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首页> 外文期刊>Journal of the Meteorological Society of Japan >Impacts of GPS-derived Water Vapor and Radial Wind Measured by Doppler Radar on Numerical Prediction of Precipitation
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Impacts of GPS-derived Water Vapor and Radial Wind Measured by Doppler Radar on Numerical Prediction of Precipitation

机译：多普勒雷达测量GPS衍生水蒸气和径向风对沉淀数值预测的多普勒雷达测量的影响

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This study conducted data assimilation experiments using the operational mesoscale four-dimensional variational data assimilation (4D-Var DA) system of the Japan Meteorological Agency (JMA). Experiments investigated the impacts of GPS-derived water vapor and Doppler radar-derived radial wind (RW) on precipitation prediction for a heavy rain event on 21 July 1999. RW data were obtained from Doppler radars at Narita and Haneda airports. GPS data were obtained from the GPS Earth Observation Network (GEONET) of the Geographical Survey Institute (GSI). Both precipitable water vapor (PWV) and slant water vapor (SWV), which is the amount of water vapor integrated along the slant path between GPS receivers and GPS satellites, were derived from the GPS data. Because SWV contains three-dimensional water vapor distribution information (Seko et al. 2000), we anticipated that assimilating SWV data would more accurately reproduce the moist air inflow at lower layers. Comparisons between observed and model-predicted precipitation regions helped define the impacts of assimilating RW, PWV, and SWV into the model. If the assimilated data included only conventional meteorological data, the model yielded small precipitation regions over a mountainous area far from Tokyo. If the assimilated data included both GPS-derived water vapor data and conventional data, lowlevel inflow in the model was more humid and precipitation occurred along the low-level convergence zone. Because the predicted position of the convergence zone differed from observations, however, the position of the precipitation region was not reproduced correctly. When RW and conventional data were assimilated into the model, low-level northerly flow was reproduced in the northwest of Tokyo. This northerly flow intensified the low-level convergence where precipitation was observed, and the position of the forecasted precipitation was more similar to that of observations. In this model run, however, lowlevel inflow from the south was less humid than observed, and precipitation onset was delayed by 1 hour. If GPS-derived water vapor data, RW data, and conventional data were all simultaneously assimilated, the precipitation position was modeled correctly, and precipitation onset occurred as observed. Comparisons between the vertical cross sections of analyzed water vapor fields and first-guess water vapor fields helped measure the impact of data assimilation on the modeled water vapor distribution. When PWV, RW, and the conventional data were assimilated, water vapor on the windward side of the low-level inflow decreased. In contrast, water vapor in the low-level inflow did not decrease when SWV data were used, instead of PWV data. For this rainfall event, the assimilation of RW and GPS-derived water vapor data improved the precipitation prediction. Assimilation of SWV data improved the representation of the vertical water vapor distribution.
机译：本研究采用日本气象学局（JMA）的运营介质四维变分数据同化（4D-VARDA）进行了数据同化实验。实验研究了GPS衍生的水蒸气和多普勒雷达衍生的径向风（RW）对1999年7月21日对大雨事件的降水预测的影响。RW数据是从纳塔塔和羽田机场的多普勒雷达获得。 GPS数据是从地理调查研究所（GSI）的GPS地球观测网络（GeOnet）获得的。可从GPS数据源自GPS数据，可沉淀的水蒸气（PWV）和倾斜水蒸气（SWV）沿着GPS接收器和GPS卫星之间的倾斜路径集成的水蒸气量。由于SWV包含三维水蒸气分布信息（Seko等人，2000），我们预计会使SWV数据更准确地再现下层的潮湿空气流入。观察和模型预测的降水区之间的比较有助于定义同化RW，PWV和SWV进入模型的影响。如果同化数据包括传统的气象数据，则该模型在远离东京的山区产生小的降水区。如果同化数据包括GPS衍生的水蒸气数据和传统数据，则模型中的Lowlevel流入更加潮湿和沿着低电平收敛区发生沉淀。因为收敛区的预测位置不同于观察结果，所以沉淀区域的位置未正确再现。当RW和常规数据被同化到模型中时，在东京西北部再现低水平的北流量。该北流动增强了观察到沉淀的低级收敛性，并且预测沉淀的位置更类似于观察结果。然而，在这种模型中，从南方的Lowlevel流入比观察到的潮湿程度较小，降水发作延迟1小时。如果GPS衍生的水蒸气数据，RW数据和常规数据均同时同时同化，则正确地建模沉淀位置，并且观察到的沉淀发作。分析的水蒸气场垂直横截面与第一猜测水蒸气场之间的比较有助于测量数据同化对模型水蒸气分布的影响。当同化PWV，RW和常规数据时，低级流入的迎风侧的水蒸气降低。相比之下，在使用SWV数据时，低级流入中的水蒸气不会降低，而不是PWV数据。对于这种降雨事件，RW和GPS衍生的水蒸气数据的同化改善了降水预测。 SWV数据的同化改善了垂直水蒸气分布的表示。

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