Repository: ARM-DOE/pyart (language: python)

Issue:
New Function for Radar Echo Classification
### Description
Adding a new function in Py-ART, implementing the wavelet-based algorithm for radar echo classification from [PyREClass](https://github.com/RBhupi/PyREClass).

## Steps
- [x] Submit GitHub issue for feedback
- [x] Implement the function in `retrieve/echo_class.py`
- [x] Add Sphinx-style documentation
- [x] Develop and add unit tests
- [x] Open a Pull Request to Py-ART repository

### References
1. Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from infrared satellite images. IEEE Geoscience and remote sensing letters, 5(3), 328-330.
2. Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique for the classification of precipitation echoes in radar data. IEEE Transactions on Geoscience and Remote Sensing, 58(8), 5409-5415.



Discussion / hints:

Fix patch:
diff --git a/examples/retrieve/wavelet_echo_class_example.ipynb b/examples/retrieve/wavelet_echo_class_example.ipynb
new file mode 100644
index 0000000000..1b92486415
--- /dev/null
+++ b/examples/retrieve/wavelet_echo_class_example.ipynb
@@ -0,0 +1,605 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fast Wavelet-Based Classification of Radar Echoes into Convection Core, Mixed-Intermediate and Stratiform Classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This cookbook demonstrates the wavelet-based radar echo classification method from Raut et al. (2008 and 2020) [1, 2]. \n",
+    "\n",
+    "\n",
+    "## Introduction:\n",
+    "In radar data, convective regions are characterized by horizontal inhomogeneity, generally high reflectivity than surrounding echoes, and vertical growth, as opposed to the more horizontally homogeneous stratiform regions. Historically, radar echoes were classified into convective and stratiform categories using reflectivity thresholds, typically around 40 dBZ. However, this approach is not reliable as significant fraction of convection has reflectivities comparable to stratiform rain below the 40dBZ threshold. To address these challenges, algorithms that consider the horizontal reflectivity structure as an additinal criteria were developed although the threshold was still used as the primary criteria [3, 4, 5]. \n",
+    "\n",
+    "The rain exhibit a wide range of spatial frequencies, or scales, embedded within self-similar structures [6] and convection and stratiform can be identified by the scale analysis of the images. Fourier transform (FT), although can compute the power spectrum of these images to study the dominant frequencies, cannot identify localized structure within the image. A multiresolution approximation separates features of different scales within the image. Wavelets iteratively decompose the image into different resolutions or scales. The á trous wavelet transform (WT) is particularly prominent in astrophysics and medical imaging.\n",
+    "\n",
+    "## The Á Trous Wavelet Transform\n",
+    "The á trous wavelet transform, as proposed by Shensa [7] and further developed by Starck and Murtagh [8], is utilized in this algorithm. This algorithm employs a scaling function at dyadically increasing scales to approximate the original image at successively coarser resolutions and the wavelet coefficients at a given scale are the difference between two successive approximations.\n",
+    "\n",
+    "This has significant implications for meteorological analysis, particularly in the classification of convection from stratiform regions in radar and satellite data. The multiresolution analysis offers an objective classification scheme to classify embedded or isolated convection without the need for specific conditions and intensity thresholds.\n",
+    "\n",
+    "## Classification Scheme\n",
+    "\n",
+    "1. **Transform Reflectivity to Rain Field**: The first step involves transforming the reflectivity field into a rain field. The standard ZR relationship should work for most radars.\n",
+    "\n",
+    " 2. **Compute Wavelet Transform (WT) of the Rain Field**: \n",
+    "The WT is computed for the rain field across `n` different scales, where `n` can be 15-30 kilometers as discussed in Raut et al (2018) [9]. This process breaks down the rain field into various scales.\n",
+    "\n",
+    "3. **Sum of Wavelet Scales (wt_sum)**: \n",
+    "The next step is to sum up all these 'n' wavelet scales. \n",
+    "\n",
+    "4. The classification of the precipitation type is then determined based on `wt_sum` and the original dBZ values (`vol_data`):\n",
+    "\n",
+    "     - **Unclassified**: If `reflectivity < min_dbz_threshold`, the precipitation is too low to be classified.\n",
+    "     - **Convective Core**: If `wt_sum ≥ conv_wt_threshold AND reflectivity > conv_dbz_threshold`, the precipitation is classified as 'Convective Core'. This implies a higher intensity and potentially active collision and coalescence.\n",
+    "     - **Mix or Intermediate**: If `conv_wt_threshold > wt_sum ≥ tran_wt_threshold AND reflectivity > conv_dbz_threshold`, the precipitation is categorized as 'Intermediate or Mix Convective'. This rain is not as intense as convective core but it has more significant liquid water content than stratiform.\n",
+    "     - **Stratiform**: If `wt_sum < tran_wt_threshold AND reflectivity > min_dbz_threshold`, the precipitation is classified as 'Stratiform or non-convective'. This is typically more uniform and less intense than convective class.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n",
+      "## library for working with weather radar data. Py-ART is partly\n",
+      "## supported by the U.S. Department of Energy as part of the Atmospheric\n",
+      "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n",
+      "## Science user facility.\n",
+      "##\n",
+      "## If you use this software to prepare a publication, please cite:\n",
+      "##\n",
+      "##     JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyart\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Case 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Reading and Preparing Radar Data\n",
+    "We load a sample radar data file using Py-ART, extracts the lowest sweep, and then interpolates this data onto a cartesian grid. The dx and dy variables represent the grid resolution in the x and y directions, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read in the test file\n",
