Reanalysis Data Research Articles

Re-analysis of data from cluster randomised trials to explore the impact of model choice on estimates of odds ratios: study protocol

BackgroundThere are numerous approaches available to analyse data from cluster randomised trials. These include cluster-level summary methods and individual-level methods accounting for clustering, such as generalised estimating equations and generalised linear mixed models. There has been much methodological work showing that estimates of treatment effects can vary depending on the choice of approach, particularly when estimating odds ratios, essentially because the different approaches target different estimands.MethodsIn this manuscript, we describe the protocol for a planned re-analysis of data from a large number of cluster randomised trials. Our main objective is to examine empirically whether and how odds ratios estimated using different approaches (for both primary and secondary binary outcomes) vary in cluster randomised trials. We describe the methods that will be used to identify the datasets for inclusion and how they will be analysed and reported.DiscussionThere have been a number of small comparisons of empirical differences between the different approaches to analysis for CRTs. The systematic approach outlined in this protocol will allow a much deeper understanding of when there are important choices around the model approach and in which settings. This will be of importance given the heightened awareness of the importance of estimands and the specification of statistical analysis plans.

Evaluating Daytime and Nighttime Land Surface Temperature Pattern and Trends in India: A Comparative Analysis of Satellite and Reanalysis Data

Evaluating Daytime and Nighttime Land Surface Temperature Pattern and Trends in India: A Comparative Analysis of Satellite and Reanalysis Data

Striatal stimulation enhances cognitive control and evidence processing in rodents and humans.

Brain disorders, in particular mental disorders, might be effectively treated by direct electrical brain stimulation, but clinical progress requires understanding of therapeutic mechanisms. Animal models have not helped, because there are no direct animal models of mental illness. Here, we propose a potential path past this roadblock, by leveraging a common ingredient of most mental disorders: impaired cognitive control. We previously showed that deep brain stimulation (DBS) improves cognitive control in humans. We now reverse translate that result using a set-shifting task in rats. DBS-like stimulation of the midstriatum improved reaction times without affecting accuracy, mirroring our human findings. Impulsivity, motivation, locomotor, and learning effects were ruled out through companion tasks and model-based analyses. To identify the specific cognitive processes affected, we applied reinforcement learning drift-diffusion modeling. This approach revealed that DBS-like stimulation enhanced evidence accumulation rates and lowered decision thresholds, improving domain-general cognitive control. Reanalysis of prior human data showed that the same mechanism applies in humans. This reverse/forward translational model could have near-term implications for clinical DBS practice and future trial design.

The impact of rainfall value on the accuracy of the SCS-CN model: selection of model parameters

ABSTRACT The soil conservation service curve number (SCS-CN) model is a widely utilized tool for estimating runoff and relies on two empirical parameters: the CN and the ratio of initial abstraction to maximum potential retention (λ). The determination of the parameters is via the empirical method or calculations based on actual data. However, few studies address the effect of rainfall on parameter selection, and collecting runoff data for model analysis is challenging. This study, taking the Nemor River Basin in Northeast China as the research region, investigates how the combination of CN and λ impacts the model in different rainfall conditions. Using runoff plots and reanalysis product data, the study reveals that: (1) the calculated methods outperformed the empirical method, increasing the Nash efficiency coefficient from 0.34 to 0.65. (2) A higher λ value (0.2 compared to 0.02) reduces runoff and smoothes the runoff curve, which becomes less obvious with increasing CN. (3) The CN values exhibit a non-monotonic relationship with rainfall, initially decreasing before rising, highlighting the need to adjust the CN based on rainfall. Moreover, the SCS-CN model's performance with reanalysis data approximates that with actual data, confirming the viability of reanalysis datasets in this region.

Improving Daily Precipitation Estimates by Merging Satellite and Reanalysis Data in Northeast China

Precipitation plays a key control in the water, energy, and carbon cycles, and it is also an important driving force for land surface modeling. This study provides an optimal least squares merging approach to merge precipitation data sets from multiple sources for an accurate daily precipitation estimate in Northeast China (NEC). Precipitation estimates from satellite-based IMERG and SM2RAIN-ASCAT, as well as reanalysis data from MERRA-2, were used in this study. The triple collocation (TC) approach was used to quantify the error uncertainties in each input data set, which are associated with the weights assigned to each data set in the merging procedure. The results revealed that IMERG provides a better consistency with the other two input data sets and thus was more relied on during the merging process. The accuracy of both SM2RAIN-ASCAT and MERRA-2 showed obvious spatio-temporal patterns due to their retrieval algorithms and resolution limits. The merged TC-based daily precipitation provides the highest correlation coefficient with ground-based measurements (R = 0.52), suggesting its capability to represent the temporal variation in daily precipitation. However, it largely overestimated the precipitation intensity in the summer, leading to a large positive bias.

