A few months ago, I started planning a series of backpacking trips to take place in New Hampshire’s White Mountains this coming summer. Because I was planning the trips so far in advance, weather forecasts were not yet available. I wanted to give my participants a good idea of what weather we might expect so that they could buy the proper equipment, particularly quilts or sleeping bags. To do this, I looked historical records to produce my own long-term weather forecasts for advanced trip planning.
Advanced Trip Planning
Over the last year, I have been organizing a series of Appalachian Trail Backpacking Trips for the Meetup group Hudson Valley Hikers. I run these trips year-round and usually post them several months in advance. I prefer that my participants sign up in advance and start preparing themselves. In order to help, I offer my own long-range climate forecasts. These are an attempt to predict the general conditions we should expect, not the exact weather. Producing long range climate forecasts is a key skill necessary for advanced trip planning.
Advanced Trip Planning Long Range Weather Forecasting
The long-range weather forecasts I produce give a rough idea of the conditions we should expect. The most important piece of information I try to offer is the overnight low temperature. Participants can use this information for purchasing sleeping bags, tents, and clothing ahead of time. I also try to give a rough idea of how much rain or precipitation we may expect. To do this, I collect data from Weather Underground. (For simplicity, this post will only discuss analysis for the overnight low temperature.)
Advanced Trip Planning: Using Weather Underground
The first step is to find a city or airport near your route. For example: over memorial day weekend we will be hiking the Mahoosuc Range. The nearest weather station with historical weather data is Berlin, NH. I then select a custom date range to cover my trip: May 27th through May 29th. Next, I record the highest and lowest temperature and the total precipitation recorded during this date range for each year as far back as records are available (23 years for this site).
Using Microsoft Excel, Adjusting for Elevation
I type the data I get from Weather Underground into Microsoft Excel, creating a table that shows the year, low temperature, high temperature, and precipitation. The temperatures I get from Weather Underground are usually not for the elevation at which I plan to hike. To adjust, I subtract 5.4 degrees F for every 1000 feet of elevation difference. The airport at Berlin, NH is at an elevation of 1121 feet. The highest site at which I plan to camp is at 3028 feet. The difference in elevation is 1907 feet. This gives me an elevation adjustment factor of 10.3 degrees.
5.4d/1000ft * (1907ft/1000) = 10.3d
Selecting Camping Gear Based on Historical Temperatures
I used to do some complicated work involving standard deviations, but have learned it is unnecessary. Most people own sleeping bags or quilts that are rated in increments of 10 degrees. I want to help people decide which quilt to bring or what to go out and buy. To do this, I sort the nightly low temperatures and then count how many fall into each temperature range (0-10, 10-20, 20-30…) .
Probability Distribution for Low Temperatures.
I then compile this data into a chart to show the chance nightly temperatures will fall into each sleeping bag or quilt range.
Sleeping Bag or Quilt Recommendation
What I really want to know here is the chance that the nightly low temperature will be greater than the rating of each quilt or bag option. For example: what is the chance that it will be warmer than 20 degrees? The chance that the temperature will be warmer than X is the cumulative probability for all the ranges warmer than X. Above, we see that there is a 35% chance the nightly low will fall between 20 and 30. There is a 48% chance it will fall between 30 and 40. Finally, there is a 9% chance it will fall between 40 and 50. Adding these up, I see that there is a 91% chance that the nightly low will be above 20 degrees. (35 + 48 + 9 = 91.) To help simplify, I make charts and tables showing the probability distribution for temperatures.
Presented with this data, I would feel safe bringing a 20 degree quilt. However, there are other factors to consider. Some people, particularly women, tend to sleep colder than others. I recommend women always pack a sleeping bag or quilt that is rated 10 degrees cooler than the recommendation for men. The data leaves a 9% chance it will be cooler than 20 degrees; but I am willing to take that chance. If you don’t tolerate cold very well, you may wish to select gear that will have a lower probability of leaving you cold.
Earlier I said that I also analyze data for anticipated precipitation and high daily temperatures. I use these factors for other elements of trip planning and will present them in future posts.