diff --git a/PySDM/backends/impl_numba/methods/seeding_methods.py b/PySDM/backends/impl_numba/methods/seeding_methods.py
index a1e6372ba..5910e794e 100644
--- a/PySDM/backends/impl_numba/methods/seeding_methods.py
+++ b/PySDM/backends/impl_numba/methods/seeding_methods.py
@@ -19,6 +19,14 @@ def body(  # pylint: disable=too-many-arguments
             seeded_particle_multiplicity,
             seeded_particle_extensive_attributes,
             number_of_super_particles_to_inject: int,
+            seeded_particle_cell_id,
+            seeded_particle_cell_origin,
+            seeded_particle_pos_cell,
+            seeded_particle_volume,
+            cell_id,
+            cell_origin,
+            pos_cell,
+            volume,
         ):
             number_of_super_particles_already_injected = 0
             # TODO #1387 start enumerating from the end of valid particle set
@@ -39,6 +47,14 @@ def body(  # pylint: disable=too-many-arguments
                         extensive_attributes[a, i] = (
                             seeded_particle_extensive_attributes[a, s]
                         )
+
+                    if cell_id is not None:
+                        cell_id[i] = seeded_particle_cell_id[s]
+                        volume[i] = seeded_particle_volume[s]
+                        for dim in range(len(cell_origin)):
+                            cell_origin[dim][i] = seeded_particle_cell_origin[dim][s]
+                            pos_cell[dim][i] = seeded_particle_pos_cell[dim][s]
+
             assert (
                 number_of_super_particles_to_inject
                 == number_of_super_particles_already_injected
@@ -56,6 +72,14 @@ def seeding(
         seeded_particle_multiplicity,
         seeded_particle_extensive_attributes,
         number_of_super_particles_to_inject: int,
+        seeded_particle_cell_id,
+        seeded_particle_cell_origin,
+        seeded_particle_pos_cell,
+        seeded_particle_volume,
+        cell_id,
+        cell_origin,
+        pos_cell,
+        volume,
     ):
         self._seeding(
             idx=idx.data,
@@ -65,4 +89,12 @@ def seeding(
             seeded_particle_multiplicity=seeded_particle_multiplicity.data,
             seeded_particle_extensive_attributes=seeded_particle_extensive_attributes.data,
             number_of_super_particles_to_inject=number_of_super_particles_to_inject,
+            seeded_particle_cell_id=seeded_particle_cell_id,
+            seeded_particle_cell_origin=seeded_particle_cell_origin,
+            seeded_particle_pos_cell=seeded_particle_pos_cell,
+            seeded_particle_volume=seeded_particle_volume,
+            cell_id=cell_id,
+            cell_origin=cell_origin,
+            pos_cell=pos_cell,
+            volume=volume,
         )
diff --git a/PySDM/dynamics/seeding.py b/PySDM/dynamics/seeding.py
index 3655e62a6..f74bcd89f 100644
--- a/PySDM/dynamics/seeding.py
+++ b/PySDM/dynamics/seeding.py
@@ -18,6 +18,10 @@ def __init__(
         super_droplet_injection_rate: callable,
         seeded_particle_extensive_attributes: dict,
         seeded_particle_multiplicity: Sized,
+        seeded_particle_cell_id=None,
+        seeded_particle_cell_origin=None,
+        seeded_particle_pos_cell=None,
+        seeded_particle_volume=None,
     ):
         for attr in seeded_particle_extensive_attributes.values():
             assert len(seeded_particle_multiplicity) == len(attr)
@@ -25,6 +29,10 @@ def __init__(
         self.super_droplet_injection_rate = super_droplet_injection_rate
         self.seeded_particle_extensive_attributes = seeded_particle_extensive_attributes
         self.seeded_particle_multiplicity = seeded_particle_multiplicity
+        self.seeded_particle_cell_id = seeded_particle_cell_id
+        self.seeded_particle_cell_origin = seeded_particle_cell_origin
+        self.seeded_particle_pos_cell = seeded_particle_pos_cell
+        self.seeded_particle_volume = seeded_particle_volume
         self.rnd = None
         self.u01 = None
         self.index = None
@@ -67,6 +75,30 @@ def post_register_setup_when_attributes_are_known(self):
             )
         )
 
+        if self.particulator.environment.mesh.n_dims > 0:
+            self.seeded_particle_cell_id = (
+                self.particulator.IndexedStorage.from_ndarray(
+                    self.index,
+                    np.asarray(self.seeded_particle_cell_id),
+                )
+            )
+            self.seeded_particle_cell_origin = (
+                self.particulator.IndexedStorage.from_ndarray(
+                    self.index,
+                    np.asarray(self.seeded_particle_cell_origin),
+                )
+            )
+            self.seeded_particle_pos_cell = (
+                self.particulator.IndexedStorage.from_ndarray(
+                    self.index,
+                    np.asarray(self.seeded_particle_pos_cell),
+                )
+            )
+            self.seeded_particle_volume = self.particulator.IndexedStorage.from_ndarray(
+                self.index,
+                np.asarray(self.seeded_particle_volume),
+            )
+
     def __call__(self):
         if self.particulator.n_steps == 0:
             self.post_register_setup_when_attributes_are_known()
@@ -91,4 +123,8 @@ def __call__(self):
                 number_of_super_particles_to_inject=number_of_super_particles_to_inject,
                 seeded_particle_multiplicity=self.seeded_particle_multiplicity,
                 seeded_particle_extensive_attributes=self.seeded_particle_extensive_attributes,
+                seeded_particle_cell_id=self.seeded_particle_cell_id,
+                seeded_particle_cell_origin=self.seeded_particle_cell_origin,
+                seeded_particle_pos_cell=self.seeded_particle_pos_cell,
+                seeded_particle_volume=self.seeded_particle_volume,
             )
diff --git a/PySDM/particulator.py b/PySDM/particulator.py
index 8176221bc..b30cb104f 100644
--- a/PySDM/particulator.py
+++ b/PySDM/particulator.py
@@ -67,6 +67,9 @@ def Random(self):
     def n_sd(self) -> int:
         return self.__n_sd
 
+    def n_sd_setter(self, value):
+        self.__n_sd += value
+
     @property
     def dt(self) -> float:
         if self.environment is not None:
@@ -446,6 +449,10 @@ def seeding(
         seeded_particle_multiplicity,
         seeded_particle_extensive_attributes,
         number_of_super_particles_to_inject,
+        seeded_particle_cell_id=None,
+        seeded_particle_cell_origin=None,
+        seeded_particle_pos_cell=None,
+        seeded_particle_volume=None,
     ):
         n_null = self.n_sd - self.attributes.super_droplet_count
         if n_null == 0:
@@ -464,15 +471,44 @@ def seeding(
                 Instead increase multiplicity of injected particles."
