Using Data Assimilation to Explore Precipitation - Cloud System - Environment Interactions

Transcription

1 Using Data Assimilation to Explore Precipitation - Cloud System - Environment Interactions Derek J. Posselt Collaborators: Samantha Tushaus, Richard Rotunno, Marcello Miglietta, Craig Bishop, Marcus van Lier-Walqui, Tomislava Vukicivic Sponsors: NASA Modeling, Analysis and Prediction National Science Foundation Office of Naval Research

2 Cloud System - Environment Interaction Controls Outcomes 3D Wind profile Land surface properties Thermodynamic environment Cloud microphysics Aerosol content and chemistry Cold pools Updraft/downdraft strength Latent heat release Vertical condensate distribution Radiative fluxes and heating rates Precipitation rate and amount Posselt et al., 2012 (J. Climate) D. J. Posselt 2

3 Data Assimilation: Quantifying Relationships The model represents the relationship between controls on, and output from, a system (e.g., cloud resolving model) Can assess sensitivity of output to changes in input P(x) All Possible Input P(x y) Model F(x) All Possible Output P(y x) P(y) Can also ask which sets of inputs could have produced a given set of outputs Uncertainties represented as probabilities D. J. Posselt 3

4 Quantifying Response: Brute Force Goal: estimate P(y x) and/or P(x y) Options: Discretize P(y x) and P(x) and compute P(x y) Specify a range of parameter values Run the model repeatedly in small increments of the control parameters Thorough, but very computationally expensive D. J. Posselt 4

5 Sensitivity Analysis via Monte Carlo Goal: estimate P(y x) and/or P(x y) Options: Randomly sample P(x y) (traditional Monte Carlo, latin hypercube) Evaluate P(y x) and P(x) at points randomly distributed in parameter space Compute sample of P(x y) Fill the response surface using Kernel density estimate Interpolation Function fitting D. J. Posselt 5

6 Cloud Sensitivity Analysis via Bayesian Sampling Goal: estimate P(y x) and/or P(x y) Options: Construct a Markov chain that samples P(x y) A random walk guided by information from observations Markov chain Monte Carlo: Avoids states that provide a poor fit to observations Flexible probability distributions Posselt and Vukicevic (2010, Mon. Wea. Rev.), Posselt and Bishop (2012, Mon. Wea. Rev.), van Lier-Walqui et al. (2012, 2013 Mon. Wea. Rev.), Posselt, Hodyss, and Bishop (2014, Mon. Wea. Rev.) D. J. Posselt 6

7 Two Experiments 1. Convection Microphysics Interaction Sensitivity of convective cloud properties to changes in cloud microphysical parameters Cloud microphysics dynamics thermodynamics interaction 2. Multivariate Sensitivity of Orographic Rainfall Sensitivity of mountain rainfall to changes in mountain geometry and upwind sounding Which combinations of geometry and sounding produce heavy upslope precipitation? D. J. Posselt 7

8 Two Experiments 1. Convection Microphysics Interaction Sensitivity of convective cloud properties to changes in cloud microphysical parameters Cloud microphysics dynamics thermodynamics interaction 2. Multivariate Sensitivity of Orographic Rainfall Sensitivity of mountain rainfall to changes in mountain geometry and upwind sounding Which combinations of geometry and sounding produce heavy upslope precipitation? D. J. Posselt 8

9 1. Convection Microphysics Interaction: 64 km (x), 24 km (y), 72 levels, dx=2km 1 x 10 6 simulations Three phases: Developing: minutes Mature: minutes Dissipating: minutes Perturb cloud microphysics Characterize parameter sensitivity Observe bulk hydrologic cycle and radiative flux constrain simulation properties Store ancillary data on dynamics, thermodynamic environment, and radiation analyze relationships D. J. Posselt 9

10 Parameters and Observations Ice Fall Speeds Warm Rain Ice PSD Ice Density 10 Microphysics Parameters Snow fall speed coefficient (a s ) Snow fall speed exponent (b s ) Graupel fall speed coefficient (a g ) Graupel fall speed exponent (b g ) cloud-rain autoconversion (q c0 ) Slope intercept Rain (N 0r ) Slope intercept Snow (N 0s ) Slope intercept Graupel (N 0g ) Snow particle density (ρ s ) Graupel particle density (ρ g ) 5 Observations Obs σ Precipitation Rate 2 mm hr -1 Liquid Water Path (LWP) 0.5 kg m -2 Ice Water Path (IWP) 1.0 kg m -2 Longwave Flux (OLR) 5 W m -2 Shortwave Flux (OSR) 5 W m -2 Obs taken in 3 time intervals Developing Mature Dissipating ( min) ( min) ( min) Defined according to cloud depth and surface rain rate D. J. Posselt 10

11 Parameter Sensitivity: Posterior Parameter PDFs P(x y) Warm Rain Graupel Fall Speed Snow Fall Speed Snow Density Graupel Density Ice PSD Given a set of outcomes (Pcp, LWP, IWP, OLR, OSR) Which sets of parameters could have produced them? Questions: What is the response of model output to changes in cloud microphysical assumptions? P(y x) How do changes in parameters affect convective structure? P(z x) D. J. Posselt 11

12 Hydrologic Cycle / Radiative Flux Response Forward observations vs parameters Developing Precip Rate LWP IWP OLR OSR Warm rain controls the solution early N 0r N 0r N 0r q c0 q c0 Mature Precip Rate LWP IWP OLR OSR Warm rain and ice processes important at maturity N 0r N 0r a g q c0 q c0 Dissipating Precip Rate LWP IWP OLR OSR a g a g a g ρ g ρ g Ice processes control the solution late D. J. Posselt 12