Estimating global precipitation fields by interpolating rain gauge observations using the local ensemble transform Kalman filter and reanalysis precipitation

Abstract. It is crucial to improve global precipitation estimates for a better understanding of water-related disasters and water resources. This study proposes a new methodology to interpolate global precipitation fields from ground rain gauge observations using the algorithm of the local ensemble transform Kalman filter (LETKF), a computationally efficient ensemble data assimilation method, in which the first guess and its error covariance are developed based on the reanalysis data of precipitation from the European Centre for Medium-Range Weather Forecasts (ERA5). For the estimation for each date, the climatological ensembles are constructed using the ERA5 data 10 years before and after that date, and thereafter they are utilized to obtain the first guess and its error covariance. Additionally, the global rain gauge observations provided by the National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) are used for observation inputs in the LETKF algorithm. Our estimates have better agreements with independent rain gauge observations compared to the existing precipitation estimates of the NOAA CPC in general. Because we utilized the same rain gauge observations for the inputs of our estimation as those used in the NOAA CPC product, this indicates that the proposed estimation method is superior to that of the NOAA CPC (i.e., optimal interpolation). Our proposed method had the advantage of constructing a physically consistent first guess and its error variance using reanalysis data for interpolating precipitation fields. Furthermore, validations against independent rain gauge observations showed that our estimates are largely improved in mountainous or rain-gauge-sparse regions compared to the CPC estimates, indicating strong benefits of the proposed method for such regions.

Differentiated Impacts of Central and Eastern Atlantic Niño on Hurricane Activity in the Tropical North Atlantic

AbstractRecent research highlights the influence of the Atlantic Niño on the likelihood of strong hurricanes forming in the tropical Atlantic. This phenomenon increases the risk of hurricanes impacting the Caribbean islands and the United States. A recent study identifies two distinct types of the Atlantic Niño, with warming concentrated in the central (CA) and eastern (EA) equatorial Atlantic, respectively. By analyzing observational and reanalysis data, we investigated how these two types of the Atlantic Niño affect hurricane activity. The findings reveal that the CA Niño enhances hurricane frequency south of 20°N, while the CA Niña promotes hurricanes north of 20°N. The CA Niño exerts a more significant influence on hurricanes than the EA Niño, primarily by affecting wind shear, relative vorticity, and vertical velocity. In contrast, the EA Niño mainly impacts relative humidity and African Easterly Waves. These insights could improve the accuracy of seasonal hurricane forecasts.

A global climatology of sting-jet extratropical cyclones

Abstract. Sting jets have been identified in the most damaging extratropical cyclones impacting northwest Europe. Unlike the cold conveyor belt and other long-lived cyclone wind jets, sting jets can lead to regions of exceptionally strong near-surface winds and damaging gusts over just a few hours and with much smaller wind “footprints”. They descend into the frontal-fracture region found in warm-seclusion cyclones. Previous research has focused almost exclusively on North Atlantic–European cyclones, but there are no known physical reasons why sting jets should not develop elsewhere, and recognition of their existence can inform weather nowcasting and wind warnings. We have produced the first climatology of sting-jet cyclones over the major ocean basins. A sting-jet precursor diagnostic has been applied to more than 10 000 warm-seclusion cyclones in the top intensity decile, tracked using 43 extended winters of ERA5 reanalysis data. Cyclones with sting-jet precursors are found to occur over the North Pacific and Southern oceans for the first time, and they are more prevalent in the Northern Hemisphere (27 % of all top decile cyclones) compared to the Southern Hemisphere (15 %). These cyclones have distinct characteristics to those without the precursor, including initiating closer to the Equator, deepening faster in mean-sea-level pressure (MSLP), and having stronger near-surface winds, even in the reanalysis data that are too coarse to fully resolve sting jets. Composite analysis reveals systematic differences in structural evolution, including in potential vorticity (PV) and jet crossing. These differences evidence the climatological consequences of strong diabatic cloud processes on cyclone characteristics, implying that sting jets are likely to be enhanced by climate change.

