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Timothy A Berkoff

Publications and source records attributed to Timothy A Berkoff.

8 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Evaluation of NASA's High-Resolution Global Composition Simulations: Understanding a Pollution Event in the Chesapeake Bay During the Summer 2017 OWLETS Campaign

Recirculation of pollutants due to a bay breeze effect is a key meteorological mechanism impacting air quality near urban coastal areas, but regional and global chemical transport models have historically struggled to capture this phenomenon. We present a case study of a high ozone (O3) episode observed over the Chesapeake Bay during the NASA Ozone Water-Land Environmental Transition Study (OWLETS) in summer 2017. OWLETS included a complementary suite of ground-based and airborne observations, with which we characterize the meteorological and chemical context of this event and develop a framework to evaluate model performance. Two publicly-available NASA global high-resolution coupled chemistry-meteorology models (CCMMs) are investigated: GEOS-CF and MERRA2-GMI. The GEOS-CF R squared value for comparisons between the NASA Sherpa C-23 aircraft measurements to the GEOS-CF resulted in good agreement (R squared: 0.67) on July 19th and fair agreement (R squared: 0.55) for July 20th. Compared to surface observations, we find the GEOS-CF product with a 25 x 25 km squared grid box, at an hourly (R squared: 0.62 to 0.87) and 15-minute (R squared: 0.64 to 0.87) interval for six regional sites outperforms the hourly nominally 50 x 50 km squared gridded MERRA2-GMI (R squared: 0.53 to 0.76) for four of the six sites, suggesting it is better capable of simulating complex chemical and meteorological features associated with ozone transport within the Chesapeake Bay airshed. When the GEOS-CF product was compared to the TOLNet LiDAR observations at both NASA Langley Research Center (LaRC) and the Chesapeake Bay Bridge Tunnel (CBBT), the median differences at LaRC were -6 to 8% and at CBBT were ± 7% between 400 to 2000 m ASL. This indicates that, for this case study, the GEOS-CF is able to simulate surface level ozone diurnal cycles and vertical ozone profiles at small scales between the surface level and 2000 m ASL. Evaluating global chemical model simulations at sub-regional scales will help air quality scientists understand the complex processes occurring at small spatial and temporal scales within complex surface terrain changes, simulating nighttime chemistry and deposition, and the potential to use global chemical transport simulations in support of regional and sub-regional field campaigns.

NASA Ozone Water-Land Environmental Transition Stu↗
NASA NTRSDate not supplied

The UV Aerosol Extinction Coefficients Retrieval Using the Ground-Based Ozone Lidar

NASA Langley Mobile Ozone Lidar (LMOL) has capability to measure the O3 concentration from 0.1 to 8 km. A new method is proposed to retrieve the aerosol backscatter and extinction coefficients from the LMOL attenuated backscatter signals. The LMOL attenuated backscatter data are corrected by the O3 concentration determined by LMOL system. The aerosol extinction coefficients are retrieved by using Fernald-type method. The retrieved aerosol extinction coefficient profiles are compared with the coincident aerosol extinction coefficient profiles provided by overflights of the airborne High Altitude Lidar Observatory (HALO) system during the 2018 LISTOS campaign. Results show the new method has the capability to retrieve aerosol extinction coefficients from 0.1-8 km at night and 0.1-4 km during the day for 5 minute temporal averaging. The profile time series of the retrieved aerosol extinction coefficient are also compared with co-located ceilometer data, showing general agreement with aerosol features and dynamics. The impact of lidar extinction to backscatter ratios on the retrievals is also presented.This new method extends LMOL’s ability to quantitatively determine the vertical distribution of aerosol optical properties. This is expected to contribute to further understanding of the relationship between the ozone and aerosol in the wildfire emissions transport and impact on local air quality during the LISTOS campaign.

Liqiao Lei↗
NASA NTRSDate not supplied

Wildfire Emission Transport and Its Impact on the Local Air Quality – Case Study Example from the LISTOS Campaign

The high O3 concentration and large aerosol backscatter were measured in New York City(NYC) region and Connecticut (CT) coastline between August 15-16, 2018 during the Long Island Sound Tropospheric Ozone Study (LISTOS) campaign. Two TOLNet ozone lidar systems, NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar (GSFC TROPOZ DIAL)and Langley Research Center (LaRC) Mobile Ozone Lidar (LMOL),were used to obtain vertical and temporal variation of local O3 concentration. The airborne High Altitude Lidar Observatory (HALO) system was used to detect the regional aerosol characteristic during this episode. The complex relationship between ozone and aerosol characteristics of wildfire emission layers was investigated. The HYSPLIT back-trajectory of the measured air parcel shows that the increase of the O3 concentration and aerosol backscatter are attributed to the significant wildfires in the Pacific Northwest and British Columbia regions during August 2018. Through correlation analyses, unique clustering relationships are identified between ozone and aerosol for different air mass types. This case study is further investigated in relation to satellite data from MODIS, MISR and CALIPSO to characterize plume behavior during transport. The importance of wildfire emission transport will be discussed in context of its impact to surface air quality at significant distances from fire events.

