Tropical disturbances in relation to general circulation modeling

The initial results of an evaluation of the performance of the Goddard Laboratory of Atmospheric Simulation general circulation model depicting the tropical atmosphere during the summer are presented. Because the results show the existence of tropical wave disturbances throughout the tropics, the characteristics of synoptic disturbances over Africa were studied and a synoptic case study of a selected disturbance in this area was conducted. It is shown that the model is able to reproduce wave type synoptic disturbances in the tropics. The findings show that, in one of the summers simulated, the disturbances are predominantly closed vortices; in another summer, the predominant disturbances are open waves

URBAN TERRAIN CLIMATOLOGY AND REMOTE SENSING *

. Urban areas have been conceived of as monolithic heat islands because traditional ground observation techniques do not lend themselves to more specific analyses. Observations of urban energy-exchange obtained from calibrated electro-optical scanners combined with energy budget simulation techniques provide tools to relate the urban land use mosaic to the heat island phenomenon. Maps of surface energy-related phenomena were made from airborne scanner outputs for selected flightpaths across the city of Baltimore, Maryland. Conditions for the flight time were simulated according to the various types of land use using an energy budget simulation model which lends itself to extrapolation of simulated grid-point conditions into a map form. Maps made by simulation compare sufficiently well with those made by aerial observation to encourage further refinement of the simulation approach.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72392/1/j.1467-8306.1976.tb01110.x.pd

Using mixed objects in the training of object-based image classifications

Image classification for thematic mapping is a very common application in remote sensing, which is sometimes realized through object-based image analysis. In these analyses, it is common for some of the objects to be mixed in their class composition and thus violate the commonly made assumption of object purity that is implicit in a conventional object-based image analysis. Mixed objects can be a problem throughout a classification analysis, but are particularly challenging in the training stage as they can result in degraded training statistics and act to reduce mapping accuracy. In this paper the potential of using mixed objects in training object-based image classifications is evaluated. Remotely sensed data were submitted to a series of segmentation analyses from which a range of under- to over-segmented outputs were intentionally produced. Training objects were then selected from the segmentation outputs, resulting in training data sets that varied in terms of size (i.e. number of objects) and proportion of mixed objects. These training data sets were then used with an artificial neural network and a generalized linear model, which can accommodate objects of mixed composition, to produce a series of land cover maps. The use of training statistics estimated based on both pure and mixed objects often increased classification accuracy by around 25% when compared with accuracies obtained from the use of only pure objects in training. So rather than the mixed objects being a problem, they can be an asset in classification and facilitate land cover mapping from remote sensing. It is, therefore, desirable to recognize the nature of the objects and possibly accommodate mixed objects directly in training. The results obtained here may also have implications for the common practice of seeking an optimal segmentation output, and also act to challenge the widespread view that object-based classification is superior to pixel-based classification

Uso de Internet y la alfabetización en eSalud con temor al COVID-19 entre estudiantes de enfermería en Filipinas

The COVID-19 outbreak situation brought into focus not only the safety but also the mental wellbeing of nursing students. At present, the level of fear of COVID-19 and associated factors among nursing students in the Philippines is not well understood. This cross-sectional online survey determined the relationship between Internet use and eHealth literacy with fear of COVID-19. One thousand three hundred and sixty-seven (n=1,367) answered an online survey using the adopted eHealth Literacy Scale (eHEALS), and Fear of COVID-19 Scale (FCV-19S) administered from May 1 to 15, 2020. Descriptive statistics, tests for differences, and correlational analysis were performed. Results indicated that the composite score of the FCV-19S was 3.65, indicating moderate to high levels of fear. Fear of COVID-19 significantly differed based on sex, year level, and location. A significant inverse relationship was found between the average daily use of the Internet and fear of COVID-19. On the other hand, no significant association was noted between eHealth literacy and fear of COVID-19. The first wave of the COVID-19 outbreak has resulted in a far-reaching impact on nursing students' psychological wellbeing. This study highlights the value of the Internet and its use during the outbreak may not always lead to higher fear related to COVID-19. Nursing schools may need to create strategies to promote regulated and responsible Internet use, address students' mental health concerns and develop interventions to respond proactively to mitigate or reduce fear among nursing students during the pandemic.El brote de COVID-19 puso de relieve no solo la seguridad sino también el bienestar mental de los estudiantes de enfermería. En la actualidad, no se comprende bien el nivel de miedo al COVID-19 y los factores asociados entre los estudiantes de enfermería en Filipinas. Esta encuesta transversal en línea determinó la relación entre el uso de Internet y la alfabetización en eSalud con el miedo al COVID-19. Mil trescientos sesenta y siete (n = 1.367) respondieron una encuesta en línea utilizando la Escala de alfabetización en salud electrónica (eHEALS) y la Escala de miedo a COVID-19 (FCV-19S) administradas del 1 al 15 de mayo de 2020. Estadísticas descriptivas, se realizaron pruebas de diferencias y análisis correlacional. Los resultados indicaron que la puntuación compuesta del FCV-19S fue de 3,65, lo que indica niveles de miedo de moderados a altos. El miedo al COVID-19 difirió significativamente según el sexo, el año y la ubicación. Se encontró una relación inversa significativa entre el uso diario promedio de Internet y el miedo al COVID-19. Por otro lado, no se observó una asociación significativa entre la alfabetización en eSalud y el miedo al COVID-19. La primera ola del brote de COVID-19 ha tenido un impacto de gran alcance en el bienestar psicológico de los estudiantes de enfermería. Este estudio destaca el valor de Internet y su uso durante el brote no siempre puede generar un mayor temor relacionado con el COVID-19. Las escuelas de enfermería pueden necesitar crear estrategias para promover el uso de Internet regulado y responsable, abordar las preocupaciones de salud mental de los estudiantes y desarrollar intervenciones para responder de manera proactiva para mitigar o reducir el miedo entre los estudiantes de enfermería durante la pandemia.Universidad Pablo de Olavid

On The analysis of seasat winds

Se formuló un método para analizar los vientos de SEASAT-A el cual elimina la ambigüedad en la medición de los vientos de SEASAT. La ambigüedad se eliminó usando vientos estimados a partir del movimiento de nubes bajas y dé patrones sinópticos de nubes (basados en fotos de satélite), de climatología de vientos de superficie así como de principios de continuidad en espacio y tiempo. El método se aplicó a datos de SEASAT tomados sobre el Atlántico Tropical. La exactitud del método ha sido evaluada comparando los resultados de nuestra técnica de análisis con observaciones independientes de barcos. La comparación muestra que la técnica de análisis produjo vientos que son comparables en exactitud a la de los vientos reportados por los barcos. doi: https://doi.org/10.22201/igeof.00167169p.1983.22.4.86

Precipitation dependence on synoptic-scale conditions and cloud seeding

Se analizó, mediante el uso de radar, la precipitación sobre el sur de la Florida para determinar su dependencia de las condiciones sinópticas y de la siembra de nubes. Los parámetros sinópticos que se usan son: el flujo predominante, la humedad, la estabilidad y el cizallamiento vertical del viento. Los resultados indican que la variación en las condiciones sinópticas es mucho más importante que la siembra múltiple de nubes en la determinación de la precipitación. Estos resultados también indican que aquellos experimentos de siembra de nubes en la Florida, que no toman en cuenta el efecto de las condiciones sinópticas, pueden conducir a conclusiones equivocadas