Airborne Campaign Research Articles

Airborne Measurements of Mesoscale Divergence at High Latitudes during HALO–(AC)3

Abstract Boundary layer cloud transformations at high latitudes play a key role for the Arctic climate and are partially controlled by large-scale dynamics such as subsidence. While measuring large-scale and mesoscale divergence on spatial scales on the order of 100 km has proven notoriously difficult, recent airborne campaigns in the subtropics have successfully applied measurement techniques using multiple dropsonde releases in circular flight patterns. In this paper, it is shown that this method can also be effectively applied at high latitudes, in spite of the considerable differences in atmospheric dynamics compared to the subtropics. To show the applicability, data collected during the airborne High Altitude and Long Range Research Aircraft–Transregional Collaborative Research Center TRR 172-Arctic Amplification: Climate Relevant Atmospheric and Surface Processes and Feedback Mechanisms [HALO–(AC)3] field campaign near Svalbard in spring 2022 were analyzed, where several flight patterns involving multiple dropsonde launches were realized by two aircraft. This study presents a first overview of the results. We find that the method indeed yields reliable estimates of mesoscale gradients in the Arctic, producing robust vertical profiles of horizontal divergence and, consequently, subsidence. Sensitivity to aspects of the method is investigated, including dependence on sampling area and the divergence calculation. Significance Statement The aim of this work is to report encouraging results with a recently proposed aircraft-based method for measuring mesoscale vertical motions at high latitudes. Dropsonde data from the recent High Altitude and Long Range Research Aircraft–Transregional Collaborative Research Center TRR 172-Arctic Amplification: Climate Relevant Atmospheric and Surface Processes and Feedback Mechanisms [HALO–(AC)3] campaign are used for this purpose. The method has so far mainly been applied at subtropical to midlatitudes, but not in the Arctic, where the weather and climate conditions are very different. Gaining insight is significant because vertical winds play a key role in the Arctic climate system, including airmass transformations and the behavior of clouds and precipitation. The results motivate the use of the measured vertical winds in follow-up studies with process models.

Can Multiscale Thermal Infrared Imaging Help Validate and Monitor Water Stress in Alluvial Forests?

ABSTRACTAlluvial forests are sensitive to drought induced by climate change and exacerbated by altered flow regimes. Our ability to detect and map their sensitivity to drought is crucial to evaluate the effects of climate change and adjust management practices. Therefore, we explore the potential of multiscale thermal infrared imagery (TIR) to diagnose their sensitivity to droughts. In summer 2022, we sampled leaves and phloem on Populus nigra trees from two sites with contrasted hydrological connectivity along the Ain River (France) to investigate the seasonality of water stress and act as ground truth for airborne TIR images. To map forest sensitivity to drought, we used TIR data from four airborne campaigns and Landsat archives over a larger spatial and temporal extent. Field data showed that stress conditions were reached for both sites but were higher in the site with lower groundwater connectivity, which was also the case for individual tree crown temperatures. At the forest plot scale, canopy temperature was linked to forest connectivity for two of four TIR campaigns, with higher values in the more degraded reaches. Landsat data were used to locate the areas of the riparian forest impacted by a historical drought event and monitor their recovery and proved useful to identify trends. TIR data showed promising results to help detect and map tree water stress in riparian environments. However, stress is not detected in all TIR campaigns, demonstrating that one‐shot TIR acquisitions alone are not enough to diagnose stress and complementary in‐field eco‐physiological measurements are necessary.

