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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。
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  • 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 received: 31 December 2020
Manuscript revised: 11 April 2021
Manuscript accepted: 20 April 2021
通讯作者: 陈斌, bchen63@163.com
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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.

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。

    • Tropical mesoscale convective systems (MCSs) contribute more than 60% of the total tropical precipitation (Mohr et al., 1999; Houze, 2004; Nesbitt et al., 2006; Liu, 2011; Roca and Fiolleau, 2020), which include tropical squall lines and other non-squall organized convective disturbances (Krishnamurti et al., 2013). In addition, tropical MCSs affect the global climate and weather across a large range of spatial scales (Madden and Julian, 1971; Held and Hou, 1980; Hoskins and Karoly, 1981; Weickmann, 1983; Satoh, 1994; Kiladis et al., 2009; Johnson and Ciesielski, 2013). It is thus important to accurately analyze and predict tropical MCSs.

      Analyses are typically used to study the dynamics, and initiate numerical predictions, of tropical MCSs. These analyses are normally produced using a class of Bayesian inference methods known as data assimilation (DA; Geer et al., 2018; Hersbach et al., 2020). DA generates these analyses by combining observations with a forecast from a preceding forecast cycle, while accounting for the uncertainties in both sources of information (e.g., Lorenc, 1986; Talagrand and Courtier, 1987; Evensen, 1994). Thus, one way to improve the analyses and predictions of tropical MCSs is to improve the DA process. Furthermore, the improved analyses produced by DA can be used to deepen our understanding of the characteristics and dynamics of tropical MCSs.

      The accuracy of DA-produced atmospheric analyses has a strong dependence on the availability of observations (Liu and Rabier, 2002; Benjamin et al., 2004; Ying and Zhang, 2018; Ying et al., 2018). Unfortunately, due to the difficulty of maintaining observation sites over ocean and tropical islands, in-situ observations of the atmosphere are generally sparse and infrequent over the Tropics. These sparse and infrequent in-situ observations are thus inadequate to capture most tropical MCSs. For instance, radiosondes and rawinsondes are usually released every 12 hours, and are typically spaced more than 500 km apart in the Tropics (Ingleby, 2017). In contrast, individual convective cells within tropical MCSs typically have length scales under 100 km and evolve on time scales less an hour, i.e., it is challenging to constrain the errors in the analyses and predictions of tropical MCSs using in-situ observations alone (Liu and Rabier, 2002).

      Supplementing in-situ observations with geostationary satellite all-sky infrared radiance observations has the potential to improve the analyses and predictions of tropical MCSs. Unlike in-situ observations, modern satellite infrared radiance observation pixels are spaced less than 10 km apart and have sampling periods of 30 minutes or less. Thus, these geostationary satellite infrared radiance observations (henceforth, IR observations) have spatiotemporal resolutions that are closer to the spatiotemporal scale of tropical moist convection than those of in-situ observations. All-sky IR observations thus have the potential to constrain errors in the analyses and prediction of tropical MCSs. This potential should be explored.

      At the time of writing, most of the research on assimilating all-sky IR observations into convection-permitting models had been done in the context of mid-latitude weather systems and tropical cyclones. Research in these contexts has found that the addition of IR observations improved the analyzed thermodynamic and cloud fields (e.g., Vukicevic et al., 2004, 2006; Otkin, 2010, 2012; Honda et al., 2018; Zhang et al., 2018; Minamide and Zhang, 2019; Sawada et al., 2019). In contrast, relatively little work has been done in the context of tropical MCSs. Nonetheless, the pattern of improvement seen in the context of convection-permitting mid-latitude severe weather and tropical cyclone prediction should also be achievable in the context of tropical MCSs.

      Ying and Zhang (2018) was among the first to examine the potential impacts of assimilating all-sky IR observations on the prediction of tropical MCSs. Using ensembles of gray-zone resolution simulations, Ying and Zhang (2018) performed idealized Observing System Simulation Experiments (OSSEs) over the tropical Indian Ocean during the active phase of the October 2011 Madden−Julian Oscillation (MJO; Madden and Julian, 1971) event. Gray-zone resolution simulations are simulations that use horizontal grid spacing fine enough to resolve mesoscale convective systems, but too coarse to resolve individual convective updrafts (Chen et al., 2018c). Ying and Zhang (2018) found that the inclusion of IR observations can improve the analyzed cloud hydrometeor fields. Chan et al. (2020b) subsequently performed real data DA experiments for a similar time period and domain and found that the assimilation of real IR observations can potentially improve cloud field analyses and predictions across a range of spatial scales, as well as short-term rainfall predictions. These improvements seen in Ying and Zhang (2018) and Chan et al. (2020b) are consistent with the improvements seen in the context of mid-latitude weather and tropical cyclone convection-permitting numerical weather prediction.

