Quantitative precipitation forecasts: a statistical adaptation of model outputs through an analogues sorting approach

Obled, Charles; Bontron, Guillaume; Garçon, Rémy

doi:10.1016/s0169-8095(02)00038-8

Cited by 132 publications

(155 citation statements)

References 15 publications

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“…In fact, the past observed values used to make the forecast contain the orographic, diabatic and other local effects which characterize the area of interest and influence the behaviour of the quantities to be predicted (e.g. Ruosteenoja, 1988;Obled et al, 2002;Diomede et al, 2008). With the advent of fast computer processors, analogue forecasting is now applied to a large variety of problems (e.g.…”

Section: Forecasting Using Analoguesmentioning

confidence: 99%

“…Recently, Obled et al (2002) and Diomede et al (2008) applied analogue-based techniques for forecasting precipitation in hydrological catchments. Obled et al (2002) produced probabilistic forecasts of daily rainfall up to 6 days ahead.…”

Section: Forecasting Using Analoguesmentioning

confidence: 99%

“…Obled et al (2002) produced probabilistic forecasts of daily rainfall up to 6 days ahead. The analogues were sought in terms of the 1000 and 700 hPa geopotential height over a window covering western Europe.…”

Section: Forecasting Using Analoguesmentioning

confidence: 99%

“…First, 120 analogues from the predictors which describe the orographic forcing are obtained (forcing analogues); second, 12 analogues are selected from the first ones by considering radar rainfall features (final analogues). As reported by Obled et al (2002), the number of analogues taken into account results from a trade-off: the more analogues we select, the better the sampling, but the less the subset is conditioned by the current situation, because of the decreasing analogy. The optimal numbers 120 and 12 were obtained by evaluating the performance of the NORA algorithm achieved with different combinations of the number of forcing and final analogues in terms of bias, scatter, and mean absolute error (these scores will be introduced in section 4.1).…”

Section: Analoguesmentioning

confidence: 99%

“…As the results were not significantly different, we opted for the simpler Euclidean solution. We also evaluated the skill of NORA by taking different combinations of mesoscale winds, following the methodology proposed by Obled et al (2002). Again, the performance of the system did not significantly change.…”

Section: Forcing Analoguesmentioning

confidence: 99%

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NORA–Nowcasting of Orographic Rainfall by means of Analogues

Panziera

Germann

Gabella

et al. 2011

Quart J Royal Meteoro Soc

100

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A novel analogue-based heuristic tool for nowcasting orographic precipitation is presented. The system takes advantage of the orographic forcing, which determines a strong relation between mesoscale flows, air mass stability and rainfall patterns. These quantities are used as predictors of precipitation. In particular, past situations with the predictors most similar to those observed at the current time are identified by searching a large historical dataset. Deterministic and probabilistic forecasts are then generated every five minutes as new observations are available, based on the rainfall observed by radar after the analogous situations. The analogue method provides a natural way to incorporate evolution of precipitation into the nowcasting system and to express forecast uncertainty by means of ensembles.A total of 127 days of long-lasting orographic precipitation constitutes the historical dataset in which the analogous situations are searched. The system is developed for the Lago Maggiore region in the southern part of the European Alps. Given the availability of radar data and the presence of a strong orographic forcing, it can be extended to other mountainous regions. An evaluation of the skill of the system shows that the heuristic tool performs better than Eulerian persistence for predictions with lead time larger than one hour, and better than the numerical model COSMO2 for forecasts with lead time up to four hours.

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Section: Forecasting Using Analoguesmentioning

confidence: 99%

Section: Forecasting Using Analoguesmentioning

confidence: 99%

Section: Forecasting Using Analoguesmentioning

confidence: 99%

Section: Analoguesmentioning

confidence: 99%

Section: Forcing Analoguesmentioning

confidence: 99%

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NORA–Nowcasting of Orographic Rainfall by means of Analogues

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Rainfall‐Runoff Models for Real‐Time Forecasting

Todini

2005

Encyclopedia of Hydrological Sciences

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This article describes the use of rainfall‐runoff models within the frame of real‐time flood forecasting and warning. After assessing the requirements of real‐time flood forecasting systems, some of the more well known ones available for operational purposes are briefly mentioned. The different types of rainfall‐runoff models used in practice are then presented, within the framework set on the one hand by the operational system requirements, and on the other hand by the historical evolution of research and development in the fields of hydrological and meteorological forecasting. After discussing the problems of real‐time updating and of the assessment of the predictive uncertainty, two example applications are illustrated. The article concludes with a description of current and future research themes for extending the forecasting lead time, assessing forecasting uncertainty, and appropriately communicating it to the end users.

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Internal variability and model uncertainty components in future hydrometeorological projections: The Alpine Durance basin

Lafaysse

Hingray

Mezghani

et al. 2014

Water Resources Research

116

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A multireplicate multimodel ensemble of hydrological simulations covering the 1860-2099 period has been produced for the Upper Durance River basin (French Alps). An original quasi-ergodic analysis of variance was applied to quantify uncertainties related to General Circulation Models (GCMs), Statistical Downscaling Models (SDMs) and the internal variability of each GCM/SDM simulation chain. For temperature, GCM uncertainty prevails and SDM uncertainty is nonnegligible. Significant warming and in turn significant changes are predicted for evaporation, snow cover and seasonality of discharges. For precipitation, GCM and SDM uncertainty components are of the same order. A high contribution of the large and smallscale components of internal variability is also obtained, inherited, respectively, from the GCMs and the different replicates of a given SDM. The same applies for annual discharge. The uncertainty in values that could be experienced for any given future period is therefore very high. For both discharge and precipitation, even the sign of future realizations is uncertain at a 90% confidence level. These findings have important implications. Similarly to GCM uncertainty, SDM uncertainty cannot be neglected. The same applies for both components of internal variability. Climate change impact studies based on a single SDM realization are likely to be no more relevant than those based on a single GCM run. They may lead to poor decisions for climate change adaptation.

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Quantitative precipitation forecasts: a statistical adaptation of model outputs through an analogues sorting approach

Cited by 132 publications

References 15 publications

NORA–Nowcasting of Orographic Rainfall by means of Analogues

NORA–Nowcasting of Orographic Rainfall by means of Analogues

Rainfall‐Runoff Models for Real‐Time Forecasting

Internal variability and model uncertainty components in future hydrometeorological projections: The Alpine Durance basin

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