+    "filename = pyart.testing.get_test_data(\"swx_20120520_0641.nc\")\n",
+    "radar = pyart.io.read(filename)\n",
+    "\n",
+    "# extract the lowest sweep\n",
+    "radar = radar.extract_sweeps([0])\n",
+    "\n",
+    "# interpolate to grid\n",
+    "grid = pyart.map.grid_from_radars(\n",
+    "    (radar,),\n",
+    "    grid_shape=(1, 201, 201),\n",
+    "    grid_limits=((0, 10000), (-50000.0, 50000.0), (-50000.0, 50000.0)),\n",
+    "    fields=[\"reflectivity_horizontal\"],\n",
+    ")\n",
+    "\n",
+    "# get dx dy\n",
+    "dx = grid.x[\"data\"][1] - grid.x[\"data\"][0]\n",
+    "dy = grid.y[\"data\"][1] - grid.y[\"data\"][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using `Peak Feature Detection`\n",
+    "Lets now performs convective-stratiform classification on the radar data using the Yuter method [4, 5], which is a part of Py-ART's retrieve module. The result is added to the grid as a new field for further analysis or visualization.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/bhupendra/anaconda3/envs/pyart/lib/python3.12/site-packages/scipy/ndimage/_filters.py:1769: RuntimeWarning: Mean of empty slice\n",
+      "  _nd_image.generic_filter(input, function, footprint, output, mode,\n"
+     ]
+    }
+   ],
+   "source": [
+    "# convective stratiform classification Yuter\n",
+    "convsf_dict = pyart.retrieve.conv_strat_yuter(\n",
+    "    grid,\n",
+    "    dx,\n",
+    "    dy,\n",
+    "    refl_field=\"reflectivity_horizontal\",\n",
+    "    always_core_thres=40,\n",
+    "    bkg_rad_km=20,\n",
+    "    use_cosine=True,\n",
+    "    max_diff=3,\n",
+    "    zero_diff_cos_val=55,\n",
+    "    weak_echo_thres=5,\n",
+    "    max_conv_rad_km=2,\n",
+    "    estimate_flag=False,\n",
+    ")\n",
+    "\n",
+    "grid.add_field(\"convsf\", convsf_dict[\"feature_detection\"], replace_existing=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using `Wavelet Scale Analysis`\n",
+    "The function processes radar data using a Py-ART Grid object and a specified reflectivity field (`refl_field`). It offers options to adjust the Z-R relationship coefficients (`zr_a` and `zr_b`) and various thresholds for tailored classification. The output is a dictionary, `reclass_dict`, ready for integration into a Py-ART Grid. This dictionary includes the classification results, a description of the categories, and a record of the used parameters for transparency and reference.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/bhupendra/projects/pyart/pyart/retrieve/_echo_class_wt.py:175: RuntimeWarning: invalid value encountered in cast\n",
+      "  return wt_class.astype(np.int32)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'data': masked_array(\n",
+       "   data=[[[--, --, --, ..., --, --, --],\n",
+       "          [--, --, --, ..., --, --, --],\n",
+       "          [--, --, --, ..., --, --, --],\n",
+       "          ...,\n",
+       "          [--, --, --, ..., --, --, --],\n",
+       "          [--, --, --, ..., --, --, --],\n",
+       "          [--, --, --, ..., --, --, --]]],\n",
+       "   mask=[[[ True,  True,  True, ...,  True,  True,  True],\n",
+       "          [ True,  True,  True, ...,  True,  True,  True],\n",
+       "          [ True,  True,  True, ...,  True,  True,  True],\n",
+       "          ...,\n",
+       "          [ True,  True,  True, ...,  True,  True,  True],\n",
+       "          [ True,  True,  True, ...,  True,  True,  True],\n",
+       "          [ True,  True,  True, ...,  True,  True,  True]]],\n",
+       "   fill_value=999999,\n",
+       "   dtype=int32),\n",
+       " 'standard_name': 'wavelet_echo_class',\n",
+       " 'long_name': 'Wavelet-based multiresolution radar echo classification',\n",
+       " 'valid_min': 0,\n",
+       " 'valid_max': 3,\n",
+       " 'classification_description': '0: Unclassified, 1: Stratiform, 2: Mixed-Intermediate, 3: Convective Cores',\n",
+       " 'parameters': {'refl_field': 'reflectivity_horizontal',\n",
+       "  'cappi_level': 0,\n",
+       "  'zr_a': 200,\n",
+       "  'zr_b': 1.6,\n",
+       "  'core_wt_threshold': 5,\n",
+       "  'conv_wt_threshold': 1.5,\n",
+       "  'conv_scale_km': 25,\n",
+       "  'scale_break_used': 32,\n",
+       "  'min_reflectivity': 5,\n",
+       "  'conv_min_refl': 25,\n",
+       "  'conv_core_threshold': 42}}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reclass_dict = pyart.retrieve.conv_strat_raut(\n",
+    "    grid, refl_field=\"reflectivity_horizontal\"\n",
+    ")\n",
+    "\n",
+    "# add field\n",
+    "grid.add_field(\"wt_reclass\", reclass_dict[\"wt_reclass\"], replace_existing=True)\n",
+    "reclass_dict[\"wt_reclass\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The classification parameters are returned in the dictionary along with the masked array. Although `conv_scale_km` was set to `25`, the algorithm calculated the `scale_break` as `32km`. This variation depends on the data resolution. Parameters outside the specified range will automatically be adjusted to fall within the permissible range. To disable this automatic adjustment and override the range checks, set `override_checks` to `True`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Required imports\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "\n",
+    "display = pyart.graph.GridMapDisplay(grid)\n",
+    "\n",
+    "# Create a colormap for reflectivity\n",
+    "magma_r_cmap = plt.get_cmap(\"magma_r\")\n",
+    "ref_cmap = mcolors.LinearSegmentedColormap.from_list(\n",
+    "    \"ref_cmap\", magma_r_cmap(np.linspace(0, 0.9, magma_r_cmap.N))\n",
+    ")\n",
+    "\n",
+    "# Define the projection\n",
+    "projection = ccrs.AlbersEqualArea(\n",
+    "    central_latitude=radar.latitude[\"data\"][0],\n",
+    "    central_longitude=radar.longitude[\"data\"][0],\n",
+    ")\n",
+    "\n",
+    "# Create a figure with a 2x2 layout\n",
+    "plt.figure(figsize=(18, 5))\n",
+    "\n",
+    "# First panel - Reflectivity (Top Left)\n",
+    "ax1 = plt.subplot(1, 3, 1, projection=projection)\n",
+    "display.plot_grid(\n",
+    "    \"reflectivity_horizontal\",\n",
+    "    vmin=0,\n",
+    "    vmax=55,\n",
+    "    cmap=ref_cmap,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    ax=ax1,\n",
+    ")\n",
+    "\n",
