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Thinning sea ice cover and earlier melt in the Arctic impact primary producer (PP) phenology, causing earlier ice algal bloom termination and phytoplankton bloom commencement. However, logistic constraints limit capturing the complete seasonal evolution of PPs and their physical drivers. Here, we combine spectral irradiance data from subsurface oceanographic moorings with synthetic aperture radar backscatter and meteorological variables to study light in Dease Strait, investigating its relation to timing and magnitude of surface PPs for 2017 and 2019. Ice algal blooms in 2017 and 2019 lasted 66 and 84 days, respectively, peaking within 2 days of snow melt onset. In 2019, lower temperatures and a deeper snowpack before snow melt extended the ice algal bloom. Melt pond formation increased light transmission, enabling a short, 6–7-day under-ice phytoplankton bloom in both years that was likely nutrient-limited. The 2019 phytoplankton bloom was less productive, possibly due to the longer ice algal bloom depleting surface nutrients. After ice break-up in 2019, a 31-day late-summer bloom occurred via wind-driven mixing. Our findings suggest that the combined remote sensing technique has novel applicability in other settings, providing insights into the changing state of PP phenology, and the need for long-term Arctic observations to discern regional climate change effects.

This paper presents a practical step-by-step approach to Frequency Modulated Continuous Wave (FMCW) radar nonlinearity correction (deconvolution), utilizing surface-based Ku- and Ka-band radar data collected over nilas ice within a newly-opened sea ice lead during the 2019/2020 MOSAiC expedition. Two performance metrics are introduced to evaluate deconvolution effectiveness: the spurious free dynamic range (SFDR), which quantifies sidelobe suppression, and the leading edge width (LEW), which quantifies the improvement in surface return clarity. The impact of deconvolution waveforms on different survey dates, radar polarizations, and surface types is examined using echograms and quantitative metrics. Deconvolution results in a maximum SFDR increase of 28 dB, with a maximum 3 dB decline in deconvolution performance observed over an 8-day period and a maximum decline of 15 dB observed over a 71-day period. The LEW values indicate that the effectiveness of deconvolution in enhancing interface clarity depends on the combination of pre-deconvolution sidelobe shape, prominence of the surface return, the influence of snowpack returns, as well as a time-dependent reduction in deconvolution performance. Deconvolution significantly improves surface return clarity for cross-polarized radar data, where weak surface returns are obscured by returns from within the snowpack. The results demonstrate that deconvolution performance is most effective shortly after deconvolution waveform characterization. Therefore, it is recommended to perform at least weekly calibrations using a large metal sheet and ideally calibration before/after data collection to ensure optimal deconvolution performance and effective sidelobe suppression.

The presence of melt ponds on the surface of Arctic Sea ice affects its albedo, thermal properties, and overall melting rate; thus, the detection of melt pond onset is of significant importance for understanding the Arctic’s changing climate. This study investigates the utility of a novel method for detecting the onset of melt ponds on sea ice using a satellite-based, dual-sensor C-band approach, whereby Sentinel-1 provides horizontally polarized (HH) data and Advanced SCATterometer (ASCAT) provides vertically polarized (VV) data. The co-polarized ratio (VV/HH) is used to detect the presence of melt ponds on landfast sea ice in the Canadian Arctic Archipelago in 2017 and 2018. ERA-5 air temperature and wind speed re-analysis datasets are used to establish the VV/HH threshold for pond onset detection, which have been further validated by Landsat-8 reflectance. The co-polarized ratio threshold of three standard deviations from the late winter season (April) mean co-pol ratio values are used for assessing pond onset detection associated with the air temperature and wind speed data, along with visual observations from Sentinel-1 and cloud-free Sentinel-2 imagery. In 2017, the pond onset detection rates were 70.59% for FYI and 92.3% for MYI. Results suggest that this method, because of its dual-platform application, has potential for providing large-area coverage estimation of the timing of sea ice melt pond onset using different earth observation satellites.

Abstract Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in “stare” nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r2 values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r2 0.15 or lower. r2 for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters.

The sub-kilometre scale distribution of snow depth on Arctic sea ice impacts atmosphere-ice fluxes of energy and mass, and is of importance for satellite estimates of sea-ice thickness from both radar and lidar altimeters. While information about the mean of this distribution is increasingly available from modelling and remote sensing, the full distribution cannot yet be resolved. We analyse 33 539 snow depth measurements from 499 transects taken at Soviet drifting stations between 1955 and 1991 and derive a simple statistical distribution for snow depth over multi-year ice as a function of only the mean snow depth. We then evaluate this snow depth distribution against snow depth transects that span first-year ice to multiyear ice from the MOSAiC, SHEBA and AMSR-Ice field campaigns. Because the distribution can be generated using only the mean snow depth, it can be used in the downscaling of several existing snow depth products for use in flux modelling and altimetry studies.

