Tuning AutoNowcaster Automatically

Lakshmanan, Valliappa; Crockett, John; Sperow, Kenneth; Ba, Mamoudou B.; Xin, Lingyan

doi:10.1175/waf-d-11-00141.1

Cited by 12 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The AutoNowCaster (ANC; Mueller et al 2003;Lakshmanan et al 2012) generates 60-min nowcasts of convective likelihood (CL). As described in BXCS, these data span the period 11 June 2012-30 September 2012 (from 1400 to 2359 UTC each day) and lie on a 0.028 3 0.028 latitude-longitude grid that spans 318-458N and 948-718W (i.e., most of the United States east of the Mississippi River).…”

Section: B Convection Occurrence Nowcastsmentioning

confidence: 99%

Using a Coevolutionary Postprocessor to Improve Skill for Both Forecasts of Surface Temperature and Nowcasts of Convection Occurrence

Roebber

Crockett

2019

Monthly Weather Review

Self Cite

View full text Add to dashboard Cite

An evolutionary programming postprocessor, using coevolution in a predator–prey ecosystem model, is developed and applied both to 72-h, 2-m temperature forecasts for the conterminous United States and southern Canada and to 60-min nowcasts of convection occurrence for the United States east of 94°W. The new approach improves deterministic and probabilistic forecasts of surface temperature relative to bias-corrected numerical weather prediction forecasts and to an earlier version of evolutionary programming forecasts for these same data. The new method also improves deterministic performance for an artificial neural network trained and evaluated for these same data. Additionally, the new approach substantially improves these forecasts’ reliability, as evidenced by reductions in the occurrence of excessive outliers in the rank histogram. The coevolutionary postprocessor also improves deterministic nowcasts of convection occurrence when compared to those produced by the National Weather Service’s AutoNowCaster system and to those obtained using multiple logistic regression. Notably, the degree of improvement relative to traditional methods appears to be problem dependent, while the training and implementation of such a system requires additional effort. However, the coevolutionary system is shown to be robust to imbalances between the frequency of positive and null events in the training data, unlike many postprocessing methods; to be implementable and effective in an adaptive mode, removing the need for retraining as inputs (such as numerical weather prediction model data) change; and to provide a useful, alternative perspective on the likelihood of event occurrence when used in combination with other methods.

show abstract

Section: B Convection Occurrence Nowcastsmentioning

confidence: 99%

Using a Coevolutionary Postprocessor to Improve Skill for Both Forecasts of Surface Temperature and Nowcasts of Convection Occurrence

Roebber

Crockett

2019

Monthly Weather Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…The clustering and assessed motion vector can be based on a user-defined band of reflectivity values, such that the motion vector applied to generate the forecast reflectivity image can be based on the motion of sections of the image defined within that reflectivity band. Therefore, by changing the reflectivity band it is possible to alter the forecast motion of convective storms (Sen Roy et al 2014;Lakshmanan et al 2012) and, if a number of different reflectivity bands are used, an ensemble of different forecast fields can be generated that provide a range of solutions representing some realistic indication of the uncertainty in the forecast storm motion (Fox et al, 2007(Fox et al, , 2008.…”

Section: Application To Nowcast Ensemblementioning

confidence: 99%

Applications of Bayesian Procrustes shape analysis to ensemble radar reflectivity nowcast verification

Fox

Micheas

Peng

2016

Atmospheric Research

View full text Add to dashboard Cite

This paper introduces the use of Bayesian full Procrustes shape analysis in object-oriented meteorological applications. In particular, the Procrustes methodology is used to generate mean forecast precipitation fields from a set of ensemble forecasts. This approach has advantages over other ensemble averaging techniques in that it can produce a forecast that retains the morphological features of the precipitation structures and present the range of forecast outcomes represented by the ensemble. The production of the ensemble mean avoids the problems of smoothing that result from simple pixel or cell averaging, while producing credible sets that retain information on ensemble spread. Also in this paper, the full Bayesian Procrustes scheme is used as an object verification tool for precipitation forecasts. This is an extension of a previously presented Procrustes shape analysis based verification approach into a full Bayesian format designed to handle the verification of precipitation forecasts that match objects from an ensemble of forecast fields to a single truth image. The methodology is tested on radar reflectivity nowcasts produced in the Warning Decision Support System -Integrated Information (WDSS-II) by varying parameters in the K-means cluster tracking scheme.

show abstract

“…These forecast equations were applied to next-day minimum temperature and were shown to improve skill by 27% relative to model output statistics (MOS; Glahn and Lowry 1972) in an independent test dataset. Lakshmanan et al (2012) used a genetic algorithm approach to provide regional tuning of the weights in an automated thunderstorm nowcasting system.…”

Section: Evolutionary Programming (Ep)mentioning

confidence: 99%

Using Evolutionary Programming to Generate Skillful Extreme Value Probabilistic Forecasts

Roebber

2013

Monthly Weather Review

View full text Add to dashboard Cite

A new method for producing large member ensemble forecasts, using a variation of evolutionary programming (EP), is presented. A series of increasingly complex datasets are used to demonstrate the method and its potential utility. First, EP performance is considered using training and test ''atmospheric'' data derived from the Lorenz low-order dynamical system. Next, a modified form of the intermediate-order Lorenz model representing 500-hPa height is used. Finally, EP performance is evaluated using real 500-hPa data and day-3 forecasts of the reforecast model. As expected, short observational records limit the potential of the EP method by preventing proper training. A kind of perfect-prog approach, in which the EP ensemble is trained using a large ''observational'' sample constructed from the imperfect model, is shown to be a potentially viable means of counteracting limits to the observed record. The EP ensembles are shown to outperform dynamical model ensembles at the extremes, and to be competitive with dynamical models across a wide range of values of the variables of interest. In particular, the EP ensembles improve resolution compared to dynamical model ensembles, which suggests that further skill might be obtainable by also improving reliability using additional postprocessing. Future applications of the ensemble EP method are briefly discussed.

show abstract

Tuning AutoNowcaster Automatically

Cited by 12 publications

References 19 publications

Using a Coevolutionary Postprocessor to Improve Skill for Both Forecasts of Surface Temperature and Nowcasts of Convection Occurrence

Using a Coevolutionary Postprocessor to Improve Skill for Both Forecasts of Surface Temperature and Nowcasts of Convection Occurrence

Applications of Bayesian Procrustes shape analysis to ensemble radar reflectivity nowcast verification

Using Evolutionary Programming to Generate Skillful Extreme Value Probabilistic Forecasts

Contact Info

Product

Resources

About