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# Improving the Analyses and Forecasts of a Tropical Squall Line Using Upper Tropospheric Infrared Satellite Observations

doi:  10.1007/s00376-021-0449-8

• The advent of modern geostationary satellite infrared radiance observations has noticeably improved numerical weather forecasts and analyses. However, compared to midlatitude weather systems and tropical cyclones, research into using infrared radiance observations for numerically predicting and analyzing tropical mesoscale convective systems remain mostly fallow. Since tropical mesoscale convective systems play a crucial role in regional and global weather, this deficit should be addressed. This study is the first of its kind to examine the potential impacts of assimilating all-sky upper tropospheric infrared radiance observations on the prediction of a tropical squall line. Even though these all-sky infrared radiance observations are not directly affected by lower-tropospheric winds, the high-frequency assimilation of these all-sky infrared radiance observations improved the analyses of the tropical squall line’s outflow position. Aside from that, the assimilation of all-sky infrared radiance observations improved the analyses and prediction of the squall line’s cloud field. Finally, reducing the frequency of assimilating these all-sky infrared radiance observations weakened these improvements to the analyzed outflow position, as well as the analyses and predictions of cloud fields.
摘要: 近年来，随着现代地球静止气象卫星技术的发展，以及资料同化方案的进步，数值天气预报的准确度得到了明显改善。然而，目前大部分关于地球静止轨道气象卫星红外亮温观测的资料同化研究都以中纬度强对流以及台风为主。相对而言，关于热带区域中尺度对流系统的红外资料同化研究非常稀缺。但是，热带中尺度对流系统却对全球天气与气候有着重要的影响，亟需开展相关研究。本研究首次分析了同化对流层上层红外观测对于热带飑线分析与预报的影响。研究结果表明，虽然对流层低层的风场不直接影响对流层上层红外亮温的观测，高频次的红外资料同化却能够改善热带飑线底层出流的位置。除此以外，红外资料的同化还改善了飑线的云场分析与预报。但是，如果同化的频次被减小，则上述改善效果会被削弱。
• Figure 1.  Plots showing (a) snapshots of the observed squall from AHI’s channel 14, and (b) longitude-time diagram of the observed ch14-BT between 3.6°S to 4.4°S. The black-and-white cross in (a) indicates the location of the surface wind observations that will be used for validation later. The dashed black line in (b) indicates the estimated longitudes of the squall’s leading edge at 4°S, and the dotted black line indicates the position of the surface station used for validation. Also shown are (c) the ch14-BT RMSD of the analysis ensemble mean, as well as (d) the consistency ratio (CR; CR$\equiv$RMSS/RMSD) of the analysis ensemble. The RMSD and CR are computed in a squall-following region that is bounded by the 15°S and 5°N latitude circles. Furthermore, said squall-following region extends 15 degrees west of the leading edge, and 5 degrees east of the leading edge. The red curves in (c) and (d) indicate the RMSD and CR of the conv experiment, the blue curves in (c) and (d) indicate the RMSD and CR of the conv+ch08_3hrly experiment, and the green curves in (c) and (d) indicate the RMSD and CR of the conv+ch08_30min experiment.

Figure 2.  A schematic showing how the PSU-EnKF system assimilates observations at times ${t}_{1}$ and ${t}_{2}$. The bubbles represent the envelop of possible model values predicted by an ensemble of model outputs, and are color-coded by time. Suppose we have three ensemble members at time ${t}_{0}$. To assimilate the observation at ${t}_{1}$, we use WRF to integrate the three members from ${t}_{0}$ to ${t}_{1}$. Then, the PSU-EnKF ingests the observation at ${t}_{1}$, which results in the three-member ensemble shifting towards and contracting around the ensemble. To assimilate the observation at ${t}_{2}$ (and at all subsequent times), the same integrate-then-ingest procedure is repeated.

Figure 3.  Squall-relative longitude-time diagrams of the analyzed zonal wind, averaged between 3.6°S to 4.4°S, at 300 m above sea level (a, b, c). The thick black contours in panels (a), (b), and (c) indicate the 224 K contour of the observed ch14-BT, averaged between the same latitudes. The gray shading indicates the storm-relative longitudes of the mountain range along the west coast of Sumatra, where no 300-m zonal wind information is available in the model. Also, the thick dotted lines indicate the relative longitude of the surface station referred to in the text. Finally, panel (d) shows time series of the observed surface zonal wind, analyzed zonal wind, as well as the observed ch14-BT at a surface station located at 2.7°S, 107.8°E.

Figure 4.  Correlations between ch08-BT and zonal wind on June 1st (0000 UTC) for (a) the conv + ch08_3hrly experiment, and (b) the conv + ch08_30min experiment. These correlations are plotted for a ch08 observation located 1 degree west of the squall’s leading edge (vertical dashed lines). Note that the plotted values are the average of correlations across 10 zonal cross-sections between 3.6°S to 4.4°S.

Figure 5.  Storm-relative longitude-time diagrams of deterministically forecasted ch14-BT (shading), as well as the 224 K contour of the observed ch14-BT (black contours). All plotted ch14-BT are averaged between 3.6°S to 4.4°S. Panels a, d, g and j show the deterministic forecasts from the conv experiment. Similar plots were also produced for the conv + ch08_3hrly (b, e, h and k) and conv + ch08_30min (c, f, i and l) experiments. The forecasted ch14-BT and the observed ch14-BT at the corresponding times are shown for the start times of the forecasts (a, b and c), at a lead time of 1 hour (d, e and f), at a lead time of 2 hours (g, h and i), and a lead time of 3 hours (j, k and l). Note that the deterministic forecasts from 13 initiation times are shown in each panel. The first deterministic forecasts were initiated on May 31 (1200 UTC), and subsequent deterministic forecasts were initiated every 3 hours, up to and including June 2 (0000 UTC).

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## Manuscript History

Manuscript revised: 11 April 2021
Manuscript accepted: 20 April 2021
###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## Improving the Analyses and Forecasts of a Tropical Squall Line Using Upper Tropospheric Infrared Satellite Observations

###### Corresponding author: Xingchao CHEN, xcz55@psu.edu
• Department of Meteorology and Atmospheric Science, and Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, Pennsylvania, PA 16802, USA

Abstract: The advent of modern geostationary satellite infrared radiance observations has noticeably improved numerical weather forecasts and analyses. However, compared to midlatitude weather systems and tropical cyclones, research into using infrared radiance observations for numerically predicting and analyzing tropical mesoscale convective systems remain mostly fallow. Since tropical mesoscale convective systems play a crucial role in regional and global weather, this deficit should be addressed. This study is the first of its kind to examine the potential impacts of assimilating all-sky upper tropospheric infrared radiance observations on the prediction of a tropical squall line. Even though these all-sky infrared radiance observations are not directly affected by lower-tropospheric winds, the high-frequency assimilation of these all-sky infrared radiance observations improved the analyses of the tropical squall line’s outflow position. Aside from that, the assimilation of all-sky infrared radiance observations improved the analyses and prediction of the squall line’s cloud field. Finally, reducing the frequency of assimilating these all-sky infrared radiance observations weakened these improvements to the analyzed outflow position, as well as the analyses and predictions of cloud fields.

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