As soon as the outer plasmasphere gets eroded during geomagnetic storms, the greatly depleted plasmasphere is replenished by cold, dense plasma from the ionosphere. A strong correlation has been revealed between plasmaspheric refilling rates and ambient densities in the topside ionosphere and exosphere, particularly that of atomic hydrogen (H). Although measurements of H airglow emission at plasmaspheric altitudes exhibit storm-time response, temporally static distributions have typically been assumed in the H density in plasmasphere modeling. In this presentation, we evaluate the impact of a realistic distribution of the dynamic H density on the plasmaspheric refilling rate during the geomagnetic storm on March 17, 2013. The temporal and spatial evolution of the plasmaspheric density is calculated by using the Ionosphere-Plasmasphere Electrodynamics (IPE) model, which is driven by a global, 3-D, and time-dependent H density distribution reconstructed from the exospheric remote sensing measurements by NASA’s TWINS and TIMED missions. We quantify the spatial and temporal scales of the refilling rate and its correlation with H densities.

Data-model comparisons are common when addressing (paleo)climate questions. Many applications require deriving continuous surface fields of scalar variables from a set of irregularly distributed data points, typically for model validation against data or data-derived model input as initial or boundary conditions. While various interpolation techniques and interfaces exist, few can simultaneously: (1) interpolate across local to global spatial scales, (2) perform anisotropic interpolation using the spatial structure derived from the data instead of an assumed one, and (3) explicitly derive uncertainty in the interpolated fields due to both data density and measurement error. We present a standalone interpolation toolbox including a graphical user interface (GUI), which is aimed at the general earth science community. It uses a kriging algorithm whose distance metric is the geodesic on an oblate spheroid, be it the WGS-84 reference ellipsoid for applications on the surface of the Earth, or an equivalent ellipsoid with varying radii for interpolation on vertical levels above the surface. While kriging algorithms exist that perform interpolation on such non-Euclidean distances, they do not provide a check for conditionally negative semi-definiteness (CNSD) of the variogram matrix, which is a requisite for the kriging method. Since mathematical theory of kriging on spheroids or ellipsoids has not yet provided a set of authorized variance-distance functions, we incorporated a numerical check for CNSD condition for each data realization and variance-distance modeling scheme. The GUI will allow the user a high degree of customization. Preliminary results are promising, with robust results for isotropic interpolation. The derivation of CNSD variogram matrices for anisotropic interpolation remains the major challenge of the project. When completed, ClimAG-Krigger will provide the community with an easy-to-use, robust tool for anisotropic global kriging that will be specifically tailored for (paleo)climate applications.

We use a newly developed global Hall MHD code to investigate how reconnection drives magnetotail asymmetries in small magnetospheres. Here, we consider a scaled-down, Earth-like magnetosphere where we have artificially inflated the ion inertial length ($\delta_i$) to one Earth radius (the real Earth’s $\delta_i\approx 1/15-1/20 R_E \approx 300-400\unit{km}$ in the magnetotail). This results in a magnetotail width on the order of $30 \delta_i$, slightly smaller than Mercury’s tail and much smaller than Earth’s. At this small size, we find that the Hall effect has significant impact on the global flow pattern, changing from a symmetric, Dungey-like convection under resistive MHD to an asymmetric pattern similar to that found in previous Hall MHD simulations of Ganymede’s subsonic magnetosphere as well as other simulations of Mercury’s using multi-fluid or embedded kinetic physics. We demonstrate that the Hall effect is sufficient to induce a dawnward asymmetry in observed dipolarization front locations and find quasi-periodic global scale dipolarizations under steady, southward solar wind conditions. On average, we find a thinner current sheet dawnward; however, the measured thickness oscillates with the dipolarization cycle. During the flux-pileup stage, the dawnward current sheet can be thicker than the duskward sheet. This could be an explanation for recent observations that suggest Mercury’s current sheet is actually thicker on the duskside: a sampling bias due to a longer-lasting “thick’ state in the sheet.