      However, several important questions remain unanswered regarding the assimilation of all-sky IR observations in the context of tropical MCSs. First, how does the assimilation of real-world IR observations improve the thermodynamic and dynamic fields of tropical MCSs? Second, how does the accuracy of the analyses and predictions of tropical MCSs vary with the frequency of IR DA? Addressing these questions could potentially inform the future assimilation of IR observations in the Tropics and improve the analyses and forecasts of tropical MCSs.

      In this article, we will attempt to answer these two important questions in the case of a tropical squall line that swept through Sumatra, the Malay Peninsula and Borneo (Fig. 1a). To study these questions, we employed ensembles of gray-zone resolution Weather Research and Forecasting (WRF) model simulations and assimilated real-world conventional observations. We also assimilated IR observations from the Advanced Himawari Imager (AHI), which is on-board the Himawari-8 geostationary satellite (Bessho et al., 2016).

    2.   Materials and Methods
    • To generate WRF simulation ensembles of the squall line, we employed the Advanced Research WRF (Skamarock et al., 2008) model (version 3.8.1). The simulation domain covers the area plotted in Fig. 1a and has 560 $ \times $ 450 horizontal grid points with 9-km grid spacing. 45 model levels were employed, with the bottommost 9 levels within the lowest 1-km of the model, and the model top pressure set to 20 hPa.

      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.

      For the WRF model’s physics and dynamics, we employed the updated Goddard shortwave scheme (Chou and Suarez, 1999) and the Global Circulation Model version of the Rapid Radiative Transfer Model (RRTMG) longwave radiation scheme (Iacono et al., 2008). Surface processes were generally simulated using the unified Noah land surface physics scheme (Chen and Dudhia, 2001), the only exception being that surface skin temperatures were diagnosed via the method of Zeng and Beljaars (2005). The Yonsei University (YSU) boundary layer scheme (Hong et al., 2006) was employed to handle subgrid-scale turbulent mixing. Finally, we utilized the scheme of Thompson et al. (2008) to parameterize cloud microphysical processes.

      Note that while the 9-km horizontal grid spacing is too coarse to resolve individual convective updrafts, it is sufficient to resolve the basic dynamics of tropical MCSs. There are several reasons behind choosing this horizontal grid spacing. First, due to the rapid motion of the tropical squall line (Fig. 1a), a large domain is required. Due to our limited computational resources, it is difficult to utilize smaller grid spacings in this large domain. Second, earlier work over the nearby Indian Ocean have shown that this 9-km grid spacing is sufficiently small to realistically replicate the precipitation, circulation, thermodynamic, and radiation features of organized mesoscale convection over tropical oceans (Wang et al., 2015). Finally, this 9-km grid spacing is widely employed in studies that examine the overturning and the physical mechanisms of organized tropical convection (Chen et al. 2018a, b, c; Chen et al., 2020; Ruppert and Chen, 2020; Ruppert et al., 2020).

      We have also opted to avoid the use of cumulus parameterizations in this study. The reason is that earlier studies in the neighboring tropical Indian Ocean were able to obtain reasonable tropical MCSs without cumulus parameterizations (Wang et al., 2015; Chen et al., 2018a, b, c; He et al., 2019; Chen et al., 2020; Ou et al., 2020; Ruppert and Chen, 2020; Ruppert et al., 2020). These studies were able to do so because the 9-km grid spacing was fine enough to resolve mesoscale convective updrafts.