Leveraging RALI‐THINICE Observations to Assess How the ICOLMDZ Model Simulates Clouds Embedded in Arctic Cyclones

AbstractDespite their essential role in the high‐latitude climate, the representation of mixed‐phase clouds is still a challenge for Global Climate Models (GCMs)'s cloud schemes. In this study we propose a methodology for robustly assessing Arctic mixed‐phase cloud properties in a climate model using airborne measurements. We leverage data collected during the RALI‐THINICE airborne campaign that took place near Svalbard in August 2022 to evaluate the simulation of mid‐level clouds associated with Arctic cyclones. Simulations are carried out with the new limited‐area configuration of the ICOLMDZ model which combines the recent icosahedral dynamical core DYNAMICO and the physics of LMDZ, the atmospheric component of the IPSL‐CM Earth System Model. Airborne radar and microphysical probes measurements are then used to evaluate the simulated clouds. A comparison method has been set‐up to guarantee as much as possible the spatiotemporal co‐location between observed and simulated cloud fields. We mostly focus on the representation of ice and liquid in‐cloud contents and on their vertical distribution. Results show that the model overestimates the amount of cloud condensates and exhibits a poor cloud phase spatial distribution, with too much liquid water far from cloud top and too much ice close to it. The downward gradual increase in snowfall flux is also not captured by the model. This in‐depth model evaluation thereby pinpoints priorities for further improvements in the ICOLMDZ cloud scheme.

Improving supercooled liquid water representation in the microphysical scheme ICE3

AbstractMost numerical weather prediction (NWP) models have a significant bias in predicting supercooled liquid water (SLW). For this reason, icing risk diagnostic tools do not use supercooled liquid water forecast by the models as an input parameter, but rather temperature and humidity, which are forecast better than SLW. The main objective of this study is to improve the SLW representation in the microphysical scheme ICE3 (Three Ice categories) used in the operational Applications de la Recherche à l'Opérationnel à Méso‐Echelle (AROME) model. For this purpose, several parametrizations of the microphysical processes were evaluated to find a better representation of SLW in the Mesoscale Non‐Hydrostatic model (MESO‐NH), which also uses ICE3. Elements of the microphysical scheme of the HARMONIE‐AROME model and work carried out by the Centre National de Recherches Météorologiques (CNRM) have been tested and compared with the current scheme. After a preliminary study, three parametrizations of the microphysical scheme were selected, in which the processes of ice initiation, snow and graupel collection of cloud droplets, condensation, Bergeron–Findeisen, and saturation adjustment were modified. Then, MESO‐NH simulations were performed and compared with observations from the In‐Cloud ICing and Large‐drop Experiment (ICICLE) airborne campaign. Three case studies were used with different icing weather conditions such as freezing rain, freezing drizzle, lake effect, etc. The results show a better representation of SLW with a greater presence of cloud droplets for colder temperatures up to 30 C. However, the liquid water content remains underestimated and the ice mass is overestimated. The ice initiation and cloud droplet collection by snow and graupel play a major role in the SLW representation. Parametrizations with restrictive ice initiation criteria reduce cloud droplet consumption and provide better agreement with observations. The results are promising and need to be investigated further with more cases and in the operational model AROME.

The ABoVE L-band and P-band airborne synthetic aperture radar surveys

Abstract. Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl).

A better representation of VOC chemistry in WRF-Chem and its impact on ozone over Los Angeles.

