and bringing abundant sediments into the SW Atlantic. Figure 3: Surface observations are local and may be subject to biases by human observers. Yet, there are very few datasets to confirm this observed trend. One of the few such independent observations are those from NASA’s constellation of ozone sensors deployed since 1978 (Nimbus 7 -TOMS and successors). They produce the Aerosol Index, a proxy used here for an independent absorbing aerosol identification (such as dust). Because observations occur once a day, they are aggregated yearly to augment the statistics and it is normalized to emphasize the trend. As shown, a completely independent dataset of satellite data confirms the increase in dust activity. One of the most outstanding climate question concerns the processes that modulate CO removal from the atmosphere. Ice-cores found in 2 Antarctica reveal correlations between temperature, CO and dust deposition variability extending for thousands of years. Stimulation of 2 phytoplankton photosynthesis through the deposition of nutrients carried by dust in the Southern Ocean (with Patagonia as the main supplier) has been proposed to explain this variability. Modern dust activity in South America can reveal whether this phenomena is currently operating and provide insights into paleo-climate questions. However, dedicated model assessment studies lack the observational datasets of dust activity in South America at time resolutions (decades) appropriate for verify the simulations. Historical time series of weather observations along with NASA’s long term series of satellite observations contribute to better understand dust activity in this sector of the world and support climate model efforts. Earth Sciences Division - Atmospheres
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