             )
 
-        self.backend.seeding(
-            idx=self.attributes._ParticleAttributes__idx,
-            multiplicity=self.attributes["multiplicity"],
-            extensive_attributes=self.attributes.get_extensive_attribute_storage(),
-            seeded_particle_index=seeded_particle_index,
-            seeded_particle_multiplicity=seeded_particle_multiplicity,
-            seeded_particle_extensive_attributes=seeded_particle_extensive_attributes,
-            number_of_super_particles_to_inject=number_of_super_particles_to_inject,
-        )
+        if self.environment.mesh.n_dims == 0:
+            self.backend.seeding(
+                idx=self.attributes._ParticleAttributes__idx,
+                multiplicity=self.attributes["multiplicity"],
+                extensive_attributes=self.attributes.get_extensive_attribute_storage(),
+                seeded_particle_index=seeded_particle_index,
+                seeded_particle_multiplicity=seeded_particle_multiplicity,
+                seeded_particle_extensive_attributes=seeded_particle_extensive_attributes,
+                number_of_super_particles_to_inject=number_of_super_particles_to_inject,
+                seeded_particle_cell_id=seeded_particle_cell_id,
+                seeded_particle_cell_origin=seeded_particle_cell_origin,
+                seeded_particle_pos_cell=seeded_particle_pos_cell,
+                seeded_particle_volume=seeded_particle_volume,
+                cell_id=None,
+                cell_origin=None,
+                pos_cell=None,
+                volume=None,
+            )
+
+        else:
+            self.backend.seeding(
+                idx=self.attributes._ParticleAttributes__idx,
+                multiplicity=self.attributes["multiplicity"],
+                extensive_attributes=self.attributes.get_extensive_attribute_storage(),
+                seeded_particle_index=seeded_particle_index,
+                seeded_particle_multiplicity=seeded_particle_multiplicity,
+                seeded_particle_extensive_attributes=seeded_particle_extensive_attributes,
+                number_of_super_particles_to_inject=number_of_super_particles_to_inject,
+                seeded_particle_cell_id=seeded_particle_cell_id.data,
+                seeded_particle_cell_origin=seeded_particle_cell_origin.data,
+                seeded_particle_pos_cell=seeded_particle_pos_cell.data,
+                seeded_particle_volume=seeded_particle_volume.data,
+                cell_id=self.attributes["cell id"].data,
+                cell_origin=self.attributes["cell origin"].data,
+                pos_cell=self.attributes["position in cell"].data,
+                volume=self.attributes["volume"].data,
+            )
+
         self.attributes.reset_idx()
         self.attributes.sanitize()
 
diff --git a/docs/bibliography.json b/docs/bibliography.json
index 8a9f1d445..8af215b4b 100644
--- a/docs/bibliography.json
+++ b/docs/bibliography.json
@@ -492,7 +492,8 @@
       "examples/docs/pysdm_examples_landing.md",
       "examples/PySDM_examples/deJong_Mackay_et_al_2023/figs_10_11_12_13.ipynb",
       "examples/PySDM_examples/Shipway_and_Hill_2012/fig_1.ipynb",
-      "examples/PySDM_examples/Shipway_and_Hill_2012/__init__.py"
+      "examples/PySDM_examples/Shipway_and_Hill_2012/__init__.py",
+      "examples/PySDM_examples/seeding/SH2012_seeding.ipynb"
     ],
     "label": "Shipway & Hill 2012 (Q. J. R. Meteorol. Soc. 138)",
     "title": "Diagnosis of systematic differences between multiple parametrizations of warm rain microphysics using a kinematic framework"
diff --git a/examples/PySDM_examples/seeding/SH2012_seeding.ipynb b/examples/PySDM_examples/seeding/SH2012_seeding.ipynb
new file mode 100644
index 000000000..6c5062f78
--- /dev/null
+++ b/examples/PySDM_examples/seeding/SH2012_seeding.ipynb
@@ -0,0 +1,7427 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Seeding dynamic for the kinematic driver introduced in Shipway & Hill 2012 \n",
+    "https://doi.org/10.1002/qj.1913\n",
+    "\n",
+    "(see the Shipway and Hill example in PySDM for more details)\n",
+    "\n",
+    "**NOTES**: \n",
+    "- constant momentum profile rather than constant velocity profile is used herein\n",
+    "- enabling precipitation interpretted as turning on sedimentation and collisions\n",
+    "- pressure at z=0 not given in the paper, assumed (see settings.py)\n",
+    "- domain extended below z=0 to mimic particle inflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-29T15:12:04.969183Z",
+     "start_time": "2023-12-29T15:12:03.369050Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from PySDM import Formulae\n",
+    "from PySDM.physics import si\n",
+    "from PySDM_examples.seeding.settings_1d import Settings\n",
+    "from PySDM_examples.seeding.simulation_1d import Simulation\n",
+    "\n",
+    "from PySDM.physics import in_unit, si\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import pyplot\n",
+    "from PySDM_examples.Shipway_and_Hill_2012 import plot\n",
+    "from open_atmos_jupyter_utils import show_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-29T15:13:57.683736Z",
+     "start_time": "2023-12-29T15:12:04.972230Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/hyfives-lamont/Desktop/cloud_seeding/PySDM/PySDM/backends/numba.py:47: UserWarning: Disabling Numba threading due to ARM64 CPU (atomics do not work yet)\n",
