DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Assimilation of Geostationary Infrared Satellite Data to Improve Forecasting of Mid-level Clouds Curtis J. Seaman with Manajit Sengupta, J. Adam Kankiewicz, Steven J. Fletcher, Andrew S. Jones, Scott Longmore and Thomas H. Vonder Haar

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Assimilation of Geostationary Infrared Satellite Data to Improve Forecasting of Mid-level Clouds Objectives: u Improve forecasting of mid-level clouds u Assimilate GOES Imager and Sounder data into the 4-DVAR RAMDAS u Compare results with observations from CLEX-9 u Investigate the assimilation of cloudy- scene radiances into a cloud-free model state Photo: Curtis Seaman

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Outline u Introduction to mid-level clouds and DoD relevance u RAMDAS and GOES u Assimilation of GOES Imager (ch. 3 & 4) u Assimilation of GOES Sounder (ch. 7 & 11) u Assimilation of water vapor channels only u Variation of decorrelation lengths and variance (background error covariance) u Conclusions and Future Work

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, DoD Relevance Mid-level Clouds have a significant impact on DoD operations u Altocumulus and altostratus clouds cover 20-25% of the globe u Form between 2 km and 7 km MSL (mission critical altitudes) u Obscure surface targets, pilot visibility u Interfere with infrared (IR) and laser based communication systems u Source of supercooled liquid droplets (aircraft icing) and turbulence t These ground Unmanned Aircraft Systems (UAS) u Difficult to forecast

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Difficult Clouds to Forecast u Operation Desert Storm prompted CLEX u During CLEX-9, eight clouds were observed by aircraft, none were forecast by models we used t RAMS, Eta, RUC, NOGAPS, MM5, ECMWF u CloudNet also found European regional and global models under- forecast mid-level clouds Image: Illingworth et al. (2007)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Quit Being So Difficult u Field studies show that altocumulus clouds are generally: t Less than 1 km thick t Have vertical velocities less than 2 m s -1 t Contain both liquid droplets and ice crystals u Operational models typically: t Have coarse vertical resolution in the mid- troposphere (~500 m or more) t Have a tough time accurately simulating vertical velocity and cloud microphysics t Have poor moisture information (radiosonde dry- bias)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Nov 2001 Altocumulus from CLEX-9 u RAMS run with 100 m vertical resolution initialized with Eta 40 km reanalysis t Peak RH of 85% too low to form cloud u Radiosonde dry bias? u Will assimilation of IR water vapor radiances help?

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Satellite DA in Cloudy Areas? u Operational forecast centers typically assimilate only cloud- free radiances t Ease, computational cost t McNally and Vespirini (1995); Garand and Hallé (1997); Ruggiero et al. (1999); McNally et al. (2000); Raymond et al. (2004); Fan and Tilley (2005) u Clouds cover 51-67% of the globe t ISCCP, CLAVR, Warren et al. (1986,1988) u Area of recent research t Vukicevic et al. (2006) t Marecal and Mahfouf (2002), Bauer et al. (2006), Weng et al. (2007) Image: NASA

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Regional Atmospheric Modeling Data Assimilation System (RAMDAS) u Mesoscale 4-DVAR assimilation system designed to assimilate clear and cloudy scene VIS, IR satellite data u Forward model: RAMS u Observational operator: VISIROO t SHDOM, OPTRAN, Deeter and Evans (1998) t Developed for GOES (Imager & Sounder) u Full adjoints for VISIROO and RAMS t Includes RAMS microphysics, but not convective parameterizations

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Regional Atmospheric Modeling Data Assimilation System (RAMDAS) Forward NWP model Satellite radiance over assimilation time window Forward Radiative Transfer to compute radiance from model output Reverse or adjoint of radiative transfer to compute adjoints Reverse or adjoint of NWP model to compute adjoints Update initial condition using propagated gradient of cost function. Compute gradient of cost function and decide whether it is small enough yet Generate forecast NoYes start end Greenwald et al. (2002) Zupanski et al. (2005)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, The 2 Nov 2001 Altocumulus Image:

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Imager Ch. 1 VIS 0.63  m Ch. 4 window 10.7  m Ch. 3 vapor 6.7  m Sounder Ch. 19 VIS 0.70  m Ch. 7 window 12.0  m Ch. 11 vapor 7.0  m

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment Set-Up u 75 x 75 (6 km horiz.) x 84 (stretched-z vert.) grid centered on North Platte, NE u Lateral boundaries masked to 50 x 50 x 84 to ignore boundary condition errors u RAMS initialized with 00 UTC FNL (GDAS) reanalysis data u 11 UTC output used to initialize RAMDAS u 1145 UTC GOES observations assimilated Control Variables: p  il u,v,w r ice r snow r total pressure (pert. Exner function) ice-liquid potential temperature winds ice water mixing ratio snow water mixing ratio total water mixing ratio

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Initial Forward Model Run Initial model is poor, but we’ll use it (and get valuable results)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #1: Imager only GOES Imager channels 3 (6.7  m) & 4 (10.7  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #1: Imager only GOES Imager channels 3 (6.7  m) & 4 (10.7  m) Before Assimilation After Assimilation Observed T b

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #1: Imager only GOES Imager channels 3 (6.7  m) & 4 (10.7  m) Before assimilation After assimilation Observed sounding = dashed Model sounding = solid

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #1: Imager only GOES Imager channels 3 (6.7  m) & 4 (10.7  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #1: Imager only GOES Imager channels 3 (6.7  m) & 4 (10.7  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #1: Imager only GOES Imager channels 3 (6.7  m) & 4 (10.7  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #1: Imager only GOES Imager channels 3 (6.7  m) & 4 (10.7  m) Surface wind (before) Surface wind (after)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #1: Imager only GOES Imager channels 3 (6.7  m) & 4 (10.7  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #2: Sounder only GOES Sounder channels 7 (12.02  m) & 11 (7.02  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #2: Sounder only GOES Sounder channels 7 (12.02  m) & 11 (7.02  m) Before Assimilation After Assimilation Observed T b

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #2: Sounder only GOES Sounder channels 7 (12.02  m) & 11 (7.02  m) Before assimilation After assimilation

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #2: Sounder only GOES Sounder channels 7 (12.02  m) & 11 (7.02  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #2: Sounder only GOES Sounder channels 7 (12.02  m) & 11 (7.02  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #2: Sounder only GOES Sounder channels 7 (12.02  m) & 11 (7.02  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #2: Sounder only GOES Sounder channels 7 (12.02  m) & 11 (7.02  m) Surface wind (before) Surface wind (after)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #2: Sounder only GOES Sounder channels 7 (12.02  m) & 11 (7.02  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #3: water vapor only GOES Imager ch 3 (6.7  m) & Sounder ch 11 (7.02  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #3: water vapor only GOES Imager ch 3 (6.7  m) & Sounder ch 11 (7.02  m) Before Assimilation After Assimilation Observed T b

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #3: water vapor only GOES Imager ch 3 (6.7  m) & Sounder ch 11 (7.02  m) Before assimilation After assimilation

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #3: water vapor only GOES Imager ch 3 (6.7  m) & Sounder ch 11 (7.02  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #3: water vapor only GOES Imager ch 3 (6.7  m) & Sounder ch 11 (7.02  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #3: water vapor only GOES Imager ch 3 (6.7  m) & Sounder ch 11 (7.02  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #3: water vapor only GOES Imager ch 3 (6.7  m) & Sounder ch 11 (7.02  m) Surface wind (before) Surface wind (after)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Experiment #3: water vapor only GOES Imager ch 3 (6.7  m) & Sounder ch 11 (7.02  m)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Control Variable r l x,y [km] r l z [km] pressure temperature u-wind v-wind w-wind501.0 total water mixing ratio501.0 rain water mixing ratio500.5 snow water mixing ratio500.5 Decorrelation Lengths u Background error covariance matrix, B, in RAMDAS based on decorrelation length and variance u Assumed values of decorrelation length for each control variable shown u Decorrelation lengths and variances doubled and halved

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, u Increasing (decreasing) decorrelation length increases (decreases) impact of observations u Variance (not shown) had little effect Decorrelation Lengths Note: dashed lines correspond to soundings based on default values of decorrelation length

