diff --git a/icedyno/metrics/__init__.py b/icedyno/metrics/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/icedyno/metrics/metrics.py b/icedyno/metrics/metrics.py
new file mode 100644
index 0000000..1e90012
--- /dev/null
+++ b/icedyno/metrics/metrics.py
@@ -0,0 +1,109 @@
+import matplotlib.pyplot as plt
+import numba
+import numpy as np
+
+## Visualization for edge metrics ##
+
+
+def plot_ice_edges_with_metrics(observed_edges, model_edges):
+    """
+    Visualize observed and model ice edges on a grid, including the calculation of metrics.
+
+    Parameters:
+    - observed_edges: numpy array of observed ice edge points.
+    - model_edges: numpy array of model ice edge points.
+    """
+    # Plotting
+    plt.figure(figsize=(8, 8))
+    plt.plot(observed_edges[:, 0], observed_edges[:, 1], "bo-", label="Example Edge 1")
+    plt.plot(model_edges[:, 0], model_edges[:, 1], "ro-", label="Example Edge 2")
+
+    # Enhance plot
+    plt.title("Ice Edge Comparison with Metrics")
+    plt.xlabel("X Coordinate")
+    plt.ylabel("Y Coordinate")
+    plt.legend()
+    plt.grid(True)
+    plt.axis("equal")
+
+    plt.show()
+
+
+## Metrics ##
+
+
+@numba.jit(nopython=True)
+def average_ice_edge_displacement(observed_edges, model_edges):
+    """
+    Calculate the average ice edge displacement (D_AVG_IE) between observed and model ice edges.
+    Credit: Validation metrics for ice edge position forecasts, Melsom et al., 2019.
+
+    Parameters:
+    - observed_edges: numpy array of shape (N, 2), where N is the number of observed ice edge points,
+      and each point is represented by its (x, y) coordinates.
+    - model_edges: numpy array of shape (M, 2), where M is the number of model ice edge points,
+      and each point is represented by its (x, y) coordinates.
+
+    Returns:
+    - D_AVG_IE: The average displacement between the observed and model ice edges.
+    """
+
+    # Initialize lists to store minimum distances for each point
+    observed_to_model_distances = []
+    model_to_observed_distances = []
+
+    # Calculate distances from each observed point to the nearest model point
+    for obs_point in observed_edges:
+        distances = np.sqrt(np.sum((model_edges - obs_point) ** 2, axis=1))
+        observed_to_model_distances.append(np.min(distances))
+
+    # Calculate distances from each model point to the nearest observed point
+    for model_point in model_edges:
+        distances = np.sqrt(np.sum((observed_edges - model_point) ** 2, axis=1))
+        model_to_observed_distances.append(np.min(distances))
+
+    # Calculate the average displacement
+    avg_displacement = (
+        sum(observed_to_model_distances) / len(observed_to_model_distances)
+        + sum(model_to_observed_distances) / len(model_to_observed_distances)
+    ) / 2
+
+    return avg_displacement
+
+
+@numba.jit(nopython=True)
+def root_mean_square_ice_edge_displacement(observed_edges, model_edges):
+    """
+    Calculate the root mean square ice edge displacement (D_RMS_IE) between observed and model ice edges.
+
+    Parameters:
+    - observed_edges: numpy array of shape (N, 2), where N is the number of observed ice edge points,
+      and each point is represented by its (x, y) coordinates.
+    - model_edges: numpy array of shape (M, 2), where M is the number of model ice edge points,
+      and each point is represented by its (x, y) coordinates.
+
+    Returns:
+    - D_RMS_IE: The root mean square displacement between the observed and model ice edges.
