Please wait a minute...

Frontiers of Earth Science

Front. Earth Sci.    2018, Vol. 12 Issue (3) : 457-467     https://doi.org/10.1007/s11707-018-0728-6
RESEARCH ARTICLE |
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
Download: PDF(4480 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
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 Authors: Zhibin SUN,Wei GAO   
Just Accepted Date: 27 July 2018   Online First Date: 09 August 2018    Issue Date: 05 September 2018
 Cite this article:   
Yan-An LIU,Zhibin SUN,Maosi CHEN, et al. Assimilation of atmospheric infrared sounder radiances with WRF-GSI for improving typhoon forecast[J]. Front. Earth Sci., 2018, 12(3): 457-467.
 URL:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-018-0728-6
http://journal.hep.com.cn/fesci/EN/Y2018/V12/I3/457
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Yan-An LIU
Zhibin SUN
Maosi CHEN
Hung-Lung Allen HUANG
Wei GAO
Fig.1  AIRS data coverage at different assimilation cycles for various analysis times (around±3 h) on 31 July, 2012. (a) 00 UTC; (b) 06 UTC; (c) 12 UTC; (d) 18 UTC.
Fig.2  Weighting functions of 120 AIRS channels assimilated by NCEP GDAS (dotted lines denote the channels discarded by model top; solid lines denote assimilated channels in regional model). Color used to distinguish the various channels. BT sensitivity is calculated with respect to the change of 1% for temperature.
Fig.3  Histogram of O-B with and without cloud detection for AIRS channel index of 253 (13.85 mm).
Fig.4  Innovation (O-B) distributions after the quality control for AIRS channel index of 1627 (7.01 mm).
Fig.5  Histogram of O-F (Observation-First Guess) before (blue) and after (red) bias correction for AIRS assimilated channels at 1800 UTC on July 30, 2012.
Fig.6  Bias comparisons of O-B and O-A for all 61 AIRS channels assimilated (the assimilation channel index corresponds to each image in Fig. 5). (a) 15 µm CO2 channels between13.2 to15.4 mm; (b) Window channels between 8.8 to 12.6 mm; (c) H2O channels between 6.2 to 8.2 mm; (d) Shortwave CO2 channels between 3.8 to 4.6 mm.
Fig.7  Comparisons of temperature increment statistics at 500 hPa on July 30, 2012. (a) Exp experiment at 1200 UTC; (b) Ctrl experiment at 1200 UTC; (c) Exp experiment at 1800 UTC; (d) Ctrl experiment at 1800 UTC.
Fig.8  Comparisons of humidity increment statistics at 850 hPa on July 30, 2012. (a) Exp experiment at 1200 UTC; (b) Ctrl experiment at 1200 UTC; (c) Exp experiment at 1800 UTC; (d) Ctrl experiment at 1800 UTC.
Fig.9  Comparisons of absolute error of 72 h track forecast for Ctrl and Exp experiment with the best track provided by JTWC (one trial starting from 1800 UTC on July 30, 2012).
Fig.10  Comparisons of 72 h minimum sea level pressure forecast for Ctrl and Exp experiment with the intensity provided by JTWC (one trial starting from 1800 UTC on July 30, 2012).
Fig.11  Exp analysis minus Ctrl analysis. (a) Temperature at 500 hPa; (b) relative humidity at 850 hPa.
Fig.12  RMSE comparisons of three typhoon cases’ 72 h track forecast with (Exp) and without (Ctrl) AIRS radiances assimilation.
Fig.13  RMSE comparisons of three typhoon cases’ 72 h intensity (minimum sea level pressure) forecast with (Exp) and without (Ctrl) AIRS radiances assimilation.
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
Related articles from Frontiers Journals
[1] Anyuan DIAO,Jiong SHU,Ci SONG,Wei GAO. Global consistency check of AIRS and IASI total CO2 column concentrations using WDCGG ground-based measurements[J]. Front. Earth Sci., 2017, 11(1): 1-10.
[2] Yin LIU,Xiaolei ZOU. Quality control of AIRS total column ozone data within tropical cyclones[J]. Front. Earth Sci., 2016, 10(2): 222-235.
[3] Hong ZHANG, Agnes LIM, Robert HOLZ, Steve DUTCHER, Fred NAGLE, Liam GUMLEY, Hung-Lung HUANG, Jinnian WANG, Runhe SHI, Wei GAO, . Analysis and characterization of the synergistic AIRS and MODIS cloud-cleared radiances[J]. Front. Earth Sci., 2010, 4(3): 363-373.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed