Hourly sea surface temperature fusion based on Himawari-8 satellite
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摘要: Himawari-8卫星是日本气象厅发射的新一代地球同步静止气象卫星，为获取逐时次海表温度产品提供了有力数据支持。本文以Himawari-8 AHI海表温度为基础，利用最优插值法融合GCOM-W1 AMSR2海表温度和NERA-GOOS现场观测资料，生成逐时次海表温度融合产品。为了充分利用邻近时刻的海表温度观测资料，利用Himawari-8 AHI海表温度和欧洲中期天气预报中心海面风速数据建立匹配数据集，研究建立海表温度日变化模型，实现邻近时刻海表温度的订正；为了消除多源海表温度间的系统偏差，以Himawari-8 AHI海表温度为目标数据，利用泊松方程对GCOM-W1 AMSR2海表温度进行偏差订正。实验验证结果表明，利用逐时次海表温度融合产品计算的日增温情况与海面风速具有较好的相关性，间接证实了逐时次海表温度融合产品的准确性；另外，逐时次海表温度融合产品与现场观测海表温度的偏差为0.09℃、均方根误差为0.89℃，二者具有较好的一致性，说明逐时次海表温度融合产品具有较高的精度。
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关键词:
- Himawari-8卫星 /
- 逐时次海表温度融合 /
- 日变化订正 /
- 偏差订正
Abstract: Himawari-8 is a new generation geostationary meteorological satellite launched by the Japan Meteorological Agency, and provides the important observation data for merging hourly sea surface temperature (SST). This paper uses the optimal interpolation method to provide the hourly Northwest Pacific SST based on the Advanced Himawari Imagery(AHI) SST, the Global Change Observation Mission-Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2) SST and North-East Asian Regional–Global Ocean Observing System (NEAR-GOOS) SST. The diurnal SST variation and the bias between multi-source SSTs are the two significant factors affecting the accuracy of hourly SST fusion products. In order to make full use of SST at the adjacent time and improve the accuracy, a diurnal variation model of Himawari-8 AHI SST is established by using the matchup data set, which contains simultaneous Himawari-8 AHI SST and European Centre for Medium-Range Weather Forecasts wind data at the same location. In addition, a bias correction method based on Poisson’s equation is applied to correct GCOM-W1 AMSR2 SST biases relative to the AHI SST. The experimental results show that the diurnal warming calculated by the hourly SST fusion product is closely related to sea surface wind speed, which indirectly confirms the accuracy of the hourly SST fusion products. The root mean square difference and bias between the hourly SST fusion product and NEAR-GOOS SST are 0.89℃ and 0.09℃, respectively. The results show that the hourly SST fusion product is basically consistent with the in situ SST. -
图 2 海表温度日变化情况（以2017年10月5日8.04°N，143.00°E海表温度变化为例）
Fig. 2 The daily variation of sea surface temperature at 8.04°N, 143.00°E on October 5, 2017
图 3 海表温度日变化幅度随太阳辐照度的变化
Fig. 3 The amplitude of diurnal sea surface temperature variation with solar irradiance
图 4 海表温度日变化幅度随海面风速的变化
Fig. 4 The amplitude of diurnal sea surface temperature variation with wind speed
图 5 Himawari-8 AHI和GCOM-W1 AMSR2海表温度的拼接产品（28.8°～31.2°N，123.2°～126.2°E）
Fig. 5 The mosaic sea surface temperature from Himawari-8 AHI and GCOM-W1 AMSR2 at 28.8°−31.2°N and 123.2°−126.2°E
图 6 Himawari-8 AHI和GCOM-W1 AMSR2海表温度的拼接产品（29.8°N）
Fig. 6 The mosaic sea surface temperature from Himawari-8 AHI and GCOM-W1 AMSR2 at 29.8°N
图 7 2017年4月初估场误差协相关的
${{\lambda _x}}$ （a）、${{\lambda _y}}$ （b）和A（c）Fig. 7 The
${{\lambda _x}}$ (a),${{\lambda _y}}$ (b), and$ A$ (c) of the estimated initial field in April 2017
图 8 经日变化订正、偏差订正后的海表温度观测数据（a）和最优插值海表温度融合产品（b）
Fig. 8 The observed sea surface temperature using the diurnal variation and bias corrections(a), and the fused sea surface temperature using the optimal interpolation(b)
图 9 2017年8月17日00:00时（a）和14:00时（b）的海表温度融合结果及日增温情况（c）
Fig. 9 The daily warming of sea surface temperature (c) and the fusion products of sea surface temperature at 00:00 (a) and 14:00 (b) on August 17, 2017
图 10 2017年8月17日14:00时的CCMP海面风速
Fig. 10 The CCMP sea surface wind speed at 14:00 on August 17, 2017
图 11 海表温度融合结果精度检验
a. 现场观测与融合海表温度的散点图；b. 现场观测与融合海表温度偏差的直方图
Fig. 11 The validation of sea surface temperature fusion products
a. Scatter plot of in situ data versus sea surface temperature fusion products; b. histogram of the bias between in situ data minus sea surface temperature fusion products
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