The ground-based NASA Langley Mobile Ozone Lidar (LMOL) has been in operation for over a decade, providing profiles of ozone at its home base in Hampton, Virginia, as well as other locations around the country during field campaign deployments. The system is based on generating wavelength tunable ultraviolet laser pulses from a ceriumdoped lithium calcium aluminum fluoride (Ce:LiCAF) laser. Two fiber-coupled telescopes are used to capture backscatter signals to enable the recovery of ozone from 100-m to 8-km altitudes at night and up to 4-km altitude during daytime. LMOL is part of the Tropospheric Ozone Lidar Network (TOLNet), which provides coordinated ozone lidar measurements at a standardized data archive available to the public. Significant changes have been implemented to the system to enable autonomous operation and remote control, as well as enhanced measurement capabilities. This paper will summarize the key instrument upgrades that occurred since 2016, provide several data examples demonstrating the lidar's measurement capabilities, and describe future plans for the instrument.

Abstract Exposure to ionizing radiation from galactic cosmic rays (GCR) and solar energetic particles (SEP) at aircraft flight altitudes can have an adverse effect on human health. Although airline crews are classified as radiation workers by the International Commission on Radiological Protection (ICRP), in most countries, their level of exposure is unquantified and undocumented throughout the duration of their career. As such, there is a need to assess pilot ionizing radiation exposure. The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS), a real‐time, global, physics‐based model is used to assess such exposure. The Automated Radiation Measurements for Aerospace Safety (ARMAS) measurement data set consists of high latitude, high altitude, and long‐duration aircraft flights between 2013 and 2023. Here, we characterize radiation exposure at aviation flight altitudes using the NAIRAS model and compare with 45 flight trajectories from the recent ARMAS flight measurement inventory.

Abstract The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS) version 3 model is available to the community through the Community Coordinated Modeling Center run‐on‐request (RoR) service. The RoR capability allows the user to run the NAIRAS model for customized applications and time‐periods using two run options. The global dosimetric run option mirrors the execution of the real‐time NAIRAS run mode. This class of run option capability provides global context and situational awareness of the atmospheric ionizing radiation environment. The flight trajectory run option allows the user to upload an aircraft, balloon, or spaceflight trajectory file. This class of run option allows detailed human radiation flight exposure characterization, detailed comparisons to onboard dosimeters, and the assessment of single event effects (SEE) in aircraft and spacecraft electronic systems. The model output includes dosimetric quantities, differential and integral flux, and fluence quantities. The flux and fluence quantities are a new feature in version 3 for the assessment of SEE. The trajectory run option and the extension of the model domain to the space environment is also a new feature in version 3.

Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (LiDAR), optical, SAR, and their combination to improve the AGB predictions, 2) examine the capability of tree-based machine learning models, and 3) compare the performance of pixel-based and object-based image analysis (OBIA). To investigate the performance of machine learning models, multiple tree-based algorithms were fitted to predictors derived from airborne LiDAR data, Landsat, Sentinel-2, Sentinel-1, and PALSAR-2/PALSAR SAR data collected within New York’s Adirondack Park. Combining remote sensing data from multiple sources improved the model accuracy (RMSE: 52.14 Mg ha−1 and R2: 0.49). There was no significant difference among gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGBoost) models. In addition, pixel-based and object-based models were compared using the airborne LiDAR-derived AGB raster as a training/testing sample. The OBIA provided the best results with the RMSE of 33.77 Mg ha−1 and R2 of 0.81 for the combination of optical and SAR data in the GBM model.

Sustainable forest management is a critical topic which contributes to ecological, economical, and socio-cultural aspect of the environment. Providing accurate AGB maps is of paramount importance for sustainable forest management, carbon accounting, and climate change monitoring. The main goal of this study was to leverage the potential of two machine learning algorithms for predicting AGB using optical and synthetic aperture radar (SAR) datasets. To achieve this goal random forest (RF) and light gradient boosting machine (LightGBM) models were deployed to predict AGB values in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. Both models were trained and evaluated based on airborne light detection and ranging (LiDAR) data, Landsat imagery, advanced land observing satellite (ALOS) phased array type L-band Synthetic Aperture Radar (PALSAR), and their combination. The integration of airborne LiDAR, optic, and SAR datasets provided the best results in terms of root mean square error (RMSE) and mean bias error (MBE). The RF model outperformed the LightGBM in all scenarios (LiDAR, Landsat 5, ALOS PALSAR, and their combination). The RF model was able to predict AGB values with the RMSE of 51.90 Mg/ha and MBE of −0.189 Mg/ha for the combination of LiDAR, optic, and SAR data, while LightGBM estimated the AGB values with the RMSE of 52.78 Mg/ha and MBE of −0.253 Mg/ha. LightGBM is more sensitive to noise and there are lots of hyperparameters that need to be tuned which highly affect its performance.

