Assimilation of radar radial velocity data with the WRF Hybrid ETKF–3DVAR system for the prediction of Hurricane Ike (2008)

Shen, Feifei; Min, Jinzhong; Xu, Dongmei

doi:10.1016/j.atmosres.2015.09.019

Cited by 58 publications

(33 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The predictability of the tracks and intensities of binary tropical cyclones (TCs), which are present simultaneously in the same region, are relatively low, due to the interactions between the TCs. Advancements in data assimilation (DA) techniques during the initialization of TCs, and the assimilation of more observations, have contributed to large reductions in the error of TC track and intensity forecasts [e.g., Li and Liu , ; Schwartz et al ., ; Liu et al ., ; Xu et al ., ; Shen et al ., ; Xu et al ., ]. Improving predictions of rainfall amounts and distributions during TCs provides another challenging task for the TC research [ Yen et al ., ; Schwartz and Liu , ].…”

Section: Introductionmentioning

confidence: 99%

“…Satellite observations play a very important role in improving the accuracy of numerical weather prediction, especially over areas with sparse conventional observations [e.g., McNally et al ., ; Zapotocny et al ., ; Xu et al ., ]; thus, the satellite observation is the key for improving TC forecasts. Recently, microwave satellite observations have contributed the most to improving numerical weather forecasts; the observations have been (or are being) ingested into DA systems [e.g., Smith et al ., ; Goodrum et al ., ; Li and Liu , ; Liu et al ., ; Schwartz et al ., ; Newman et al ., ; Shen et al ., ]. Examples include the Suomi National Polar‐orbiting Partnership (NPP)‐Advanced Technology Microwave Sounder (ATMS) data, used in the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) systems (NIELS) [ Niels et al , ; Collard et al ., ]; the NOAA‐18 and MetOp‐A Microwave Humidity Sounders (MHSs), used in the Weather Research and Forecasting DA system (WRFDA) [ Schwartz et al ., ; Newman et al ., ] as well as most operational centers [ Thépaut , ]; and the FY‐3A and FY‐3B MWHSs, used in the ECMWF system, which is the first operational use of Chinese polar orbiter satellite data by an NWP center outside China [ Lu et al ., a; Chen et al ., ].Notably, the MHS and MWHS are designed to retrieve profiles of atmospheric water vapor.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assimilation of MWHS radiance data from the FY‐3B satellite with the WRF Hybrid‐3DVAR system for the forecasting of binary typhoons

Min

Shen

et al. 2016

J Adv Model Earth Syst

Self Cite

View full text Add to dashboard Cite

Chan‐hom and Linfa were binary typhoons that occurred in the western North Pacific in 2015. In this study, the impacts of FY‐3B satellite Microwave Humidity Sounder (MWHS) radiance observations on the analyses and forecasts of Linfa and Chan‐hom are assessed. The regional Weather Research and Forecasting model and its data assimilation (DA) systems, using three‐dimensional variational (3DVAR) and Hybrid (ensemble/3DVAR) methods are used. Assimilation of the FY‐3B MWHS data using the 3DVAR method slightly improves the descriptive wind and temperature fields. Positive impacts on the specific humidity forecasts, for levels higher than 850 hPa, are also obvious. 3DVAR adjusts the typhoons' initial positions and their dynamic structures favorably, yielding better tracks, intensities, and precipitation forecasts, compared to the experiment run without MWHS data (control). With the Hybrid method, the water vapor information from the MWHS data better improve the analyses through multivariable correlations with the flow‐dependent background error. The Hybrid method further improves the track, intensities, and precipitation forecasts. For Typhoon Linfa, with the coexistence of Typhoon Chan‐hom, the Hybrid method provides a more descriptive background error covariance matrix, than using 3DVAR. Experiments on multiple binary typhoon cases are also provided to further validate the robustness of the results on the FY‐3B satellite MWHS radiance data assimilation.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assimilation of MWHS radiance data from the FY‐3B satellite with the WRF Hybrid‐3DVAR system for the forecasting of binary typhoons

