A Four-Dimensional Variational System for Skillful Operational Prediction of Convective Storms

Nathan SNOOK; Qinghong ZHANG

doi:10.1007/s00376-016-6170-3

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A Four-Dimensional Variational System for Skillful Operational Prediction of Convective Storms

1. Center for the Analysis and Prediction of Storms, University of Oklahoma, Norman, OK 73072, USA

2. Department of Atmospheric & Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China

Manuscript accepted: 2016-07-12

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Chen X. C., K. Zhao, J. Z. Sun, B. W. Zhou, and W. C. Lee, 2016: Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case. Adv. Atmos. Sci.,33(10), doi: 10.1007/s00376-016-5290-0.10.1007/s00376-016-5290-0

1fce16de8882bed0d338e98f2117c986

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F304349214_Assimilating_Surface_Observations_in_a_Four-Dimensional_Variational_Doppler_Radar_Data_Assimilation_System_to_Improve_the_Analysis_and_Forecast_of_a_Squall_Line_Case

http://www.researchgate.net/publication/304349214_Assimilating_Surface_Observations_in_a_Four-Dimensional_Variational_Doppler_Radar_Data_Assimilation_System_to_Improve_the_Analysis_and_Forecast_of_a_Squall_Line_Case

Dawson, D. T., II, L. J. Wicker, E. R. Mansell, R. L. Tanamachi, 2012: Impact of the environmental low-level wind profile on ensemble forecasts of the 4 May 2007 Greensburg,Kansas, tornadic storm and associated mesocyclones. Mon. Wea. Rev., 140, 696-716, doi: 10.1175/MWR-D-11-00008.1.10.1175/MWR-D-11-00008.1

da666f8db9627c827f463e46b80d7135

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012MWRv..140..696D

http://adsabs.harvard.edu/abs/2012MWRv..140..696DNot Available

Kalnay E., H. Li, T. Miyoshi, S.-C. Yang, and J. Ballabrera-Poy, 2007: 4-D-Var or ensemble Kalman filter. Tellus A, 59, 758- 773.10.1111/j.1600-0870.2007.00261.x

eeaeb031663375c71249d7ee9fdd5858

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.1600-0870.2007.00261.x%2Fcitedby

http://onlinelibrary.wiley.com/doi/10.1111/j.1600-0870.2007.00261.x/citedbyABSTRACT We consider the relative advantages of two advanced data assimilation systems, 4-D-Var and ensemble Kalman filter (EnKF), currently in use or under consideration for operational implementation. With the Lorenz model, we explore the impact of tuning assimilation parameters such as the assimilation window length and background error covariance in 4-D-Var, variance inflation in EnKF, and the effect of model errors and reduced observation coverage. For short assimilation windows EnKF gives more accurate analyses. Both systems reach similar levels of accuracy if long windows are used for 4-D-Var. For infrequent observations, when ensemble perturbations grow non-linearly and become non-Gaussian, 4-D-Var attains lower errors than EnKF. If the model is imperfect, the 4-D-Var with long windows requires weak constraint. Similar results are obtained with a quasi-geostrophic channel model. EnKF experiments made with the primitive equations SPEEDY model provide comparisons with 3-D-Var and guidance on model error and 鈥榦bservation localization鈥. Results obtained using operational models and both simulated and real observations indicate that currently EnKF is becoming competitive with 4-D-Var, and that the experience acquired with each of these methods can be used to improve the other. A table summarizes the pros and cons of the two methods.

Schenkman A. D., M. Xue, A. Shapiro, K. Brewster, and J. D. Gao, 2011: The analysis and prediction of the 8-9 May 2007 Oklahoma tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using 3DVAR. Mon. Wea. Rev., 139, 224- 246.182a191f6387f27bd0454a0b6ed76247

http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2011MWRv..139..224S%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D12602

http://xueshu.baidu.com/s?wd=paperuri%3A%286f6fdfe93c08e7720b569feb3e50daca%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2011MWRv..139..224S%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D12602&ie=utf-8&sc_us=15673482078073305551

Snook N., M. Xue, and Y. Jung, 2015: Multiscale EnKF assimilation of radar and conventional observations and ensemble forecasting for a tornadic mesoscale convective system. Mon. Wea. Rev.,143, 1035-1057, doi: 10.1175/MWR-D-13-00262.1.10.1175/MWR-D-13-00262.1

b70d2a68a3390c8d93eefd7254f44d4b

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015MWRv..143.1035S

http://adsabs.harvard.edu/abs/2015MWRv..143.1035SNot Available

Stensrud, D. J., Coauthors, 2009: Convective-scale warn-on-forecast system: A vision for 2020. Bull. Am. Meteor. Soc., 90, 1487- 1499.10.1175/2009BAMS2795.1

bb0329c3bf2699ef090d188cfd78df93

http%3A%2F%2Fconnection.ebscohost.com%2Fc%2Farticles%2F45399304%2Fconvective-scale-warn-on-forecast-system-vision-2020

