Until you get some data on the dependent variable (snow quality) it will be tough to make much progress. One thing you might try to use as a surrogate for this is change in snow depth from day to day which may at least give you something to start modeling with. I would assume that on days where considerable depth is lost due to wind, heat or radiation, subjective quality will go down, while when depth is increasing, quality will go up.
Instead of dumping everything into a linear regression, it's usually a good idea to do some exploratory data analysis: Plot the dependent variable against each potential explanatory variable and see if there is much of a relationship and whether it appears to be linear or not. If one sees a strong non-linear relationship, try and transform the explanatory variable to produce a more linear relationship.
You may also want to try and reshape the distributions of any variables to get them closer to a normal distribution before putting everything into a linear regression. Otherwise the results can be pretty strange. (Recover the actual forecast then by inverting the transformation on the forecast values). Do some histograms of the variables in your data set to see how weird they might be.
You also want to look for outliers in your data (they will show up in histograms) and clip them or ditch them entirely before doing any regressions, since they will tend to throw everything off.
Good luck!
"I just want to thank everyone who made this day necessary." -Yogi Berra
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