Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)

Zhang, Yunji; Sieron, Scott B.; Lu, Yinghui; Chen, Xingchao; Nystrom, Robert G.; Minamide, Masashi; Chan, Man‐Yau; Hartman, Christopher M.; Yao, Zhu; Ruppert, James H.; Okazaki, Atsushi; Greybush, Steven J.; Clothiaux, Eugene E.; Zhang, Fuqing

doi:10.1029/2021gl096410

Cited by 19 publications

(19 citation statements)

References 48 publications

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“…We also employ 80% relaxation to prior perturbations to maintain ensemble dispersion (F. Zhang et al., 2004 ). Similar combinations of heuristic strategies are commonly seen in the EnKF‐based DA of infrared radiance observations (Chan & Chen, 2022 ; Chan, Zhang, et al., 2020 ; Minamide & Zhang, 2018 ; F. Zhang et al., 2016 ; Y. Zhang et al., 2019 ; Y. Zhang et al., 2021 ).…”

Section: Methodsmentioning

confidence: 71%

The Potential Benefits of Handling Mixture Statistics via a Bi‐Gaussian EnKF: Tests With All‐Sky Satellite Infrared Radiances

Chan

Chen

Anderson

2023

J Adv Model Earth Syst

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The meteorological characteristics of cloudy atmospheric columns can be very different from their clear counterparts. Thus, when a forecast ensemble is uncertain about the presence/absence of clouds at a specific atmospheric column (i.e., some members are clear while others are cloudy), that column's ensemble statistics will contain a mixture of clear and cloudy statistics. Such mixtures are inconsistent with the ensemble data assimilation algorithms currently used in numerical weather prediction. Hence, ensemble data assimilation algorithms that can handle such mixtures can potentially outperform currently used algorithms. In this study, we demonstrate the potential benefits of addressing such mixtures through a bi-Gaussian extension of the ensemble Kalman filter (BGEnKF). The BGEnKF is compared against the commonly used ensemble Kalman filter (EnKF) using perfect model observing system simulated experiments (OSSEs) with a realistic weather model (the Weather Research and Forecast model). Synthetic all-sky infrared radiance observations are assimilated in this study. In these OSSEs, the BGEnKF outperforms the EnKF in terms of the horizontal wind components, temperature, specific humidity, and simulated upper tropospheric water vapor channel infrared brightness temperatures. This study is one of the first to demonstrate the potential of a Gaussian mixture model EnKF with a realistic weather model. Our results thus motivate future research toward improving numerical Earth system predictions though explicitly handling mixture statistics. Plain Language SummaryThe accuracy of a computer weather forecast often depends on the accuracy of the information inputted into the computer forecast system. The accuracy of the input in turn depends on the accuracy of the input-constructing algorithm. Such algorithms often use probabilistic forecasts from an earlier point in time and current atmospheric measurements to construct the inputs. A common assumption in such algorithms is that the probabilistic forecasts follow a multivariate normal distribution (henceforth called the normality assumption). However, in the frequent situation where the probabilistic forecast is uncertain about the presence/absence of clouds, the normality assumption is violated. This is because clear atmospheric columns and cloudy atmospheric columns have distinctly different thermodynamic and dynamic characteristics. These two types of columns thus have different statistics. As such, when a probabilistic forecast is uncertain about the presence/absence of clouds, it has mixed statistics (henceforth termed mixed probabilistic forecast). Addressing such mixtures can potentially improve forecasts. In this study, we propose a new input-constructing algorithm that can explicitly handle mixed probabilistic forecasts. This new algorithm is nearly as fast as currently available algorithms. More importantly, our experiments demonstrate that our new algorithm can produce more accurate forecast inputs than an existing popular algorithm. Our work thus suggests that...

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Section: Methodsmentioning

confidence: 71%

The Potential Benefits of Handling Mixture Statistics via a Bi‐Gaussian EnKF: Tests With All‐Sky Satellite Infrared Radiances

Chan

Chen

Anderson

2023

J Adv Model Earth Syst

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“…At the time of writing, the microwave DA capability in the PSU-EnKF is under development (Y. Zhang et al, 2021). From the experiences of operational forecasting, the assimilation of space-borne microwave radiometer data from sounding channels is likely to improve the quality of the TMeCSR data set (Geer et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

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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“…Introduction Throughout much of the tropics and mid-latitudes, tropical cyclones (TCs) are the cause of societal and scientific attention due to the destructive threat to life, property, and crops they pose [1][2][3][4] . Most of these threats are caused by the TC's strong winds, either directly or indirectly through waves, storm surge, or moisture convergence driving extreme rain 2,13,14 . Thus, knowing and forecasting the full wind field of a storm well in advance is essential for effective preparation, evacuation, and mitigation 2,9 .…”

Section: Main Textmentioning

confidence: 99%

Physics-Informed Neural Networks for Hurricane Reconstruction and Data Assimilation

Eusebi

Vecchi

Lai

et al. 2023

Preprint

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Tropical cyclones (TCs) are responsible for large-scale loss of life and property, and billions of dollars worth of damage annually. Forecasting is typically accomplished through the use of dynamical models which directly integrate the governing equations of motion. These forecasts, and especially intensity forecasts, rely on accurate initial conditions obtained by assimilating real-time observational data. However, current methods used to generate these initial conditions are computationally expensive and limited, and TC intensity forecasts have much room for improvement. Here, we show that a Physics-Informed Neural Network can provide a promising alternative for data assimilation for TC initial conditions. Using synthetic training data sparsely sampled from hurricanes simulated in a forecast model, a PINN is able to accurately reconstruct full 2- and 3-dimensional wind and pressure fields of a TC. We also demonstrate how a set of sparse, real-time observations, can be used to accurately reconstruct Hurricane Ida. Our results demonstrate how recent advances in computational capabilities and deep learning can be applied to TC data assimilation, offering an accurate and efficient complement to existing data assimilation methods.

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Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)

Cited by 19 publications

References 48 publications

The Potential Benefits of Handling Mixture Statistics via a Bi‐Gaussian EnKF: Tests With All‐Sky Satellite Infrared Radiances

The Potential Benefits of Handling Mixture Statistics via a Bi‐Gaussian EnKF: Tests With All‐Sky Satellite Infrared Radiances

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

Physics-Informed Neural Networks for Hurricane Reconstruction and Data Assimilation

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