Correction to: Correlation Structures between Satellite All-Sky Infrared Brightness Temperatures and the Atmospheric State at Storm Scales

The article “Correlation Structures between Satellite All-Sky Infrared Brightness Temperatures and the Atmospheric State at Storm Scales”, written by Yunji ZHANG, Eugene E. CLOTHIAUX, and David J. STENSRUD was originally published electronically on the publisher’s internet portal on 30 of April 2021 without open access. With the author(s)’ decision to opt for Open Choice, the copyright of the article changed on 26 of October 2021 to © The Author(s), 2021 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0.The original article has been corrected.

An ALMA study of hub-filament systems I. On the clump mass concentration within the most massive cores

The physical processes behind the transfer of mass from parsec-scale clumps to massive-star-forming cores remain elusive. We investigate the relation between the clump morphology and the mass fraction that ends up in its most massive core (MMC) as a function of infrared brightness, i.e. a clump evolutionary tracer. Using ALMA 12 m and ACA we surveyed 6 infrared-dark hubs in 2.9 mm continuum at ∼3″ resolution. To put our sample into context, we also re-analysed published ALMA data from a sample of 29 high mass-surface density ATLASGAL sources. We characterise the size, mass, morphology, and infrared brightness of the clumps using Herschel and Spitzer data. Within the 6 newly observed hubs, we identify 67 cores, and find that the MMCs have masses between 15–911 M⊙ within a radius of 0.018–0.156 pc. The MMC of each hub contains 3–24 per cent of the clump mass (fMMC), becoming 5–36 per cent once core masses are normalised to the median core radius. Across the 35 clumps, we find no significant difference in the median fMMC values of hub and non-hub systems, likely the consequence of a sample bias. However, we find that fMMC is ∼7.9 times larger for infrared-dark clumps compared to infrared-bright ones. This factor increases up to ∼14.5 when comparing our sample of 6 infrared-dark hubs to infrared-bright clumps. We speculate that hub-filament systems efficiently concentrate mass within their MMC early on during its evolution. As clumps evolve, they grow in mass, but such growth does not lead to the formation of more massive MMCs.

Spaceborne infrared imagery for early detection of Weddell Polynya opening

Knowing when sea ice will open is crucial, notably for scientific deployments. This was particularly obvious when the Weddell Polynya, a large opening in the winter Southern Ocean sea ice, unexpectedly re-appeared in 2016. As no precursor had been detected, observations were limited to chance autonomous sensors, and the exact cause of the opening could not be determined accurately. We investigate here whether the signature of the vertical ocean motions or that of the leads, which ultimately re-open the polynya, are detectable in spaceborne infrared temperature before the polynya opens. From the full historical sea ice concentration record, we find 30 polynyas starting from 1980. Then, using the full time series of the spaceborne infrared Advanced Very High Resolution Radiometer, we determine that these events can be detected in the 2 weeks before the polynya opens as a reduction in the variance of the data. For the three commonly used infrared brightness temperature bands, the 15 d sum and 15 d standard deviation of their area median and maximum are systematically lower than the climatology when a polynya will open. Moreover, by comparing the infrared brightness temperature to atmospheric reanalysis, hydrographic mooring data, and autonomous profilers, we find that temporal oscillations in one band and the decrease in the difference between bands may be used as proxies for upwelling of warm water and presence of leads, respectively, albeit with caution. Therefore, although infrared data are strongly limited by their horizontal resolution and sensitivity to clouds, they could be used for studying ocean or atmosphere preconditioning of polynyas in the historical record.

Thermal sea ice classification during the MOSAiC expedition

<p>The MOSAiC expedition took place in the Arctic from September 2019 to October 2020 while having measurements under, in, and above sea ice for a complete annual cycle. Airborne thermal infrared imaging was conducted during 41 helicopter survey flights along the MOSAiC drift track. We analyze the infrared brightness temperature of snow, sea ice, and ocean water surfaces from October 2019 until May 2020 from the airborne measurements. While the snow-covered sea ice appears very cold, thin ice and open water are significantly warmer. These surface types will be considered with particular attention because they dominate the heat exchange between the ocean, ice, and atmosphere during wintertime. This influences the Arctic Climate and becomes even more important in the currently changing Arctic, where the sea ice gets thinner, moves faster, and breaks up easier. After georeferencing and merging the recorded images to a mosaic, we can provide maps of infrared brightness temperatures in a high spatial resolution of 1 m for each flight. The spatial range of the maps varies from local (~5 km) up to regional (~30 km). This data set provides a basis to study the spatial and temporal variability of sea ice characteristics in the Arctic winter. We derive the physical surface temperature from the brightness temperature, surface emissivity, and downwelling radiation from the sky or clouds. Using the surface temperature, we calculate the heat flux from a local up to a regional scale based on thermodynamic assumptions and atmospheric measurements on the ice floe. From more complex thermodynamic simulations, we estimate ice thickness and ice age based on the airborne measured surface temperatures. The model calculates for each surface temperature a specific ice thickness and heat flux based on the knowledge about the surface’s thermodynamic history. The simulated ice thickness allows a sea ice classification which is compared to our first classification approach which deals with the flight's temperature distribution only. In the future, we will investigate the sub-footprint scale variability of ice surface temperature and thin ice thickness for satellite data, e.g. MODIS and Sentinel-3.</p>

First steps to mapping of exoplanets: modeling secondary eclipses and search inhomogeneities in the infrared brightness profiles

To detect inhomogeneities in the thermal radiation of exoplanet HD209458b, based on observations of the Spitzer space telescope processing and modeling of the light curves of secondary transits (eclipses) were carried out. Current work is the basis for the mapping of the temperature distribution on the dayside of the exoplanet. Tidal and electromagnetic star-planet interactions, together with climate effects, lead to nonuniform heating of the surface. We obtained that the depth of the eclipse was 0.101±0.009% of the total flux of the system and is consistent with the results of other works.

Assessing the Impact of Stochastic Perturbations in Cloud Microphysics using GOES-16 Infrared Brightness Temperatures

In this study, infrared brightness temperatures (BTs) are used to examine how applying stochastic perturbed parameter (SPP) methodology to the widely used Thompson–Eidhammer cloud microphysics scheme impacts the cloud field in high-resolution forecasts. Modifications are made to add stochastic perturbations to three parameters controlling cloud generation and dissipation processes. Two five-member ensembles are generated, one using the microphysics parameter perturbations (SPP-MP) and another where white noise perturbations were added to potential temperature fields at initialization time (Control). The impact of the SPP method was assessed using simulated and observed GOES-16 BTs. This analysis uses pixel-based and object-based methods to assess the impact on the cloud field. Pixel-based methods revealed that the SPP-MP BTs are slightly more accurate than the Control BTs. However, too few pixels with a BT lower than 270 K result in a positive bias compared to the observations. A negative bias compared to the observations is observed when only analyzing lower BTs. The spread of the ensemble BTs was analyzed using the continuous ranked probability score differences, with the SPP-MP ensemble BTs having less (more) spread during May (January) compared to the Control. Object-based analysis using the Method for Object-Based Diagnostic Evaluation revealed the upper-level cloud objects are smaller in the SPP-MP ensemble than the Control but a lower bias exists in the SPP-MP BTs compared to the Control BTs when overlapping matching objects. However, there is no clear distinction between the SPP-MP and Control ensemble members during the evolution of objects, Overall, the SPP-MP perturbations result in lower BTs compared to the Control ensemble and more cloudy pixels.