Earth’s thermosphere is driven by a combination of meteorological, magnetospheric, and solar forcing that exhibits significant variation from day-to-day. The relative importance of these drivers and their combined affects in determining daily thermospheric variability on global and local scales is an important science question particularly under solar minimum conditions. Far-ultraviolet, satellite-based airglow observations are a valuable tool to probe the thermosphere and can provide the spatial coverage and temporal resolution required to improve our understanding of thermospheric day-to-day variability in response to driver variability. This paper presents first results from principal component analysis and ensemble sensitivity analysis to quantify the major modes of dayglow variability in both OI 135.6 nm emissions and N2 Lyman-Birge-Hopfield emissions and the sensitivity of these modes to geomagnetic and lower atmosphere drivers. The ensemble simulations are performed with NOAA’s Whole Atmosphere Model that extends from Earth’s surface to the exobase and NCAR’s Global Airglow Model for a recent period with low-to-moderate levels of geomagnetic activity and low solar activity. The ensemble simulations are compared to thermospheric observations over the same period by the NASA Global-scale Observations of the Limb and Disk (GOLD) mission.

The latest generation of coupled models, the sixth Coupled Models Intercomparison Project (CMIP6), is used to study the changes in the El Niño Southern Oscillation (ENSO) in a warming climate. For the four future scenarios studied, the sea surface temperature variability increases in most CMIP6 models, but to varying degrees. This increase is linked to a weakening of the east-west temperature gradient in the tropical Pacific Ocean, which is evident across all models. Just as in previous generations of climate models, we find that many characteristics of future ENSO remain uncertain. This includes changes in dominant timescale, extra-tropical teleconnection patterns and amplitude of El Niño and La Niña events. For models with the strongest increase in future variability, the majority of the increase happens in the Eastern Pacific, where the strongest El Niño events usually occur.

The Atmosphere-Space Interactions Monitor (ASIM) on the International Space Station (ISS) provides optical radiances and images of lightning flashes in several spectral bands. This work presents a lightning flash simultaneously observed from space by ASIM, the Geostationary Lightning Mapper (GLM) and the Lightning Imaging Sensor on the International Space Station (ISS-LIS); and from ground by the Colombia Lightning Mapping Array (Colombia-LMA). Volumetric weather radar provides reflectivity data to help to interpret the effects of the cloud particles on the observed optical features. We found that surges in radiance in the band at 777.4 nm, appear to be related mostly with lightning processes involving currents as well with branching of lightning leaders with new leader development. In cloud areas with reflectivity <18 dBZ above the lightning leader channels at altitudes >7 km, these have been imaged by ASIM and GLM. But in the region with reflectivity <23 dBZ, despite its lower cloud tops and similar altitudes of lightning channels, these have been almost undetectable. The estimated relative optical depth results consistent with the observed optical features at the different locations of the flash. Despite of the effects of the cloud particles and the altitude of the lightning channels on the attenuation of the luminosity, the luminosity of the lightning channels due to different processes is fundamental for the imaging of lightning from space.

GNSS networks with multi-frequency data can be used to monitor the activity of the Earth’s ionosphere and to generate global maps of the vertical total electron content (VTEC). This paper introduces and evaluates operational GFZ VTEC maps. The processing is based on a rigorous least-squares approach using uncombined code and phase observations, and does not entail leveling techniques. A single-layer model with a spherical harmonic VTEC representation is used. The solutions are generated in a daily post-processing mode with GPS, GLONASS, and Galileo data, and are provided for the period since the beginning of 2000. A comparison of the GFZ VTEC maps with the final combined IGS product shows a high consistency with the solutions of the IGS analysis centers. A validation with about four years of Jason-3 altimetry-derived VTEC data is provided, in which the GFZ solution has the smallest bias of 1.2 TEC units compared to the solutions of the IGS analysis centers, and with 3.0 TEC units one of the smallest standard deviations.

