Influence of Assimilating Wind Profiling Radar Observations in Distinct Dynamic Instability Regions on the Analysis and Forecast of an Extreme Rainstorm Event in Southern China
Abstract
:1. Introduction
2. Weather Cases, Model, and Experimental Design
2.1. Weather Cases
2.2. Model Configuration and Data Assimilation System
2.3. Experimental Design and Evaluation
3. Results
3.1. Spatial Instability
3.2. Impact of Assimilating WPR Data on Initial Analysis
3.3. Impact of WPR Data on the Forecast
3.4. Sensitivity of the Selection of WPR Observations
3.5. A Case Study
4. Summary and Discussions
5. Conclusions
- The WPR observations have a larger impact on the analysis in the SIA than in the WIA, resulting in about 12% lower observational fitting errors of the analysis in the SIA than in the WIA. The analysis of E_SIA accounts for about 60% error reduction in E_ALL with all of the WPR observations assimilated.
- Although the selection of the percentile of observations will affect the number of the observations in SIA and WIA, a sensitivity analysis shows that the E_SIA always has more improvements in the analysis than the E_WIA.
- The WPR observations in SIA help to reduce the background errors corresponding to the shear line, which contributes to the improvement of the wind and precipitation forecast.
- The critical regions with strong dynamic instability can be effectively identified by the ensemble spread, which may be helpful in the future optimal design of the observing network.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Zhang, F.; Sippel, J.A. Effects of moist convection on hurricane predictability. J. Atmos. Sci. 2009, 66, 1944–1961. [Google Scholar] [CrossRef]
- Sippel, J.A.; Zhang, F. Factors affecting the predictability of Hurricane Humberto (2007). J. Atmos. Sci. 2010, 67, 1759–1778. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.; Wang, X. Direct assimilation of radar reflectivity without tangent linear and adjoint of the nonlinear observation operator in the GSI-Based EnVar System: Methodology and experiment with the 8 May 2003 Oklahoma City tornadic supercell. Mon. Weather Rev. 2017, 145, 1447–1471. [Google Scholar] [CrossRef]
- Wang, H.; Liu, Y.; Cheng, W.Y.Y.; Zhao, T.; Xu, M.; Liu, Y.; Shen, S.; Calhoun, K.M.; Fierro, A.O. Improving lightning and precipitation prediction of severe convection using lightning data assimilation with NCAR WRF-RTFDDA. J. Geophys. Res. Atmos. 2017, 122, 12296–12316. [Google Scholar] [CrossRef]
- Feng, J.; Toth, Z.; Peña, M. Spatial Extended Estimates of Analysis and Short-Range Forecast Error Variances. Tellus A 2017, 69, 1325301. [Google Scholar] [CrossRef] [Green Version]
- Wang, B.; Zou, X.L.; Zhu, J. Data assimilation and its applications. Proc. Natl. Acad. Sci. USA 2000, 97, 11143–11144. [Google Scholar] [CrossRef] [Green Version]
- Kalnay, E. Atmospheric Modeling, Data Assimilation and Predictability, 1st ed.; Cambridge Univerity Press: Cambridge, UK, 2002; pp. 136–240. [Google Scholar]
- Toth, Z.; Kalnay, E. Ensemble Forecasting at NMC: The Generation of Perturbations. Bull. Amer. Meteor. Soc. 1993, 74, 2317–2330. [Google Scholar] [CrossRef] [Green Version]
- Toth, Z.; Kalnay, E. Ensemble Forecasting at NCEP and the Breeding Method. Mon. Weather Rev. 1997, 125, 3297–3319. [Google Scholar] [CrossRef]
- Mu, M.; Zhou, F.; Wang, H. A Method for Identifying the Sensitive Areas in Targeted Observations for Tropical Cyclone Prediction: Conditional Nonlinear Optimal Perturbation. Mon.Weather Rev. 2009, 137, 1623–1639. [Google Scholar] [CrossRef] [Green Version]
- Majumdar, S.J. A review of targeted obaservations. Bull. Amer. Meteor. Soc. 2016, 97, 2287–2303. [Google Scholar] [CrossRef]
