Weather pattern climatology of the United States

Barchet, W.R.; Davis, W.E.

doi:10.2172/5447969

Cited by 12 publications

(5 citation statements)

References 1 publication

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“…Comrie and Yarnal, 1992;Yarnal and Draves 1993;Takle et al 1995;Yarnal and Frakes, 1997b). Several investigators, such as Barchet and Davis (1984), Heidorn and Yap (1986), and Yu and Pielke (1986), have also used similar schemes successfully.…”

Section: The Manual Classi®cationmentioning

confidence: 99%

A procedure for blending manual and correlation‐based synoptic classifications

Frakes

Yarnal

1997

Int. J. Climatol.

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Manual and correlation-based (also known as Lund or Kirchhofer) classi®cations are important to synoptic climatology, but both have signi®cant drawbacks. Manual classi®cations are inherently subjective and labour intensive, whereas correlationbased classi®cations give the investigator little control over the map-patterns generated by the computer. This paper develops a simple procedure that combines these two classi®cation methods, thereby minimizing these weaknesses. The hybrid procedure utilizes a relatively short-term manual classi®cation to generate composite pressure surfaces, which are then used as seeds in a long-term correlation-based computer classi®cation. Overall, the results show that the hybrid classi®cation reproduces the manual classi®cation while optimizing speed, objectivity and investigator control, thus suggesting that the hybrid procedure is superior to the manual or correlation classi®cations as they are currently used. More speci®cally, the results demonstrate little difference between the hybrid procedure and the original manual classi®cation at monthly and longer timescales , with less internal variation in the hybrid types than in the subjective categories. However, the two classi®cations showed substantial differences at the daily level, not because of poor performance by the hybrid procedure, but because of errors introduced by the subjectivity of the manual classi®cation.

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Section: The Manual Classi®cationmentioning

confidence: 99%

A procedure for blending manual and correlation‐based synoptic classifications

Frakes

Yarnal

1997

Int. J. Climatol.

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“…Of the 90 total clusters selected for 1984, the 54 largest were subjectively grouped into 10 major synoptic categories based upon meteorological similarities. Similar subjective classification schemes have been developed by Barchet and Davis (1984) and Lindsey (1980) in which synoptic categories are identified based upon locations relative to the classical polar front model of a mid-latitude cyclone. This grouping was only conducted to enhance the user's ability to follow the progression of general synoptic categories from day to day; the original 90-cluster designations should be used in any analysis of environmental parameters.…”

Section: Resultsmentioning

confidence: 99%

Development of an automated spatial synoptic climatological classification

Davis

Kalkstein

1990

Intl Journal of Climatology

126

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A spatial synoptic climatological index is developed that both characterizes the dynamic and thermodynamic surface weather regime and is capable of assessing and analysing the impact of synoptic-scale variations on numerous environmental parameters. Based upon surface weather data for the contiguous USA for 1984, daily maps are produced that show those areas under the influence of similar weather conditions. The index is developed by applying principal components analysis to a daily matrix of 141 stations by 24 weather elements (chosen to represent the thermal-moisture and surface flow characteristics). The resulting component scores are grouped using a two-stage clustering technique. This yields a map showing lobes of air having relatively homogeneous weather characteristics. These lobes (clusters) are spatially coherent and correspond closely to surface weather map features.To allow for daily tracking of the clusters and identification days or weeks after disappearance, the means of each of the daily clusters are grouped across days. The resulting 90-cluster categorization describes all of the major synoptic situations that occurred in 1984. Some of the common categories include maritime and continental polar air masses, frontal passages, and continental high dominance. These groups demonstrate seasonality, regionality, and high withincategory similarity. The resulting maps are useful since they are surface weather maps yet add clarity for environmental interpretation.

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“…The satellite image of University of California Davis (Figure 2) shows an area equivalent to about 150 ha, which, under the assumption of an average total annual rainfall in Davis being about 500 mm [63], would have the potential to collect as much as 7.5 million L of rainwater, if roof-based water catchment systems were installed and combined with the collection of run-off from streets and sidewalks (ignoring ground with vegetation and assuming near 100% collection efficiency). Even if this estimate of potential rain catchment is somewhat optimistic and only 5 million L of rainwater could be collected, this seems to represent missed opportunities.…”

Section: Urban Water Footprintmentioning

confidence: 99%

“…Moreover, this example is used to illustrate how the roofs of individual households can be used to sequester large amounts of green water and use it for vegetable gardening and to provide water for chickens. Under the assumption of 500 mm of total annual rainfall [63], a house roof of 100 m 2 , and 100% capture efficiency, 50,000 L of water can be collected and used for green water irrigation. As the house has solar panels, the pumping of water to/from water tanks and to perform irrigation is carbon neutral.…”

Section: Urban Water Footprintmentioning

confidence: 99%

Active Learning, Living Laboratories, Student Empowerment, and Urban Sustainability

Nansen

2024

Sustainability

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In schools and universities, we instructors carry the responsibility of informing and inspiring students. Traditional and more theoretical educational programs (here referred to as passive learning) may be tied to projects and activities (active learning), in which students gain hands-on practical experience with planning, development, implementation, maintenance, and presentation of different solution-focused activities. Complementary to passive learning, the needs for active learning activities and living laboratories have become more pertinent as global trends, such as climate change, weigh heavily on the shoulders of young people. Unless properly guided and given tangible sources of inspiration, the sense of being overwhelmed and incapable of effectively contributing to a more sustainable future may cast a dark shadow over students, their ability to engage in active learning, and their long-term career aspirations. Schools and universities are being evaluated for their “greenness”. Accordingly, operational improvements (carbon, water, waste, and nutrient footprints) to meet sustainability targets are being implemented. Structural sustainability improvements represent unique opportunities for students and instructors to engage in active learning. As a broader message to school and university administrators, it is argued that efforts to plan and implement sustainability initiatives should also involve transformations of educational curricula. It is argued that educational institutions could and should be more than sums of buildings and infra-structure and represent living laboratories. Descriptions of topics taught, learning outcomes, and links to examples of student assignments of a specific course, Urban Food and Society, are included and discussed in the broader contexts of urban food sustainability and active learning. The main purpose of this article is to promote the notion that active learning activities and the need for improved sustainability of schools and universities can go hand in hand and provide compelling educational opportunities.

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Weather pattern climatology of the United States

Cited by 12 publications

References 1 publication

A procedure for blending manual and correlation‐based synoptic classifications

A procedure for blending manual and correlation‐based synoptic classifications

Development of an automated spatial synoptic climatological classification

Active Learning, Living Laboratories, Student Empowerment, and Urban Sustainability

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