Since its formation in 2015, the National Centers for Environmental Information (NCEI) has used disparate, legacy systems spread across several IT networks of the National Environmental Satellite, Data, and Information Service (NESDIS) to fulfill its data-stewardship functions. As part of its modernization and consolidation of these functions, NCEI implemented Common Ingest as the functional component that ingests approximately 200 data streams every month into its enterprise archival information system. In parallel, NESDIS completed the Secure Ingest Gateway Project (SIGP), a pilot project to establish standard-enterprise secure methods for NESDIS and the rest of the National Oceanic and Atmospheric Administration (NOAA) to receive data in a cloud environment from their external partners. SIGP is now transitioning to operations as the Operational Secure Ingest Service (OSIS), which will be the on-ramp to NCEI’s “Common Ingest” functionality when it too moves to the cloud. In addition, this ingest function will populate and use a cloud-based metadata catalog, which will be the beating heart of the NESDIS and NCEI information systems in the cloud environment. The vision is to scale their ingest of environmental data to keep pace with its ever increasing volume, veracity, variety, and velocity. In this presentation to the ocean data community, the authors describe NESDIS and NCEI’s challenges and successes with the implementation of the ingest function of their archival information system in a cloud environment.

We examine characteristics of the seasonal variation of thermospheric composition using column number density ratio ∑O/N2 observed by the NASA Global Observations of Limb and Disk (GOLD) mission from low-mid to mid-high latitudes. We found that the ∑O/N2 seasonal variation is hemispherically asymmetric: in the southern hemisphere, it exhibits the well-known annual and seminal pattern, with highs near the equinoxes, and primary and secondary lows near the solstices. In the northern hemisphere, it is dominated by an annual variation, with a minor semiannual component with the highs shifting towards the wintertime. We also found that the durations of the December and June solstice seasons in terms of ∑O/N2 are highly variable with longitude. Our hypothesis is that ion-neutral collisional heating in the equatorial ionization anomaly region, ion drag, and auroral Joule heating play substantial roles in this longitudinal dependency. Finally, the rate of change in ∑O/N2 from one solstice season to the other is dependent on latitude, with more dramatic changes at higher latitudes.

Science is fueled by data. Throughout history, scientists have operated sensors-from astronomical observatories to particle accelerators-that accumulate observations for analysis or to evaluate a hypothesis. However, as available technologies have increased both the volume of data and the efficiency of data storage and transmission, a new model of data access has emerged. The concept of a data buy is where an entity purchases access to a set of data or a data stream, instead of operating the sensors themselves. But why might a data consumer, whether a researcher or an end-user, prefer this kind of data access over the more traditional methods of running a network themselves? The simple answer, in some cases, is efficiency and, possibly, cost. Space weather forecasting and analysis has a growing private sector, and the extension to data gathering can be considered as a natural next step in the maturation of the field and the growing public-private partnerships. Operational applications require consistent, clean, and (in some cases) real-time data access that can be hard to support through the existing model of sensor deployment. Even in scientific applications, where access to raw information can be critical to discovery, there are benefits to the data buy model. Consistent access to a trusted data set allows more time to be spent on the scientific analysis, instead of maintaining machines that require consistent development, maintenance, and monitoring. The outsourcing of data infrastructure and pipelines can be particularly beneficial when the sensors are in distributed networks, spread over wide areas, and when there is a need to provide local data in observational gaps in existing networks. In the ideal case, a data buy can supplement the traditional observational networks in a beneficial and symbiotic way. It is important to note that data buys should not replace traditional observational networks, nor compete for funding with future observatories and infrastructure that the scientific community has deemed necessary (for example, through decadal survey processes).

Being in an arid zone that is frequently submitted to high winds, south-central Arizona regularly gets impacted by several blowing dust events or dust storms every year. Major consequences of these events are visibility impairment and ensuing road traffic accidents, and a variety of health issues induced by inhalation of polluted air loaded with fine particulate matter produced by wind erosion. Despite such problems, and thus a need for guidance on mitigation efforts, studies dealing with dust source attribution for the region are largely missing. Furthermore, existing dust models exhibit large uncertainties and deficiencies in simulating dust events, rendering them of limited use in attribution studies or early warning systems. Therefore, to address some of these model issues, we have developed a high-resolution (1 km) dust modeling system by building upon an existing modeling framework consisting of Weather Research and Forecasting (WRF), FENGSHA (a dust emission model), and Community Multiscale Air Quality (CMAQ) models. In addition to incorporating new representations in the dust emission scheme, including roughness correction factor, sandblasting efficiency, and dust source mask, we implemented, in the dust model, up-to-date and very high-resolution data on land use, soil texture, and vegetation index. We used the revised dust modeling system to simulate a springtime dust storm (08–09 April 2013) of relatively long duration that caused a regional traffic incident involving minor injuries. The model simulations compared reasonably well against observations of concentration of particulate matter with a diameter of 10 μm and smaller (PM₁₀) and satellite-derived dust optical depth and vertical profile of aerosol subtypes. Interestingly, simulation results revealed that the anthropogenic (cropland) dust sources contributed more than half (~53 % or 260 µg/m³) of total PM₁₀, during the dust storm, over the region including Phoenix and western Pinal County. Contrary to the conventional wisdom that desert is the main dust source, our findings for this region challenge such belief and suggest that the regional air quality modeling over dryland regions should emphasize an improved representation of dust from agricultural lands as well, especially during high wind episodes. Such representations have the potential to inform decision-making in order to reduce windblown dust-related hazards on public health and safety.

