Assimilation of water vapor sensitive infrared brightness temperature observations during a high impact weather event

Journal of Geophysical Research Atmospheres

Volumn 117, Issue 19, 2012, Pages

  Otkin, Jason A ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EXPERIMENTS; TEMPERATURE; TROPOSPHERE; VAPORS; WEATHER FORECASTING; WEATHER INFORMATION SERVICES;

ACCURATE ANALYSIS; DATA ASSIMILATION; DATA ASSIMILATION SYSTEMS; ENSEMBLE DATA ASSIMILATION; ENSEMBLE KALMAN FILTER; EQUITABLE THREAT SCORE; FORECAST ACCURACY; HIGH IMPACT; INFRARED BRIGHTNESS TEMPERATURE; MOISTURE DISTRIBUTION; OBSERVING SYSTEM SIMULATION EXPERIMENTS; PRECIPITATION THRESHOLD; SHORT-RANGE PRECIPITATION FORECAST; TEST BED SYSTEMS; UPPER TROPOSPHERE; WEATHER EVENTS; WIND FIELD;

ATMOSPHERIC HUMIDITY;

BRIGHTNESS TEMPERATURE; DATA ASSIMILATION; ENSEMBLE FORECASTING; ERROR ANALYSIS; MOISTURE CONTENT; THRESHOLD; TROPOSPHERE; WATER VAPOR; WEATHER FORECASTING; WIND FIELD;

UNITED STATES;

EID: 84867559648     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/2012JD017568     Document Type: Article

References (58)