Liqiao Lei↗
NASA NTRSDate not supplied

Origins and Chemical Characteristics of a Plume Observed on 31 August 2019 by the Langley Mobile Ozone Lidar During the FIREX-AQ Field Campaign

During the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign, the Langley Mobile Ozone Lidar (LMOL) observed an aerosol plume over Hampton, VA between 0000–1200 UTC on August 31, 2019. This plume is of interest because it did not have any associated ozone (O3) and was only observable in LMOL’s new aerosol backscatter product derived while performing the aerosol correction procedure (Leietal.,2021). Here we investigate the origins and evolution of the chemical composition throughout the plume’s lifetime. This study also evaluates LMOL’s new aerosol capability, which allows us to better identify and understand smoke plumes.

Daniel B Phoenix↗
NASA NTRSDate not supplied

Analysis of Atmospheric Conditions Responsible for an Ozone Exceedance Event in Southeast Virginia on June 15, 2022

On June 15, 2022, the Virginia Department of Environmental Quality monitoring site at Suffolk/Holland, a rural site in southeast Virginia, recorded its first ozone exceedance since 2016. An ozone exceedance day occurs when the daily maximum 8-hour average surface ozone concentration is greater than 70 ppb. On this day, it was 75 ppb. This event is also noteworthy due to the rapid increase in surface ozone between 6 am and 9 am EDT as well as the hourly maximum ozone concentration of 82 ppb that was measured at 1 pm EDT. In this analysis, we utilize various observational and model data to determine the atmospheric conditions responsible for this ozone exceedance event. The analysis is conducted in two parts: (1) an evaluation of the accuracy of the GEOS-CF and WRF-Chem model forecasts and (2) an investigation of the mechanisms responsible for the high surface ozone at the Virginia DEQ Suffolk/Holland monitoring site. Comparisons of model forecasted ozone time-height cross sections with measured ozone by lidars from the Tropospheric Ozone Lidar Network (TOLNet) at NASA Langley and NASA Goddard reveal that both models forecasted ozone reasonably well near the surface. Model and observational data reveal that an upper-level ridge was present over much of the eastern United States, with a broad anticyclonic circulation near the surface, resulting in north-northeasterly flow over southeast Virginia. Back trajectory calculations using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) and model data suggest that a low-level plume rich in ozone and precursor species originating from the New York City and New Jersey region was transported along the east coast between June 14 and June 15, arriving in southeast Virginia around 6 am EDT on June 15. In the early morning hours, this plume mixed down to the surface, elevating the background ozone mixing ratio as well as the mixing ratios of several precursor species. Other potential contributions are also explored and discussed. Lastly, the authors note that this work represents the analysis of the authors and not the Virginia Department of Environmental Quality.

Daniel B Phoenix↗
NASA NTRSDate not supplied

Atmospheric Composition Forecast Model Evaluation Using Ozone Measurements Collected by the Langley Mobile Ozone Lidar

The Langley Mobile Ozone Lidar (LMOL) is a mobile ground based lidar system based at NASA Langley in Hampton, Virginia. Between 2022 and 2024, LMOL collected over 2500 hours of ozone measurements for a range of different atmospheric conditions, including calm days, stratospheric intrusions, surface frontal passages, and long-range transported wildfire smoke plumes. Here, the data is used to evaluate the forecast accuracy of NASA’s Global GEOS Composition Forecasting (GEOS-CF) model. GEOS-CF makes daily three-dimensional forecasts of trace gases and aerosol species. Overall, for calm periods, the forecast model predicts lower tropospheric ozone at NASA Langley with reasonable accuracy (within 20%). The model best predicts the timing and extent of stratospheric intrusions but often vary in the magnitude of the ozone mixing ratio. Among the other types of atmospheric conditions, there is more variability in the model forecasts. Based on this analysis, model forecasts are utilized to determine future data acquisition opportunities with the goal of providing feedback to the modeling teams, thereby enabling them to better understand the model biases and improve the model forecasts of ozone during these different atmospheric conditions.

Daniel B Phoenix↗