dataesr/scientific-paragraphs-categorization huggingface dataset:
Li et al. (2019)  provided a detailed study on the benefits and challenges of GRACE data assimilation for GW and drought monitoring on a global scale and identified major problems in regions with intensive GW abstraction. Another possibility to merge information from different data sources (e. g. , output from different hydrological models or GRACE solutions based on different algorithms) is Bayesian Model Averaging (BMA). The posterior distribution of a variable of interest (e. g. , TWSA or GW) is constructed by averaging the posterior distribution of each model weighted by the corresponding posterior model probabilities. The posterior distribution of each individual model with respect to a set of observations of the variable of interest is obtained from the Bayes' theorem. Long et al. (2017)  applied BMA to derive uncertainties for different GRACE TWSA products in the major river basins of the world.
Mehrnegar et al. (2020)  went further and developed a novel approach of Dynamic Model Data Averaging (DMDA), which combines the benefits of BMA and Kalman Filter techniques. Applying DMDA, they computed time-variable weights for six different hydrological and landsurface models to derive averaged outputs that best fit to GRACE TWS observations for the 33 major river basins of the world. Indeed, DMDA was found to introduce the impact from anthropogenic influences (e. g. , GW pumping) into the models. GW trends significantly changed due to DMDA compared to the original models for most of the considered basin. Furthermore, a synthetic example for the Niger basin showed significantly improved representation of derived GW storage change when applying DMDA instead of BMA. Over the United States,  Mehrnegar et al.
They found that extreme gradient boosting outperforms MLR and ANN for local applications. Finally, GW variations at increased spatial resolution were extracted from downscaled GRACE data and the land-surface model using eq. (  2 ). Downscaled GW changes showed higher correspondence to well measurements compared to original GW changes. However, the best method for reconstructing TWS and GW storages might depend on the respective setup, the input data sets, and the study region. Ahmed et al. (2019)  used an ARX model to forecast GRACE like TWS in 10 major catchments over central Africa based on climate input data and to predict drought events with very good performance for a majority of the catchments. In order to predict GW abstraction rates, Gemitzi and Lakshmi (2018) trained an artificial neural network against GW abstraction estimates for farms using GRACE TWS and climate data as input data.
2015). We used this data to generate GWBL maps in the IND following the approach proposed by  Pfeffer et al. (2014). Annual minima of water levels were first identified. Then, GWBL were computed interpolating annual minima of water levels over the IND. Only water levels from ERS-2 and ENVISAT were considered to avoid introducing artifacts due to biases between the different RA missions which have no overlapping period. The 1995-2010 average GWBL map over the IND is presented in Fig. 7a. It shows a slope of ∼0. 09 m/km in the flow direction. Standard deviations (stds) of GWBL derived from ERS-2 and ENVISAT data over 1995-2010 were smaller than 0. 5 m over most of the IND, except in the upstream part, downstream Douna (-5. 9 • W, 13. 22 • N), on the Bani River, and also in the downstream part of the IND in a region     centered on 4.
(2019)  in an integrated framework including GRACE data, altimetry, numerical modeling, climate indices, well data and secondary information on soil types and water quality. GW variations in nine major aquifer systems were derived and analyzed with respect to recharge and correlation with rainfall and climate indices. The authors derived the potential for irrigated agriculture, which is particularly high in the Upper Nile, the Karoo Sandstone, and the Ethiopian highland. At a more local scale, Awange (2022a) sub-divided Ethiopia into 10 regions and extracted year to year changes and intra-annual changes of GW from GRACE and GLDAS model output. They found significant variability of GW in all regions for almost all months of the year -and particular strong variability during rainy seasons. Abiy and Melesse (2017)  analyzed GRACE data together with aquifer potential zones over the Tana basin (Ethiopia) to derive aquifer characteristics.
