diff --git a/fme/ace/aggregator/inference/video.py b/fme/ace/aggregator/inference/video.py
index 1c4803ae..0e1f9af6 100644
--- a/fme/ace/aggregator/inference/video.py
+++ b/fme/ace/aggregator/inference/video.py
@@ -509,9 +509,13 @@ def _make_video(
     video_data = np.minimum(video_data, 255)
     video_data = np.maximum(video_data, 0)
     video_data[np.isnan(video_data)] = 0
+    # Convert from single-channel grayscale to three-channel RGB to work
+    # around imageio error
+    video_data = video_data.repeat(3, axis=1)
     caption += f"; vmin={data_min:.4g}, vmax={data_max:.4g}"
     return wandb.Video(
         np.flip(video_data, axis=-2),
         caption=caption,
         fps=4,
+        format="gif",
     )
diff --git a/fme/sht_fix.py b/fme/sht_fix.py
index ba2484b1..d1696e4a 100644
--- a/fme/sht_fix.py
+++ b/fme/sht_fix.py
@@ -42,7 +42,6 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 #
 
-import numpy as np
 import torch
 import torch.nn as nn
 import torch.fft
@@ -100,15 +99,14 @@ def __init__(self, nlat, nlon, lmax=None, mmax=None, grid="lobatto", norm="ortho
             raise(ValueError("Unknown quadrature mode"))
 
         # apply cosine transform and flip them
-        tq = np.flip(np.arccos(cost))
+        tq = torch.flip(torch.arccos(cost), dims=(0,))
 
         # determine the dimensions
         self.mmax = mmax or self.nlon // 2 + 1
 
         # combine quadrature weights with the legendre weights
-        weights = torch.from_numpy(w)
         pct = torch.as_tensor(_precompute_legpoly(self.mmax, self.lmax, tq, norm=self.norm, csphase=self.csphase))
-        weights = torch.einsum('mlk,k->mlk', pct, weights)
+        weights = torch.einsum('mlk,k->mlk', pct, w)
 
         # remember quadrature weights
         self.weights = weights.float().to(get_device())
@@ -181,7 +179,7 @@ def __init__(self, nlat, nlon, lmax=None, mmax=None, grid="lobatto", norm="ortho
             raise(ValueError("Unknown quadrature mode"))
 
         # apply cosine transform and flip them
-        t = np.flip(np.arccos(cost))
+        t = torch.flip(torch.arccos(cost), dims=(0,))
 
         # determine the dimensions
         self.mmax = mmax or self.nlon // 2 + 1
diff --git a/requirements.txt b/requirements.txt
index 03f317c5..52dd58ca 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,7 @@ dacite
 gcsfs
 h5netcdf
 h5py
-imageio<=2.27.0
+imageio>=2.28.1
 matplotlib
 moviepy
 netcdf4
@@ -14,7 +14,7 @@ plotly
 s3fs
 tensorly
 tensorly-torch
-torch-harmonics==0.7.4  # pinned since we use private API
+torch-harmonics==0.8.0  # pinned since we use private API
 torch>=2
 wandb[media]>=0.19.0
 xarray