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Introduction: Experiences of violence are important risk factors for worse outcome in people with mental health conditions; however, they are not routinely collected be mental health services, so their ascertainment depends on extraction from text fields with natural language processing (NLP) algorithms. Methods: Applying previously developed neural network algorithms to routine mental healthcare records, we sought to describe the distribution of recorded violence victimisation by demographic and diagnostic characteristics. We ascertained recorded violence victimisation from the records of 60,021 patients receiving care from a large south London NHS mental healthcare provider during 2019. Descriptive and regression analyses were conducted to investigate variation by age, sex, ethnic group, and diagnostic category (ICD-10 F chapter sub-headings plus post-traumatic stress disorder (PTSD) as a specific condition). Results: Patients with a mood disorder (adjusted odds ratio 1.63, 1.55-1.72), personality disorder (4.03, 3.65-4.45), schizophrenia spectrum disorder (1.84, 1.74-1.95) or PTSD (2.36, 2.08-2.69) had a significantly increased likelihood of victimisation compared to those with other mental health diagnoses. Additionally, patients from minority ethnic groups (1.10 (1.02-1.20) for Black, 1.40 (1.31-1.49) for Asian compared to White groups) had significantly higher likelihood of recorded violence victimisation. Males were significantly less likely to have reported recorded violence victimisation (0.44, 0.42-0.45) than females. Discussion: We thus demonstrate the successful deployment of machine learning based NLP algorithms to ascertain important entities for outcome prediction in mental healthcare. The observed distributions highlight which sex, ethnicity and diagnostic groups had more records of violence victimisation. Further development of these algorithms could usefully capture broader experiences, such as differentiating more efficiently between witnessed, perpetrated and experienced violence and broader violence experiences like emotional abuse. [ABSTRACT FROM AUTHOR]

Abstract Background Clozapine is the only recommended antipsychotic medication for individuals diagnosed with treatment-resistant schizophrenia. Unfortunately, its wider use is hindered by several possible adverse effects, some of which are rare but potentially life threatening. As such, there is a growing interest in studying clozapine use and safety in routinely collected healthcare data. However, previous attempts to characterise clozapine treatment have had low accuracy. Aim To develop a methodology for identifying clozapine treatment dates by combining several data sources and implement this on a large clinical database. Methods Non-identifiable electronic health records from a large mental health provider in London and a linked database from a national clozapine blood monitoring service were used to obtain information regarding patients' clozapine treatment status, blood tests and pharmacy dispensing records. A rule-based algorithm was developed to determine the dates of starting and stopping treatment based on these data, and more than 10% of the outcomes were validated by manual review of de-identified case note text. Results A total of 3,212 possible clozapine treatment periods were identified, of which 425 (13.2%) were excluded due to insufficient data to verify clozapine administration. Of the 2,787 treatments remaining, 1,902 (68.2%) had an identified start-date. On evaluation, the algorithm identified treatments with 96.4% accuracy; start dates were 96.2% accurate within 15 days, and end dates were 85.1% accurate within 30 days. Conclusions The algorithm produced a reliable database of clozapine treatment periods. Beyond underpinning future observational clozapine studies, we envisage it will facilitate similar implementations on additional large clinical databases worldwide.

Background: Clozapine is the only recommended antipsychotic medication for individuals diagnosed with treatment-resistant schizophrenia. Unfortunately, its wider use is hindered by several possible adverse effects, some of which are rare but potentially life threatening. As such, there is a growing interest in studying clozapine use and safety in routinely collected healthcare data. However, previous attempts to characterise clozapine treatment have had low accuracy. Aim: To develop a methodology for identifying clozapine treatment dates by combining several data sources and implement this on a large clinical database. Methods: Non-identifiable electronic health records from a large mental health provider in London and a linked database from a national clozapine blood monitoring service were used to obtain information regarding patients' clozapine treatment status, blood tests and pharmacy dispensing records. A rule-based algorithm was developed to determine the dates of starting and stopping treatment based on these data, and more than 10% of the outcomes were validated by manual review of de-identified case note text. Results: A total of 3,212 possible clozapine treatment periods were identified, of which 425 (13.2%) were excluded due to insufficient data to verify clozapine administration. Of the 2,787 treatments remaining, 1,902 (68.2%) had an identified start-date. On evaluation, the algorithm identified treatments with 96.4% accuracy; start dates were 96.2% accurate within 15 days, and end dates were 85.1% accurate within 30 days. Conclusions: The algorithm produced a reliable database of clozapine treatment periods. Beyond underpinning future observational clozapine studies, we envisage it will facilitate similar implementations on additional large clinical databases worldwide. [ABSTRACT FROM AUTHOR]