The declining trend in vehicle emissions has underscored the growing significance of Volatile Organic Compound (VOC) emissions from Volatile Chemical Products (VCP). However, accurately representing VOC chemistry in simplified chemical mechanisms remains challenging due to its chemical complexity including speciation and reactivity. Previous studies have predominantly focused on VOCs from fossil fuel sources, leading to an underrepresentation of VOC chemistry from VCP sources. We developed an integrated chemical mechanism, RACM2B-VCP, that is compatible with WRF-Chem and is aimed to enhance the representation of VOC chemistry, particularly from VCP sources, within the present urban environment. Evaluation against the Air Quality System (AQS) network data demonstrates that our model configured with RACM2B-VCP reproduces both the magnitude and spatial variability of O3 as well as PM2.5 in Los Angeles. Furthermore, evaluation against comprehensive measurements of O3 and PM2.5 precursors from the Reevaluating the Chemistry of Air Pollutants in California (RECAP-CA) airborne campaign and the Southwest Urban NO x and VOC Experiment (SUNVEx) ground site and mobile laboratory campaign, confirm the model's accuracy in representing NOx and many VOCs and highlight remaining biases. Although there exists an underprediction in the total VOC reactivity of observed VOC species, our model with RACM2B-VCP exhibits good agreement for VOC markers emitted from different sectors, including biogenic, fossil fuel, and VCP sources. Through sensitivity analyses, we probe the contributions of VCP and fossil fuel emissions to total VOC reactivity and O3. Our results reveal that 52% of the VOC reactivity and 35% of the local enhancement of MDA8 O3 arise from anthropogenic VOC emissions in Los Angeles. Significantly, over 50% of this anthropogenic fraction of either VOC reactivity or O3 is attributed to VCP emissions. The RACM2B-VCP mechanism created, described, and evaluated in this work is ideally suited for accurately representing ozone for the right reasons in the present urban environment where mobile, biogenic, and VCP VOCs are all important contributors to ozone formation.

Observations and modeling of areal surface albedo and surface types in the Arctic

Abstract. An accurate representation of the annual evolution of surface albedo of the Arctic Ocean, especially during the melting period, is crucial to obtain reliable climate model predictions in the Arctic. Therefore, the output of the surface albedo scheme of a coupled regional climate model (HIRHAM–NAOSIM) was evaluated against airborne and ground-based measurements. The observations were conducted during five aircraft campaigns in the European Arctic at different times of the year between 2017 and 2022; one of them was part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2020. We applied two approaches for the evaluation: (a) relying on measured input parameters of surface type fraction and surface skin temperature (offline) and (b) using HIRHAM–NAOSIM simulations independently of observational data (online). From the offline method we found a seasonally dependent bias between measured and modeled surface albedo. In spring, the cloud effect on surface broadband albedo was overestimated by the surface albedo parametrization (mean albedo bias of 0.06), while the surface albedo scheme for cloudless cases reproduced the measured surface albedo distributions for all seasons. The online evaluation revealed an overestimation of the modeled surface albedo resulting from an overestimation of the modeled cloud cover. Furthermore, it was shown that the surface type parametrization contributes significantly to the bias in albedo, especially in summer (after the drainage of melt ponds) and autumn (onset of refreezing). The lack of an adequate model representation of the surface scattering layer, which usually forms on bare ice in summer, contributed to the underestimation of surface albedo during that period. The difference between modeled and measured net irradiances for selected flights during the five airborne campaigns was derived to estimate the impact of the model bias for the solar radiative energy budget at the surface. We revealed a negative bias between modeled and measured net irradiances (median: −6.4 W m−2) for optically thin clouds, while the median value of only 0.1 W m−2 was determined for optically thicker clouds.

Airborne lidar measurements of atmospheric CO2 column concentrations to cloud tops made during the 2017 ASCENDS/ABoVE campaign

Abstract. We measured the column-averaged atmospheric CO2 mixing ratio (XCO2) to a variety of cloud tops with an airborne pulsed multi-wavelength integrated path differential absorption (IPDA) lidar during NASA's 2017 ASCENDS/ABoVE airborne campaign. Measurements of height-resolved atmospheric backscatter profiles allow this lidar to retrieve XCO2 to cloud tops, as well as to the ground, with accurate knowledge of the photon path length. We validated these measurements with those from an onboard in situ CO2 sensor during spiral-down maneuvers. These lidar measurements were 2–3 times better than those from previous airborne campaigns due to our using a wavelength step-locked laser transmitter and a high-efficiency detector for this campaign. Precisions of 0.6 parts per million (ppm) were achieved for 10 s average measurements to mid-level clouds and 0.9 ppm to low-level clouds at the top of the planetary boundary layer. This study demonstrates the lidar's capability to fill in XCO2 measurement gaps in cloudy regions and to help resolve the vertical and horizontal distributions of atmospheric CO2. Future airborne campaigns and spaceborne missions with this capability can be used to improve atmospheric transport modeling, flux estimation and carbon data assimilation.