+    "# Second panel - CSY (Top Right)\n",
+    "ax2 = plt.subplot(1, 3, 2, projection=projection)\n",
+    "display.plot_grid(\n",
+    "    \"convsf\",\n",
+    "    vmin=0,\n",
+    "    vmax=3,\n",
+    "    cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4),\n",
+    "    ax=ax2,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    ticks=[1 / 3, 1, 5 / 3],\n",
+    "    ticklabs=[\"< 5dBZ\", \"Stratiform\", \"Convective\"],\n",
+    ")\n",
+    "\n",
+    "# Third panel - WT (Bottom Left)\n",
+    "ax3 = plt.subplot(1, 3, 3, projection=projection)\n",
+    "display.plot_grid(\n",
+    "    \"wt_reclass\",\n",
+    "    vmin=0,\n",
+    "    vmax=4,\n",
+    "    cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4),\n",
+    "    ax=ax3,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    ticks=[0.5, 1.5, 2.5, 3.5],\n",
+    "    ticklabs=[\"< 5dBZ\", \"Non-Convective\", \"Convective (Mixed)\", \"Convective (Cores)\"],\n",
+    ")\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Case 2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in file\n",
+    "nexrad_file = \"s3://noaa-nexrad-level2/2022/09/28/KTBW/KTBW20220928_190142_V06\"\n",
+    "radar = pyart.io.read_nexrad_archive(nexrad_file)\n",
+    "\n",
+    "# extract the lowest sweep\n",
+    "radar = radar.extract_sweeps([0])\n",
+    "\n",
+    "# interpolate to grid\n",
+    "grid = pyart.map.grid_from_radars(\n",
+    "    (radar,),\n",
+    "    grid_shape=(1, 201, 201),\n",
+    "    grid_limits=((0, 10000), (-200000.0, 200000.0), (-200000.0, 200000.0)),\n",
+    "    fields=[\"reflectivity\"],\n",
+    ")\n",
+    "\n",
+    "# get dx dy\n",
+    "dx = grid.x[\"data\"][1] - grid.x[\"data\"][0]\n",
+    "dy = grid.y[\"data\"][1] - grid.y[\"data\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/bhupendra/anaconda3/envs/pyart/lib/python3.12/site-packages/scipy/ndimage/_filters.py:1769: RuntimeWarning: Mean of empty slice\n",
+      "  _nd_image.generic_filter(input, function, footprint, output, mode,\n"
+     ]
+    }
+   ],
+   "source": [
+    "# convective stratiform classification Yuter\n",
+    "convsf_dict = pyart.retrieve.conv_strat_yuter(\n",
+    "    grid,\n",
+    "    dx,\n",
+    "    dy,\n",
+    "    refl_field=\"reflectivity\",\n",
+    "    always_core_thres=40,\n",
+    "    bkg_rad_km=20,\n",
+    "    use_cosine=True,\n",
+    "    max_diff=3,\n",
+    "    zero_diff_cos_val=55,\n",
+    "    weak_echo_thres=5,\n",
+    "    max_conv_rad_km=2,\n",
+    "    estimate_flag=False,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# add to grid object\n",
+    "# mask zero values (no surface echo)\n",
+    "convsf_masked = np.ma.masked_equal(convsf_dict[\"feature_detection\"][\"data\"], 0)\n",
+    "# mask 3 values (weak echo)\n",
+    "convsf_masked = np.ma.masked_equal(convsf_masked, 3)\n",
+    "# add dimension to array to add to grid object\n",
+    "convsf_dict[\"feature_detection\"][\"data\"] = convsf_masked\n",
+    "# add field\n",
+    "grid.add_field(\"convsf\", convsf_dict[\"feature_detection\"], replace_existing=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/bhupendra/projects/pyart/pyart/retrieve/_echo_class_wt.py:175: RuntimeWarning: invalid value encountered in cast\n",
+      "  return wt_class.astype(np.int32)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'wt_reclass': {'data': masked_array(\n",
+       "    data=[[[--, --, --, ..., --, --, --],\n",
+       "           [--, --, --, ..., --, --, --],\n",
+       "           [--, --, --, ..., --, --, --],\n",
+       "           ...,\n",
+       "           [--, --, --, ..., 1, 1, 1],\n",
+       "           [--, --, --, ..., 1, 1, 1],\n",
+       "           [--, --, --, ..., 1, 1, 1]]],\n",
+       "    mask=[[[ True,  True,  True, ...,  True,  True,  True],\n",
+       "           [ True,  True,  True, ...,  True,  True,  True],\n",
+       "           [ True,  True,  True, ...,  True,  True,  True],\n",
+       "           ...,\n",
+       "           [ True,  True,  True, ..., False, False, False],\n",
+       "           [ True,  True,  True, ..., False, False, False],\n",
+       "           [ True,  True,  True, ..., False, False, False]]],\n",
+       "    fill_value=999999,\n",
+       "    dtype=int32),\n",
+       "  'standard_name': 'wavelet_echo_class',\n",
+       "  'long_name': 'Wavelet-based multiresolution radar echo classification',\n",
+       "  'valid_min': 0,\n",
+       "  'valid_max': 3,\n",
+       "  'classification_description': '0: Unclassified, 1: Stratiform, 2: Mixed-Intermediate, 3: Convective Cores',\n",
+       "  'parameters': {'refl_field': 'reflectivity',\n",
+       "   'cappi_level': 0,\n",
+       "   'zr_a': 200,\n",
+       "   'zr_b': 1.6,\n",
+       "   'core_wt_threshold': 5,\n",
+       "   'conv_wt_threshold': 1.5,\n",
+       "   'conv_scale_km': 25,\n",
+       "   'scale_break_used': 32,\n",
+       "   'min_reflectivity': 5,\n",
+       "   'conv_min_refl': 25,\n",
+       "   'conv_core_threshold': 42}}}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reclass_dict = pyart.retrieve.conv_strat_raut(grid, refl_field=\"reflectivity\")\n",
+    "\n",
+    "# add field\n",
+    "grid.add_field(\"wt_reclass\", reclass_dict[\"wt_reclass\"], replace_existing=True)\n",
+    "reclass_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Required imports\n",
+    "\n",
+    "display = pyart.graph.GridMapDisplay(grid)\n",
+    "\n",
+    "# Create a colormap for reflectivity\n",
+    "magma_r_cmap = plt.get_cmap(\"magma_r\")\n",
+    "ref_cmap = mcolors.LinearSegmentedColormap.from_list(\n",
+    "    \"ref_cmap\", magma_r_cmap(np.linspace(0, 0.9, magma_r_cmap.N))\n",
+    ")\n",
+    "\n",
+    "# Define the projection\n",
+    "projection = ccrs.AlbersEqualArea(\n",
+    "    central_latitude=radar.latitude[\"data\"][0],\n",
+    "    central_longitude=radar.longitude[\"data\"][0],\n",
+    ")\n",
+    "\n",
+    "# Create a figure with a 2x2 layout\n",
+    "plt.figure(figsize=(18, 5))\n",
+    "\n",
+    "# First panel - Reflectivity (Top Left)\n",
+    "ax1 = plt.subplot(1, 3, 1, projection=projection)\n",
+    "display.plot_grid(\n",
+    "    \"reflectivity\", vmin=0, vmax=55, cmap=ref_cmap, transform=ccrs.PlateCarree(), ax=ax1\n",
+    ")\n",
+    "\n",
+    "# Second panel - csy (Bottom Left)\n",
+    "ax2 = plt.subplot(1, 3, 2, projection=projection)\n",
