Hour-by-Month Climograms as a Planning, Decision-aid, and General Information Tool in the Visualization and Interpretation of Extreme Temperature, Wind Speed, and Relative Humidity Data

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Wednesday, 5 February 2014
Hall C3 (The Georgia World Congress Center )
Charles Fisk, Naval Base Ventura County, Pt. Mugu, California
Manuscript (1.6 MB)

The study describes and illustrates the use of “hour-by-month” climograms as a means of visualizing and interpreting single-station extreme hourly temperature, wind speed, and relative humidity data. A visual analog to the topographic map, the scheme has calendar month comprising the y-axis, hour of the day, the x-axis. Upon the 2-D grid, a parameter of interest's diurnal/seasonal variation is depicted in the form of contours, shadings, vector arrows, etc. In addition, sunrise/sunset demarcation lines are overlain, lending an extra physical perspective.

Previous studies (Fisk, 2004, 2008, 2009, and 2010) demonstrated hour-by-month climograms' use in portraying seasonal and diurnal variations in means, percent occurrence frequencies, mean vector wind orientations, median ceilings' heights, and others. However, climatological extremes were not investigated in this context, and since in some applications these may be of primary importance, the climogram approach is explored here for these types of cases.

Construction of extremes' climograms is likely a more complicated exercise, as the uncertain nature of many parameters' distributions, (symmetrical?, skewed?, peaked?, bounded on one side?, mixed?), which manifests itself most significantly in the tail regions, suggests that simple standard deviation derived exceedance levels (implying distributional symmetry) in extreme regions may not be accurate in some cases. Indeed, distributional shape properties (skewness/kurtosis) as well as those of variability (standard deviation) may have a physical basis for differing from one hour/month combination to another. Thus, for a selected exceedance level, either at the high or low end, a “true” hour-by-month extremes' climogram pattern might differ noticeably from that of its hourly mean counterpart. Given these uncertainties, an additional, straightforward, “pre-processing” approach would be percentile rankings, by hour and month (288 separate arrays). From these arrays, a given extremes' climogram would be derived from a single 288-point array of hour/month parameter values extracted from those at the specified percentile ranking level (e.g., “upper 99th percentile”).

Utilizing this methodology and to demonstrate examples. climograms of extremes in temperature (mostly warm), sustained wind speeds (high), and relative humidities (low) are constructed at either the 99th or 1st percentile levels for a selection of stations, both domestic and international. And, to evaluate the notion of differing extremes vs. means patterns, the extremes' climograms are compared with their counterpart means' charts.