diff --git a/modules/cosmic/__init__.py b/modules/cosmic/__init__.py
index 8a9136e..9f46409 100644
--- a/modules/cosmic/__init__.py
+++ b/modules/cosmic/__init__.py
@@ -1,12 +1,16 @@
 from io import BytesIO
+import pandas as pd
 from quart import Blueprint, request, send_file
 from matplotlib import pyplot as plt
 
+from CONSTANT import DATA_BASEPATH
 from modules.cosmic.gravityw_multiday import GravityMultidayPlot
 from modules.cosmic.gravityw_perday import CosmicGravitywPlot
 from modules.cosmic.planetw_daily import cosmic_planetw_daily_plot
 from quart.utils import run_sync
 
+from modules.cosmic.planetw_daily_process import cosmic_planet_daily_process
+
 
 cosmic_module = Blueprint("Cosmic", __name__)
 
@@ -19,9 +23,24 @@ def get_meta():
 @cosmic_module.route('/render/planet_wave/daily')
 @cosmic_module.route('/temp_render')
 async def render():
+    year = request.args.get("year", 2008)
     T = request.args.get("T", 16)
     k = request.args.get("k", 1)
-    await run_sync(cosmic_planetw_daily_plot)(T=int(T), k=int(k))
+    target_h = request.args.get("target_h", 40)
+    target_latitude = request.args.get("target_lat", 30)
+
+    # path: str = f"{DATA_BASEPATH.cosmic}/cosmic.txt"
+    temp_df = await run_sync(cosmic_planet_daily_process)(
+        year=int(year),
+        target_h=int(target_h),
+        target_latitude=int(target_latitude)
+    )
+
+    await run_sync(cosmic_planetw_daily_plot)(
+        temp_df,
+        T=int(T),
+        k=int(k)
+    )
     buf = BytesIO()
 
     plt.savefig(buf, format="png")
diff --git a/modules/cosmic/planetw_daily.py b/modules/cosmic/planetw_daily.py
index 476a9b1..f751bf9 100644
--- a/modules/cosmic/planetw_daily.py
+++ b/modules/cosmic/planetw_daily.py
@@ -18,7 +18,7 @@ matplotlib.rcParams['axes.unicode_minus'] = False  # 正常显示负号
 
 
 def cosmic_planetw_daily_plot(
-        path: str = f"{DATA_BASEPATH.cosmic}/cosmic.txt",
+        df: pd.DataFrame,
         T=16,
         k=0
 ):
@@ -36,7 +36,6 @@ def cosmic_planetw_daily_plot(
             + a9 * np.sin((2 * np.pi / T) * t + 4 * x + b9)
         )
 