1. Anderson, J. L. (2001), An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884-2903, doi:10.1175/1520-0493(2001) 129<2884AEAKFF>2.0.CO;2. (Pubitemid 34004796)
2. Anderson, J. L. (2007), An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus, Ser. A, 59, 210-224. (Pubitemid 46671846)
3. Anderson, J. L. (2009), Spatially and temporally varying adaptive covari-ance inflation for ensemble filters, Tellus, Ser. A, 61, 72-83.
4. Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano (2009), The Data Assimilation Research Testbed: A community facility, Bull. Am. Meteorol. Soc., 90, 1283-1296, doi:10.1175/2009BAMS2618.1.
5. Andersson, E., E. Holm, P. Bauer, A. Beljaars, G. A. Kelly, A. P. McNally, A. J. Simmons, J.-N. Thepaut, and A. M. Tompkins (2007), Analysis and forecast impact of the main humidity observing systems, Q. J. R. Meteorol. Soc., 133, 1473-1485. (Pubitemid 350002133)
6. Baum, B. A., P. Yang, A. J. Heymsfield, S. Platnick, M. D. King, Y.-X. Hu, and S. T. Bedka (2005), Bulk scattering properties for the remote sensing of ice clouds. Part II: Narrowband models, J. Appl. Meteorol., 44, 1896-1911, doi:10.1175/JAM2309.1. (Pubitemid 43186323)
7. Bormann, N., A. J. Geer, and P. Bauer (2011), Estimates of observation-error characteristics in clear and cloudy regions for microwave imager radiances from numerical weather prediction, Q. J. R. Meteorol. Soc., 137, 2014-2023, doi:10.1002/qj.833.
8. Carlson, T. N., S. G. Benjamin, G. S. Forbes, and Y. F. Li (1983), Elevated mixed layers in the regional severe storm environment-Conceptual modelandcasestudies, Mon. WeatherRev., 111, 1453-1474, doi:10.1175/1520- 0493(1983)111<1453:EMLITR>2.0.CO;2.
9. Donner, L. J. (1988), An initialization for cumulus convection in numerical weather prediction, Mon. Weather Rev., 116, 377-385, doi:10.1175/1520-0493(1988)116<0377:AIFCCI>2.0.CO;2.
10. Evensen, G. (1994), Sequential data assimilation with a nonlinear quasi- geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99(C5), 10, 143-10, 162, doi:10.1029/94JC00572.
11. Fabry, F., and J. Sun (2010), For how long should what data be assimilated for the mesoscale forecasting of convection and why? Part I: On the propagation of initial condition errors and their implications for data assimilation, Mon. Weather Rev., 138, 242-255.
12. Gandin, L. S., and A. H. Murphy (1992), Equitable skill scores for categorical forecasts, Mon. WeatherRev., 120, 361-370, doi:10.1175/1520- 0493(1992)120<0361:ESSFCF>2.0.CO;2.
13. Gaspari, G., and S. E. Cohn (1999), Construction of correlation functions in two and three dimensions, Q. J. R. Meteorol. Soc., 125, 723-757, doi:10.1002/qj.49712555417. (Pubitemid 29247364)
14. Geer, A. J., and P. Bauer (2011), Observation errors in all-sky data assimi-lation, Q. J. R. Meteorol. Soc., 137, 2024-2037, doi:10.1002/qj.830.
15. Hamill, T. M., J. S. Whitaker, and C. Snyder (2001), Distance-dependent filtering ofbackground error covariance estimates in an ensemble Kalman filter, Mon. Weather Rev., 129, 2776-2790, doi:10.1175/1520-0493 (2001)129<2776: DDFOBE>2.0.CO;2. (Pubitemid 33079980)
16. Han, Q., W. Rossow, R. Welch, A. White, and J. Chou (1995), Validation of satellite retrievals of cloud microphysics and liquid water path using observations from FIRE, J. Atmos. Sci., 52, 4183-4195, doi:10.1175/1520- 0469(1995)052<4183:VOSROC>2.0.CO;2. (Pubitemid 27002259)
17. Han, Y., P. van Delst, Q. Liu, F. Weng, B. Yan, R. Treadon, and J. Derber (2006), Community Radiative Transfer Model (CRTM): Version 1, NOAA Tech. Rep. NESDIS 122, 122 pp., NOAA, Washington, D. C.
18. Heidinger, A. K., C. O'Dell, R. Bennartz, and T. Greenwald (2006), The successive-order-of-interaction radiative transfer model. Part I: Model development, J. Appl. Meteorol. Climatol., 45, 1388-1402, doi:10.1175/JAM2387.1. (Pubitemid 44781977)
19. Heymsfield, A. J., S. Matrosov, and B. Baum (2003), Ice water path-optical depth relationships for cirrus and deep stratiform ice cloud layers, J. Appl. Meteorol., 42, 1369-1390, doi:10.1175/1520-0450(2003) 042<1369:IWPDRF>2.0.CO;2. (Pubitemid 37470772)
20. Hong, S.-Y., and J.-O. Lim (2006), The WRF single-moment 6-class micro-physics scheme (WSM6), J. Korean Meteorol. Soc., 42, 129-151.
21. Hong, S.-Y., Y. Noah, and J. Dudhia (2006), A new vertical diffusion pack-age with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318-2341, doi:http://dx.doi.org/10.1175/MWR3199.1. (Pubitemid 44445432)