Separating Drivers of Groundwater Trends Freshwater resources in Africa are sensitive to (1) natural variability, i. e. , cycles in precipitation and temperature due to climate modes like ENSO (e. g. ,  Ni et al. 2018 ), (  2 ) direct anthropogenic interventions, e. g. , population growth, deforestation, GW extraction, and (3) an increasing number of extreme events that are related to climate change  (Fasullo et al. 2016; Taylor et al. 2013). It is essential to understand the impact of climate modes on GW storages in order to separate them from human contributions to GW trends and from impacts through an intensification of the water cycle. Ahmed et al. (2014)  and  Ahmed and Wiese (2019)  provide a detailed overview on natural and anthropogenic causes for mass variations observed by GRACE by analyzing several remote sensing, geological, and hydrological data sets.
They show inconsistencies between model-based and observation-based drought classification. Based on GRACE TWSA, several instances of hydrological droughts were classified (see also Awange 2021b). Likewise, Awange (2022a) computed TSD indices for ten regions over Ethiopia and related water loss to possible human exploitation for irritation, but also to lagged impact of rainfall variability. Hasan et al. (2019)  developed a physical water scarcity index for the African continent by combining GRACE data and remotely sensed precipitation and dividing by population density, called the Potential Available Water Storage 1 3 (PAWS) indicator. The PAWS shows water stress affecting the African population. High water stress was found for Northern Africa, for most parts of the Greater Horn of Africa, and for South Africa.
Further on, Awange (2021a) provided a detailed ICA-based analysis of influences of climate modes on the hydro-meteorological conditions in the individual subbasins of the Nile based on GRACE TWSA. However,  Shamsudduha et al. (2017)  highlighted the lack of correlation between GRACE-derived GW variations and in situ data in the Upper Nile basin. Indeed, GRACE-derived GW variations are greatly constrained by uncertainties in both soil moisture and low long-term amplitude of GW changes as the Upper Nile basin aquifers are in deeply weathered crystalline rocks. Consequently, as 40 % of SSA aquifers are located in that environment (e. g. ,  MacDonald et al. 2012; Shamsudduha et al. 2017) , GRACE-derived GW variation must be used with caution in these specific areas.
g. ,  Sultan et al. 2014; Oiro et al. 2020 ), 1 3 -identification of hydro-climatological patterns that need to be considered for decision making (e. g. , Carvalho Resende et al. 2019), -regional GW resources monitoring for deriving local-scale GW availability (e. g. ,  Nanteza et al. 2016). Over the course of the past decade, the contribution of satellite observations to GW management concepts has become increasingly important. Indeed, Ahmed (2020) suggested management scenarios for North African aquifers based on GW sustainable withdrawal rates derived from GRACE. Agutu et al. (2019)  used GRACE-based TWSA and hydrological models to assess the sensitivity of GW variability over the Greater Horn of Africa to climate modes, and subsequently, explored the potential of irrigated agriculture based on auxiliary data sets.
2015). Rescaling factors on a global grid including uncertainty estimates are provided by e. g. ,  Landerer and Swenson (2012). In his seminal paper,  Wahr et al. (1998)  derives the computation of TWSA in terms of Equivalent Water Height (EWH) from the (filtered) Stokes coefficients. c nm and s nm as following: where and are geocentric colatitude and longitude, respectively, R is the Earth's mean radius, M is the mass of the Earth, P nm are the fully normalized associated Legendre functions of degree n and order m, n max is the maximum degree of the SH coefficients, and k ′ n are the dimensionless degree-dependent gravitational load Love numbers, which represent the average Earth properties  (Farrell 1972). In many applications, GRACE data are evaluated for river basins described by a given polygon or basin function.
Expressing the basin function in terms of SH coefficients c nm and s nm spatially averaged TWSA can be directly computed from the Stokes coefficients according to  Swenson and Wahr (2002) : The accuracy of catchment averaged TWSA depends on regional mass distribution, which governs signal leaking inside or out of the basin, and the catchment size  (Longuevergne et al. 2010). Again signal attenuation can be taken into account by rescaling, e. g. , according to  Long et al. (2015). (ii) All processing centers also provide gridded time series of monthly TWS anomalies (TWSA) where post processing is already applied. The user can chose the filter that should be applied. If rescaling is necessary for the application, rescaling factors must either be computed or downloaded from data provided by previous studies, e.