The Cross‐track Infrared Sounder (CrIS) observations (O) contributed greatly to numerical weather prediction. Further contribution depends on the success of all‐sky data assimilation, which requires a method to produce realistic cloud/rain band structures from background fields (i.e., 6‐hr forecasts), and to remove large biases of all‐sky simulation of brightness temperature (TB) in the presence of clouds. In this study, CrIS all‐sky simulations of brightness temperatures at an arbitrarily selected window channel within Typhoon Hinnamnor (2022) are investigated. The 3‐km Weather Research and Forecasting model with three microphysics schemes were used to produce 6‐hr background forecasts (B). The O − B statistic deviate greatly from Gaussian distribution with large biases in either water clouds, or thin ice clouds, or thick ice clouds within Typhoon Hinnamnor. By developing a linear regression function of three all‐sky simulations of TB from 6‐hr forecasts with three microphysics schemes, the O − B statistics approximate a Gaussian normal distribution in water clouds, thin ice clouds and thick ice clouds. Taking the regression function that is established by a training data set to combine 6‐hr background forecasts at later times, the cloud/rain band structures compared much more favorably with CrIS observations than those from an individual microphysic. Furthermore, the regression coefficients derived from Typhoon Hinnamnor (2022) also work for Typhoon Khanun (2023). The work aims to quantify and remove biases in background fields of TB and generating realistic typhoon cloud/rain band structures in background fields will allow a better description of center position, intensity and size to improve typhoon forecasts. Plain Language Summary: This study focuses on enhancing weather forecasts using the Cross‐track Infrared Sounder (CrIS) by improving cloud and rainband structure predictions, especially in typhoons. We examine Typhoon Hinnamnor from 2022 and use a 3‐km resolution Weather Research and Forecasting model, which employs three different microphysics schemes to produce 6‐hr forecasts. However, these forecasts often differ significantly from CrIS observations, especially in optical thick clouds. To address this discrepancy, we develop a linear regression method to rebuild the forecasts, making them more accurate in simulating the cloud patterns observed by CrIS. This method effectively reduces errors and aligns our model's predictions more closely with CrIS real observations. This work is crucial in refining cloud structure predictions within typhoons, leading to a better understanding of a typhoon's center, strength, and size, thus contributing to more reliable typhoon forecasts. Key Points: This study aims to quantify and remove biases in background fields of brightness temperatures of NOAA‐20 Cross‐track Infrared Sounder (CrIS) window channelsA multiple linear regression is used to combine the all‐sky brightness temperature (TB) simulations of CrIS window channel from three microphysics schemesThis work also aims to generate realistic typhoon cloud/rain band structures in background fields of TB of NOAA‐20 CrIS [ABSTRACT FROM AUTHOR]

IntroductionExperiences of violence are important risk factors for worse outcome in people with mental health conditions; however, they are not routinely collected be mental health services, so their ascertainment depends on extraction from text fields with natural language processing (NLP) algorithms.MethodsApplying previously developed neural network algorithms to routine mental healthcare records, we sought to describe the distribution of recorded violence victimisation by demographic and diagnostic characteristics. We ascertained recorded violence victimisation from the records of 60,021 patients receiving care from a large south London NHS mental healthcare provider during 2019. Descriptive and regression analyses were conducted to investigate variation by age, sex, ethnic group, and diagnostic category (ICD-10 F chapter sub-headings plus post-traumatic stress disorder (PTSD) as a specific condition).ResultsPatients with a mood disorder (adjusted odds ratio 1.63, 1.55-1.72), personality disorder (4.03, 3.65-4.45), schizophrenia spectrum disorder (1.84, 1.74-1.95) or PTSD (2.36, 2.08-2.69) had a significantly increased likelihood of victimisation compared to those with other mental health diagnoses. Additionally, patients from minority ethnic groups (1.10 (1.02-1.20) for Black, 1.40 (1.31-1.49) for Asian compared to White groups) had significantly higher likelihood of recorded violence victimisation. Males were significantly less likely to have reported recorded violence victimisation (0.44, 0.42-0.45) than females.DiscussionWe thus demonstrate the successful deployment of machine learning based NLP algorithms to ascertain important entities for outcome prediction in mental healthcare. The observed distributions highlight which sex, ethnicity and diagnostic groups had more records of violence victimisation. Further development of these algorithms could usefully capture broader experiences, such as differentiating more efficiently between witnessed, perpetrated and experienced violence and broader violence experiences like emotional abuse.

The COVID‐19 pandemic perturbed air pollutant emissions as cities shut down worldwide. Peroxyacyl nitrates (PANs) are important tracers of photochemistry that are formed through the oxidation of non‐methane volatile organic compounds in the presence of nitrogen oxide radicals (NOx = NO + NO2). We use satellite measurements of free tropospheric PANs from the Suomi‐National Polar‐orbiting Partnership Cross‐track Infrared Sounder (CrIS) over eight of the world's megacities. We quantify the seasonal cycle of PANs over these megacities and find seasonal maxima in PANs correspond to seasonal peaks in local photochemistry. CrIS is used to explore changes in PANs in response to the COVID‐19 lockdowns. Statistically significant changes to PANs occurred over four megacities: with decreases over Los Angeles and Delhi, and increases over Mexico City and Beijing in the winter. Our analysis suggests that large perturbations in NOx may not result in significant declines in NOx export potential of megacities. Plain Language Summary: The COVID‐19 pandemic led to the lockdown of urban centers worldwide, drastically perturbing the concentrations of global air pollutants. Peroxyacyl nitrates (PANs) are important photochemical pollutants formed from reactions between NOx and volatile organic compounds, which were substantially reduced during the pandemic. We use satellite measurements of PANs from the Suomi‐National Polar‐orbiting Partnership Cross‐track Infrared Sounder in the free troposphere over and surrounding eight of the world's megacities. Seasonal cycles of PANs are pronounced and the seasonal maxima correspond to seasonal peaks in local photochemistry. Significant changes to PANs in response to COVID‐19 occurred over four out of the eight cities: PANs decreased over Los Angeles and Delhi, and PANs increased over Mexico City and Beijing in the winter. Our results indicate that large changes in NOx may not result in equally significant changes to PANs and the NOx export potential of megacities. Key Points: There are pronounced seasonal cycles of peroxyacyl nitrates (PANs) over each megacity that align with seasonal maximums in photochemistryObserved free tropospheric mixing ratios of PANs during COVID‐19 were significantly different over four out of eight surveyed megacitiesSensitivity of free tropospheric PANs to the abundance of precursors is seasonally dependent in some locations [ABSTRACT FROM AUTHOR]

Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH4) spectral features, including the ν1 vibrational band near 1300 cm−1 (7.7 μm); therefore, they can be used to monitor CH4 concentrations in the atmosphere. However, retrieving CH4 remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH4 is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH4 data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH4 profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH4 signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements. An inter-comparison study revealed that the spectral fingerprinting results can capture the vertical variation characteristics of CH4 profiles that operational sounder products may not provide. The latitudinal variations in CH4 concentration in these results appear more realistic than those shown in existing sounder products. The methodology presented herein could enhance the utilization of satellite data to comprehend methane's role as a greenhouse gas and facilitate the tracking of methane sources and sinks with increased reliability. [ABSTRACT FROM AUTHOR]