22. Houtekamer, P. L., H. L. Mitchell, G. Pellerin, M. Buehner, M. Charron, L. Spacek, and B. Hansen (2005), Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations, Mon. Weather Rev., 133, 604-620, doi:10.1175/MWR-2864.1.
23. Iacono, M.J., J.S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins (2008), Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103, doi:10.1029/2008JD009944.
24. Kain, J. S. (2004), The Kain-Fritsch convective parameterization: An update, J. Appl. Meteorol., 43, 170-181, doi:10.1175/1520-0450(2004) 043<0170:TKCPAU>2.0.CO;2.
25. Kain, J. S., and J. M. Fritsch (1993), Convective parameterization for mesoscale models: The Kain-Fritsch scheme, in The Representation of Cumulus Convection in Numerical Models, edited by K. A. Emanuel and D. J. Raymond, pp. 165-170, Am. Meteorol. Soc., Boston, Mass.
26. Köpken, C., G. Kelly, and J.-N. Thepaut (2004), Assimilation of Meteosat radiance data within the 4D-Var system at ECMWF: Assimilation experiments and forecast impact, Q. J. R. Meteorol. Soc., 130, 2277-2292, doi:10.1256/qj.02.230. (Pubitemid 39320239)
27. Krishnamurti, T. N., K. Ingles, S. Cocke, T. Kitade, and R. Pasch (1984), Details of low latitude medium range numerical weather prediction using a global spectral model, J. Meteorol. Soc. Jpn., 62, 613-649.
28. Li, J., and H. Liu (2009), Improved hurricane track and intensity forecast using single field-of-view advanced IR sounding measurements, Geophys. Res. Lett., 36, L11813, doi:10.1029/2009GL038285.
29. Liu, Y.-C., S.-H. Chen, and F.-C. Chien (2011), Impact of MODIS and AIRS total precipitable water on modifying the vertical shear and Hurricane Emily simulations, J. Geophys. Res., 116, D02126, doi:10.1029/2010JD014528.
30. McBride, J. L., and R. M. Zehr (1981), Observational analysis of tropical cyclone formation. Part II: Comparison ofnon-developing versus develop-ing systems, J. Atmos. Sci., 38, 1132-1151, doi:10.1175/1520-0469(1981) 038<1132:OAOTCF>2.0.CO;2.
31. Miloshevich, L. M., H. Vomel, D. N. Whiteman, B. M. Lesht, F. J. Schmidlin, and F. Russo (2006), Absolute accuracy ofwater vapor measurements from six operational radiosonde types launched during AWEX-G, and implica-tions for AIRS validation, J. Geophys. Res., 111, D09S10, doi:10.1029/ 2005JD006083.
32. Mitchell, H. L., P. L. Houtekamer, and G. Pellerin (2002), Ensemble size, balance, and model-error representation in an ensemble Kalman filter, Mon. Weather Rev., 130, 2791-2808, doi:10.1175/1520-0493(2002) 130<2791: ESBAME>2.0.CO;2. (Pubitemid 35372080)
33. Munro, R., C. Kopken, G. Kelly, J.-N. Thepaut, and R. Sanders (2004), Assimilation of Meteosat radiance data within the 4D-Var system at ECMWF: Data quality monitoring, bias correction, and single-cycle experiments, Q. J. R. Meteorol. Soc., 130, 2293-2313, doi:10.1256/ qj.02.229. (Pubitemid 39320240)
34. O'Dell, C. W., A. K. Heidinger, T. Greenwald, P. Bauer, and R. Bennartz (2006), The successive-order-of-interaction radiative transfer model. Part II: Model performance and applications, J. Appl. Meteorol. Climatol., 45, 1403-1413, doi:10.1175/JAM2409.1. (Pubitemid 44781978)
35. Otkin, J. A. (2010), Clear and cloudy sky infrared brightness temperature assimilation using an ensemble Kalman filter, J. Geophys. Res., 115, D19207, doi:10.1029/2009JD013759.
36. Otkin, J. A. (2012), Assessing the impact of the covariance localization radius when assimilating infrared brightness temperature observations using an ensemble Kalman filter, Mon. Weather Rev., 140, 543-561, doi:10.1175/MWR-D-11- 00084.1.
37. Otkin, J. A., and T. J. Greenwald (2008), Comparison of WRF model-simulated and MODIS-derived cloud data, Mon. Weather Rev., 136, 1957-1970, doi:10.1175/2007MWR2293.1.
38. Otkin, J. A., T. J. Greenwald, J. Sieglaff, and H.-L. Huang (2009), Validation of a large-scale simulated brightness temperature dataset using SEVIRI satellite observations, J. Appl. Meteorol. Climatol., 48, 1613-1626, doi:10.1175/2009JAMC2142.1.
39. Palmen, E. (1948), On the formation and structure of tropical cyclones, Geophysics, 3, 26-38.
40. Pu, A., and L. Zhang (2010), Validation of Atmospheric Infrared Sounder temperature and moisture profiles over tropical oceans and their impact on numerical simulations of tropical cyclones, J. Geophys. Res., 115, D24114, doi:10.1029/2010JD014258.
