Impact of adaptively thinned GOES-16 all-sky radiances in an ensemble Kalman filter based WoFS

Mallick, Swapan; Jones, Thomas A.

doi:10.1016/j.atmosres.2022.106304

Cited by 5 publications

(3 citation statements)

References 77 publications

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“…( 2020 ), Li et al. ( 2021 ), Mallick and Jones ( 2022 ), M. Zhang et al. ( 2022 ), Chan and Chen ( 2022 ), Hartman et al.…”

Section: Introductionmentioning

confidence: 99%

“…More evidence of mixed statistics can be found in Supporting Information S1. Though current EDA methods have been remarkably successful at assimilating cloud-affected satellite radiance observations (e.g., F. Zhang et al (2016), Geer et al (2018), Chan, Zhang, et al (2020), Jones et al (2020), Li et al (2021), Mallick and Jones (2022), M. Zhang et al (2022), , Hartman et al (2021), and Chan, Chen, and Leung (2022), Chandramouli et al (2022)) the effectiveness of current operational EDA methods is likely limited when the ensemble statistics are mixed.…”

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confidence: 99%

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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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“…( 2020 ), Li et al. ( 2021 ), Mallick and Jones ( 2022 ), M. Zhang et al. ( 2022 ), Chan and Chen ( 2022 ), Hartman et al.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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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“…Recently, a 2 km, all-sky, hourly LST product was generated by Jia et al [21]. The Geostationary Operational Environmental Satellites (GOES)-R, which is operated by the National Oceanic and Atmospheric Administration (NOAA), can provide global diurnal observations [22]. The LST product retrieved from GOES-16 Advanced Baseline Imager (ABI) can provide 10 km, hourly LST over North and South America, and 2 km, hourly data covering the contiguous US (CONUS) and Mexico [23].…”

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confidence: 99%

Generation of 100-m, Hourly Land Surface Temperature Based on Spatio-Temporal Fusion

Tang,

Wang,

Tong

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Landsat surface temperature (LST) is an important physical quantity for global climate change monitoring. Over the past decades, several LST products have been produced by satellite thermal infrared (TIR) bands or land surface models (LSMs). Recent research has increased the spatio-temporal resolution of LST products to 2 km, hourly based on Geostationary Operational Environmental Satellites (GOES)-R Advanced Baseline Imager (ABI) LST data. The spatial resolution of 2 km, however, is insufficient for monitoring at the regional scale. This paper investigates the feasibility of applying spatio-temporal fusion to generate reliable 100 m, hourly LST data based on fusion of the newly released 2 km, hourly GOES-16 ABI LST and 100 m Landsat LST data. The most accurate fusion method was identified through a comparison between several popular methods. Furthermore, a comprehensive comparison was performed between fusion (with Landsat LST) involving satellite-derived LST (i.e., GOES) and model-derived LSMs (i.e., European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysis v.5 (ERA5)-Land). The spatial and temporal adaptive reflectance fusion model (STARFM) method was demonstrated to be an appropriate method to generate 100 m, hourly data, which produced an average root mean square error (RMSE) of 2.640 K, mean absolute error (MAE) of 2.159 K and average coefficient of determination (R 2 ) of 0.982 referring to the in situ time-series. Furthermore, inheriting the advantages of direct observation, and the fusion of Landsat and GOES for the generation of 100 m, hourly LST produced greater accuracy compared to the fusion of Landsat and ERA5-Land LST in the experiments. The generated 100 m, hourly LST can provide important diurnal data with fine spatial resolution for various monitoring applications.  Index Terms-Land surface temperature (LST); Landsat; GOES; ERA5; spatio-temporal fusion. I. INTRODUCTION Land surface temperature (LST), as an important parameter in the energy exchange between the land surface and atmosphere, has been researched extensively in recent years [1]-[3]. LST is central to many applications including mapping the urban heat island effect [4], [5], forest fire monitoring [6], [7] and drought Manuscript

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Assessing the Impacts of Assimilating GOLD Disk O/N₂ Observations on the Thermosphere‐Ionosphere System

Laskar,

Pedatella,

Codrescu

et al. 2024

JGR Space Physics

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The Global‐scale Observations of Limb and Disk (GOLD) imagers scan the Earth's Thermosphere‐Ionosphere (TI) in the far ultraviolet wavelengths. Measurements from GOLD daylit spectrum are used to retrieve the column integrated atomic oxygen to molecular nitrogen density ratio (O/N2) over about one fourth of the globe. The present investigation assesses the impact of assimilating GOLD disk O/N2 on the Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension (WACCMX) using the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter. Two Observing System Simulation Experiments (OSSEs) are performed, and improvements are quantified by calculating root mean square error (RMSE) and bias with respect to a truth run. In addition to solar and geomagnetic forcing, we introduced gravity wave forcing perturbations to increase the ensemble spread, which has not previously been applied in ensemble assimilation. One of the OSSEs assimilates only the lower atmosphere (LA, 100 km) observations, referred to as LA experiment, and the second assimilates GOLD O/N2 and LA observations, referred to as Whole Atmosphere (WA) experiment. The WA‐analysis O/N2 RMSE and bias are about 60% and 87% better compared to LA‐analysis. Also, the O/N2 RMSE and bias for the WA‐analysis are about 23% and 54% better compared to WA 1‐hr forecasts. The improvement in WA electron column density (ECD), a model equivalent of Total Electron Content (TEC), is about 24% compared to the LA experiment. These results demonstrate that the assimilation of GOLD O/N2 improves both the thermosphere and ionosphere in a whole atmosphere data assimilation system.

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Impact of adaptively thinned GOES-16 all-sky radiances in an ensemble Kalman filter based WoFS

Cited by 5 publications

References 77 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

Generation of 100-m, Hourly Land Surface Temperature Based on Spatio-Temporal Fusion

Assessing the Impacts of Assimilating GOLD Disk O/N₂ Observations on the Thermosphere‐Ionosphere System

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Impact of adaptively thinned GOES-16 all-sky radiances in an ensemble Kalman filter based WoFS

Cited by 5 publications

References 77 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

Generation of 100-m, Hourly Land Surface Temperature Based on Spatio-Temporal Fusion

Assessing the Impacts of Assimilating GOLD Disk O/N2 Observations on the Thermosphere‐Ionosphere System

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Assessing the Impacts of Assimilating GOLD Disk O/N₂ Observations on the Thermosphere‐Ionosphere System