Improved atmospheric constraints on Southern Ocean CO2 exchange

We present improved estimates of air-sea CO2 exchange over three latitude bands of the Southern Ocean using atmospheric CO2 measurements from global airborne campaigns and an atmospheric 4-box inverse model based on a mass-indexed isentropic coordinate (Mθe). These flux estimates show two features not clearly resolved in previous estimates based on inverting surface CO2 measurements: a weak winter-time outgassing in the polar region and a sharp phase transition of the seasonal flux cycles between polar/subpolar and subtropical regions. The estimates suggest much stronger summer-time uptake in the polar/subpolar regions than estimates derived through neural-network interpolation of pCO2 data obtained with profiling floats but somewhat weaker uptake than a recent study by Long et al. [Science 374, 1275-1280 (2021)], who used the same airborne data and multiple atmospheric transport models (ATMs) to constrain surface fluxes. Our study also uses moist static energy (MSE) budgets from reanalyses to show that most ATMs tend to have excessive diabatic mixing (transport across moist isentrope, θe, or Mθe surfaces) at high southern latitudes in the austral summer, which leads to biases in estimates of air-sea CO2 exchange. Furthermore, we show that the MSE-based constraint is consistent with an independent constraint on atmospheric mixing based on combining airborne and surface CO2 observations.

CAIPEEX: Indian Cloud Seeding Scientific Experiment

Abstract The demand for effective methods to augment precipitation over arid regions of India has been increasing over the past several decades as the changing climate brings warmer average temperatures. In the fourth phase of the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX IV), a scientific investigation was conducted over a rain-shadow region of the Western Ghats mountains in India. The primary objective was to investigate the efficacy of hygroscopic seeding in convective clouds and to develop a cloud seeding protocol. CAIPEEX IV followed the World Meteorological Organization (WMO) recommendations in a peer-reviewed report with physical, statistical, and numerical investigations. The initial results of the campaign in the monsoon period of 2018 and 2019 with two instrumented aircraft, a ground-based dual-polarization C-band radar, a network of rain gauges, radiosondes, and surface aerosol measurements are reported here. The hygroscopic seeding material was detected in cloud droplets and key cloud microphysical processes in the seeding hypothesis were tracked. The formidable challenges of assessing seeding impacts in convective clouds and the results from 150 seed and 122 no-seed samples of randomized experiments are illustrated. Over 5,000 cloud passes from the airborne campaign provided details about the convective cloud properties as the key indicators for a seeding strategy and the evaluation protocol. The experimental results suggest that cloud seeding can be approached scientifically to reduce uncertainty. The results from this study should interest the scientific community and policymakers concerned with climate change’s impact on precipitation and how to mitigate rainfall deficiencies.

Verification of different Fizeau fringe analysis algorithms based on airborne wind lidar data in support of ESA's Aeolus mission.

The Aeolus mission by the European Space Agency was launched in August 2018 and stopped operations in April 2023. Aeolus carried the direct-detection Atmospheric LAser Doppler INstrument (ALADIN). To support the preparation of Aeolus, the ALADIN Airborne Demonstrator (A2D) instrument was developed and applied in several field campaigns. Both ALADIN and A2D consist of so-called Rayleigh and Mie channels used to measure wind from both molecular and particulate backscatter signals. The Mie channel is based on the fringe-imaging technique, which relies on determining the spatial location of a linear interference pattern (fringe) that originated from multiple interference in a Fizeau spectrometer. The accuracy of the retrieved winds is among others depending on the analytic algorithm used for determining the fringe location on the detector. In this paper, the performance of two algorithms using Lorentzian and Voigt fit functions is investigated by applying them to A2D data that were acquired during the AVATAR-I airborne campaign. For performance validation, the data of a highly accurate heterodyne detection wind lidar (2-µm DWL) that was flown in parallel are used as a reference. In addition, a fast and non-fit-based algorithm based on a four-pixel intensity ratio approach (R 4) is developed. It is revealed that the Voigt-fit-based algorithm provides 50% more data points than the Lorentzian-based algorithm while applying a quality control that yields a similar random error of about 1.5m/s. The R 4 algorithm is shown to deliver a similar accuracy as the Voigt-fit-based algorithms, with the advantage of a one to two orders of magnitude faster computation time. Principally, the R 4 algorithm can be adapted to other spectroscopic applications where sub-pixel knowledge of the location of measured peak profiles is needed.