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Imager Experiment Sounder Experiment Decorrelation Lengths Doubled Mid-level Cloud

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Summary u GOES Imager experiment t Cooled the surface, increased upper-tropospheric humidity t Increased fog t No closer to producing mid-level cloud u GOES Sounder experiment t Produced subsidence inversion t Cooled, humidified atmosphere near 2 km AGL t Some surface cooling t No mid-level cloud, but closer to producing one u Water Vapor-only experiment t Produced weaker subsidence inversion near 2 km AGL t Almost no effect on the surface t Similar to GOES Sounder results otherwise

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Conclusions u The assimilation modifies the model state where the observations are sensitive to model variables u When no cloud is present in the model, the adjoint calculates sensitivities based on having no cloud t In the GOES Imager case, ch. 4 is most sensitive to surface temperature in the absence of cloud, ch. 3 is most sensitive to upper-tropospheric humidity t GOES Sounder ch. 7 & 11 are more sensitive to low- to mid- troposphere temperature and humidity u Significant innovations achieved with only one observation time t Biggest changes to temperature, dew point and winds u Many implications for other cases t Clouds in multiple layers t Clouds over snow t Clouds of different scales

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Back to the Future u More channels, more observation times u More case studies t CLEX-9, CLEX-10 u Ideal decorrelation lengths u Add constraints t Surface temperature t Additional cloud information u Log-normal distributions (Fletcher and Zupanski 2007)

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Questions?

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Aircraft studies of Mid-level Clouds Field Study Cloud Depth [m] LWC [g m -3 ] T [  C] Continental North American Tulich and Vonder Haar (1998) to -23 Fleishauer et al. (2002) to -31 Seaman and Vonder Haar (2003) to -25 Heymsfield et al. (1991) to -31 Marine and Arctic Hobbs and Rangno (1985) <.1 – to -26 Hobbs and Rangno (1998) to -31 Pinto (1998) to -20 Continental Australian Paltridge et al. (1986) to -11 = CLEX studies

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Model Resolution u Models typically have course vertical resolution in the mid- troposphere  RUC layers are set at 2 K (  v ) in mid- troposphere  Fleishauer et al. (2002) showed  v varied by less than 2 K within cloud u ~500 m resolution not uncommon in other models

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, What We Have Learned about Mid- level Mixed-Phase Clouds Optically Opaque Mixed-Phase Region (~ m deep) Precipitating Ice Region (~ km deep) Generating Cells ~ km in Length Supercooled Liquid Ice = =

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, What We Have Learned about Mid- level Mixed-Phase Clouds Supercooled Liquid Ice = = Icing and Turbulence Region!

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Forecast Model Microphysics AVN model Models typically assume the colder it is, the more ice there is

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, More Difficulty u Vertical velocities are typically 1-2 m s -1 at most u Numerous studies have shown that radiosondes have a dry-bias that increases with decreasing temperature t Soden et al. (1994); Ferrare et al. (1995); Lesht and Liljegren (1997); Kley et al. (2000); Miloshevich et al. (2001); Wang et al. (2002); Turner et al. (2003) Photo: Larry Carey 2 Nov 2001 Altocumulus

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, RAMS operational forecast during CLEX-9 (500 m vertical resolution) The 2 Nov 2001 Altocumulus

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Geostationary Operational Environmental Satellites (GOES) u Imager t Higher spatial, temporal resolution u Sounder t More channels u Experiments use window and water vapor channels t Imager ch. 3 & 4 t Sounder ch. 7 & 11 t Water vapor-only (3 & 11) Theoretical Weighting Functions

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Initial (No Assimilation) Forward Model Run

DoD Center for Geosciences/Atmospheric Research at Colorado State University January 29, Summary u Decorrelation lengths are important t Increasing (decreasing) decorrelation length increases (decreases) effect of observations t With GOES Sounder data, doubled decorrelation length produced cloud near 2 km AGL t Based on broad, horizontally homogeneous cloud? u Variance found not to be important u Surface temperature important when assimilating IR window data u Initial model simulation was poor and the assimilation was unable to overcome it