Visualization of 40 Years of Tropical Cyclone Positions and Their Rainfall

Correos de investigadores: [email protected] || [email protected] || [email protected] || [email protected] article focuses on a visualization of tropical cyclone track data occurring over a 40- year period (1970–2010) and their relationship with (extremely) heavy rainfall reported by 88 Central American weather stations. The purpose of the visualization is to associate the paths of tropical cyclones in oceanic areas with heavy rainfall inland. Thus, the potential for producing a set of rainfall patterns might somehow help in predicting where different impacts like flooding might occur when tropical cyclones develop in specific oceanic regions. The visualization will serve as a key tool for CIGEFI scientists to apply in their work to determine critical positions of the tropical cyclones associated with extremely heavy rainfall events at daily timescales.Universidad de Costa Rica/[805-B9-454]/UCR/Costa RicaUniversidad de Costa Rica/[805-C0-610]/UCR/Costa RicaUniversidad de Costa Rica/[EC-497]/UCR/Costa RicaUniversidad de Costa Rica/[805-A4-906]/UCR/Costa RicaUniversidad de Costa Rica/[805-C0-074]/UCR/Costa RicaUniversidad de Costa Rica/[805-A1-715]/UCR/Costa RicaUniversidad de Costa Rica/[805-B0-810]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI)UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de FísicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Ciencias del Mar y Limnología (CIMAR

Uso de herramientas estadísticas para la predicción estacional del campo de precipitación en América Central como apoyo a los Foros Climáticos Regionales. 2: Análisis de Correlación Canónica.

Artículo científico -- Universidad de Costa Rica. Centro de Investigaciones Geofísicas, 2012Se elaboró un pronóstico climático estacional en América Central basado en el Análisis de Correlación Canónica (ACC). Como predictores se usaron las temperaturas superficiales del mar de los océanos circundantes al istmo y como predictante el campo de precipitación, de 146 estaciones meteorológicas en Mesoamérica con registros mensuales para el período 1971-2000. El área oceánica asociada en el campo de la temperatura superficial del mar fue 60 N- 60 S y 270-0 O. En general, para cada uno de los períodos sobre los cuales se realizaron los pronósticos, se utilizaron las temperaturas superficiales del mar del trimestre anterior. El ACC, mostró los mejores resultados para el trimestre de ASO, trimestre de máxima precipitación anual en la vertiente del Pacífico. Algunos de los modos identificados en el análisis presentaron patrones espaciales asociados a fuentes de variabilidad conocidas como el ENOS, por lo que el ACC aparte de ser una herramienta útil para realizar pronósticos estacionales, también resultó útil para explicar predictores y asociaciones con otros índices climáticos. Los resultados de este trabajo mostraron que la introducción del ACC puede ayudar a los pronósticos estacionales de la región centroamericana, realizados en los foros de predicción climática de América Central, con análisis objetivos sobre las relaciones predictivas encontradas en el istmo.A seasonal climate prediction was elaborated for Central America based on Canonical Correlation Analysis (CCA). Sea surface temperatures from the oceans around the isthmus were used as predictors. Precipitation was used as predictand field, using 146 meteorological stations located in Mesoamerica with monthly records from 1971 to 2000. The sea surface temperature area used was 60 N-60 S and 270-0 W. In general, the sea surface temperature associated with previous trimester was used for every predicted season. The CCA showed the best precipitation results for ASO, which is the trimester associated with the season of maximum precipitation over the Central American Pacific slope. Some of the modes identified in the analysis display spatial patterns associated with known climate variability sources as ENSO, meaning that CCA is useful for seasonal prediction in Central America and for predictor patterns explanation and possible climate indices associated. Results showed that CCA use in Central America can also help in the Regional Climate Outlook Forum tasks, providing objective analysis for the predictive relationships found in the region.Servicios Meteorológicos e Hidrológicos de América CentralUniversidad de Costa Rica. Instituto de Investigaciones GeofísicasUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI

Uso de herramientas estadísticas para la predicción estacional del campo de precipitación en América Central como apoyo a los Foros Climáticos Regionales. 1: Análisis de tablas de contingencia.