Seasonal Tropospheric Distribution and Air‐Sea Fluxes of Atmospheric Potential Oxygen From Global Airborne Observations

AbstractSeasonal change of atmospheric potential oxygen (APO ∼ O2 + CO2) is a tracer for air‐sea O2 flux with little sensitivity to the terrestrial exchange of O2 and CO2. In this study, we present the tropospheric distribution and inventory of APO in each hemisphere with seasonal resolution, using O2 and CO2 measurements from discrete airborne campaigns between 2009 and 2018. The airborne data are represented on a mass‐weighted isentropic coordinate (Mθe) as an alternative to latitude, which reduces the noise from synoptic variability in the APO cycles. We find a larger seasonal amplitude of APO inventory in the Southern Hemisphere relative to the Northern Hemisphere, and a larger amplitude in high latitudes (low Mθe) relative to low latitudes (high Mθe) within each hemisphere. With a box model, we invert the seasonal changes in APO inventory to yield estimates of air‐sea flux cycles at the hemispheric scale. We found a larger seasonal net outgassing of APO in the Southern Hemisphere (518 ± 52.6 Tmol) than in the Northern Hemisphere (342 ± 52.1 Tmol). Differences in APO phasing and amplitude between the hemispheres suggest distinct physical and biogeochemical mechanisms driving the air‐sea O2 fluxes, such as fall outgassing of photosynthetic O2 in the Northern Hemisphere, possibly associated with the formation of the seasonal subsurface shallow oxygen maximum. We compare our estimates with four model‐ and observation‐based products, identifying key limitations in these products or in the tools used to create them.

An observation-based, reduced-form model for oxidation in the remote marine troposphere

The hydroxyl radical (OH) fuels atmospheric chemical cycling as the main sink for methane and a driver of the formation and loss of many air pollutants, but direct OH observations are sparse. We develop and evaluate an observation-based proxy for short-term, spatial variations in OH (ProxyOH) in the remote marine troposphere using comprehensive measurements from the NASA Atmospheric Tomography (ATom) airborne campaign. ProxyOH is a reduced form of the OH steady-state equation representing the dominant OH production and loss pathways in the remote marine troposphere, according to box model simulations of OH constrained with ATom observations. ProxyOH comprises only eight variables that are generally observed by routine ground- or satellite-based instruments. ProxyOH scales linearly with in situ [OH] spatial variations along the ATom flight tracks (median r2 = 0.90, interquartile range = 0.80 to 0.94 across 2-km altitude by 20° latitudinal regions). We deconstruct spatial variations in ProxyOH as a first-order approximation of the sensitivity of OH variations to individual terms. Two terms modulate within-region ProxyOH variations-water vapor (H2O) and, to a lesser extent, nitric oxide (NO). This implies that a limited set of observations could offer an avenue for observation-based mapping of OH spatial variations over much of the remote marine troposphere. Both H2O and NO are expected to change with climate, while NO also varies strongly with human activities. We also illustrate the utility of ProxyOH as a process-based approach for evaluating intermodel differences in remote marine tropospheric OH.

