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

Abstract

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.

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

“…For vertical localization, all variables had the same radius (i.e., 4 km). The V r , Z , and N observation errors were 3 m·s −1 , 5 dBZ (Tsai et al ., 2014), and 4.1 N ‐units (approximately 0.8 g·kg − 1 ; Feng et al ., 2021) respectively.…”

“…Feng et al . (2021) characterized N fields in the SoWMEX field experiment. The N value was higher in the convective environment than in the non‐precipitation environment.…”

“…The N dimension was 480 × 300 (480 azimuth angles with a resolution of 0.75 • and 300 gates with a 0.15-km interval). Feng et al (2021) characterized N fields in the SoWMEX field experiment. The N value was higher in the convective environment than in the non-precipitation environment.…”

“…The change in N is most affected by variation in moisture. Feng et al (2021) reported that the change in moisture was five times more sensitive to the change in N than was the change in temperature.…”

“…This method is based on the phase change detected from a stationary target located in the vicinity of the radar site. The radar‐retrieved N field represents air moisture at approximately 20 m above the ground and covers approximately a 40‐km range from the radar location (Feng et al ., 2021). The moist (dry) air is represented by high (low) N values.…”