Historical fog climate dataset for Carpathian Basin from 1886 to 1919

This paper presents the historical fog climate dataset from 1886 to 1919 for Hungary and its neighbouring countries in the Carpathian Basin. The dataset was obtained from the yearbooks of the Royal Hungarian Central Institute of Meteorology and Earth Magnetism (RHCIMEM) established in 1870 to investigate the climatic features of Hungary during the time of the Austro-Hungarian Monarchy. Monthly observations were recorded from 1871 and daily observations were recorded from 1886. The yearbooks contain daily meteorological records of temperature, relative humidity, rainfall, pressure, wind speed and direction, cloudiness and surface weather conditions along with monthly summaries for 24 meteorological stations. The daily weather observations were recorded three times a day, namely at 07:00, 14:00 and 21:00 local time. Station information (location, environment, instrumentation, observations etc.) can also be found in the yearbooks as metadata. For example, the definition of fog in the case of historical observations is the same as that of today, i.e., fog is detected if the maximum horizontal visibility is less than 1 km. In this way fog observations are easily comparable to today's observations without requiring further data correction and homogenisation. The longest 13 continuously recorded fog observation datasets have the length between 15 and 34 years. The stations are located in 5 countries of the Carpathian Basin at present. These datastests are suitable for conducting historical climatic investigations and can also serve as reference datasets. The historical dataset can be used to study the annual and seasonal changes in frequency and duration of fog events in the Carpathian Basin as a reference, thus facilitating research in the field of fog climatology and forecast

Households’ perceptions on impact of drought on water resources in Makindu sub-county, Kenya

Drought is a major threat to water sources in the world. It causes variation in volumes of water flow. Once compounded with other factors, water scarcity arises. However, perceptions of households on the impact of drought on water sources vary from region to region. Understanding the perceptions of households is critical in ensuring people cope with water shortages. Thus, this paper sought to examine household’s perception on the impact of drought on water resources in Makindu Sub-County, Kenya. The study employed descriptive survey research design. A total of 370 households were sampled using simple random sampling. Purposive sampling was used to select the key informants. Questionnaires and key informants’ interview schedules were used to collect primary data. Data from questionnaires was coded and analyzed using SPSS Version 20. As perceived by the households’ drought led to drying up of water sources and further its impact varied from one drought intensity to another. The study also established that overuse by households, high rates of evaporation and encroachment of people to water sources were also affecting water sources. It was concluded that households should embrace adaptation and coping strategies to minimize water shortages. It is recommended that sensitization is required to equip individuals with knowledge to conserve water sources. The study provides new knowledge that is beneficial for water resource saving policy making, governance as well as for education at community and institutional levels.Keywords: Perceptions, drought, household

The 2022 symposium on dementia and brain aging in low‐ and middle‐income countries: Highlights on research, diagnosis, care, and impact

Two of every three persons living with dementia reside in low‐ and middle‐income countries (LMICs). The projected increase in global dementia rates is expected to affect LMICs disproportionately. However, the majority of global dementia care costs occur in high‐income countries (HICs), with dementia research predominantly focusing on HICs. This imbalance necessitates LMIC‐focused research to ensure that characterization of dementia accurately reflects the involvement and specificities of diverse populations. Development of effective preventive, diagnostic, and therapeutic approaches for dementia in LMICs requires targeted, personalized, and harmonized efforts. Our article represents timely discussions at the 2022 Symposium on Dementia and Brain Aging in LMICs that identified the foremost opportunities to advance dementia research, differential diagnosis, use of neuropsychometric tools, awareness, and treatment options. We highlight key topics discussed at the meeting and provide future recommendations to foster a more equitable landscape for dementia prevention, diagnosis, care, policy, and management in LMICs. Highlights: Two‐thirds of persons with dementia live in LMICs, yet research and costs are skewed toward HICs. LMICs expect dementia prevalence to more than double, accompanied by socioeconomic disparities. The 2022 Symposium on Dementia in LMICs addressed advances in research, diagnosis, prevention, and policy. The Nairobi Declaration urges global action to enhance dementia outcomes in LMICs