Global change is a change in the planetary energy balance. It is usually expressed as a change in near-surface (2 m) air temperature (Ta), but changes to Ta represent only part of the atmospheric energy balance, which includes specific humidity (q) and more. We analyzed MERRA-2 reanalysis data and 15 Atmospheric Model Intercomparison Project (AMIP) models over the 1980-2014 period. Some 41%, 37%, and 49% of the near-surface atmosphere showed significant increases in ET, ESH, and E, respectively. The average increase in ET (ESH) was 10.6 J kg−1 year−1 (11.5 J kg−1 year−1) but AMIP models estimated that ET (14.5 J kg−1 year−1) exceeded ESH (13.7 J kg−1 year−1). Global near-surface Ta would have increased at more than twice the observed rate if energy was not partitioned into latent heat. Results demonstrate the critical role that q plays in recent changes to near-surface atmospheric energy.

Lava flows are one of the main hazards related to effusive basaltic volcanism. To minimize their impact during emplacement, we use lava flow potential distance-to-run predicted by propagation models. These models are partly based on infrared (IR) measurements of lava radiative heat fluxes by remote sensing (RS) methods (ground-based or satellite-based detectors) [1]. These results are however subjected to important errors related to the poor knowledge of spectral emissivity (ε), commonly considered constant by these well-established techniques[2, 3]. This oversimplification is an important source of uncertainties in derived temperatures, which restrain our capacity to accurately model active lava flows. In this study, we developed new algorithms that take into account the effect of spectral emissivity for calculating radiative heat fluxes. We describe the temperature-emissivity relationship with equations established at two wavelengths of interest for RS (10.9 μm and 1.6 μm) that are retrieved from in situ measurements of spectral emissivity for basaltic magma from the 2014–2015 Holuhraun eruption. Spectral emissivity data were systematically acquired over a wide spectral range (400–8000 cm−1) covering TIR, MIR and SWIR, and up to 1473 K [4]. Our results show that spectral emissivity varies linearly with temperature in TIR (10.9 μm), and nonlinearly in SWIR (1.6 μm). We confronted our lab-based results to the field IR data retrieved by [5] and found that temperature precision increases compared to data using constant emissivity value. These new insights will ultimately improve the thermo-rheological models of lava flows [6] in order to support hazard assessment in volcanic systems. References: [1] Kolzenburg et al. 2017. Bull. Volc. 79:45. [2] Harris, A. 2013: Cambridge University press. 728. [3] Rogic et al. 2019 Remote Sens., 11, 662 [4] De Sousa Meneses et al. 2015. Infrared Physics & Technology 69. [5] Aufaristama et al. 2018, Remote Sens, 10,151. [6] Thompson and Ramsey, 2021, Bulletin of Volcanology, 83:41. Keywords: Spectral emissivity, temperature, IR spectroscopy, remote sensing, basalt

Here, we present new radio interferometer beamforming observations of lightning initiation using data from the Low Frequency Array (LOFAR). We show that the first lightning source in the flash increases exponentially in intensity by two orders of magnitude in 15 microseconds, while propagating 88 meters away from the initiation location at a constant speed of 4.8 ± 0.1 x 10^6 m/s. A second source replaces the first source at the initiation location, and subsequent propagation of the lightning leader follows. We interpret the first source to be a rapidly propagating and intensifying positive streamer discharge that subsequently produces a hot leader channel near the initiation point. How lightning initiates is one of the greatest unsolved problems in the atmospheric sciences, and these results shed light on this longstanding mystery.