+      "  warnings.warn(\n",
+      "/Users/hyfives-lamont/Desktop/cloud_seeding/PySDM/PySDM/backends/numba.py:47: UserWarning: Disabling Numba threading due to ARM64 CPU (atomics do not work yet)\n",
+      "  warnings.warn(\n",
+      "/Users/hyfives-lamont/Desktop/cloud_seeding/PySDM/PySDM/backends/numba.py:47: UserWarning: Disabling Numba threading due to ARM64 CPU (atomics do not work yet)\n",
+      "  warnings.warn(\n",
+      "/Users/hyfives-lamont/Desktop/cloud_seeding/PySDM/PySDM/backends/numba.py:47: UserWarning: Disabling Numba threading due to ARM64 CPU (atomics do not work yet)\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "np.random.seed(123)\n",
+    "\n",
+    "common_params = {\n",
+    "    \"n_sd_per_gridbox\": 32,\n",
+    "    \"n_sd_seeding\": 200,\n",
+    "    \"dt\": 5 * si.s,\n",
+    "    \"dz\": 50 * si.m,\n",
+    "    \"p0\": 990 * si.hPa,\n",
+    "    \"kappa\": .3,\n",
+    "    \"particles_per_volume_STP\": 50 / si.cm**3,\n",
+    "    \"seed_particles_per_volume_STP\": 50 / si.cm**3,\n",
+    "    \"seed_kappa\": .8,\n",
+    "}\n",
+    "\n",
+    "rho_times_w= 2 * si.kg/si.m**3 * si.m/si.s\n",
+    "simulations = {\n",
+    "    case: Simulation(\n",
+    "        Settings(\n",
+    "            **common_params,\n",
+    "            rho_times_w_1=rho_times_w,\n",
+    "            formulae= Formulae(seed= np.random.randint(1000)),\n",
+    "            super_droplet_injection_rate = {\n",
+    "                'seeding': lambda time: 3 if 5 * si.min < time < 10 * si.min else 0,\n",
+    "                'no seeding': lambda _: 0,\n",
+    "            }[case],\n",
+    "            precip=True,\n",
+    "        )\n",
+    "    )\n",
+    "    for case in ('seeding', 'no seeding')\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n",
+      "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n"
+     ]
+    }
+   ],
+   "source": [
+    "outputs = {case: simulations[case].run() for case in simulations}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-29T15:14:06.730859Z",
+     "start_time": "2023-12-29T15:13:57.682941Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b5971ee9b4a486893551258651c9da8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HTML(value=\"<a href='./qc_seeding.pdf' target='_blank'>./qc_seeding.pdf</a><br>\")"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28e055c31b2546c184f95f4ef028304a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HTML(value=\"<a href='./qc_no seeding.pdf' target='_blank'>./qc_no seeding.pdf</a><br>\")"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for case, output in outputs.items():\n",
+    "    plot(var='cloud water mixing ratio', qlabel='cloud water mixing ratio [g/kg]', fname= 'qc_' + case +'.pdf', cmin= 0, cmax= 3, output= output.products)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-29T15:14:11.163980Z",
+     "start_time": "2023-12-29T15:14:06.728728Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fda1c714b6314c1db2357a69d2cc3949",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HTML(value=\"<a href='./qr_seeding.pdf' target='_blank'>./qr_seeding.pdf</a><br>\")"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1c64c570a0e40c0aef5f415910df047",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HTML(value=\"<a href='./qr_no seeding.pdf' target='_blank'>./qr_no seeding.pdf</a><br>\")"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for case, output in outputs.items():\n",
+    "    plot(var='rain water mixing ratio', qlabel='rain water mixing ratio [g/kg]', fname= 'qr_' + case +'.pdf', cmin= 0, cmax= 4, output= output.products)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"856.8pt\" height=\"285.84pt\" viewBox=\"0 0 856.8 285.84\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
+       " <metadata>\n",
+       "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2025-01-09T23:15:20.974562</dc:date>\n",
+       "    <dc:format>image/svg+xml</dc:format>\n",
+       "    <dc:creator>\n",
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+       "L 3481 3500 \n",
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+       "z\n",
+       "\" transform=\"scale(0.015625)\"/>\n",
+       "    </defs>\n",
+       "    <use xlink:href=\"#DejaVuSans-6e\"/>\n",
+       "    <use xlink:href=\"#DejaVuSans-6f\" x=\"63.378906\"/>\n",
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+       "    <use xlink:href=\"#DejaVuSans-67\" x=\"486.132812\"/>\n",
+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"pddf6eb9f1d\">\n",
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+       "   <rect x=\"597.527083\" y=\"34.65375\" width=\"244.121354\" height=\"213.63\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a94c40564943434b9d77f2633e098827",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HTML(value=\"<a href='./SH2012_no_seeding.pdf' target='_blank'>./SH2012_no_seeding.pdf</a><br>\")"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for case, output in outputs.items():\n",
+    "    time = output.products['t']\n",
+    "    water_mass = output.products['effective radius']\n",
+    "    \n",
+    "    fig, axs = pyplot.subplot_mosaic(\n",
+    "        [['a', 'b', 'c']],\n",
+    "        sharey=True,\n",
+    "        figsize=(12, 4),\n",
+    "        tight_layout=True\n",