+    "display.plot_grid(\n",
+    "    \"convsf\",\n",
+    "    vmin=0,\n",
+    "    vmax=3,\n",
+    "    cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4),\n",
+    "    ax=ax2,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    ticks=[1 / 3, 1, 2],\n",
+    "    ticklabs=[\"< 5dBZ\", \"Stratiform\", \"Convective\"],\n",
+    ")\n",
+    "\n",
+    "# third  panel - reclass (Bottom Right)\n",
+    "ax3 = plt.subplot(1, 3, 3, projection=projection)\n",
+    "display.plot_grid(\n",
+    "    \"wt_reclass\",\n",
+    "    vmin=0,\n",
+    "    vmax=4,\n",
+    "    cmap=plt.get_cmap(\"pyart_HomeyerRainbow\", 4),\n",
+    "    ax=ax3,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    ticks=[0.5, 1.5, 2.5, 3.5],\n",
+    "    ticklabs=[\"< 5dBZ\", \"Non-Convective\", \"Convective (Mixed)\", \"Convective (Cores)\"],\n",
+    ")\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Remarks:\n",
+    "Both the methods primarily agree on the location of the convection; however, the wavelet transform reveals more intricate details in the shape of the identified convective regions. The further separation of convection into `cores` and `intermediate-mixed` category is particularly notable. The comparison of Drop Size Distributions (DSD) for these classes shows the WT method's efficiency in segregating radar rainfall regions that are microphysically distinct [2]. The stratiform or non-convective precipitation, characterized by smaller drops and the lowest drop density, contrasts with convective core precipitation, which exhibits a high drop density and abundance of large drops. The intermediate or mixed rain category, marked by a high concentration of small and medium size drops and a lack of large drops, provides further insights into the microphysical processes and the convective-stratiform organization."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### References:\n",
+    "1. Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). \"Wavelet-based technique to extract convective clouds from infrared satellite images.\" IEEE Geosci. Remote Sens. Lett., 5(3), 328–330. [DOI](https://doi.org/10.1109/LGRS.2008.916072)\n",
+    "2. Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). \"A Multiresolution Technique for the Classification of Precipitation Echoes in Radar Data.\" IEEE Trans. Geosci. Remote Sens., 58(8), 5409. [DOI](https://doi.org/10.1109/TGRS.2020.2965649)\n",
+    "3. Churchill, D. D., & Houze, R. A. (1984). \"Development and structure of winter monsoon cloud clusters on 10 December 1978.\" J. Atmos. Sci., 41(6), 933-960. [DOI](https://doi.org/10.1175/1520-0469(1984)041<0933:DASOWM>2.0.CO;2)\n",
+    "4. Steiner, M. R., Houze Jr., R. A., & Yuter, S. E. (1995). \"Climatological Characterization of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge Data.\" J. Appl. Meteor., 34, 1978-2007. [DOI](https://doi.org/10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2)\n",
+    "5. Yuter, S. E., & Houze Jr., R. A. (1997). \"Measurements of raindrop size distributions over the Pacific warm pool and implications for Z-R relations.\" J. Appl. Meteor., 36, 847-867. [DOI](https://doi.org/10.1175/1520-0450(1997)036<0847:MORSDO>2.0.CO;2)\n",
+    "6. Lovejoy, S., & Schertzer, D. (1985). \"Generalized scale invariance in the atmosphere and fractal models of rain.\" Water Resour. Res., 21(8), 1233–1250. [DOI](https://doi.org/10.1029/WR021i008p01233)\n",
+    "7. Starck, J.-L., Murtagh, F. D., & Bijaoui, A. (1998). \"Image Processing and Data Analysis: The Multiscale Approach.\" Cambridge Univ. Press.\n",
+    "8. Shensa, M. J. (1992). \"The discrete wavelet transform: Wedding the à trous and Mallat algorithms.\" IEEE Trans. Signal Process., 40(10), 2464–2482. [DOI](https://doi.org/10.1109/78.157290)\n",
+    "9. Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). \"A multiplicative cascade model for high-resolution space-time downscaling of rainfall.\" J. Geophys. Res. Atmos., 123(4), 2050–2067. [DOI](https://doi.org/10.1002/2017JD027148)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pyart",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyart/retrieve/__init__.py b/pyart/retrieve/__init__.py
index 3b79ba5391..0deedf7f8f 100644
--- a/pyart/retrieve/__init__.py
+++ b/pyart/retrieve/__init__.py
@@ -10,6 +10,7 @@
 from .echo_class import get_freq_band  # noqa
 from .echo_class import hydroclass_semisupervised  # noqa
 from .echo_class import steiner_conv_strat  # noqa
+from .echo_class import conv_strat_raut  # noqa
 from .gate_id import fetch_radar_time_profile, map_profile_to_gates  # noqa
 from .kdp_proc import kdp_maesaka, kdp_schneebeli, kdp_vulpiani  # noqa
 from .qpe import est_rain_rate_a  # noqa
diff --git a/pyart/retrieve/_echo_class_wt.py b/pyart/retrieve/_echo_class_wt.py
new file mode 100644
index 0000000000..e9d38365e3
--- /dev/null
+++ b/pyart/retrieve/_echo_class_wt.py
@@ -0,0 +1,313 @@
+"""
+Classification of Precipitation Echoes in Radar Data.
+
+Created on Thu Oct 12 23:12:19 2017
+@author: Bhupendra Raut
+@modifed: 11/19/2023
+@references: 10.1109/TGRS.2020.2965649
+
+.. autosummary::
+    wavelet_reclass
+    label_classes
+    calc_scale_break
+    atwt2d
+"""
+
+
+import numpy as np
+
+
+def wavelet_reclass(
+    grid,
+    refl_field,
+    level,
+    zr_a,
+    zr_b,
+    core_wt_threshold,
+    conv_wt_threshold,
+    scale_break,
+    min_reflectivity,
+    conv_min_refl,
+    conv_core_threshold,
+):
+    """
+    Compute ATWT described as Raut et al (2008) and classify radar echoes using scheme of Raut et al (2020).
+    First, convert dBZ to rain rates using standard Z-R relationship or user given coefficients. This is to
+    transform the normally distributed dBZ to gamma-like distribution, enhancing the structure of the field.
+
+    Parameters
+    ----------
+    dbz_data : ndarray
+        2D array containing radar data. Last dimension should be levels.
+    res_km : float
+        Resolution of the radar data in km
+    scale_break : int
+        Calculated scale break between convective and stratiform scales. Dyadically spaced in grid pixels.
+
+    Returns
+    -------
+    wt_class : ndarray
+        Precipitation type classification: 0. N/A 1. stratiform/non-convective,
+        2. convective cores and 3. moderate+transitional (mix) convective
+        regions.
