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

Chan, Man‐Yau; Chen, Xingchao

doi:10.1007/s00376-021-0449-8

Cited by 5 publications

(14 citation statements)

References 84 publications

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“…Finally, the WV‐BT analysis increments were horizontally localized using a 100‐km HROI, and no vertical localization was applied to the WV‐BT analysis increments. These ROI values follow the GeoIR DA study of Chan and Chen (2021), which was able to constrain the dynamical structure of a tropical squall line. Note that it is possible to produce better results by further tuning the various radii of influence (ROIs).…”

Section: Data and Methods Used To Generate The Tmecsrsupporting

confidence: 57%

“…We also employed an adaptive background error inflation scheme (ABEI; Minamide & Zhang, 2019) to mitigate the overly small forecast ensemble spread (often called under‐dispersion) that often manifests when the forecast ensemble is cloud‐free and the observed atmospheric column is cloudy. The AOEI and ABEI are frequently applied in the PSU‐EnKF when assimilating GeoIR observations (Chan & Chen, 2021; Chan, Zhang, et al., 2020; F. Zhang, Minamide, et al., 2019; F. Zhang et al., 2016; Hartman et al., 2021; Y. Zhang, Stensrud, et al., 2019; Y. Zhang et al., 2018).…”

Section: Data and Methods Used To Generate The Tmecsrmentioning

confidence: 99%

“…Earlier work has found that 9 km is sufficiently fine to realistically resolve the precipitation, circulation, thermodynamic, and radiation features of organized convection over the tropical oceans (Wang et al., 2015). As such, 9‐km grid spacing WRF simulations without cumulus parameterization have been widely used to examine the overturning and physical mechanisms of organized tropical convection (Chan & Chen, 2021; Ruppert & Chen, 2020; X. Chen et al., 2018a, 2018b, 2018c; X. Chen & Zhang, 2019; Ruppert et al., 2020), to study the multiscale predictability of tropical convection (Ying & Zhang, 2017) and to evaluate the impacts of assimilating GeoIR observations on tropical MCSs (Chan & Chen, 2021; Chan, Zhang, et al., 2020; Ying & Zhang, 2018). In the interest of having a large domain to resolve the multiscale interactions of tropical MCSs, we thus opted to use the 9‐km grid spacing.…”

Section: Data and Methods Used To Generate The Tmecsrmentioning

confidence: 99%

“…Geostationary satellite infrared radiance (GeoIR) observations, for instance, are usually assimilated with horizontal densities of less than one observation per 100 km 2 (ECMWF, 2016a). In contrast, applications of GeoIR in mesoscale DA generally assimilate GeoIR with horizontal densities greater than one observation per 30 km 2 (Chan & Chen, 2021; Chan, Zhang, et al., 2020; F. Zhang et al., 2016; Hartman et al., 2021; Honda, Miyoshi, et al., 2018; Minamide & Zhang, 2018; Okamoto et al., 2019; Otkin & Potthast, 2019; Sawada et al., 2019a, 2019b; Y. Zhang, Stensrud, et al., 2019; Y. Zhang et al., 2018). Creating an accurate tropical MCS analysis data set thus requires addressing these model‐ and observation‐related issues.…”

Section: Introductionmentioning

confidence: 99%

“…Similar impacts have also been found in the context of subtropical and midlatitude mesoscale systems (Honda, Kotsuki, et al., 2018; Okamoto et al., 2019; Otkin, 2010, 2012; Otkin & Potthast, 2019; Sawada et al., 2019a; Y. Zhang, Stensrud, et al., 2019; Y. Zhang et al., 2018). More recently, the scope of convection‐permitting all‐sky GeoIR DA research was extended to include tropical MCSs (Chan & Chen, 2021; Chan, Zhang, et al., 2020). These tropical MCS all‐sky GeoIR DA studies indicate that all‐sky GeoIR DA can improve the analyzed and forecasted cloud fields, forecasted rainfall, and even the analyzed and forecasted gust fronts of tropical MCSs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A High‐Resolution Tropical Mesoscale Convective System Reanalysis (TMeCSR)

