Land surface temperature (LST) derived from satellite observations and weather modeling has been widely used for investigating Earth surface-atmosphere energy exchange and radiation budget. However, satellite-derived LST has a trade-off between spatial and temporal resolutions and missing observations caused by clouds, while there are limitations such as potential bias and expensive computation in model calibration and simulation for weather modeling. To mitigate those limitations, we proposed a WRFM framework to estimate LST at a spatial resolution of 1 km and temporal resolution of an hour by integrating the Weather Research and Forecasting (WRF) model and MODIS satellite data using the morphing technique. We tested the framework in eight counties, Iowa, USA, including urban and rural areas, to generate hourly LSTs from June 1st to August 31st, 2019, at a 1 km resolution. Upon evaluation with in-situ LST measurements, our WRFM framework has demonstrated its ability to capture hourly LSTs under both clear and cloudy conditions, with a root mean square error (RMSE) of 2.63 K and 3.75 K, respectively. Additionally, the assessment with satellite LST observations has shown that the WRFM framework can effectively reduce the bias magnitude in LST from the WRF simulation, resulting in a reduction of the average RMSE over the study area from 4.34 K (daytime) and 4.12 K (nighttime) to 2.89 K (daytime) and 2.75 K (nighttime), respectively, while still capturing the hourly patterns of LST. Overall, the WRFM is effective in integrating the complementary advantages of satellite observations and weather modeling and can generate LSTs with high spatiotemporal resolutions in areas with complex landscapes (e.g., urban).

中文翻译：

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通过将天气建模与卫星观测相结合，改进对昼夜地表温度的估计


从卫星观测和天气建模得出的地表温度 （LST） 已被广泛用于研究地球表面-大气能量交换和辐射收支。然而，卫星衍生的 LST 需要在空间和时间分辨率与云造成的观测缺失之间进行权衡，同时在用于天气建模的模型校准和模拟中存在诸如潜在偏差和昂贵计算等局限性。为了减轻这些限制，我们提出了一个 WRFM 框架，通过使用变形技术集成天气研究和预报 （WRF） 模型和 MODIS 卫星数据，以 1 公里的空间分辨率和一小时的时间分辨率估计 LST。我们在美国爱荷华州的八个县（包括城市和农村地区）测试了该框架，以 2019 年 6 月 1 日至 8 月 31 日以 1 公里的分辨率生成每小时的 LST。在用原位 LST 测量进行评估后，我们的 WRFM 框架已经证明它能够在晴朗和多云的条件下捕获每小时的 LST，均方根误差 （RMSE） 分别为 2.63 K 和 3.75 K。此外，卫星 LST 观测的评估表明，WRFM 框架可以有效降低 WRF 模拟中 LST 的偏差幅度，从而将研究区域的平均 RMSE 分别从 4.34 K（白天）和 4.12 K（夜间）降低到 2.89 K（白天）和 2.75 K（夜间），同时仍然捕获 LST 的每小时模式。总体而言，WRFM 可以有效地整合卫星观测和天气建模的互补优势，并且可以在具有复杂景观的地区（例如城市）生成具有高时空分辨率的 LST。