+    """
+
+    # Extract grid data, save mask and get the resolution in km
+    try:
+        dbz_data = grid.fields[refl_field]["data"][level, :, :]
+    except:
+        dbz_data = grid.fields[refl_field]["data"][:, :]
+
+    # save the radar original mask for missing data.
+    radar_mask = np.ma.getmask(dbz_data)
+
+    wt_sum = conv_wavelet_sum(dbz_data, zr_a, zr_b, scale_break)
+
+    wt_class = label_classes(
+        wt_sum,
+        dbz_data,
+        core_wt_threshold,
+        conv_wt_threshold,
+        min_reflectivity,
+        conv_min_refl,
+        conv_core_threshold,
+    )
+
+    wt_class_ma = np.ma.masked_where(radar_mask, wt_class)  # add mask back
+    wt_class_ma = wt_class_ma.squeeze()
+
+    return wt_class_ma
+
+
+def conv_wavelet_sum(dbz_data, zr_a, zr_b, scale_break):
+    """
+    Computes the sum of wavelet transform components for convective scales from dBZ data.
+
+    Parameters
+    ------------
+    dbz_data : ndarray
+        2D array containing radar dBZ data.
+    zr_a, zr_b : float
+        Coefficients for the Z-R relationship.
+    res_km : float
+        Resolution of the radar data in km.
+    scale_break : int
+        Calculated scale break (in pixels) between convective and stratiform scales
+
+    Returns
+    ---------
+    wt_sum : ndarray
+        Sum of convective scale wavelet transform components.
+    """
+    try:
+        dbz_data = dbz_data.filled(0)
+    except Exception:
+        pass
+
+    dbz_data[np.isnan(dbz_data)] = 0
+    rr_data = ((10.0 ** (dbz_data / 10.0)) / zr_a) ** (1.0 / zr_b)
+
+    wt, _ = atwt2d(rr_data, max_scale=scale_break)
+    wt_sum = np.sum(wt, axis=(0))
+
+    return wt_sum
+
+
+def label_classes(
+    wt_sum,
+    dbz_data,
+    core_wt_threshold,
+    conv_wt_threshold,
+    min_reflectivity,
+    conv_min_refl,
+    conv_core_threshold,
+):
+    """
+    Labels classes using given thresholds:
+        - 0: No precipitation or unclassified
+        - 1: Stratiform/non-convective regions
+        - 2: Transitional and mixed convective regions
+        - 3: Convective cores
+
+    Following hard coded values are optimized and validated using C-band radars
+    over Darwin, Australia (2.5 km grid spacing) and tested for Solapur, India (1km grid spacing) [Raut et al. 2020].
+    core_wt_threshold = 5  # WT value more than this is strong convection
+    conv_wt_threshold = 2  # WT value for moderate convection
+    min_reflectivity = 10  # pixels below this value are not classified.
+     conv_min_refl = 30  # pixel below this value are not convective. This works for most cases.
+
+    Parameters
+    -----------
+    wt_sum : ndarray
+        Integrated wavelet transform
+    vol_data : ndarray
+        Array, vector or matrix of data
+
+    Returns
+    ---------
+    wt_class : ndarray
+        Precipitation type classification.
+    """
+
+    # I first used negative numbers to annotate the categories. Then multiply it by -1.
+    wt_class = np.where(
+        (wt_sum >= conv_wt_threshold) & (dbz_data >= conv_core_threshold), -3, 0
+    )
+    wt_class = np.where(
+        (wt_sum >= core_wt_threshold) & (dbz_data >= conv_min_refl), -3, 0
+    )
+    wt_class = np.where(
+        (wt_sum < core_wt_threshold)
+        & (wt_sum >= conv_wt_threshold)
+        & (dbz_data >= conv_min_refl),
+        -2,
+        wt_class,
+    )
+    wt_class = np.where((wt_class == 0) & (dbz_data >= min_reflectivity), -1, wt_class)
+
+    wt_class = -1 * wt_class
+    wt_class = np.where((wt_class == 0), np.nan, wt_class)
+
+    return wt_class.astype(np.int32)
+
+
+def calc_scale_break(res_meters, conv_scale_km):
+    """
+    Compute scale break for convection and stratiform regions. WT will be
+    computed upto this scale and features will be designated as convection.
+
+    Parameters
+    -----------
+    res_meters : float
+        resolution of the image.
+    conv_scale_km : float
+        expected size of spatial variations due to convection.
+
+    Returns
+    --------
+    dyadic scale break : int
+        integer scale break in dyadic scale.
+    """
+    res_km = res_meters / 1000
+    scale_break = np.log(conv_scale_km / res_km) / np.log(2) + 1
+
+    return int(round(scale_break))
+
+
+def atwt2d(data2d, max_scale=-1):
+    """
+    Computes a trous wavelet transform (ATWT). Computes ATWT of the 2D array
+    up to max_scale. If max_scale is outside the boundaries, number of scales
+    will be reduced.
+
+    Data is mirrored at the boundaries. 'Negative WT are removed. Not tested
+    for non-square data.
+
+    @authors: Bhupendra A. Raut and Dileep M. Puranik
+    @references: Press et al. (1992) Numerical Recipes in C.
+
+    Parameters
+    -----------
+    data2d : ndarray
+        2D image as array or matrix.
+    max_scale :
+        Computes wavelets up to max_scale. Leave blank for maximum possible
+        scales.
+
+    Returns
+    ---------
+    tuple of ndarray
+        ATWT of input image and the final smoothed image or background image.
+    """
+
+    if not isinstance(data2d, np.ndarray):
+        raise TypeError("The input data2d must be a numpy array.")
+
+    data2d = data2d.squeeze()
+
+    dims = data2d.shape
+    min_dims = np.min(dims)
+    max_possible_scales = int(np.floor(np.log(min_dims) / np.log(2)))
+
+    if max_scale < 0 or max_possible_scales <= max_scale:
+        max_scale = max_possible_scales - 1
+
+    ny = dims[0]
+    nx = dims[1]
+
+    # For saving wt components
+    wt = np.zeros((max_scale, ny, nx))
+
+    temp1 = np.zeros(dims)
+    temp2 = np.zeros(dims)
+
+    sf = (0.0625, 0.25, 0.375)  # scaling function
+
+    # start wavelet loop
+    for scale in range(1, max_scale + 1):
+        # print(scale)
+        x1 = 2 ** (scale - 1)
+        x2 = 2 * x1
+
+        # Row-wise smoothing
+        for i in range(0, nx):
+            # find the indices for prev and next points on the line
+            prev2 = abs(i - x2)
+            prev1 = abs(i - x1)
+            next1 = i + x1
+            next2 = i + x2
+
+            # If these indices are outside the image, "mirror" them
+            # Sometime this causes issues at higher scales.