Global temperatures are continuing to rise as atmospheric carbon dioxide (CO2) concentrations increase, and climate warming has become a major challenge to global sustainable development. The Cross-Track Infrared Sounder (CrIS) instrument is a Fourier transform spectrometer with 0.625 cm−1 spectral resolution covering a 15 μm CO2-absorbing band, providing a way of monitoring CO2 with on a large scale twice a day. This paper proposes a method to predict the concentration of column-averaged CO2 (XCO2) from thermal infrared satellite data using ensemble learning to avoid the iterative computations of radiative transfer models, which are necessary for optimization estimation (OE). The training data set is constructed with CrIS satellite data, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) meteorological parameters, and ground-based observations. The training set was processed using two methods: correlation significance analysis (abbreviated as CSA) and principal component analysis (PCA). Extreme Gradient Boosters (XGBoost), Extreme Random Trees (ERT), and Gradient Boost Regression Tree (GBRT) are used for training and learning to develop the new retrieval model. The results showed that the R2 of XCO2 prediction built from the PCA dataset was bigger than that from the CSA dataset. These three learning models were verified by validation sets, and the ERT model showed the best agreement between model predictions and the truth (R2 = 0.9006, RMSE = 0.7994 ppmv, MAE = 0.5804 ppmv). The ERT model was finally selected to estimate the concentrations of XCO2. The deviation of XCO2 predictions of 12 TCCON sites in 2019 was within ±1 ppm. The monthly averages of XCO2 concentrations in close agreement with TCCON ground observations were grouped into four regions: Asia (R2 = 0.9671, RMSE = 0.7072 ppmv), Europe (R2 = 0.9703, RMSE = 0.8733 ppmv), North America (R2 = 0.9800, RMSE = 0.6187 ppmv), and Oceania (R2 = 0.9558, RMSE = 0.4614 ppmv). [ABSTRACT FROM AUTHOR]

The results are presented of the analysis of 214 foreign current research information systems (–CRISs) as basic resources reflecting the state of research of specific scientific institutions. A quantitative assessment of the research data registered in the system is given. Emphasis is placed on open access data collected in information systems. It is found that the number of scientific research results in open access is increasing every year. Today, the share of publicly available scientific products in the considered CRIS systems averages about 25%. [ABSTRACT FROM AUTHOR]

Abstract The COVID‐19 pandemic perturbed air pollutant emissions as cities shut down worldwide. Peroxyacyl nitrates (PANs) are important tracers of photochemistry that are formed through the oxidation of non‐methane volatile organic compounds in the presence of nitrogen oxide radicals (NOx = NO + NO2). We use satellite measurements of free tropospheric PANs from the Suomi‐National Polar‐orbiting Partnership Cross‐track Infrared Sounder (CrIS) over eight of the world's megacities. We quantify the seasonal cycle of PANs over these megacities and find seasonal maxima in PANs correspond to seasonal peaks in local photochemistry. CrIS is used to explore changes in PANs in response to the COVID‐19 lockdowns. Statistically significant changes to PANs occurred over four megacities: with decreases over Los Angeles and Delhi, and increases over Mexico City and Beijing in the winter. Our analysis suggests that large perturbations in NOx may not result in significant declines in NOx export potential of megacities.

Uncertainty in ammonia (NH3) emissions causes the inaccuracy of fine particulate matter simulations, which is associated with human health. To address this uncertainty, in this work, we employ the iterative finite difference mass balance (iFDMB) technique to revise NH3 emissions over East Asia using the Cross-track Infrared Sounder (CRIS) satellite for July, August, and September 2019. Compared to the emissions, the revised NH3 emissions show an increase in China, particularly in the North China Plain (NCP) region, corresponding to agricultural land use in July, August, and September and a decrease in South Korea in September. The enhancement in NH3 emissions resulted in a remarkable increase in concentrations of NH3 by 5 ppb. in July and September, there is an increase in ammonium (NH4+) and nitrate (NO3-) concentrations by 5 μg/m3, particularly in the NCP region, while in August, both NH4+ and NO3- concentrations exhibit a decrease. For sulfate (SO42-), in August and September, the concentrations decreased over most regions of China and Taiwan, as a result of the production of ammonium sulfate; increased concentrations of SO42-, however, were simulated over South Korea, Japan, and the southern region of Chengdu, caused by higher relative humidity (RH). In contrast, during the month of July, our simulations showed an increase in SO42- concentrations over most regions of China. To gain a more comprehensive understanding, we defined a sulfur conversion ratio (SCR=(Spost-Sprior)/(SO42-post-SO42-prior)), which explains how changes in sulfur in the gas phase affect changes in sulfate concentrations. A subsequent sensitivity analysis performed in this study indicated the same relationship between changes in ammonia and its effect on inorganic fine particulate matter (PM2.5). This study highlights the challenge of controlling and managing inorganic PM2.5 and indicates that reducing the emissions of air pollutants do not necessarily lead to a reduction in their concentrations.

Helen Keller, a child who was deaf and blind from the second year of life, describes, in her autobiography, her childhood and the character of her closest family members. Anne Sullivan, the teacher who will be able to connect her with the world, is an essential example for anyone who wants to measure themselves with the relational dynamics of a painfully dysfunctional family. [ABSTRACT FROM AUTHOR]

To optimize the performance of the Collinear Resonance Ionization Spectroscopy (CRIS) experiment at CERN-ISOLDE, technical upgrades are continuously introduced, aiming to enhance its sensitivity, precision, stability, and efficiency. Recently, a voltage-scanning setup was developed and commissioned at CRIS, which improved the scanning speed by a factor of three as compared to the current laser-frequency scanning approach. This leads to faster measurements of the hyperfine structure for systems with high yields (more than a few thousand ions per second). Additionally, several beamline sections have been redesigned and manufactured, including a new field-ionization unit, an electrostatic bend with a larger deflection angle, and improved ion optics. The beamline upgrades are expected to yield an improvement of at least a factor of 5 in the signal-to-noise ratio by avoiding the use of high-power lasers (which yield non-resonantly produced ions) and providing time-of-flight separation between the resonant ions and the collisional background. [ABSTRACT FROM AUTHOR]