http://connection.ebscohost.com/c/articles/45399304/convective-scale-warn-on-forecast-system-vision-2020Abstract The National Oceanic and Atmospheric Administration's (NOAA's) National Weather Service (NWS) issues warnings for severe thunderstorms, tornadoes, and flash floods because these phenomena are a threat to life and property. These warnings are presently based upon either visual confirmation of the phenomena or the observational detection of proxy signatures that are largely based upon radar observations. Convective-scale weather warnings are unique in the NWS, having little reliance on direct numerical forecast guidance. Because increasing severe thunderstorm, tornado, and flash-flood warning lead times are a key NOAA strategic mission goal designed to reduce the loss of life, injury, and economic costs of these high-impact weather phenomena, a new warning paradigm is needed in which numerical model forecasts play a larger role in convective-scale warnings. This new paradigm shifts the warning process from warn on detection to warn on forecast, and it has the potential to dramatically increase warning lead times. A warn-on-forecast system is envisioned as a probabilistic convective-scale ensemble analysis and forecast system that assimilates in-storm observations into a high-resolution convection-resolving model ensemble. The building blocks needed for such a system are presently available, and initial research results clearly illustrate the value of radar observations to the production of accurate analyses of convective weather systems and improved forecasts. Although a number of scientific and cultural challenges still need to be overcome, the potential benefits are significant. A probabilistic convective-scale warn-on-forecast system is a vision worth pursuing.

Stensrud, D. J., Coauthors, 2013: Progress and challenges with Warn-on-Forecast. Atmos. Res.,123, 2-16, doi: 10.1016/j.atmosres.2012.04.004.10.1016/j.atmosres.2012.04.004

c3691d105fd03a0114de3772f8be7f41

http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS016980951200110X

http://www.sciencedirect.com/science/article/pii/S016980951200110XThe current status and challenges associated with two aspects of Warn-on-Forecast鈥攁 National Oceanic and Atmospheric Administration research project exploring the use of a convective-scale ensemble analysis and forecast system to support hazardous weather warning operations鈥攁re outlined. These two project aspects are the production of a rapidly-updating assimilation system to incorporate data from multiple radars into a single analysis, and the ability of short-range ensemble forecasts of hazardous convective weather events to provide guidance that could be used to extend warning lead times for tornadoes, hailstorms, damaging windstorms and flash floods. Results indicate that a three-dimensional variational assimilation system, that blends observations from multiple radars into a single analysis, shows utility when evaluated by forecasters in the Hazardous Weather Testbed and may help increase confidence in a warning decision. The ability of short-range convective-scale ensemble forecasts to provide guidance that could be used in warning operations is explored for five events: two tornadic supercell thunderstorms, a macroburst, a damaging windstorm and a flash flood. Results show that the ensemble forecasts of the three individual severe thunderstorm events are very good, while the forecasts from the damaging windstorm and flash flood events, associated with mesoscale convective systems, are mixed. Important interactions between mesoscale and convective-scale features occur for the mesoscale convective system events that strongly influence the quality of the convective-scale forecasts. The development of a successful Warn-on-Forecast system will take many years and require the collaborative efforts of researchers and operational forecasters to succeed.

Sun J. Z., N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 1642- 1661.10.1175/1520-0469(1997)0542.0.CO;2

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1997jats...54.1642s

refpaperuri:(3442f4439a64c47314ac768eea6c1f42)

http://adsabs.harvard.edu/abs/1997jats...54.1642sThe purpose of the research reported in this paper is to develop a variational data analysis system that can be used to assimilate data from one or more Doppler radars. In the first part of this two-part study, the technique used in this analysis system is described and tested using data from a simulated warm rain convective storm. The analysis system applies the 4D variational data assimilation technique to a cloud-scale model with a warm rain parameterization scheme. The 3D wind, thermodynamical, and microphysical fields are determined by minimizing a cost function, defined by the difference between both radar observed radial velocities and reflectivities (or rainwater mixing ratio) and their model predictions. The adjoint of the numerical model is used to provide the sensitivity of the cost function with respect to the control variables.Experiments using data from a simulated convective storm demonstrated that the variational analysis system is able to retrieve the detailed structure of wind, thermodynamics, and microphysics using either dual-Doppler or single-Doppler information. However, less accurate velocity fields are obtained when single-Doppler data were used. In both cases, retrieving the temperature field is more difficult than the retrieval of the other fields. Results also show that assimilating the rainwater mixing ratio obtained from the reflectivity data results in a better performance of the retrieval procedure than directly assimilating the reflectivity. It is also found that the system is robust to variations in the Z-qrelation, but the microphysical retrieval is quite sensitive to parameters in the warm rain scheme. The technique is robust to random errors in radial velocity and calibration errors in reflectivity.

Yussouf N., D. C. Dowell, L. J. Wicker, K. H. Knopfmeier, and D. M. Wheatley, 2015: Storm-scale data assimilation and ensemble forecasts for the 27 April 2011 severe weather outbreak in Alabama. Mon. Wea. Rev.,143, 3044-3066, doi: 10.1175/MWR-D-14-00268.1.10.1175/MWR-D-14-00268.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2015MWRv..143.3044Y

http://adsabs.harvard.edu/abs/2015MWRv..143.3044YNot Available

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A Four-Dimensional Variational System for Skillful Operational Prediction of Convective Storms

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