Impact of a New Bias Correction Predictor for FY‐4A AGRI All‐Sky Data Assimilation on Typhoon Forecast

Shi, Bingying; Yang, Chun; Min, Jinzhong; Sha, Lina

doi:10.1029/2023jd039063

Cited by 3 publications

(2 citation statements)

References 65 publications

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“…We have extended this work to include multiple WV channels available in advanced sensors like the FY-4A Advanced Geostationary Radiation Imager (AGRI). Additionally, our study, conducted over a month-long period, contrasts with the latest case studies by other researchers (Shi et al, 2023;Xu, Zhang, et al, 2023). Furthermore, the key to this work is the use of the Community Radiative Transfer Model (CRTM) observation operator, which allows for the inclusion of five different types of hydrometeors (cloud water, ice, rain, snow, and graupel) as independent control variables.…”

Section: Introductionmentioning

confidence: 99%

All‐sky assimilation of FY‐4A AGRI water vapor channels: An observing system experiment study for south Asian monsoon prediction

Niu,

Tang,

Wang

2024

Quart J Royal Meteoro Soc

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In this study, a month‐long parallel experiment is conducted to assess the assimilation effects of all‐sky radiance (ASR) versus clear‐sky radiance (CSR) in the single and multiple water vapor (WV) channels provided by the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun‐4A (FY‐4A) satellite. The Weather Research and Forecasting model data assimilation system (WRFDA) is utilized for assimilating the FY‐4A AGRI WV observations. Compared with the CSR, the assimilated data of the ASR increases by ~1.7 times in WV channel 9 and around 2.4 times in the multiple WV channels (channels 9 and 10). Both the CSR and ASR assimilation results demonstrate that the simulated brightness temperature (TB) from the WRF analyses is closer to the TB observations from Sondeur Atmospherique du Profil d'Humidite Intertropicale par Radiometrie (SAPHIR) channels 1–2 and Microwave Humidity Sounder (MHS) channel 3, as compared with the control experiment where only conventional data are assimilated. Furthermore, a larger improvement is noted in assimilation of multiple WV channel against assimilation of a single WV channel for both CSR and ASR runs. Overall, the analyses and forecasts generated from the ASR run exhibit superior performance compared with the CSR run when verified against SAPHIR and MHS measurements, as well as the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analyses. The verification of the rainfall predicted by the WRF against the Global Satellite Mapping of Precipitation (GSMaP) products over India indicates that the assimilation of the ASR in multiple WV channels has the largest impact on rainfall prediction. Moreover, the ASR run demonstrates an accurate prediction of heavy rainfall events that are missed in the control experiment and underestimated in the CSR run.

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Section: Introductionmentioning

confidence: 99%

All‐sky assimilation of FY‐4A AGRI water vapor channels: An observing system experiment study for south Asian monsoon prediction

Niu,

Tang,

Wang

2024

Quart J Royal Meteoro Soc

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“…Since the Gridpoint Statistical Interpolation system showed weak correlations between observation-minus-background (O B) samples and two lapse raterelated airmass predictions, Li et al (2019) proposed a modified bias correction (BC) scheme for CrIS clear-sky data assimilation and thus improved the temperature and humidity forecasts. There exit challenges to remove biases of IR channels under the cloudy conditions (Chandramouli et al, 2022;Niu et al, 2023;Okamoto, 2017;Shi et al, 2023;Xu et al, 2021). Otkin et al (2018) introduced a novel nonlinear BC method that employs a Taylor series polynomial expansion to effectively remove linear and nonlinear biases from all-sky simulations of IR brightness temperatures.…”

Section: Introductionmentioning

confidence: 99%

Improving All‐Sky Simulations of Typhoon Cloud/Rain Band Structures of NOAA‐20 CrIS Window Channel Observations