The relative cross sections for radioactive negative ion production via double electron capture have been measured for collisions between a 40 keV projectile beam of uranium-238 and potassium vapor. This was performed at the collinear resonance ionization spectroscopy (CRIS) experiment at CERN-ISOLDE and is a step towards measuring the electron affinities (EAs) of elements that cannot be efficiently produced in negative ion sources at radioactive ion beam (RIB) facilities. This includes short-lived radioactive isotopes that have low production quantities and heavy and superheavy elements that systematically have smaller EAs than work functions of available ion source materials. Negative ions are particularly sensitive to electron–electron correlation effects, which make such studies ideal for benchmarking atomic structure models that go beyond the independent particle model. While the EAs of most light elements have been measured, experimental investigations on heavier elements, namely the actinides, remain scarce due to their radioactive nature and production difficulty. By developing negative ion production by charge exchange, we aim to make these studies feasible at RIB facilities. [ABSTRACT FROM AUTHOR]

High spectral resolution infrared sounders are an integral component of the global observing system and are used in a broad range of applications. This is enabled by their high accuracies which are ensured by rigorous calibration/validation activities. One of these activities is the post‐launch intercomparison with other high spectral resolution infrared sounders using simultaneous nadir overpasses (SNOs). This paper introduces a novel application of the previously developed SNO methodology by including time difference histogram symmetrization and a spatial sampling uncertainty. Where possible, radiometric measurement uncertainties are included and propagated through the statistics. Comparisons of Atmospheric Infrared Sounder (AIRS), METOP‐A/B/C Infrared Atmospheric Sounding Interferometer (IASI), and Cross‐track Infrared Sounder (CrIS) from 1 October 2019, to 1 October 2020, are analyzed. Results show AIRS and IASI differences as well as CrIS and AIRS differences are generally less than 0.4 K across the spectrum, and CrIS and IASI differences are generally less than 0.25 K. Comparison of the Suomi National Polar‐Orbiting Partnership and NOAA‐20 CrIS instruments via IASI and AIRS shows differences are generally less than 0.1 K across all bands and that the two CrIS instruments statistically agree within their radiometric uncertainties except for the narrow 2,370 cm−1 region where artifacts due to brightness temperature conversion are prone to occur. Plain Language Summary: One of the primary sources of information used to monitor Earth's weather patterns are satellite based meteorological instruments that measure radiation in the infrared region with high spectral resolution. Agencies worldwide use data from these types of instruments to provide input into weather prediction models. The long term data sets are also very valuable for multi‐decadal climate studies. To ensure the value of these data, the instruments need to be properly calibrated using external references. This paper details and expands upon the previously established methodology of one commonly used calibration technique—called SNOs. This method compares nadir‐viewing data from different instruments measuring radiation coming from the same mass of atmosphere in time and space. Introduced in this paper are two additions to the methodology: (a) a symmetrization of the time differences between when the instruments measure the specific air mass, and (b) an uncertainty estimate on the comparison based on the spatial coincidence of the measurements. Results from this methodology demonstration show how well the current suite of satellite‐based instrumentation measuring infrared radiation at high spectral resolution agree with each other. Key Points: Simultaneous nadir overpass comparisons include time difference symmetrization, spatial sampling uncertainty, and Cross‐track Infrared Sounder (CrIS) radiometric uncertaintyDifferences between Suomi National Polar‐Orbiting Partnership (SNPP) CrIS and NOAA‐20 CrIS are generally less than 0.1 K and within combined radiometric measurement uncertaintiesDifferences between Atmospheric Infrared Sounder (AIRS) and CrIS or AIRS and Infrared Atmospheric Sounding Interferometer (IASI) generally less than 0.4 K, differences between CrIS and IASI generally less than 0.25 K [ABSTRACT FROM AUTHOR]

This study highlights hyperspectral infrared observations from the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) collected as part of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) deployment on the icebreaker RV Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. The ARM M-AERI directly measures the infrared radiance emission spectrum between 520 cm−1 and 3000 cm−1 (19.2–3.3 μm) at 0.5 cm−1 spectral resolution. These ship-based observations provide a valuable set of radiance data for the modeling of snow/ice infrared emission as well as validation data for the assessment of satellite soundings. Remote sensing using hyperspectral infrared observations provides valuable information on sea surface properties (skin temperature and infrared emissivity), near-surface air temperature, and temperature lapse rate in the lowest kilometer. Comparison of the M-AERI observations with those from the DOE ARM meteorological tower and downlooking infrared thermometer are generally in good agreement with some notable differences. Operational satellite soundings from the NOAA-20 satellite were also assessed using ARM radiosondes launched from the RV Polarstern and measurements of the infrared snow surface emission from the M-AERI showing reasonable agreement. [ABSTRACT FROM AUTHOR]

Methane (CH4) is the second most significant contributor to climate change after carbon dioxide (CO2), accounting for approximately 20% of the contributions from all well-mixed greenhouse gases. Understanding the spatiotemporal distributions and the relevant long-term trends is crucial to identifying the sources, sinks, and impacts on climate. Hyperspectral thermal infrared (TIR) sounders, including the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI), have been used to measure global CH4 concentrations since 2002. This study analyzed nearly 20 years of data from AIRS and CrIS and confirmed a significant increase in CH4 concentrations in the mid-upper troposphere (around 400 hPa) from 2003 to 2020, with a total increase of approximately 85 ppb, representing a +4.8% increase in 18 years. The rate of increase was derived using global satellite TIR measurements, which are consistent with in situ measurements, indicating a steady increase starting in 2007 and becoming stronger in 2014. The study also compared CH4 concentrations derived from the AIRS and CrIS against ground-based measurements from NOAA Global Monitoring Laboratory (GML) and found phase shifts in the seasonal cycles in the middle to high latitudes of the northern hemisphere, which is attributed to the influence of stratospheric CH4 that varies at different latitudes. These findings provide insights into the global budget of atmospheric composition and the understanding of satellite measurement sensitivity to CH4. [ABSTRACT FROM AUTHOR]