Min

Shen

et al. 2016

J Adv Model Earth Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is found that radar Vr data are dealiased correctly after the quality control procedure. The radar Vr observation error is prescribed as 3 m/s for data assimilation, which is consistent with Xu and Gong (2003), Xiao et al (2009) (Xue and Dong, 2013;Shen et al, 2016), to explore the impact of the determination of the background error length and variance scale factors on the track and intensity forecast of the hurricane system for radar Vr data assimilation. Experiments were performed with version 3.8.1 of the WRF model (Skamarock et al, 2008).…”

Section: Methodology Of Radar Vr Data Assimilationmentioning

confidence: 99%

Effect of background error tuning on assimilating radar radial velocity observations for the forecast of hurricane tracks and intensities

Shen

Min

2019

Meteorological Applications

Self Cite

View full text Add to dashboard Cite

The background error covariance is an important component in data assimilation systems, which largely dominates the error correlations between different analysis variables. This study aimed to assess the impact of the determination of the background error length and variance scale factors on the track and intensity forecast of hurricane systems for radar radial velocity (Vr) data assimilation. To test the sensitivity of the background error length scale and variance scale, multiple data assimilation analyses were performed using a 3D variational data assimilation method with a parameter‐sweeping strategy for the case of Hurricane Ike (2008) by varying the decorrelation length scale (LEN_SCALING) and the variance (VAR_SCALING) of the background error step by step. It is shown that, in radar Vr data assimilation, generally smaller variance scale and length scale factors tend to improve the prediction of the hurricane track. Setting both VAR_SCALING and LEN_SCALING equivalent to 0.4 provides a better track than other combinations. In particular, the impact of the variance scale factor on the track forecasts is larger than that of the length scale factor. There is no direct impact of tuning variance scales and length scales on the forecast intensity for the first few forecast hours. Tuning length scale factors less than 0.4 provides smaller forecast errors of minimum sea level pressure and maximum surface wind with increasing forecast lead time.

show abstract

“…Using the dual‐polarization observations, single‐Doppler perspectives and the dual‐Doppler volumes, we anticipate widespread, diverse use of the data. For example, surface rainfall validation (Tokay et al , ), rainband process studies (Didlake and Houze, , ) and data assimilation/numerical modelling (Gall et al , ) are active areas in which research is being pursued heavily through airborne instrumentation (Allen et al , ; Braun et al , ; Gall et al , ; Shen et al , ), but may strongly benefit from the surface‐based observations documented here. Particularly during landfall of TCs, airborne platforms are often unavailable as the aircraft missions are frequently focused on open‐ocean observations.…”

Section: Significance and Use Of Datamentioning

confidence: 99%

Mobile ground‐based SMART radar observations and wind retrievals during the landfall of Hurricane Harvey (2017)

Alford

Biggerstaff

Carrie

2019

Geoscience Data Journal

View full text Add to dashboard Cite

This article describes a unique ground‐based weather radar dataset collected during the landfall of Hurricane Harvey (2017) along the United States Gulf Coast, documents the wind retrievals conducted with this dataset, and reports on the location of the archive of the data and wind retrievals so that others may gain access and use of these data. Datasets from C‐band dual‐polarimetric Shared Mobile Atmospheric Research and Teaching (SMART) radar and the United States National Weather Service WSR‐88D in Corpus Christi, Texas, are presented, along with dual‐Doppler analyses before and during Harvey's landfall on the United States Gulf Coast. The quality assurance and dual‐Doppler wind synthesis procedures are detailed. Nearly 8 hr of dual‐Doppler wind analyses were constructed, providing an unprecedented dataset for use in understanding hurricane dynamics at landfall and validating numerical simulations of Hurricane Harvey. In addition, raw, dual‐polarization data not used in the dual‐Doppler syntheses, but included in each radar dataset, are also summarized.

show abstract

Assimilation of radar radial velocity data with the WRF Hybrid ETKF–3DVAR system for the prediction of Hurricane Ike (2008)

Cited by 58 publications

References 55 publications

Assimilation of MWHS radiance data from the FY‐3B satellite with the WRF Hybrid‐3DVAR system for the forecasting of binary typhoons

Assimilation of MWHS radiance data from the FY‐3B satellite with the WRF Hybrid‐3DVAR system for the forecasting of binary typhoons

Effect of background error tuning on assimilating radar radial velocity observations for the forecast of hurricane tracks and intensities

Mobile ground‐based SMART radar observations and wind retrievals during the landfall of Hurricane Harvey (2017)

Contact Info

Product

Resources

About