基于Polar WRF-格点统计插值(GSI)的北极区域大气再分析系统的构建与评估

    Construction and evaluation of an Arctic regional atmospheric reanalysis system based on Polar WRF-Gridpoint Statistical Interpolation (GSI)

    • 摘要: 本文采用Polar WRF极地中尺度数值模式和格点统计插值(Gridpoint Statistical Interpolation, GSI)变分同化系统, 构建了一套适用于北极地区的高分辨率区域大气再分析系统。该系统同化地面观测、探空及卫星遥感等多源资料, 生成了2012—2016年水平分辨率为10 km的北极区域大气再分析数据集, 并利用地面探空观测、ERA5再分析资料及GPM降水产品进行系统评估。结果表明: (1)近地面要素方面, WRF对2 m气温和10 m风速的模拟优于ERA5, 其均方根误差(ERMSE)分别为2.008 ℃和1.707 m·s−1, 低于ERA5的2.196 ℃和2.018 m·s−1; 但2 m露点温度和海平面气压ERMSE分别为1.984 ℃和1.303 hPa, 高于ERA5。(2)高空要素方面, WRF对位势高度和相对湿度具有优势, 其中相对湿度ERMSE为11.5%~12.4%, 低于ERA5的18.0%~19.5%; 温度和风速ERMSE则略高于ERA5。(3)降水方面, WRF能够再现“外围海域高、中央海盆低”的空间格局, 7月误差最小, 平均误差(EME)和ERMSE分别为0.04 mm·d−1和0.57 mm·d−1; 1月和10月ERMSE分别为1.11 mm·d−1和1.03 mm·d−1, 冬季和秋季降水量级误差较大。(4)2012年8月北极强气旋个例表明, WRF能够刻画500 hPa低压中心、冷心结构、湿度分布和外围高风速带。总体而言, 该系统在北极区域大气状态和降水空间结构重建方面具有较好适用性。

       

      Abstract: This study developed a high-resolution regional atmospheric reanalysis system for the Arctic using the Polar WRF mesoscale numerical model and the Gridpoint Statistical Interpolation (GSI) variational data assimilation system. The system assimilates multi-source observations, including surface observations, radiosonde data, and satellite remote sensing data, to generate a 10 km-resolution Arctic regional atmospheric reanalysis dataset for 2012–2016. The system was evaluated using surface and radiosonde observations, ERA5 reanalysis data, and GPM precipitation products. The results show that: (1) for near-surface variables, WRF performs better than ERA5 in simulating 2 m air temperature and 10 m wind speed, with root mean square error (ERMSE) values of 2.008 ℃ and 1.707 m·s–1, respectively, lower than the corresponding ERA5 values of 2.196 ℃ and 2.018 m·s–1; however, the ERMSE values for 2 m dew-point temperature and sea-level pressure are 1.984 ℃ and 1.303 hPa, respectively, higher than those of ERA5. (2) For upper-air variables, WRF shows advantages in geopotential height and relative humidity, with relative humidity ERMSE values of 11.5%~12.4%, lower than the ERA5 values of 18.0%~19.5%; however, the ERMSE values for temperature and wind speed are slightly higher than those of ERA5. (3) For precipitation, WRF can reproduce the spatial pattern characterized by higher precipitation over the peripheral seas and lower precipitation over the central Arctic Basin. The smallest errors occur in July, with mean error (EME) and ERMSE values of 0.04 mm·d–1 and 0.57 mm·d–1, respectively; the ERMSE values in January and October are 1.11 mm·d–1 and 1.03 mm·d–1, respectively, indicating larger precipitation magnitude errors in winter and autumn. (4) A case study of the strong Arctic cyclone in August 2012 shows that WRF can capture the 500 hPa low-pressure center, cold-core structure, humidity distribution, and peripheral high-wind-speed belt. Overall, the system demonstrates good applicability in reconstructing the atmospheric state and precipitation spatial structure over the Arctic.

       

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