Metric (other than RMSE, MSE, etc.) to select the best model to better detect peaks in weather forecasting I have created multiple regression models and wanted to choose the best one. One common metric would be RMSE, as you know. 

When I looked at the results, second model (RMSE = 0.15) was better able to detect some of the peaks than the first one (RMSE = 0.1). For example look at the two results:

Best model (RMSE = 0.1):


Here is the second model's result(RMSE = 0.15):

The model which is able to detect all the peaks, as much as possible (although its RMSE is not the least among all of the models), is more preferable than a model which has less RMSE but is not able to detect peaks.
I searched through the web but didn't find what I was looking for.

Can anyone suggest me a code to evaluate the results of the models, based on their ability to detect peaks better?

Suppose the results are simply 2 arrays:

import numpy as np
np.random.seed(10)
predicted_1 = np.random.rand(10, 1)
predicted_2 = np.random.rand(10, 1)
original = np.random.rand(10, 1)
I had this problem in my work before and used weighted RMSE and assigned higher weights to the peaks.

Some ideas I took from here https://stats.stackexchange.com/questions/230517/weighted-root-mean-square-error