+            if next1 > nx - 1:
+                next1 = 2 * (nx - 1) - next1
+
+            if next2 > nx - 1:
+                next2 = 2 * (nx - 1) - next2
+
+            if prev1 < 0 or prev2 < 0:
+                prev1 = next1
+                prev2 = next2
+
+            for j in range(0, ny):
+                left2 = data2d[j, prev2]
+                left1 = data2d[j, prev1]
+                right1 = data2d[j, next1]
+                right2 = data2d[j, next2]
+                temp1[j, i] = (
+                    sf[0] * (left2 + right2)
+                    + sf[1] * (left1 + right1)
+                    + sf[2] * data2d[j, i]
+                )
+
+        # Column-wise smoothing
+        for i in range(0, ny):
+            prev2 = abs(i - x2)
+            prev1 = abs(i - x1)
+            next1 = i + x1
+            next2 = i + x2
+
+            # If these indices are outside the image use next values
+            if next1 > ny - 1:
+                next1 = 2 * (ny - 1) - next1
+            if next2 > ny - 1:
+                next2 = 2 * (ny - 1) - next2
+            if prev1 < 0 or prev2 < 0:
+                prev1 = next1
+                prev2 = next2
+
+            for j in range(0, nx):
+                top2 = temp1[prev2, j]
+                top1 = temp1[prev1, j]
+                bottom1 = temp1[next1, j]
+                bottom2 = temp1[next2, j]
+                temp2[i, j] = (
+                    sf[0] * (top2 + bottom2)
+                    + sf[1] * (top1 + bottom1)
+                    + sf[2] * temp1[i, j]
+                )
+
+        wt[scale - 1, :, :] = data2d - temp2
+        data2d[:] = temp2
+
+    return wt, data2d
diff --git a/pyart/retrieve/echo_class.py b/pyart/retrieve/echo_class.py
index 7a15fa448c..039034bfa5 100644
--- a/pyart/retrieve/echo_class.py
+++ b/pyart/retrieve/echo_class.py
@@ -7,8 +7,11 @@
 
 import numpy as np
 
+# Local imports
 from ..config import get_field_name, get_fillvalue, get_metadata
+from ..core import Grid
 from ._echo_class import _feature_detection, steiner_class_buff
+from ._echo_class_wt import calc_scale_break, wavelet_reclass
 
 
 def steiner_conv_strat(
@@ -978,3 +981,175 @@ def get_freq_band(freq):
     warn("Unknown frequency band")
 
     return None
+
+
+def conv_strat_raut(
+    grid,
+    refl_field,
+    cappi_level=0,
+    zr_a=200,
+    zr_b=1.6,
+    core_wt_threshold=5,
+    conv_wt_threshold=1.5,
+    conv_scale_km=25,
+    min_reflectivity=5,
+    conv_min_refl=25,
+    conv_core_threshold=42,
+    override_checks=False,
+):
+    """
+    A computationally efficient method to classify radar echoes into convective cores, mixed convection,
+    and stratiform regions for gridded radar reflectivity field.
+
+    This function uses à trous wavelet transform (ATWT) for multiresolution (i.e. scale) analysis of radar field,
+    focusing on precipitation structure over reflectivity thresholds for robust echo classification (Raut et al 2008, 2020).
+
+    Parameters
+    ----------
+    grid : PyART Grid
+        Grid object containing radar data.
+    refl_field : str
+        Field name for reflectivity data in the Py-ART grid object.
+    zr_a : float, optional
+        Coefficient 'a' in the Z-R relationship Z = a*R^b for reflectivity to rain rate conversion.
+        The algorithm is not sensitive to precise values of 'zr_a' and 'zr_b'; however,
+        they must be adjusted based on the type of radar used.
+        Default is 200.
+    zr_b : float, optional
+        Coefficient 'b' in the Z-R relationship Z = a*R^b. Default is 1.6.
+    core_wt_threshold : float, optional
+        Threshold for wavelet components to separate convective cores from mix-intermediate type.
+        Default is 5. Recommended values are between 4 and 6.
+    conv_wt_threshold : float, optional
+        Threshold for significant wavelet components to separate all convection from stratiform.
+        Default is 1.5. Recommended values are between 1 and 2.
+    conv_scale_km : float, optional
+        Approximate scale break (in km) between convective and stratiform scales.
+        Scale break may vary over different regions and seasons
+        (Refere to Raut et al 2018 for more discussion on scale-breaks). Note that the
+        algorithm is insensitive to small variations in the scale break due to the
+        dyadic nature of the scaling. The actual scale break used in the calculation of wavelets
+        is returned in the output dictionary by parameter `scale_break_used`.
+        Default is 25 km. Recommended values are between 16 and 32 km.
+    min_reflectivity : float, optional
+        Minimum reflectivity threshold. Reflectivities below this value are not classified.
+        Default is 5 dBZ. This value must be greater than or equal to '0'.
+    conv_min_refl : float, optional
+        Reflectivity values lower than this threshold will be always considered as non-convective.
+        Default is 25 dBZ. Recommended values are between 25 and 30 dBZ.
+    conv_core_threshold : float, optional
+        Reflectivities above this threshold are classified as convective cores if wavelet components are significant (See: conv_wt_threshold).
+        Default is 42 dBZ.
+        Recommended value must be is greater than or equal to 40 dBZ. The algorithm is not sensitive to this value.
+    override_checks : bool, optional
+        If set to True, the function will bypass the sanity checks for above parameter values.
+        This allows the user to use custom values for parameters, even if they fall outside
+        the recommended ranges. The default is False.
+
+    Returns
+    -------
+
+    dict :
+    A dictionary structured as a Py-ART grid field, suitable for adding to a Py-ART Grid object. The dictionary
+    contains the classification data and associated metadata. The classification categories are as follows:
+        - 3: Convective Cores: associated with strong updrafts and active collision-coalescence.
+        - 2: Mixed-Intermediate: capturing a wide range of convective activities, excluding the convective cores.
+        - 1: Stratiform: remaining areas with more uniform and less intense precipitation.
+        - 0: Unclassified: for reflectivity below the minimum threshold.
+
+
+    References
+    ----------
+    Raut, B. A., Karekar, R. N., & Puranik, D. M. (2008). Wavelet-based technique to extract convective clouds from
+    infrared satellite images. IEEE Geosci. Remote Sens. Lett., 5(3), 328-330.
+
+    Raut, B. A., Seed, A. W., Reeder, M. J., & Jakob, C. (2018). A multiplicative cascade model for high‐resolution
+    space‐time downscaling of rainfall. J. Geophys. Res. Atmos., 123(4), 2050-2067.
