Short period forecasting of catchment-scale precipitation. Part I: the role of Numerical Weather Prediction

Pedder, M. A.; Haile, Mebrahtu; Thorpe, Alan

doi:10.5194/hess-4-627-2000

Cited by 10 publications

(12 citation statements)

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“…Use of frequency analysis is also possible in which a probability distribution function is empirically fitted to the observed data and prediction is made from the population of the distribution fitted (Stidd, 1973). Precipitation can also be forecast by numerical weather prediction models together with radar information (Pedder et al, 2000;Ganguly and Bras, 2003;Kaufmann et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network models for forecasting intermittent monthly precipitation in arid regions

Dahamsheh

Aksoy

2009

Meteorological Applications

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Forecasting monthly precipitation in arid regions is investigated by means of feed forward back propagation (FFBP) artificial neural networks (ANNs) and compared to the linear regression technique with multiple inputs (MLR). Four meteorological stations from different geographical regions in Jordan are selected. The ANNs and MLR processes are analysed based on the mean square error, relative/absolute error, determination coefficient as well as the central statistical moments such as mean, standard deviation, and minimum and maximum values. It is found that whilst on one hand the ANNs are slightly better than the MLR in forecasting the monthly total precipitation, on the other hand, both are found with to have limitations which should be improved by means of either changing the type and architecture of the ANNs or incorporating modelling tools such as Markov chains into the forecast model.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network models for forecasting intermittent monthly precipitation in arid regions

Dahamsheh

Aksoy

2009

Meteorological Applications

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“…Numerous studies (Bell and Moore, 2000;Moore et al, 2000;Pedder et al, 2000;Cluckie et al, 2000) have been conducted regarding the catchment, notably during the period of the Hydrological Radar EXperiment (HYREX) which was a UK Natural Environment Research Council (NERC) Special Topic Program. Figure 1 shows the locations of the Brue catchment and the gauging stations.…”

Section: The Brue Catchmentmentioning

confidence: 99%

Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction

Xuan

Cluckie

Wang

2009

Hydrol. Earth Syst. Sci.

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Abstract. Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate highresolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a shortrange high-resolution mesoscale weather model. The results show that: (1) The hydrological model driven by QPF can produce forecasts comparable with those from a raingaugedriven one; (2) The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3) the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.

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“…Forecasting the intensity and spatial distribution of precipitation, on the other hand, remains a significant challenge even on large scales. The reasons for this are numerous, but generally involve difficulties in both modeling the underlying moist physical processes (Sellers 1965;Hartmann 1994;Pedder et al 2000) and providing adequately constrained initial conditions in cloudy and precipitating regions. With regard to the latter, millions of observations are now actively assimilated during each analysis cycle in most modern NWP systems.…”

Section: Introductionmentioning

confidence: 99%

The Framework for Assimilating All-Sky GPM Microwave Imager Brightness Temperature Data in the NASA GEOS Data Assimilation System

Kim

Jin

Akkraoui

et al. 2020

Monthly Weather Review

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Satellite radiance observations combine global coverage with high temporal and spatial resolution, and bring vital information to NWP analyses especially in areas where conventional data are sparse. However, most satellite observations that are actively assimilated have been limited to clear-sky conditions due to difficulties associated with accounting for non-Gaussian error characteristics, nonlinearity, and the development of appropriate observation operators for cloud- and precipitation-affected satellite radiance data. This article provides an overview of the development of the Gridpoint Statistical Interpolation (GSI) configurations to assimilate all-sky data from microwave imagers such as the GPM Microwave Imager (GMI) in the NASA Goddard Earth Observing System (GEOS). Electromagnetic characteristics associated with their wavelengths allow microwave imager data to be highly sensitive to precipitation. Therefore, all-sky data assimilation efforts described in this study are primarily focused on utilizing these data in precipitating regions. To utilize data in cloudy and precipitating regions, state and analysis variables have been added for ice cloud, liquid cloud, rain, and snow. This required enhancing the observation operator to simulate radiances in heavy precipitation, including frozen precipitation. Background error covariances in both the central analysis and EnKF analysis in the GEOS hybrid 4D-EnVar system have been expanded to include hydrometeors. In addition, the bias correction scheme was enhanced to reduce biases associated with thick clouds and precipitation. The results from single observation experiments demonstrate the capability of assimilating all-sky microwave brightness temperature data in GEOS both when the model forecast produces excessive precipitation and too little precipitation. Additional experiments show that hydrometeors and dynamic variables such as winds and pressure are adjusted in physically consistent ways in response to the assimilation.

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Short period forecasting of catchment-scale precipitation. Part I: the role of Numerical Weather Prediction

Cited by 10 publications

References 0 publications

Artificial neural network models for forecasting intermittent monthly precipitation in arid regions

Artificial neural network models for forecasting intermittent monthly precipitation in arid regions

Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction

The Framework for Assimilating All-Sky GPM Microwave Imager Brightness Temperature Data in the NASA GEOS Data Assimilation System

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