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Marcin Paszkuta
,
Maciej Markowski
, and
Adam Krężel

Abstract

Empirical verification of the reliability of estimating the amount of solar radiation entering the sea surface is a challenging topic due to the quantity and quality of data. The collected measurements of total and diffuse radiation from the Multifilter Rotating Shadowband Radiometer (MRF-7) commercial device over the Baltic Sea were compared with the satellite results of using modeling data. The obtained results, also divided into individual spectral bands, were analyzed for usefulness in satellite cloud and aerosol detection. The article presents a new approach to assessing radiation and cloud cover based on the use of models supported by satellite data. Measurement uncertainties were estimated for the obtained results. To reduce uncertainty, the results were averaged to the time constant of the device, day, and month. The effectiveness of the method was determined by comparison against the SM Hel measurement point. The empirical results obtained confirm the effectiveness of using satellite methods for estimating radiation along with cloud-cover detection over the sea with the adopted uncertainty values.

Significance Statement

The difference in the amount of solar energy reaching the sea surface between cloudless and cloudy areas reaches tens of percent. Empirical results confirm the effectiveness of using satellite methods to estimate solar radiation along with cloud-cover detection. Over the sea in comparison to land, the amount of empirical data is limited. This research uses new empirical results of radiation to determine the accuracy of satellite estimation results. Experimental results show that the proposed method is effective and adequately parameterizes the detection of satellite image features.

Open access
Joseph S. Schlosser
,
Ryan Bennett
,
Brian Cairns
,
Gao Chen
,
Brian L. Collister
,
Johnathan W. Hair
,
Michael Jones
,
Michael A. Shook
,
Armin Sorooshian
,
Kenneth L. Thornhill
,
Luke D. Ziemba
, and
Snorre Stamnes

Abstract

Suborbital (e.g., airborne) campaigns that carry advanced remote sensing and in situ payloads provide detailed observations of atmospheric processes, but can be challenging to use when it is necessary to geographically collocate data from multiple platforms that make repeated observations of a given geographic location at different altitudes. This study reports on a data collocation algorithm that maximizes the volume of collocated data from two coordinated suborbital platforms and demonstrates its value using data from the NASA Aerosol Cloud Meteorology Interactions Over the western Atlantic Experiment (ACTIVATE) suborbital mission. A robust data collocation algorithm is critical for the success of the ACTIVATE mission goal to develop new and improved remote sensing algorithms, and quantify their performance. We demonstrate the value of these collocated data to quantify the performance of a recently developed vertically resolved lidar + polarimeter–derived aerosol particle number concentration (Na ) product, resulting in a range-normalized mean absolute deviation (NMAD) of 9% compared to in situ measurements. We also show that this collocation algorithm increases the volume of collocated ACTIVATE data by 21% compared to using only nearest-neighbor finding algorithms alone. Additional to the benefits demonstrated within this study, the data files and routines produced by this algorithm have solved both the critical collocation and the collocation application steps for researchers who require collocated data for their own studies. This freely available and open-source collocation algorithm can be applied to future suborbital campaigns that, like ACTIVATE, use multiple platforms to conduct coordinated observations, e.g., a remote sensing aircraft together with in situ data collected from suborbital platforms.

Significance Statement

This study describes a data collocation (i.e., selection) process that aims to maximize the volume of data identified to be simultaneously collected in time and space from two coordinated measurement platforms. The functional utility of the resultant dataset is also demonstrated by extending the validation of aerosol particle number concentration derived from standard lidar and polarimeter data products from a suborbital mission that used two aircraft platforms.

Open access
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
Erica L. McGrath-Spangler
,
N. C. Privé
,
Bryan M. Karpowicz
,
Isaac Moradi
, and
Andrew K. Heidinger

Abstract

The Geostationary eXtended Observations (GeoXO) program plans to include a hyperspectral infrared (IR) sounder on its central satellite, expected to launch in the mid-2030s. As part of the follow-on to the GOES program, the NOAA/NASA GeoXO Sounder (GXS) instrument will join several international counterparts in a geostationary orbit. In preparation, the NASA Global Modeling and Assimilation Office (GMAO) assessed the potential effectiveness of GXS both as a single GEO IR sounder and as part of a global ring that includes international partners. Using a global observing system simulation experiment (OSSE) framework, GXS was assessed from a numerical weather prediction (NWP) perspective. Evaluation of the ability of GXS, both alone and as part of a global ring ofGEOsounders, to improveweather prediction of thermodynamic variables was performed globally and regionally. GXS dominated regional analysis and forecast improvements, and contributed significantly to global increases in forecast skill relative to a Control. However, more sustained global improvements, on the order of 4 days, relied on international partnerships. Additionally, GXS showed the capability to improve hurricane forecast track errors on the timescales necessary for evacuation warnings. The FSOI metric over CONUS showed that the GXS observations provided the largest radiance impact on the moist energy error norm reduction. The high temporal resolution atmospheric profile information over much of the western hemisphere from GXS provides an opportunity to improve the representation of weather systems and their forecasts.