+
+    Raut, B. A., Louf, V., Gayatri, K., Murugavel, P., Konwar, M., & Prabhakaran, T. (2020). A multiresolution technique
+    for the classification of precipitation echoes in radar data. IEEE Trans. Geosci. Remote Sens., 58(8), 5409-5415.
+    """
+
+    # Check if the grid is a Py-ART Grid object
+    if not isinstance(grid, Grid):
+        raise TypeError("The 'grid' is not a Py-ART Grid object.")
+
+    # Check if dx and dy are the same, and warn if not
+    dx = grid.x["data"][1] - grid.x["data"][0]
+    dy = grid.y["data"][1] - grid.y["data"][0]
+    if dx != dy:
+        warn(
+            "Warning: Grid resolution `dx` and `dy` should be comparable for correct results.",
+            UserWarning,
+        )
+
+    # Compute scale break (dyadic) here to paas it on as parameter to user dictionary
+    scale_break = calc_scale_break(res_meters=dx, conv_scale_km=conv_scale_km)
+
+    # From dyadic scale to km
+    scale_break_km = (2 ** (scale_break - 1)) * dx / 1000
+
+    # Sanity checks for parameters if override_checks is False
+    if not override_checks:
+        conv_core_threshold = max(
+            40, conv_core_threshold
+        )  # Ensure conv_core_threshold is at least 40 dBZ
+        core_wt_threshold = max(
+            4, min(core_wt_threshold, 6)
+        )  # core_wt_threshold should be between 4 and 6
+        conv_wt_threshold = max(
+            1, min(conv_wt_threshold, 2)
+        )  # conv_wt_threshold should be between 1 and 2
+        conv_scale_km = max(
+            16, min(conv_scale_km, 32)
+        )  # conv_scale_km should be between 15 and 30 km
+        min_reflectivity = max(
+            0, min_reflectivity
+        )  # min_reflectivity should be non-negative
+        conv_min_refl = max(
+            25, min(conv_min_refl, 30)
+        )  # conv_min_refl should be between 25 and 30 dBZ
+
+    # Call the actual wavelet_relass function to obtain radar echo classificatino
+    reclass = wavelet_reclass(
+        grid,
+        refl_field,
+        cappi_level,
+        zr_a,
+        zr_b,
+        core_wt_threshold=core_wt_threshold,
+        conv_wt_threshold=conv_wt_threshold,
+        scale_break=scale_break,
+        min_reflectivity=min_reflectivity,
+        conv_min_refl=conv_min_refl,
+        conv_core_threshold=conv_core_threshold,
+    )
+
+    reclass = np.expand_dims(reclass, axis=0)
+
+    # put data into a dictionary to be added as a field
+    reclass_dict = {
+        "wt_reclass": {
+            "data": reclass,
+            "standard_name": "wavelet_echo_class",
+            "long_name": "Wavelet-based multiresolution radar echo classification",
+            "valid_min": 0,
+            "valid_max": 3,
+            "classification_description": "0: Unclassified, 1: Stratiform, 2: Mixed-Intermediate, 3: Convective Cores",
+            "parameters": {
+                "refl_field": refl_field,
+                "cappi_level": cappi_level,
+                "zr_a": zr_a,
+                "zr_b": zr_b,
+                "core_wt_threshold": core_wt_threshold,
+                "conv_wt_threshold": conv_wt_threshold,
+                "conv_scale_km": conv_scale_km,
+                "scale_break_used": int(scale_break_km),
+                "min_reflectivity": min_reflectivity,
+                "conv_min_refl": conv_min_refl,
+                "conv_core_threshold": conv_core_threshold,
+            },
+        }
+    }
+
+    return reclass_dict


Test patch:
diff --git a/pyart/testing/sample_objects.py b/pyart/testing/sample_objects.py
index fb4f1f0212..e9fc2833a4 100644
--- a/pyart/testing/sample_objects.py
+++ b/pyart/testing/sample_objects.py
@@ -378,6 +378,69 @@ def make_normal_storm(sigma, mu):
     return test_grid
 
 
+def make_gaussian_storm_grid(
+    min_value=5, max_value=45, grid_len=32, sigma=0.2, mu=0.0, masked_boundary=3
+):
+    """
+    Make a 1 km resolution grid with a Gaussian storm pattern at the center,
+    with two layers having the same data and masked boundaries.
+
+    Parameters
+    -----------
+    min_value : float
+        Minimum value of the storm intensity.
+    max_value : float
+        Maximum value of the storm intensity.
+    grid_len : int
+        Size of the grid (grid will be grid_len x grid_len).
+    sigma : float
+        Standard deviation of the Gaussian distribution.
+    mu : float
+        Mean of the Gaussian distribution.
+    masked_boundary : int
+        Number of pixels around the edge to be masked.
+
+    Returns
+    --------
+    A Py-ART grid with the Gaussian storm field added.
+    """
+
+    # Create an empty Py-ART grid
+    grid_shape = (2, grid_len, grid_len)
+    grid_limits = (
+        (1000, 1000),
+        (-grid_len * 1000 / 2, grid_len * 1000 / 2),
+        (-grid_len * 1000 / 2, grid_len * 1000 / 2),
+    )
+    grid = make_empty_grid(grid_shape, grid_limits)
+
+    # Creating a grid with Gaussian distribution values
+    x, y = np.meshgrid(np.linspace(-1, 1, grid_len), np.linspace(-1, 1, grid_len))
+    d = np.sqrt(x * x + y * y)
+    gaussian = np.exp(-((d - mu) ** 2 / (2.0 * sigma**2)))
+
+    # Normalize and scale the Gaussian distribution
+    gaussian_normalized = (gaussian - np.min(gaussian)) / (
+        np.max(gaussian) - np.min(gaussian)
+    )
+    storm_intensity = gaussian_normalized * (max_value - min_value) + min_value
+    storm_intensity = np.stack([storm_intensity, storm_intensity])
+
+    # Apply thresholds for storm intensity and masking
+    mask = np.zeros_like(storm_intensity, dtype=bool)
+    mask[:, :masked_boundary, :] = True
+    mask[:, -masked_boundary:, :] = True
+    mask[:, :, :masked_boundary] = True
+    mask[:, :, -masked_boundary:] = True
+
+    storm_intensity = np.ma.array(storm_intensity, mask=mask)
+    # Prepare dictionary for Py-ART grid fields
+    rdic = {"data": storm_intensity, "long_name": "reflectivity", "units": "dBz"}
+    grid.fields = {"reflectivity": rdic}
+
+    return grid
+
+
 def make_empty_spectra_radar(nrays, ngates, npulses_max):
     """
     Return a Spectra Radar object.
diff --git a/tests/retrieve/test_echo_class.py b/tests/retrieve/test_echo_class.py
index 288ca32dd9..ce9e276250 100644
--- a/tests/retrieve/test_echo_class.py
+++ b/tests/retrieve/test_echo_class.py
@@ -300,3 +300,98 @@ def test_standardize():
     assert_allclose(field_std_no_limits[0], [1.0, 1.0, 1.0, 1.0, 1.0], atol=1e-6)
 
     pytest.raises(ValueError, pyart.retrieve.echo_class._standardize, rhohv, "foo")
+
+
+def test_conv_strat_raut_outDict_valid():
+    """
+    Test that function returns a valid dictionary with all expected keys'.
