Most problems encounted in understanding weather relate to gathering and displaying the information and not understanding weather dynamics or processes. Weather does not stand still and wait for us; it is constantly in motion, and we are always playing catch-up, looking at what has happened in the recent past and using known weather behavior patterns to predict the future.
Why is high density important? Because we make analyses and decisions through comparisonbig versus little, bright versus dark, hot versus cold, cloudy versus sunny. When more data is available, then more comparisons are possible and the better our decisions will be. But we need information displayed in the proper format, and here is the keythe human brain is very efficient at scanning, but not so efficient when it comes to looking at one item at a time. When you want to examine photographs, what do you do? You lay them out side by side on a table. Do you stack them in a pile? No, of course not. You lay things along side each other so you can compare them. You rearrange the order and sequence until you find the correct perspective.
Then you can take this a step further with a variety of colors. Perhaps you can use a red marker to highlight the upper-air storm track path, a blue one to mark surface lows, and yellow to mark surface highs. Use blue arrows to trace movement of lows, and yellow to trace movement of highs. Now correlate troughs with lows and ridges with highs.
At this point, the weather should begin to take on motion. Patterns should begin to appear and you should be seeing weather moving through increments of time. The smaller the increments of time the better, since the human brain sees change through comparison and the more opportunities for comparison, the more easily change is appreciated. Watch a house being built every day and you see change, watch it every week or every month and you do not see the change as well as you see completion.
In the process of forcing yourself to make visual comparisons you also need to demonstrate causality. What is causality? It is demonstrating what causes an event. Weather events do not occur mysteriously. There are no unpredictable rogue waves nor are there any unpredicted stormseven the conditions reported in The Perfect Storm were well predicted, days in advance. For every surface low-pressure system there exists an upper level trough, and for every surface high-pressure system there is a supporting ridge.
|"Thus the key is to gather sufficient data so that you can articulate why events are occurring."|
You are seeing relationships and causality. If you are unable to visualize and show causality, the reasons are simpleyou either have insufficient data, inaccurate data, the data is displayed improperly, or the data is distorted.
By establishing data credibility early in a weather analysis process, you are able to gain high confidence over time. That is, if we know our initial data is correct and we have verified this by observing weather features and events, then we have a base upon which to build future analyses and forecasts. This is quality control, making sure you have good data now so that future decisions come from a base of high confidence. Weather follows patterns and conforms to the laws of physics, but since we are always playing catch-up with it, we depend on detecting relationships and establishing probabilities of events, not relying too much on predicting the particulars of an absolute, deterministic event.
Rules for Viewing and Displaying Weather
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