Artículo científico -- Universidad de Costa Rica. Centro de Investigaciones Geofísicas, 2012Se utilizó la técnica estadística del análisis de tablas de contingencia para elaborar esquemas predictivos de los campos de precipitación en América Central. Como primer paso, se produjeron índices de estos campos utilizando el análisis de componentes principales a partir de los registros de 146 estaciones con datos diarios. Se obtuvieron dos componentes principales para la precipitación, asociados con las vertientes Pacífico y Caribe de América Central. Debido a que uno de los objetivos de este estudio era el de apoyar al proceso de los Foros Regionales de Predicción Climática, los esquemas predictivos utilizaron los trimestres de Mayo-Junio-Julio, Agosto-Setiembre-Octubre y el cuatrimestre de Diciembre-Enero-Febrero-Marzo como periodos a predecir de la precipitación. Como predictores se utilizaron diferentes índices asociados con fuentes de variabilidad climática que influencian los patrones climáticos de América Central, usando uno o dos bimestres anteriores a la estación predicha. Se encontraron esquemas predictivos útiles para prácticamente todas las relaciones señaladas anteriormente y se observó que gran parte de la variabilidad de América Central se puede explicar con los índices asociados a El Niño (La Niña) (variabilidad interanual) y del Atlántico (AMO, principalmente, variabilidad multidecadal).The statistical technique of contingency table analysis was used to produce predictive schemes associated with rainfall in Central America. As a first step, principal component analysis was used to produce indices using 146 daily station records. Two rainfall components were obtained associated with Central America Pacific and Caribbean slopes. Keeping in mind that one of the work objectives is to support the Regional Climate Outlook Forums process, the predictive schemes used the trimesters of May-June-July, August- September-October and the four month period of December-January-February-March as targets for predictions in rainfall. Different climate indices were used as predictors, associated with several climate variability sources that influence the climate patterns in Central America, using one or two bimester previous to the predicted season. Useful predictive schemes were found for practically all the relationships mentioned previously, noticing that most of the Central America climate variability could be explained by the El Niño (La Niña) (e.g. interanual variability) and the Atlantic (AMO, mainly, e.g. multidecadal variability) indices.Servicios Meteorológicos e Hidrológicos de América CentralUniversidad de Costa Rica, Vicerrectoría de Investigación 805-B0-402 (apoyo de CORBANA)Universidad de Costa Rica, Vicerrectoría de Investigación 805-A8-606 (apoyo de Florida Ice & Farm CO.)Universidad de Costa Rica, Vicerrectoría de Investigación 805-A9-532 (apoyo de ASDI-CSUCA)Universidad de Costa Rica, Vicerrectoría de Investigación 805-A7-002 (apoyo de CRN2050-IAI)Universidad de Costa Rica, Escuela de FísicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI

Predicción estacional de las temperaturas máximas y mínimas en América Central

Artículo científico -- Universidad de Costa Rica. Centro de Investigaciones Geofísicas, 2014Se utilizaron las técnicas estadísticas del análisis de tablas de contingencia y el análisis de correlación canónica para elaborar esquemas predictivos de los campos utilizando el análisis de componentes principales a partir de los registros de 146 estaciones con datos diarios, en los cuales se obtuvo un índice para temperatura máxima y otro para la mínima. Además, se obtuvieron sendos índices para pronosticar los campos de las temperaturas máximas y mínimas. Debido a que uno de los objetivos del estudio es el de apoyar el proceso de los Foros Regionales de Predicción Climática, los esquemas predictivos utilizaron los trimestres de Mayo-Junio-Julio, Agosto-Setiembre-Octubre y el cuatrimestre de Diciembre-Enero-Febrero-Marzo como periodos a predecir de los predictantes, es decir, de las temperaturas máximas y mínimas. Como predictores se utilizaron diferentes índices asociados con fuentes de variabilidad climática, que influencian los patrones climáticos de América central, como Niño 3 y NAO, esto para el bimestre anterior y transanterior al del predictante, también se formaron nuevos índices a partir de la combinación lineal de varios de ellos. Se encontraron esquemas predictivos útiles para todas las relaciones señaladas anteriormente y se observó que gran parte de la variabilidad multidecadal como el de la Oscilación Multidecadal del Atlántico (AMO, por sus siglas en inglés).The statistical technique of contingency table analysis and canonical correlation analysis were used to produce predictive schemes associated with maximum and minimum temperatures in Central America. As a first step, principal component analysis was used to produce indices using 146 daily station records. One index was obtained for both, maximum and minimum temperatures. Keeping in mind that one of the work objectives is to support the Regional Climate Outlook Forum process, the predictive schemes used the trimesters of May-June-July, August-September-October and the four month period of December-January-February-March as targets for predictions of maximum and minimum temperatures. Different climate indices like Niño 3 and NAO were used as predictors, associated with several climate variability sources that influence the climate patterns in Central America, using one or two bimester previous to the predicted season. Linear combination of climate indices was also to create new ones. Useful predictive schemes were found for practically all the relationships mentioned previously, noticing that most of the Atlantic (AMO, mainly, e.g. multidecadal variability) indices.Universidad de Costa Rica. Escuela de Física. Centro de Investigaciones GeofísicasVicerrectoría de Investigación, Universidad de Costa Rica: 805-B0-402 (apoyo de CORBANA)Vicerrectoría de Investigación, Universidad de Costa Rica: 805-A9-224 (Fondo de Estímulo)Vicerrectoría de Investigación, Universidad de Costa Rica: 805-B3-600 (Fondo de Estímulo)Vicerrectoría de Investigación, Universidad de Costa Rica: 805-A7-002 (Apoyo CRN 2050-IAI)UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI

