Slides are collected from: Zhiquan Liu, Thomas Auligne, Xin Zhang, Hui Shao,
Zhiquan Liu, Craig Schwartz, Chris Snyder, and So-Young Ha NCAR/NESL/MMM Boulder, Colorado, USA
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Transcript of Zhiquan Liu, Craig Schwartz, Chris Snyder, and So-Young Ha NCAR/NESL/MMM Boulder, Colorado, USA
Impact of Assimilating AMSU-A Radiances on forecasts of 2008 Atlantic TCs Initialized
with a limited-area EnKF
Zhiquan Liu, Craig Schwartz, Chris Snyder, and So-Young Ha
NCAR/NESL/MMMBoulder, Colorado, USA
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NCAR is sponsored by the National Science Foundation
Outline
• Radiance DA in WRF/DART
• Results from 2008 Atlantic hurricane season
• Conclusions
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DART/EAKF: vector-matrix formLiu et al., 2012, MWR
• a two-step square-root filter (Anderson, 2003)– adjustment step (shift+compact) for observation space analysis
– regression step from observation space to model space analysis increment
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€
y ia = A y
1/ 2(HBHT )−1/ 2 y ib − yb
( ) + ya, i = 1,K ,N
€
x ia − x i
b = BHT(HBHT )−1 y ia − y i
b( )
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Radiance DA in WRF/DART
• Make use of observation operators built in the WRFDA-3DVAR.– Radiance obs prior is calculated from WRFDA-3DVAR using CRTM
– Inflate the background before the radiance calculation
• Outlier check performed in DART
• Peak level of weighting function used for vertical localization
• Make use of bias correction utility in WRFDA-3DVAR
May June July Aug Sept.
Radiance Bias correction coefficient spin-up
Recent work indicates spinning up coeffs for O(months) is beneficial
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J(β ) =1
2(β − β b )TB β
−1(β − β b ) +1
2[y − ˜ H (x r ,β )]TR −1[y − ˜ H (x r ,β )]
Reference field for coeffs training can be: from global analysis (NCEP GFS used in this study), or EnKF analysis, or other regional analysis
Experimental period
• 11 Aug-15 Sep 2008– 5 storms: Fay, Gustav, Hannah, Ike,
Josephine
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Experimental Design• Two principal WRF/DART 6-hourly cycling experiments
– (1) Assimilate solely “conventional” (i.e., non-radiance) observations
– (2) Assimilate conventional observations and AMSU-A channels 5~7 radiances from NOAA-18 and METOP-2.
• WRF V3.2.1: 36km, 36 levels to 20hPa.– Deterministic 72-h forecasts from ensemble mean analyses at 00Z,
12Z
• DART: – 96 members, ±1.5 h time-window, LBCs from GFS– Adaptive inflation and localization, No surface obs except
altimeter
• Radiances:– Thinned to 72 km– Static bias correction coefficients from offline monitoring spun-
up for 3 months prior to experiment using GFS as the reference field
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Obs coverage @ 00Z 17 Aug. 2008
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Mean T analyses vs. ERA-Interim
(domain-averaged)
Time (x-axis)Model level (y-axis)
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Single 3-d forecast before landfall
Fay Gustav
Hanna Ike
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Track/intensity errors for all storms
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Solid: no-radianceDashed: radiance
48-h forecasts vs. dropwindsondes obs
NOAA G-IV dropwindsondes sampled TC environment, not TC core.
208 dropsondes used in verification
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Mean difference of EnKF analyses – ERA-Interim
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Mean difference of Radiance minus non-radiance analyses
Radiances made the analyses colder over Atlantic, therefore reduced the warm bias and weakened steering flow.
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Importance of simultaneouslyassimilating radiances and satellite winds
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Conclusions
• Assimilating AMSU-A radiance improved TC track and intensity forecasts, particularly for forecast range beyond 36-h.
• Track improvement likely caused by improved environmental analysis, e.g, reduce T bias and improve the wind depiction.
• Simultaneous assimilation of radiances and satellite winds is important to maximize the benefit from both data sources.
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