Evaluation of NU-WRF rainfall forecasts for IFloodS

Journal of Hydrometeorology

Volumn 17, Issue 5, 2016, Pages 1317-1335

  Wu, Di ^a,b   Peters Lidard, Christa ^a   Tao, Wei Kuo ^a   Petersen, Walter ^a  

^a NASA GODDARD SPACE FLIGHT CENTER   (United States)

^b SCIENCE SYSTEMS AND APPLICATIONS INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FLOODING; LAND INFORMATION SYSTEM; PRECIPITATION INTENSITY; PROBABILITY DENSITY FUNCTION; RAINFALL; REAL TIME; WEATHER FORECASTING;

IOWA; UNITED STATES;

EID: 84966269237     PISSN: 1525755X     EISSN: 15257541     Source Type: Journal    
DOI: 10.1175/JHM-D-15-0134.1     Document Type: Article

References (62)

1. Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future. J. Climate, 17, 2493-2525, doi:10.1175/1520-0442(2004)017,2493:RATCPP.2.0.CO;2.
2. Baldauf, M., A. Seifert, J. Förstner, D. Majewski, M. Raschendorfer, and T. Reinhardt, 2011: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Mon. Wea. Rev., 139, 3887-3905, doi:10.1175/MWR-D-10-05013.1.
3. Bélair, S., and J. Mailhot, 2001: Impact of horizontal resolution on the numerical simulation of a midlatitude squall line: Implicit versus explicit condensation. Mon. Wea. Rev., 129, 2362-2376, doi:10.1175/1520-0493(2001)129,2362:IOHROT.2.0.CO;2.
4. Betts, A. K., 1986: A new convective adjustment scheme. Part I:Observational and theoretical basis. Quart. J. Roy. Meteor. Soc., 112, 677-691, doi:10.1002/qj.49711247307.
5. Betts, A. K., and M. J. Miller, 1986: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Quart. J. Roy. Meteor. Soc., 112, 693-709, doi:10.1002/qj.49711247308.
6. Carbone, R. E., J. D. Tuttle, D. A. Ahijevych, and S. B. Trier, 2002: Inferences of predictability associated with warm season precipitation episodes. J. Atmos. Sci., 59, 2033-2056, doi:10.1175/1520-0469(2002)059,2033:IOPAWW.2.0.CO;2.
7. Case, J. L., S. V. Kumar, J. Srikishen, and G. J. Jedlovec, 2011: Improving numerical weather predictions of summertime precipitation over the southeastern United States through a high-resolution initialization of the surface state. Wea. Forecasting, 26, 785-807, doi:10.1175/2011WAF2222455.1.
8. Chin, M., R. B. Rood, S.-J. Lin, J. F. Muller, and A. M. Thomspon, 2000: Atmospheric sulfur cycle in the global modelGOCART:Model description and global properties. J. Geophys. Res., 105, 24 671-24 687, doi:10.1029/2000JD900384.
9. Chou, M.-D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. Tech. Memo. NASA/TM-1999-104606, Vol. 15, 38 pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/Chou136.pdf]
10. Cosgrove, B. A., and Coauthors, 2003a: Land surface model spinup behavior in the North American Land Data Assimilation System (NLDAS). J. Geophys. Res., 108, 8845, doi:10.1029/2002JD003316.
11. Crago, R., 1996: Conservation and variability of the evaporative fraction during the daytime. J. Hydrol., 180, 173-194, doi:10.1016/0022-1694(95)02903-6.
12. Crago, R., and W. Brutsaert, 1996: Daytime evaporation and the selfpreservation of the evaporative fraction and the Bowen ratio. J. Hydrol., 178, 241-255, doi:10.1016/0022-1694(95)02803-X.
13. Cunha, L. K., J. A. Smith, W. F. Krajewski, M. L. Baeck, and B.-C. Seo, 2015: NEXRADNWSpolarimetric precipitation product evaluation for I Flood S. J. Hydrometeor., 16, 1676-1699, doi:10.1175/JHM-D-14-0148.1.
