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137 results on '"Stefanova, Lydia"'

3. The Impact of Tropical SST Biases on the S2S Precipitation Forecast Skill over the Contiguous United States in the UFS Global Coupled Model

Author

Bai, Hedanqiu, primary, Li, Bin, additional, Mehra, Avichal, additional, Meixner, Jessica, additional, Moorthi, Shrinivas, additional, Ray, Sulagna, additional, Stefanova, Lydia, additional, Wang, Jiande, additional, Wang, Jun, additional, Worthen, Denise, additional, Yang, Fanglin, additional, and Stan, Cristiana, additional

Published
2023
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6. Scale Interactions

Author

Krishnamurti, T. N., Stefanova, Lydia, Misra, Vasubandhu, Krishnamurti, T.N., Stefanova, Lydia, and Misra, Vasubandhu

Published
2013
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8. Hurricane Observations

Author

Krishnamurti, T. N., Stefanova, Lydia, Misra, Vasubandhu, Krishnamurti, T.N., Stefanova, Lydia, and Misra, Vasubandhu

Published
2013
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10. Monsoons

Author

Krishnamurti, T. N., Stefanova, Lydia, Misra, Vasubandhu, Krishnamurti, T.N., Stefanova, Lydia, and Misra, Vasubandhu

Published
2013
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21. Radiative Forcing

Author

Krishnamurti, T. N., Stefanova, Lydia, Misra, Vasubandhu, Krishnamurti, T.N., Stefanova, Lydia, and Misra, Vasubandhu

Published
2013
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23. The impact of tropical Pacific SST biases on the S2S forecast skill over the North America in the UFS global coupled model

Author

Stan, Cristiana, primary, Krishnamurthy, V., additional, Bai, Hedanqiu, additional, Li, Bin, additional, Mehra, Avichal, additional, Meixner, Jessica, additional, Moorthi, Shrinivas, additional, Stefanova, Lydia, additional, Wang, Jiande, additional, Wang, Jun, additional, Worthen, Denise, additional, and Yang, Fanglin, additional

Published
2022
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26. The Impact of Tropical Pacific SST Biases on the S2S Forecast Skill over North America in the UFS Global Coupled Model.

Author

Stan, Cristiana, Krishnamurthy, V., Bai, Hedanqiu, Li, Bin, Mehra, Avichal, Meixner, Jessica, Moorthi, Shrinivas, Stefanova, Lydia, Wang, Jiande, Wang, Jun, Worthen, Denise, and Yang, Fanglin

Subjects
OCEAN temperature, TROPICAL cyclones, REGRESSION analysis, FORECASTING, SURFACE temperature, NUMERICAL weather forecasting, SUMMER
Abstract

The impact of tropical Pacific sea surface temperature (SST) biases on the deterministic skill of the Unified Forecast System (UFS) coupled model Prototype 5 is evaluated during weeks 1–4 of the forecast. The evaluation is limited to the contiguous United States (CONUS) and two seasons: boreal summer (June–September) and winter (December–March). The tropical SST in the UFS model is warmer than in observations and bias patterns show seasonal dependence especially in the central and western Pacific. During boreal summer, the bias is located north of the equator whereas in winter, the bias is located in the Southern Hemisphere. A regression analysis indicates a significant relationship between these SST biases and the biases in the surface temperature and precipitation over the CONUS along with midtroposphere large-scale circulation and North Pacific storm-track activity. The SST biases affect the biases in other fields from week 1 of the forecast and the impact becomes stronger as the lead time increases to week 4. The impact of SST biases on the biases in other fields show a qualitative relationship to the patterns of forecast errors of the fields. [ABSTRACT FROM AUTHOR]

Published
2023
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39. High-Resolution Subtropical Summer Precipitation Derived from Dynamical Downscaling of the NCEP-DOE Reanalysis: How Much Small-Scale Information Is Added by a Regional Model?

Author

Lim, Young-Kwon, Stefanova, Lydia B, Chan, Steven C, Schubert, Siegfried D, and OBrien, James J

Subjects
Meteorology And Climatology
Abstract

This study assesses the regional-scale summer precipitation produced by the dynamical downscaling of analyzed large-scale fields. The main goal of this study is to investigate how much the regional model adds smaller scale precipitation information that the large-scale fields do not resolve. The modeling region for this study covers the southeastern United States (Florida, Georgia, Alabama, South Carolina, and North Carolina) where the summer climate is subtropical in nature, with a heavy influence of regional-scale convection. The coarse resolution (2.5deg latitude/longitude) large-scale atmospheric variables from the National Center for Environmental Prediction (NCEP)/DOE reanalysis (R2) are downscaled using the NCEP Environmental Climate Prediction Center regional spectral model (RSM) to produce precipitation at 20 km resolution for 16 summer seasons (19902005). The RSM produces realistic details in the regional summer precipitation at 20 km resolution. Compared to R2, the RSM-produced monthly precipitation shows better agreement with observations. There is a reduced wet bias and a more realistic spatial pattern of the precipitation climatology compared with the interpolated R2 values. The root mean square errors of the monthly R2 precipitation are reduced over 93 (1,697) of all the grid points in the five states (1,821). The temporal correlation also improves over 92 (1,675) of all grid points such that the domain-averaged correlation increases from 0.38 (R2) to 0.55 (RSM). The RSM accurately reproduces the first two observed eigenmodes, compared with the R2 product for which the second mode is not properly reproduced. The spatial patterns for wet versus dry summer years are also successfully simulated in RSM. For shorter time scales, the RSM resolves heavy rainfall events and their frequency better than R2. Correlation and categorical classification (above/near/below average) for the monthly frequency of heavy precipitation days is also significantly improved by the RSM.

Published
2010
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