Impacts of the All-Sky Assimilation of FY-3C and FY-3D MWHS-2 Radiances on Analyses and Forecasts of Typhoon Hagupit

Chen, Keyi; Chen, Zhenxuan; Xian, Zhipeng; Li, Guancheng

doi:10.3390/rs15092279

Cited by 4 publications

(3 citation statements)

References 51 publications

(68 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the impact of our new sea surface roughness scheme on the ability of the coupled model to predict sea surface winds, we conduct five sets of experiments, with each set corresponding to a typhoon. These five typhoons landed in mainland China in August 2020 and had severe impacts, resulting in direct economic losses of billions of dollars (Chen et al., 2023). Each experiment starts at 00:00 UTC and extends for 72 hr of forecast.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning‐Based Sea Surface Roughness Parameterization Scheme Improves Sea Surface Wind Forecast

Fu,

Huang,

Luo

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

Accurate offshore surface wind forecasting is crucial for navigation safety and disaster prevention. However, significant biases exist in forecasting sea surface winds due to the uncertainties in estimating sea surface roughness. In this study, we propose a deep learning‐based scheme (DL2023) for estimating sea surface roughness and integrate it into a regionally coupled ocean‐atmosphere‐wave model. Single‐point experiments demonstrate that DL2023 achieves a remarkable 50% reduction in the Root Mean Square Error (RMSE) compared to the four traditional schemes. During five typhoon cases in August 2020, compared to the four traditional schemes, the RMSEs of forecasted surface winds using DL2023 are reduced by 6.02%–14.75%, 11.17%–18.30%, and 11.91%–19.46% at lead times of 24, 48, and 72 hr, respectively. Thus, the DL2023 scheme, trained using data from the Atlantic Ocean, successfully improves the forecast of surface winds over the Northwest Pacific Ocean.

show abstract

Section: Methodsmentioning

confidence: 99%

Deep Learning‐Based Sea Surface Roughness Parameterization Scheme Improves Sea Surface Wind Forecast

Fu,

Huang,

Luo

et al. 2023

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…With the launch of the FY-3D and FY-3C satellites, MWHS2 observations are becoming more crucial to assimilation systems. In their research on assimilating FY-3C MWHS2 data, Chen et al [18,19] made essential contributions. The effect of MWHS2/FY-3C data assimilation using a dynamic emissivity retrieved on precipitation forecast over a complex terrain was examined by Chen et al [18].…”

Section: Introductionmentioning

confidence: 99%

“…Their results indicated that, with the exception of rainfall above 100 mm, employing the dynamic emissivity retrieved from an 89 GHz channel improved the initial fields and the 24 h forecast of intensity and precipitation distribution. The influence of assimilated FY-3C MWHS2 radiance on Typhoon Hagupit forecast was evaluated by Chen et al [19] with FNL analysis data as the background field, and they found that it contributed to the improved analysis of wind fields, relative humidity, and temperature, thus improving the typhoon track and intensity forecast. In addition, the effect of assimilation of MWHS2 data from the FY-3C on the analysis and forecasting of binary typhoons was investigated by Xian et al [20] with FNL reanalysis data as the background field.…”

Section: Introductionmentioning

confidence: 99%

Assimilating FY-3D MWHS2 Radiance Data to Predict Typhoon Muifa Based on Different Initial Background Conditions and Fast Radiative Transfer Models

Huang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

In this study, the impact of assimilating MWHS2 radiance data under different background conditions on the analyses and deterministic prediction of the Super Typhoon Muifa case, which hit China in 2022, was explored. The fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data and the Global Forecast System (GFS) analysis data from the National Centers for Environmental Prediction (NCEP) were used as the background fields. To assimilate the Microwave Humidity Sounder II (MWHS2) radiance data into the numerical simulation experiments, the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system were employed. The results show that after the data assimilation, the standard deviation and root-mean-square error of the analysis significantly decrease relative to the observation, indicating the effectiveness of the assimilation process with both background fields. In the MWHS_GFS experiment, a subtropical high-pressure deviation to the east is observed around the typhoon, resulting in its northeast movement. In the differential field of the MWHS_ERA experiment, negative sea-level pressure differences around the typhoon are observed, which increases its intensity. In the deterministic predictions, assimilating the FY3D MWHS2 radiance data reduces the typhoon track error in the MWHS_GFS experiment and the typhoon intensity error in the MWHS_ERA experiment. In addition, it is found that the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for Tovs (RTTOV) model show similar performance in assimilating MWHS2 radiance data for this typhoon case. It seems that the data assimilation experiment with the CRTM significantly reduces the typhoon track error than the experiment with the RTTOV model does, while the intensity error of both experiments is rather comparable.

show abstract

Improving Typhoon Muifa (2022) Forecasts with FY-3D and FY-3E MWHS-2 Satellite Data Assimilation under Clear Sky Conditions

Shen,

Yuan,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

This study investigates the impacts of assimilating the Microwave Humidity Sounder II (MWHS-2) radiance data carried on the FY-3D and FY-3E satellites on the analyses and forecasts of Typhoon Muifa in 2022 under clear-sky conditions. Data assimilation experiments are conducted using the Weather Research and Forecasting (WRF) model coupled with the Three-Dimensional Variational (3D-Var) Data Assimilation method to compare the different behaviors of FY-3D and FY-3E radiances. Additionally, the data assimilation strategies are assessed in terms of the sequence of applying the conventional and MWHS-2 radiance data. The results show that assimilating MWHS-2 data is able to enhance the dynamic and thermal structures of the typhoon system. The experiment with FY-3E MWHS-2 assimilated demonstrated superior performance in terms of simulating the typhoon’s structure and providing a prediction of the typhoon’s intensity and track than the experiment with FY-3D MWHS-2 did. The two-step assimilation strategy that assimilates conventional observations before the radiance data has improved the track and intensity forecasts at certain times, particularly with the FY-3E MWHS-2 radiance. It appears that large-scale atmospheric conditions are more refined by initially assimilating the Global Telecommunication System (GTS) data, with subsequent satellite data assimilation further adjusting the model state. This strategy has also confirmed improvements in precipitation prediction as it enhances the dynamic and thermal structures of the typhoon system.

show abstract

Impacts of the All-Sky Assimilation of FY-3C and FY-3D MWHS-2 Radiances on Analyses and Forecasts of Typhoon Hagupit

Cited by 4 publications

References 51 publications

Deep Learning‐Based Sea Surface Roughness Parameterization Scheme Improves Sea Surface Wind Forecast

Deep Learning‐Based Sea Surface Roughness Parameterization Scheme Improves Sea Surface Wind Forecast

Assimilating FY-3D MWHS2 Radiance Data to Predict Typhoon Muifa Based on Different Initial Background Conditions and Fast Radiative Transfer Models

Improving Typhoon Muifa (2022) Forecasts with FY-3D and FY-3E MWHS-2 Satellite Data Assimilation under Clear Sky Conditions

Contact Info

Product

Resources

About