diff --git a/book/chapters/amsr.ipynb b/book/chapters/amsr.ipynb
index 635c628..1be1c62 100644
--- a/book/chapters/amsr.ipynb
+++ b/book/chapters/amsr.ipynb
@@ -1,5 +1,93 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "aab13cdf",
+   "metadata": {},
+   "source": [
+    "## Utility Functions for Data Processing and Analysis Pipeline\n",
+    "\n",
+    "### 1. Library Imports and Setup for Snow Data\n",
+    "\n",
+    "- `KDTree`: From `scipy.spatial`, used for performing efficient nearest-neighbor searches in spatial datasets.\n",
+    "- `plot_all_variables_in_one_csv`: A custom function from `convert_results_to_images`, used for visualizing processed data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6d98378",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import h5py\n",
+    "import subprocess\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "from scipy.spatial import KDTree\n",
+    "import time\n",
+    "from datetime import datetime, timedelta, date\n",
+    "import warnings\n",
+    "import sys\n",
+    "# from convert_results_to_images import plot_all_variables_in_one_c\n",
+    "\n",
+    "homedir = os.path.expanduser('~')\n",
+    "work_dir = \"../data/gridmet_test_run\"\n",
+    "test_start_date = \"2024-07-18\"\n",
+    "western_us_coords = \"../data/dem_file.tif.csv\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2550b08d",
+   "metadata": {},
+   "source": [
+    "### 2. Identifying Binary Files\n",
+    "\n",
+    "Here we determine whether a given file is a binary file or a text file.\n",
+    "\n",
+    "- We attempt to open the file in binary mode (`'rb'`) and read a chunk of bytes (1024 bytes).\n",
+    "- And the we check for null bytes (`b'\\x00'`), which are common in binary files. If a null byte is found, then it is binary file.\n",
+    "- Next, we check for a high percentage of non-printable ASCII characters by converting the byte chunk to characters and filtering out non-printable ones. If the chunk has no printable characters, the file is considered binary.\n",
+    "- If neither of the above conditions are met, the function assumes the file is a text file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "7004281e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def is_binary(file_path):\n",
+    "    try:\n",
+    "        with open(file_path, 'rb') as file:\n",
+    "            # Read a chunk of bytes from the file\n",
+    "            chunk = file.read(1024)\n",
+    "\n",
+    "            # Check for null bytes, a common indicator of binary data\n",
+    "            if b'\\x00' in chunk:\n",
+    "                return True\n",
+    "\n",
+    "            # Check for a high percentage of non-printable ASCII characters\n",
+    "            text_characters = \"\".join(chr(byte) for byte in chunk if 32 <= byte <= 126)\n",
+    "            if not text_characters:\n",
+    "                return True\n",
+    "\n",
+    "            # If none of the binary indicators are found, assume it's a text file\n",
+    "            return False\n",
+    "\n",
+    "    except FileNotFoundError:\n",
+    "        print(f\"File '{file_path}' not found.\")\n",
+    "        return False\n",
+    "    except Exception as e:\n",
+    "        print(f\"An error occurred: {e}\")\n",
+    "        return False\n",
+    "  "
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "05b77e80",
@@ -1507,94 +1595,6 @@
     "                # Copy the file to the destination directory\n",
     "                shutil.copy(source_file_path, destination_dir)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aab13cdf",
-   "metadata": {},
-   "source": [
-    "## Utility Functions for Data Processing and Analysis Pipeline\n",
-    "\n",
-    "### 1. Library Imports and Setup for Snow Data\n",
-    "\n",
-    "- `KDTree`: From `scipy.spatial`, used for performing efficient nearest-neighbor searches in spatial datasets.\n",
-    "- `plot_all_variables_in_one_csv`: A custom function from `convert_results_to_images`, used for visualizing processed data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e6d98378",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import h5py\n",
-    "import subprocess\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from datetime import datetime\n",
-    "from scipy.spatial import KDTree\n",
-    "import time\n",
-    "from datetime import datetime, timedelta, date\n",
-    "import warnings\n",
-    "import sys\n",
-    "# from convert_results_to_images import plot_all_variables_in_one_c\n",
-    "\n",
-    "homedir = os.path.expanduser('~')\n",
-    "work_dir = \"../data/gridmet_test_run\"\n",
-    "test_start_date = \"2024-07-18\"\n",
-    "western_us_coords = \"../data/dem_file.tif.csv\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2550b08d",
-   "metadata": {},
-   "source": [
-    "### 2. Identifying Binary Files\n",
-    "\n",
-    "Here we determine whether a given file is a binary file or a text file.\n",
-    "\n",
-    "- We attempt to open the file in binary mode (`'rb'`) and read a chunk of bytes (1024 bytes).\n",
-    "- And the we check for null bytes (`b'\\x00'`), which are common in binary files. If a null byte is found, then it is binary file.\n",
-    "- Next, we check for a high percentage of non-printable ASCII characters by converting the byte chunk to characters and filtering out non-printable ones. If the chunk has no printable characters, the file is considered binary.\n",
-    "- If neither of the above conditions are met, the function assumes the file is a text file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "7004281e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def is_binary(file_path):\n",
-    "    try:\n",
-    "        with open(file_path, 'rb') as file:\n",
-    "            # Read a chunk of bytes from the file\n",
-    "            chunk = file.read(1024)\n",
-    "\n",