Tropopause‐overshooting convection transports air from the lower troposphere to the upper troposphere and lower stratosphere (UTLS) where the resulting chemistry and mixing of trace gases can modify the radiation budget. While recent work has examined output from model simulations as well as aircraft and satellite observations of the impacts of tropopause‐overshooting convection on UTLS composition, the range of potential impacts and their dependence on characteristics of storms and their environments is not known. Here, two 10‐day periods, one representative of springtime convection and one representative of summertime convection, were simulated with the Weather Research and Forecasting (WRF) model with Chemistry to examine the range of UTLS composition impacts from tropopause‐overshooting convection. Overall, springtime convection has a larger impact on UTLS composition than summertime convection, with a net effect of increasing water vapor (H 2 O) in the lower stratosphere and increasing ozone (O 3 ) in the upper troposphere. Springtime convection frequently increases the domain average H 2 O mixing ratio in the lowermost stratosphere by over 20% while changes in stratospheric H 2 O from summertime convection are much lower (∼7%–11% increase), reflecting a dependence of the maximum possible H 2 O increase on UTLS temperature. Increases in upper troposphere O 3 mixing ratios span the range 8%–19% from springtime convection and are minimal from summertime convection. Changes in the composition of the UTLS from tropopause‐overshooting convection are largely dependent on the height and temperature of the tropopause, with the largest changes being in environments with relatively low tropopause heights between 11 and 13 km (typical of springtime environments in the United States).

Forest is one of the most crucial Earth’s resources. Forest above-ground biomass (AGB) mapping has been research endeavors for a long time in many applications since it provides valuable information for carbon cycle monitoring, deforestation, and forest degradation monitoring. A methodology to rapidly and accurately estimate AGB is essential for forest monitoring purposes. Thus, the main objective of this paper was to investigate the performance of decision tree-based models to predict AGB at a site in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. The results of decision tree, random forest, and deep forest regression models were compared using light detection and ranging (LiDAR), Landsat 5 TM, and a combination of them. The results illustrated the importance of integration of Landsat 5 TM and LiDAR data, which benefits from both vertical forest structure and spectral information reflected by canopy cover. In addition, the deep forest model with a root mean square error (RMSE) of 51.63 Mg/ha and R-squared (R2) of 0.45 outperformed other regression tree-based models, regardless of the dataset.

Recent observational studies have shown that stratospheric air rich in ozone (O3) is capable of being transported into the upper troposphere in association with tropopause‐penetrating convection (anvil wrapping). This finding challenges the current understanding of upper tropospheric sources of O3, which is traditionally thought to come from thunderstorm outflows where lightning‐generated nitrogen oxides facilitate O3 formation. Since tropospheric O3 is an important greenhouse gas and the frequency and strength of tropopause‐penetrating storms may change in a changing climate, it is important to understand the mechanisms driving this transport process so that it can be better represented in chemistry‐climate models. Simulations of a mesoscale convective system (MCS) around which this transport process was observed are performed using the Weather Research and Forecasting model coupled with Chemistry. The Weather Research and Forecasting model coupled with Chemistry model adequately simulates anvil wrapping of ozone‐rich air. Possible mechanisms that influence the transport, including small‐scale static and dynamic instabilities and MCS‐induced mesoscale circulations, are evaluated. Model results suggest that anvil wrapping is a two‐step transport process (1) compensating subsidence surrounding the MCS, which is driven by mass conservation as the MCS transports tropospheric air into the upper troposphere and lower stratosphere, followed by (2) differential advection beneath the core of the MCS upper‐tropospheric outflow jet which wraps high O3 air around and under the MCS cloud anvil. Static and dynamic instabilities are not a leading contributor to this transport process. Continued fine‐scale modeling of these events is needed to fully represent the stratosphere‐to‐troposphere transport process.

The projected increase in global air traffic raises concerns about the potential impact aviation emissions have on climate and air quality. Previous studies have shown that aircraft non-landing and take-off (non-LTO) emissions (emitted above 1 km) can affect surface air quality by increasing concentrations of ozone (O-3) and fine particles (PM2.5). Here, we examine the global impacts of aviation non-LTO emissions on surface air quality for present day and mid-century (2050) using the Community Atmosphere Model with Chemistry, version 5 (CAMS). An important update in CAMS over previous versions is the modal aerosol module (MAM), which provides a more accurate aerosol representation. Additionally we evaluate of the aviation impact at mid-century with two fuel scenarios, a fossil fuel (SC1) and a biofuel (Alt). Monthly-mean results from the present day simulations show a northern hemisphere (NH) mean surface O-3 increase of 1.3 ppb (2.7% of the background) and a NH maximum surface PM2.5 increase of 1.4 mu g/m(3) in January. Mid-century simulations show slightly greater surface O-3 increases (mean of 1.9 ppb (4.2%) for both scenarios) and greater PM2.5 increases (maximum of 3.5 mu g/m(3) for SC1 and 2.2 mu g/m(3) for Alt). While these perturbations do not significantly increase the frequency of extreme air quality events (increase is less than 1.5%), they do contribute to the background concentrations of O-3 and PM2.5, making it easier for urban areas to surpass these standards.