- Majumdar, S.J.; Bishop, C.H.; Buizza, R.; Gelaro, R. A comparison of ensemble-transform Kalman-filter targeting guidance with ECMWF and NRL total-energy singular-vector guidance. Q. J. Roy. Meteor. Soc. 2010, 128, 2527–2549. [Google Scholar] [CrossRef]
- Huang, L.; Meng, Z. Quality of the Target Area for Metrics with Different Nonlinearities in a Mesoscale Convective System. Mon.Weather Rev. 2014, 142, 2379–2397. [Google Scholar] [CrossRef] [Green Version]
- Feng, J.; Ding, R.Q.; Li, J.P.; Toth, Z. Comparison of nonlinear local Lyapunov vectors and bred vectors in estimating the spatial distribution of error growth. J. Atmos. Sci. 2018, 75, 1073–1087. [Google Scholar] [CrossRef]
- Qin, X.; Mu, M. Influence of conditional nonlinear optimal perturbations sensitivity on typhoon track forecasts. Q. J. Roy. Meteor. Soc. 2012, 138, 185–197. [Google Scholar] [CrossRef]
- Aksoy, A.; Lorsolo, S.; Vukicevic, T.; Sellwood, K.J.; Aberson, S.D.; Zhang, F. The HWRF HurricaneEnsemble Data Assimilation System (HEDAS) forhigh-resolution data: The impact of airborne Dopplerradar observations in an OSSE. Mon. Weather Rev. 2012, 140, 1843–1862. [Google Scholar] [CrossRef] [Green Version]
- Oger, N.; Pannekoucke, O.; Doerenbecher, A.; Arbogast, P. Assessing the influence of the model trajectory in the adaptiveobservation Kalman Filter Sensitivity method. Q. J. Roy. Meteor. Soc. 2012, 138, 813–825. [Google Scholar] [CrossRef]
- Zeng, X.; Atlas, R.; Birk, R.J.; Carr, F.H.; Carrier, M.J.; Cucurull, L.; Hooke, W.H.; Kalnay, E.; Murtugudde, R.; Posselt, D.J.; et al. Use of observing system simulation expeiments in the United States. Bull. Amer. Meteor. Soc. 2020, 101, E1427–E1438. [Google Scholar] [CrossRef]
- Wang, D.; Zheng, R.; Wang, G.L.; Zhu, L.J.; Tian, W.H.; Li, F. A study on assimilation of wind profiling radar data in GRAPES-Meso model (in Chinese). Chin. J. Atmos. Sci. 2019, 43, 634–654. [Google Scholar]
- Zhong, S.Y.; Fast, J.D.; Bian, X.D. A case study of the Great Plains low-level jet using wind profiler network data and a high-resolution mesoscale model. Mon. Weather Rev. 1996, 124, 785–806. [Google Scholar] [CrossRef] [Green Version]
- Liu, S.Y.; Zheng, Y.G.; Tao, Z.Y. The analysis of the relationship between pulse of LLJ and heavy rain using wind profiler data. J. Trop. Meteor. 2003, 9, 158–163. [Google Scholar]
- Zhang, X.; Luo, Y.; Wan, Q.; Ding, W.; Sun, J. Impact of Assimilating Wind Profiling Radar Observations on Convection-Permitting Quantitative Precipitation Forecasts during SCMREX. Weather Forecast. 2016, 31, 1271–1292. [Google Scholar] [CrossRef]
- Kuo, Y.H.; Guo, Y.R. Dynamic initialization using observations from a hypothetical network of profilers. Mon. Weather Rev. 1989, 117, 1975–1998. [Google Scholar] [CrossRef] [Green Version]
- Smith, T.L.; Benjamin, S.G. Impact of network wind profiler data on a 3-h data assimilation system. Bull. Amer. Meteor. Soc. 1993, 74, 801–807. [Google Scholar] [CrossRef] [Green Version]
- Ishihara, M.; Kato, Y.; Abo, T.; Kobayashi, K.; Izumikawa, Y. Characteristics and performance of the operational wind profiler network of the Japan Meteorological Agency. J. Meteor. Soc. Jpn. 2006, 84, 1085–1096. [Google Scholar] [CrossRef] [Green Version]
- Park, S.Y.; Lee, H.W.; Lee, S.H.; Kim, D.H. Impact of wind profiler data assimilation on wind field assessment over coastal areas. Asian J. Atmos. Environ. 2010, 4, 198–210. [Google Scholar] [CrossRef] [Green Version]
- Benjamin, S.G.; Schwartz, B.E.; Koch, S.E.; Szoke, E.J. The value of wind profiler data in U.S. weather forecasting. Bull. Amer. Meteor. Soc. 2004, 85, 1871–1886. [Google Scholar] [CrossRef]