+    """
+
+    # Test that function raises `TypeError` with invalid grid object as input.
+    pytest.raises(TypeError, pyart.retrieve.conv_strat_raut, None, "foo")
+
+    # Create a Gaussian storm grid
+    grid_len = 32
+    gaussian_storm_2d = pyart.testing.make_gaussian_storm_grid(
+        min_value=5, max_value=45, grid_len=grid_len, sigma=0.2, mu=0, masked_boundary=3
+    )
+    wtclass = pyart.retrieve.conv_strat_raut(
+        gaussian_storm_2d, "reflectivity", cappi_level=0
+    )
+
+    # First check that it's a Python dictionary
+    assert isinstance(wtclass, dict), "Output is not a dictionary"
+    # then test 'wt_reclass' key exists in the dict
+    assert "wt_reclass" in wtclass.keys()
+
+    # Now test other expected keys
+    expected_keys = [
+        "data",
+        "standard_name",
+        "long_name",
+        "valid_min",
+        "valid_max",
+        "classification_description",
+        "parameters",
+    ]
+
+    # Get the keys of the 'wt_reclass' field
+    reclass_keys = wtclass["wt_reclass"].keys()
+
+    # Check each expected key
+    for key in expected_keys:
+        assert key in reclass_keys
+
+    # check grid shape
+    assert wtclass["wt_reclass"]["data"].shape == (1, grid_len, grid_len)
+
+
+def test_conv_strat_raut_results_correct():
+    """
+    Checks the correctness of the results from the function.
+
+    I created a fixed Gaussian storm with masked boundaries as pyart grid and classified it.
+    Then constructed manually the expected classification results and compared it to the actual output from the function.
+    """
+
+    # Create a Gaussian storm grid
+    grid_len = 32
+    mask_margin = 3
+
+    gaussian_storm_2d = pyart.testing.make_gaussian_storm_grid(
+        min_value=5,
+        max_value=45,
+        grid_len=grid_len,
+        sigma=0.2,
+        mu=0,
+        masked_boundary=mask_margin,
+    )
+
+    wtclass = pyart.retrieve.conv_strat_raut(gaussian_storm_2d, "reflectivity")
+
+    # Create a 32x32 array of ones
+    test_reclass = np.ones((grid_len, grid_len))
+
+    # Mask the edges
+    test_reclass[:mask_margin, :] = np.nan
+    test_reclass[-mask_margin:, :] = np.nan
+    test_reclass[:, :mask_margin] = np.nan
+    test_reclass[:, -mask_margin:] = np.nan
+
+    # Define the center and create the 4x4 area
+    center = grid_len // 2
+    # these are actual results from successful run
+    test_reclass[center - 3 : center + 3, center - 3 : center + 3] = 2
+    test_reclass[center - 2 : center + 2, center - 2 : center + 2] = 3
+
+    test_reclass[13, 13] = 1
+    test_reclass[13, 18] = 1
+    test_reclass[18, 13] = 1
+    test_reclass[18, 18] = 1
+
+    # Creating a mask for NaN values
+    mask = np.isnan(test_reclass)
+    masked_reclass = np.ma.array(test_reclass, mask=mask).astype(np.int32)
+    masked_reclass = np.expand_dims(masked_reclass, axis=0)
+
+    assert_allclose(masked_reclass, wtclass["wt_reclass"]["data"], atol=0.1)
diff --git a/tests/testing/test_sample_objects.py b/tests/testing/test_sample_objects.py
new file mode 100644
index 0000000000..1cdaad28c1
--- /dev/null
+++ b/tests/testing/test_sample_objects.py
@@ -0,0 +1,63 @@
+""" Unit Tests for Py-ART's testing/sample_objects.py module. """
+
+import numpy as np
+
+from pyart.testing.sample_objects import make_gaussian_storm_grid
+
+
+def test_gaussian_storm_grid_results_correct():
+    """
+    Test for the make_gaussian_storm_grid function.
+
+    Checks grid shape, limits, field data, and masking.
+    These test are focusing on statistical properties of the storm and not on comparing exact storm values.
+    """
+    grid_len = 32
+    min_value = 5
+    max_value = 45
+    mask_margin = 3
+
+    # Create grid
+    gaussian_storm_2d = make_gaussian_storm_grid()
+
+    # Test Shape
+    assert gaussian_storm_2d.fields["reflectivity"]["data"].shape == (
+        2,
+        grid_len,
+        grid_len,
+    ), "Grid shape mismatch"
+
+    # Test Data
+    assert (
+        gaussian_storm_2d.fields["reflectivity"]["data"] is not None
+    ), "No data in reflectivity field"
+
+    # Test Masking
+    mask = gaussian_storm_2d.fields["reflectivity"]["data"].mask
+    assert np.all(mask[:, :mask_margin, :]), "Masking at the boundary is incorrect"
+    assert np.all(mask[:, -mask_margin:, :]), "Masking at the boundary is incorrect"
+    assert np.all(mask[:, :, :mask_margin]), "Masking at the boundary is incorrect"
+    assert np.all(mask[:, :, -mask_margin:]), "Masking at the boundary is incorrect"
+
+    storm_data = gaussian_storm_2d.fields["reflectivity"]["data"]
+
+    # Test for Max and Min
+    assert np.isclose(
+        np.max(storm_data), max_value
+    ), "Maximum value does not match expected"
+    assert np.isclose(
+        np.min(storm_data[~storm_data.mask]), min_value
+    ), "Minimum value does not match expected"
+
+    # Test Mean and SD
+    expected_mean = 8.666844653650797
+    expected_std = 7.863066829145
+    assert np.isclose(
+        np.mean(storm_data), expected_mean, atol=5
+    ), "Mean value out of expected range"
+    assert np.isclose(
+        np.std(storm_data), expected_std, atol=5
+    ), "Standard deviation out of expected range"
+
+    # Test Central Value
+    assert storm_data[0, 15, 15] == max_value, "Maximum value is not at the center"