14. Cuo, L., T. C. Pagano, and Q. J. Wang, 2011: A review of quantitative precipitation forecasts and their use in short-to medium-range streamflow forecasting. J. Hydrometeor., 12, 713-729, doi:10.1175/2011JHM1347.1.
15. Done, J., C. A. Davis, and M. Weisman, 2004: The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecasting (WRF) Model. Atmos. Sci. Lett., 5, 110-117, doi:10.1002/asl.72.
16. Ebert, E. E., and J. L. McBride, 2000: Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol., 239, 179-202, doi:10.1016/S0022-1694(00)00343-7.
17. Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.
18. Fritsch, J. M., and R. E. Carbone, 2004: Improving quantitative precipitation forecasts in the warm season: A USWRP research and development strategy. Bull. Amer. Meteor. Soc., 85, 955-965, doi:10.1175/BAMS-85-7-955.
19. Gallus, W. A., Jr., 1999: Eta simulations of three extreme rainfall events:Impact of resolution and choice of convective scheme. Wea. Forecasting, 14, 405-426, doi:10.1175/1520-0434(1999)014,0405:ESOTEP.2.0.CO;2.
20. Gallus, W. A., Jr., and M. Segal, 2000: Sensitivity of forecast rainfall in a Texas convective system to soil moisture and convective scheme. Wea. Forecasting, 15, 509-526, doi:10.1175/1520-0434(2000)015,0509:SOFRIA.2.0.CO;2.
21. Gochis, D. J., W. J. Shuttleworth, and Z.-L. Yang, 2002: Sensitivity of the modeled North American monsoon regional climate to convective parameterization. Mon. Wea. Rev., 130, 1282-1298, doi:10.1175/1520-0493(2002)130,1282:SOTMNA.2.0.CO;2.
22. Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev., 121, 764-787, doi:10.1175/1520-0493(1993)121,0764:PEOAUB.2.0.CO;2.
23. Grell, G. A., and D. Dévényi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 1693, doi:10.1029/2002GL015311.
24. Iowa-Cedar Watershed Interagency Coordination Team, 2015: Iowa Cedar Watershed. Accessed 10 November 2015. [Available online at http://iowacedarbasin.org/.]
25. Iowa Flood Center, 2013: IFC-2013 progress report. Accessed 10 November 2015. [Available online at http://iowafloodcenter.org/wordpress/wp-content/uploads/2011/09/IFC-2013ProgressReport.pdf.]
26. Kalb, M. W., 1987: The role of convective parameterization in the simulation of aGulfCoast precipitation system. Mon. Wea. Rev., 115, 214-234, doi:10.1175/1520-0493(1987)115,0214:TROCPI.2.0.CO;2.
27. Koster, R. D., M. J. Suarez, R. W. Higgins, and H. M. Van den Dool, 2003: Observational evidence that soil moisture variations affect precipitation. Geophys. Res. Lett., 30, 1241, doi:10.1029/2002GL016571.
28. Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927-945, doi:10.1175/1520-0493(1994)122,0927:TSMECM.2.0.CO;2.
29. Jankov, I., W. A. Gallus Jr., M. Segal, B. Shaw, and S. E. Koch, 2005: The impact of different WRF Model physical parameterizations and their interactions on warm season MCS rainfall. Wea. Forecasting, 20, 1048-1060, doi:10.1175/WAF888.1.
30. Kain, J. S., and Coauthors, 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 23, 931-952, doi:10.1175/WAF2007106.1.
31. Kumar, S. V., and Coauthors, 2006: Land Information System: An interoperable framework for high resolution land surface modeling. Environ. Modell. Software, 21, 1402-1415, doi:10.1016/j.envsoft.2005.07.004.