-    df = pd.read_csv(path,  sep='\s+')
     # 删除有 NaN 的行
     df = df.dropna(subset=['Temperature'])
     # 设置初始参数
diff --git a/modules/cosmic/planetw_daily_process.py b/modules/cosmic/planetw_daily_process.py
new file mode 100644
index 0000000..220535c
--- /dev/null
+++ b/modules/cosmic/planetw_daily_process.py
@@ -0,0 +1,150 @@
+# 高度和target_latitude可以自己选择，纬度可以选+-30，+-60，高度可以每10km
+import netCDF4 as nc
+import pandas as pd
+import numpy as np
+import os
+from scipy.interpolate import interp1d
+import matplotlib.pyplot as plt
+
+from CONSTANT import DATA_BASEPATH
+
+
+def cosmic_planet_daily_process(
+        year=2008,
+        target_latitude=30,
+        target_h=40
+):
+    # 用于存储所有DataFrame的列表
+    dfs = []
+    # 设置文件夹的路径模板
+    base_folder_path = f"{DATA_BASEPATH.cosmic}/{year}"
+
+    cache_path = f"{base_folder_path}/planet_{target_h}_{target_latitude}.parquet"
+    if os.path.exists(cache_path):
+        return pd.read_parquet(cache_path)
+
+    # 遍历文件夹序号1到365
+    for i in range(1, 365):
+        # 根据i的值调整文件夹名称
+        if i < 10:
+            folder_name = f"atmPrf_repro2021_2008_00{i}"  # 一位数，前面加两个0
+        elif i < 100:
+            folder_name = f"atmPrf_repro2021_2008_0{i}"  # 两位数，前面加一个0
+        else:
+            folder_name = f"atmPrf_repro2021_2008_{i}"   # 三位数，不加0
+
+        # 构建当前文件夹的路径
+        folder_path = os.path.join(base_folder_path, folder_name)
+
+        # 检查文件夹是否存在
+        if os.path.exists(folder_path):
+            # 遍历文件夹中的文件
+            for file_name in os.listdir(folder_path):
+                if file_name.endswith('.0390_nc'):
+                    finfo = os.path.join(folder_path, file_name)
+                    print(f"正在处理文件: {finfo}")
+                    try:
+                        dataset = nc.Dataset(finfo, 'r')
+                        # 提取变量数据
+                        temp = dataset.variables['Temp'][:]
+                        altitude = dataset.variables['MSL_alt'][:]
+                        lat = dataset.variables['Lat'][:]
+                        lon = dataset.variables['Lon'][:]
+                        # 读取month, hour, minute
+                        month = dataset.month
+                        hour = dataset.hour
+                        minute = dataset.minute
+                        # 将i的值作为day的值
+                        day = i
+                        # 将day, hour, minute拼接成一个字符串
+                        datetime_str = f"{day:03d}{hour:02d}{minute:02d}"
+                        # 确保所有变量都是一维数组
+                        temp = np.squeeze(temp)
+                        altitude = np.squeeze(altitude)
+                        lat = np.squeeze(lat)
+                        lon = np.squeeze(lon)
+                        # 检查所有数组的长度是否相同
+                        assert len(temp) == len(altitude) == len(lat) == len(
+                            lon), "Arrays must be the same length"
+                        # 创建DataFrame，并将datetime_str作为第一列添加
+                        df = pd.DataFrame({
+                            'Datetime_str': datetime_str,
+                            'Longitude': lon,
+                            'Latitude': lat,
+                            'Altitude': altitude,
+                            'Temperature': temp
+                        })
+                        dataset.close()
+                        # 仅筛选高度
+
+                        df_filtered = df[(df['Altitude'] >= target_h - 0.5)
+                                         & (df['Altitude'] <= target_h + 0.5)]
+                        dfs.append(df_filtered)
+                    except Exception as e:
+                        print(f"处理文件 {finfo} 时出错: {e}")
+        else:
+            print(f"文件夹 {folder_path} 不存在。")
+
+    # 按行拼接所有DataFrame
+    final_df = pd.concat(dfs, axis=0, ignore_index=True)
+    # 假设 final_df 是你的大 DataFrame
+    final_df.to_csv("output365.txt", index=False, sep=",")  # 逗号分隔