South Atlantic meridional overturning circulation and its respective heat and freshwater transports from sustained observations near 34.5°S

The Atlantic Meridional Overturning Circulation (AMOC) drives northward Meridional Heat Transport (MHT) and affects climate and weather patterns, regional sea levels, and ecosystems. This study uses a methodology recently applied in 22.5°S to estimate the strength and structure of the AMOC, MHT and freshwater (FWT) transports at 34.5°S since 2005. For this, temperature and salinity profiles from sustained observations were used in conjunction with satellite and reanalysis data under two mapping methodologies: (i) an optimized mapping and (ii) a two-layer feed-forward neural network approach. There is strong agreement between both methods for AMOC, MHT and FWT estimates, thus showing the mapping methodologies are robust. In addition, the AMOC variability estimate is significantly correlated with the monthly SAMBA array data (correlation of 0.41). The mean AMOC transport of 17.0 ± 1.6 Sv, a MHT of 0.6 ± 0.1 PW, and a FWT of -0.02 ± 0.01 Sv are estimated between January 2005 and May 2023 at 34.5°S. The MHT and FWT are analyzed in terms of their horizontal (Hhor and Fhor) and overturning contributions (Hov and Fov), and vertical structures. The MHT is dominated by the overturning contribution (correlation of 0.92), while the FWT is controlled by the overturning contribution driven by the wind via Ekman transport at seasonal timescale, and by horizontal contribution at longer timescales. Both horizontal heat (Hhor) and freshwater (Fhor) components are mostly confined to the upper 500 m, with the geostrophic Fov and Fhor offsetting each other between 50 m and 500 m, and the Ekman Fov in the upper 50 m determining the negative FWT. Finally, the estimated mean Fov of -0.15 Sv agrees with previous estimates that the AMOC exports freshwater in the South Atlantic, and suggests that the AMOC is unstable. Although a long-term trend in the Fov was not detected in the past 20 years, there is a salinification trend (0.05 ± 0.01 PSU/decade) in the upper 300 m near 34.5°S since 2005.

Atmospheric and Surface Dynamics During Winter Warm Spells in the Southern Great Plains: Insights From the 2021 Case Study

AbstractTwo 2021 winter warm spell events experienced near record extreme surface temperatures, anomalies exceeding C, during the winter season in 2021 across the Southern Great Plains (SGP). Extreme heat during the winter season results in similar detrimental socioeconomic impacts compared to their counterpart summer heat wave events. Winter warm spell events across the SGP have been historically increasing over the last several decades, and as such, it is crucial to investigate the drivers of these extreme events. In this study, we use ERA‐5 reanalysis data to investigate the atmospheric and surface characteristics associated with these two extreme events (i.e., (a) 29 November–17 December 2021 and (b) 22 December–31 December 2021). A prolonged period of positive geopotential height anomalies amplified subsidence in combination with increased incoming solar radiation and surface heat fluxes aiding extreme surface temperatures during the first winter warm spell event. However, a more prominent atmospheric blocking high (i.e., Alaskan Ridge) initiated and intensified the extreme heat during the second winter warm spell. Increased incoming solar radiation and positive sensible heat flux due to a dry surface fostered extreme heat during the second winter warm spell event. Warm air advection throughout both winter warm spell events supported the extreme surface temperatures. Discovering potential crucial drivers to winter warm spells identifies the sources of predictability to improve prediction of these extreme heat events.

Needs assessment of people living with cognitive impairment/dementia, a requirement of comprehensive psychogeriatric assessment and person-centered care. Empirical validation of the model in a community study

IntroductionCognitive impairment and dementia are part of a continuum that progressively leads to functional impairment and dependency. Dementia is a paradigmatic example of chronic and complex psychogeriatric diseases, requiring a comprehensive assessment. The authors underline the importance of implementing a formal assessment of needs (whether met or unmet) as an essential element of comprehensive assessment. The aim of this paper is to empirically validate this model of approach towards dementia, the needs assessment, demonstrating the relationship between needs and functionality/dependency in people with cognitive impairment/dementia in the community.Material and methodsCommunity-based, cross-sectional, descriptive epidemiological study based on the reanalysis of data from a two-phase community epidemiological study conducted in Santiago de Compostela, Spain, of 800 people over 65 years of age. The present study reanalyzes a subsample of 368 people, including those with dementia/cognitive impairment. The comprehensive assessment of the sample included sociodemographic variables, the presence of chronic diseases, health self-perception, assessment of affective, cognitive and functional state, as well as the needs assessment. The main instruments used were the MMSE, the Geriatric Depression Scale (GDS), the Barthel and Katz Indices, the Lawton Scale, and, for the needs assessment, the Camberwell Assessment of Need for the Elderly (CANE). For the clinical diagnosis of dementia, the ICD-10 criteria were followed. Statistical analysis: A study of the association between variables was carried out through hypothesis testing and a multivariate study was performed using regression models to analyze the relationship between the different variables defining disability/dependency, other health conditions and sociodemographic variables and the MMSE score as an expression of the cognitive impairment/dementia continuum.ResultsPeople with cognitive impairment/dementia had a higher number of needs compared to the healthy population. The severity of cognitive impairment is a significant predictor of dependency in Basic Activities of Daily Living (BADL) and is also a predictor of a greater number of needs (both met and unmet).DiscussionThe present study provides empirical evidence of the importance of implementing scales to assess the needs of people with cognitive impairment, as part of the process of comprehensive biopsychosocial assessment and person-centered care for dementia.

Unclouding the Correlations: A Principal Component Analysis of Convective Environments

AbstractIn this study, we leverage 25 years of observations from spaceborne radars, along with coincident reanalysis data, to determine how the depth and width of precipitating convective storms are related to the large‐scale environments in which they are observed. We find that the deepest convective features are observed in environments markedly different from the environments of other convective features, including organized convection. Deep storms co‐occur with relatively dry, unstable conditions, while wide storms are observed in moist, relatively stable environments. We identify eight large‐scale environmental variables that serve to distinguish between storm modes, and then show that principal component analysis can be used to condense this information into just two scalar variables. The methodology presented offers a succinct way to describe a storm's environment and will allow us to better relate a storm's initial environment to its dynamical characteristics.

An Investigation of the SOCOLv4 Model’s Suitability for Predicting the Future Evolution of the Total Column Ozone

The anthropogenic impact on the ozone layer is expressed in anomalies in the total ozone content (TOC) on a global scale, with periodic enhancements observed in high-latitude areas. In addition, there are significant variations in TOC time trends at different latitudes and seasons. The reliability of the TOC future trends projections using climate chemistry models must be constantly monitored and improved, exploiting comparisons against available measurements. In this study, the ability of the Earth’s system model SOCOLv4.0 to predict TOC is evaluated by using more than 40 years of satellite measurements and meteorological reanalysis data. In general, the model overpredicts TOC in the Northern Hemisphere (by up to 16 DU) and significantly underpredicts it in the South Pole region (by up to 28 DU). The worst agreement was found in both polar regions, while the best was in the tropics (the mean difference constitutes 4.2 DU). The correlation between monthly means is in the range of 0.75–0.92. The SOCOLv4 model significantly overestimates air temperature above 1 hPa relative to MERRA2 and ERA5 reanalysis (by 10–20 K), particularly during polar nights, which may be one of the reasons for the inaccuracies in the simulation of polar ozone anomalies by the model. It is proposed that the SOCOLv4 model can be used for future projections of TOC under the changing scenarios of human activities.

German higher education managers in the multiple hybrid university and their positioning with respect to third space

Based on a re-analysis of data from the KaWuM and WiMaKo research projects, this article furthers our understanding of higher education management in German universities by focusing on communicative skills and competences, as opposed to roles and job descriptions. While higher education management is a relatively heterogeneous field of activity, resulting in a huge variety of different higher education manager roles, we argue that similarities between these managers are found in the skills and competences they need to cope in the unique organisation of the university. German universities can be described as multiple hybrid organisations, characterised by their many structural inconsistencies in their key decision premises. As a result, third space opens up between three university subsystems: academia, administration and leadership roles (mainly the presidium). We suggest that higher education managers need communicative skills and competences, in particular negotiation and mediation, to work in the third space bridging these three subsystems. It is proposed, therefore, that future research on higher education managers should focus more on their skills and competences than on precise tasks, as outlined, for example, in a job description.