Acknowledgements The authors would like to thank the support given by the  International Space Science Institute (Bern, Switzerland) , to propose this review paper, which was originally presented in a solicited presentation at the multidisciplinary workshop " Global Change in Africa" held in January 2021 in Bern  and remotely. A. S. was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) as part of the Research Unit 2736 NEROGRAV and by the  DFG -SFB 1502/1-2022  -Projektnummer: 450058266. F. F acknowledges funding from  Centre National d'Etudes Spatiales  (CNES) trough TOSCA PRIAM and SWHYM grants. The authors would like to thank  Laurent Longuevergne  for fruitful discussions during the writing process of this paper and  Anno Löcher  for proofreading the manuscript.
The LES approach showed better agreement with experimental data. In the present work, the Partially Averaged Navier-Stokes (PANS) approach was implemented in OpenFOAM. Only a few articles discussed the use of this method for highly swirl and anisotropic flows and applications. For instance, Foroutan et al. [18]  used PANS method for a swirling flow in an abrupt expansion. Comparisons were done with both experimental data and DDES results, and PANS was found to be closer to the experimental than DDES. But in fact it was proved to be superior over RANS and as effective as LES in many applications. Lakshmipathy et al. [19]  have tested the PANS method while changing the viscosity reduction factor in a flow across a cylinder for a high Reynolds number and compared their results to those of LES  [20]  and some experimental results  [21].
The instrumentation of the High Altitude and Long Range (HALO) research aircraft is extended by the new Broadband AirCrAft RaDiometer Instrumentation (BAC-ARDI) to quantify the radiative energy budget. Two sets of pyranometers and pyrgeometers are mounted to measure upward and downward solar (0. 3-3 µm) and thermal-infrared (3-100 µm) irradiances. The radiometers are installed in a passively ventilated fairing to reduce the effects of the dynamic environment, e. g. , fast changes in altitude and temperature. The remaining thermal effects range up to 20 W m -2 for the pyranometers and 10 W m -2 for the pyrgeometers. Using data collected by BACARDI during a night flight, it is demonstrated that the dynamic components of the offsets can be parameterized by the rate of change of the radiometer sensor temperatures, providing a greatly simplifying correction of the dynamic thermal effects.
The parameterization provides a linear correction function (200-500 W m -2 K -1 s) that depends on the radiometer type and the mounting position of the radiometer on HALO. Furthermore, BACARDI measurements from the EUREC 4 A (Elucidating the Role of Clouds-Circulation Coupling in Climate) field campaign are analyzed to characterize the performance of the radiometers and to evaluate all corrections applied in the data processing. Vertical profiles of irradiance measurements up to 10 km altitude show that the thermal offset correction limits the bias due to temperature changes to values below 10 W m -2. Measurements with BACARDI during horizon-tal, circular flight patterns in cloud-free conditions demonstrate that the common geometric attitude correction of the solar downward irradiance provides reliable measurements in this typical flight section of EUREC 4 A, even without active stabilization of the radiometer.
2 • C. The output voltage signals have a bandwidth of 3 Hz and are recorded by the HALO basic data acquisition system BAHAMAS (BAsic HAlo Measurement And Sensor system;  Krautstrunk and Giez, 2012)  with a 10 Hz data rate and 18bit resolution. A signal path calibration is performed after aircraft installation of the radiometers, which includes all wiring, connectors, electronics, and the data acquisition. For the calibration, the radiometers are replaced by either a highprecision constant voltage source (Burster 4463) to simulate the thermopile output or a high-precision resistance decade (Burster 1427) to simulate the Pt-100. Both calibration references are set to values covering the operating range of the radiometer and Pt-100 thermometer by a computer-controlled calibration routine. The calibration factors are implemented in the first post-processing step of the BACARDI raw data.
Therefore, the coefficients reported here for BACARDI operated on HALO cannot reliably be transferred to other broadband radiometers on other research aircraft. For the thermal-infrared irradiance measured by the pyrgeometers, the assumption of F ir = 0 W m -2 does not apply. On timescales of several minutes, F ir also varies with changing atmospheric conditions and altitude and cannot be assumed to be constant. Therefore, only the detrending with the 100 s high-pass filter is applied. Additionally, only selected flight segments are used to determine F. These sections are characterized by small variations in the thermal-infrared irradiance that match strong variations in temperature. The selected data are shown in Fig. 5b  and d  for both pyrgeometers. The remaining fluctuations of the pyrgeometers show an excellent correlation with ∂T ref /∂t, which is inverse to the correlation of the pyranometers.
