参考引用虽未直接提供海洋热浪计算代码，但提及了热浪特征计算及相关数据处理思路。在Python中进行类似热浪计算可参考以下示例代码，以下代码基于引用中热浪寒浪统计思路模拟计算海洋热浪相关特征： ```python import numpy as np import pandas as pd # 模拟多年每日数据 np.random.seed(0) years = 10 days_per_year = 365 data = np.random.rand(years * days_per_year) * 30 # 模拟温度数据 # 转换为DataFrame dates = pd.date_range(start='2010-01-01', periods=years * days_per_year) df = pd.DataFrame({'date': dates, 'Tmax': data}) # 计算每日的多年平均值 daily_avg = df.groupby(df['date'].dt.dayofyear)['Tmax'].mean() # 对每日多年平均值进行15日滑动平均 rolling_avg = daily_avg.rolling(window=15, center=True).mean() # 对滑动平均后的每日多年平均值求10%和90%分位数 q10 = rolling_avg.quantile(0.1) q90 = rolling_avg.quantile(0.9) # 提取每年5 - 9月的数据 hot_season = df[(df['date'].dt.month >= 5) & (df['date'].dt.month <= 9)] # 标记热浪日 hot_season['hot_day'] = (hot_season['Tmax'] > q90).astype(int) # 计算热浪特征 hot_spells = [] current_spell = None for index, row in hot_season.iterrows(): if row['hot_day'] == 1: if current_spell is None: current_spell = {'SDHD': row['date'], 'start_index': index, 'intensities': []} current_spell['intensities'].append((row['Tmax'] - q90) / q90) else: if current_spell is not None: current_spell['EDHD'] = hot_season.loc[index - 1, 'date'] current_spell['LHD'] = index - current_spell['start_index'] current_spell['IHD'] = sum(current_spell['intensities']) hot_spells.append(current_spell) current_spell = None # 输出热浪特征 FHW = len(hot_spells) if FHW > 0: MCHW = max([spell['LHD'] for spell in hot_spells]) IHW = sum([spell['IHD'] for spell in hot_spells]) else: MCHW = 0 IHW = 0 print(f"热浪次数 (FHW): {FHW}") print(f"最长热浪天数 (MCHW): {MCHW}") print(f"热浪强度 (IHW): {IHW}") ``` 上述代码首先模拟了多年的每日温度数据，然后按照引用中提及的步骤计算每日多年平均值、进行滑动平均、求分位数，接着提取5 - 9月的数据来标记热浪日，最后计算热浪的相关特征。