      In this study, we initiated 50-member WRF ensemble simulations, starting at 0000 UTC 30 May 2017. To construct the needed 50 sets of initial conditions, we utilized the European Centre for Medium-Range Weather Forecast (ECMWF) fifth-generation global reanalysis data (ERA5; Hersbach et al., 2020) 10-member ensemble fields at 0000 UTC on 26, 27, 28, 29, and 30 May, as well as the ERA5 reanalysis fields at 0000 UTC 30 May. To be precise, for each of the 5 dates, we generated 10 perturbations from the ERA5 10-member ensemble by subtracting the ensemble mean on said dates from each of the 10-members. This procedure produced a set of 50 perturbations. We then added the ERA5 reanalysis fields at 0000 UTC May 30 to the 50 perturbations to produce 50 sets of initial conditions. The ERA5 reanalysis fields were also used to generate the boundary conditions, i.e., all 50-members were subjected to the same boundary conditions. The ensemble was then integrated forward for 12 hours to generate flow-dependent ensemble statistics at 1200 UTC 30 May. All data assimilation experiments began at 1200 UTC 30 May.

    • To examine the questions raised in section 1, we performed experiments where we assimilated non-IR conventional observations and satellite all-sky IR observations. The non-IR conventional observations include surface station, pilot, rawinsonde, radiosonde, and ship observations from the Global Telecommunication System from the National Center for Atmospheric Research’s Research Data Archive (NCAR RDA). The non-IR conventional observations also include satellite-derived atmospheric motion vectors from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) Tropical Cyclone Archive. These non-IR conventional observations were assimilated every 3 hours in all experiments.

      Aside from the non-IR conventional observations, we also assimilated AHI channel 8 (6.2 µm) infrared brightness temperature observations (ch08-BT). The AHI instrument is on board the Himawari-8 geostationary satellite, which is located above the Equator, at 140.7°E. Under clear-sky conditions, ch08-BT observations typically reflect the upper tropospheric water vapor content (Otkin, 2012). These observations are generally available every 10 minutes.

    • To investigate the important questions raised in section 1, we performed three DA experiments. The first experiment assimilated non-IR conventional observations every three hours (conv experiment). In the second experiment, we assimilated both ch08-BT and non-IR conventional observations every 3 hours (conv+ch08_3hrly). Finally, in the third experiment, we assimilated non-IR conventional observations every 3 hours, and ch08-BT observations every 30 minutes (conv+ch08_30min).

      In this study, we employed the Pennsylvania State University WRF Ensemble Kalman Filter system (PSU-EnKF; Meng and Zhang, 2008) as our DA system. The ensemble Kalman filter (EnKF) constructs ensemble analyses by combining an ensemble of forecasts with observations (Evensen, 1994; Burgers et al., 1998). To be precise, the EnKF communicates information from the observations into the modeled atmospheric fields using forecast-estimated covariances between the model state variables and the observable quantities predicted by the forecast. The PSU-EnKF utilizes the method of Whitaker and Hamill (2002) to construct the ensemble analyses. A schematic of how the PSU-EnKF assimilates observations over time is shown in Fig. 2.

      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.

      The PSU-EnKF algorithm employed here is a slightly modified version of the PSU-EnKF described in Chan et al. (2020b) [the Ensemble Square-Root Filter algorithm (EnSRF) in section 2c]. The only modification to the algorithm is that the mean of the forecasted observations is no longer calculated using the ensemble mean. Instead, the mean of the forecasted observations is calculated by first computing the simulated observations for all forecast members, and then taking the ensemble mean of the result. This modification was made to mitigate the artificial dry bias produced by the previous EnSRF implementation when infrared observations are assimilated. The cause of this artificial dry bias is described in Section 3b of Chan et al (2020b).

      Aside from that, we also performed 80% relaxation to prior perturbations to prevent filter divergence (Zhang et al., 2004). The variables updated by the PSU-EnKF include the three-dimensional winds, water vapor mixing ratio, liquid cloud mixing ratio, ice cloud mixing ratio, rain mixing ratio, snow mixing ratio, graupel mixing ratio, temperature, and pressure. All localizations are performed using the Gaspari−Cohn 1999 fifth-order polynomial (Gaspari and Cohn, 1999).

      With regards to the assimilation of conventional observations, we assimilated the atmospheric motion vectors with a horizontal radius of influence (HROI) of 100 km, and no vertical localization. Sounding and aircraft observations were assimilated with a 700 km HROI and a 5 model layer vertical radius of influence (VROI). With regards to surface observations, Metéorologique Aviation Régulière (METAR) observations, surface synoptic observations (SYNOP) observations and ship observations were respectively assimilated with HROIs of 600 km, 300 km and 1400 km. All three surface observation types were assimilated with a 45 model layer VROI.

