The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities
Abstract
Rapid advancements of computer technologies in recent years made the real-time transferring and integration of high-volume, multisource data at a centralized location a possibility. The Multi-Radar Multi-Sensor (MRMS) system recently implemented at the National Centers for Environmental Prediction demonstrates such capabilities by integrating about 180 operational weather radars from the conterminous United States and Canada into a seamless national 3D radar mosaic with very high spatial (1 km) and temporal (2 min) resolution. The radar data can be integrated with high-resolution numerical weather prediction model data, satellite data, and lightning and rain gauge observations to generate a suite of severe weather and quantitative precipitation estimation (QPE) products. This paper provides an overview of the initial operating capabilities of MRMS QPE products.
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Cites
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., & Pasteris, P. P. (2008) · International Journal of Climatology · doi:10.1002/joc.1688 · Cited by 3,100
A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain
Daly, C., Neilson, R. P., & Phillips, D. L. (1994) · Journal of Applied Meteorology · doi:10.1175/1520-0450(1994)033<0140:astmfm>2.0.co;2 · Cited by 2,782
The WSR-88D Rainfall Algorithm
Fulton, R. A., Breidenbach, J. P., Seo, D.-J., Miller, D. A., & O’Bannon, T. (1998) · Weather and Forecasting · doi:10.1175/1520-0434(1998)013<0377:twra>2.0.co;2 · Cited by 968
How Well Are We Measuring Snow: The NOAA/FAA/NCAR Winter Precipitation Test Bed
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer, A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., et al. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00052.1 · Cited by 697
National Mosaic and Multi-Sensor QPE (NMQ) System: Description, Results, and Future Plans
Zhang, J., Howard, K., Langston, C., Vasiloff, S., Kaney, B., Arthur, A., Van Cooten, S., Kelleher, K., Kitzmiller, D., Ding, F., et al. (2011) · Bulletin of the American Meteorological Society · doi:10.1175/2011bams-d-11-00047.1 · Cited by 452
Self-citationThe Warning Decision Support System–Integrated Information
Lakshmanan, V., Smith, T., Stumpf, G., & Hondl, K. (2007) · Weather and Forecasting · doi:10.1175/waf1009.1 · Cited by 288
Accuracy of precipitation measurements for hydrologic modeling
Larson, L. W., & Peck, E. L. (1974) · Water Resources Research · doi:10.1029/wr010i004p00857 · Cited by 272
Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities
Smith, T. M., Lakshmanan, V., Stumpf, G. J., Ortega, K. L., Hondl, K., Cooper, K., Calhoun, K. M., Kingfield, D. M., Manross, K. L., Toomey, R., et al. (2016) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-14-00173.1 · Cited by 248
Self-citationThe Great Colorado Flood of September 2013
Gochis, D., Schumacher, R., Friedrich, K., Doesken, N., Kelsch, M., Sun, J., Ikeda, K., Lindsey, D., Wood, A., Dolan, B., et al. (2015) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-13-00241.1 · Cited by 233
Challenges in obtaining reliable measurements of point rainfall
Sieck, L. C., Burges, S. J., & Steiner, M. (2007) · Water Resources Research · doi:10.1029/2005wr004519 · Cited by 196
Cited by
Skilful precipitation nowcasting using deep generative models of radar
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03854-z · Cited by 1,024
Skilful nowcasting of extreme precipitation with NowcastNet
Zhang, Y., Long, M., Chen, K., Xing, L., Jin, R., Jordan, M. I., & Wang, J. (2023) · Nature · doi:10.1038/s41586-023-06184-4 · Cited by 465
Dowell, D. C., Alexander, C. R., James, E. P., Weygandt, S. S., Benjamin, S. G., Manikin, G. S., Blake, B. T., Brown, J. M., Olson, J. B., Hu, M., et al. (2022) · Weather and Forecasting · doi:10.1175/waf-d-21-0151.1 · Cited by 399
Geographically weighted regression based methods for merging satellite and gauge precipitation
Chao, L., Zhang, K., Li, Z., Zhu, Y., Wang, J., & Yu, Z. (2018) · Journal of Hydrology · doi:10.1016/j.jhydrol.2018.01.042 · Cited by 292
Qi, Y., Martinaitis, S., Zhang, J., & Cocks, S. (2016) · Journal of Hydrometeorology · doi:10.1175/jhm-d-15-0188.1 · Cited by 73
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Metadata
- Authors
- Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H., Wang, Y., Cocks, S., Martinaitis, S., Arthur, A., Cooper, K., Brogden, J., & Kitzmiller, D.
- Venue
- Bulletin of the American Meteorological Society
- Publisher
- American Meteorological Society
- Published
- 2016-04-01
- Volume
- 97
- Issue
- 4
- Pages
- 621-638
- ISSN
- 0003-0007 (ISSN-L)
- 1520-0477
- OpenAlex
- W1522265489
- Cited by
- 657
- References
- 20
- FWCI
- 23.83
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, J., Howard, K., Langston, C., Kaney, B., Qi, Y., Tang, L., Grams, H., Wang, Y., Cocks, S., Martinaitis, S., Arthur, A., Cooper, K., Brogden, J., & Kitzmiller, D. (2016). Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities. Bulletin of the American Meteorological Society. https://doi.org/10.1175/bams-d-14-00174.1
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