Quantifying the Error of Radar-Estimated Refractivity by Multiple Elevation and Dual-Polarimetric Data

Feng, Ya-Chien; Fabry, Frédéric

doi:10.1175/jtech-d-18-0008.1

Cited by 2 publications

(2 citation statements)

References 44 publications

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“…For the N observation, Fabry et al (1997), Feng et al (2016), and Feng and Fabry (2018 gave comprehensive descriptions of N estimation for air near the surface by using the Doppler weather radar. This study employed N fields retrieved using the NCAR S-Pol radar for assimilation (https://doi.org/10.5065/D6WH2N9T).…”

Section: Observations For Assimilationmentioning

confidence: 99%

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Assimilation of radar‐derived refractivity and radar data in the context of ensemble Kalman filter: Cases study of the Southwest Monsoon Experiment

Chung

Feng

et al. 2023

Quart J Royal Meteoro Soc

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Radar‐derived refractivity provides moisture information near the surface, which plays a crucial role in convective initiation and of heavy rain. In this study, the high‐resolution Weather Research and Forecasting local ensemble transform Kalman filter data assimilation system was employed for two cases in the Southwest Monsoon Experiment to conduct two experimental sets. The first set was applied in both cases to investigate the effects of assimilating radar‐retrieved refractivity along with reflectivity and radial wind. The second set was conducted in the second case to examine the benefit of increasing the frequency of refractivity assimilation and investigate the optimal strategy to assimilate refractivity. Results of the two cases revealed that assimilating reflectivity and radial velocity modified near‐surface humidity on the basis of the flow‐dependent error correlation estimated by the ensemble, but the spatial distribution may not be fully accurate, causing underestimation of rainfall. With further refractivity assimilation, stronger convergence and more accurate low‐level moisture, temperature, and wind field corrections were obtained, leading to superior forecasts for both light and heavy rainfall during six hours. The results of the second set indicated that increasing the assimilation interval of refractivity enabled capturing the dramatic moisture variation and enhancing wind convergence, resulting in short‐term forecast improvement. The strategy that solely assimilated refractivity before the appearance of the convection system optimized the correction of environmental moisture, then accurately represented the humidity and strengthened the wind convergence when precipitation occurred. Consequently, the achieved improvement of the short‐term forecast was most noteworthy, particularly for heavy rain.

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Section: Observations For Assimilationmentioning

confidence: 99%

“…The moist (dry) air is represented by high (low) N values. The spatial and temporal resolution of N varies by scan strategy and clutter target density (Feng and Fabry, 2018). Studies have employed N to correct the initial humidity field in the NWP model through data assimilation.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of radar‐derived refractivity and radar data in the context of ensemble Kalman filter: Cases study of the Southwest Monsoon Experiment

Chung

Feng

et al. 2023

Quart J Royal Meteoro Soc

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The spatiotemporal characteristics of near-surface water vapor in a coastal region revealed from radar-derived refractivity

Feng

Hsu

Weckwerth

et al. 2021

Monthly Weather Review

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The radar-retrieved refractivity fields provide detailed depictions of the near-surface moisture distribution at the meso-gamma scale. This study represents a novel application of the refractivity fields by examining the spatiotemporal characteristics of moisture variability in a summertime coastal region in Taiwan over four weeks. The physiography in Taiwan lends itself to a variety of flow features and corresponding moisture behavior, which has not been well-studied. High-resolution of refractivity analyses demonstrate how a highly variable moisture field is related to the complex interaction between the synoptic-scale winds, diurnal local circulations, terrain, storms, and heterogeneous land-use. On average, higher refractivity (water vapor) is observed along the coastline and refractivity decreases inland toward the foothills. Under weak synoptic forcing conditions, the daytime refractivity field develops differently under local surface wind directions determined by the synoptic-scale prevailing wind and the sea breeze fronts. High moisture penetrates inland toward the foothills with southwesterly winds, but it stalls along the coastline with southerly and the northwesterly winds. The moisture distribution may further affect the occurrence of the inland afternoon storms. During the nighttime, the dry downslope wind decreases the moisture from the foothills toward the coast and forms a refractivity gradient perpendicular to the meridionally-oriented mountains. Furthermore, the refractivity fields illustrate higher resolution moisture distribution over surface station point measurements by showing the lagged daytime sea-breeze front between the urban and rural areas and the detailed nighttime heterogeneous moisture distribution related to land-use and rivers.

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Quantifying the Error of Radar-Estimated Refractivity by Multiple Elevation and Dual-Polarimetric Data

Cited by 2 publications

References 44 publications

Assimilation of radar‐derived refractivity and radar data in the context of ensemble Kalman filter: Cases study of the Southwest Monsoon Experiment

Assimilation of radar‐derived refractivity and radar data in the context of ensemble Kalman filter: Cases study of the Southwest Monsoon Experiment

The spatiotemporal characteristics of near-surface water vapor in a coastal region revealed from radar-derived refractivity

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