Satellites provide the primary dataset for monitoring the earth system and constraining analyses in numerical models. A challenge for utilizing satellite radiances is the estimation of their biases. High-accuracy non-radiance data are typically employed to anchor radiance bias corrections. This study provides the first assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program that samples ARs over the Northeast Pacific on the radiance assimilation using the Global Forecast System (GFS) and the Global Data Assimilation System. Including this dropsonde dataset has provided better anchoring for bias corrections and improved model background, leading to an increase of ~5-10% in the amount of assimilated microwave radiance in the lower/middle troposphere over the Northeast Pacific and North America. The impact on tropospheric infrared radiance is small but also beneficial. This result points to the usefulness of dropsondes, along with other conventional data, in the assimilation of satellite radiance.

\sout The proposed work presents a model to understand the lower hybrid turbulence as observed by the Magnetospheric Multiscale (MMS) mission in the magnetic reconnection regions of magnetopause by the energetic electron beams (generated by the magnetic reconnection process). The magnetic reconnection process has been substituted by the “energetic electron beam source” in this model. Therefore, in the proposed model, dynamical equations for beam-driven lower hybrid wave (LHW) have been formulated, foreseeing that it will evolve from noise level and then attain large amplitude due to beam energy. At large amplitude, non-linear effects due to ponderomotive force dominate, causing LHW localization and the turbulent state. A non-linear two-dimensional model with the help of the two-fluid dynamics has been developed. The present mathematical model considers the interaction between pump LHW and low frequency magnetosonic wave (MSW). The MSW, present in the background, has been contemplated as the source of density perturbations in LHW dynamics. The LHW is the source of ponderomotive nonlinearity in the medium and is incorporated in the MSW wave dynamics. With the help of the growth term associated with the electron beam, dynamical equations for LHW and MSW have been established. The two coupled equations, thus obtained, were solved with the help of numerical simulation techniques. The results show the temporal evolution of the LHW from noise level and formation of localized structures and turbulence consistent with MMS mission observations.

Organization metrics were originally developed to measure how densely convective clouds are arranged at mesoscales. In this work, we apply organization metrics to describe tropical synoptic scale convective activity. Such activity is identified by cloud-precipitation (hybrid) regimes defined at 1-degree and 1-hourly resolution. Existing metrics were found to perform inadequately for such convective regime aggregates because the large domain size and co-existence of sparse aggregate occurrences with noisy isolated convection often violate assumptions inherent in these metrics. In order to capture these characteristics, the existing “convective organization potential” (COP) metric was modified so as to: (1) focus on local organization and (2) provide increased weight to aggregate size. The resulting “area-based COP” (ABCOP) is found to outperform existing metrics in tropical convective events at synoptic scales. Moreover, this new organization metric can match the performance of existing metrics, or arguably be better, over a wide range of domain sizes.

The isotopic composition of water vapor (e.g. its Deuterium content) evolves along the water cycle as phase changes are associated with isotopic fractionation. In the tropics, it is especially sensitive to convective processes. Consequently the isotopic composition of precipitation recorded in paleoclimate archives has significantly contributed to the reconstruction of past hydrological changes. It has also been suggested that observed isotopic composition of water vapor could help better understand convective processes and evaluate their representation in climate models. Yet, water isotopes remain rarely used beyond the isotopic community to answer today’s pressing climate questions. A prerequisite to better assess the strengths and weaknesses of the isotopic tool is to better understand what controls spatio-temporal variations in water vapor isotopic composition through the tropical atmosphere. A first step towards this better understanding is to understand what controls the isotopic composition of the sub-cloud layer water vapor over the ocean. Isotopic measurements show that the water vapor is the most enriched in trade-wind regions, and becomes more depleted as precipitation increases. To understand this pattern, we use global simulations with the isotope-enabled general circulation model LMDZ, large-eddy simulation in radiative-convective equilibrium and with large-scale ascent or descent, with the isotope-enabled model SAM and simple analytical models. We show that increased precipitation rate is associated with increased isotopic depletion if it is associated with stronger large-scale ascent, but with decreased isotopic depletion if it is associated with warmer surface temperature. As large-scale ascent increases, the isotopic vertical gradient in the lower troposphere is steeper, which makes downdrafts and updrafts more efficient in depleting the sub-cloud layer water vapor. The steeper gradient is caused mainly by the larger quantity of snow falling down to the melting level, forming rain whose evaporation depletes the water vapor.

