08-A. Jet Lag: Prevention and Cure

理解飞行时差的本质及其对身体的影响,通过合理的饮食安排和适量的咖啡摄入,可以显著减轻其不适症状,使长途飞行变得更加轻松愉快。

The problem of Jet Lag is one every international traveller comes across at some time. But do you have to suffer? Understand what it is, and how a careful diet can minimize its worst effects, and your flights will be less stressful.

The effects of rapid travel on the body are actually far more disturbing than we realize. Jet Lag is not a psychological consequence of having to readjust to a different time zone. It is due to changes in the body's physiological regulatory mechanisms, specifically the hormonal systems, in a different environment.

Confused? So was John Foster Dulles, the American Secretary of State, when he flew to Egypt to conduct negotiations on the Aswan Dam. He later blamed his poor judgement on Jet Lag.

The effects can be used to advantage, too. President Johnson once conducted an important meeting in Guam and kept the entire proceedings at Washington DC time. The White House working personnel were as fresh as paint, while the locals, in this case, were jet-lagged. Essentially, they had been instantaneously transported to America.

Now that we understand what Jet Lag is, we can go some way to overcoming it. A great number of the body's events are scheduled to occur at a certain time of day. Naturally these have to be regulated, and there are two regulatory systems which interact.

One timing system comes from the evidence of our senses and stomachs, and the periodicity we experience when living in a particular time zone. The other belongs in our internal clocks (the major one of which may be physically located in a part of the brain called the suprachiasmatic nucleus) which, left alone, would tie the body to a 25 hour -- yes, 25 -- rhythm. Normally the two timers are in step, and the external cues tend to regularise the internal clocks to the more convenient 24 hour period.

If, however, you move the whole body to a time zone which is four hours different, the two clocks will be out of step, like two alarm clocks which are normally set together, but which have been reset a few hours apart. Whereas the two clocks would normally sound their alarms together, now they ring at different times. Similarly, the body can he set for evening while the sun is rising.

In time the physiological system will reset itself, but it does take time. One easily monitored rhythm is palm sweating. A man flown to a time zone different by 10 hours will take eight days to readjust his palm sweat. Blood pressure, which is also rhythmical, takes four days to readjust.

One reason for this discrepancy is that different bodily events are controlled by different factors. The hormone cortisol, which controls salt and water excretion, is made in the morning, wherever the body is. But the growth hormone is released during sleep, whenever in the day that sleep occurs. Normally these two hormones are separated by seven or eight hours, but if the body arrives at a destination in the early morning (local) and goes to sleep as soon as possible, the two hormones will be released simultaneously.

What can we do about it? It is not feasible to wait four days until the body is used to the new time zone. Fortunately there is a short cut. It relies on two things -- the power of the stomach to regulate the timing of other events, and the pharmacological actions of coffee.

The basic assumptions are:

Coffee delays the body clock in the morning, and advances it at night. Coffee at mid-afternoon is neutral.

Protein in meals stimulates wakefulness, while carbohydrates promote sleep.

Putting food into an empty stomach helps synchronize the body clock.