      To assimilate the ch08-BT observations, we employed the Community Radiative Transfer Model (CRTM), release 2.3.0, to calculate the IR AHI brightness temperatures from the WRF ensemble. The ch08-BT observations were thinned to a spacing of 30 km, and then assimilated with a HROI of 100 km (Chan et al., 2020b). Furthermore, we employed the adaptive observation error inflation scheme (AOEI) of Minamide and Zhang (2017), as well as the adaptive background error inflation scheme (ABEI) of Minamide and Zhang (2019), to assimilate the ch08-BT observations. No vertical localization was performed for the ch08-BT observations. Better results might be possible with more tuning, which can be explored in future studies.

      Aside from these settings, no ch08-BT observations were rejected in this study. Observations with large innovations are often rejected because these observations might be erroneous (e.g., Järvinen and Undén, 1997; Cardinali et al., 2003). In our setup, the AOEI inflates the observation error of ch08-BT observations with large innovations. The AOEI thus weakens the magnitude of the analysis increments coming from these large innovation ch08-BT observations, i.e., AOEI functions as a safety measure against potentially problematic ch08-BT observations. As such, we did not reject any ch08-BT observations in this study.

      It should also be noted that no bias correction for the ch08-BT observations were employed here because we found the ch08-BT biases are small in this tropical squall line case. As discussed in Chan et al. (2020b), the square of the bias is a component of the mean-squared difference (MSD) between the ensemble mean and observed ch08-BT. The conv, conv+ch08_3hrly and conv+ch08_30min experiments’ squared prior ch08-BT biases are 0.49 K2, 0.25 K2, and 0.00 K2, respectively. In contrast, the prior ch08-BT MSDs of the conv, conv+ch08_3hrly and conv+ch08_30min experiments are 68.9 K2, 23.0 K2, and 16.0 K2, respectively, i.e., the squared prior ch08-BT biases account for less than 1.1% of the prior ch08-BT MSD. Thus, we consider the ch08-BT biases to be small and did not employ bias correction when assimilating ch08-BT observations.

    3.   Results and discussion
    • Before delving into the important questions raised in section 1, we will describe the observed behavior of this study’s tropical squall line case. Tropical squall lines are a frequently occurring type of tropical MCSs over our study’s domain (Lo and Orton, 2016; Chan et al., 2019; Sun et al., 2020). On 30 May 2017, the AHI observed a southeastward propagating MCS over the equatorial Indian Ocean (Fig. 1a). By 1500 UTC 30 May, the mesoscale convective system evolved into a southeastward propagating, bow-shaped squall line. At this time, the squall line was generally aligned in the northeast-southwest direction, with the northeastern tip of the squall line grazing the west coast of Sumatra (not shown here).

      For the next 12 hours, AHI observations indicate that the squall did not penetrate the mountain range that runs along most of the west coast of Sumatra. At approximately 0900 UTC 31 May, the northeastern half of the squall line penetrated the mountain range. Within the next 3 hours, the northeastern half and the southwestern half of the squall split apart. For the remainder of the study, we will focus on the northeastern half because it passed over surface meteorological stations. The northeastern half developed a north-south alignment and propagated eastwards (Fig. 1a), passing through Sumatra, and then the Strait of Malacca. This propagation behavior and morphology is rather typical of squalls in the Sumatra-Borneo region (Lo and Orton, 2016; Chan et al., 2019; Sun et al., 2020). By 0000 UTC 2 June, the squall line had reached the southwestern coast of Borneo (Fig. 1a) and proceeded to dissipate.

      According to the ERA5, the observed squall line is collocated with the advancing edge of a westerly wind burst (not shown). Furthermore, the Australian Bureau of Meteorology’s (BoM’s) Real-time Multivariate MJO index (Wheeler and Hendon, 2004) indicated that during the squall line’s time frame, the MJO’s active phase was advancing from the Indian Ocean into the Maritime Continent. As westerly wind bursts are often associated with the onset of the MJO’s active phase (Zhang, 2005), the MJO index supports the association between the squall line and a westerly wind burst seen in the ERA5 data. Future work can examine the association between westerly wind bursts and tropical squall lines in the region.