Niu,

Zou

2024

JGR Atmospheres

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The Cross‐track Infrared Sounder (CrIS) observations (O) contributed greatly to numerical weather prediction. Further contribution depends on the success of all‐sky data assimilation, which requires a method to produce realistic cloud/rain band structures from background fields (i.e., 6‐hr forecasts), and to remove large biases of all‐sky simulation of brightness temperature (TB) in the presence of clouds. In this study, CrIS all‐sky simulations of brightness temperatures at an arbitrarily selected window channel within Typhoon Hinnamnor (2022) are investigated. The 3‐km Weather Research and Forecasting model with three microphysics schemes were used to produce 6‐hr background forecasts (B). The O − B statistic deviate greatly from Gaussian distribution with large biases in either water clouds, or thin ice clouds, or thick ice clouds within Typhoon Hinnamnor. By developing a linear regression function of three all‐sky simulations of TB from 6‐hr forecasts with three microphysics schemes, the O − B statistics approximate a Gaussian normal distribution in water clouds, thin ice clouds and thick ice clouds. Taking the regression function that is established by a training data set to combine 6‐hr background forecasts at later times, the cloud/rain band structures compared much more favorably with CrIS observations than those from an individual microphysic. Furthermore, the regression coefficients derived from Typhoon Hinnamnor (2022) also work for Typhoon Khanun (2023). The work aims to quantify and remove biases in background fields of TB and generating realistic typhoon cloud/rain band structures in background fields will allow a better description of center position, intensity and size to improve typhoon forecasts.

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Seasonal Variation in Total Cloud Cover and Cloud Type Characteristics in Xinjiang, China Based on FY-4A

Zeng,

Yang,

Tong

et al. 2024

Remote Sensing

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In order to deepen the knowledge of the seasonal variation in total cloud cover (TCC) in Xinjiang, China (XJ), a typical arid region, and to broaden the understanding of the seasonal variation in cloud type (CLT) in the region, we used TCC and CLT datasets from the latest generation of the geostationary satellite Fengyun 4A (FY-4A) from 2018 to 2022 to investigate the seasonal variation characteristics of TCC and CLT in XJ. Meanwhile, to verify the accuracy of TCC from FY-4A, ground observation (GROB) TCC datasets from 105 national meteorological stations (NMSs) in XJ and TCC datasets from ERA5 during the same period were used. In addition, the correlation between TCC from FY-4A and meteorological factors from ERA5 was also analyzed in this study. The TCC from FY-4A, GROB, and ERA5 can all well reflect the significant seasonal variation in TCC in XJ, with the highest (lowest) mean TCC and a distribution pattern of high in the southwest (northwest) and low in the northeast (southeast) in spring (fall) in XJ. Although the mean TCC from FY-4A in all four seasons was lower than that from GROB, the two were comparable in spring (44.09% and 47.32%) and summer (42.88% and 43.17%), while there was a significant difference between the two in fall (27.86% and 40.19%) and winter (30.58% and 46.93%) for 105 NMSs in XJ. The TCC from FY-4A was lower (higher) than that from GROB in spring and summer at most NMSs in northern (southern) XJ, while the TCC from FY-4A was lower than that from GROB for the vast majority of NMSs in fall and winter, especially in northern XJ. The seasonal variation in the spatial distribution of different CLTs (clear, water-type, supercooled-type, mixed-type, ice-type, cirrus-type, and overlap-type) from FY-4A exhibited diverse variation characteristics. Water-type (supercooled-water-type) had a high-frequency center of over 30% in the Tarim Basin (Kunlun Mountains) during summer. Mixed-type (ice-type and cirrus-type) had the highest frequency in winter (spring), while overlap-type had the highest frequency in summer. The correlation between TCC and water vapor conditions (total column vertically integrated water vapor, specific humidity at 250 hPa, 500 hPa, and 700 hPa) was positive in XJ.

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Impact of a New Bias Correction Predictor for FY‐4A AGRI All‐Sky Data Assimilation on Typhoon Forecast

Cited by 3 publications

References 65 publications

All‐sky assimilation of FY‐4A AGRI water vapor channels: An observing system experiment study for south Asian monsoon prediction

All‐sky assimilation of FY‐4A AGRI water vapor channels: An observing system experiment study for south Asian monsoon prediction

Improving All‐Sky Simulations of Typhoon Cloud/Rain Band Structures of NOAA‐20 CrIS Window Channel Observations

Seasonal Variation in Total Cloud Cover and Cloud Type Characteristics in Xinjiang, China Based on FY-4A

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