Assessing Impacts of the High-Frequency Assimilation of Surface Observations for the Forecast of Convection Initiation on 3 April 2014 within the Dallas–Fort Worth Test Bed
The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system and the continued advancement toward accurate high-resolution numerical weather prediction. The research test bed known as the Dalla...
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| Published in: | Monthly weather review Vol. 146; no. 11; pp. 3845 - 3872 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
01.11.2018
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| ISSN: | 0027-0644, 1520-0493 |
| Online Access: | Get full text |
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| Summary: | The Nationwide Network of Networks (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system and the continued advancement toward accurate high-resolution numerical weather prediction. The research test bed known as the Dallas–Fort Worth (DFW) Urban Demonstration Network was created to experiment with many kinds of mesoscale observations that could be used in a data assimilation system. Many nonconventional observations, including Earth Networks and Citizen Weather Observer Program surface stations, are combined with conventional operational data to form the test bed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information. In this study, the GSI-based EnKF system was used together with the WRF-ARW Model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hail storm case. Data denial experiments tested the impact of high-frequency (5 min) assimilation of nonconventional data on the timing and location of CI and subsequent storm evolution. Results showed nonconventional observations were necessary to capture details in the dryline structure causing localized enhanced convergence and leading to CI. Diagnosis of denial-minus-control fields showed the cumulative influence each observing network had on the resulting CI forecast. It was found that most of this impact came from the assimilation of thermodynamic observations in sensitive areas along the dryline gradient. Accurate metadata were found to be crucial toward the future application of nonconventional observations in high-resolution assimilation and forecast systems. |
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| ISSN: | 0027-0644 1520-0493 |
| DOI: | 10.1175/MWR-D-18-0177.1 |