+    """
+
+    # Initialize lists to store distances for each point
+    observed_to_model_distances = []
+    model_to_observed_distances = []
+
+    # Calculate distances from each observed point to the nearest model predicted point
+    for obs_point in observed_edges:
+        distances = np.sqrt(np.sum((model_edges - obs_point) ** 2, axis=1))
+        observed_to_model_distances.append(np.min(distances) ** 2)
+
+    # Calculate distances from each model point to the nearest observed point
+    for model_point in model_edges:
+        distances = np.sqrt(np.sum((observed_edges - model_point) ** 2, axis=1))
+        model_to_observed_distances.append(np.min(distances) ** 2)
+
+    # Calculate the root mean square displacement
+    rms_displacement = np.sqrt(
+        (sum(observed_to_model_distances) + sum(model_to_observed_distances))
+        / (len(observed_to_model_distances) + len(model_to_observed_distances))
+    )
+
+    return rms_displacement
diff --git a/icedyno/metrics/process_edges.py b/icedyno/metrics/process_edges.py
new file mode 100644
index 0000000..e82d206
--- /dev/null
+++ b/icedyno/metrics/process_edges.py
@@ -0,0 +1,24 @@
+import numpy as np
+
+
+def process_binary_edge_image_into_coordinates(activated: np.array) -> np.array:
+    """
+    Cannot take in a tensorflow tensor, input array must be between 0 and 1.
+    Input array is the result of running edge-detection on Ice/No Ice array.
+
+    Something like:
+    sobel_edges = tf.image.sobel_edges(model_prediction)
+    activated = Activation('sigmoid')(tf.square(sobel_edges[:, :, :, :, 0]) + tf.square(sobel_edges[:, :, :, :, 1]))
+
+    Parameters:
+        activated: Numpy array with >= 0.5 indicating sea-ice edge.
+
+    Returns:
+        numpy array of integer indices in input array corresponding to edge. Of shape (N Edges, 2)
+
+    """
+    edges = np.where(np.round(activated) >= 1)
+    x, y = edges[0], edges[1]
+    edge_coordinates = np.stack((x, y), axis=-1)
+
+    return edge_coordinates
diff --git a/pixi.lock b/pixi.lock
index e920862..bf35adf 100644
--- a/pixi.lock
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+  name: llvmlite
+  version: 0.42.0
+  build: py312h534208b_1
+  build_number: 1
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/llvmlite-0.42.0-py312h534208b_1.conda
+  sha256: 507d3fb788a7a7fefcb9b4c5091331a961785503d7ecdd8ee83a57380f786d0c
+  md5: ac1d435c2f56e72746eb5d7ed4b2e1a1
+  depends:
+  - libcxx >=16
+  - libllvm14 >=14.0.6,<14.1.0a0
+  - libzlib >=1.2.13,<1.3.0a0
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  license: BSD-2-Clause
+  license_family: BSD
+  size: 373369
+  timestamp: 1706921870859
+- kind: conda
+  name: llvmlite
+  version: 0.42.0
+  build: py312h7894644_1
+  build_number: 1
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/llvmlite-0.42.0-py312h7894644_1.conda
+  sha256: ecc36569c8c657e478bfd8673bd376581bef46197625ff59a1dc603060f30aa1
+  md5: b0c8b1ebd145b7810cf30258115624ab
+  depends:
+  - libzlib >=1.2.13,<1.3.0a0
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  - vs2015_runtime
+  license: BSD-2-Clause
+  license_family: BSD
+  size: 17125461
+  timestamp: 1706922264652
+- kind: conda
+  name: llvmlite
+  version: 0.42.0
+  build: py312hb06c811_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.42.0-py312hb06c811_1.conda
+  sha256: a2e0495effb5897c06a7407d4f318958643008dab270e54591086c2e8a3dc44b
+  md5: ae7c5ecce1fd0b7006149bd020421379
+  depends:
+  - libgcc-ng >=12
+  - libllvm14 >=14.0.6,<14.1.0a0
+  - libstdcxx-ng >=12
