The effects of data selection on The effects of data selection on the assimilation of AIRS data the assimilation of AIRS data Joanna Joiner Joanna Joiner Genia Brin Brin Genia Robert Atlas Robert Atlas
Outline Outline • Description of the data assimilation system • Description of the data assimilation system • Description of experimental setups • Description of experimental setups – Channel selection and weights Channel selection and weights – – Spatial Spatial subsetting subsetting – • Assimilation results • Assimilation results – Data coverage and Observed-Background statistics Data coverage and Observed-Background statistics – – Cloud detection Cloud detection – – Forecast Skill – Forecast Skill • Discussion on the effect of water vapor channels • Discussion on the effect of water vapor channels • Conclusions and future plans • Conclusions and future plans 9 March 2006 Joiner AIRS team mtg 2 9 March 2006 Joiner AIRS team mtg 2
AIRS Assimilation Experiments AIRS Assimilation Experiments fvSSI Assimilation System Assimilation System fvSSI • NCEP SSI (Spectral Statistical Interpolation) • NCEP SSI (Spectral Statistical Interpolation) analysis and satellite data (Derber Derber et al) et al) analysis and satellite data ( T63L64 T63L64 • Finite volume GCM (Lin et al.) 1 • Finite volume GCM (Lin et al.) 1 o X 1.25 o o X 1.25 o • AIRS and AMSU-A radiances assimilated in a • AIRS and AMSU-A radiances assimilated in a variety of ways including variety of ways including • warmest FOV or center FOV in a warmest FOV or center FOV in a golfball golfball • • clear radiances with different channel selections and clear radiances with different channel selections and • specified errors specified errors • Progress since last presentation (September): • Progress since last presentation (September): • Completed a full set of experiments with Aqua Completed a full set of experiments with Aqua • AMSU-A radiances as well as AIRS on NCCS SGI AMSU-A radiances as well as AIRS on NCCS SGI platform (decommisioned platform ( decommisioned at end of Jan. 2006) at end of Jan. 2006) 9 March 2006 Joiner AIRS team mtg 3 9 March 2006 Joiner AIRS team mtg 3
Step 1: Find optimal set of Step 1: Find optimal set of channels and errors channels and errors • Start with two sets of channel errors (Small and • Start with two sets of channel errors (Small and Large) Large) • Channel errors affect assimilation in 2 ways • Channel errors affect assimilation in 2 ways – Affects the weight a channel receives in the analysis (how Affects the weight a channel receives in the analysis (how – much to weight data vs vs forecast and other observations) forecast and other observations) much to weight data – Affects quality control threshold (toss data that has Affects quality control threshold (toss data that has – difference from forecast > 3 σ or 4.5K) difference from forecast > 3 σ or 4.5K) • Try different channel selections using two sets of • Try different channel selections using two sets of errors errors – Start with nearly full channel set (note: do not use channels Start with nearly full channel set (note: do not use channels – > 2240 cm -1 currently) > 2240 cm -1 currently) – Eliminate 6.7 Eliminate 6.7 µ m water vapor channels (1080-1620 cm -1 ) – µ m water vapor channels (1080-1620 cm -1 ) – Eliminate also 9.7 Eliminate also 9.7 µ m ozone channels (920-1080 cm -1 ) – µ m ozone channels (920-1080 cm -1 ) 9 March 2006 Joiner AIRS team mtg 4 9 March 2006 Joiner AIRS team mtg 4
Channel errors and selection Channel errors and selection Large errors Small errors Try eliminating H 2 O channels and ozone channels 9 March 2006 Joiner AIRS team mtg 5 9 March 2006 Joiner AIRS team mtg 5
Step 2: Use optimized channel set Step 2: Use optimized channel set to investigate effects of to investigate effects of • Assimilating AIRS and Aqua AMSU-A • Assimilating AIRS and Aqua AMSU-A separately and together separately and together • Different spatial • Different spatial subsetting subsetting (warmest FOV (warmest FOV vs center FOV) center FOV) vs • Not feeding back humidity analysis to • Not feeding back humidity analysis to model model 9 March 2006 Joiner AIRS team mtg 6 9 March 2006 Joiner AIRS team mtg 6
