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L. J. Gelinas
,
J. H. Hecht
, and
R. J. Rudy

Abstract

The OH airglow layer is a persistent feature of Earth’s upper mesosphere, centered near 87 km altitude, that can be perturbed by atmospheric gravity waves (AGWs) and instabilities. While ground-based airglow imaging has been used to study these perturbations locally, this technique is limited by tropospheric weather. Space-based remote sensing provides a platform to measure these processes globally. In addition, portions of the OH airglow band span an atmospheric window, allowing airglow illumination of the ground for imaging of nighttime clouds and Earth terrain features. The Near-Infrared Airglow Camera (NIRAC) images the airglow at 1.6 μm and while deployed to the International Space Station (ISS) from May 2019 to November 2021 demonstrated these applications. The camera uses a patented motion-compensation system with a custom rectilinear lens that allows multisecond, nearly smear-free imaging (∼<1.5 pixels) at a ground pixel resolution of ∼83 m. With a ∼170 km × 170 km ground swath, NIRAC acquires overlapping images at a 7–10-s cadence. Parallax considerations enable detection of both AGWs and instabilities in the airglow, and scenes can be analyzed for terrain and cloud height. NIRAC also has a short-exposure daytime mode for cloud and ground imagery. This study describes NIRAC and its operations on the ISS and presents imagery examples of Earth terrain and surface phenomenology (such as fires), cloud imagery at all moon phases day and night, and the nighttime detection of AGWs and instabilities above 80 km altitude.

Significance Statement

The Near-Infrared Airglow Camera (NIRAC) is the first space-based instrument to exploit the bright 1.6 μm OH Meinel airglow emission band for Earth surface imager at resolution of ∼83 m. During its 2.5-yr deployment on the International Space Station (ISS), NIRAC obtained over a half million images of Earth’s surface and OH airglow layer. NIRAC has been able to capture images of the very small-scale (<30 km) AGWs and instabilities under a wide range of viewing conditions, including (i) in the vicinity of city lights, (ii) over complex cloud scenes, and (iii) under both moondown and moonup illumination. NIRAC also acquired daytime and nighttime images of clouds, hurricanes and typhoons, human lighting, and forest fires in the 1.6 μm band.

Restricted access
Naoyuki Kurita
,
Takao Kameda
,
Hideaki Motoyama
,
Naohiko Hirasawa
,
David Mikolajczyk
,
Lee J. Welhouse
,
Linda M. Keller
,
George A. Weidner
, and
Matthew A. Lazzara

Abstract

The interior of Dronning Maud Land (DML) in East Antarctica is one of the most data-sparse regions of Antarctica for studying climate change. A monthly mean near-surface temperature dataset for the last 30 years has been compiled from the historical records from automatic weather stations (AWSs) at three sites in the region (Mizuho, Relay Station, and Dome Fuji). Multiple AWSs have been installed along the route to Dome Fuji since the 1990s, and observations have continued to the present day. The use of passive-ventilated radiation shields for the temperature sensors at the AWSs may have caused a warm bias in the temperature measurements, however, due to insufficient ventilation in the summer, when solar radiation is high and winds are low. In this study, these warm biases are quantified by comparison with temperature measurements with an aspirated shield and subsequently removed using a regression model. Systematic error resulting from changes in the sensor height due to accumulating snow was insignificant in our study area. Several other systematic errors occurring in the early days of the AWS systems were identified and corrected. After the corrections, multiple AWS records were integrated to create a time series for each station. The percentage of missing data over the three decades was 21% for Relay Station and 28% for Dome Fuji. The missing rate at Mizuho was 49%, more than double that at Relay Station. These new records allow for the study of temperature variability and change in DML, where climate change has so far been largely unexplored.

Significance Statement

Antarctic climate change has been studied using temperature data at staffed stations. The staffed stations, however, are mainly located on the Antarctic Peninsula and in the coastal regions. Climate change is largely unknown in the Antarctic plateau, particularly in the western sector of the East Antarctic Plateau in areas such as the interior of Dronning Maud Land (DML). To fill the data gap, this study presents a new dataset of monthly mean near-surface climate data using historical observations from three automatic weather stations (AWSs). This dataset allows us to study temperature variability and change over a data-sparse region where climate change has been largely unexplored.