Patient symptoms and experience following COVID-19: results from a UK-wide survey

Objectives: To investigate the experience of people who continue to be unwell after acute COVID-19, often referred to as ‘long COVID’, both in terms of their symptoms and their interactions with healthcare. Design: We conducted a mixed-methods analysis of responses to a survey accessed through a UK online post-COVID support and information hub between April and December 2020 about people’s experiences after having acute COVID-19. Participants: 3290 respondents, 78% female 92.1% white ethnicity and median age range 45-54 years; 12.7% had been hospitalised. 494(16.5%) completed the survey between 4 and 8 weeks of the onset of their symptoms, 641(21.4%) between 8 and 12 weeks and 1865(62.1%) >12 weeks after. Results: The ongoing symptoms most frequently reported were; breathing problems (92.1%), fatigue (83.3%), muscle weakness or joint stiffness (50.6%), sleep disturbances (46.2%), problems with mental abilities (45.9%) changes in mood, including anxiety and depression (43.1%) and cough (42.3%). Symptoms did not appear to be related to the severity of the acute illness or to the presence of pre-existing medical conditions. Analysis of free text responses revealed three main themes (1) Experience of living with COVID-19 – physical and psychological symptoms that fluctuate unpredictably; (2) Interactions with healthcare that were unsatisfactory; (3) Implications for the future – their own condition, society and the healthcare system, and the need for research Conclusion: Consideration of patient perspective and experiences will assist in the planning of services to address problems persisting in people who remain symptomatic after the acute phase of COVID-19

Physical factors contributing to rural water supply functionality performance in Uganda

This report communicates the findings generated from one of the project surveys – deconstruction and forensic analysis of 50 individual water points in Uganda. The report presents the new data generated to Uganda’s groundwater resource potential; the nature and condition of hand-pump borehole installations; and the significance of both of these factors to service performance. Based on the evidence collected, the main physical factors affecting functionality performance within Uganda are the poor condition of handpump components, and the complex aquifer resource. The impact of these factors can be mitigated through appropriate material choice for handpump components (non GI), increased investment in borehole siting and testing, and adequate accessibility to repairs and maintenance capacity with breakdowns. These factors should not be considered to be the only driving forces of functionality outcomes in these regions of Uganda, however, and the results of this survey need to be examined alongside the wider project findings. Wider institutional arrangements, resources and dynamics, are likely to play a significant role in the implementation of appropriate borehole construction, siting and design; procurement processes; and the management capacity available for water points at national to local levels

Permeability of the crystalline basement in Uganda : evidence from 665 pumping tests and implications for solar pumping