Influence of atmospheric rivers and associated weather systems on precipitation in the Arctic

Abstract. In this study, we analyse the contribution of atmospheric rivers (ARs), cyclones, and fronts to the total precipitation in the Arctic. We focus on two distinct periods of different weather conditions from two airborne campaigns: ACLOUD (Arctic Cloud Observations Using airborne measurements during polar day; May/June 2017) and AFLUX (Aircraft campaign Arctic Boundary Layer Fluxes; March/April 2019). Both campaigns covered the northern North Atlantic sector, the area in the Arctic that is affected by the highest precipitation rates. Using ERA5 reanalysis, we identify pronounced regional anomalies with enhanced precipitation rates compared to the climatology during ACLOUD due to these weather systems, whereas during AFLUX enhanced precipitation rates occur over most of the area. We have established a new methodology that allows us to analyse the contribution of ARs, cyclones, and fronts to precipitation rates based on ERA5 reanalysis and different detection algorithms. Here, we distinguish whether these systems occur co-located or separately. The contributions differ between the two periods. During ACLOUD (early summer), the precipitation rates are mainly associated with AR- (40 %) and front-related (55 %) components, especially if they are connected, while cyclone-related components (22 %) play a minor role. However, during AFLUX (early spring) the precipitation is mainly associated with cyclone-related components (62 %). For both campaign periods, snow is the dominant form of precipitation, and the small rain occurrence is almost all associated with ARs. About one-third of the precipitation can not be attributed to one of the weather systems, the so-called residual. While the residual can be found more frequently as convective than as large-scale precipitation, the rare occasion of convective precipitation (roughly 20 %) can not completely explain the residual. The fraction of precipitation classified as residual is reduced significantly when a precipitation threshold is applied that is often used to eliminate “artificial” precipitation. However, a threshold of 0.1 mm h−1 reduces the total accumulated precipitation by a factor of 2 (ACLOUD) and 3 (AFLUX), especially affecting light precipitation over the Arctic Ocean. We also show the dependence of the results on the choice of the detection algorithm serving as a first estimate of the uncertainty. In the future, we aim to apply the methodology to the full ERA5 record to investigate whether the differences found between the campaign periods are typical for the different seasons in which they were performed and whether any trends in precipitation associated with these weather systems can be identified.

Airborne observations of the surface cloud radiative effect during different seasons over sea ice and open ocean in the Fram Strait

Abstract. This study analyses the cloud radiative effect (CRE) obtained from near-surface observations of three airborne campaigns in the Arctic north-west of Svalbard: Airborne measurements of radiative and turbulent FLUXes of energy and momentum in the Arctic boundary layer (AFLUX, March/April 2019), Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD, May/June 2017), and Multidisciplinary drifting Observatory for the Study of Arctic Climate – Airborne observations in the Central Arctic (MOSAiC-ACA, August/September 2020). The surface CRE quantifies the potential of clouds to modify the radiative energy budget at the surface and is calculated by combining broadband radiation measurements during low-level flight sections in mostly cloudy conditions with radiative transfer simulations of cloud-free conditions. The significance of surface albedo changes due to the presence of clouds is demonstrated, and this effect is considered in the cloud-free simulations. The observations are discussed with respect to differences of the CRE between sea ice and open-ocean surfaces and between the seasonally different campaigns. The results indicate that the CRE depends on cloud, illumination, surface, and thermodynamic properties. The solar and thermal-infrared (TIR) components of the CRE, CREsol and CRETIR, are analysed separately, as well as combined for the study of the total CRE (CREtot). The inter-campaign differences of CREsol are dominated by the seasonal cycle of the solar zenith angle, with the strongest cooling effect in summer. The lower surface albedo causes a stronger solar cooling effect over open ocean than over sea ice, which amounts to −259 W m−2 (−108 W m−2) and −65 W m−2 (−17 W m−2), respectively, during summer (spring). Independent of campaign and surface type, CRETIR is only weakly variable and shows values around 75 W m−2. In total, clouds show a negative CREtot over open ocean during all campaigns. In contrast, over sea ice, the positive CREtot suggests a warming effect of clouds at the surface, which neutralizes during mid-summer. Given the seasonal cycle of the sea ice distribution, these results imply that clouds in the Fram Strait region cool the surface during the sea ice minimum in late summer, while they warm the surface during the sea ice maximum in spring.