+    "    )\n",
+    "    axs['a'].plot(\n",
+    "        output.products['super droplet count per gridbox'].mean(axis= 0),\n",
+    "        in_unit(time, si.min),\n",
+    "        marker='.',\n",
+    "        color='red',\n",
+    "    )\n",
+    "    axs['a'].set_ylabel(\"time [min]\")\n",
+    "    axs['a'].set_xlabel(\"mean super droplet count\")\n",
+    "    axs['a'].grid()\n",
+    "    axs['a'].set_xlim(10, 40)\n",
+    "\n",
+    "    axs['b'].plot(\n",
+    "        output.products['coalescence_rate'].mean(axis= 0),\n",
+    "        in_unit(time, si.min),\n",
+    "        color='green',\n",
+    "    )\n",
+    "    axs['b'].set_ylabel(\"time [min]\")\n",
+    "    axs['b'].set_xlabel(\"mean coalescence rate [1/s]\")\n",
+    "    axs['b'].grid()\n",
+    "    axs['b'].set_xlim(-1000, 65000)\n",
+    "\n",
+    "    axs['c'].plot(\n",
+    "        in_unit(output.products['surface precipitation'], si.m/si.s),\n",
+    "        in_unit(time, si.min),\n",
+    "        color='blue',\n",
+    "        label= r'Total precipitation = %.5f m/s'%in_unit(output.products['surface precipitation'].sum(), si.m/si.s)    \n",
+    "    )\n",
+    "    axs['c'].set_xlabel(f\"surface precipitation [m/s]\")\n",
+    "    axs['c'].grid()\n",
+    "    axs['c'].legend(loc= 'upper right')\n",
+    "    axs['c'].set_xlim(-1E-6, 2E-5)\n",
+    "\n",
+    "    fig.suptitle(case)\n",
+    "    show_plot(f\"SH2012_{case.replace(' ', '_')}.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "b43cf254c70d60c2e21a7f71ba113e70c1694742e72407132919c841d907074b"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/PySDM_examples/seeding/kinematic_1d_seeding.py b/examples/PySDM_examples/seeding/kinematic_1d_seeding.py
new file mode 100644
index 000000000..ec8795abe
--- /dev/null
+++ b/examples/PySDM_examples/seeding/kinematic_1d_seeding.py
@@ -0,0 +1,92 @@
+"""
+Single-column time-varying-updraft framework with moisture advection handled by
+[PyMPDATA](http://github.com/open-atmos/PyMPDATA/)
+"""
+
+import numpy as np
+
+from PySDM.environments.impl.moist import Moist
+
+from PySDM.impl import arakawa_c
+from PySDM.initialisation.equilibrate_wet_radii import equilibrate_wet_radii
+from PySDM.environments.impl import register_environment
+
+
+@register_environment()
+class Kinematic1D(Moist):
+    def __init__(self, *, dt, mesh, thd_of_z, rhod_of_z, z0=0):
+        super().__init__(dt, mesh, [])
+        self.thd0 = thd_of_z(z0 + mesh.dz * arakawa_c.z_scalar_coord(mesh.grid))
+        self.rhod = rhod_of_z(z0 + mesh.dz * arakawa_c.z_scalar_coord(mesh.grid))
+        self.formulae = None
+
+    def register(self, builder):
+        super().register(builder)
+        self.formulae = builder.particulator.formulae
+        rhod = builder.particulator.Storage.from_ndarray(self.rhod)
+        self._values["current"]["rhod"] = rhod
+        self._tmp["rhod"] = rhod
+
+    def get_water_vapour_mixing_ratio(self) -> np.ndarray:
+        return self.particulator.dynamics["EulerianAdvection"].solvers.advectee
+
+    def get_thd(self) -> np.ndarray:
+        return self.thd0
+
+    def init_attributes(
+        self,
+        *,
+        spatial_discretisation,
+        spectral_discretisation,
+        kappa,
+        n_sd,
+        z_part=None,
+        collisions_only=False
+    ):
+        super().sync()
+        self.notify()
+
+        attributes = {}
+        with np.errstate(all="raise"):
+            positions = spatial_discretisation.sample(
+                backend=self.particulator.backend,
+                grid=self.mesh.grid,
+                n_sd=n_sd,
+                z_part=z_part,
+            )
+            (
+                attributes["cell id"],
+                attributes["cell origin"],
+                attributes["position in cell"],
+            ) = self.mesh.cellular_attributes(positions)
+
+            if collisions_only:
+                v_wet, n_per_kg = spectral_discretisation.sample(
+                    backend=self.particulator.backend, n_sd=n_sd
+                )
+                attributes["volume"] = v_wet
+            else:
+                r_dry, n_per_kg = spectral_discretisation.sample(
+                    backend=self.particulator.backend, n_sd=n_sd
+                )
+                attributes["dry volume"] = self.formulae.trivia.volume(radius=r_dry)
+                attributes["kappa times dry volume"] = attributes["dry volume"] * kappa
+                r_wet = equilibrate_wet_radii(
+                    r_dry=r_dry,
+                    environment=self,
+                    cell_id=attributes["cell id"],
+                    kappa_times_dry_volume=attributes["kappa times dry volume"],
+                )
+                attributes["volume"] = self.formulae.trivia.volume(radius=r_wet)
+
+            rhod = self["rhod"].to_ndarray()
+            cell_id = attributes["cell id"]
+            domain_volume = np.prod(np.array(self.mesh.size))
+
+        attributes["multiplicity"] = n_per_kg * rhod[cell_id] * domain_volume
+
+        return attributes
+
+    @property
+    def dv(self):
+        return self.mesh.dv
diff --git a/examples/PySDM_examples/seeding/settings_1d.py b/examples/PySDM_examples/seeding/settings_1d.py
new file mode 100644
index 000000000..c366edeea
--- /dev/null
+++ b/examples/PySDM_examples/seeding/settings_1d.py
@@ -0,0 +1,203 @@