32. Lang, S. E., W.-K. Tao, R. Cifelli, W. Olson, J. Halverson, S. Rutledge, and J. Simpson, 2007: Improving simulations of convective system from TRMM LBA: Easterly and westerly regimes. J. Atmos. Sci., 64, 1141-1164, doi:10.1175/JAS3879.1.
33. Lang, S. E., W.-K. Tao, X. Zeng, and Y. Li, 2011: Reducing the biases in simulated radar reflectivities from a bulk microphysics scheme:Tropical convective systems. J. Atmos. Sci., 68, 2306-2320, doi:10.1175/JAS-D-10-05000.1.
34. Lang, S. E., W.-K. Tao, J.-D. Chern, D. Wu, and X. Li, 2014: Benefits of a fourth ice class in the simulated radar reflectivities of convective systems using a bulk microphysics scheme. J. Atmos. Sci., 71, 3583-3612, doi:10.1175/JAS-D-13-0330.1.
35. Lean, H. W., P. A. Clark, M. Dixon, N. M. Roberts, A. Fitch, R. Forbes, and C. Halliwell, 2008: Characteristics of highresolution versions of the Met Office Unified Model for forecasting convection over the United Kingdom. Mon. Wea. Rev., 136, 3408-3424, doi:10.1175/2008MWR2332.1.
36. Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and application. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.]
37. Liu, C., M. W. Moncrieff, and W. W. Grabowski, 2001: Hierarchical modelling of tropical convective systems using explicit and parametrized approaches. Quart. J. Roy. Meteor. Soc., 127, 493-515, doi:10.1002/qj.49712757213.
38. Matsui, T., X. Zeng, W.-K. Tao, H. Masunaga, W. S. Olson, and S. Lang, 2009: Evaluation of long-term cloud-resolving model simulations using satellite radiance observations and multifrequency satellite simulators. J. Atmos. Oceanic Technol., 26, 1261-1274, doi:10.1175/2008JTECHA1168.1.
39. Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851-875, doi:10.1029/RG020i004p00851.
40. Mesinger, F., 1996: Improvements in quantitative precipitation forecasts with the eta regional model at the National Centers for Environmental Prediction: The 48-km upgrade. Bull. Amer. Meteor. Soc., 77, 2637-2649, doi:10.1175/1520-0477(1996)077,2637:IIQPFW.2.0.CO;2.
41. Molinari, J., and M. Dudek, 1986: Implicit versus explicit convective heating in numerical weather prediction models. Mon. Wea. Rev., 114, 1822-1831, doi:10.1175/1520-0493(1986)114,1822:IVECHI.2.0.CO;2.
42. Molinari, J., and M. Dudek, 1992: Parameterization of convective precipitation in mesoscale numerical models: A critical review. Mon. Wea. Rev., 120, 326-344, doi:10.1175/1520-0493(1992)120,0326:POCPIM.2.0.CO;2.
43. Mukhopadhyay, P., S. Taraphdar, B. N. Goswami, and K. Krishnakumar, 2010: Indian summer monsoon precipitation climatology in a high-resolution regional climate model: Impacts of convective parameterization on systematic biases. Wea. Forecasting, 25, 369-387, doi:10.1175/2009WAF2222320.1.
44. NCAR, 2012: Changes in Noah LSM versions. Accessed 10 November 2015. [Available online at http://www.ral.ucar.edu/research/land/technology/lsm/noahlsm-v3.4.1/CHANGES.]
45. Nichols, W. E., and R. H. Cuenca, 1993: Evaluation of the evaporative fraction for parameterization of the surface energy balance. Water Resour. Res., 29, 3681-3690, doi:10.1029/93WR01958.
46. Peters-Lidard, C. D., and Coauthors, 2007: High-performance Earth system modeling with NASA/GSFC's Land Information System. Innovations Syst. Software Eng., 3, 157-165, doi:10.1007/s11334-007-0028-x.
47. Peters-Lidard, C. D., and Coauthors, 2015: Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales. Environ. Modell. Software, 67, 149-159, doi:10.1016/j.envsoft.2015.01.007.
