A hurricane off the east coast, strong cold front, gulf moisture, Atlantic moisture. These are just a few of the factors that will drive the rain and wind this weekend. In my opinion, one of the most difficult forecasts in recent memory.
The various computer projections are taking Hurricane Joaquin and the other tropical disturbances in all sorts of directions as it begins its interaction with the other variables above. Each line represents a different computer model representation of each outcome. Are you confused yet?
Our minds don't easily handle probability. We especially hate probabilities in our weather forecasts especially when confronted with a map like this.
Why is this?
For our minds to grasp probabilities, we need to be able to handle multiple possible outcomes at once. Just our luck. Weather has many, many outcomes over a large area over a significant period of time. Change the initial weather conditions (humidity, wind flow, frontal position, upper level energy, etc) and you create more uncertainty. Factor in time and the probability becomes significantly higher.
Typically, our brains work much better with a theme that is linear: A story that has a beginning, middle and an end. We want to visualize a line of showers that moves in at a specific time, stays for a select amount of time and then moves out without fanfare. Unfortunately, rain events rarely behave in this manner.
Much to the chagrin of the general public—probabilities are the only way to tell the weather story. We use 90% chance of rain, 40% chance of rain, etc. Yet if it doesn't rain over their house when the probability is 90% chance of rain, the forecaster is wrong even if the rest of the area was hit with a good downpour. We want to know if it will rain or not; a black and white scenario without caveats. Yet the behavior of some small scale weather events like warm frontal rain/storms can behave semi-independently of the overall large scale pattern. Meteorologist try to convey this idea on the air. Most of the time this falls on deaf ears.
It all goes back to basic human nature. A good weather narrative (a feel-good forecast with some folklore) is desired versus something data/science driven. Nebulous weather data and science makes most of us feel uncomfortable even if the on-air meteorologist has the best of intentions.
In recent years, some highly sophisticated models of the atmosphere have been developed that can make some very good “probabilistic” outcomes for weather events. Yet a level of uncertainty still remains and we humans don’t like it! We try to rationalize the irrational. Our biases quickly dismiss the probabilistic science as irrelevant or at the very worst, an excuse.
Instead, we favor more simplified stories even though that story might gloss over important details. Our minds involuntarily cherry-pick elements of the story so that it fits our biases. Think of a time when someone told you a weather fact or forecast which you didn’t believe. You felt uneasy. Your mind shrugged it aside only to be replaced by a story, forecast or explanation that made you feel better…accuracy be damned.
* Narratives (straight forward simple weather forecasts) are about hitting emotional buttons making the reader feel good by focusing on less qualitative aspects (weather science and probability) of an issue.
* Narratives (weather forecast) are/is about the outcome not the process (explanation of the science and probability)
* The process (weather science) is important in developing solid results
So remember the psychology. How you react when you hear a weather forecast? Do you dismiss the science? How do you handle probability? Do you like hearing an explanation to why the weather does what it does? Do you overly simplify the weather? Are you aware of your biases?
Wednesday, September 30, 2015
Thursday, September 24, 2015
Monday, September 07, 2015
|North Country Public Radio|
I'm thinking out loud today so bare with me.
Over the last few days, I was playing around with the constructed analogs of similar winter years. Using a set of 6 best fit years and weighting them equally, I constructed a blend for each month starting in November and ending with February showing the upper level pattern across the North American Continent. The color colors show where the storm system would develop (Low pressure). The warmer colors show where High Pressure systems reside.
The first burst of colder air occurs in November, the pattern relaxes in December then reloads after the first of the year. Can you see how the southern jet stream becomes very active, dominant force in the second half of the winter. February shows the much higher frequency and strength of low pressure systems from the southwest. This indicates a higher propensity for wetter snows from northern Texas, northeast into the Mid Atlantic states and Ohio Valley similar to 2010.
UPPER AIR PRESSURE (500 mB)
Last winter's actual temperatures versus our Winter Outlook issued in October. Not bad.
(recap of last winter at this link) or 2013-14.
Will this strong El Nino have some central based signature tendencies? Maybe more eastern El Nino characteristics thus a warmer winter overall with little snow?
The Constructive Analog from Huug van den Dool's page on the CPC site shows the core of the ENSO warmth in the ENSO 3.4 region with a slow drift WEST toward the Dateline by late winter.
Will this verify? Only time will tell.
Many meteorologists in the private sector don't like using the Analog Method too literally because the probability of finding an exact match over such a large area is incredibly small. I get that. However, I have found that analogs give a glimpse into how the atmosphere responds to the various drivers in closely matched years. This group of "best fits" for this winter has stayed consistent over the last several months. Several notes: Analogs are only one of several tools that I use in assessing seasonal outlooks. El Nino is only one of the many factors that will play a role in our winter weather. I did not include any ENSO dynamic model guidance in this post. That is a story for another time.