    • The first question we will address in this study is whether adding all-sky IR observations into DA can improve the analyses and prediction of a tropical squall line. To that end, we will be comparing the conv and the conv+ch08_30min experiments. Furthermore, to focus on the squall line itself, the experiments will be compared in an area around the squall line’s leading edge.

      To determine the position of the leading edge, we computed the meridional average of the observed AHI channel 14 brightness temperatures (ch14-BT) for latitudes between 3.6°S and 4.4°S. The result is plotted as a longitude-time diagram in Fig. 1b. Because the squall line’s clouds show up as strong cold signals in ch14-BT, we can determine the position of the leading edge of the squall line from Fig. 1b, which is indicated by a dashed black line. For the rest of this article, we will be comparing the experiments in the vicinity of these inferred positions.

      We begin with examining the impacts of including half-hourly ch08-BT observations, in addition to 3-hourly conventional observations, on the cloud fields. Since ch14-BT was not assimilated and indicates the presence and absence of clouds, the root-mean-square difference (RMSD) between the analyzed and observed ch14-BT can be used to gauge the quality of the analyzed cloud fields. To be more precise, we will be comparing the ch14-BT RMSDs computed for a region surrounding the squall line’s leading edge (Fig. 1c). The region covers a rectangular area between 10°S and 5°S, as well as between 15 degrees west and 5 degrees east of the leading edge (Fig. 1b). As seen in Fig. 1c, conv+ch08_30min’s RMSDs are at all times substantially smaller than that of conv. On average, conv+ch08_30min’s RMSD (16.2 K) is ~35% smaller than conv’s RMSD (25.0 K). In other words, the assimilation of IR observations, on top of conventional observations, can improve analyses of the squall line’s cloud field. This improvement is in line with earlier work (Ying and Zhang, 2018). Finally, conv+ch08_30min’s consistency ratio (CR$ \equiv $ root-mean-square spread (RMSS)/RMSD) is closer to the optimal value of 1 than that of conv. This indicates that the assimilation of half-hourly IR observations improved the ensemble dispersion for cloud fields.

    • Aside from improving the cloud field, the inclusion of half-hourly ch08-BT observations also improved the position of the squall line surface outflow. Surface outflows can generate converging surface winds ahead of the squall line, and thus encourage the formation of new cells ahead of the squall line (e.g., Gamache and Houze, 1982; Moncrieff and Liu, 1999). Since the initiation of new cells ahead of the squall line is important for squall line propagation (e.g., Fovell et al., 2006), improving the position of the analyzed surface outflow can improve the analyses and prediction of the squall line.

      To see the improvement in surface outflow, consider that the outflow in this squall line is characterized by strong surface westerlies. Figure 3a and 3c show longitude−time diagrams of the meridionally-averaged analysis mean zonal wind at 300-m above sea level in the conv and conv+ch08_30min experiments. The thick black contours trace the observed 224 K ch14-BT contour, which indicates the position of the squall line. The meridional average was performed between 3.6°S and 4.4°S. Between 1200 UTC 31 May and 1800 UTC 1 June, the predicted outflow in the conv experiment is mostly located east of the observed leading edge (Fig. 3a; black contours). In contrast, the analyzed zone of strong westerlies in conv+ch08_30min overlaps substantially with the observed squall (Fig. 3c). These longitude-time diagrams suggest that the half-hourly assimilation of ch08-BT observations improved the positions of the surface outflow.

      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.

      To further confirm the improvement of the surface outflow position, we examined three-hourly unassimilated surface zonal wind observations from a surface station located at 2.7°S, 107.8°E (marked with white cross in Fig. 1a). These surface observations were obtained from the Integrated Surface Database (ISD), which is maintained by the National Oceanic and Atmospheric Administration (NOAA). The time-series of the observed and analyzed zonal winds for this station is plotted in Fig. 3d.