Sea ice concentration algorithms using brightness temperatures ($T_{B}$) from satellite microwave radiometers are used to compute sea ice concentration ($c_{\text{ice}}$), sea ice extent, and generate sea ice climate data records. Therefore, it is important to minimize the sensitivity of $c_{\text{ice}}$ estimates to geophysical noise caused by snow/sea ice thermal microwave emission signature variations, and presence of WV and clouds in the atmosphere and/or near-surface winds. In this study, we investigate the effect of geophysical noise leading to systematic $c_{\text{ice}}$ biases and affecting $c_{\text{ice}}$ standard deviations (STD) using simulated top of the atmosphere $T_{B}$s over open water and 100% sea ice. We consider three case studies for the Arctic and the Antarctic and eight different $c_{\text{ice}}$ algorithms, representing different families of algorithms based on the selection of channels and methodologies. Our simulations show that, over open water and low $c_{\text{ice}}$, algorithms using gradients between V-polarized 19-GHz and 37-GHz $T_{B}$s show the lowest sensitivity to the geophysical noise, while the algorithms exclusively using near-90-GHz channels have by far the highest sensitivity. Over sea ice, the atmosphere plays a much smaller role than over open water, and the $c_{\text{ice}}$ STD for all algorithms is smaller than over open water. The hybrid and low-frequency (6 GHz) algorithms have the lowest sensitivity to noise over sea ice, while the polarization type of algorithms has the highest noise levels.

Arctic sea ice is declining in areal extent and seasonal duration, affecting stable landfast sea ice regimes. These landfast ice regimes are vital platforms for local indigenous subsistence hunters and epontic primary production species supporting local and regional food webs. Under changing thermodynamic and mechanical stresses, landfast ice can become more prone to break out events - unexpected mid-season detachments from the coastline. However, the prevalence of break out events in the landfast ice annual cycle on an interannual and decadal basis is generally understudied. Here, we detect and quantify landfast ice break-out events at St. Lawrence Island in the Northern Bering Sea. We accomplished this using C-band Synthetic Aperture Radar (SAR) imagery to detect landfast ice cover. We also advanced a geospatial analysis method to identify break-out events and distinguish such occurrences from end-of-season breakups. Results reveal that 35 break-outs occurred across four sections of the St. Lawrence Island coastline from 1996 − 2019, with 74 % of break-outs occurring on the northern coastlines. Break-outs generally occurred during annual cycles with higher than average landfast ice cover. During the break-out events, maximum temperatures seldom exceed conditions promoting melt, whereas wind speeds are, on average, 2 m/s faster compared to end-of-season breakup events. However, additional datasets are needed to understand better the influence of tidal amplitude and ocean currents on detected break-out occurrence and location. Our study provides the basis for future research to understand break-out events in landfast ice annual cycles at fine spatial scales unavailable in existing sea ice datasets.

Uncertainty in snow properties impacts the accuracy of Arctic sea ice thickness estimates from radar altimetry. On first-year sea ice (FYI), spatiotemporal variations in snow properties can cause the Ku-band main radar scattering horizon to appear above the snow/sea ice interface. This can increase the estimated sea ice freeboard by several centimeters, leading to FYI thickness overestimations. This article examines the expected changes in Ku-band main scattering horizon and its impact on FYI thickness estimates, with variations in snow temperature, salinity, and density derived from ten naturally occurring Arctic FYI Cases encompassing saline/nonsaline, warm/cold, simple/complexly layered snow (4-45 cm) overlying FYI (48-170 cm). Using a semi-empirical modeling approach, snow properties from these Cases are used to derive layer-wise brine volume and dielectric constant estimates, to simulate the Ku-band main scattering horizon and delays in radar propagation speed. Differences between modeled and observed FYI thickness are calculated to assess sources of error. Under both cold and warm conditions, saline snow covers are shown to shift the main scattering horizon above from the snow/sea ice interface, causing thickness retrieval errors. Overestimates in FYI thicknesses of up to 65% are found for warm, saline snow overlaying thin sea ice. Our simulations exhibited a distinct shift in the main scattering horizon when the snow layer densities became greater than 440 kg/m3, especially under warmer snow conditions. Our simulations suggest a mean Ku-band propagation delay for snow of 39%, which is higher than 25%, suggested in previous studies.