+  - libzlib >=1.2.13,<1.3.0a0
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  license: BSD-2-Clause
+  license_family: BSD
+  size: 3392211
+  timestamp: 1706921771933
 - kind: conda
   name: lockfile
   version: 0.12.2
@@ -11853,6 +11993,119 @@ packages:
   license_family: MOZILLA
   size: 1908769
   timestamp: 1708065399315
+- kind: conda
+  name: numba
+  version: 0.59.1
+  build: py312h04e34b5_0
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/numba-0.59.1-py312h04e34b5_0.conda
+  sha256: e4bc3426d6b023c121328e6a9878ec35a71487bc1cb3424f96d9f2808ad0ee43
+  md5: 46499b9d015d01c22ceb1c37e1dc6ccc
+  depends:
+  - libcxx >=16
+  - llvm-openmp >=16.0.6
+  - llvm-openmp >=18.1.2
+  - llvmlite >=0.42.0,<0.43.0a0
+  - numpy >=1.26.4,<2.0a0
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  constrains:
+  - tbb >=2021.6.0
+  - cudatoolkit >=11.2
+  - numpy >=1.22.3,<1.27
+  - cuda-version >=11.2
+  - cuda-python >=11.6
+  - scipy >=1.0
+  - libopenblas !=0.3.6
+  license: BSD-2-Clause
+  license_family: BSD
+  size: 5586866
+  timestamp: 1711475677299
+- kind: conda
+  name: numba
+  version: 0.59.1
+  build: py312h115d327_0
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/numba-0.59.1-py312h115d327_0.conda
+  sha256: 9a04cfd0886170036bdbc3c2b3e4805e8629c126c0b55e498e3401d4aee9331c
+  md5: 514d477e6114b692d400ed23deb73348
+  depends:
+  - llvmlite >=0.42.0,<0.43.0a0
+  - numpy >=1.26.4,<2.0a0
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  constrains:
+  - tbb >=2021.6.0
+  - cuda-version >=11.2
+  - cudatoolkit >=11.2
+  - scipy >=1.0
+  - numpy >=1.22.3,<1.27
+  - libopenblas !=0.3.6
+  - cuda-python >=11.6
+  license: BSD-2-Clause
+  license_family: BSD
+  size: 5591106
+  timestamp: 1711475839209
+- kind: conda
+  name: numba
+  version: 0.59.1
+  build: py312hacefee8_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/numba-0.59.1-py312hacefee8_0.conda
+  sha256: 13445a4ea0677336aa882c519f31ce397cbb5ac848adc68b0c951ca038784562
+  md5: 84c93029c60916c59a914a3ba579c4a8
+  depends:
+  - _openmp_mutex >=4.5
+  - libgcc-ng >=12
+  - libstdcxx-ng >=12
+  - llvmlite >=0.42.0,<0.43.0a0
+  - numpy >=1.26.4,<2.0a0
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  constrains:
+  - cuda-version >=11.2
+  - numpy >=1.22.3,<1.27
+  - libopenblas !=0.3.6
+  - tbb >=2021.6.0
+  - scipy >=1.0
+  - cuda-python >=11.6
+  - cudatoolkit >=11.2
+  license: BSD-2-Clause
+  license_family: BSD
+  size: 5613943
+  timestamp: 1711475331686
+- kind: conda
+  name: numba
+  version: 0.59.1
+  build: py312hbaff935_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/numba-0.59.1-py312hbaff935_0.conda
+  sha256: 300dde5a10d35eeba537b1d3e689841a7ea11fef7e8bd273a83af1535696c486
+  md5: e4b79ed152a0499e5ccfe660140aeca7
+  depends:
+  - libcxx >=16
+  - llvm-openmp >=16.0.6
+  - llvm-openmp >=18.1.2
+  - llvmlite >=0.42.0,<0.43.0a0
+  - numpy >=1.26.4,<2.0a0
+  - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
+  - python_abi 3.12.* *_cp312
+  constrains:
+  - cudatoolkit >=11.2
+  - scipy >=1.0
+  - numpy >=1.22.3,<1.27
+  - tbb >=2021.6.0
+  - libopenblas >=0.3.18, !=0.3.20
+  - cuda-python >=11.6
+  - cuda-version >=11.2
+  license: BSD-2-Clause
+  license_family: BSD
+  size: 5590634
+  timestamp: 1711475851039
 - kind: conda
   name: numpy
   version: 1.26.4
diff --git a/pixi.toml b/pixi.toml
index ee6fa86..ff355e1 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -28,3 +28,4 @@ pip = ">=24.0,<25"
 gdal = ">=3.8.4,<3.9"
 h5netcdf = ">=1.3.0,<1.4"
 luigi = ">=3.5.0,<3.6"
+numba = ">=0.59.1,<0.60"