Restricted access
Gijs de Boer
,
Brian J. Butterworth
,
Jack S. Elston
,
Adam Houston
,
Elizabeth Pillar-Little
,
Brian Argrow
,
Tyler M. Bell
,
Phillip Chilson
,
Christopher Choate
,
Brian R. Greene
,
Ashraful Islam
,
Ryan Martz
,
Michael Rhodes
,
Daniel Rico
,
Maciej Stachura
,
Francesca M. Lappin
,
Antonio R. Segales
,
Seabrooke Whyte
, and
Matthew Wilson

Abstract

Small uncrewed aircraft systems (sUAS) are regularly being used to conduct atmospheric research and are starting to be used as a data source for informing weather models through data assimilation. However, only a limited number of studies have been conducted to evaluate the performance of these systems and assess their ability to replicate measurements from more traditional sensors such as radiosondes and towers. In the current work, we use data collected in central Oklahoma over a 2-week period to offer insight into the performance of five different sUAS platforms and associated sensors in measuring key weather data. This includes data from three rotary-wing and two fixed-wing sUAS and included two commercially available systems and three university-developed research systems. Flight data were compared to regular radiosondes launched at the flight location, tower observations, and intercompared with data from other sUAS platforms. All platforms were shown to measure atmospheric state with reasonable accuracy, though there were some consistent biases detected for individual platforms. This information can be used to inform future studies using these platforms and is currently being used to provide estimated error covariances as required in support of assimilation of sUAS data into weather forecasting systems.

Open access
Seung-Tae Lee
,
Yang-Ki Cho
,
Jihun Jung
,
Byoung-Ju Choi
,
Young-Ho Kim
, and
Sangil Kim

Abstract

The North Pacific is divided into different regions based on ocean currents and sea surface temperature (SST) distribution. Data assimilation is a useful tool for generating accurate ocean estimates because of the limited availability of observational data. This study compared the performances of two data assimilation methods, ensemble optimal interpolation (EnOI) and ensemble Kalman filter (EnKF), in various North Pacific subregions using an ocean model configured with the Regional Ocean Modeling System (ROMS). Both methods assimilated spaceborne SST observations, and the simulation results varied by subregion. The study found that EnKF and EnOI methods performed better than the control model in all regions when compared against satellite SST. EnOI reproduced SST as well as EnKF and required fewer computational resources. However, EnOI performed worse than the control model at sea surface height (SSH) in the equatorial region, while EnKF’s performance improved. This was due to the crushed mean state in the EnOI, which used long-term historical data as an ensemble member. El Niño–Southern Oscillation at the equator drove substantial interannual variability that crushed the ensemble mean of SSH in the EnOI. It is crucial to use a suitable assimilation method for the target area, considering the regional properties of ocean variables. Otherwise, the performance of the assimilated model may be even worse than that of the control model. While EnKF is better suited for regions with high variability in ocean variables, EnOI requires fewer computational resources. Thus, it is crucial to use a suitable assimilation method for accurately predicting and understanding the dynamics of the North Pacific.

Restricted access
H. M. Aravind
,
Helga S. Huntley
,
A. D. Kirwan Jr.
, and
Michael R. Allshouse

Abstract

Surface convergence in the ocean is associated with accumulation of buoyant pollutants as well as with vertical transport that is important to biological activity. Such surface convergence regions are marked by a high dilation rate, i.e., the finite time Lagrangian average divergence. Dilation-rate observations are most easily derived from the change of the area encompassed by a drifter swarm over time. The technological advances that have enabled the deployment of large numbers of drifters in a single experiment have raised new questions about optimal deployment strategies for extracting dilation-rate information with acceptable accuracy and as much spatial coverage as possible. Using a submesoscale-resolving operational model of the Mediterranean Sea, we analyze synthetic trajectories of drifter polygons to evaluate the impact of the number of drifters and their initial separation on the accuracy of the resulting dilation-rate estimates. The results confirm that estimates improve as the circumradius of the polygon decreases and as more drifters are added, but with only a marginal improvement for drifter polygons containing more than four drifters. Moreover, GPS positions obtained from drifters in the ocean are subject to uncertainty on the order of 2–50 m, and when this uncertainty is taken into account, an optimal circumradius can be identified that balances uncertainty from position measurements with that from the area approximations.