The FSSCat mission was the 2017 ESA Sentinel Small Satellite (S⌃3) Challenge winner and the Copernicus Masters competition overall winner. It was successfully launched on 3 September 2020 onboard the VEGA SSMS PoC (VV16). FSSCat aims to provide coarse and downscaled soil moisture data and over polar regions, sea ice cover, and coarse resolution ice thickness using a combined L-band microwave radiometer and GNSS-Reflectometry payload. As part of the calibration and validation activities of FSSCat, a GNSS-R instrument was deployed as part of the MOSAiC polar expedition. The Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition was an international one-year-long field experiment led by the Alfred Wegener Institute to study the climate system and the impact of climate change in the Arctic Ocean. This paper presents the first results of the PYCARO-2 instrument, focused on the GNSS-R techniques used to measure snow and ice thickness of an ice floe. The Interference Pattern produced by the combination of the GNSS direct and reflected signals over the sea-ice has been modeled using a four-layer model. The different thicknesses of the substrate layers (i.e., snow and ice) are linked to the position of the fringes of the interference pattern. Data collected by MOSAiC GNSS-R instrument between December 2019 and January 2020 for different GNSS constellations and frequencies are presented and analyzed, showing that under general conditions, sea ice and snow thickness can be retrieved using multiangular and multifrequency data.

Ku- and C-band spaceborne scatterometer sigma nought (σ°) backscatter data of snow covered landfast first-year sea ice from the Canadian Arctic Archipelago are acquired during the winter season with coincident in situ snow-thickness observations. Our objective is to describe a methodological framework for estimating relative snow thickness on first-year sea ice based on the variance in σ° from daily time series ASCAT and QuikSCAT scatterometer measurements during the late winter season prior to melt onset. We first describe our theoretical basis for this approach, including assumptions and conditions under which the method is ideally suited and then present observational evidence from four independent case studies to support our hypothesis. Results suggest that the approach can provide a relative measure of snow thickness prior to σ° detected melt onset at both Ku- and C-band frequencies. We observe that, during the late winter season, a thinner snow cover displays a larger variance in daily σ° compared to a thicker snow cover on first-year sea ice. This is because for a given increase in air temperature, a thinner snow cover manifests a larger increase in basal snow layer brine volume owing to its higher thermal conductivity, a larger increase in the dielectric constant and a larger increase in σ° at both Ku- and C bands. The approach does not apply when snow thickness distributions on first-year sea ice being compared are statistically similar, indicating that similar late winter σ° variances likely indicate regions of similar snow thickness.

Snow thickness measurements over relatively smooth Arctic first-year sea ice, obtained near Cambridge Bay in the Canadian Arctic (2014, 2016 and 2017) and near Elson Lagoon in the Alaskan Arctic (2003 and 2006), are analyzed to quantify physical length-scales and their relevant scaling behaviors. We use the multi-fractal temporally weighted detrended fluctuation analysis method to detect two major physical length-scales from the two independent study locations. Our results suggest that physical processes underlying the formation of snow dunes are consistent and that the wind is the main process shaping the snow thickness variability and redistribution. One scale, around 10 m, appears to be related to the formation of the snow ‘dunes’, while the other scale, between 30 and 100 m, is likely associated with the various interactions of the snow dunes such as merging, calving and lateral linking. Results imply that snow on level sea ice shows self-organized characteristics.

In this study, we inter-compared observed Ku-, X- and C-band microwave backscatter from saline 14 cm, 8 cm, and 4 cm snow covers on smooth first-year sea ice. A Ku-, X- and C-band surface-borne polarimetric microwave scatterometer system was used to measure fully-polarimetric backscatter from the three snow covers, near-coincident with corresponding in situ snow thermophysical measurements. The study investigated differences in co-polarized backscatter observations from the scatterometer system for all three frequencies, modeled penetration depths, utilized co-pol ratios, and introduced dual-frequency ratios to discriminate dominant polarization-dependent frequencies from these snow covers. Results demonstrate that the measured co-polarized backscatter magnitude increased with decreasing snow thickness for all three frequencies, owing to stronger gradients in snow salinity within thinner snow covers. The innovative dual-frequency ratios suggest greater sensitivity of Ku-band microwaves to snow grain size as snow thickness increases and X-band microwaves to snow salinity changes as snow thickness decreases. C-band demonstrated minimal sensitivity to changes in snow salinities. Our results demonstrate the influence of salinity associated dielectric loss, throughout all layers of the three snow covers, as the governing factor affecting microwave backscatter and penetration from all three frequencies. Our “plot-scale” observations using co-polarized backscatter, co-pol ratios and dual-frequency ratios suggest the future potential to up-scale our multi-frequency approach to a “satellite-scale” approach, towards effective development of snow geophysical and thermodynamic retrieval algorithms on smooth first-year sea ice.