Chan

Chen

Leung

2022

J Adv Model Earth Syst

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Modern global reanalysis products have greatly accelerated meteorological research in synoptic‐to‐planetary‐scale phenomena. However, their use in studying tropical mesoscale convective systems (MCSs) and their regional‐to‐global impact has mostly been limited to supplying initial and boundary conditions for MCS‐resolving simulations and providing information about the large‐scale environments of MCSs. These limitations are due to difficulties in resolving tropical MCS dynamics in the relatively low‐resolution global models and that tropical MCSs often occur over poorly observed regions. In this work, a Tropical MCS‐resolving Reanalysis product (TMeCSR) was created over a region with frequent tropical MCSs. This region spans the tropical Indian Ocean, tropical continental Asia, Maritime Continent, and Western Pacific. TMeCSR is produced by assimilating all‐sky infrared radiances from geostationary satellites and other conventional observations into an MCS‐resolving regional model using the Ensemble Kalman Filter. The resulting observation‐constrained high‐resolution (9‐km grid spacing) data set is available hourly during the boreal summer (June‐August) of 2017, during which widespread severe flooding occurred. Comparisons of TMeCSR and European Center for Medium Range Weather Forecast Reanalysis version 5 (ERA5) against independent satellite retrievals indicate that TMeCSR's cloud and multiscale rain fields are better than those of ERA5. Furthermore, TMeCSR better captured the diurnal variability of rainfall and the statistical characteristics of MCSs. Forecasts initialized from TMeCSR also have more accurate rain and clouds than those initialized from ERA5. The TMeCSR and ERA5 forecasts have similar performances with respect to sounding and surface observations. These results indicate that TMeCSR is a promising MCS‐resolving data set for tropical MCS studies.

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Section: Data and Methods Used To Generate The Tmecsrsupporting

confidence: 57%

Section: Data and Methods Used To Generate The Tmecsrmentioning

confidence: 99%

Section: Data and Methods Used To Generate The Tmecsrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A High‐Resolution Tropical Mesoscale Convective System Reanalysis (TMeCSR)

Chan

Chen

Leung

2022

J Adv Model Earth Syst

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Benefit of assimilating satellite all‐sky infrared radiances on the cloud and precipitation prediction of a long‐lasting mesoscale convective system over the Tibetan plateau

Chen

2023

Quart J Royal Meteoro Soc

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Heavy rainfall events in the warm season (May–September) over the Tibetan Plateau (TP) region and its downstream areas are often closely related to eastward‐propagating Tibetan Plateau Vortices (TPVs). Hence, improving the prediction of TPVs and their associated convective activity is of paramount importance, given the significant potential impacts they can have on densely populated downstream regions, including but not limited to flooding and damages. In this study, a typical long‐lived TPV that occurred in July 2008 was used for the first time to explore the benefit of assimilating satellite all‐sky infrared radiances on the cloud and precipitation prediction of the TPV‐induced eastward‐propagating mesoscale convective system (MCS). The all‐sky infrared radiances from the water vapor (WV) channel of the geostationary Meteosat‐7 and other conventional observations were assimilated into a 4‐km grid spacing regional model using the ensemble Kalman filter. The results revealed that the all‐sky infrared data assimilation improved the cloud, precipitation, dynamical, and thermodynamical analyses as well as 0–12‐hr deterministic and ensemble forecasts. Compared with the experiment in which the all‐sky infrared radiances were not assimilated (non‐radiance experiment), the experiment with assimilated all‐sky infrared radiances yielded clearly improved initial wind and cloud fields, 1–12‐hr cloud forecasts, and 1–6‐hr precipitation forecasts. This study indicates that assimilation of all‐sky satellite radiances has the potential for improving the operational cloud and precipitation forecasts over the TP and its downstream areas.

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Preface to the Special Issue: Predictability, Data Assimilation, and Dynamics of High Impact Weather—In Memory of Dr. Fuqing ZHANG

Meng

2022

Adv. Atmos. Sci.

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

Cited by 5 publications

References 84 publications

A High‐Resolution Tropical Mesoscale Convective System Reanalysis (TMeCSR)

A High‐Resolution Tropical Mesoscale Convective System Reanalysis (TMeCSR)

Benefit of assimilating satellite all‐sky infrared radiances on the cloud and precipitation prediction of a long‐lasting mesoscale convective system over the Tibetan plateau

Preface to the Special Issue: Predictability, Data Assimilation, and Dynamics of High Impact Weather—In Memory of Dr. Fuqing ZHANG

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