You can use np.concatenate
but you should broadcast 1d array first, to multiply first axis with 84 times to accomodate shapes.
a = np.random.randn(84, 13, 1036800)
b = np.random.randn(1036800)
print(a.shape, b.shape)
>>>
(84, 13, 1036800) (1036800,)
b_broadcasted = np.broadcast_to(b, [a.shape[0], 1, a.shape[2]])
print(b_broadcasted.shape)
>>>
(84, 1, 1036800)
np.concatenate([a, b_broadcasted], axis=1).shape
>>>
(84, 14, 1036800)
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