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首页> 外文期刊>Atmospheric research >First steps towards an all-sky assimilation framework for tropical cyclone event over Bay of Bengal region: Evaluation and assessment of GMI radiances
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First steps towards an all-sky assimilation framework for tropical cyclone event over Bay of Bengal region: Evaluation and assessment of GMI radiances

机译:孟加拉湾热带气旋活动全天同化框架的第一步:GMI广域的评估和评估

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摘要

The present study designs a data assimilation framework for all-sky GMI radiances in the Weather Research Forecast (WRF) model for the prediction of tropical cyclone events over the Bay of Bengal (BOB) region. Experiments are performed using the RTTOV-SCATT radiative transfer model for three cyclone events (Hudhud (2014), Vardah (2016), and Kyant (2016)) for 19 V, 23 V, 37 V, 89 V, 166 V, 183 +/- 3, and 183 +/- 7 V channels. Results show that observed and simulated brightness temperatures (Tb) agree well for low-frequency channels (89 GHz). However, significant discrepancies do exist when simulating low Tb at high frequency channels. Therefore, a set of 26 simulations is considered using snow particle shapes from single scattering property databases. A new methodology is devised in order to compare the 26 simulations with observations; taking into account the uncertainties on hydrometeor radiative properties. Results reveal that model simulations are characterized by an underestimation of occurrences within several Tb ranges (similar to 200-300 K). The probability distribution function (PDF) of normalized observed-minus first guess is close to a Gaussian shape after applying a dedicated model of observation errors. The potential to integrate the GMI sensor data within a WRF data assimilation system is thus demonstrated.
机译:本研究为天空研究预测(WRF)模型中的全天GMI放射设计了数据同化框架,用于预测孟加拉湾(BOB)地区的热带气旋事件。使用RTTOV-SCATT散热模型进行三个旋风事件(Hudhud(2014),Vardah(2016)和Kyant(2016))进行实验,为19 V,23 V,37 V,89 V,166 V,183 + / - 3和183 +/- 7 V通道。结果表明,观察到的和模拟亮度温度(TB)对低频通道(&lt 199 ghz)很好。然而,在高频信道中模拟低TB时确实存在显着的差异。因此,使用来自单个散射属性数据库的雪粒子形状考虑一组26个模拟。设计了一种新的方法,以便将26个模拟与观察结果进行比较;考虑到水流仪辐射特性的不确定性。结果表明,模型仿真的特征在于,在几TB范围内低估发生(类似于200-300 k)。在应用观察误差的专用模型之后,归一化观察 - 负第一猜测的概率分布函数(PDF)接近高斯形状。因此证明了在WRF数据同化系统中集成GMI传感器数据的可能性。

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