|
|
Assimilation of atmospheric infrared sounder radiances with WRF-GSI for improving typhoon forecast |
Yan-An LIU1,2,3,4,5, Zhibin SUN3,5( ), Maosi CHEN5, Hung-Lung Allen HUANG3,6, Wei GAO3,4,5( ) |
1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China 2. School of Geographic Sciences, East China Normal University, Shanghai 200241, China 3. ECNU-CSU Joint Research Institute for New Energy and the Environment, East China Normal University, Shanghai 200062, China 4. Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University, Shanghai 200241, China 5. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, United States 6. Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, Madison, WI 53706, United States |
|
|
Abstract The Atmospheric Infrared Sounder (AIRS) can provide the profile information on atmospheric temperature and humidity in high vertical resolution. The assimilation of its radiances has been proven to improve the Numerical Weather Prediction (NWP) in global models. In this study, regional assimilation of AIRS radiances was carried out in a community assimilation system, using Gridpoint Statistical Interpolation (GSI) coupled with the Weather Research and Forecasting (WRF) model. The AIRS channel selection, quality control, and radiances bias correction were examined and illustrated for optimized assimilation. The bias correction scheme in the regional model showed that corrections on most of the channels produce satisfactory results except for several land surface channels. The assimilation and forecast experiments were carried out for three typhoon cases (Saola, Damrey, and Haikui in 2012) with and without including AIRS radiances. Results show that the assimilation of AIRS radiances into the WRF/GSI model improves both the typhoon track and intensity in a 72-hour forecast.
|
Keywords
AIRS
WRF/GSI model
radiance assimilation
typhoon forecast
|
Corresponding Author(s):
Zhibin SUN,Wei GAO
|
Just Accepted Date: 27 July 2018
Online First Date: 09 August 2018
Issue Date: 05 September 2018
|
|
1 |
Aumann H H, Chahine M T, Gautier C, Goldberg M D, Kalnay E, Mcmillin L M, Revercomb H, Rosenkranz P W, Smith W L, Staelin D H, Strow L L, Susskind J (2003). AIRS/AMSU/HSB on the aqua mission: design, science objectives, data products, and processing systems. IEEE Trans Geosci Remote Sens, 41(2): 253–264
https://doi.org/10.1109/TGRS.2002.808356
|
2 |
Bauer P, Thorpe A, Brunet G (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567): 47–55
https://doi.org/10.1038/nature14956
|
3 |
Benjamin S G, Weygandt S S, Brown J M, Hu M, Alexander C R, Smirnova T G, Olson J B, James E P, Dowell D C, Grell G A, Lin H, Peckham S E, Smith T L, Moninger W R, Kenyon J S, Manikin G S (2016). A North American hourly assimilation and model forecast cycle: the rapid refresh. Mon Weather Rev, 144(4): 1669–1694
https://doi.org/10.1175/MWR-D-15-0242.1
|
4 |
Bernardet L, Tallapragada V, Bao S, Trahan S, Kwon Y, Liu Q, Tong M, Biswas M, Brown T, Stark D, Carson L, Yablonsky R, Uhlhorn E, Gopalakrishnan S, Zhang X, Marchok T, Kuo B, Gall R (2015). Community support and transition of research to operations for the hurricane weather research and forecasting model. Bull Am Meteorol Soc, 96(6): 953–960
https://doi.org/10.1175/BAMS-D-13-00093.1
|
5 |
Carrier M J, Zou X, Lapenta W M (2008). Comparing the vertical structures of weighting functions and adjoint sensitivity of radiance and verifying mesoscale forecasts using AIRS radiance observations. Mon Weather Rev, 136(4): 1327–1348
https://doi.org/10.1175/2007MWR2057.1
|
6 |
Carrier M, Zou X, Lapenta W M (2007). Identifying cloud-uncontaminated AIRS spectra from cloudy FOV based on cloud-top pressure and weighting functions. Mon Weather Rev, 135(6): 2278–2294
https://doi.org/10.1175/MWR3384.1
|
7 |
Chahine M T, Pagano T S, Aumann H H, Atlas R, Barnet C, Blaisdell J, Chen L, Divakarla M, Fetzer E J, Goldberg M, Gautier C, Granger S, Hannon S, Irion F W, Kakar R, Kalnay E, Lambrigtsen B H, Lee S Y, Le MARSHALL J, McMillan W W, McMillin L, Olsen E T, Revercomb H, Rosenkranz P, Smith W L, Staelin D, Strow L L, Susskind J, Tobin D, Wolf W, Zhou L (2006). Improving weather forecasting and providing new data on greenhouse gases. Bull Am Meteorol Soc, 87: 911–926
https://doi.org/10.1175/BAMS-87-7-911
|
8 |
De Pondeca M S F V, Manikin G S, DiMego G, Benjamin S G, Parrish D F, Purser R J, Wu W S, Horel J D, Myrick D T, Lin Y, Aune R M, Keyser D, Colman B, Mann G, Vavra J (2011). The real-time mesoscale analysis at NOAA’s National Centers for Environmental Prediction: current status and development. Weather Forecast, 26(5): 593–612
https://doi.org/10.1175/WAF-D-10-05037.1
|
9 |