+from typing import Iterable, Optional
+from pystrict import strict
+
+import numpy as np
+from numdifftools import Derivative
+from scipy.integrate import solve_ivp
+from scipy.interpolate import interp1d
+
+from PySDM import Formulae
+from PySDM.dynamics import condensation
+from PySDM.initialisation import spectra
+from PySDM.physics import si
+from PySDM.dynamics.collisions.collision_kernels import Geometric
+
+
+# @strict
+class Settings:
+    def __dir__(self) -> Iterable[str]:
+        return (
+            "n_sd_per_gridbox",
+            "n_sd_seeding",
+            "super_droplet_injection_rate",
+            "p0",
+            "kappa",
+            "rho_times_w_1",
+            "particles_per_volume_STP",
+            "seed_particles_per_volume_STP",
+            "seed_kappa" "dt",
+            "dz",
+            "precip",
+            "z_max",
+            "z_part",
+            "t_max",
+            "cloud_water_radius_range",
+            "rain_water_radius_range",
+            "r_bins_edges_dry",
+            "r_bins_edges",
+        )
+
+    def __init__(
+        self,
+        *,
+        n_sd_per_gridbox: int,
+        n_sd_seeding: Optional[int] = None,
+        super_droplet_injection_rate: Optional[callable] = None,
+        p0: float = 1007 * si.hPa,  # as used in Olesik et al. 2022 (GMD)
+        kappa: float = 1,
+        rho_times_w_1: float = 2 * si.m / si.s * si.kg / si.m**3,
+        particles_per_volume_STP: int = 50 / si.cm**3,
+        seed_particles_per_volume_STP: int = 0 / si.cm**3,
+        seed_kappa: float = 0.8,
+        dt: float = 1 * si.s,
+        dz: float = 25 * si.m,
+        z_max: float = 3000 * si.m,
+        z_part: Optional[tuple] = None,
+        t_max: float = 60 * si.minutes,
+        precip: bool = True,
+        enable_condensation: bool = True,
+        formulae: Formulae = None,
+        save_spec_and_attr_times=(),
+        collision_kernel=None
+    ):
+        self.formulae = formulae or Formulae()
+        self.n_sd_per_gridbox = n_sd_per_gridbox
+        self.n_sd_seeding = n_sd_seeding
+        self.super_droplet_injection_rate = super_droplet_injection_rate
+        self.p0 = p0
+        self.kappa = kappa
+        self.rho_times_w_1 = rho_times_w_1
+        self.particles_per_volume_STP = particles_per_volume_STP
+        self.seed_particles_per_volume_STP = seed_particles_per_volume_STP
+        self.seed_kappa = seed_kappa
+        self.dt = dt
+        self.dz = dz
+        self.precip = precip
+        self.enable_condensation = enable_condensation
+        self.z_part = z_part
+        self.z_max = z_max
+        self.t_max = t_max
+        self.collision_kernel = collision_kernel or Geometric(collection_efficiency=1)
+
+        t_1 = 600 * si.s
+        self.rho_times_w = lambda t: (
+            rho_times_w_1 * np.sin(np.pi * t / t_1) if t < t_1 else 0
+        )
+        apprx_w1 = rho_times_w_1 / self.formulae.constants.rho_STP
+        self.particle_reservoir_depth = (
+            (2 * apprx_w1 * t_1 / np.pi) // self.dz + 1
+        ) * self.dz
+
+        self.wet_radius_spectrum_per_mass_of_dry_air = spectra.Lognormal(
+            norm_factor=particles_per_volume_STP / self.formulae.constants.rho_STP,
+            m_mode=0.08 / 2 * si.um,
+            s_geom=1.4,
+        )
+
+        self._th = interp1d(
+            (0.0 * si.m, 740.0 * si.m, 3260.00 * si.m),
+            (297.9 * si.K, 297.9 * si.K, 312.66 * si.K),
+            fill_value="extrapolate",
+        )
+
+        self.water_vapour_mixing_ratio = interp1d(
+            (-max(self.particle_reservoir_depth, 1), 0, 740, 3260),
+            (0.015, 0.015, 0.0138, 0.0024),
+            fill_value="extrapolate",
+        )
+
+        self.thd = (
+            lambda z_above_reservoir: self.formulae.state_variable_triplet.th_dry(
+                self._th(z_above_reservoir),
+                self.water_vapour_mixing_ratio(z_above_reservoir),
+            )
+        )
+
+        g = self.formulae.constants.g_std
+        self.rhod0 = self.formulae.state_variable_triplet.rho_d(
+            p=p0,
+            water_vapour_mixing_ratio=self.water_vapour_mixing_ratio(0 * si.m),
+            theta_std=self._th(0 * si.m),
+        )
+
+        def drhod_dz(z_above_reservoir, rhod):
+            if z_above_reservoir < 0:
+                return 0
+            water_vapour_mixing_ratio = self.water_vapour_mixing_ratio(
+                z_above_reservoir
+            )
+            d_water_vapour_mixing_ratio__dz = Derivative(
+                self.water_vapour_mixing_ratio
+            )(z_above_reservoir)
+            T = self.formulae.state_variable_triplet.T(
+                rhod[0], self.thd(z_above_reservoir)
+            )
+            p = self.formulae.state_variable_triplet.p(
+                rhod[0], T, water_vapour_mixing_ratio
+            )
+            lv = self.formulae.latent_heat.lv(T)
+            return self.formulae.hydrostatics.drho_dz(
+                g, p, T, water_vapour_mixing_ratio, lv
+            ) / (
+                1 + water_vapour_mixing_ratio
+            ) - rhod * d_water_vapour_mixing_ratio__dz / (
+                1 + water_vapour_mixing_ratio
+            )
+
+        z_span = (-self.particle_reservoir_depth, self.z_max)
+        z_points = np.linspace(*z_span, 2 * self.nz + 1)
+        rhod_solution = solve_ivp(