      As can be inferred from Fig. 3d, the analyzed squall line surface outflow from the conv+ch08_30min experiment outperformed that of conv in several ways. The first way is in terms of the onset time of the squall line surface outflow at the station. The onset of the squall line surface outflow is indicated by strengthening westerlies. As can be seen in Fig. 3d, the onset time from the conv+ch08_30min experiment analyzed surface outflow (around 1200 UTC May 31) is slightly closer to the observed onset time (around 1800 UTC May 31) than that of conv (around 0600 UTC May 31). Aside from the difference in onset times, Fig. 3d indicates that the squall line outflow in conv leaves the station (around 0600 UTC June 1) 6 hours earlier than the observed squall line (around 1200 UTC June 1), whereas the squall line outflow from conv+ch08_30min leaves the station at roughly the same time as the observed squall line. These differences in onset and exit times imply that the squall line outflow in conv is displaced east of the actual outflow, and that this displacement error is largely reduced in conv+ch08_30min. This reduction in surface outflow displacement error is consistent with Figs. 3a and 3c.

      It should also be noted here that the conv+ch08_30min experiment also outperforms conv in terms of the fit to the unassimilated zonal wind observations when the squall line is passing over the surface station (from 2100 UTC May 31 to 1200 UTC June 1). This improvement is likely because the eastward displacement error is substantially smaller in conv+ch08_30min than in conv.

      Interestingly, the improvements in surface outflow position introduced by spatiotemporally dense IR DA occurred in the absence of dense surface observations. Previous studies have suggested that dense surface observations can improve squall line surface outflows and the associated gust front positions (e.g., Chen et al., 2016). Our results thus suggest that spatiotemporally dense IR observations can be a potential substitute for dense surface observations when constraining errors associated with squall line surface outflows and gust front positions.

      The surface outflow position improvements introduced by half-hourly IR DA can be plausibly explained by the combination of two complementary effects. The first effect comes from the cloud field improvements introduced by half-hourly ch08-BT DA. These cloud field improvements include improvements to the position of the analyzed squall line clouds, which in turn leads to improved positioning of the squall line rainfall. Since the surface outflow is induced by rainfall, it is plausible that an improved rainfall position can improve the position of the surface outflow.

      The second effect arises from the positive correlation between forecasted ch08-BT just behind the observed leading edge, and the ~950 hPa zonal wind near the leading edge of the surface outflow (henceforth, gust front). For instance, at 0000 UTC on 1 June, the forecasted ensemble mean gust front is at roughly 1°E relative longitude (similar to the analyzed gust front in Fig. 3c). Likewise, Fig. 4b indicates that the ~950 hPa zonal wind at ~1°E relative longitude is positively correlated with forecasted ch08-BT just behind the leading edge (-1°E relative longitude), i.e., this positive correlation is collocated with the gust front.

      To see how this positive gust front zonal wind correlation allows the EnKF to shift the gust front, consider that the forecasted squall line clouds tend to be displaced eastwards of the observed squall line clouds. Since the observed squall line’s highest clouds tend to occur at around −1°E relative longitude (not shown, but can be inferred from Fig. 1b), this eastward displacement indicates that the forecasted clouds at −1°E relative longitude should be lower than that of the observations. In other words, the forecasted ch08-BT is higher than that of the observations at −1°E relative longitude. This results in a negative ch08-BT innovation at −1°E relative longitude. Because of the positive correlation, this negative innovation results in a negative westerly (or easterly) analysis increment at the gust front. Since the wind in the gust front is mostly westerly, the analysis increment weakens the westerly winds at the gust front. This weakening means that the forecasted gust front is forced westward. As such, the assimilation of ch08-BT observations near the observed leading edge improved the position of the gust front through the correlations between ch08-BT and the gust front’s zonal wind.

    • Aside from improving the analyzed cloud fields and surface outflow position, the half-hourly assimilation of IR observations also improved the short-term deterministic forecasts of the squall line. These deterministic forecasts were initiated from the EnKF analysis means. Figure 5 shows the meridionally-averaged, deterministically forecasted ch14-BT. Like Figs. 1c, 3, and 4, the meridional average was done between 3.6°S and 4.4°S. As can be seen in Fig. 5, for the first 3 hours of the deterministic forecasts, the positions of the forecasted clouds from conv+ch08_30min are closer to the observations than those from conv, especially for the forecasts initialized after 1200 UTC June 1. Furthermore, the squall-relative ch14-BT RMSD (calculated as in Fig. 1c) of the conv+ch08_30min deterministic forecasts are lower than those of the conv (not shown) for the first three hours. To be more precise, on average, conv+ch08_30min ch14-BT deterministic forecast RMSDs are about 20% smaller than those of conv from initiation to up to a lead time of 2 hours. For lead times of 3 hours and longer, the ch14-BT deterministic forecast RMSDs of both experiments are statistically indistinguishable. These improvements to the short-term deterministic forecasts of cloud fields are akin to those seen in Chan et al. (2020b).