Presented is a methodology to explicitly identify and account for cloud-free satellite measurements below a sensor's measurement detection level. These low signals can often be found in satellite observations of minor atmospheric species with weak spectral signals (e.g., ammonia (NH3)). Not accounting for these non-detects can high-bias averaged measurements in locations that exhibit conditions below the detection limit of the sensor. The approach taken here is to utilize the information content from the satellite signal to explicitly identify non-detects and then account for them with a consistent approach. The methodology is applied to the CrIS Fast Physical Retrieval (CFPR) ammonia product and results in a more realistic averaged dataset under conditions where there are a significant number of non-detects. These results show that in larger emission source regions (i.e., surface values > 7.5 ppbv) the non-detects occur less than 5% of the time and have a relatively small impact (decreases by less than 5%) on the gridded averaged values (e.g., annual ammonia source regions). However, in regions that have low ammonia concentration amounts (i.e., surface values < 1 ppbv) the fraction of non-detects can be greater than 70%, and accounting for these values can decrease annual gridded averaged values by over 50% and make the distributions closer to what is expected based on surface station observations. [ABSTRACT FROM AUTHOR]

Abstract High spectral resolution infrared sounders are an integral component of the global observing system and are used in a broad range of applications. This is enabled by their high accuracies which are ensured by rigorous calibration/validation activities. One of these activities is the post‐launch intercomparison with other high spectral resolution infrared sounders using simultaneous nadir overpasses (SNOs). This paper introduces a novel application of the previously developed SNO methodology by including time difference histogram symmetrization and a spatial sampling uncertainty. Where possible, radiometric measurement uncertainties are included and propagated through the statistics. Comparisons of Atmospheric Infrared Sounder (AIRS), METOP‐A/B/C Infrared Atmospheric Sounding Interferometer (IASI), and Cross‐track Infrared Sounder (CrIS) from 1 October 2019, to 1 October 2020, are analyzed. Results show AIRS and IASI differences as well as CrIS and AIRS differences are generally less than 0.4 K across the spectrum, and CrIS and IASI differences are generally less than 0.25 K. Comparison of the Suomi National Polar‐Orbiting Partnership and NOAA‐20 CrIS instruments via IASI and AIRS shows differences are generally less than 0.1 K across all bands and that the two CrIS instruments statistically agree within their radiometric uncertainties except for the narrow 2,370 cm−1 region where artifacts due to brightness temperature conversion are prone to occur.

Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH4) spectral features, including the ν1 vibrational band near 1300 cm−1 (7.7 μm); therefore, they can be used to monitor CH4 concentrations in the atmosphere. However, retrieving CH4 remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH4 is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH4 data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH4 profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH4 signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements. An inter-comparison study revealed that the spectral fingerprinting results can capture the vertical variation characteristics of CH4 profiles that operational sounder products may not provide. The latitudinal variations in CH4 concentration in these results appear more realistic than those shown in existing sounder products. The methodology presented herein could enhance the utilization of satellite data to comprehend methane’s role as a greenhouse gas and facilitate the tracking of methane sources and sinks with increased reliability.

Global temperatures are continuing to rise as atmospheric carbon dioxide (CO2) concentrations increase, and climate warming has become a major challenge to global sustainable development. The Cross-Track Infrared Sounder (CrIS) instrument is a Fourier transform spectrometer with 0.625 cm−1 spectral resolution covering a 15 μm CO2-absorbing band, providing a way of monitoring CO2 with on a large scale twice a day. This paper proposes a method to predict the concentration of column-averaged CO2 (XCO2) from thermal infrared satellite data using ensemble learning to avoid the iterative computations of radiative transfer models, which are necessary for optimization estimation (OE). The training data set is constructed with CrIS satellite data, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) meteorological parameters, and ground-based observations. The training set was processed using two methods: correlation significance analysis (abbreviated as CSA) and principal component analysis (PCA). Extreme Gradient Boosters (XGBoost), Extreme Random Trees (ERT), and Gradient Boost Regression Tree (GBRT) are used for training and learning to develop the new retrieval model. The results showed that the R2 of XCO2 prediction built from the PCA dataset was bigger than that from the CSA dataset. These three learning models were verified by validation sets, and the ERT model showed the best agreement between model predictions and the truth (R2 = 0.9006, RMSE = 0.7994 ppmv, MAE = 0.5804 ppmv). The ERT model was finally selected to estimate the concentrations of XCO2. The deviation of XCO2 predictions of 12 TCCON sites in 2019 was within ±1 ppm. The monthly averages of XCO2 concentrations in close agreement with TCCON ground observations were grouped into four regions: Asia (R2 = 0.9671, RMSE = 0.7072 ppmv), Europe (R2 = 0.9703, RMSE = 0.8733 ppmv), North America (R2 = 0.9800, RMSE = 0.6187 ppmv), and Oceania (R2 = 0.9558, RMSE = 0.4614 ppmv).