The dynamic thermal offset correction coefficient amounts to β = -491 W m -2 K -1 s for the upper pyrgeometer and β = -404 W m -2 K -1 s for the lower pyrgeometer. Compared to the CMP22 pyranometers, β values of both CGR4 pyrgeometers show only a small difference. This might be a consequence of the less exposed domes of the CGR4 compared to the CMP22 in combination with the more efficient ventilation of the CGR4 inside the BACARDI sensor mounting where the CGR4 is placed in front of CMP22 with respect to the flight direction. Applying these parameterizations, all irradiances measured during the night flight were corrected. In Fig. 6 , the upward and downward solar irradiances are compared to the uncorrected measurements for the entire flight. The data are presented as vertical profiles to compare the ascent and descent, which should agree after correction in the absence of any solar radiation.
Consequently, the atmospheric heating rates, defined as the vertical change in net irradiance, ∂T ∂t = 1 ρ • c p • ∂F net ∂z , (19) also show only a minor impact of the radiometer dynamic thermal offsets. In Eq. (  19 ) ρ represents the air density and c p the specific heat capacity of the air. From the example profile, heating rates are calculated for a 50 m layer thickness, showing the strongest heating rates of down to -4 K h -1 at the top of the low-level cloud layer. However, the differences between corrected and uncorrected data are less than ±0. 2 K h -1 for the entire profile. These results demonstrate that for this specific application of BACARDI, which is based on differences of upper and lower broadband radiometer measurements, a dynamic thermal offset correction could be neglected.
Oscillations with higher frequencies between 0. 5 and 1 Hz, e. g. , most obvious between 17:00 and 19:30 UTC, result from variations of the roll and pitch angle due to turbulence and the aircraft autopilot. The postcorrection of F ↓ sol does remove most of these fast and slow oscillations. This confirms that the roll and pitch angle offsets are determined with sufficient accuracy and that the sensor response time of BACARDI is corrected so that the oscillations are synchronized in time with the aircraft attitude. Subsequently, the remaining slow oscillations are in phase with the simulations and are only caused by the changes in the local solar zenith angle (latitude). A statistically more robust comparison of measured and simulated F ↓ sol is performed by merging 12 EUREC 4 A flights and filtering the data for cloud-free conditions and reliable attitude corrections.
Data are assumed to be valid when the attitude correction factors are less than 25 %. For larger correction factors, roll and pitch angles need to be smaller than 5 •. A one-to-one comparison is shown in Fig. 11a. From the total number of almost 3 million individual measurement samples, 97. 6 % agree with the simulations within an uncertainty range of less than 5 %. This indicates that the general performance of BACARDI including the thermal and attitude correction is stable over the entire campaign. A linear regression of all reliable filtered data shows only a slight deviation from the 1 : 1 slope with a correlation coefficient of 0. 999. The absolute differences are limited by the applied filter but illustrate that for high solar irradiances, the outliers of the measurements tend to be lower than the simulations.
Conclusions A new radiometer package, the Broadband AirCrAft Ra-Diometer Instrumentation (BACARDI) for the HALO research aircraft, is introduced and characterized. BACARDI comprises two sets of upward-and downward-looking broadband radiometers covering the solar and thermal-infrared spectral ranges. The operation of broadband pyranometers and pyrgeometers as mounted on the HALO research aircraft is investigated in this paper. Especially for a fast and highflying aircraft such as HALO, for which the environmental conditions such as air temperature and density can change rapidly, a minimization of the related dynamic effects is required for the data to fulfill their scientific potential. Three basic corrections are applied to the measurements of BAC-ARDI. -The post-processing of BACARDI measurements accounts for the temperature dependence of the sensor thermopile sensitivity.