Derber J C, Wu W S (1998). The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon Weather Rev, 126(8): 2287–2299
https://doi.org/10.1175/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2
|
10 |
Eyre J R, Kelly G A, Mcnally A P, Andersson E, Persson A (1993). Assimilation of TOVS radiance information through one-dimensional variational analysis. Q J R Meteorol Soc, 119(514): 1427–1463
https://doi.org/10.1002/qj.49711951411
|
11 |
Fourrié N, Thépaut J J (2002). Validation of the NESDIS Near Real Time AIRS channel selection. ECMWF Technical Memorandum, 1–14
|
12 |
Goldberg M D, Kilcoyne H, Cikanek H, Mehta A (2013). Joint polar satellite system: the United States next generation civilian polar-orbiting environmental satellite system. J Geophys Res Atmos, 118(24): 13,463–13,475
https://doi.org/10.1002/2013JD020389
|
13 |
Klaes K D, Cohen M, Buhler Y, Schlüssel P, Munro R, Luntama J P, von Engeln A, Clérigh E Ó, Bonekamp H, Ackermann J, Schmetz J (2007). An introduction to the EUMETSAT polar system. Bull Am Meteorol Soc, 88(7): 1085–1096
https://doi.org/10.1175/BAMS-88-7-1085
|
14 |
Le Marshall J, Jung J, Derber J, Chahine M, Treadon R, Lord S J, Goldberg M, Wolf W, Liu H C, Joiner J, Woollen J, Todling R, van Delst P, Tahara Y (2006). Improving global analysis and forecasting with AIRS. Bull Am Meteorol Soc, 87(7): 891–894
https://doi.org/10.1175/BAMS-87-7-891
|
15 |
Li J, Liu H (2009). Improved hurricane track and intensity forecast using single field-of-view advanced IR sounding measurements. Geophys Res Lett, 36(11): L11813
https://doi.org/10.1029/2009GL038285
|
16 |
Lim A H N, Jung J A, Huang H A, Ackerman S A, Otkin J A (2014). Assimilation of clear sky Atmospheric Infrared Sounder radiances in short-term regional forecasts using community models. J Appl Remote Sens, 8(1): 083655
https://doi.org/10.1117/1.JRS.8.083655
|
17 |
Liu Y A, Huang H L A, Gao W, Lim A H N, Liu C, Shi R (2015). Tuning of background error statistics through sensitivity experiments and its impact on typhoon forecast. J Appl Remote Sens, 9(1): 096051
https://doi.org/10.1117/1.JRS.9.096051
|
18 |
Liu Y, Huang H A, Lim A H N, Gao W (2018). Adaptive bias correction of advanced infrared sounding radiance assimilation in a regional model and its impact on typhoon forecast. J Appl Remote Sens, 12: 1
https://doi.org/10.1117/1.JRS.12.026012
|
19 |
McCarty W, Jedlovec G, Miller T L (2009). Impact of the assimilation of Atmospheric Infrared Sounder radiance measurements on short-term weather forecasts. J Geophys Res Atmos, 114: D18122
https://doi.org/10.1029/2008JD011626
|
20 |
McNally A P, Watts P D (2003). A cloud detection algorithm for high-spectral-resolution infrared sounders. Q J R Meteorol Soc, 129(595): 3411–3423
https://doi.org/10.1256/qj.02.208
|
21 |
McNally P, Watts P D, Smith J, Engelen R, Kelly G, Thépaut J N, Matricardi M (2006). The assimilation of AIRS radiance data at ECMWF. Q J R Meteorol Soc, 132(616): 935–957
https://doi.org/10.1256/qj.04.171
|
22 |
Menzel W P, Schmit T J, Zhang P, Li J (2018). Satellite based atmospheric infrared sounder development and applications. Bull Am Meteorol Soc, 99(3): 583–603
https://doi.org/10.1175/BAMS-D-16-0293.1
|
23 |
Miyoshi T, Kunii M (2012). Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction. Tellus, Ser A, Dyn Meterol Oceanogr, 64(1): 18408
https://doi.org/10.3402/tellusa.v64i0.18408
|
24 |
Pu Z, Zhang L (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 Atmos, 115(D24): 1–13
https://doi.org/10.1029/2010JD014258
|
25 |
Rabier F, Fourrie N, Chafai D, Prunet P (2002). Channel selection methods for infrared atmospheric sounding interferometer radiances. Q J R Meteorol Soc, 128(581): 1011–1027
https://doi.org/10.1256/0035900021643638
|
26 |
Skamarock W C, Klemp J B, Dudhia J, Gill D O, Barker D M, Duda M G, Huang X Y, Wang W, Powers J G (2008). A Description of the Advanced Research WRF Version 3. NCAR Tech. Notes NCAR/TN-475+ STRT, 1–113
https://doi.org/10.5065/D6DZ069T.
|
27 |
Xu D, Liu Z, Huang X Y, Min J, Wang H (2013). Impact of assimilating IASI radiance observations on forecasts of two tropical cyclones. Meteorol Atmos Phys, 122(1‒2): 1–18
https://doi.org/10.1007/s00703-013-0276-2
|
28 |
Zheng J, Li J J, Schmit T J, Li J J, Liu Z (2015). The impact of AIRS atmospheric temperature and moisture profiles on hurricane forecasts: Ike (2008) and Irene (2011). Adv Atmos Sci, 32(3): 319–335
https://doi.org/10.1007/s00376-014-3162-z
|
29 |
Zhou Y P, Lau K M, Reale O, Rosenberg R (2010). AIRS impact on precipitation analysis and forecast of tropical cyclones in a global data assimilation and forecast system. Geophys Res Lett, 37: L02806
https://doi.org/10.1029/2009GL041494
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|