- Benjamin, S.G.; Jamison, B.D.; Moninger, W.R.; Sahm, S.R.; Schwartz, B.E.; Schlatter, T.W. Relative short-range forecast impact from aircraft, profiler, radiosonde, VAD, GPS-PW, METAR, and mesonet observations via the RUC hourly assimilation cycle. Mon. Weather Rev. 2010, 138, 1319–1343. [Google Scholar] [CrossRef]
- Zhang, X.; Wan, Q.L.; Xue, J.S.; Ding, W.Y.; Li, H.R. Quality control of wind profile radar data and its application to assimilation. Acta Meteor. Sin. 2015, 73, 159–176. (In Chinese) [Google Scholar]
- Elsberry, R.L.; Carr, L.E., III. Consensus of dynamical tropical cyclone track forecasts—Errors versus spread. Mon. Weather Rev. 2000, 128, 4131–4138. [Google Scholar] [CrossRef]
- Goerss, J.S. Tropical cyclone track forecasts using an ensemble of dynamical models. Mon. Weather Rev. 2000, 128, 1187–1193. [Google Scholar] [CrossRef]
- Lewis, J.M. Roots of Ensemble Forecasting. Mon. Weather Rev. 2005, 133, 1865–1885. [Google Scholar] [CrossRef]
- Leutbecher, M.; Palmer, T.N. Ensemble forecasting. J. Comput. Phys. 2008, 227, 3515–3539. [Google Scholar] [CrossRef]
- Skamarock, W.C.; Klemp, B.; Dudhia, J.; Gill, O.; Barker, D.; Duda, G.; Huang, X.G.; Wang, W.; Powers, G. A Description of the Advanced Research WRF Version 3; NCAR Technical Notes (No. NCAR/TN-4751STR); National Center for Atmospheric Research: Boulder, CO, USA, 2008; pp. 3–7. [Google Scholar]
- Hong, S.Y.; Dudhia, J.; Chen, S.H. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Weather Rev. 2004, 132, 103–120. [Google Scholar] [CrossRef]
- Mlawer, E.J.; Taubman, S.J.; Brown, P.D.; Lacono, M.J.; Clough, S.A. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlate-k model for the longwave. J. Geophys. Res. Atmos. 1997, 102, 16663–16682. [Google Scholar] [CrossRef] [Green Version]
- Chou, M.D.; Suarez, M.J. A solar radiation parameterization for atmospheric studies. In Technical Report Series on Global Modeling and Data Assimilation; No. NASA/TM-1999-104606; Suarez, M.J., Ed.; NASA Technical Memorandum: Greenbelt, MD, USA, 1999; Volume 15, p. 40. [Google Scholar]
- Grell, G.A.; Devenyi, D. A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophy. Res. Lett. 2002, 29, 38-1–38-4. [Google Scholar] [CrossRef] [Green Version]
- Janjic, Z.I. The step-mountain Eta coordinate model: Further developments of the convection, viscous layer, and turbulence closure schemes. Mon. Weather Rev. 1994, 122, 927–945. [Google Scholar] [CrossRef] [Green Version]
- Barker, D.M.; Huang, W.; Guo, Y.R.; Xiao, Q.N. A three-dimensional (3DVAR) data assimilation system for use with MM5: Implementation and initial results. Mon. Weather Rev. 2004, 132, 897–914. [Google Scholar] [CrossRef] [Green Version]
- Parrish, D.F.; Derber, J.C. The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Weather Rev. 1992, 120, 1747–1763. [Google Scholar] [CrossRef]
Absolute Error | Standard Deviation | |||
---|---|---|---|---|
U | V | U | V | |
SIA | 2.68 | 2.97 | 2.78 | 2.95 |
WIA | 2.36 | 2.42 | 2.51 | 2.53 |
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Liu, D.; Huang, C.; Feng, J. Influence of Assimilating Wind Profiling Radar Observations in Distinct Dynamic Instability Regions on the Analysis and Forecast of an Extreme Rainstorm Event in Southern China. Remote Sens. 2022, 14, 3478. https://doi.org/10.3390/rs14143478
Liu D, Huang C, Feng J. Influence of Assimilating Wind Profiling Radar Observations in Distinct Dynamic Instability Regions on the Analysis and Forecast of an Extreme Rainstorm Event in Southern China. Remote Sensing. 2022; 14(14):3478. https://doi.org/10.3390/rs14143478
Chicago/Turabian StyleLiu, Deqiang, Chuanrong Huang, and Jie Feng. 2022. "Influence of Assimilating Wind Profiling Radar Observations in Distinct Dynamic Instability Regions on the Analysis and Forecast of an Extreme Rainstorm Event in Southern China" Remote Sensing 14, no. 14: 3478. https://doi.org/10.3390/rs14143478