Significance Statement

Locating regions of convergence over a finite time interval on the ocean surface can help in pollution mitigation, locating biological hotspots, and even search-and-rescue operations. Finite time convergence can be quantified using the dilation rate, but it is hard to measure in the ocean. Hence, we present a method to estimate the dilation rate using trajectories of drifters, which are instruments widely used by oceanographers during field experiments to understand the local flow features. We show that even though the drifter-based dilation rates are prone to error as a result of a finite number of drifters and limited GPS accuracy, the estimates locate around 90% of the strongest convergent features in our model.

Restricted access
Mircea Grecu
and
John E. Yorks

Abstract

In this study, we investigate the synergy of elastic backscatter lidar, Ku-band radar, and submillimeter-wave radiometer measurements in the retrieval of ice from satellite observations. The synergy is analyzed through the generation of a large dataset of ice water content (IWC) profiles and simulated lidar, radar and radiometer observations. The characteristics of the instruments (frequencies, sensitivities, etc.) are set based on the expected characteristics of instruments of the Atmosphere Observing System (AOS) mission. A hold-out validation methodology is used to assess the accuracy of the IWC profiles retrieved from various combinations of observations from the three instruments. Specifically, the IWC and associated observations are randomly divided into two datasets, one for training and the other for evaluation. The training dataset is used to train the retrieval algorithm, while the evaluation dataset is used to assess the retrieval performance. The dataset of IWC profiles is derived from CloudSat reflectivity and CALIOP lidar observations. The retrieval of the ice water content IWC profiles from the computed observations is achieved in two steps. In the first step, a class, of 18 potential classes characterized by different vertical distribution of IWC, is estimated from the observations. The 18 classes are predetermined based on the k-means clustering algorithm. In the second step, the IWC profile is estimated using an ensemble Kalman smoother algorithm that uses the estimated class as a priori information. The results of the study show that the synergy of lidar, radar, and radiometer observations is significant in the retrieval of the IWC profiles. Nevertheless, it should be mentioned that this synergy was found under idealized conditions, and additional work might be required to materialize it in practice. The inclusion of the lidar backscatter observations in the retrieval process has a larger impact on the retrieval performance than the inclusion of the radar observations. As ice clouds have a significant impact on atmospheric radiative processes, this work is relevant to ongoing efforts to reduce uncertainties in climate analyses and projections.

Open access
Sergey Sokolovskiy
,
Zhen Zeng
,
Douglas C. Hunt
,
Jan-Peter Weiss
,
John J. Braun
,
William S. Schreiner
,
Richard A. Anthes
,
Ying-Hwa Kuo
,
Hailing Zhang
,
Donald H. Lenschow
, and
Teresa Vanhove

Abstract

Superrefraction at the top of the atmospheric boundary layer introduces problems for assimilation of radio occultation data in weather models. A method of detection of superrefraction by spectral analysis of deep radio occultation signals introduced earlier has been tested using 2 years of COSMIC-2/FORMOSAT-7 radio occultation data. Our analysis shows a significant dependence of the probability of detection of superrefraction on the signal-to-noise ratio, which results in a certain sampling nonuniformity. Despite this nonuniformity, the results are consistent with the known global distribution of superrefraction (mainly over the subtropical oceans) and show some additional features and seasonal variations. Comparisons to the European Centre for Medium-Range Weather Forecasts analyses and limited set of radiosondes show reasonable agreement. Being an independent measurement, detection of superrefraction from deep radio occultation signals is complementary to its prediction by atmospheric models and thus should be useful for assimilation of radio occultation data in the atmospheric boundary layer.