+            fun=drhod_dz,
+            t_span=z_span,
+            y0=np.asarray((self.rhod0,)),
+            t_eval=z_points,
+            max_step=dz / 2,
+        )
+        assert rhod_solution.success
+        self.rhod = interp1d(z_points, rhod_solution.y[0])
+
+        self.mpdata_settings = {"n_iters": 3, "iga": True, "fct": True, "tot": True}
+        self.condensation_rtol_x = condensation.DEFAULTS.rtol_x
+        self.condensation_rtol_thd = condensation.DEFAULTS.rtol_thd
+        self.condensation_adaptive = True
+        self.condensation_update_thd = False
+        self.coalescence_adaptive = True
+
+        self.number_of_bins = 100
+        self.r_bins_edges_dry = np.logspace(
+            np.log10(0.001 * si.um),
+            np.log10(1 * si.um),
+            self.number_of_bins + 1,
+            endpoint=True,
+        )
+        self.r_bins_edges = np.logspace(
+            np.log10(0.001 * si.um),
+            np.log10(10 * si.mm),
+            self.number_of_bins + 1,
+            endpoint=True,
+        )
+        self.cloud_water_radius_range = [1 * si.um, 50 * si.um]
+        self.cloud_water_radius_range_igel = [1 * si.um, 25 * si.um]
+        self.rain_water_radius_range = [50 * si.um, np.inf * si.um]
+        self.rain_water_radius_range_igel = [25 * si.um, np.inf * si.um]
+        self.save_spec_and_attr_times = save_spec_and_attr_times
+
+    @property
+    def n_sd(self):
+        return self.nz * self.n_sd_per_gridbox
+
+    @property
+    def nz(self):
+        assert (
+            self.particle_reservoir_depth / self.dz
+            == self.particle_reservoir_depth // self.dz
+        )
+        nz = (self.z_max + self.particle_reservoir_depth) / self.dz
+        assert nz == int(nz)
+        return int(nz)
+
+    @property
+    def nt(self):
+        nt = self.t_max / self.dt
+        assert nt == int(nt)
+        return int(nt)
diff --git a/examples/PySDM_examples/seeding/simulation_1d.py b/examples/PySDM_examples/seeding/simulation_1d.py
new file mode 100644
index 000000000..a3390b517
--- /dev/null
+++ b/examples/PySDM_examples/seeding/simulation_1d.py
@@ -0,0 +1,346 @@
+from collections import namedtuple
+
+import numpy as np
+from PySDM_examples.Shipway_and_Hill_2012.mpdata_1d import MPDATA_1D
+
+import PySDM.products as PySDM_products
+from PySDM import Builder
+from PySDM.backends import CPU
+from PySDM.dynamics import (
+    AmbientThermodynamics,
+    Coalescence,
+    Condensation,
+    Displacement,
+    EulerianAdvection,
+    Seeding,
+)
+from PySDM_examples.seeding.kinematic_1d_seeding import Kinematic1D
+from PySDM.impl.mesh import Mesh
+from PySDM.initialisation import spectra
+from PySDM.initialisation.sampling import spatial_sampling, spectral_sampling
+from PySDM.initialisation.equilibrate_wet_radii import equilibrate_wet_radii
+from PySDM.physics import si
+
+
+class Simulation:
+    def __init__(self, settings, backend=CPU):
+        self.nt = settings.nt
+        self.z0 = -settings.particle_reservoir_depth
+        self.save_spec_and_attr_times = settings.save_spec_and_attr_times
+        self.number_of_bins = settings.number_of_bins
+
+        self.particulator = None
+        self.output_attributes = None
+        self.output_products = None
+
+        self.mesh = Mesh(
+            grid=(settings.nz,),
+            size=(settings.z_max + settings.particle_reservoir_depth,),
+        )
+
+        env = Kinematic1D(
+            dt=settings.dt,
+            mesh=self.mesh,
+            thd_of_z=settings.thd,
+            rhod_of_z=settings.rhod,
+            z0=-settings.particle_reservoir_depth,
+        )
+
+        def zZ_to_z_above_reservoir(zZ):
+            z_above_reservoir = zZ * (settings.nz * settings.dz) + self.z0
+            return z_above_reservoir
+
+        mpdata = MPDATA_1D(
+            nz=settings.nz,
+            dt=settings.dt,
+            mpdata_settings=settings.mpdata_settings,
+            advector_of_t=lambda t: settings.rho_times_w(t) * settings.dt / settings.dz,
+            advectee_of_zZ_at_t0=lambda zZ: settings.water_vapour_mixing_ratio(
+                zZ_to_z_above_reservoir(zZ)
+            ),
+            g_factor_of_zZ=lambda zZ: settings.rhod(zZ_to_z_above_reservoir(zZ)),
+        )
+
+        _extra_nz = settings.particle_reservoir_depth // settings.dz
+        _z_vec = settings.dz * np.linspace(
+            -_extra_nz, settings.nz - _extra_nz, settings.nz + 1
+        )
+        self.g_factor_vec = settings.rhod(_z_vec)
+
+        self.builder = Builder(
+            n_sd=settings.n_sd + settings.n_sd_seeding,
+            backend=backend(formulae=settings.formulae),
+            environment=env,
+        )
+        self.builder.add_dynamic(AmbientThermodynamics())
+
+        if settings.enable_condensation:
+            self.builder.add_dynamic(
+                Condensation(
+                    adaptive=settings.condensation_adaptive,
+                    rtol_thd=settings.condensation_rtol_thd,
+                    rtol_x=settings.condensation_rtol_x,
+                    update_thd=settings.condensation_update_thd,
+                )
+            )
+        self.builder.add_dynamic(EulerianAdvection(mpdata))
+
+        self.products = []
+        if settings.precip:
+            self.add_collision_dynamic(self.builder, settings, self.products)
+
+        displacement = Displacement(
