The NASA Lightning Nitrogen Oxides Model (LNOM): Application to Air Quality Modeling

Recent improvements to the NASA Marshall Space Flight Center Lightning Nitrogen Oxides Model (LNOM) and its application to the Community Multiscale Air Quality (CMAQ) modeling system are discussed. The LNOM analyzes Lightning Mapping Array (LMA) and National Lightning Detection Network(TradeMark)(NLDN) data to estimate the raw (i.e., unmixed and otherwise environmentally unmodified) vertical profile of lightning NO(x) (= NO + NO2). The latest LNOM estimates of lightning channel length distributions, lightning 1-m segment altitude distributions, and the vertical profile of lightning NO(x) are presented. The primary improvement to the LNOM is the inclusion of non-return stroke lightning NOx production due to: (1) hot core stepped and dart leaders, (2) stepped leader corona sheath, K-changes, continuing currents, and M-components. The impact of including LNOM-estimates of lightning NO(x) for an August 2006 run of CMAQ is discussed

The NASA Lightning Nitrogen Oxides Model (LNOM): Recent Updates and Applications

No abstract availabl

TEMPO Applications to Air-Quality and Health

No abstract availabl

Ozone Lidar Observations for Air Quality Studies

Tropospheric ozone lidars are well suited to measuring the high spatio-temporal variability of this important trace gas. Furthermore, lidar measurements in conjunction with balloon soundings, aircraft, and satellite observations provide substantial information about a variety of atmospheric chemical and physical processes. Examples of processes elucidated by ozone-lidar measurements are presented, and modeling studies using WRF-Chem, RAQMS, and DALES/LES models illustrate our current understanding and shortcomings of these processes

Investigate Wildfire Impacts on Ozone Production by Vertical Observations and Photochemical Modeling

In troposphere, ozone is a toxic secondary pollutant produced when its precursors react in sunlight. An important source of ozone precursors is biomass burning. Here we investigate the impacts of 2016 Southeast U.S. Wildfires on ozone production by integrating vertical resolved ozone profiles and photochemical modeling. The results show that wildfires contributed to ozone lamina at the top of boundary layer and enhanced surface ozone up to about 10ppbv in Southeast U.S.. Ozone lidar observed a lower ozone change with respect to a fast growth of aerosol plume, of which the reason is also investigated. Current results indicate an effective integration of vertical observations and modeling for us to understand the ozone production from fires in troposphere

Investigate Wildfire Impacts on Ozone Production by Vertical Observations and Photochemical Modeling

In troposphere, ozone is a toxic secondary pollutant produced when its precursors react in sunlight. An important source of ozone precursors is biomass burning. Here we investigate the impacts of 2016 Southeast U.S. Wildfires on ozone production by integrating vertical resolved ozone profiles and photochemical modeling. The results show that wildfires contributed to ozone lamina at the top of boundary layer and enhanced surface ozone up to about 10ppbv in Southeast U.S.. Ozone lidar observed a lower ozone change with respect to a fast growth of aerosol plume, of which the reason is also investigated. Current results indicate an effective integration of vertical observations and modeling for us to understand the ozone production from fires in troposphere

Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review

The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented

Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review

The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented

Incorporating GOES Satellite Photosynthetically Active Radiation (PAR) Retrievals to Improve Biogenic Emission Estimates in Texas

This study examines the influence of insolation and cloud retrieval products from the Geostationary Operational Environmental Satellite (GOES) system on biogenic emission estimates and ozone simulations in Texas. Compared to surface pyranometer observations, satellite‐retrieved insolation and photosynthetically active radiation (PAR) values tend to systematically correct the overestimation of downwelling shortwave radiation in the Weather Research and Forecasting (WRF) model. The correlation coefficient increases from 0.93 to 0.97, and the normalized mean error decreases from 36% to 21%. The isoprene and monoterpene emissions estimated by the Model of Emissions of Gases and Aerosols from Nature are on average 20% and 5% less, respectively, when PAR from the direct satellite retrieval is used rather than the control WRF run. The reduction in biogenic emission rates using satellite PAR reduced the predicted maximum daily 8 h ozone concentration by up to 5.3 ppbV over the Dallas‐Fort Worth (DFW) region on some days. However, episode average ozone response is less sensitive, with a 0.6 ppbV decrease near DFW and 0.3 ppbV increase over East Texas. The systematic overestimation of isoprene concentrations in a WRF control case is partially corrected by using satellite PAR, which observes more clouds than are simulated by WRF. Further, assimilation of GOES‐derived cloud fields in WRF improved CAMx model performance for ground‐level ozone over Texas. Additionally, it was found that using satellite PAR improved the model's ability to replicate the spatial pattern of satellite‐derived formaldehyde columns and aircraft‐observed vertical profiles of isoprene