Wind power density characterization in arid and semi-arid Taita-Taveta and Garissa counties of Kenya

Wind Power Density (WPD) is a crucial parameter that can be used in assessing the potential of a given site for energy development and determining the suitability of wind turbine installation. A 7-year long-term data (2014–2020) of temperature, relative humidity, and wind speeds were obtained from Voi and Garissa synoptic station with a 3-h resolution. The objective of the study was to characterize wind power density in selected arid regions in Kenya. Analysis was performed using Weibull distribution parameters statistical tools i.e. Moment of Methods, Empirical Method (Justus), and Empirical Method (Lyssen), and error analysis using Mean Absolute Percentage Error, Mean Absolute Deviation (MAD), Coefficient of determination (R2) and Root Mean squared Error to determine the WPD accurate characteristics. Results show that Moment of Methods (MoM) performed better compared to other statistical tools, while the Taita Taveta had a better coefficient of Variance (CoV) ranging between 0.20 and 0.28% compared to 0.28–0.43% in Garissa. Based on the wind power density, the sites were found to be within Class II on the wind power classification from IEC and thus not viable for commercial power purposes. Results imply that power produced can be used in supplementing Kenya Offgrid Solar Access Project (KoSAP) which supplements power production used in gazetted marginalized counties by Kenya Power

Analysis of Short-Term Drought Episodes Using Sentinel-3 SLSTR Data under a Semi-Arid Climate in Lower Eastern Kenya

This study uses Sentinel-3 SLSTR data to analyze short-term drought events between 2019 and 2021. It investigates the crucial role of vegetation cover, land surface temperature, and water vapor amount associated with drought over Kenya’s lower eastern counties. Therefore, three essential climate variables (ECVs) of interest were derived, namely Land Surface Temperature (LST), Fractional Vegetation Cover (FVC), and Total Column Water Vapor (TCWV). These features were analyzed for four counties between the wettest and driest episodes in 2019 and 2021. The study showed that Makueni and Taita Taveta counties had the highest density of FVC values (60–80%) in April 2019 and 2021. Machakos and Kitui counties had the lowest FVC estimates of 0% to 20% in September for both periods and between 40% and 60% during wet seasons. As FVC is a crucial land parameter for sequestering carbon and detecting soil moisture and vegetation density losses, its variation is strongly related to drought magnitude. The land surface temperature has drastically changed over time, with Kitui and Taita Taveta counties having the highest estimates above 20 °C in 2019. A significant spatial variation of TCWV was observed across different counties, with values less than 26 mm in Machakos county during the dry season of 2019, while Kitui and Taita Taveta counties had the highest estimates, greater than 36 mm during the wet season in 2021. Land surface temperature variation is negatively proportional to vegetation density and soil moisture content, as non-vegetated areas are expected to have lower moisture content. Overall, Sentinel-3 SLSTR products provide an efficient and promising data source for short-term drought monitoring, especially in cases where in situ measurement data are scarce. ECVs-produced maps will assist decision-makers with a better understanding of short-term drought events as well as soil moisture loss episodes that influence agriculture under arid and semi-arid climates. Furthermore, Sentinel-3 data can be used to interpret hydrological, ecological, and environmental changes and their implications under different environmental conditions

Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya

Kenya is dominated by a rainfed agricultural economy. Recurrent droughts influence food security. Remotely sensed data can provide high-resolution results when coupled with a suitable machine learning algorithm. Sentinel-1 SAR and Sentinel-3 SLSTR sensors can provide the fundamental characteristics for actual evapotranspiration (AET) estimation. This study aimed to estimate the actual monthly evapotranspiration in Busia County in Western Kenya using Sentinel-1 SAR and Sentinel-3 SLSTR data with the application of the gradient boosting machine (GBM) model. The descriptive analysis provided by the model showed that the estimated mean, minimum, and maximum AET values were 116, 70, and 151 mm/month, respectively. The model performance was assessed using the correlation coefficient (r) and root mean square error (RMSE). The results revealed a correlation coefficient of 0.81 and an RMSE of 10.7 mm for the training dataset (80%), and a correlation coefficient of 0.47 and an RMSE of 14.1 mm for the testing data (20%). The results are of great importance scientifically, as they are a conduit for exploring alternative methodologies in areas with scarce meteorological data. The study proves the efficiency of high-resolution data retrieved from Sentinel sensors coupled with machine learning algorithms, focusing on GBM as an alternative to accurately estimate AET. However, the optimal solution would be to obtain direct evapotranspiration measurements

The 2022 symposium on dementia and brain aging in low- and middle-income countries: Highlights on research, diagnosis, care, and impact.

Publication status: PublishedFunder: Alzheimer's Association, USAFunder: ICMR: Indian Council for Medical ResearchFunder: GOK: Government of KarnatakaFunder: RBM: Rotary Bangalore MidtownFunder: LSIPL: M/s Lowes Services India Private LimitedFunder: Wellcome Trust, UKFunder: Chinese Neuroscience Society, ChinaFunder: International Society for Neurochemistry; doi: http://dx.doi.org/10.13039/501100008992Funder: AXA Research Fund; doi: http://dx.doi.org/10.13039/501100001961Funder: Appel à Projet des Equipes Émergentes et Labellisées scheme (APREL)Funder: Global Brain Health Institute (GBHI)Funder: Health Professionals Education Partnership Initiative EthiopiaFunder: Rainwater Charitable Foundation – The Bluefield project to cure FTD, and Global Brain Health InstituteFunder: National Research Foundation (NRF)Funder: Michael J. Fox Foundation for Parkinson's Research, USAFunder: National Institute for Health and Care Research, United KingdomFunder: UK National Health Service, Newcastle University,Funder: Alzheimer's Drug Discovery Foundation (ADDF)Funder: Canadian Institute of Health ResearchFunder: National Council for Scientific and Technological Development; doi: http://dx.doi.org/10.13039/501100003593Funder: Bluefield Project, the Olav Thon Foundation, the Erling‐Persson Family Foundation, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, SwedenFunder: Marie Skłodowska‐CurieFunder: National Institute for Health and Care Research University College London Hospitals Biomedical Research CentreTwo of every three persons living with dementia reside in low- and middle-income countries (LMICs). The projected increase in global dementia rates is expected to affect LMICs disproportionately. However, the majority of global dementia care costs occur in high-income countries (HICs), with dementia research predominantly focusing on HICs. This imbalance necessitates LMIC-focused research to ensure that characterization of dementia accurately reflects the involvement and specificities of diverse populations. Development of effective preventive, diagnostic, and therapeutic approaches for dementia in LMICs requires targeted, personalized, and harmonized efforts. Our article represents timely discussions at the 2022 Symposium on Dementia and Brain Aging in LMICs that identified the foremost opportunities to advance dementia research, differential diagnosis, use of neuropsychometric tools, awareness, and treatment options. We highlight key topics discussed at the meeting and provide future recommendations to foster a more equitable landscape for dementia prevention, diagnosis, care, policy, and management in LMICs. HIGHLIGHTS: Two-thirds of persons with dementia live in LMICs, yet research and costs are skewed toward HICs. LMICs expect dementia prevalence to more than double, accompanied by socioeconomic disparities. The 2022 Symposium on Dementia in LMICs addressed advances in research, diagnosis, prevention, and policy. The Nairobi Declaration urges global action to enhance dementia outcomes in LMICs

The Nairobi Declaration—Reducing the burden of dementia in low‐ and middle‐income countries (LMICs): Declaration of the 2022 Symposium on Dementia and Brain Aging in LMICs

[No abstract available