13 Storm-Scale Dynamic Thermodynamic interactions Mature Phase Low Level W+ Mature Phase Low Level W- Dissipating Phase Low Level LHR Dissipating Phase Low Level RH Dissipating Phase Low Level T clear Probability Probability Probability Probability Probability Updraft Downdraft Latent Heat Release Relative Humidity Clear Air Temperature Bimodal solution: Weaker up/downdrafts, near-zero LHR, dry, warm Stronger up/downdrafts, negative LHR, moist, cool D. J. Posselt 13

14 Microphysics Latent Heating Developing Phase, Low Level Mature Phase, Low Level Dissipating Phase, Low Level Low level latent heating profiles sensitive to warm rain parameters at all times. Note different solutions associated with cold pools. Latent Heat Release Latent Heat Release Latent Heat Release q c0 q c0 q c0 Developing Phase, Low Level Mature Phase, Low Level Dissipating Phase, Low Level Latent Heat Release Latent Heat Release Latent Heat Release N 0r N 0r N 0r D. J. Posselt 14

15 Two Experiments 1. Convection Microphysics Interaction Sensitivity of convective cloud properties to changes in cloud microphysical parameters Cloud microphysics dynamics thermodynamics interaction 2. Multivariate Sensitivity of Orographic Rainfall Sensitivity of mountain rainfall to changes in mountain geometry and upwind sounding Which combinations of geometry and sounding produce heavy upslope precipitation? D. J. Posselt 15

16 Two Experiments 1. Convection Microphysics Interaction Sensitivity of convective cloud properties to changes in cloud microphysical parameters Cloud microphysics dynamics thermodynamics interaction 2. Multivariate Sensitivity of Orographic Rainfall Sensitivity of mountain rainfall to changes in mountain geometry and upwind sounding Which combinations of geometry and sounding produce heavy upslope precipitation? D. J. Posselt 16

17 2. Orographic Precipitation (S. Tushaus, Poster 1080) CM1 model, stable upslope rainfall 2D (x-z) 800 km long, dx = 2 km, 59 levels Analytical moist-stable sounding, Gaussian bell mountain, warm rain microphysics Observe precipitation in 6 regions 6 Environment Parameters Wind speed (U) Moist Brunt Vaisala Freq (N m2 ) Surface potential temperature (T s ) Relative Humidity (RH) Mountain height Mountain half-width D. J. Posselt 17

18 Precipitation (mm/hr) Parameter Sensitivity Examine functional response of precipitation Explore joint parameter PDFs Upslope Precipitation Response to Change in Parameters Mtn Ht (m) Mtn Width (m) Wind (m/s) RH N m2 (s -2 ) Tsfc (K) D. J. Posselt 18

19 Parameter Sensitivity Tipping point in T s examine change in structure Interaction between T s and stability Back-propagating mountain wave Strong precipitation response to a small change in profile D. J. Posselt 19

20 Multivariate Precipitation Response How does sensitivity change with parameter value? Change two parameters at a time Change all parameters simultaneously D. J. Posselt 20

21 Summary: MCMC-based Bayesian sensitivity experiments identify key microphysical parameters and their relationship to dynamics and environment Joint PDF of parameters with model states lends information on cloud-environment interaction 1. There are multiple stable states in each system; different combinations of parameters produce similar integral observations in very different dynamic and thermodynamic environments 2. Convective cold pools responsive to changes in PSD parameters, but with two distinct preferred states 3. Tipping point in orographic precipitation system: rapid change in outcome for a small change in input 4. Strong changes in sensitivity in multivariate orographic case D. J. Posselt 21

22 Next Steps Composite analysis of large ensembles of model states processes Extension to other dynamical systems Tropical and extratropical cyclones (in progress) Cloud-aerosol interaction (in progress) Examination of observation information content Dual-polarization radar Combined cloud radar microwave retrievals References: Posselt and Vukicevic (2010, MWR) Posselt and Bishop (2012, MWR) van Lier-Walqui et al. (2012, 2014, MWR) Posselt, Hodyss, and Bishop (2014, MWR) Posselt and Mace (2014, JAMC) D. J. Posselt 22

23 PDF Sampling: Markov Chain Monte Carlo Parameter 2 Parameter 1 D. J. Posselt 23

24 Storm-Scale Dynamics Column integral constraint on microphysical parameters leads to constraint on storm scale dynamics Long tail toward stronger storms (stronger updrafts and downdrafts) Probability Probability Mid-level, Mature Updraft Mid-level, Mature Probability Probability Mid-level, Dissipating Updraft Mid-level, Dissipating Downdraft Downdraft D. J. Posselt 24

25 PSD Cold Pool Relationships Mature Phase, Low Level Dissipating Phase, Low Level Dissipating Phase, Low Level Downdraft Relative Humidity T (clear) N 0r N 0r Rain particle size distribution influence on lowlevel downdrafts at maturity and on cold pools at dissipation N 0r D. J. Posselt 25

26 3D Control Simulation, w and cloud Three Phases: Developing: minutes Mature: minutes Dissipating: minutes Model Configuration: 64 x 24 km domain 2 km dx, dy 72 vertical levels D. J. Posselt 26

27 3D Control Simulation, w and cloud Three Phases: Developing: minutes Mature: minutes Dissipating: minutes Model Configuration: 64 x 24 km domain 2 km dx, dy 72 vertical levels D. J. Posselt 27

28 3D Control Simulation, w and cloud Three Phases: Developing: minutes Mature: minutes Dissipating: minutes Model Configuration: 64 x 24 km domain 2 km dx, dy 72 vertical levels D. J. Posselt 28