Data assimilation (DA) has been applied to estimate the time-mean state such as annual mean surface temperature for paleoclimate reconstruction. There are two types of DA for this purpose: online-DA and offline-DA. The online-DA estimates the time-mean states and the initial conditions for the next DA cycles while the offline-DA only estimates the former. If there is sufficiently long predictability in the system of interest compared to the temporal resolution of the observations, online-DA is expected to outperform offline-DA by utilizing information in the initial conditions. However, previous studies failed to show the superiority of online-DA when time-averaged observations are assimilated, and the reason has not been investigated thoroughly. This study compares online-DA and offline-DA and investigates the relation to the predictability using an intermediate complexity general circulation model with perfect-model observing system simulation experiments. The result shows that the online-DA outperforms offline-DA when the length of predictability is longer than the averaging time of the observations. We also found that the longer the predictability, the more skillful the online-DA. Here, the ocean plays a crucial role in extending predictability, which helps online-DA to outperform offline-DA. Interestingly, the observations of near-surface air temperature over land are found to be highly valuable to update the ocean variables in the analysis steps, suggesting the importance to use cross-domain covariance information between the atmosphere and the ocean when online-DA is applied to reconstruct paleoclimate.

Lightning and transient luminous events (TLEs) emit a short burst (~1 ms) of broadband electromagnetic waves, whose frequencies can range from a few Hz to the optical band, but the bulk of their energy is radiated as longwaves (<500 kHz). These longwave radio signals are named radio atmospherics, or colloquially sferics. Due to their low frequency, sferics can propagate in the Earth-ionosphere waveguide at global distances with relatively low attenuation (~3 dB per 1000 km). This allows a sparse network of longwave receiver stations, placed hundreds of kilometers apart, to geolocate lightning strikes at a global scale. Hardware performance of the receivers at these stations significantly impacts the data quality and determines the detection efficiency and location accuracy of the lightning detection network. In this work, we present a low-frequency remote sensing instrument for lightning geolocation in the form of an ultra-sensitive broadband electric field receiver. It is capable of detecting extremely weak sferics, enabled by its ideal sensitivity of 1 nV/(m√Hz), or 0.003 fT/√Hz. We present this receiver’s antenna-amplifier co-design and the design considerations to achieve this low sensitivity. We then report its performance characteristics, validated both theoretically and empirically. Finally, we present some of the novel applications of this device in the scope of lightning geolocation and remote sensing.

Scattering of longwave radiation by cloud particles has been regarded unimportant and hence commonly neglected in global climate models. However, it has been demonstrated by recent studies that cloud longwave scattering plays an unignorable role in modulating the energy budget of the Earth System. Offline radiative transfer calculation showed that excluding cloud longwave scattering could overestimate outgoing longwave radiation and underestimate downward irradiance to the surface, and thus impose excessive cooling onto the atmosphere column. How this physical process interacts with other processes in the Arctic climate system, however, has not been thoroughly evaluated yet. Given the fact that the melting of ice and snow that cover the vast surface of the Arctic region is sensitive to energy budget, and such melting may trigger further feedback mechanisms, the neglection of cloud longwave scattering could bias the regional climate simulations to a considerable extent. We have incorporated cloud longwave scattering into the NCAR CESM and the DoE E3SM and this study analyzed the impact on the simulated polar climates in both earth system models. Cloud longwave scattering leads to a warmer surface air temperature in both models, especially over the wintertime. A detailed surface energy budget analysis is performed, for both the mean state and the temporal variability. Preliminary results suggest that the leading change is downward longwave flux and upward longwave flux, followed by the changes of turbulent heat flux. How the longwave scattering treatments can couple with cloud microphysics and precipitation physics to affect Arctic precipitation is further explored.