<think> 我们遇到的问题是:在绘制相关系数场时,滞后0天和滞后-30天的相关系数范围显示为nan - nan。这通常意味着数据全为NaN(缺失值),导致无法计算有效范围。下面将逐步分析原因并提供解决方案。 ### 原因分析: 1. **数据本身全为NaN**:可能由于计算相关系数时出现错误,导致整个数组都是NaN。 2. **数据选择错误**:可能选择的数据区域没有有效值,例如区域选择超出了数据范围或数据在该区域确实不存在。 3. **数据计算错误**:在计算相关系数时,可能由于数据不匹配(如时间维度不一致)导致无法计算相关系数,从而得到全NaN。 4. **数据读取问题**:原始数据可能存在问题,比如缺失值过多,导致计算出的相关系数全为NaN。 ### 解决步骤: #### 步骤1: 验证相关系数数据是否全为NaN ```python for lag in [0, -30]: corr_da = corr_fields[lag] # 计算NaN的比例 nan_ratio = corr_da.isnull().sum().values / corr_da.size print(f"滞后{lag}天的相关系数数据中NaN的比例: {nan_ratio:.2%}") # 检查是否全部为NaN if nan_ratio == 1: print(f"错误: 滞后{lag}天的相关系数数据全为NaN!") ``` #### 步骤2: 检查原始数据 如果相关系数数据全为NaN,我们需要检查用于计算相关系数的原始数据(海温和降水)是否存在问题。 ##### 检查海温数据(sst): ```python # 假设sst是包含海温数据的DataArray,且有时间、纬度、经度维度 print("海温数据检查:") print(f"时间范围: {sst.time.min().values} - {sst.time.max().values}") print(f"纬度范围: {sst.latitude.min().values} - {sst.latitude.max().values}") print(f"经度范围: {sst.longitude.min().values} - {sst.longitude.max().values}") print(f"缺失值比例: {sst.isnull().sum().values / sst.size:.2%}") ``` ##### 检查降水数据(precip): ```python print("降水数据检查:") print(f"时间范围: {precip.time.min().values} - {precip.time.max().values}") print(f"纬度范围: {precip.latitude.min().values} - {precip.latitude.max().values}") print(f"经度范围: {precip.longitude.min().values} - {precip.longitude.max().values}") print(f"缺失值比例: {precip.isnull().sum().values / precip.size:.2%}") ``` #### 步骤3: 检查时间对齐 计算超前滞后相关系数时,时间对齐至关重要。例如,滞后-30天表示海温超前降水30天,因此需要将海温的时间提前30天与降水对齐。 ```python # 假设我们计算滞后-30天的相关系数 # 正确的对齐方式应该是: # sst_shifted = sst.shift(time=-30) # 将海温提前30天 # precip_lagged = precip # 降水保持不变 # 然后计算sst_shifted与precip_lagged在相同时间段的相关系数 # 检查时间重叠: # 假设原始海温和降水时间范围一致,那么将海温提前30天后,海温的最后一个时间点会缺失30天的降水数据 # 因此,我们需要截取重叠的时间段 # 示例:计算滞后-30天相关系数的代码片段 if lag < 0: sst_shifted = sst.shift(time=lag) # lag为负数,例如-30,表示提前30天 # 截取重叠时间段 common_time = precip.time.intersection(sst_shifted.time) sst_common = sst_shifted.sel(time=common_time) precip_common = precip.sel(time=common_time) # 然后计算相关系数 ``` #### 步骤4: 重新计算相关系数(如果需要) 如果发现时间对齐问题或原始数据问题,需要重新计算相关系数。 ##### 计算两个DataArray在时间维度上的相关系数(逐网格点): ```python def compute_correlation(sst_data, precip_data): # 确保时间维度对齐 assert len(sst_data.time) == len(precip_data.time) # 使用xarray的corr函数 corr_da = xr.corr(sst_data, precip_data, dim='time') return corr_da # 示例:计算滞后0天 corr_0 = compute_correlation(sst, precip) # 示例:计算滞后-30天 sst_shifted = sst.shift(time=-30) common_time = precip.time.intersection(sst_shifted.time) sst_common = sst_shifted.sel(time=common_time) precip_common = precip.sel(time=common_time) corr_minus30 = compute_correlation(sst_common, precip_common) ``` #### 步骤5: 验证重新计算的相关系数 重新计算后,再次检查相关系数的范围: ```python print(f"滞后0天相关系数范围: {corr_0.min().values:.2f} - {corr_0.max().values:.2f}") print(f"滞后-30天相关系数范围: {corr_minus30.min().values:.2f} - {corr_minus30.max().values:.2f}") ``` #### 步骤6: 绘制相关系数场 如果重新计算后相关系数范围正常,则按照之前的方法绘制。 ### 常见问题总结: 1. **时间未对齐**:超前滞后相关系数计算时,时间错位后可能导致没有共同时间点,从而计算出全NaN。务必检查共同时间段是否存在。 2. **数据缺失过多**:如果原始海温或降水在某个区域全为NaN,那么该区域的相关系数也会是NaN。需要检查原始数据的缺失情况。 3. **计算函数错误**:确保使用xarray的`corr`函数时指定了正确的维度(通常是时间维度)。 ### 相关问题: 1. 如何有效处理气象数据中的缺失值? 2. 在计算超前滞后相关系数时,如何处理不同时间分辨率的数据? 3. 除了相关系数,还有哪些方法可以分析两个气象变量之间的关系? </think> # 相关系数范围显示为nan - nan的原因及解决方案 当使用xarray和matplotlib绘制相关系数场时,滞后0天和滞后-30天的相关系数范围显示为`nan - nan`,这表示数据集中所有值都是缺失值(NaN)。以下是详细分析和解决方案: ## 原因分析 ### 1. 数据读取或预处理问题 - 原始数据本身包含大量缺失值 - 数据读取时坐标不匹配导致切片失败 - 数据预处理步骤引入错误 ```python # 验证原始数据 print(f"原始数据缺失值比例: {corr_da.isnull().mean().values*100:.1f}%") ``` ### 2. 时间对齐错误 计算滞后相关性时最常见的问题: - 时间维度未正确对齐 - 滞后计算时超出时间范围边界 - 时间索引不一致 ```python # 验证时间对齐 if lag < 0: print(f"滞后{abs(lag)}天时可用时间点: {len(common_time)}") print(f"第一个可用时间点: {common_time[0].values}") print(f"最后一个可用时间点: {common_time[-1].values}") ``` ### 3. 区域选择错误 - 经纬度范围超出数据边界 - 坐标名称不匹配 - 切片方向错误(纬度需从高到低) ```python # 验证区域选择 print(f"数据纬度范围: {corr_da.latitude.min().values} - {corr_da.latitude.max().values}") print(f"数据经度范围: {corr_da.longitude.min().values} - {corr_da.longitude.max().values}") ``` ### 4. 相关性计算错误 - 统计方法应用不正确 - 输入数据维度不匹配 - 样本量不足导致无法计算 ```python # 验证相关计算 sample_size = corr_da.attrs.get('sample_size', '未知') print(f"用于计算相关性的样本量: {sample_size}") ``` ## 解决方案 ### 步骤1: 诊断NaN来源 ```python # 检查原始数据缺失情况 print("=== 原始数据诊断 ===") print(f"海温数据缺失比例: {sst.isnull().mean().values*100:.1f}%") print(f"降水数据缺失比例: {precip.isnull().mean().values*100:.1f}%") # 检查相关系数数据 print("\n=== 相关系数数据诊断 ===") for lag in [0, -30]: corr_da = corr_fields[lag] nan_ratio = corr_da.isnull().mean().values print(f"滞后{lag}天 - NaN比例: {nan_ratio*100:.1f}%") if nan_ratio == 1.0: print(f" → 警告: 滞后{lag}天数据全为NaN!") ``` ### 步骤2: 修复时间对齐问题 ```python # 重新计算滞后相关性(确保时间对齐) def recalculate_correlation(lag): if lag < 0: # 滞后计算(海温超前) sst_shifted = sst.shift(time=lag) # 负值表示超前 common_time = precip.time.intersection(sst_shifted.time) sst_common = sst_shifted.sel(time=common_time) precip_common = precip.sel(time=common_time) else: # 同期计算 common_time = precip.time.intersection(sst.time) sst_common = sst.sel(time=common_time) precip_common = precip.sel(time=common_time) # 计算逐点相关系数 corr_da = xr.corr(sst_common, precip_common, dim='time') # 添加样本量属性 corr_da.attrs['sample_size'] = len(common_time) return corr_da # 重新计算相关系数 corr_fields[0] = recalculate_correlation(0) corr_fields[-30] = recalculate_correlation(-30) ``` ### 步骤3: 验证数据区域 ```python # 定义目标区域(长江中下游) target_lon = slice(113, 122) target_lat = slice(32, 26) # 纬度从高到低 # 验证数据是否覆盖目标区域 for lag in [0, -30]: corr_da = corr_fields[lag] print(f"\n滞后{lag}天数据范围验证:") print(f"纬度: {corr_da.latitude.min().values} - {corr_da.latitude.max().values}") print(f"经度: {corr_da.longitude.min().values} - {corr_da.longitude.max().values}") # 检查目标区域是否在数据范围内 lat_in_range = (target_lat.start >= corr_da.latitude.min().item() and target_lat.stop <= corr_da.latitude.max().item()) lon_in_range = (target_lon.start >= corr_da.longitude.min().item() and target_lon.stop <= corr_da.longitude.max().item()) print(f"目标区域纬度覆盖: {'是' if lat_in_range else '否'}") print(f"目标区域经度覆盖: {'是' if lon_in_range else '否'}") ``` ### 步骤4: 数据填充与插值 ```python # 对于少量缺失值使用插值填补 for lag in [0, -30]: corr_da = corr_fields[lag] if corr_da.isnull().any(): print(f"\n滞后{lag}天缺失值处理:") print(f"填补前缺失值数量: {corr_da.isnull().sum().item()}") # 使用最近邻插值填补缺失值 corr_filled = corr_da.interpolate_na(dim='longitude', method='nearest') corr_filled = corr_filled.interpolate_na(dim='latitude', method='nearest') print(f"填补后缺失值数量: {corr_filled.isnull().sum().item()}") corr_fields[lag] = corr_filled ``` ### 步骤5: 验证并绘制结果 ```python # 最终验证 for lag in [0, -30]: corr_da = corr_fields[lag] print(f"\n滞后{lag}天最终验证:") print(f"纬度大小: {len(corr_da.latitude)}") print(f"经度大小: {len(corr_da.longitude)}") print(f"纬度范围: {corr_da.latitude.min().values} - {corr_da.latitude.max().values}") print(f"经度范围: {corr_da.longitude.min().values} - {corr_da.longitude.max().values}") print(f"相关系数范围: {corr_da.min().values:.2f} - {corr_da.max().values:.2f}") print(f"样本量: {corr_da.attrs.get('sample_size', '未知')}") # 绘制验证图 plt.figure(figsize=(10, 6)) corr_da.plot() plt.title(f"滞后{lag}天相关系数验证图") plt.savefig(f'correlation_validation_lag{abs(lag)}.png') plt.close() ``` ## 关键预防措施 1. **数据质量检查**:在计算前验证原始数据的完整性和范围 ```python print(f"海温数据时间范围: {sst.time.min().values} - {sst.time.max().values}") print(f"降水数据时间范围: {precip.time.min().values} - {precip.time.max().values}") ``` 2. **时间对齐验证**:确保滞后计算时时间索引正确匹配 ```python if lag < 0: common_time = precip.time.intersection(sst.shift(time=lag).time) print(f"滞后{abs(lag)}天共同时间点数量: {len(common_time)}") ``` 3. **坐标系统一**:确保所有数据集使用一致的坐标命名 ```python # 统一坐标名称 sst = sst.rename({'lat': 'latitude', 'lon': 'longitude'}) precip = precip.rename({'lat': 'latitude', 'lon': 'longitude'}) ``` 4. **样本量监控**:记录用于计算相关性的有效样本量 ```python corr_da.attrs['sample_size'] = len(common_time) ``` 通过系统性地诊断和解决这些问题,您应该能够成功绘制出包含有效相关系数值的场图。如果问题仍然存在,建议检查原始数据源的质量和时间覆盖范围。 ## 相关问题 1. 如何处理气象数据中的时间维度不一致问题? 2. 当遇到全NaN数据时,有哪些有效的调试策略? 3. 如何优化大型气象数据集的相关性计算性能? 4. 在气象分析中,除了相关系数还有哪些有效的统计方法? 5. 如何验证气象数据插值方法的合理性?
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