Acknowledgements. We thank the  Max Planck Institute for Meteorology, Hamburg, Germany , for the funding of the new radiometer system and providing it to the HALO community. We are further grateful for funding of project grant nos. 422897361  and  316500630  by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  within the framework of  Priority Programme SPP 1294  to promote research with HALO. We gratefully acknowledge the funding by the  DFG  -project number  268020496 -TRR 172 , within the  Transregional Collaborative Research Center " ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms  (AC)3". We thank the  Max Planck Institute for Meteorology  for designing and coordinating the EUREC 4 A campaign and the  German Aerospace Center (Deutsches Luft und Raumfahrtzentrum, DLR)  for campaign support.
Method 3 The general idea of this method is described in the following three steps: Step 1: we start from any initialization a 0 , l 0 , s 0 then we execute the optim function with several restarts until convergence to a local minimum a 1 , l 1 , s 1. Step 2: We draw a sample of points around a 1 , l 1 , s 1 according to the truncated Gaussian law of average a 1 , l 1 , s 1 with the constraint l > l 1 , by keeping the same variance and using the function rtnorm of the package msm. For each of the points we repeat step 1. We thus collect a new list of local minimum. Initialization: Assign to each (a, l, s) an initial value (a 0 , l 0 , s 0 ) calculate (a opt 0 , l opt 0 , s opt 0 , min opt 0 ) = opt(T, c(a 0 , l 0 , s 0 ), K), using the opt() function with K restarts proposed in Algorithm 1.
Aim The present systematic review analyzes existing strategies and policies used for adult vaccination of seven countries of the European area, emphasizing weaknesses and strengths of immunization schedules. Selected countries were Germany, France, the United Kingdom, Italy, Spain, Sweden, and Romania. Subject and methods Three main scientific databases (PubMed, Scopus, and Web of Science) were queried and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies assessing weaknesses or strengths factors, facilitators and barriers related to the adult vaccination plans were considered eligible. We included ten studies with a medium/high score at the quality assessment.
Search strategy Data sources used were PubMed, Scopus, Web of Science. Each electronic database was searched to identify relevant articles published in English, French, Spanish and Italian language. Since in a preliminary analysis the documents published before 2011 were found to be excessively dated, documents published from 01/01/2011 to 01/01/2023 were included. The search string was developed consisting of the following keywords "adult vaccinations," "healthy adults," "adult immunization," "schedule," "European countries," "strengths," "weaknesses," "barriers," and "management. " The full search string is available in the Online Resource 2.
Eligibility criteria The search question and the eligibility criteria for inclusion in the systematic review were developed according to the PICOS framework. P (population) -Healthy adult populations (aged 18-64) in the selected European Countries (France, Italy, Spain, Germany, UK, Sweden, and Romania); I (intervention) -Vaccination/immunization schedules/ plan available; C (comparator) -There is not a comparator applicable for the studies in this systematic review; O (outcome) -Weakness and strengths of the vaccination plans S (study type) -Either quantitative and qualitative studies reporting primary/secondary data were included. Based on the PICOS developed for this systematic review, studies assessing weaknesses or strengths factors, facilitators and barriers related to the adult vaccination schedules/plans in the selected European countries were considered eligible.
Study selection The identified articles from all the databases were uploaded to RAYYAN software (Rayyan Systems 2023) and the duplicates were removed. Four researchers (CC; TEL; AP; ST) independently performed the initial step of screening based on title and abstracts. As a second step, five independent researchers (CC; TEL; GSL; AP; ST) carefully read the articles with full texts available to decide upon the final articles to be included in the systematic review. Articles satisfying the eligibility criteria were selected for inclusion in the systematic review. Reasons for exclusion of full texts were recorded and were reported in the PRISMA flow chart (Fig. 1 ). The reference lists of the included articles were carefully hand-searched to retrieve additional eligible articles. Any discrepancies in the screening process and study selection were resolved by consulting a sixth researcher (ChC).
Data extraction and synthesis Five researchers (CC; TEL; GSL; AP; ST) independently extracted from each article the following data: • Study identification (first author, year of publication). • Study characteristics (country, study design, population, study size). • Information related to the vaccination plan/schedule reported (type, age groups). • Barriers and success factors of the vaccination plan. Any discrepancies in the data extraction process were resolved through discussion with a sixth author (ChC). We performed a synthetic descriptive analysis of the studies according to the area of contextual factors.