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Kermit K. Keeter and Joel W. Cline

Abstract

The Local Objective Guidance for Predicting Precipitation Type (LOG/PT) consists of regression equations and nomograms. LOG/PT was designed to address problems inherent in forecasting wintry precipitation across North Carolina, where frozen and freezing precipitation are relatively infrequent, often occurring from mixed precipitation events, and where even small amounts can disrupt communities. Moreover, LOG/PT is an example of employing developmental strategies to maximize the yield from limited resources to produce an objective forecast tool for a critical local-forecast problem.

Stepwise linear regression, with modifications to approximate the sigmoid curve associated with logit regression, was used to derive relationships between precipitation type and 1000–700-, 850–700-, and 1000–850-mb thickness values from radiosonde observations (raobs). The soundings were concurrent with, or within 12 h prior to, the onset of the precipitation at the prediction sites.

The regression portion of LOG/PT discriminates frozen from liquid precipitation. LOG/PT demonstrated skill in detecting frozen events and in correctly specifying frozen-precipitation forecasts. When used in a perfect prog sense with the nested grid model (NGM) thickness forecasts, LOG/PT showed a tendency to overforecast the frequency of snow. LOG/PT's forecast success was limited by its dependence upon a one-raob prediction site with raobs taken 12 h part, and the characteristics of the NGM 1000–850-mb thickness forecasts. Operationally, the regression portion has been useful in predicting the location of the snow/rain boundary in storms with relatively narrow precipitation-type transition zones. In addition, nomograms were prepared to differentiate mixed-precipitation events that resulted in measurable amounts of frozen precipitation from those producing only a trace of frozen precipitation, and to identify icing events. Operationally, the nomograms are used to specify precipitation type in storms with broad bands of mixed precipitation.

In addition to statistical samples, the operational experience of local forecasters was used to gain insight concerning the forecast performance of LOG/PT and the Model Output Statistics (MOS) Probability of Precipitation Type (PoPT) guidance from the Limited-Area Fine Mesh (LFM) model. LOG/PT provides the forecaster with an additional source of objective precipitation-type guidance that can be helpful, especially when forecast errors in the LFM limit the accuracy of the resulting MOS guidance.

Future research efforts directed toward improving the LOG/PT guidance, and increasing the forecaster's knowledge of synoptic features and physical processes that determine precipitation type are also discussed.

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Irina V. Djalalova, Joseph Olson, Jacob R. Carley, Laura Bianco, James M. Wilczak, Yelena Pichugina, Robert Banta, Melinda Marquis, and Joel Cline

Abstract

During the summer of 2004 a network of 11 wind profiling radars (WPRs) was deployed in New England as part of the New England Air Quality Study (NEAQS). Observations from this dataset are used to determine their impact on numerical weather prediction (NWP) model skill at simulating coastal and offshore winds through data-denial experiments. This study is a part of the Position of Offshore Wind Energy Resources (POWER) experiment, a Department of Energy (DOE) sponsored project that uses National Oceanic and Atmospheric Administration (NOAA) models for two 1-week periods to measure the impact of the assimilation of observations from 11 inland WPRs. Model simulations with and without assimilation of the WPR data are compared at the locations of the inland WPRs, as well as against observations from an additional WPR and a high-resolution Doppler lidar (HRDL) located on board the Research Vessel Ronald H. Brown (RHB), which cruised the Gulf of Maine during the NEAQS experiment. Model evaluation in the lowest 2 km above the ground shows a positive impact of the WPR data assimilation from the initialization time through the next five to six forecast hours at the WPR locations for 12 of 15 days analyzed, when offshore winds prevailed. A smaller positive impact at the RHB ship track was also confirmed. For the remaining three days, during which time there was a cyclone event with strong onshore wind flow, the assimilation of additional observations had a negative impact on model skill. Explanations for the negative impact are offered.

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Laura Bianco, Irina V. Djalalova, James M. Wilczak, Joel Cline, Stan Calvert, Elena Konopleva-Akish, Cathy Finley, and Jeffrey Freedman

Abstract

A wind energy Ramp Tool and Metric (RT&M) has been developed out of recognition that during significant ramp events (large changes in wind power over short periods of time ) it is more difficult to balance the electric load with power production than during quiescent periods between ramp events. A ramp-specific metric is needed because standard metrics do not give special consideration to ramp events and hence may not provide an appropriate measure of model skill or skill improvement. This RT&M has three components. The first identifies ramp events in the power time series. The second matches in time forecast and observed ramps. The third determines a skill score of the forecast model. This is calculated from a utility operator’s perspective, incorporates phase and duration errors in time as well as power amplitude errors, and recognizes that up and down ramps have different impacts on grid operation. The RT&M integrates skill over a matrix of ramp events of varying amplitudes and durations.

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