Probabilistic Weather Prediction with an Analog Ensemble

Monache, Luca Delle; Eckel, F. Anthony; Rife, Daran L.; Nagarajan, Badrinath; Searight, K.

doi:10.1175/mwr-d-12-00281.1

Cited by 292 publications

(260 citation statements)

References 63 publications

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“…The study was conducted over Uganda which geographically approximately extends from 29.55 o E to 35.09 o E and 1.50 o S to 4.36 o N . The country largely experiences tropical savanna climate with isolated cases like the Lake Victoria Basin experiencing equatorial climate [30].…”

Section: The Study Areamentioning

confidence: 99%

Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case study of Uganda

Mugume¹,

Mesquita²,

Bamutaze³

et al. 2017

Preprint

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; Tel.: +256-701-349477 ‡ These authors contributed equally to this work. Abstract:Accurate and timely rainfall prediction enhances productivity and can aid proper planning in sectors such as agriculture, health, transport and water resources. This study is aimed at improving rainfall prediction using ensemble methods. It first assesses the performance of six convective schemes (Kain-Fritsch (KF); Betts-Miller-Janjić (BMJ); Grell-Fretas (GF); Grell 3D ensemble (G3); New-Tiedke (NT) and Grell-Devenyi (GD)) using the root mean square error (RMSE) and mean error (ME) focusing on the March-May 2013 rainfall period over Uganda. 18 ensemble members are generated from the three best performing convective schemes (i.e. KF, GF & G3). The performance of three ensemble methods (i.e. ensemble mean (EM); ensemble mean analogue (EMA) and multi-member analogue ensemble (MAEM)) is also analyzed using the RMSE and ME. The EM presented a smaller RMSE compared to individual schemes

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Section: The Study Areamentioning

confidence: 99%

Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case study of Uganda

Mugume¹,

Mesquita²,

Bamutaze³

et al. 2017

Preprint

View full text Add to dashboard Cite

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“…This approach for constructing prior information is similar to analog techniques applied in statistical downscaling (Zorita and von Storch 1999;Tian and Martinez 2012;Shao and Li 2013) and weather and climate forecasting (Fernandez-Ferrero et al 2010;Orlowsky et al 2010;Delle Monache et al 2013;Martin et al 2014). Analog techniques take advantage of a pattern recognition approach in which the probability of the future state of the system is estimated using observed patterns that have evolved from an analog state in the past.…”

Section: Prior Generationmentioning

confidence: 99%

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

Alemohammad

McLaughlin

Entekhabi

2015

Monthly Weather Review

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Ensemble-based data assimilation techniques are often applied to land surface models in order to estimate components of terrestrial water and energy balance. Precipitation forcing uncertainty is the principal source of spread among the ensembles that is required for utilizing information in observations to correct model priors. Precipitation fields may have both position and magnitude errors. However, current uncertainty characterizations of precipitation forcing in land data assimilation systems often do no more than applying multiplicative errors to precipitation fields. In this paper, an ensemble-based Bayesian method for characterization of uncertainties associated with precipitation retrievals from spaceborne instruments is introduced. This method is used to produce stochastic replicates of precipitation fields that are conditioned on precipitation observations. Unlike previous studies, the error likelihood is derived using an archive of historical measurements. The ensemble replicates are generated using a stochastic method, and they are intermittent in space and time. The replicates are first projected in a low-dimension subspace using a problem-specific set of attributes. The attributes are derived using a dimensionality reduction scheme that takes advantage of singular value decomposition. A nonparametric importance sampling technique is formulated in terms of the attribute vectors to solve the Bayesian sampling problem. Examples are presented using retrievals from operational passive microwave instruments, and performance of the method is assessed using ground validation measurements from a surface weather radar network. Results indicate that this ensemble characterization approach provides a useful description of precipitation uncertainties with a posterior ensemble that is narrower in distribution than its prior while containing both precipitation position and magnitude errors.

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“…Others have used analogs of past forecasts based 75 on weighted atmospheric predictors to quantify forecast uncertainty (Delle Monache et al, 2013;Junk et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

Worsnop

2018

Preprint

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Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind energy production must be balanced by other power 15 generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to twelve hours of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80-m wind speed observations from towers in Boulder, Colorado and near the Columbia River Gorge in eastern 20Oregon.We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill 25 scores for each method at predicting up-and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake Shuffle method yields the highest skill at predicting ramp events for these datasets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO site using any of the multivariate methods, because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize 30 decisions before ramp events occur.Wind Energ. Sci. Discuss., https://doi

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Probabilistic Weather Prediction with an Analog Ensemble

Cited by 292 publications

References 63 publications

Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case study of Uganda

Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case study of Uganda

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

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