Background: Pertussis is a highly contagious, vaccine-preventable acute respiratory infection caused by Bordetella pertussis (B. Pertussis) and affects all age groups. Since 1947, the Global Expanded Program on Immunization (EPI) was executed, and its incidence has been effectively curtailed. The reasons for the resurgence are multifactorial including the waning of vaccine acquired immunity, the difference in vaccination strategies, the adaptation of B. Pertussis strains, and an increase in disease awareness due to the strengthening of diagnostic sensitivity and surveillance sensitivity. Notably, Hewlett and Edwards proposed that the shift in pertussis transmission patterns plays an important role in pertussis resurgence. Diphtheria is an acute infectious disease of upper respiratory tract caused by toxigenic strains of Corynebacterium Diphtheriae (CD) and Corynebacterium other than diphtheriae (COD). The organism is locally invasive and causes exotoxin - mediated illness and can lead to complications like stridor, respiratory obstruction, myocarditis, nerve palsy, renal insufficiency and death in severe cases. The objective of the present study is to isolate and identify the etiological agents in clinically suspected cases of Diphtheria and Pertussis. Materials & Methods: This Prospective study has been conducted in the department of Microbiology from Feb 2022 to Aug 2024. Samples with high clinical suspicion were received and processed. Two Throat swabs were collected in patients with clinical suspicion of Diphtheria. They were inoculated on Blood agar, Potassium Tellurite agar, Tinsdale agar. Gram staining and Albert staining were done. Molecular testing also performed for C. diphtheriae. Two nasopharyngeal swabs were collected in patients with clinical suspicion of Pertussis. They were inoculated on Bordet Gengou medium, Charcoal Agar with Cephalexin and Blood Agar and Mac Conkey agar. Gram Staining was performed and Biochemical testing was carried out. Molecular testing was carried out for B. Pertussis and B. parapertussis. Results: Out of 53 samples with clinical suspicion of Diphtheriae, 6 were positive for Corynebacterium diphtheria by culture. Molecular testing was also done for testing of rpo B gene and Tox A gene. 6 samples came to be positive for C. diphtheria rpo B gene where as one was positive for tox gene production. Out of 20 cases with strong suspicion of Pertussis, culture was positive for Bordetella pertussis in 2 samples. Molecular testing came out to be positive for one sample. Testing for the antibodies by ELISA two patients were serologically positive for Pertussis. Both Culture and molecular testing was positive in one sample. Both culture and serology was positive in one sample. Only serology was positive in one sample. Conclusion: A constant surveillance is needed for vaccine preventable diseases to predict the outbreak or resurgence of cases. [ABSTRACT FROM AUTHOR]

Salt is often present in the snow overlying seasonal sea ice, and has profound thermodynamic and electromagnetic effects. However, its provenance and behaviour within the snow remain uncertain. We describe two investigations tracing upward brine movement in snow: one conducted in the laboratory and one in the field. The laboratory experiments involved the addition of dyed brine to the base of terrestrial snow samples, with subsequent wicking being measured. Our field experiment involved dye being added directly (without brine) to bare sea-ice and lake ice surfaces, with snow then accumulating on top over several days. On the sea ice, the dye migrated upwards into the snow by up to 5 cm as the snow's basal layer became more salty, whereas no migration occurred in our control experiment over non-saline lake ice. This occurred in relatively dry snowpacks where brine took up $< 6\%$ of the snow's calculated pore volume, suggesting pore saturation is not required for upward salt transport. Our results highlight the potential role of microstructural parameters beyond those currently retrievable with penetrometry, and the potential value of longitudinal, process-based field studies of young snowpacks.

Background: Injections are preferred by patients because they believe they provide rapid relief and more effective than oral medications. Worldwide statistics suggested that unsafe injections account for 32% of new Hepatitis B, 40% new Hepatitis C and 5% of new HIV infections. The prevention of disease transmission through injection requires good knowledge and practice regarding safe injection practices. The present study was undertaken to determine awareness on safe injection practices among healthcare providers, to find out the current injection practices among healthcare providers and to compare the knowledge and practice regarding injection safety among various classes of healthcare providers in Chandragiri Mandal, Chittoor district. Methodology: A hospital based cross sectional study was conducted for 24 months i.e. November 2019 to October 2021 in Chandragiri mandal of Chittoor district among 203 healthcare providers in one area hospital, one community health centre, one primary health centre, one rural health training centre and 5 private clinics. Data collected was entered in MS Excel and analyzed using SPSS Version 16. Results: Among the 203 participants, mean age was found to be 33.65± 8.39 years with a range of 21-73. Majority of the subjects were females (83.7%). Differences were noted among knowledge and practice in preparing and administering injections. Conclusions: The present study shows that though the knowledge about safe injection practices is good among healthcare providers, the practice is poor. Unsafe practices like not washing hands, not wearing gloves and not following the correct steps in giving injections were observed in many. [ABSTRACT FROM AUTHOR]