41. Puri, K., and N. E. Davidson (1992), The use of infrared satellite cloud imagery data as proxy data for moisture and diabatic heating in data assimilation, Mon. Weather Rev., 120, 2329-2341, doi:10.1175/1520-0493(1992) 120<2329:TUOISC>2.0.CO;2. (Pubitemid 23378626)
42. Puri, K., and M. J. Miller (1990), The use of satellite data in the specifica-tion of convective heating for diabatic initialization and moisture adjust-ment in numerical weather prediction models, Mon. Weather Rev., 118, 67-93, doi:10.1175/1520-0493(1990)118<0067:TUOSDI>2.0.CO;2.
43. Raymond, W. H., G. S. Wade, and T. H. Zapotocny (2004), Assimilating GOES brightness temperatures. Part I: Upper-tropospheric moisture, J. Appl. Meteorol., 43, 17-27, doi:10.1175/1520-0450(2004)043<0017: AGBTPI>2.0.CO;2. (Pubitemid 38259137)
44. Riehl, H. (1948), On the formation oftyphoons, J. Meteorol., 5, 247-265, doi:10.1175/1520-0469(1948)005<0247:OTFOT>2.0.CO;2.
45. Sanders, F., and J. R. Gyakum (1980), Synoptic-dynamic climatology of the bomb, Mon. Weather Rev., 108, 1589-1606, doi:10.1175/1520-0493(1980) 108<1589:SDCOT>2.0.CO;2.
46. Schmit, T. J., W. F. Feltz, W. P. Menzel, J. Jung, A. P. Noel, J. N. Heil, J. P. Nelson, and G. S. Wade (2002), Validation and use of GOES sounder moisture information, Weather Forecast., 17, 139-154, doi:10.1175/ 1520-0434(2002)017<0139:VAUOGS>2.0.CO;2. (Pubitemid 34554050)
47. Schmit, T. J., M. M. Gunshor, W. P. Menzel, J. J. Gurka, J. Li, and A. S. Bachmeier (2005), Introducing the next-generation Advanced Baseline Imager on GOES-R, Bull. Am. Meteorol. Soc., 86, 1079-1096, doi:10.1175/BAMS-86-8-1079. (Pubitemid 41194723)
48. Seaman, C. J., M. Sengupta, and T. H. Vonder Haar (2010), Mesoscale satellite data assimilation: Impact ofcloud-affected infrared observations on a cloud-free initial model state, Tellus, Ser. A, 62, 298-318.
49. Seemann, S. W., E. E. Borbas, R. O. Knuteson, G. R. Stephenson, and H.-L. Huang (2008), Development of a global infrared land surface emissivity database for application to clear sky sounding retrievals from multispectral satellite radiance measurements, J. Appl. Meteorol. Climatol., 47, 108-123, doi:10.1175/2007JAMC1590.1. (Pubitemid 351351469)
50. Singh, R., P. K. Pal, C. M. Kishtawal, and P. C. Joshi (2008), Impact of Atmospheric Infrared Sounder data on the numerical simulation ofahis-torical Mumbai rain event, Weather Forecast., 23, 891-913, doi:10.1175/ 2008WAF2007060.1.
51. Singh, R., P. K. Pal, and P. C. Joshi (2010), Assimilation of Kalpana very high resolution radiometer water vapor channel radiances into a mesoscale model, J. Geophys. Res., 115, D18124, doi:10.1029/2010JD014027.
52. Singh, R., C. M. Kishtawal, and P. K. Pal (2011), A comparison of the performance of Kalpana and HIRS water vapor radiances in the WRF 3D-Var assimilation system for mesoscale weather predictions, J. Geophys. Res., 116, D08113, doi:10.1029/2010JD014969.
53. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers (2005), A description of the Advanced Research WRF Version 2, NCAR Tech. Note/TN-468+STR, 88 pp., NCAR, Boulder, Colo.
54. Stengel, M., M. Lindskog, P. Unden, N. Gustafsson, and R. Bennartz (2010), An extended observation operator in HIRLAM 4D-VAR for the assimilation of cloud-affected satellite radiances, Q. J. R. Meteorol. Soc., 136, 1064-1074, doi:10.1002/qj.621.
55. Torn, R. D., G. J. Hakim, and C. Snyder (2006), Boundary conditions for limited-area ensemble Kalman filters, Mon. Weather Rev., 134, 2490-2502, doi:10.1175/MWR3187.1. (Pubitemid 44445440)
56. Whitaker, J. S., and T. M. Hamill (2002), Ensemble data assimilation without perturbed observations, Mon. Weather Rev., 130, 1913-1924, doi:10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2. (Pubitemid 34825252)
57. Wu, X., G. R. Diak, C. M. Hayden, and J. A. Young (1995), Short-range precipitation forecasts using assimilation of simulated satellite water vapor profiles and column cloud liquid water amounts, Mon. Weather Rev., 123, 347-365, doi:10.1175/1520-0493(1995)123<0347:SRPFUA>2.0.CO;2.
58. Zapotocny, T. H., W. P. Menzel, J. Jung, and J. P. Nelson (2005), A four-season impact study ofrawinsonde, GOES, and POES data in the Eta Data Assimilation System. Part I: The total contribution, Weather Forecast., 20, 161-177, doi:10.1175/WAF837.1. (Pubitemid 40687177)