Sensitivity analysis and performance estimation of refractivity from clutter techniques

Abstract

[1] Refractivity from clutter (RFC) refers to techniques that estimate the atmospheric refractivity profile from radar clutter returns. A RFC algorithm works by finding the environment whose simulated clutter pattern matches the radar measured one. This paper introduces a procedure to compute RFC estimator performance. It addresses the major factors such as the radar parameters, the sea surface characteristics, and the environment (region, time of the day, season) that affect the estimator performance and formalizes an error metric combining all of these. This is important for applications such as calculating the optimal radar parameters, selecting the best RFC inversion algorithm under a set of conditions, and creating a regional performance map of a RFC system. The performance metric is used to compute the RFC performance of a non-Bayesian evaporation duct estimator. A Bayesian estimator that incorporates meteorological statistics in the inversion is introduced and compared to the non-Bayesian estimator. The performance metric is used to determine the optimal radar parameters of the evaporation duct estimator for six scenarios. An evaporation duct inversion performance map for a S band radar is created for the larger Mediterranean/Arabian Sea region.

Statistical estimations of atmospheric duct over the South China Sea and the tropical eastern Indian Ocean

“…For evaporation duct, the duct height is less than 40 m. Most of the heights are concentrated between 10 and 20 m, and the mean value is 15.3 m. This value is higher than the global mean height 13 m [13] and the height off the southeastern coastal area of China is 10 m [14]. Although the evaporation ducts are near the sea surface, they have a significant impact on ship-borne communications above 2 GHz [15].…”

Evaporation Duct Retrieval Using Changes in Radar Sea Clutter Power Versus Receiving Height

“…Because the clutter statistical characteristics affect the evaporation duct estimation significantly [17], we consider it in the actual clutter power modeling. The K-distribution was introduced as an effective means to represent the sea clutter amplitude, and is used extensively in applications with low grazing angles.…”

“…Besides, because of the low-resolution nature of the RFC, very spiky clutter is less likely to be encountered in RFC applications. The clutter is expected to be mildly to weakly spiky with the shape factor v = 1-5 for most RFC applications [17]. For generality, the shape factor v is taken as 2.5 in this study.…”

“…Yardim et al [15] tracked the temporal and spatial evolution of evaporation duct parameters from radar sea clutter using an extended Kalman filter. Later, they investigated the performance of an evaporation duct RFC estimator by introducing an error metric as a function of the radar parameters, sea surface characteristics, and the environment; the authors also developed a Bayesian estimator that incorporates meteorological statistics into RFC inversion [16,17]. Very recently, Karimian et al [18] provided a good review of the many applications of the RFC technique.…”

“…In retrieving the modified refractivity profile for an evaporation duct, this technique is generally performed on the basis of the clutter power received at a fixed antenna height, as in Rogers et al [10], Yardim et al [17], and Wang et al [19]. That is, only one observed clutter power pattern is used as input into an RFC inversion algorithm.…”