+            enable_sedimentation=settings.precip,
+            precipitation_counting_level_index=int(
+                settings.particle_reservoir_depth / settings.dz
+            ),
+        )
+        self.builder.add_dynamic(displacement)
+
+        self.attributes = self.builder.particulator.environment.init_attributes(
+            spatial_discretisation=spatial_sampling.Pseudorandom(),
+            spectral_discretisation=spectral_sampling.ConstantMultiplicity(
+                spectrum=settings.wet_radius_spectrum_per_mass_of_dry_air
+            ),
+            kappa=settings.kappa,
+            collisions_only=not settings.enable_condensation,
+            n_sd=settings.n_sd,  # only initialize with background SDs
+            z_part=settings.z_part,
+        )
+        r_dry, n_in_dv = spectral_sampling.ConstantMultiplicity(
+            spectra.Lognormal(
+                norm_factor=(
+                    settings.seed_particles_per_volume_STP
+                    / settings.formulae.constants.rho_STP
+                ),
+                m_mode=1 * si.um,
+                s_geom=1.4,
+            )
+        ).sample(
+            n_sd=settings.n_sd_seeding
+        )  # TODO #1387: does not have to be the same?
+        v_dry = settings.formulae.trivia.volume(radius=r_dry)
+        self.seeded_particle_extensive_attributes = {
+            "water mass": np.array([0.0001 * si.ng] * settings.n_sd_seeding),
+            "dry volume": v_dry,
+            "kappa times dry volume": settings.seed_kappa
+            * v_dry,  # include kappa argument for seeds
+        }
+        self.seeded_particle_multiplicity = n_in_dv * np.prod(np.array(self.mesh.size))
+
+        positions = spatial_sampling.Pseudorandom().sample(
+            backend=backend(formulae=settings.formulae),
+            grid=self.mesh.grid,
+            n_sd=settings.n_sd_seeding,
+        )
+        cell_id, cell_origin, pos_cell = self.mesh.cellular_attributes(positions)
+        self.seeded_particle_cell_id = cell_id
+        self.seeded_particle_cell_origin = cell_origin
+        self.seeded_particle_pos_cell = pos_cell
+
+        r_wet = equilibrate_wet_radii(
+            r_dry=settings.formulae.trivia.radius(volume=v_dry),
+            environment=self.builder.particulator.environment,
+            cell_id=cell_id,
+            kappa_times_dry_volume=settings.seed_kappa
+            * v_dry,  # include kappa argument for seeds
+        )
+        self.seeded_particle_volume = settings.formulae.trivia.volume(radius=r_wet)
+        self.builder.particulator.backend.mass_of_water_volume(
+            self.seeded_particle_extensive_attributes["water mass"],
+            self.seeded_particle_volume,
+        )
+
+        self.builder.add_dynamic(
+            Seeding(
+                super_droplet_injection_rate=settings.super_droplet_injection_rate,
+                seeded_particle_multiplicity=self.seeded_particle_multiplicity,
+                seeded_particle_extensive_attributes=self.seeded_particle_extensive_attributes,
+                seeded_particle_cell_id=self.seeded_particle_cell_id,
+                seeded_particle_cell_origin=self.seeded_particle_cell_origin,
+                seeded_particle_pos_cell=self.seeded_particle_pos_cell,
+                seeded_particle_volume=self.seeded_particle_volume,
+            )
+        )
+
+        self.products += [
+            PySDM_products.WaterMixingRatio(
+                name="cloud water mixing ratio",
+                unit="g/kg",
+                radius_range=settings.cloud_water_radius_range,
+            ),
+            PySDM_products.WaterMixingRatio(
+                name="rain water mixing ratio",
+                unit="g/kg",
+                radius_range=settings.rain_water_radius_range,
+            ),
+            PySDM_products.AmbientDryAirDensity(name="rhod"),
+            PySDM_products.AmbientDryAirPotentialTemperature(name="thd"),
+            PySDM_products.ParticleSizeSpectrumPerVolume(
+                name="wet spectrum", radius_bins_edges=settings.r_bins_edges
+            ),
+            PySDM_products.ParticleConcentration(
+                name="nc", radius_range=settings.cloud_water_radius_range
+            ),
+            PySDM_products.ParticleConcentration(
+                name="nr", radius_range=settings.rain_water_radius_range
+            ),
+            PySDM_products.ParticleConcentration(
+                name="na", radius_range=(0, settings.cloud_water_radius_range[0])
+            ),
+            PySDM_products.MeanRadius(),
+            PySDM_products.EffectiveRadius(
+                radius_range=settings.cloud_water_radius_range
+            ),
+            PySDM_products.SuperDropletCountPerGridbox(),
+            PySDM_products.AveragedTerminalVelocity(
+                name="rain averaged terminal velocity",
+                radius_range=settings.rain_water_radius_range,
+            ),
+            PySDM_products.AmbientRelativeHumidity(name="RH", unit="%"),
+            PySDM_products.AmbientPressure(name="p"),
+            PySDM_products.AmbientTemperature(name="T"),
+            PySDM_products.AmbientWaterVapourMixingRatio(
+                name="water_vapour_mixing_ratio"
+            ),
+        ]
+        if settings.enable_condensation:
+            self.products.extend(
+                [