-    "            # Check for null bytes, a common indicator of binary data\n",
-    "            if b'\\x00' in chunk:\n",
-    "                return True\n",
-    "\n",
-    "            # Check for a high percentage of non-printable ASCII characters\n",
-    "            text_characters = \"\".join(chr(byte) for byte in chunk if 32 <= byte <= 126)\n",
-    "            if not text_characters:\n",
-    "                return True\n",
-    "\n",
-    "            # If none of the binary indicators are found, assume it's a text file\n",
-    "            return False\n",
-    "\n",
-    "    except FileNotFoundError:\n",
-    "        print(f\"File '{file_path}' not found.\")\n",
-    "        return False\n",
-    "    except Exception as e:\n",
-    "        print(f\"An error occurred: {e}\")\n",
-    "        return False\n",
-    "  "
-   ]
   }
  ],
  "metadata": {
diff --git a/book/chapters/fsCA.ipynb b/book/chapters/fsCA.ipynb
index 2fff4b7..f626e96 100644
--- a/book/chapters/fsCA.ipynb
+++ b/book/chapters/fsCA.ipynb
@@ -79,6 +79,28 @@
     "create an account in urs.earthdata.nasa.gov for earth access"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "377eb1f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "work_dir = '../data/fsca'\n",
+    "def date_to_julian(date_str):\n",
+    "    \"\"\"\n",
+    "    Convert a date to Julian date.\n",
+    "    \"\"\"\n",
+    "    date_object = datetime.strptime(date_str, \"%Y-%m-%d\")\n",
+    "    tt = date_object.timetuple()\n",
+    "    \n",
+    "\n",
+    "    # Format the result as 'YYYYDDD'\n",
+    "    julian_format = str('%d%03d' % (tt.tm_year, tt.tm_yday))\n",
+    "\n",
+    "    return julian_format"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -336,28 +358,6 @@
     "## 3.5.6 Utility Functions"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "377eb1f1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "work_dir = '../data/fsca'\n",
-    "def date_to_julian(date_str):\n",
-    "    \"\"\"\n",
-    "    Convert a date to Julian date.\n",
-    "    \"\"\"\n",
-    "    date_object = datetime.strptime(date_str, \"%Y-%m-%d\")\n",
-    "    tt = date_object.timetuple()\n",
-    "    \n",
-    "\n",
-    "    # Format the result as 'YYYYDDD'\n",
-    "    julian_format = str('%d%03d' % (tt.tm_year, tt.tm_yday))\n",
-    "\n",
-    "    return julian_format"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 12,
diff --git a/book/chapters/gridmet.ipynb b/book/chapters/gridmet.ipynb
index 3bb1811..84e667f 100644
--- a/book/chapters/gridmet.ipynb
+++ b/book/chapters/gridmet.ipynb
@@ -12,6 +12,54 @@
     "GridMET is a high-resolution dataset that provides daily surface meteorological data for the contiguous United States"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "524ca247",
+   "metadata": {},
+   "source": [
+    "## Calculate specific Operation Day\n",
+    "\n",
+    "Here we calculate the date exactly three days before the current date and returns it as a formatted string.\n",
+    "\n",
+    "- `current_date`: A `datetime` object representing the current date and time.\n",
+    "- `three_days_ago`: A `datetime` object representing the date three days before the current date.\n",
+    "- `three_days_ago_string`: A string representing the date three days ago, formatted as \"YYYY-MM-DD\".\n",
+    "- `test_start_date`: A string that stores the returned value from `get_operation_day()`, representing the operation day used in other parts of the code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "29df8904",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-08-16\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datetime import datetime, timedelta\n",
+    "def get_operation_day():\n",
+    "  # Get the current date and time\n",
+    "  current_date = datetime.now()\n",
+    "\n",
+    "  # Calculate three days ago\n",
+    "  three_days_ago = current_date - timedelta(days=3)\n",
+    "\n",
+    "  # Format the date as a string\n",
+    "  three_days_ago_string = three_days_ago.strftime(\"%Y-%m-%d\")\n",
+    "\n",
+    "  print(three_days_ago_string)\n",
+    "\n",
+    "  return three_days_ago_string\n",
+    "\n",
+    "test_start_date = get_operation_day()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4aac33f5",
@@ -1340,54 +1388,6 @@
     "    return (abs(array - value)).argmin()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "524ca247",
-   "metadata": {},
-   "source": [
-    "## 11. Calculate specific Operation Day\n",
-    "\n",
-    "Here we calculate the date exactly three days before the current date and returns it as a formatted string.\n",
-    "\n",
-    "- `current_date`: A `datetime` object representing the current date and time.\n",
-    "- `three_days_ago`: A `datetime` object representing the date three days before the current date.\n",
-    "- `three_days_ago_string`: A string representing the date three days ago, formatted as \"YYYY-MM-DD\".\n",
-    "- `test_start_date`: A string that stores the returned value from `get_operation_day()`, representing the operation day used in other parts of the code."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "29df8904",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2024-08-16\n"
-     ]
-    }
-   ],
-   "source": [
-    "from datetime import datetime, timedelta\n",
-    "def get_operation_day():\n",
-    "  # Get the current date and time\n",
-    "  current_date = datetime.now()\n",
-    "\n",
-    "  # Calculate three days ago\n",
-    "  three_days_ago = current_date - timedelta(days=3)\n",
-    "\n",
-    "  # Format the date as a string\n",
-    "  three_days_ago_string = three_days_ago.strftime(\"%Y-%m-%d\")\n",
-    "\n",
-    "  print(three_days_ago_string)\n",
-    "\n",
-    "  return three_days_ago_string\n",
-    "\n",
-    "test_start_date = get_operation_day()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "ea2c5e26",