Das Sammeln, Aufbereiten und Bilanzieren von Informationen über wissenschaftliche Aktivitäten ist eine zentrale Aufgabe des Forschungsmanagements auf allen Ebenen. Diese Aufgabe unterstützen Forschungsinformationssysteme (FIS). Obwohl FIS zunehmend zu einem zentralen Element der IT-Ökologie des Wissenschaftssystems geworden sind, fehlen noch immer belastbare empirische Daten darüber, wie verbreitet Forschungsinformationssysteme überhaupt sind und ob es dahingehend Unterschiede zwischen unterschiedlichen Typen und Größen von Forschungseinrichtungen gibt. Aufbauend auf einer telefonischen Organisationsbefragung in Niederösterreich diagnostizieren wir eine zweite digitale Welle des Forschungsmanagements. Insbesondere sind avancierte Forschungsinformationssysteme auf dem Vormarsch. Es gibt eine starke Wandlungsdynamik. Allerdings bringt die Welle auch Tiefenströmungen mit sich, die auf eine Fragmentierung in der Digitalisierung des Forschungsmanagements hinweisen und für öffentliche Projekte von großer Bedeutung sind, will man Nachteile für kleine Forschungseinrichtungen nicht reproduzieren.

The Joint Polar Satellite System (JPSS) mission has provided over ten years of high-quality data products for environment forecasting and monitoring through the current Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. Particularly, the sensor data record (SDR) and the derived environmental data record (EDR) products from the Visible Infrared Imaging Radiometer Suite (VIIRS), the Cross-track Infrared Sounder (CrIS), the Advanced Technology Microwave Sounder (ATMS), and the Ozone Mapping and Profiler Suite (OMPS) offer an unprecedented opportunity to observe severe weather and environmental events over the Earth. This paper presents the observations about atmospheric features of the Hunga Tonga Volcanic eruption of January 2022, e.g., the gravity wave, volcanic cloud, and aerosol (sulfate) plume phenomena, by using the ATMS, CrIS, OMPS, and VIIRS SDR and EDR products. Powerful gravity waves ringing through the atmosphere after the eruption of the Hunga Tonga volcano are discovered at two CrIS upper sounding channels (670 cm−1 and 2320 cm−1) in the deviations of the observed brightness temperature (O) from the simulated baseline brightness temperature (B) using the Community Radiative Transfer Model (CRTM), i.e., O—B. A similar pattern is also observed in the ATMS global maps at channel 15, whose peak weighting function is around 40 km, showing the atmospheric disturbance caused by the eruption that reached 40 km above the surface. The Tonga volcanic cloud (plume) was also captured by the OMPS SO2 EDR product. The gravity wave features were also captured in the native resolution image of the S-NPP VIIRS I-5 band nighttime observations. In addition, the VIIRS Aerosol Optical Depth (AOD) captured and tracked the volcanic aerosol (sulfate) plume successfully. These discoveries demonstrate the scientific potential of the JPSS SDR and EDR products in monitoring and tracking the eruption of the Hunga Tonga volcano and its severe environmental impacts. This paper presents the atmospheric features of the Hunga Tonga volcano eruption that is uniquely captured by all four advanced sensors onboard JPSS satellites, with different spectral coverages and spatial resolutions. [ABSTRACT FROM AUTHOR]

This paper provides a procedure for the simulation of radiances from the U. S. National Oceanic and Atmospheric Administration (NOAA) Cross-track Infrared Sounder (CrIS) Fourier Transform Spectrometer to include spectral ringing effects caused by the finite-band, non-flat instrument spectral response to incident radiation. A simulation using a line-by-line radiative transfer model is performed to illustrate the magnitude of the effect and to indicate which spectral channels are likely to be impacted. Comparisons with CrIS observations are made to show that for most channels this effect is negligibly small compared to errors in the radiative transfer calculations but for the longwave edge of the CrIS longwave band and a few other regions, the brightness temperature ringing is significant. While the ringing artifact described in this paper may appear to be removed when Hamming apodization is applied, as is done for the assimilation of CrIS data into Numerical Weather Prediction (NWP) models, it is still present, and its influence reappears if the spectral correlation induced by apodization is properly handled to preserve the information content that derives from high spectral resolution. Inclusion of the instrument responsivity in calculated spectra to properly mimic the observed spectra as defined here eliminates artifacts from this type of ringing. Users of CrIS radiances should consider whether this effect is important for their application. [ABSTRACT FROM AUTHOR]

Collecting, processing and balancing information about research activities is a central task of research management at all levels. Research information systems support this task. Although the topic is increasingly discussed, there is still a lack of robust empirical data on the spread of research information systems among research institutions and what differences there are between different types and sizes of research institutions. Building on a telephone survey of organizations in Lower Austria, we diagnose a second digital wave of research management. Advanced research information systems are on the rise and there is a strong dynamic of digital transformation. However, the wave also brings deep currents that point to a fragmentation of the field. These are of great importance for public projects, if one does not want to reproduce disadvantages for small research institutions. [ABSTRACT FROM AUTHOR]

This article presents a concept of a research performance monitoring system. The following models are developed, described, and introduced: a set-theoretic model that includes three logical layers—organization, information, and function; an information model that details the content of the information layer of the semiotic model; and a function model of the system for scientometric monitoring of research performance, based on the principles of automation, identification, reliability, reproducibility, verification, and indicator variability and reusability, as well as the FAIR methodological principles for scientific data exchange. It is demonstrated that the proposed conceptual model is in line with current trends in the development of global research communication, and its implementation would create a number of positive technological and economic effects for Russian science. [ABSTRACT FROM AUTHOR]

The article presents upgrades and features of the new SICRIS application version, based on updated trasfer of data from ARRS, migration to open source environment and updated web services. In 2022, the project of migration from the Windows platform (MS SQL, ASP.NET, IIS) to the Linux platform (PostgreSQL, Java EE, Wildfly) was completed. The new version SICRIS2 (V1.0) was issued. The new version includes most of the functionalities of the old SICRIS application, in addition it includes enhanced browser and data display as well as a higher speed of operation. Due to the migration to the platform used also by other IZUM services, the maintenance is simpler and security is improved.

The article presents issues related to CRIS (Current Research Information System): it provides a definition of the system, outlines the benefits of integration with other systems and connection to a repository, discusses the prospects for the development of the Polish Platform of Medical Research strategy and the use of the potential of the implemented system.