48. Roberts, R. D., and S. Rutledge, 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562-584, doi:10.1175/1520-0434(2003)018,0562:NSIAGU.2.0.CO;2.
49. Rodell, M., P. R. Houser, A. A. Berg, and J. S. Famiglietti, 2005: Evaluation of 10 methods for initializing a land surface model. J. Hydrometeor., 6, 146-155, doi:10.1175/JHM414.1.
50. Seity, Y., P. Brousseau, S. Malardel, G. Hello, P. Bénard, F. Bouttier, C. Lac, and V. Masson, 2011: The AROMEFrance convective-scale operational model. Mon. Wea. Rev., 139, 976-991, doi:10.1175/2010MWR3425.1.
51. Seo, B.-C., L. K. Cunha, and W. F. Krajewski, 2013: Uncertainty in radar-rainfall composite and its impact on hydrologic prediction for the eastern Iowa flood of 2008. Water Resour. Res., 49, 2747-2764, doi:10.1002/wrcr.20244.
52. Tang, L., Y. Tian, and X. Lin, 2014: Validation of precipitation retrievals over land from satellite-based passive microwave sensors. J. Geophys. Res. Atmos., 119, 4546-4567, doi:10.1002/2013JD020933.
53. Tao, W.-K., and Coauthors, 2003: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97-137.
54. Tao, W.-K., and Coauthors, 2011: High-resolution numerical simulation of the extreme rainfall associatedwithTyphoonMorakot. Part I:Comparing the impact of microphysics and PBL parameterizations with observations. Terr. Atmos. Oceanic Sci., 22, 673-696, doi:10.3319/TAO.2011.08.26.01(TM).
55. Tao, W.-K., D. Wu, S. Lang, J.-D. Chern, C. Peters-Lidard, A. Fridlind, and T. Matsui, 2016: High-resolution NU-WRF simulations of a deep convective-precipitation system during MC3E:Further improvements and comparisons between Goddard microphysics schemes and observations. J. Geophys. Res. Atmos., 121, 1278-1305, doi:10.1002/2015JD023986.
56. Wang, W., and N. L. Seaman, 1997: A comparison study of convective schemes in a mesoscale model. Mon. Wea. Rev., 125, 252-278, doi:10.1175/1520-0493(1997)125,0252:ACSOCP.2.0.CO;2.
57. Weiss, S. J., M. E. Pyle, Z. Janjić, D. R. Bright, J. S. Kain, and G. J. DiMego, 2008: The operational High Resolution Window WRF Model runs at NCEP: Advantages of multiple model runs for severe convective weather forecasting. 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., P10.8. [Available online at https://ams.confex.com/ams/24SLS/techprogram/paper_142192.htm.]
58. Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527-548, doi:10.1175/1520-0493(1997)125,0527:TRDOEM.2.0.CO;2.
59. Weisman, M. L., C. Davis, W. Wang, K. W. Manning, and J. B. Klemp, 2008: Experiences with 0-36-h explicit convective forecasts with the WRF-ARWModel. Wea. Forecasting, 23, 407-437, doi:10.1175/2007WAF2007005.1.
60. Wu, D., X. Dong, B. Xi, Z. Feng, A. Kennedy, G. Mullendore, M. Gilmore, and W.-K. Tao, 2013: Impacts of microphysical scheme on convective and stratiform characteristics in two high precipitation squall line events. J. Geophys. Res. Atmos., 118, 11 119-11 135, doi:10.1002/jgrd.50798.
61. Zhang, D.-L., E.-Y. Hsie, and M. W. Moncrieff, 1988: A comparison of explicit and implicit predictions of convective and stratiform precipitating weather systems with a meso-b-scale numerical model. Quart. J. Roy. Meteor. Soc., 114, 31-60, doi:10.1002/qj.49711447903.
62. Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-14-00174.1, in press.