复杂山区地形下GFS预报气温降尺度方法

A Downscaling Method for GFS Forecast Air Temperature in Complex Mountainous Terrain

摘要:

为获取复杂山区地形下可靠的精细化预报气温产品，以地形复杂的重庆地区为试验区，利用高空间分辨率的高程数据，基于神经网络的气温降尺度方法，将GFS(Global Forecast System)预报气温从0.25 °×0.25 °降尺度到200 m×200 m。使用2020年重庆市35个气象站点、逐3 h的气温数据进行模型训练及验证，并将其应用于2022年GFS 3 h的预报气温，生成200 m×200 m气温栅格数据。经验证，降尺度后的模型气温总体误差更小，均方根误差为1.65 ℃，相关系数为0.98，平均偏差为0.02 ℃；误差时空特征分析表明，降尺度后的气温在不同月、日、时、预报时效和台站位置下都更加稳定可靠；对比降尺度前后的气温空间分布，降尺度后的气温空间分辨率明显提高，能展示更多的空间细节，特别是在海拔差异明显的山区。

Abstract:

Near-surface air temperature (T_{a) at high spatiotemporal resolution is of great significance for meteorology, hydrology, agroecology, production and life, and disaster prevention and reduction. The heterogeneity of Ta in mountainous areas is strong, and there are few stations. How to obtain high spatial resolution forecast Ta under complex mountainous terrain remains a challenge in operational applications and scientific fields. The Global Forecast System (GFS) provides forecast Ta products with global coverage, but its low spatial resolution is not applicable to small-scale areas, especially mountainous areas with complex terrain. Currently, downscaling methods are widely used, which transform large-scale Ta into small-scale Ta. Taking Chongqing with complex terrain as a test area, this study uses elevation data with two spatial resolutions (0.25° and 200 m), latitude and longitude, temporal information, and GFS forecast Ta (0.25°) to construct a downscaling model of GFS forecast Ta for Chongqing based on the statistical downscaling method of neural networks. Validated using Chongqing station data in 2020, the root-mean-square error (RMSE) between the downscaled Ta and station Ta reduces from 2.55 ℃ to 1.65 ℃, the correlation coefficient (R) improves from 0.959 to 0.98, and the absolute value of the mean bias (Bias) reduces from 1 ℃ to 0.02 ℃. To assess the spatiotemporal stability of the model, this paper analyses the model errors by four aspects: month, day, different moments, and different forecast timescales. The results show that the downscaled Ta performance is more stable, with the 12-month RMSE decreasing from 2.22-3.15 ℃ to 1.37-1.86 ℃, and the absolute value of Bias decreasing from 0.10-2.24 ℃ to 0.01-0.29 ℃. The improvement is more obvious in the hot season (July to October). Comparing the time series of modelled Ta, GFS Ta, and station Ta at the three sites, the downscaled Ta aligns better with the station Ta, and the RMSE reduces from 1.63-2.54 ℃ to 1.52-1.87 ℃. Comparing the model performance at different moments, the downscaled Ta has smaller RMSE and Bias, and the model significantly improves accuracy even at moments when the GFS Ta errors are larger (03:00 and 09:00). Comparing the two forecast time scales, there is no significant difference in model performance after downscaling. In addition, the error spatial analysis results show that the station locations throughout Chongqing perform better after downscaling, and the effect is obvious at some stations, with the RMSE decreasing from 3.61 ℃ to 1.70 ℃, the R improving from 0.965 to 0.978, and the Bias reducing from -2.95 ℃ to -0.25 ℃. Finally, the model applies to GFS 3 h forecast Ta (0.25°×0.25°) to obtain small-scale Ta (200 m×200 m). The effect of Ta spatial distribution before and after downscaling is demonstrated for four moments (January 1, 03:00; April 1, 03:00; July 1, 03:00; and October 1, 03:00, 2022) in Chongqing. In contrast, the spatial resolution of GFS Ta before downscaling is low and shows an obvious sense of fuzzy patches, while the spatial resolution of Ta after downscaling is significantly improved and shows richer Ta spatial details, especially in high-altitude mountainous areas.}