Restricted access
Mahsa Payami
,
Yunsoo Choi
,
Ahmed Khan Salman
,
Seyedali Mousavinezhad
,
Jincheol Park
, and
Arman Pouyaei

Abstract

In this study, we developed an emulator of the Community Multiscale Air Quality (CMAQ) model by employing a 1-dimensional Convolutional Neural Network (CNN) algorithm to predict hourly surface nitrogen dioxide (NO2) concentrations over the most densely populated urban regions in Texas. The inputs for the emulator were the same as those for the CMAQ model, which includes emission, meteorology, and land use land cover data. We trained the model over June, July, and August (JJA) of 2011 and 2014 and then tested it on JJA of 2017, achieving an Index of Agreement (IOA) of 0.95 and a correlation of 0.90. We also employed temporal 3-fold cross-validation to evaluate the model’s performance, ensuring the robustness and generalizability of the results. To gain deeper insights and understand the factors influencing the model’s surface NO2 predictions, we conducted a Shapley Additive Explanations analysis. The results revealed solar radiation reaching the surface, Planetary Boundary Layer height, and NOx (NO + NO2) emissions are key variables driving the model’s predictions. These findings highlight the emulator’s ability to capture the individual impact of each variable on the model’s NO2 predictions. Furthermore, our emulator outperformed the CMAQ model in terms of computational efficiency, being more than 900 times faster in predicting NO2 concentrations, enabling the rapid assessment of various pollution management scenarios. This work offers a valuable resource for air pollution mitigation efforts, not just in Texas, but with appropriate regional data training, its utility could be extended to other regions and pollutants as well.

Open access
Nicholas M. Leonardo
and
Brian A. Colle

Abstract

Nested idealized baroclinic wave simulations at 4-km and 800-m grid spacing are used to analyze the precipitation structures and their evolution in the comma head of a developing extratropical cyclone. After the cyclone spins up by hour 120, snow multi-bands develop within a wedge-shaped region east of the near-surface low center within a region of 700-500-hPa potential and conditional instability. The cells deepen and elongate northeastward as they propagate north. There is also an increase in 600-500-hPa southwesterly vertical wind shear prior to band development. The system stops producing bands 12 hours later as the differential moisture advection weakens, and the instability is depleted by the convection.

Sensitivity experiments are run in which the initial stability and horizontal temperature gradient of the baroclinic wave are adjusted by 5-10%. A 10% decrease in initial instability results in less than half the control run potential instability by 120 h and the cyclone fails to produce multi-bands. Meanwhile, a 5% decrease in instability delays the development of multi-bands by 18 h. Meanwhile, decreasing the initial horizontal temperature gradient by 10% delays the growth of vertical shear and instability, corresponding to multi-bands developing 12-18 hours later. Conversely, increasing the horizontal temperature gradient by 10% corresponds to greater vertical shear, resulting in more prolific multi-band activity developing ∼12 hours earlier. Overall, the relatively large changes in band characteristics over a ∼12-hour period (120-133 h) and band evolutions for the sensitivity experiments highlight the potential predictability challenges.

Restricted access
Isaac Davis
and
Brian Medeiros

Abstract

The Community Earth System Model, version 2 (CESM2) has a very high climate sensitivity driven by strong positive cloud feedbacks. To evaluate the simulated clouds in the present climate and characterize their response with climate warming, a clustering approach is applied to three independent satellite cloud products and a set of coupled climate simulations. Using k-means clustering with a Wasserstein distance cost function, a set of typical cloud configurations is derived for the satellite cloud products. Using satellite simulator output, the model clouds are classified into the observed cloud regimes in both current and future climates. The model qualitatively reproduces the observed cloud configurations in the historical simulation using the same time period as the satellite observations, but it struggles to capture the observed heterogeneity of clouds which leads to an overestimation of the frequency of a few preferred cloud regimes. This problem is especially apparent for boundary layer clouds. Those low-level cloud regimes also account for much of the climate response in the late 21st Century in four Shared Socioeconomic Pathway simulations. The model reduces the frequency of occurrence of these low-cloud regimes, especially in tropical regions under large-scale subsidence, in favor of regimes that have weaker cloud radiative effects.

Restricted access
Jingyi Wen
,
Zhiyong Meng
,
Lanqiang Bai
, and
Ruilin Zhou

Abstract

This study documents the features of tornadoes, their parent storms and the environments of the only two documented tornado outbreak events in China. The two events were associated with tropical cyclone (TC) Yagi on 12 August 2018, with 11 tornadoes, and with an extratropical cyclone (EC) on 11 July 2021 (EC 711), with 13 tornadoes. Most tornadoes in TC Yagi were spawned from discrete mini-supercells, while a majority of tornadoes in EC 711 were produced from supercells imbedded in QLCSs or cloud clusters. In both events, the high-tornado-density area was better collocated with K index rather than MLCAPE, and with entraining rather than non-entraining parameters possibly due to their sensitivity to mid-level moisture. EC 711 had a larger displacement between maximum entraining CAPE and vertical wind shear than TC Yagi, with the maximum entraining CAPE better collocated with the high-tornado-density area than vertical wind shear. Relative to TC Yagi, EC 711 had stronger entraining CAPE, 0–1-km storm relative helicity, 0–6-km vertical wind shear, and composite parameters such as entraining significant tornado parameter, which caused its generally stronger tornado vortex signatures (TVSs) and mesocyclones with a larger diameter and longer lifespan. No significant differences were found in composite parameter of these two events from U.S. statistics. Although obvious dry air intrusions were observed in both events, no apparent impact was observed on the potential of tornado outbreak in EC 711. In TC Yagi, however, the dry air intrusion may have helped tornado outbreak due to cloudiness erosion and thus increase in surface temperature and low-level lapse rate.

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