Stratosphere-troposphere exchange via extreme extratropical convection has implications for climate change. We test the ability of the ARW-WRF model to simulate the physical aspects of a real case of extreme extratropical convection that injected cloud particles into the stratosphere. We find that the model resolves storm structure sufficiently, and proceed to examine the representation of trace gas transport within the same case of convection. Additionally, distributions of trace gas concentrations across the nested model domain are considered in diagnosing irreversible transport. Trace gas transport is seen in model output within the cloud, but little evidence exists for out of cloud transport.

Tropopause-penetrating convection is capable of rapidly transporting air from the lower troposphere to the upper troposphere and lower stratosphere (UTLS), where it can have important impacts on chemistry, the radiative budget, and climate. However, obtaining in situ measurements of convection and convective transport is difficult and such observations are historically rare. Modeling studies, on the other hand, offer the advantage of providing output related to the physical, dynamical, and chemical characteristics of storms and their environments at fine spatial and temporal scales. Since these characteristics of simulated convection depend on the chosen model design, we examine the sensitivity of simulated convective transport to the choice of physical (bulk microphysics or BMP and planetary boundary layer or PBL) and chemical parameterizations in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). In particular, we simulate multiple cases where in situ observations are available from the recent (2012) Deep Convective Clouds and Chemistry (DC3) experiment. Model output is evaluated using ground-based radar observations of each storm and in situ trace gas observations from two aircraft operated during the DC3 experiment. Model results show measurable sensitivity of the physical characteristics of a storm and the transport of water vapor and additional trace gases into the UTLS to the choice of BMP. The physical characteristics of the storm and transport of insoluble trace gases are largely insensitive to the choice of PBL scheme and chemical mechanism, though several soluble trace gases (e.g., SO2,CH2O, and HNO3) exhibit some measurable sensitivity.

Atmospheric chemistry-climate models are often used to calculate the effect of aviation NOx emissions on atmospheric ozone (O-3) and methane (CH4). Due to the long (similar to 10 yr) atmospheric lifetime of methane, model simulations must be run for long time periods, typically for more than 40 simulation years, to reach steady-state if using CH4 emission fluxes. Because of the computational expense of such long runs, studies have traditionally used specified CH4 mixing ratio lower boundary conditions (BCs) and then applied a simple parameterization based on the change in CH4 lifetime between the control and NOx-perturbed simulations to estimate the change in CH4 concentration induced by NOx emissions. In this parameterization a feedback factor (typically a value of 1.4) is used to account for the feedback of CH4 concentrations on its lifetime. Modeling studies comparing simulations using CH4 surface fluxes and fixed mixing ratio BCs are used to examine the validity of this parameterization. The latest version of the Community Earth System Model (CESM), with the CAMS atmospheric model, was used for this study. Aviation NOx emissions for 2006 were obtained from the AEDT (Aviation Environmental Design Tool) global commercial aircraft emissions. Results show a 31.4 ppb change in CH4 concentration when estimated using the parameterization and a 1.4 feedback factor, and a 28.9 ppb change when the concentration was directly calculated in the CH4 flux simulations. The model calculated value for CH4 feedback on its own lifetime agrees well with the 1.4 feedback factor. Systematic comparisons between the separate runs indicated that the parameterization technique overestimates the CH4 concentration by 8.6%. Therefore, it is concluded that the estimation technique is good to within similar to 10% and decreases the computational requirements in our simulations by nearly a factor of 8. Published by Elsevier Ltd.

Due to the non-linear nature of ozone production in the troposphere, ozone production as a function of aviation nitrogen oxide (NOx = NO + NO2) emissions varies based on the background NOx levels. Of the several different sources of background NOx in the atmosphere, NOx from lightning (LNOx) contributes a substantial amount of NOx to the upper troposphere and has an effect on the ozone production efficiency, even though the LNOx source still has significant uncertainty. In this study, CAM5, the atmospheric component of the Community Earth System Model (CESM), was used to study the effect of uncertainties in NOx emissions from lightning on the production of aviation-induced ozone. Three sensitivity studies were analyzed with varying LNOx values of 3.7, 5, and 7.4 TgN/yr, representing the best current range estimates for LNOx. Results show a decrease in the aviation -induced ozone production rate and radiative forcing (RF) as LNOx increases. This is tied to the decreased ozone production under NOx saturated conditions. The ozone production per unit of NOx emission from lightning ranges from 2.38 TgO(3)/TgN for the case with 3.7 TgN from lightning to 0.97 TgO(3)/TgN for the case with 7.4 TgN from lightning. Similarly, the O-3 RF decreases from 43.9 mW/m2 for the 3.7 TgN/yr case to 34.3 mW/m2 for 7.4 TgN/yr case. Understanding the current sensitivity of aviation-induced ozone production to the LNOx strength is important for reducing the uncertainty in ozone production from aviation NOx emissions.