Impacts of Changing Atmospheric Circulation Patterns on Aviation Turbulence Over Europe

AbstractAddressing aviation turbulence not only enhances passenger comfort but also help to reduce fuel consumption and environmental impact, supporting aviation sustainability. By using ERA5 reanalysis data we explore how changing atmospheric circulation, monitored via sea level pressure trends, impacts aviation turbulence over Europe. Our results show coherent climate anomalies, with rising turbulence intensity over the UK and Northern Europe where most events involve clear air turbulence, which occurs unexpectedly and without warning, particularly at flight altitudes. We also highlight a clear seasonal patterns in moderate‐or‐greater turbulence encounters, most frequent and intense in winter, with a key role of wind shears due to the sub‐tropical jet position over Southern Mediterranean. Our approach adds to previous studies on the same topic by analyzing individual atmospheric circulation pattern changes and their effects on turbulence‐related factors. This offers insight into how climate change affecting atmospheric dynamics may contribute to increased aviation turbulence.

Recent Trends in Extreme Temperature Events Across the Contiguous United States

ABSTRACTExtreme heat events (EHEs) are becoming prevalent across the globe and are a major factor in terms of temperature‐related mortality in the United States (US). In this study, we compare trends in extreme temperature events (ETEs) across the Contiguous US, from 3 reanalysis products, namely: European Centre for Medium‐Range Weather Forecasts Reanalysis Version 5 (ERA5), Modern‐Era Retrospective Analysis for Research and Applications Version 2 (MERRA2) and North American Regional Reanalysis (NARR). We focused on the trends (1980–2022) in absolute extreme heat and cold events (ECE) as well as seasonally relative extreme heat and cold events (REHE and RECE). ETEs are defined based on a duration‐intensity metric calculated from excess apparent temperature factors, based on the exceedance of apparent temperature beyond local percentile thresholds while incorporating an acclimatisation factor. Our results show that the reanalysis data sets generally produced consistent climatology of ETEs, though with some inconsistencies in their number and spatial distribution. ETE trends in the study region are spatially heterogeneous and were more consistent between MERRA2 and ERA5. Nonetheless, all data sets agree that the frequency of EHEs is significantly increasing in the western parts of the US, whereas REHEs are significantly increasing in the southern parts. The highest increase in the frequency of EHEs occurs in southern California and Nevada, while REHE trends are maximal in Florida. RECEs are significantly decreasing more in spatial scale and magnitude than ECEs, especially towards the coastal regions. The highest decrease in RECEs is in Florida peninsula, southern California and Nevada. The data sets show inconsistency in ECE trends. Trends in excess temperature factors further indicated that extreme cold conditions are decreasing faster compared to the increasing trends of extreme heat conditions. Our findings highlight the need for improving the monitoring of ETEs across the US and for policies that mitigate the impact of ETEs on biological systems.

Tibetan Plateau Vortex Activity and Its Relationship With the Tibetan Plateau Summer Monsoon and Precipitation

ABSTRACTThe unique topography and location of the Tibetan Plateau (TP) often result in extreme weather events, which have led to disastrous consequences for the TP and its downstream regions. The TP summer monsoon (TPSM) and the TP vortex (TPV) play key roles in the transfer and redistribution of water vapour during the summer months on the TP and are increasingly active in summer and disappear in winter. However, it remains uncertain if a relationship between these systems. Understanding the relationship between these two systems is crucial for uncovering precipitation patterns on the TP, improving weather forecasting accuracy and reducing socioeconomic losses resulting from weather‐related disasters. On the basis of GLDAS and ERA5 reanalysis data from 1996 to 2022, the relationships between TPVs and the TPSM were explored in terms of their intensity and spatial characteristics, and their impacts on the spatial distributions of precipitation across the TP were examined in this study. The results indicated that the monthly mean TPSM index agreed very well with the TPV in terms of the annual number formed, duration and intensity, especially in July and August. The investigation of the movement of the center of the TPSM from May to October revealed that the center of the TPSM moves westward when the TPV is active and moves eastward when the TPV is less active. In years with a strong TPSM, the precipitation location generated by TPVs was biased toward the east. This finding could be attributed to the greater number of TPV events and the fact that the TPVs in years with a stronger TPSM moved eastward across a greater distance than those in years with a weaker TPSM. These findings highlightthe contribution of the joint relationship between the TPSM and TPV to seasonal circulation changes and could provide a new perspective for the study and prediction of precipitation distributions on the TP.

