Copula-based assimilation of radar and gauge information  to derive bias-corrected precipitation fields

Vogl, Stefanie; Laux, Patrick; Qiu, Wei; Mao, Ganquan; Kunstmann, Harald

doi:10.5194/hess-16-2311-2012

Cited by 50 publications

(36 citation statements)

References 46 publications

(48 reference statements)

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“…This is quite a new way of measuring rainfall and has been under study by several researchers recently (Overeem et al, 2013;Zinevich et al, 2009). Most of the mentioned methods seek to use alternative devices which were intended originally for other purposes.…”

Section: E Rabiei Et Al: Rainfall Estimation Using Moving Cars As Rmentioning

confidence: 99%

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Rainfall estimation using moving cars as rain gauges – laboratory experiments

Rabiei¹,

Haberlandt²,

Sester

et al. 2013

Hydrol. Earth Syst. Sci.

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Abstract. The spatial assessment of short time-step precipitation is a challenging task. Low density of observation networks, as well as the bias in radar rainfall estimation motivated the new idea of exploiting cars as moving rain gauges with windshield wipers or optical sensors as measurement devices. In a preliminary study, this idea has been tested with computer experiments (Haberlandt and Sester, 2010). The results have shown that a high number of possibly inaccurate measurement devices (moving cars) provide more reliable areal rainfall estimations than a lower number of precise measurement devices (stationary gauges). Instead of assuming a relationship between wiper frequency (W ) and rainfall intensity (R) with an arbitrary error, the main objective of this study is to derive valid W -R relationships between sensor readings and rainfall intensity by laboratory experiments. Sensor readings involve the wiper speed, as well as optical sensors which can be placed on cars and are usually made for automating wiper activities. A rain simulator with the capability of producing a wide range of rainfall intensities is designed and constructed. The wiper speed and two optical sensors are used in the laboratory to measure rainfall intensities, and compare it with tipping bucket readings as reference. Furthermore, the effect of the car speed on the estimation of rainfall using a car speed simulator device is investigated. The results show that the sensor readings, which are observed from manual wiper speed adjustment according to the front visibility, can be considered as a strong indicator for rainfall intensity, while the automatic wiper adjustment show weaker performance. Also the sensor readings from optical sensors showed promising results toward measuring rainfall rate. It is observed that the car speed has a significant effect on the rainfall measurement. This effect is highly dependent on the rain type as well as the windshield angle.

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Section: E Rabiei Et Al: Rainfall Estimation Using Moving Cars As Rmentioning

confidence: 99%

“…Ehret (2002) applied another method for merging radar data with rain gauge data, called conditional merging. Other approaches for merging radar data with rain gauge data were suggested by Erdin et al (2012) and Vogl et al (2012).…”

Section: E Rabiei Et Al: Rainfall Estimation Using Moving Cars As Rmentioning

confidence: 99%

Rainfall estimation using moving cars as rain gauges – laboratory experiments

Rabiei¹,

Haberlandt²,

Sester

et al. 2013

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…This function is unique if the marginals are continuous functions (Nelsen 2006;Vogl et al 2012). According to Sklar's theorem,30 the joint multivariate distribution H of m variables Zi equals a copula C of m variables ui as:…”

Section: Copulasmentioning

confidence: 99%

“…Here, to obtain a single value for air temperature, a choice for either the mean, or the median or the mode of a simulation provides a single value ̂1. In the literature, the mean value of the simulations is considered as a single realization (Laux et al 2011;Vogl et al 2012). The number of samples in the simulations, however, influences the 25 simulation of conditional quantiles.…”

Section: Simulation Of Conditional Quantilementioning

confidence: 99%

“…Mao et al (2015) investigated daily precipitation data and showed that a copula-based bias correction performs better than quantile mapping. Vogl et al (2012) proposed the "Multiple Theta" and the "Maximum Theta" approaches for bias correction of rainfall data. So far, copulas have mainly been applied to precipitation time series retrieved from regional climate models.…”

mentioning

confidence: 99%

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Three novel copula-based bias correction methods for daily ECMWF air temperature data

Alidoost

Stein

et al. 2017

Preprint

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Abstract. Data retrieved from global weather forecast systems are typically biased with respect to measurements at local weather stations. This paper presents three copula-based methods for bias correction of daily air temperature data derived from the European Centre for Medium-range Weather Forecasts (ECMWF). The aim is to predict conditional copula quantiles at different unvisited locations, assuming spatial stationarity of the underlying random field. The three new methods are: bivariate copula quantile mapping (types I and II), and a quantile search. These are compared with commonly applied methods, using 10 data from an agricultural area in the Qazvin Plain in Iran containing five weather stations. Cross-validation is carried out to assess the performance. The study shows that the new methods are able to predict the conditional quantiles at unvisited locations, improve the higher order moments of marginal distributions, and take the spatial variabilities of the bias-corrected variable into account. It further illustrates how a choice of the bias correction method affects the bias-corrected variable and highlights both theoretical and practical issues of the methods. We conclude that the three new methods improve local 15 refinement of weather data, in particular if a low number of observations is available.

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Assessing the relative effectiveness of statistical downscaling and distribution mapping in reproducing rainfall statistics based on climate model results

Langousis

Mamalakis

Deidda

et al. 2016

Water Resources Research

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To improve the level skill of climate models (CMs) in reproducing the statistics of daily rainfall at a basin level, two types of statistical approaches have been suggested. One is statistical correction of CM rainfall outputs based on historical series of precipitation. The other, usually referred to as statistical rainfall downscaling, is the use of stochastic models to conditionally simulate rainfall series, based on large-scale atmospheric forcing from CMs. While promising, the latter approach attracted reduced attention in recent years, since the developed downscaling schemes involved complex weather identification procedures, while demonstrating limited success in reproducing several statistical features of rainfall. In a recent effort, Langousis and Kaleris (2014) developed a statistical framework for simulation of daily rainfall intensities conditional on upper-air variables, which is simpler to implement and more accurately reproduces several statistical properties of actual rainfall records. Here we study the relative performance of: (a) direct statistical correction of CM rainfall outputs using nonparametric distribution mapping, and (b) the statistical downscaling scheme of Langousis and Kaleris (2014), in reproducing the historical rainfall statistics, including rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e., the Flumendosa catchment, using rainfall and atmospheric data from four CMs of the ENSEMBLES project. The obtained results are promising, since the proposed downscaling scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the CM used and the characteristics of the calibration period. This is particularly the case for yearly rainfall maxima. To date, the assessment of the overall performance of different GCM/RCM combinations in modeling Earth's past, present and future climate has been greatly facilitated by open-access databases of climate model simulations developed through projects like PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects, http://prudence.dmi.dk/), PCMDI/CMIP3 (Program for Climate Model Diagnosis and Intercomparison/Coupled Model Intercomparison Project -Phase 3, http:// www-pcmdi.llnl.gov), ENSEMBLES (ENSEMBLE-based Predictions of Climate Changes and their Impacts,

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Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields

Cited by 50 publications

References 46 publications

Rainfall estimation using moving cars as rain gauges – laboratory experiments

Rainfall estimation using moving cars as rain gauges – laboratory experiments

Three novel copula-based bias correction methods for daily ECMWF air temperature data

Assessing the relative effectiveness of statistical downscaling and distribution mapping in reproducing rainfall statistics based on climate model results

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