+                    PySDM_products.RipeningRate(name="ripening"),
+                    PySDM_products.ActivatingRate(name="activating"),
+                    PySDM_products.DeactivatingRate(name="deactivating"),
+                    PySDM_products.PeakSupersaturation(unit="%"),
+                    PySDM_products.ParticleSizeSpectrumPerVolume(
+                        name="dry spectrum",
+                        radius_bins_edges=settings.r_bins_edges_dry,
+                        dry=True,
+                    ),
+                ]
+            )
+        if settings.precip:
+            self.products.extend(
+                [
+                    PySDM_products.CollisionRatePerGridbox(
+                        name="collision_rate",
+                    ),
+                    PySDM_products.CollisionRateDeficitPerGridbox(
+                        name="collision_deficit",
+                    ),
+                    PySDM_products.CoalescenceRatePerGridbox(
+                        name="coalescence_rate",
+                    ),
+                    PySDM_products.SurfacePrecipitation(),
+                ]
+            )
+        self.particulator = self.builder.build(
+            attributes={
+                k: np.pad(
+                    array=v,
+                    pad_width=(
+                        ((0, 0), (0, settings.n_sd_seeding))
+                        if k in ("position in cell", "cell origin")
+                        else (0, settings.n_sd_seeding)
+                    ),
+                    mode="constant",
+                    constant_values=np.nan if k == "multiplicity" else 0,
+                )
+                for k, v in self.attributes.items()
+            },
+            products=tuple(self.products),
+        )
+
+        self.output_attributes = {
+            "cell origin": [],
+            "position in cell": [],
+            "radius": [],
+            "multiplicity": [],
+        }
+        self.output_products = {}
+        for k, v in self.particulator.products.items():
+            if len(v.shape) == 0:
+                self.output_products[k] = np.zeros(self.nt + 1)
+            elif len(v.shape) == 1:
+                self.output_products[k] = np.zeros((self.mesh.grid[-1], self.nt + 1))
+            elif len(v.shape) == 2:
+                number_of_time_sections = len(self.save_spec_and_attr_times)
+                self.output_products[k] = np.zeros(
+                    (self.mesh.grid[-1], self.number_of_bins, number_of_time_sections)
+                )
+
+    @staticmethod
+    def add_collision_dynamic(builder, settings, _):
+        builder.add_dynamic(
+            Coalescence(
+                collision_kernel=settings.collision_kernel,
+                adaptive=settings.coalescence_adaptive,
+            )
+        )
+
+    def save_scalar(self, step):
+        for k, v in self.particulator.products.items():
+            if len(v.shape) > 1:
+                continue
+            if len(v.shape) == 1:
+                self.output_products[k][:, step] = v.get()
+            else:
+                self.output_products[k][step] = v.get()
+
+    def save_spectrum(self, index):
+        for k, v in self.particulator.products.items():
+            if len(v.shape) == 2:
+                self.output_products[k][:, :, index] = v.get()
+
+    def save_attributes(self):
+        for k, v in self.output_attributes.items():
+            v.append(self.particulator.attributes[k].to_ndarray())
+
+    def save(self, step):
+        self.save_scalar(step)
+        time = step * self.particulator.dt
+        if len(self.save_spec_and_attr_times) > 0 and (
+            np.min(
+                np.abs(
+                    np.ones_like(self.save_spec_and_attr_times) * time
+                    - np.array(self.save_spec_and_attr_times)
+                )
+            )
+            < 0.1
+        ):
+            save_index = np.argmin(
+                np.abs(
+                    np.ones_like(self.save_spec_and_attr_times) * time
+                    - np.array(self.save_spec_and_attr_times)
+                )
+            )
+            self.save_spectrum(save_index)
+            self.save_attributes()
+
+    def run(self):
+        mesh = self.particulator.mesh
+
+        assert "t" not in self.output_products and "z" not in self.output_products
+        self.output_products["t"] = np.linspace(
+            0, self.nt * self.particulator.dt, self.nt + 1, endpoint=True
+        )
+        self.output_products["z"] = np.linspace(
+            self.z0 + mesh.dz / 2,
+            self.z0 + (mesh.grid[-1] - 1 / 2) * mesh.dz,
+            mesh.grid[-1],
+            endpoint=True,
+        )
+
+        self.save(0)
+        for step in range(self.nt):
+            mpdata = self.particulator.dynamics["EulerianAdvection"].solvers
+            mpdata.update_advector_field()
+            if "Displacement" in self.particulator.dynamics:
+                self.particulator.dynamics["Displacement"].upload_courant_field(
+                    (mpdata.advector / self.g_factor_vec,)
+                )
+            self.particulator.run(steps=1)
+            self.save(step + 1)
+
+        Outputs = namedtuple("Outputs", "products attributes")
+        output_results = Outputs(self.output_products, self.output_attributes)
+        return output_results