ABSTRACT

A broad spectrum of model-derived weather datasets are available in the US. Because each product integrates atmospheric conditions with different model processes, each produces different statistical biases. This study validated air temperature from NLDAS-2 and a novel statistically downscaled NLDAS-2 against observational weather station data for the state of Florida. We statistically downscaled NLDAS-2 to a 1-km grid product using MODIS land surface temperature. We investigated mean biases and Pearson correlation coefficients between daily observational data and the two model-derived datasets. We then calculated multiple Climate Extremes Indices to further scrutinize differences in capturing extreme temperatures. Finally, we quantified potential causes of systematic NLDAS-2 biases related to distance from the coast, urban heat island, land cover, and type of observational stations. Two model-derived datasets showed similar mean biases and correspondence with observational data, underestimating maximum temperature by 1°C and overestimating minimum temperature by 2°C. Extreme temperatures were well simulated in both datasets. However, we still found overestimated extreme minimum temperatures and underestimated extreme maximum temperatures. Systematic biases tended to be higher for coastal stations and grids having a high fraction of water. Our study suggests that including physical processes covering land surface and ocean interactions may improve the model accuracy.

摘要

美国提供了广泛的模型衍生天气数据集。由于每种产品都将大气条件与不同的模型过程相结合，因此每种产品都会产生不同的统计偏差。这项研究根据佛罗里达州的观测气象站数据验证了 NLDAS-2 和新的统计学上缩小比例的 NLDAS-2 的气温。我们使用 MODIS 地表温度将 NLDAS-2 统计缩小为 1 公里网格产品。我们调查了日常观测数据和两个模型衍生数据集之间的平均偏差和皮尔逊相关系数。然后，我们计算了多个极端气候指数，以进一步审查捕捉极端温度的差异。最后，我们量化了与海岸距离、城市热岛、土地覆盖、和观测站的类型。两个模型衍生的数据集显示出相似的平均偏差和与观测数据的对应关系，将最高温度低估了 1°C，将最低温度高估了 2°C。在两个数据集中都很好地模拟了极端温度。然而，我们仍然发现高估了极端最低温度和低估了极端最高温度。具有高比例水的沿海站点和电网的系统偏差往往更高。我们的研究表明，包括覆盖地表和海洋相互作用的物理过程可能会提高模型的准确性。在两个数据集中都很好地模拟了极端温度。然而，我们仍然发现高估了极端最低温度和低估了极端最高温度。具有高比例水的沿海站点和电网的系统偏差往往更高。我们的研究表明，包括覆盖地表和海洋相互作用的物理过程可能会提高模型的准确性。在两个数据集中都很好地模拟了极端温度。然而，我们仍然发现高估了极端最低温度和低估了极端最高温度。具有高比例水的沿海站点和电网的系统偏差往往更高。我们的研究表明，包括覆盖地表和海洋相互作用的物理过程可能会提高模型的准确性。