Atmospheric drag describes the main perturbing force of the atmosphere on the orbital trajectories of near-Earth orbiting satellites. The ability to accurately model atmospheric drag is critical for precise satellite orbit determination and collision avoidance. Assuming we know atmospheric winds and satellite velocity, area and mass, the primary sources of uncertainty in atmospheric drag include mass density of the space environment and the spacecraft drag coefficient, CD. Historically, much of the focus has been on physically or empirically estimating mass density, while CD is treated as a fitting parameter or fixed value. Presently, CD can be physically modeled through energy and momentum exchange processes between the atmospheric gas particles and the satellite surface. However, physical CD models rely on assumptions regarding the scattering and adsorption of atmospheric particles, and these responses are driven by atmospheric composition and temperature. Modifications to these assumptions can cause CD to change by up to ~40%. The nature and magnitude of these changes also depend on the shape of the spacecraft. We can check the consistency of the CD model assumptions by comparing densities derived from satellite drag measurements and computed CD values for satellites of different shapes orbiting in the same space environment. Since all of the satellites should see the same density, offsets in the derived densities should be attributable to inconsistencies in the CD model. Adjusting the CD model scattering assumptions can improve derived density consistency among the different satellites and inform the physics behind CD modeling. In turn, these efforts will help to reduce uncertainty in CD, leading to improved atmospheric drag estimates.

It is increasingly recognized that light-absorbing impurities deposited on a surface can reduce its albedo and lead to increased absorption of solar radiation. Natural dust can travel substantial distances in the Earth’s atmosphere from its original source. It affects all climatic zones from the tropics to the poles and it may have a regional or global impact on air quality and human health. In the Arctic, a rapid increase in temperature compared to the global change, known as Arctic Amplification, is closely linked to snow albedo feedback. Furthermore, recent studies detail an extreme climate change scenario in the history of our planet that lead to catastrophic cascading events and global mass extinction triggered by atmospheric soot injections. Therefore, knowledge of optical properties of dust particles is important for improved climate models and dust effect studies. Here we report detailed results of multi-angular polarized measurements of light scattered by volcanic sand particles obtained with the FIGIFIGO goniospectrometer (Peltoniemi et al. 2014). The design concept of this custom made instrument has a well designed user friendly interface, a high level of automation, and an excellent adaptability to a wide range of weather conditions during field measurements. The foreoptics is connected to an ASD FieldSpec Pro FR 350-2500 nm spectroradiometer by an optical fiber. A calcite Glan-Thompson prism is used as a polarizer, covering the full spectral range with better than 1% accuracy. The samples studied in this work were collected from the Mýrdalssandur area in Iceland (in March 2016) and from the Villarica area in Chile (in July 2019). Following established FGI practices in laboratory conditions samples are further divided into the following categories: (1) natural volcanic sand, (2) sieved volcanic sand (dust) where the size of the particles is less than 250 μm, including dry and wet sample condition, and (3) a fine-grained powder of milled volcanic sand measurable also as aerosol. The potential use of the results from our measurements are diverse, including their use as a ground truth reference for Earth Observation and remote sensing studies, estimating climate change over time, as well as measuring other ecological effects caused by changes in atmospheric composition or land cover.

Semi-implicit time-stepping schemes for atmosphere and ocean models require elliptic solvers that work efficiently on modern supercomputers. This paper reports our study of the potential computational savings when using mixed precision arithmetic in the elliptic solvers. Precision levels as low as half (16 bits) are used and a detailed evaluation of the impact of reduced precision on the solver convergence and the solution quality is performed. This study is conducted in the context of a novel semi-implicit shallow-water model on the sphere, purposely designed to mimic numerical intricacies of modern all-scale weather and climate (W&C) models. The governing algorithm of the shallow-water model is based on the non-oscillatory MPDATA methods for geophysical flows, whereas the resulting elliptic problem employs a strongly preconditioned non-symmetric Krylov-subspace solver GCR, proven in advanced atmospheric applications. The classical longitude/latitude grid is deliberately chosen to retain the stiffness of global W&C models. The analysis of the precision reduction is done on a software level, using an emulator, whereas the performance is measured on actual reduced precision hardware. The reduced-precision experiments are conducted for established dynamical-core test-cases, like the Rossby-Haurwitz wavenumber 4 and a zonal orographic flow. The study shows that selected key components of the elliptic solver, most prominently the preconditioning and the application of the linear operator, can be performed at the level of half precision. For these components, the use of half precision is found to yield a speed-up of a factor 4 compared to double precision for a wide range of problem sizes.