The objective of this article is to discuss the researcher profile available on the local website of the Polish Platform of Medical Research of the Medical University of Silesia in Katowice (ppm.sum.edu.pl). The Polish Platform of Medical Research (Polska Platforma Medyczna, PPM) is a current research information system (CRIS) knowledge base based on the Omega-PSIR software developed by a team of IT specialists of the Warsaw University of Technology. Presentation of the scientific output and achievements of a researcher in CRIS makes it possible to aggregate in one area – the researcher profile – consistent information about the publications, citations, ministerial scores, collaborations or statistics. The value of the profile is determined by reliability of the presented information. It should be emphasised that when this article was developed, work was being conducted to improve the consistency and quality of data contained in the system, which are aggregated from external sources. The profile is not only used to showcase the researcher or university, but ultimately will also provide a set of data informing the bibliometric assessment.

The community of Polish medical libraries has been cooperating on numerous projects and initiatives for many years. Recently, eight libraries have created a joint platform for the promotion of research outputs and research potential of eight medical institutions. The project titled “Polish Platform of Medical Research: a knowledge and research potential management platform” (PPM) was created following the identification and analysis of the needs of the scientific community in the area of medical research. It was implemented between November 2017 and January 2021 under the Operational Program Digital Poland for 2014–2020, co-financed with funds of the European Union in connection with the European Regional Development Fund. This paper presents the assumptions, stages, effects and internal organization of the project as well as the conclusions from this complicated implementation.

This paper describes the PublicUM database created in connection with the InterScienceCloud project implemented by the Medical University in Łódź. The text discusses the implementation of a CRIS-type system at the university and the individual components of the integration of the created solution with other systems – operating on the institutional, national or international level. In particular, the paper describes the integration of PublicUM with the ORCID platform, the purposes of the integration, the method of implementation, the current advancement of the activities performed by the university and plans for the future.

The complexity of the current crisis is a big challenge for sustainable development, in the context of several overlapping shocks generated by the Coronavirus disease, geostrategic conflict, energy breaches, and food security threats. An appropriate answer to address these issues is to create a new approach to sustainable growth based on the knowledge economy. At this point, related studies have referring to the knowledge triangle, the triple (quintuple) helix, smart specialization, and the knowledge-based economy. In this research, compared with other studies in the field, knowledge structures created on the foundation of the knowledge economy were designed to work together to generate synergy in a knowledge environment where the stakeholders are universities, technology providers, governments, local communities, and entrepreneurs. In this mechanism, innovation, creativity, and entrepreneurship are the premises for increasing productivity and competitiveness, with a positive impact on smart growth and the standard of living. [ABSTRACT FROM AUTHOR]

Dust outflows off Northwest Africa often propagate westward across the North Tropical Atlantic Basin (NTAB) into the greater Caribbean and US. From a health perspective, weather forecasters in these regions often monitor hazardous air quality associated with this dust. However, forecasters can be constrained by sparse data observations upwind over the Atlantic of the impacted populated areas. Global satellite sounding retrievals can potentially augment and enhance the operational forecasting toolkit for monitoring Saharan dust episodes. The focus of this paper was to examine the skill of the NOAA Unique Combined Atmospheric Processing System (NUCAPS) temperature and water vapor profiles within the dust and non-dust conditions during the March 2019 NOAA Aerosols and Ocean Science Expedition (AEROSE). During this time, the NOAA Ron Brown research ship launched radiosondes to coincide with satellite overpasses that served as independent ground truth data for evaluating NUCAPS. Compared to RAOBs from the Ron Brown, the SNPP and NOAA-20 NUCAPS-derived soundings showed skill in profiling atmospheric conditions supporting Saharan dust monitoring. Outside of dust regions, the NOAA-20 NUCAPS surface temperature bias peaks at 2.0 K; the surface water vapor bias is minimal (~1000 hPa), with a small cold bias that peaks at −50% between 742 and 790 hPa. Corresponding temperature RMS values are less than 2.0 K; water vapor RMS values are generally below 70%. Within the dust regions, NOAA-20 NUCAPS temperature soundings show a cold bias peak of 2.6 K at 918 hPa and 113% of a moist bias peak at the same level. Corresponding temperature RMS values maximize at 3.5 K at 945 hPa; the water vapor RMS shows a peak value of 106% at the same level. Weather forecasters can apply NUCAPS across the NTAB in issuing timely and accurate hazardous air quality warnings and visibility alerts to health officials and the general public. [ABSTRACT FROM AUTHOR]

In recent years, academic publications related to the Current Research Information System in the Web of Science and Scopus multidisciplinary databases have increased. Other bibliographic data sources such as Dimensions have appeared. The objective of this study is to analyze the thematic coverage of the Current Research Information System in the Web of Science, Scopus and Dimensions databases during the period 2000-2020 for a holistic view of the behavior and visibility of the scientific literature in this domain. A search strategy has been designed and datasets exported from these sources are available in Zenodo. For the analysis, dimensions were defined with their variables and bibliometric indicators and the data was processed in the biblioshiny and VOSviewer tools. The Dimensions database provides broad coverage of the topic, which can be seen in the high number of records, sources and total citations retrieved. Scopus has a greater diversity of types of retrieved documents. In the analyzed indicators, it shares with the WoS a high degree of overlap in the other sources. The selectivity offered by Scopus and WoS consolidate it as sources for the recovery of literature related to CRIS. However, these three databases could be complemented with other sources, such as the euroCRIS repository, to retrieve information and obtain a holistic view of behavior, sources and authorship in the CRIS domain. [ABSTRACT FROM AUTHOR]

The Spanish regulatory framework governing university doctoral studies, establishes that, for all purposes, PhD students are researchers in training and, as such, all the activities carried out by these students during their training as doctors, must be recorded. These activities must be detailed in a formal "doctoral activity document" (DAD) that serves to evaluate the student. If these students are considered, to all intents and purposes, researchers (in training), their activities (all or some of them) should be part of the scientific production of the institution and, therefore, be included in their CRIS systems. Through the results of a survey conducted in 18 relevant Spanish universities, this presentation aims to analyse how the research activities carried out during the doctoral training period impact, or may impact, on CRIS systems. [ABSTRACT FROM AUTHOR]

