Rainfall estimation by rain gauge‐radar combination: A concurrent multiplicative‐additive approach

Abstract

[1] A procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) is proposed for operational rainfall estimation using rain gauges and radar data. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on Barnes' objective analysis scheme (OAS), whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km 2 . Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, a spatially variable adjustment with multiplicative factors, and ordinary cokriging.

Data Transformation and Uncertainty in Geostatistical Combination of Radar and Rain Gauges

“…Methods for real-time radar-gauge combination have been developed within several methodological frameworks, including objective analysis (e.g., Brandes 1975;García-Pintado et al 2009), interpolation with deterministic weights (e.g., DeGaetano and Wilks 2008;He et al 2011), interpolation with splines (Schneider and Steinacker 2009), and Bayesian conditioning (Todini 2001). A particularly broad class of combination methods is constructed in the framework of geostatistics, using a stochastic interpolation procedure known as kriging (Gandin 1965;Cressie 1993).…”

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

“…For instance, the US Next Generation Radar (NEXRAD) Weather Surveillance Radar-1988 Doppler (WSR-88D) provides up to 4 km spatial and 6 min temporal resolution QPE [36]. This makes up for the spatial poverty of gauged rainfall through radar-rain gauge fusion [37,[40][41][42][43][44][45]. Although radar stations can provide relatively high spatiotemporal resolution QPE, a single station can only be suitable for small range applications.…”

“…One idea is to improve the precipitation estimation directly, e.g., to improve the identification of Z-R relationship and bright band so as to improve radar rainfall estimates and streamflow simulations [72]. Another solution, as mentioned before, is to reduce uncertainties in radar QPE by incorporating rain gauge records [37,[40][41][42][43][44][45]. The radar-rain gauge merged QPEs can then be used for streamflow/flood modelling, and it has been found that these combined products generally result in an optimal streamflow prediction compared to the use of a single product [61,68,69,72].…”

Kernel smoothing dos dados de chuva no Nordeste

“…Na literatura científica mundial se encontram diversos estudos, como os de Li & Shao (2010) e Garcia-Pintado et al (2009) na direção de aproveitar os benefícios e diminuir as falhas das fontes de dados com o objetivo geral de estabelecer padrões e regras para o tratamento dos dados extraindo o máximo de informações neles contidas, bem como estabelecer os procedimentos e regras para medidas futuras.…”