      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).

    • Another important question raised in section 1 is how the accuracy and prediction of the tropical squall line vary with the update frequency of IR DA. To examine this question, we will be comparing the conv+ch08_30min and conv+ch08_3hrly experiments.

      Reducing the frequency of ch08-BT DA generally degraded the analyses and deterministic forecasts of cloud fields (Figs. 1 and 5). According to Fig. 1c, decreasing ch08-BT DA frequency from half-hourly to three-hourly generally increased the analysis ch14-BT RMSD. On average, the RMSDs increased by ~10%, from 16.2 K to 18.1 K. Furthermore, Fig. 5 indicates that reduction in ch08-BT DA frequency degraded the deterministically forecasted positions of the squall line for the three hours of the deterministic forecast, and the degradations are especially noticeable for the forecasts initialized after 1200 UTC June 1 (Figs. 5e and 5h). Taken together, these degradations imply that a higher frequency of data assimilation is preferable for short-term cloud field analyses and forecasts.

      The analyzed positions of the squall line’s outflow were also degraded when the frequency of ch08-BT DA was reduced from half-hourly to three-hourly. Between 31 May (1200 UTC) and 1 June (1800 UTC), the analyzed strong zonal wind zone from conv+ch08_3hrly does not overlap much with the observed squall (Fig. 3b), whereas that of the conv+ch08_30min substantially overlaps the observed squall (Fig. 3c). Furthermore, while the validation surface station data indicates the analyzed outflows of conv+ch08_3hrly and conv+ch08_30min arrived at the station at the same time (Fig. 3d), the conv+ch08_30min analyzed outflow is clearly better than that of conv+ch08_3hrly when the squall line passed over the surface station during 0000 UTC - 1200UTC June 1. In other words, high frequency data assimilation is preferable for improving the analyzed position of storm outflows.

      This degradation in squall line outflow position is also consistent with the ensemble correlations between simulated ch08-BT and zonal wind (Fig. 4a). While similar near-surface dipole correlation patterns exist in both the conv+ch08_30min and conv+ch08_3hrly experiments, the dipole pattern in conv+ch08_3hrly is dislocated ~1.5 degrees east of the ch08-BT observation location. In other words, the capability of conv+ch08_3hrly in moving the squall line surface outflow position westwards is limited when compared to that of conv+ch08_30min.

      It should be noted here that while decreasing the frequency of ch08-BT DA did dramatically degrade the quality of the analyzed cloud fields, analyzed outflow, and forecasted clouds, these quantities in conv+ch08_3hrly still outperformed those conv. In other words, even a moderate frequency of ch08-BT DA was able to improve these quantities beyond what is achieved by non-IR conventional observation DA.

    4.   Conclusions
    • In this study, we have delved into the hitherto unexplored dynamic and thermodynamic impacts of assimilating IR observations for the case of a tropical squall line. In line with earlier work (e.g., Ying and Zhang, 2018), we confirmed that introducing ch08-BT observations into DA improved the analyses and predictions of a tropical squall line’s cloud field. Even though the ch08-BT observations are insensitive to lower-tropospheric zonal winds, the assimilation of these observations improved the position of the squall line’s lower tropospheric outflow zone. This improvement was likely due to the improved cloud fields, as well as the near-surface dipole forecast ensemble correlations between ch08-BT and lower-tropospheric zonal winds. Finally, we also found that increasing the frequency of ch08-BT observations further improved the analyzed cloud field, forecasted cloud field, and the analyzed outflow position.

      Since this study is the first of its kind to specifically examine the improvements from introducing IR observations into tropical MCS analyses and prediction, there are many areas for future work. These areas can be broken into three types: completeness, the assimilation of other observations and areas for future DA algorithm development.

