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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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Robert M. Banta, Yelena L. Pichugina, W. Alan Brewer, Eric P. James, Joseph B. Olson, Stanley G. Benjamin, Jacob R. Carley, Laura Bianco, Irina V. Djalalova, James M. Wilczak, R. Michael Hardesty, Joel Cline, and Melinda C. Marquis

Abstract

To advance the understanding of meteorological processes in offshore coastal regions, the spatial variability of wind profiles must be characterized and uncertainties (errors) in NWP model wind forecasts quantified. These gaps are especially critical for the new offshore wind energy industry, where wind profile measurements in the marine atmospheric layer spanned by wind turbine rotor blades, generally 50–200 m above mean sea level (MSL), have been largely unavailable. Here, high-quality wind profile measurements were available every 15 min from the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL)’s high-resolution Doppler lidar (HRDL) during a monthlong research cruise in the Gulf of Maine for the 2004 New England Air Quality Study. These measurements were compared with retrospective NWP model wind forecasts over the area using two NOAA forecast-modeling systems [North American Mesoscale Forecast System (NAM) and Rapid Refresh (RAP)]. HRDL profile measurements quantified model errors, including their dependence on height above sea level, diurnal cycle, and forecast lead time. Typical model wind speed errors were ∼2.5 m s−1, and vector-wind errors were ∼4 m s−1. Short-term forecast errors were larger near the surface—30% larger below 100 m than above and largest for several hours after local midnight (biased low). Longer-term, 12-h forecasts had the largest errors after local sunset (biased high). At more than 3-h lead times, predictions from finer-resolution models exhibited larger errors. Horizontal variability of winds, measured as the ship traversed the Gulf of Maine, was significant and raised questions about whether modeled fields, which appeared smooth in comparison, were capturing this variability. If not, horizontal arrays of high-quality, vertical-profiling devices will be required for wind energy resource assessment offshore. Such measurement arrays are also needed to improve NWP models.

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Yelena L. Pichugina, Robert M. Banta, Joseph B. Olson, Jacob R. Carley, Melinda C. Marquis, W. Alan Brewer, James M. Wilczak, Irina Djalalova, Laura Bianco, Eric P. James, Stanley G. Benjamin, and Joel Cline

Abstract

Evaluation of model skill in predicting winds over the ocean was performed by comparing retrospective runs of numerical weather prediction (NWP) forecast models to shipborne Doppler lidar measurements in the Gulf of Maine, a potential region for U.S. coastal wind farm development. Deployed on board the NOAA R/V Ronald H. Brown during a 2004 field campaign, the high-resolution Doppler lidar (HRDL) provided accurate motion-compensated wind measurements from the water surface up through several hundred meters of the marine atmospheric boundary layer (MABL). The quality and resolution of the HRDL data allow detailed analysis of wind flow at heights within the rotor layer of modern wind turbines and data on other critical variables to be obtained, such as wind speed and direction shear, turbulence, low-level jet properties, ramp events, and many other wind-energy-relevant aspects of the flow. This study will focus on the quantitative validation of NWP models’ wind forecasts within the lower MABL by comparison with HRDL measurements. Validation of two modeling systems rerun in special configurations for these 2004 cases—the hourly updated Rapid Refresh (RAP) system and a special hourly updated version of the North American Mesoscale Forecast System [NAM Rapid Refresh (NAMRR)]—are presented. These models were run at both normal-resolution (RAP, 13 km; NAMRR, 12 km) and high-resolution versions: the NAMRR-CONUS-nest (4 km) and the High-Resolution Rapid Refresh (HRRR, 3 km). Each model was run twice: with (experimental runs) and without (control runs) assimilation of data from 11 wind profiling radars located along the U.S. East Coast. The impact of the additional assimilation of the 11 profilers was estimated by comparing HRDL data to modeled winds from both runs. The results obtained demonstrate the importance of high-resolution lidar measurements to validate NWP models and to better understand what atmospheric conditions may impact the accuracy of wind forecasts in the marine atmospheric boundary layer. Results of this research will also provide a first guess as to the uncertainties of wind resource assessment using NWP models in one of the U.S. offshore areas projected for wind plant development.

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