Center FOV Center FOV brightness temps brightness temps in 11 µ m window in 11 µ m window 20 December 2002 20 December 2002 Warmest-Center FOV radiances in Warmest-Center FOV radiances in 11 µ m window (mean difference 11 µ m window (mean difference 4.4K, 4.4K, σ σ = = 6.3K). Largest 6.3K). Largest differences occur in and on edges differences occur in and on edges of cloudy areas where forecast of cloudy areas where forecast sensitivity is expected to be sensitivity is expected to be highest. highest. 9 March 2006 Joiner AIRS team mtg 7 9 March 2006 Joiner AIRS team mtg 7
Outline Outline • Description of the data assimilation system • Description of the data assimilation system • Description of experimental setups • Description of experimental setups – Channel selection and weights Channel selection and weights – – Spatial Spatial subsetting subsetting – • Assimilation results • Assimilation results – Data coverage and Observed-Calc (Background) statistics Data coverage and Observed-Calc (Background) statistics – – Cloud detection Cloud detection – – Forecast Skill – Forecast Skill • Discussion on the effect of water vapor channels • Discussion on the effect of water vapor channels • Conclusions and future plans • Conclusions and future plans 9 March 2006 Joiner AIRS team mtg 8 9 March 2006 Joiner AIRS team mtg 8
Percentage of input data accepted by analysis: Percentage of input data accepted by analysis: ∆ : Large errors, warmest FOV; : Large errors, warmest FOV; ◊ ◊ : Small errors, : Small errors, ∆ warmest FOV; +: Small errors, center FOV +: Small errors, center FOV warmest FOV; Specification of channel errors can play a significant role in determining how much data enters analysis (can be larger effect than FOV selection method) 9 March 2006 Joiner AIRS team mtg 9 9 March 2006 Joiner AIRS team mtg 9
Coverage: Warmest FOV (left) vs vs Coverage: Warmest FOV (left) Center FOV (right) Center FOV (right) Note: warmest FOV has ~10% more observations accepted for this mid-tropospheric temperature channel 9 March 2006 Joiner AIRS team mtg 10 9 March 2006 Joiner AIRS team mtg 10
Coverage: Small errors (left) vs Large errors (right) • Note: Large error set allows in ~40% • Note: Large error set allows in ~40% more observations, particularly at higher more observations, particularly at higher latitudes where Obs-Calcs Obs-Calcs are larger are larger latitudes where 9 March 2006 Joiner AIRS team mtg 11 9 March 2006 Joiner AIRS team mtg 11
Comparing coverage and Obs-Calc of temperature (704.4 cm -1 ) and water vapor channels (1524.4 cm -1 ) that peak at similar altitudes (336 mb) Water vapor channel has significantly more coverage (~50%!) and larger Obs-Calc due to 1) larger quality control threshold 2) more sharply peaked weighting function (more often peaks above low clouds). This channel has more small-scale structure in Obs-Calc due to forecast humidity errors. This leads to water vapor channels having large impact on temperature analysis. 9 March 2006 Joiner AIRS team mtg 12 9 March 2006 Joiner AIRS team mtg 12
Observed – – Calc (forecast) brightness temps Calc (forecast) brightness temps Observed mean (top curves); standard deviation (bottom curves) mean (top curves); standard deviation (bottom curves) Dashed: Large errors (warmest FOV) Solid: Small errors (warmest FOV) Dotted: Small errors (center FOV) FOV selection does not impact Obs-Calc (good) As expected, changing the channel errors (quality control thresholds) does impact Obs-Calc. Note slightly more negative mean with Large Errors (more cloud contamination?) 9 March 2006 Joiner AIRS team mtg 13 9 March 2006 Joiner AIRS team mtg 13
Outline Outline • Description of the data assimilation system • Description of the data assimilation system • Description of experimental setups • Description of experimental setups – Channel selection and weights Channel selection and weights – – Spatial Spatial subsetting subsetting – • Assimilation results • Assimilation results – Data coverage and Observed-Background statistics Data coverage and Observed-Background statistics – – Cloud detection Cloud detection – – Forecast Skill – Forecast Skill • Discussion on the effect of water vapor channels • Discussion on the effect of water vapor channels • Conclusions and future plans • Conclusions and future plans 9 March 2006 Joiner AIRS team mtg 14 9 March 2006 Joiner AIRS team mtg 14
NCEP cloud detection does a reasonable job of MODIS detecting high gridbox tropical convective minimum clouds and lower and midlatitude storm maximum track clouds cloud pressures 9 March 2006 Joiner AIRS team mtg 15 9 March 2006 Joiner AIRS team mtg 15
Recommend
More recommend
