Improved Tropical Storm Forecasts with GOES-13/15 Imager Radiance Assimilation and Asymmetric Vortex Initialization in HWRF

Zou, Xiaolei; Qin, Zhengkun; Zheng, Youtong

doi:10.1175/mwr-d-14-00223.1

Cited by 61 publications

(40 citation statements)

References 38 publications

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“…The first category contains scenes in which both the observation and model prior are classified as clear‐sky, which includes studies that masked out the cloud‐affected observations and model priors (e.g. Zou et al, ; ; Wang et al, ). The second category contains scenes in which both observation and model prior are cloudy.…”

Section: Introductionmentioning

confidence: 99%

An adaptive background error inflation method for assimilating all‐sky radiances

Minamide

Zhang

2019

Quart J Royal Meteoro Soc

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An adaptive background error inflation (ABEI) method is proposed for assimilating all-sky satellite brightness temperatures with an ensemble Kalman filter. This empirical cloud-scene-dependent covariance inflation method is designed to mitigate the model's difficulties in initiating convection in the observed cloudy regions where the background prior estimated from the ensemble mean incorrectly simulates clear-sky conditions. This new approach calculates a spatially varying, flow-dependent, multiplicative ensemble covariance inflation factor based on error statistics produced by a well-constructed, off-line observing system simulation experiment (OSSE) that assimilates similar all-sky radiance observations but were generated by the model, in which case the truth is known for all the state variables and the assimilated radiances. The adaptive inflation factor is a linear function of a cloud parameter which is only applied to the observed cloudy regions where there are less or no cloud in the prior ensemble mean estimates. The performance of ABEI is evaluated through assimilating synthetic and real-data all-sky radiance experiments from the Advanced Baseline Imager on board GOES-16 for Hurricanes Karl of 2010 and Harvey of 2017. Assimilation experiments with ABEI allow adaptive inflation of the ensemble covariance in the model-simulated clear-sky regions when there are observed clouds while avoiding unnecessarily large ensemble spread in other cloud scenarios. This new approach alleviates the difficulty in estimating the appropriate inflation factors in the model state space using the innovation statistics in the observation space (radiance) with a highly nonlinear observation operator. It serves as an alternative to existing methods using spatially varying adaptive inflations; their relative performance and potential combinations are to be further assessed in the future.

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Section: Introductionmentioning

confidence: 99%

An adaptive background error inflation method for assimilating all‐sky radiances

Minamide

Zhang

2019

Quart J Royal Meteoro Soc

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“…There are 61 vertical levels. Zou et al (2015) discussed the importance of assimilating the upper-level sounding channels of AMSU-A, ATMS, and AIRS. A model top higher than the 0.5-hPa layer with more upper vertical levels is needed to resolve the upper-level sounding channels of AMSU-A, ATMS, and AIRS.…”

Section: Experimental Designmentioning

confidence: 99%

“…Wu et al (2002) described the theory behind and the development of the GSI system. We have applied this same GSI data assimilation and ARW modeling system in previous studies to assimilate measurements from geostationary operational environmental satellite imagers (Zou et al 2011(Zou et al , 2015Qin et al 2013Qin et al , 2017Qin and Zou 2018), the MHS (Zou et al 2013b;Qin and Zou 2016), the ATMS (Zou et al 2013a), the AMSU-A , the AMSU-A and MHS combined data stream , and the CrIS (Li and Zou 2017). Three data assimilation and forecast experiments were conducted.…”

Section: Experimental Designmentioning

confidence: 99%

Impact of AMSU-A Data Assimilation over High Terrains on QPFs Downstream of the Tibetan Plateau

Qin

Zou

2019

Journal of the Meteorological Society of Japan

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The Tibetan Plateau (TP) and the atmospheric conditions over it strongly affect downstream regional weather. Advanced Microwave Sounding Unit-A (AMSU-A) brightness temperature observations provide temperature sounding information and have long been successfully assimilated for numerical weather prediction. AMSU-A brightness temperatures observed from the polar-orbiting NOAA-15 and 18 satellites during July and August 2016 were collected. During these months, the equator crossing time of these particular satellites was approximately 0600 local time. Observations collected within the 3-h periods centered at 0000 UTC and 1200 UTC, covering the TP, were assimilated. The weighting coefficients for mid-tropospheric AMSU-A channels 6 and 7 were significantly reduced over areas with terrain heights greater than 2 km and 4 km, respectively, in the National Centers for Environmental Prediction Gridpoint Statistical Interpolation system. The assimilation of AMSU-A observations was improved to better exploit the role of AMSU-A channels 6 and 7 over the TP. This was achieved by not decreasing the weighting coefficients of the two channels over the grassy surface of the TP's high terrain such that they were consistent with the inverse error variances. This modification produced larger positive impacts of satellite data assimilation on the 48-h forecasts of the mid-tropospheric trough, water vapor, and quantitative precipitation forecasts downstream of the TP. This study also suggests the importance of AMSU-A observations from early-morning satellite orbits for numerical weather prediction downstream of the TP.

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“…For example, positive impacts of the GOES radiance assimilation on regional weather forecasts were demonstrated by Köpken et al (2004) and Okamoto (2013) for the Meteosat Visible and Infrared Imager (MVIRI); Szyndel et al (2005), Guedj et al (2011) and Stengel et al (2009) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat-8, Zou et al (2011Zou et al ( , 2015 and Qin et al (2013) for the GOES imagers on board the United States On 7 October 2014, the Japan Meteorological Agency (JMA) successfully launched a Geostationary Meteorological Satellite (GMS) -Himawari-8. It carries the newest generation of the geostationary visible and infrared imager -the Advanced Himawari Imager (AHI).…”

Section: Introductionmentioning

confidence: 99%

Impacts of assimilating all or GOES-like AHI infrared channels radiances on QPFs over Eastern China

Qin

Zou

Weng

2017

Tellus A: Dynamic Meteorology and Oceanography

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The launch of the Japanese Advanced Himawari Imager (AHI) on 7 October 2014 represents a new era of geostationary operational environmental satellite (GOES) imagers, providing many more channels than any previously launched GOES imagers for the first time. In this study, we compare the impacts of assimilating all AHI versus GOES-like infrared channels radiances on regional forecasts over Eastern China. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and Advanced Research Weather Research and Forecast model are employed. Positive impacts are obtained on quantitative precipitation forecasts (QPFs) associated with a typical summer precipitation case over eastern China in both set-ups, i.e. one assimilating all 10 AHI infrared channels (AHIA) and the other assimilating only four GOES-like AHI channels (AHIG). It is found that a southwest to northeast oriented band of the atmosphere with high water vapor content that was formed and moved inland with time under the influence of a subtropical high and an eastward-propagating middle-latitude trough was responsible for the persistent precipitation in the eastern China of the selected case. The AHIA experiment generated the largest improvement on QPFs due to it generating a wetter atmosphere in the middle and low troposphere over the ocean off the southeast coast of China than the AHIG experiment and a control experiment without assimilating any AHI channel.

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Improved Tropical Storm Forecasts with GOES-13/15 Imager Radiance Assimilation and Asymmetric Vortex Initialization in HWRF

Cited by 61 publications

References 38 publications

An adaptive background error inflation method for assimilating all‐sky radiances

An adaptive background error inflation method for assimilating all‐sky radiances

Impact of AMSU-A Data Assimilation over High Terrains on QPFs Downstream of the Tibetan Plateau

Impacts of assimilating all or GOES-like AHI infrared channels radiances on QPFs over Eastern China

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