+    print("数据已保存为 output365.txt")
+    # 计算差值找到最接近的索引来筛选纬度为30的数据
+    # 目标值
+
+    # 计算 'Latitude' 列与目标值的差异
+    final_df['Latitude_diff'] = np.abs(final_df['Latitude'] - target_latitude)
+    # 按天分组并找到每一天最接近的行
+
+    def find_closest_row(group):
+        return group.loc[group['Latitude_diff'].idxmin()]
+
+    # 使用groupby按Day分组并找到每个分组的最接近的记录
+    closest_per_day = final_df.groupby('Datetime_str', as_index=False).apply(
+        find_closest_row, include_groups=False)
+    # 删除不需要的列
+    closest_per_day = closest_per_day.drop(columns=['Latitude_diff'])
+    closest_per_day = closest_per_day[(closest_per_day['Latitude'] >= (
+        target_latitude - 2)) & (closest_per_day['Latitude'] <= (target_latitude + 2))]
+    # 将经度转换为弧度制单位
+    closest_per_day['Longitude_rad'] = np.radians(closest_per_day['Longitude'])
+    print(closest_per_day)
+    # 函数：将 Datetime_str 转换为以天为单位的时间
+
+    def convert_to_days(datetime_str):
+        # 获取日期、小时和分钟
+        dd = int(datetime_str[:3])  # 日期部分 (DDD)
+        hh = int(datetime_str[3:5])  # 小时部分 (HH)
+        mm = int(datetime_str[5:])  # 分钟部分 (MM)
+
+        # 计算总分钟数
+        total_minutes = dd * 1440 + hh * 60 + mm
+
+        # 转换为天数
+        return total_minutes / 1440
+
+    # 应用函数转换
+    closest_per_day['Time'] = closest_per_day['Datetime_str'].apply(
+        convert_to_days)
+    # 时间t
+    day_data = closest_per_day['Time']
+    # 计算背景温度，时间Day和弧度经度的函数
+    background_temperature = closest_per_day['Temperature'].mean()
+    # 计算温度扰动
+    closest_per_day['Temperature_perturbation'] = closest_per_day['Temperature'] - \
+        background_temperature
+    # 选择需要的列并重命名
+    selected_columns = closest_per_day[[
+        'Longitude_rad', 'Time', 'Temperature']]
+    selected_columns.columns = ['Longitude_Radians', 'Time', 'Temperature']
+    # 输出到 txt 文件
+    # save to parquet
+    selected_columns.to_parquet(cache_path)
+
+    return selected_columns
+
+    # output_file_path = 'cosmic.txt'  # 你可以修改这个路径来指定文件的保存位置
+# selected_columns.to_csv(output_file_path, sep='\t', index=False)
+# # 输出成功提示
+# print("输出成功，文件已保存为 'cosmic.txt'")
diff --git a/modules/saber/__init__.py b/modules/saber/__init__.py
index a771fe5..8971971 100644
--- a/modules/saber/__init__.py
+++ b/modules/saber/__init__.py
@@ -152,11 +152,15 @@ async def do_gravity_year():
     T = request.args.get("T")
     k = request.args.get("k")
     H = request.args.get("H")
-    lat_range = request.args.get("lat_range")
+    lat_target = request.args.get("lat_target")
     year = int(year)
     T = int(T)
     if T not in [5, 10, 16]:
         raise ValueError("T must be in [5, 10, 16]")
-    data = await run_sync(saber_planetw_process)(year)
+    data = await run_sync(saber_planetw_process)(
+        year,
+        target_h=int(H),
+        target_lat=int(lat_target)
+    )
     await run_sync(saber_planetw_plot)(data, T, k=int(k))
     return await extract_payload()
diff --git a/modules/saber/planetw_process.py b/modules/saber/planetw_process.py
index 771c8f5..65eae00 100644
--- a/modules/saber/planetw_process.py
+++ b/modules/saber/planetw_process.py
@@ -20,7 +20,11 @@ months = [
 ]
 
 
-def saber_planetw_process(year):
+def saber_planetw_process(
+    year,
+    target_h=70,
+        target_lat=60
+):
     # 定义文件路径模板和年份
 
     base_path = DATA_BASEPATH.saber + \
@@ -116,7 +120,7 @@ def saber_planetw_process(year):
     adjusted_time_day = adjusted_time_hour / 24.0  # 转换为天
     # 找到第一个事件中与70最接近的高度的索引
     # 输入高度
-    target_h = 70
+
     first_event_altitudes = tpaltitude[0]
     diff = np.abs(first_event_altitudes - target_h)
     closest_height_index = np.argmin(diff)
@@ -130,7 +134,7 @@ def saber_planetw_process(year):
     # 1. 创建经度分组（0到360°，每20°一个区间）
     longitude_bins = np.arange(0, 361, 20)
     # 2. 创建纬度目标值
-    target_lat = 60  # 假设目标纬度为60°
+ # 假设目标纬度为60°
     # 3. 用于存储结果
     closest_event_indices = []