Harvested metadata on research objects can include links between the primary domain objects such as organizational identifiers associated with dataset, persons identified with ORCIDs linked to publications and publications connected through ISSNs to publishing channels. This kind of linkage is the bread-and-butter of the CRIS systems and usually comprehensively maintained. When it comes to the more subjective description of a domain object, such as keywords, themes, or subject headings, the issues related to data management and modeling become prominent with challenges such as flexibility of free text keywords as opposed to authoritative, but rigid classification systems. Many CRIS objects also already contain an extensive description of the content, just meant for human consumption, in the form of an abstract or similar summary text.With the help of automated data mining and annotation tools, these textual representations can be processed into structured data. This paper presents the processing pipelines implemented as part of the research.fi portal for automatic linking of different research inputs based on automatically extracted ontology concepts and discusses the implications of utilizing them as part of the research.fi platform. But more than simply discussing the annotation of research objects and the creation of word clusters for representation of the semantic content of research objects, we also discuss challenges related to maintaining the automatically produced metadata [ABSTRACT FROM AUTHOR]

CRIS systems are an important resource about the university's scientists, research projects and publications. The functionality of FAU's CRIS was constantly extended by several additional entity types in the last years, such as affiliations, research infrastructure, internal applications and more. Most of them go beyond standards like CERIF or "Kerndatensatz Forschung", but follow their general principles. In this paper, we present the current state of our approach in supporting FAIR research data management in our CRIS. We achieve synergy effects between research data and research information, since some parts of research data's meta data are already contained in the CRIS. [ABSTRACT FROM AUTHOR]

An increasing number of organizations are encouraging and accepting the research information management system in the institute level to assess the research progress of the organization and bring out a report on scholarly communication activities. Open and commercial product on the Current Research Information System (CRIS) is emerging in the last decade. The Directory of Current Research Information system listed 25 CRIS system across European countries. The European Current Research Information System (euroCRIS) support the CRIS thought the European countries through the conference, working group and pioneer in the advocacy since 2002. This paper considers the recent development in the CRIS related activities around the world and recent progress in India. This paper describes the various challenges to setting up the RIM for the higher education institutions including identification of nodal officers, collecting faculty profile information, retrieving scholarly communication activities from internal and external sources, etc. Also, discuss the training programme to the nodal officer level and institute level to promote the academic identities and their importance. The paper also discusses the impact of the IRINS in the higher education system in India and its use case with different stakeholders such as students, scholars, faculty members, scientists, research administrators, funding agencies and policymakers, etc. [ABSTRACT FROM AUTHOR]

Development and implementation of Current Research Information Systems (CRIS) is one of the most transparent and practical approaches to curate research information on a national level. The process of building and implementing such systems is a complex and time consuming where successful results heavily depend on the established research information infrastructure of a country, the interoperability of the systems and the quality of the information which reside in them. The purpose of this paper is to analyse the existing Ukrainian Research Information Infrastructure and identify which databases could be reused and integrated with a national Ukrainian Current Research Information System (URIS). The analysis showed that there are functional databases and registries that collect data on research activities and could be used as a data sources for the URIS. In particular, the Unified State Electronic Database on Education is a potential data source on higher educational institutions, the National Repository of Academic Texts — on metadata on research output, internal database of the National Research Foundation of Ukraine and database on research projects maintained by Ukrainian Institute of Scientific Technical and Economic Information — on projects. Secondly, it was identified that Ukrainian research infrastructure lacks complete, up-to-date registry on researchers. Finally, we discussed the challenges and solutions for further steps in building national CRIS. [ABSTRACT FROM AUTHOR]

This paper introduces the ongoing development of the Finnish Research Information Hub and, in particular, how researchers' can manage their profiles in the Research.fi portal. Introducing researchers' profiles into Research.fi includes integrating information from organizational CRIS systems and researchers' ORCID records and implementing a service where researchers have full control of the publicity and disclosure of their personal information. [ABSTRACT FROM AUTHOR]

The effectiveness of a Current Research Information System (CRIS) is based on satisfying essential institutional needs, or purposes, regarding the capture, processing and reporting of research-related activities and outcomes. These needs, or purposes, exemplified in the Code of Good Practice and introduced in 1998, have remained relatively constant over time. However, the scope and nature of the underlying data supporting these needs have grown in complexity, thus necessitating a concurrent increase in sophistication for how data quality is addressed and improved. This publication aims to introduce and analyse the implications and solutions required to improve data quality within the scope of fit for purpose in a CRIS context. Drawing from, and building on, data and information quality foundations and descriptions, a data quality framework is introduced, and detailed product functionality is described. A discussion on the combination of framework and functionality highlights how data quality can be improved in CRIS products. [ABSTRACT FROM AUTHOR]

This paper summarises the half-day workshop on national and regional portals, organised by the RIS Synergy project for the CRIS2022 conference. The goal of the workshop was to discuss challenges and opportunities of national and regional research portals with representatives of six different portals and implementation projects. In an expert panel talk, participants shared their experiences with the initiation and implementation of their national/regional portal. Moderated questions also addressed various topics such as target groups, stakeholders, technical specifications and standards. The workshop contributes to the international collaboration of research portal operators and promotes the exchange of lessons learned. [ABSTRACT FROM AUTHOR]

This paper introduces the RIS Synergy project, an Austrian digitisation project that aims to synergise CRIS Systems across a diverse research landscape. The project brings together various Austrian research institutions and funders to collaborate on the development and implementation of open, standardised interfaces and shared services for research information services. In this paper, we present how the project's bottom up approach proves to provide an effective method to manage system interoperability and information exchange in a multidisciplinary consortium. Furthermore, we offer an insight into RIS Synergy's approach to the conception of an Austrian research portal. [ABSTRACT FROM AUTHOR]