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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >Performance and analysis of the constructed analogue method applied to US soil moisture over 1981-2001 - art. no. 8617
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Performance and analysis of the constructed analogue method applied to US soil moisture over 1981-2001 - art. no. 8617

机译：1981-2001年间应用于美国土壤水分的模拟方法的性能和分析-艺术。没有。 8617

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We have conducted an experiment to assess the real time skill in monthly and seasonal predictions based solely on patterns of antecedent hydrological information over the United States. The hydrological information is contained in a proxy for soil moisture at 102 locations over the lower 48 states. This soil moisture is calculated over the years 1931 to present from a local hydrological equation taking monthly precipitation (P) and temperature (T) as input, and producing soil moisture (w), evaporation (E), runoff (R), and loss to groundwater (G) as output. The initial condition (IC) for the forecast procedure is soil moisture over the United States at the end of the month (w30). We constructed an analogue to the w30 fields, i.e., made linear combinations of soil moisture fields at the same time of year in years past to reproduce the IC to within a small tolerance. The coefficients assigned to the years past are then made to persist, and the subsequent development in the historical years is linearly combined to form a forecast. This method has been running at CPC in real time since 1998, and we added 1981-1997 in "retroactive real time" mode to form a large enough sample. In total, we considered both seasonal and monthly forecasts at leads of -1 to +6 months for 1981-2001, for the elements w30, E, T, and P. From the outset, we wanted to investigate nonlocal forecast methods, considering local effects, on evaporation and temperature mainly, as being established already and well documented [ Huang et al., 1996]. In a nonlocal method we entertain the possibility of precipitation (the response) falling downstream of a soil moisture anomaly (forcing). We found that we have about a 0.6 correlation in forecasting monthly soil moisture with a lead of one month (i.e., July at the end of May). This figure is higher in spring and somewhat lower in the early fall. The capability to forecast evaporation anomalies is very seasonal. During the cold half of the year, when E anomalies resemble T anomalies, the correlation is only 0.2-0.3, but in summer, when E anomalies resemble w anomalies, the skill of forecasts goes up to 0.6. We thus have some insight into patterns of anomalous water vapor input from the land surface into the lower atmosphere on a continental scale. Skill of forecasting T is modest, reaching 0.2-0.3 in many months and seasons, but there is no clear seasonal dependence that relates to the presumed physics of land atmosphere interactions. Skill in forecasting P is quite low, barely 0.1 in correlation, but +ve in all months and seasons. We did alternative experiments where the constructed analogue was built on E, T, or P instead of w and verified the forecast of all elements likewise. We found initial w to be the best for forecasting w itself and indeed for forecasting the other fields as well! This is important testimony that soil moisture is indeed the key, as has been suspected by many for ages. [References: 29]
机译：我们已经进行了一项实验，仅根据美国以前的水文信息模式来评估月度和季节预报中的实时技能。水文信息包含在下48个州的102个位置的土壤湿度的代理中。该土壤湿度是在1931年间计算得出的，它是根据当地的水文方程式得出的，以月降水量（P）和温度（T）为输入，并产生土壤湿度（w），蒸发量（E），径流（R）和损失地下水（G）作为输出。预报程序的初始条件（IC）是月底（w30）上美国的土壤湿度。我们构建了w30场的类似物，即过去几年中每年的同一时间对土壤湿度场进行线性组合，以将IC复制到很小的公差范围内。然后使分配给过去年份的系数保持不变，并将历史年份中的后续发展线性组合以形成预测。此方法自1998年以来一直在CPC上实时运行，并且我们以“追溯实时”模式添加1981-1997年以形成足够大的样本。总的来说，对于w30，E，T和P元素，我们考虑了1981-2001年-1到+6个月的潜在季节和月度预报。从一开始，我们想研究非本地预报方法，考虑本地如已经建立并有充分的文献记载[Huang et al。，1996]。在非局部方法中，我们考虑了降水（响应）落在土壤水分异常（强迫）下游的可能性。我们发现预测月土壤湿度的相关性约为0.6，提前一个月（即5月底的7月）。这个数字在春季较高，而在秋季初较低。预测蒸发异常的能力是非常季节性的。在寒冷的半年中，当E异常类似于T异常时，相关系数仅为0.2-0.3，而在夏季，当E异常类似于W异常时，预测技能达到了0.6。因此，我们对大陆表面从陆地表面向低层大气的异常水汽输入模式有一些了解。预报T的技巧不高，在许多月份和季节中都达到0.2-0.3，但是没有明显的季节依赖性与推测的陆地大气相互作用的物理性质有关。预测P的技巧很低，相关系数仅为0.1，但在所有月份和季节中都为+ ve。我们进行了替代实验，其中构造的类似物基于E，T或P而不是w进行构建，并且同样验证了所有元素的预测。我们发现初始w对于预测w本身以及对其他字段的预测都是最佳的！这是一个重要的证明，土壤湿度确实是关键，正如许多人早已怀疑的那样。 [参考：29]

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