Crystalline basement rocks of Precambrian age underlie nearly three quarters of Uganda, providing groundwater supplies to meet ever increasing demand from rural areas and urban growth centres. Development of groundwater sources is commonly based on several factors including physical and socio-economic considerations that have a bearing on their functionality and long term reliability. Here we present new transmissivity data from 665 boreholes across basement aquifers in Uganda calculated from previously unanalyzed pumping test data. Other data are available to help interpret the transmissvity values, including borehole lithological logs, weathering thickness, well design and depth to groundwater. Spatial and depth comparisons are made to relate aquifer permeability to lithology and weathering, and also to relate borehole yields to well design. The data provide an improved understanding of the physical permeability of weathered crystalline basement rock aquifers across Uganda, complimenting earlier studies of vertical permeability profiles in focused areas. The analysis helps inform the physical capacity of the aquifer to supply the borehole yields to meet increasing demands, and application the potential for higher abstraction technologies, such as solar pumps

Seasonal prediction of extreme precipitation events and frequency of rainy days over Costa Rica, Central America, using Canonical Correlation Analysis

Artículo científico -- Universidad de Costa Rica. Centro de Investigaciones Geofísicas, 2013High mountains divide Costa Rica, Central America, into two main climate regions, the Pacific and Caribbean slopes, which are lee and windward, respectively, according to the North Atlantic trade winds – the dominant wind regime. The rain over the Pacific slope has a bimodal annual cycle, having two maxima, one in May–June and the other in August-September-October (ASO), separated by the midsummer drought in July. A first maximum of deep convection activity, and hence a first maximum of precipitation, is reached when sea surface temperature (SST) exceeds 29 C (around May). Then, the SST decreases to around 1 C due to diminished downwelling solar radiation and stronger easterly winds (during July and August), resulting in a decrease in deep convection activity. Such a reduction in deep convection activity allows an increase in down welling solar radiation and a slight increase in SST (about 28.5 C) by the end of August and early September, resulting once again in an enhanced deep convection activity, and, consequently, in a second maximum of precipitation. Most of the extreme events are found during ASO. Central American National Meteorological and Hydrological Services (NMHS) have periodic Regional Climate Outlook Fora (RCOF) to elaborate seasonal predictions. Recently, meetings after RCOF with different socioeconomic stakeholders took place to translate the probable climate impacts from predictions. From the feedback processes of these meetings has emerged that extreme event and rainy days seasonal predictions are necessary for different sectors. As is shown in this work, these predictions can be tailored using Canonical Correlation Analysis for rain during ASO, showing that extreme events and rainy days in Central America are influenced by interannual variability related to El Ni˜no-Southern Oscillation and decadal variability associated mainly with Atlantic Multidecadal Oscillation. Analyzing the geographical distribution of the ASO- 2010 disaster reports, we noticed that they did not necessarily agree with the geographical extreme precipitation event distribution, meaning that social variables, like population vulnerability, should be included in the extreme events impact analysis.Universidad de Costa Rica. Instituto de Investigaciones GeofísicasInstituto Costarricense de ElectricidadUniversity of Costa Rica, Graduate Program in Atmospheric SciencesUniversity of Costa Rica, Center for Research in Marine Sciences and LimnologyUniversity of Costa Rica, School of PhysicsNational Meteorological InstituteUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI

Generation of ordered protein assemblies using rigid three-body fusion

Protein nanomaterial design is an emerging discipline with applications in medicine and beyond. A longstanding design approach uses genetic fusion to join protein homo-oligomer subunits via α-helical linkers to form more complex symmetric assemblies, but this method is hampered by linker flexibility and a dearth of geometric solutions. Here, we describe a general computational method that performs rigid three-body fusion of homo-oligomer and spacer building blocks to generate user-defined architectures, while at the same time significantly increasing the number of geometric solutions over typical symmetric fusion. The fusion junctions are then optimized using Rosetta to minimize flexibility. We apply this method to design and test 92 dihedral symmetric protein assemblies from a set of designed homo-dimers and repeat protein building blocks. Experimental validation by native mass spectrometry, small angle X-ray scattering, and negative-stain single-particle electron microscopy confirms the assembly states for 11 designs. Most of these assemblies are constructed from DARPins (designed ankyrin repeat proteins), anchored on one end by α-helical fusion and on the other by a designed homo-dimer interface, and we explored their use for cryo-EM structure determination by incorporating DARPin variants selected to bind targets of interest. Although the target resolution was limited by preferred orientation effects, small scaffold size, and the low-order symmetry of these dihedral scaffolds, we found that the dual anchoring strategy reduced the flexibility of the target-DARPIN complex with respect to the overall assembly, suggesting that multipoint anchoring of binding domains could contribute to cryo-EM structure determination of small proteins