Neural network algorithm for precipitation estimation from atms radiometer data

The paper presents a neural network method for precipitation estimation using microwave measurements from ATMS radiometer on board Suomi NPP and NOAA-20/21 satellites. The algorithms based on two fully-connected neural networks, the first one is used to detect precipitation clouds and the other one is used to quantify precipitation rate. When training the neural networks, the reference source of information was an array of measurements simulated using the fast radiation transfer model RTTOV in the bands of ATMS instrument and the corresponding precipitation rates were taken from ECMWF ERA5 reanalysis data. Validation of the obtained precipitation estimates was carried out using the results of the MIRS and GPROF algorithms for satellite radiometer ATMS, as well as ground-based radar observations from NIMROD. The results of the validation showed a high accuracy level consistent with many others works in this research field. The validation was carried out for land and water surface separately. The comparison with MIRS algorithm showed the correlation coefficient was more 0.9, and the RMSE error was approximately 0.78 mm/h for water and 0.84 mm/h for land surface. The same metrics for GPROF algorithm showed the correlation coefficient was ~0.8, and the RMSE error was approximately 1.27 mm/h and 0.9 for water and land surface, respectively. When compared with ground-based NIMROD radar data, the correlation and the RMSE were 0.47 and 1.37 mm/h, respectively. The results of the validation confirm the performance of the presented neural network method for precipitation estimation. In addition, further minor refinement of the presented algorithm will make it possible to apply it to measurements of other microwave satellite instruments, including Russian ones, such as MTVZA-GY, installed on Meteor-M satellites.

Recent global temperature surge intensified by record-low planetary albedo.

In 2023, the global mean temperature soared to almost 1.5K above the pre-industrial level, surpassing the previous record by about 0.17K. Previous best-guess estimates of known drivers including anthropogenic warming and the El Niño onset fall short by about 0.2K in explaining the temperature rise. Utilizing satellite and reanalysis data, we identify a record-low planetary albedo as the primary factor bridging this gap. The decline is apparently caused largely by a reduced low-cloud cover in the northern mid-latitudes and tropics, in continuation of a multi-annual trend. Further exploring the low-cloud trend and understanding how much of it is due to internal variability, reduced aerosol concentrations, or a possibly emerging low-cloud feedback will be crucial for assessing the current and expected future warming.

Assessing the ducting phenomenon and its potential impact on Global Navigation Satellite System (GNSS) radio occultation refractivity retrievals over the northeast Pacific Ocean using radiosondes and global reanalysis

Abstract. In this study, high-resolution radiosondes from the Marine Atmospheric Radiation Measurement (ARM) Global Energy and Water Experiment (GEWEX) Cloud System Study (GCSS) Pacific Cross-section Intercomparison (GPCI) Investigation of Clouds (MAGIC) field campaign and ECMWF Reanalysis version 5 (ERA5) global reanalysis data are used to assess characteristics of the elevated ducting layer along a transect over the northeastern Pacific Ocean from Los Angeles, California, to Honolulu, Hawaii. The planetary boundary layer (PBL) height (PBLH) increases as the strength of the refractivity gradient decreases westward along the transect. The thickness of the prevailing ducting layer remains remarkably consistent (∼ 110 m) in the radiosonde data. On the other hand, the ERA5 reanalysis generally resolves the ducting features well, but it underestimates the ducting height and strength, especially over the trade cumulus region near Hawaii. A simple two-step end-to-end simulation is used to evaluate the impact of the elevated ducting layer on radio occultation (RO) refractivity retrievals. A systematic negative refractivity bias (N bias) below the ducting layer is observed throughout the transect, peaking (−5.42 %) slightly below the PBLH and gradually decreasing towards the surface (−0.5 %). The N bias shows a strong positive correlation with the ducting strength. The ERA5 data underestimate the N bias, with the magnitude of the underestimation increasing westward along the transect.