      The first area for completeness concerns the fact that we have only examined the impacts for one tropical squall line in the Sumatra-Borneo region. As such, future work can examine if similar improvements can be found in other tropical MCSs, including other tropical areas. Second, comparisons against surface station temperature observations (not shown) indicate that the simulated squall line cold pools tend to be much stronger than observed. These overly strong simulated cold pools might be due to model errors in the surface parameterization or radiation schemes. Future work can thus examine tuning these schemes to produce more realistic cold pools. Aside from that, future work can also explore whether the improvements obtained here are retained when the model’s horizontal grid spacing is reduced to a value that allows for the explicit resolution of convective updrafts (usually 1 km or less).

      Future work can also explore the assimilation of other uncommonly used observations. For instance, it is possible that the inclusion of observations from additional water vapor channels can improve the analysis and prediction tropical MCSs. This improvement might be possible because different channels respond to different parts of the atmospheric column (Schmit et al., 2005). Aside from that, several studies have indicated that sub-hourly AMV data assimilation has the potential to improve forecasts of moist convection (Otsuka et al., 2015; Kunii et al., 2016). Presently, AMVs are generally assimilated every 3~6 hours. Thus, the impact of frequently assimilating infrared-derived atmospheric motion vector observations is also another avenue of future study.

      Finally, future work can also delve into methods that address certain assumptions that are made in IR DA. One important assumption in the EnKF is that the simulated observations have an approximately linear relationship with modelled atmospheric variables. However, the relationship between simulated IR observations and modelled atmospheric is often nonlinear (e.g., Steward, 2012). Other cutting-edge DA methods might be able handle this nonlinearity. Such methods include particle filters (e.g., van Leeuwen, 2011; Penny and Miyoshi, 2016; Poterjoy, 2016), iterative EnKF methods (Lorentzen and Naevdal, 2011), and Gaussian-mixture model methods (e.g., Anderson and Anderson, 1999; Sondergaard and Lermusiaux, 2013; Chan et al., 2020a). Aside from linearity, the EnKF also assumes that the forecast ensemble follows a Gaussian distribution. However, the displacements of cloud features among ensemble members can result in non-Gaussian properties, violating this Gaussian assumption. As such, future work can explore methods that address displacement errors. Such methods include variants of the feature calibration and alignment technique (Hoffman and Grassotti, 1996; Brewster, 2003; Nehrkorn et al., 2015; Stratman et al., 2018) and the recently developed multiscale alignment data assimilation (Ying, 2019).

      The advent of modern geostationary satellite IR observations has ushered in a new era of high-frequency, high spatial-density, and globe-encompassing remote observations. While the data assimilation community is well on its way to exploit these observations, many unexplored avenues remain, especially in the Tropics. With continued advancements in computing power and data assimilation techniques, the day we can fully exploit these IR observations is just beyond the horizon.

    Samples and Data
    • The ERA5 pressure level and surface data were obtained from the ECMWF’s Climate Data Store (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels, and https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels). Aside from that, the in-situ conventional observations were obtained from the NCAR RDA (https://rda.ucar.edu/datasets/ds351.0, and, https://rda.ucar.edu/datasets/ds461.0), and the atmospheric motion vector observations were obtained from the CIMSS Tropical Cyclone Archive (http://tropic.ssec.wisc.edu/archive/). The Himawari-8 AHI data was obtained from the Japan Aerospace Exploration Agency (JAXA) P-Tree system via File Transfer Protocol (FTP; ftp://ftp.ptree.jaxa.jp/).

      Acknowledgements. This study is supported by the Water Cycle and Climate Extremes Modelling (WACCEM) project, which is funded by the U.S. Department of Energy Office of Science Biological and Environmental Research, as part of the Regional and Global Climate Modeling program. This work is also partially supported by ONR Grant N00014-18-1-2517. Computations were performed on the National Energy Research Scientific Computing Center Cori supercluster and the Texas Advanced Computer Center. This paper is dedicated to Dr. Fuqing ZHANG, who passed away unexpectedly in 2019. Dr. Fuqing ZHANG made tremendous contributions to the atmospheric sciences. His contributions include innovative and pioneering research on atmospheric dynamics, predictability, and ensemble-based data assimilation. He devoted boundless energy to engaging, mentoring, and inspiring undergraduate and graduate students, as well as early-career scientists, including us, the two authors of this paper. His death is a great loss to his family, friends, students, colleagues, and our entire community.

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