Library of Simulated Gamma‐Ray Glows and Application to Previous Airborne Observations

AbstractGamma‐Ray Glows (GRGs) are high energy radiation originating from thunderclouds, in the MeV energy regime, with typical duration of seconds to minutes, and sources extended over several to tens of square kilometers. GRGs have been observed from detectors placed on ground, inside aircraft and on balloons. In this paper, we present a general purpose Monte‐Carlo model of GRG production and propagation. This model is first compared to a model from Zhou et al. (2016, https://doi.org/10.1016/j.astropartphys.2016.08.004) relying on another Monte‐Carlo framework, and small differences are observed. We then have built an extensive simulation library, made available to the community. This library is used to reproduce five previous gamma‐ray glow observations, from five airborne campaigns: balloons from Eack et al. (1996b, https://doi.org/10.1029/96gl02570), Eack et al. (2000, https://doi.org/10.1029/1999gl010849); and aircrafts from ADELE (Kelley et al., 2015, https://doi.org/10.1038/ncomms8845), ILDAS (Kochkin et al., 2017, https://doi.org/10.1002/2017jd027405) and ALOFT (Østgaard et al., 2019, https://doi.org/10.1029/2019jd030312). Our simulation results confirm that fluxes of cosmic‐ray secondary particles present in the background at a given altitude can be enhanced by several percent (MOS process), and up to several orders of magnitude (RREA process) due to the effect of thunderstorms' electric fields, and explain the five observations. While some GRG could be explained purely by the MOS process, E‐fields significantly larger than Eth are required to explain the strongest GRGs observed. Some of the observations also came with in‐situ electric field measurements, that were always lower than Eth, but may not have been obtained from regions where the glows are produced. This study supports the claim that kilometer‐scale E‐fields magnitudes of at least the level of Eth must be present inside some thunderstorms.

Mapping tropical forest aboveground biomass using airborne SAR tomography

Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency’s BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination. TropiSAR airborne campaign data in French Guiana, inventory plots, and airborne LiDAR measurements are used as reference data to develop models and calculate the AGB estimation uncertainty. We used univariate and multivariate regression models to estimate AGB at 4-ha grid cells, the nominal resolution of the BIOMASS mission. Our results show that the BP-based variables produced better AGB estimates compared to their counterparts, suggesting a more straightforward TomoSAR processing for the mission. The tomographic FH and AGB estimation have an average relative uncertainty of less than 10% with negligible systematic error across the entire biomass range (~ 200–500 Mg ha−1). We show that the backscattered power at 30 m height at HV polarization is the best single measurement to estimate AGB with significantly better accuracy than the LiDAR height metrics, and combining it with FH improved the accuracy of AGB estimation to less than 7% of the mean. Our study implies that using multiple information from P-band TomoSAR data from the BIOMASS mission provides a new capability to map tropical forest biomass and its changes accurately.

Characteristics of supersaturation in midlatitude cirrus clouds and their adjacent cloud-free air

Abstract. Water vapor measurements of midlatitude cirrus clouds, obtained by the WAter vapour Lidar Experiment in Space (WALES) lidar system during the Mid-Latitude Cirrus (ML-CIRRUS) airborne campaign, which took place in the spring of 2014 over central Europe and the NE Atlantic Ocean, are combined with model temperatures from the European Centre for Medium-Range Weather Forecasts (ECMWF) and analyzed. Our main focus is to derive the distribution and temporal evolution of humidity with respect to ice within cirrus clouds and in their adjacent cloud-free air. We find that 34.1 % of in-cloud data points are supersaturated with respect to ice. Supersaturation is also detected in 6.8 % of the cloud-free data points. When the probability density of the relative humidity over ice (RHi) is calculated with respect to temperature for the in-cloud data points from the ML-CIRRUS dataset, there are two peaks: one around 225 K and close to saturation, RHi = 100 %, and a second one at colder temperatures around 218 K in subsaturation, RHi = 79 %. These two regions seem to represent two cirrus cloud categories: in situ formed and liquid origin. Regarding their vertical structure, most clouds have higher supersaturations close to the cloud top and become subsaturated near the cloud bottom. Finally, we find that the vertical structure of RHi within the clouds is also indicative of their life stage. RHi skewness tends to go from positive to negative values as the cloud ages. RHi modes are close to saturation in young clouds, supersaturated in mature clouds and subsaturated in dissipating clouds.