Open access
Rich Pawlowicz
,
Cédric Chavanne
, and
Dany Dumont

Abstract

Many different surface drifter designs have been developed recently to track near-surface ocean currents, but the degree to which these drifters slip through the water because of mechanisms associated with the wind is poorly known. In the 2020 Tracer Release Experiment (TReX), 19 drifters of eight different designs, both commercially available and home-built, were simultaneously released with a patch of rhodamine dye. The dye rapidly spread vertically through the mixed layer but also more slowly dispersed horizontally. Although winds were light, drifters moved downwind from the dye patch at speeds of 3–17 cm s−1 (0.6%–4% of wind speed) depending on the design type. Measurements were made of wind and ocean conditions, and these were incorporated into a boundary layer model at the air–sea interface to estimate complete velocity profiles above and below the surface. Then, a steady-state drag model is used with these profiles to successfully predict drifter slip. Drogued drifters (those with a subsurface drag element) can be affected by Eulerian shear in the upper 0.5 m of the water column, as well as the Stokes drift, but undrogued drifters are in addition greatly affected by direct wind drag, and possibly by resonant interactions with waves. The dye, cycling vertically in the mixed layer, is largely unaffected by all of these factors; therefore, even “perfect” surface drifters do not move with a mixed layer tracer.

Significance Statement

Surface drifters are used by oceanographers to measure ocean surface currents. However, different designs also slip downwind through the water at rates that are poorly known but are typically around a few percent of the wind speed. In 2020 we simultaneously deployed drifters of eight different designs along with rhodamine dye in a field experiment to see how well the different designs track the water. Here we independently and successfully model drifter slippage for the different designs. Slip factors are then estimated for a range of wind and ocean conditions.

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Matteo Bramati
,
Martin Schön
,
Daniel Schulz
,
Vasileios Savvakis
,
Yongtan Wang
,
Jens Bange
, and
Andreas Platis

Abstract

The use of small uncrewed aircraft systems (UAS) can effectively capture the wind profile in the lower atmospheric boundary layer. This study presents a calibration process to estimate the horizontal wind vector using a rotary-wing UAS in hovering conditions. This procedure does not require wind tunnels or meteorological masts, only the data from the flight control unit and a specific set of calibration flights. A model based on the UAS drag coefficient was proposed and compared to a traditional approach. Validation flights at the German Weather Service MOL-RAO observatory showed that the system can accurately predict wind speed and direction. A modified DJI S900 hexacopter with a Styrofoam sphere casing was used for the study and calibrated for wind speeds between 1 and 14 m s−1. Power spectral density analysis showed the system’s ability to resolve atmospheric eddies up to 0.1 Hz. The overall root-mean-square error was less than 0.7 m s−1 for wind speed and less than 8° for wind direction.

Open access
Cathrine Hancock
and
Olaf Boebel

Abstract

In sea ice–covered polar oceans, profiling Argo floats are often unable to surface for 9 months or longer, rendering acoustic RAFOS tracking the only method to obtain unambiguous under-ice positions. Tracking RAFOS-enabled floats has historically relied on the ARTOA3 software, which had originally been tailored toward nonprofiling floats in regions featuring the sound fixing and ranging (SOFAR) channel with acoustic ranges of approximately 1000 km. However, in sea ice–covered regions, RAFOS tracking is challenged due to (i) reduced acoustic ranges of RAFOS signals, and (ii) enhanced uncertainties in float and sound source clock offsets. A new software, built on methodologies of previous ARTOA versions, called artoa4argo, has been created to overcome these issues by exploiting additional float satellite fixes, resolving ambiguous float positions when tracking with only two sources and systematically resolving float and sound source clock offsets. To gauge the performance of artoa4argo, 21 RAFOS-enabled profiling floats deployed in the Weddell Sea during 2008–12 were tracked. These have previously been tracked in independent studies with a Kalman smoother and a multiconstraint method. The artoa4argo improves tracking by automating and streamlining methods. Although artoa4argo does not necessarily produce positions for every time step, which the Kalman smoother and multiconstraint methods do, whenever a track location is available, it outperforms both methods.

Significance Statement

Argo is an international program that collects oceanic data using floats that drift with ocean currents and sample the water column from 2000-m depth to the surface every 7–10 days. Upon surfacing, the float acquires a satellite position and transmits its data via satellite. In polar regions, with extensive seasonal sea ice coverage, floats are unable to surface for many months. Thus, any under-ice samples collected are missing positions, hampering their use in scientific endeavors. Since monitoring of polar regions is imperative to better understand and predict the effects of climate change, hydroacoustic tracking is employed there. Here a new acoustic tracking software, artoa4argo, is introduced, which improves tracking of these floats.

Open access