Gaussian process regression-based forest above ground biomass retrieval from simulated L-band NISAR data

Accurate mapping of forest above ground biomass (AGB) is essential for understanding changes in the rate of ecosystem processes such as biomass productivity, litter productivity, actual litter decomposition, and potential litter decomposition during secondary succession. They also play a vital role in evaluating forest carbon pools. This study presents a Matérn kernel-based Gaussian process regression (GPR) approach for biomass estimation integrating Synthetic Aperture Radar (SAR) backscatter intensities with LiDAR measurements. We use the backscatter intensities from five scenes of L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) simulated NISAR data collected during the 2019 AM–PM NASA airborne campaign. The biomass map derived from LVIS (Land, Vegetation, and Ice Sensor) Lidar point clouds over the Lenoir landing site, Alabama, was used with these simulated NISAR data. We have utilized the GPR model to estimate the AGB of the entire forest alongside the major forest classes in the study area, namely deciduous, evergreen, and woody wetlands. To examine the dependency of the model on acquisition conditions, we calibrated and validated our proposed model utilizing scene combinations. The experiment shows that multiple-scene retrieval delivered improved AGB estimates compared to single-scene retrieval. We evaluated the efficacy of the GPR model for three AGB ranges, i.e., (i) 8to100Mgha−1, (ii) 8to230Mgha−1, and (iii) 8to470Mgha−1 at 20m, 30m, 50m and 100m spatial resolution, respectively. The results indicate that the RMSE incurred by the GPR model for all these AGB ranges seemed to reduce as we increased spatial extent or reduced spatial resolution from 20m to 100m. Further, we demonstrate the performance of the optimized GPR model to retrieve AGB within the biomass range of 8Mgha−1 to 100Mgha−1 for all the above-mentioned forest types. Finally, this research highlights the advantages of the Matérn kernel-based GPR model over other statistical regression models towards improved AGB mapping.

Shape from spectra

We introduce a new unified atmospheric–topographic correction approach that estimates surface geometry directly from the radiance measurement. Surface topography influences the at-sensor radiance measurement, making precise topography modeling critical in applications like vegetation or snow studies in mountainous terrain. Currently, elevation maps are used to derive topographic variables such as the slope and sky-view factor. This process is error-prone since static global digital elevation models do not generally achieve the accuracy required, and even minor mismatches in spatial resolution can introduce significant artifacts in downstream processing. Here we demonstrate that it is possible to estimate topographic parameters directly from spectral data, ensuring perfect physical consistency, temporal coincidence, and spatial alignment. We present experiments estimating topographic slope in two scenes in Southern California, with data from NASA’s Next Generation Airborne Visible/Near Infrared Imaging Spectrometer (AVIRIS-NG). We compared our radiance-based estimates against high-resolution lidar datasets. Our initial validation result showed a correlation of R2=0.864 (n=160) over the homogeneous surface of Beckman Auditorium’s cone-shaped roof on the Caltech campus in Pasadena, California. We then validate the model over a larger study site near Santa Clarita, California, finding R2=0.923 (n=40,000) in a 350×350 m area. The accuracy of our model estimates, combined with its systematic advantages over the alternative, show the potential of the approach for use in both airborne campaigns and orbital missions.