Uncertainties in Clouds and Precipitation Processes Benjamin T. Johnson, Ph.D. Atmospheric and Environmental NOAA / STAR / JCSDA Image: D. Stuart Broce NASA ER-2 Research Pilot IPHEX field campaign Where are the uncertainties in scientific understanding of cloud and precipitation processes? (Answer: Above our heads) In this talk Information and Uncertainty Observation Challenges Forward Modeling Challenges Uncertainty as Information Discussion In this talk, I will (continued) Increase your overall uncertainty Make assertions with minimal/no support Possibly offend some of you (or even myself) Provide an incomplete sampling of the many sources of uncertainty in measurement and simulation of cloud and precipitation processes Categories of Uncertainty Parameter uncertainty: model parameters whose exact values are unknown and cannot be controlled in physical experiments, or whose values cannot be exactly inferred by statistical methods. Parametric variability: variability of input variables of the model. Model uncertainty: the lack of knowledge of the underlying true physics. Algorithmic uncertainty: numerical errors and numerical approximations per implementation of a computer model Experimental uncertainty (observation error): the variability of experimental measurements. Interpolation uncertainty: a lack of available data collected from computer model simulations and/or experimental measurements. Cloud Information The general concerns most users have about clouds and precipitation are: (1)Identification of clouds and/or precipitation at the desired scales of observation or simulation (2)Estimation (retrieval or simulation) of some physical aspects of the cloud and/or precipitation (3)Application of information and known uncertainties in other systems (e.g., analysis, forecast), conflating uncertainties and scales (4)Propagation of information and uncertainties The Knowns and The Unknowns Scope and scales of clouds and precipitation Different scopes for different folks What do we know and not know? Observation Retrieval / Estimation Physically-based Modeling Whats the flow of information and uncertainty (e.g., R2O)? How can we make optimal use of high quality information and uncertainty in an operational capacity? gpw NASA-STS107-E-5059-Earth-from-space-clouds-von-Karman-vortices-Atlantic-Ocean Scope and Scales of Clouds MODIS // December 7, 2010 at 17:05 UTC Precipitation 11 Cloud and Precipitation Information Scale and Scope are driven by some combination of user needs, scientific requirements, cost, system capability and feasibility, and potential information content. User #1: I want to know the precise mass, size, shape, and orientation of every single cloud-ice particle on the planet, at all times. (spatial-temporal observation limited) User #2: I want to know if theres a cloud in a given 1 degree by 1 degree grid box at a given time, so I can promptly throw that information away. (fixed time/space scale, but whats the definition of cloud from the users perspective?) User #3: Yeah, man, is it going to snow today? (general questions get general answers, i.e., lack of specificity in scale.) User #4: Whats the winter going to be like in Kiribati in 2035? (scope and scale are well defined, but subject to future uncertainties in both weather and climate it might be under water by then). Even with perfect knowledge of the atmospheric and surface state at a given time and/or location, the uncertainty of that knowledge immediately increases if you move forward in time or to a different spatial location, even if immediately adjacent. General approaches: assume persistence or coherence over appropriate spatial / temporal scales update state and/or observations (real or virtual) The 4-D problem Cloud and Precipitation Processes Formation Processes: saturation: lifting, cooling, advection, available CN nucleation and growth: hetero/homogeneous nucleation, vapor deposition, collision, aggregation, advection Depletion/Inhibition Processes: subsaturation: lowering, warming, advection, Lack of CN mass loss: sublimation, evaporation, scavenging, advection Environmental Interaction Processes: latent heat exchange (enthalpy) sensible heat exchange (e.g., entropy) Radiation Interaction Processes: e.g., UV, VIS, IR, MW physical radiative interaction radiative impacts on cooling / warming of layers / clouds. remote sensing of clouds (active vs. passive) Very well understood* Individual cloud ice and droplet physical processes: Dynamic nucleation methods, phase change, deposition growth, thermodynamic interactions within and with environment General precipitation formation/loss mechanisms Moderately well understood The possible range of shapes/orientations of individual ice particles The possible ranges of integrated mass (content) of cloud or precipitation within a given 3-D box. General, large-medium scale cloud-radiative processes (e.g., OLR, vis. albedo) Poorly understood Actual distributions of particle sizes and shapes Rates of growth/loss within a dynamic, real environment Environmental thermodynamic feedback in cloud / precipitation growth / decay regions (e.g., spatial/temporal structure of latent-heating). Aerosol influence on cloud and precipitation formation at large scales, in dynamically complex environments. small scale radiative interactions between incident radiation and individual particles or distributions of particles (e.g., scattering problem, NUBF) State of Knowledge of Clouds Aerosols make clouds and precipitation possible! Homogeneous nucleation is has a high energy barrier: the world would be very different without heterogeneous nucleation. Understanding aerosol physics, chemistry, and transport is absolutely crucial to understanding cloud physical processes. Aerosol Impact Example: The Twomey Effect A quick plug for aerosols Twomey, S. (1977). "The Influence of Pollution on the Shortwave Albedo of Clouds". J. Atmos. Sci. 34 (7): 11491152.J. Atmos. Sci. Same CWC, double N The Twomey Effect Example (vis. albedo aerosol conc.*) How do we know what we (think) we know? Direct and in-situ observations of cloud and precipitation hydrometeors: - Particle size & shape measurements (e.g., optical disdrometers, aircraft probes, particle photography, etc.) - Particle mass (e.g., hotwire probes, accumulated mass) - Cloud growth Observations (Truthiness) Most of our knowledge of cloud and precipitation physical processes comes from direct observations Instrument errors Human errros Measurement Limitations Spatial Temporal resolution Size range, interval of observation Physical-property-specific limitations: tradeoffs Underrepresentation of the population Limited scope (i.e., entire cloud cannot be consistently sampled) Uncertainties in Direct Measurements Images: Kenneth LibbrechtPristine ice-crystals/snowflakes Thats nice, but < 100 m 400 600 m In the CloudsCloud Particle Imager In the clouds High Volume Precipitation Spectometer Lawson, R. P., Woods, S., & Morrison, H. (2015). The Microphysics of Ice and Precipitation Development in Tropical Cumulus Clouds. Journal of the Atmospheric Sciences, (2015). (figures used with permission of the author) Jackson, R.C. ASSESSING THE DEPENDENCE OF BULK ICE PROPERTIES FROM PROBES WITH ANTI-SHATTER TIPS ON ENVIRONMENTAL CONDITIONS, Ph. D. Dissertation, UIUC, Death by a thousand cuts: assumptions and parameterizations Surface Precipitation Measurements Cam. A Cam. B Ground-based instrumentation, much like their aircraft counterparts use visible light / scanning / photography as particles pass through the field of view of the sensor. Same issues apply: resolution, range of sensitivity, locality Cam 1 Cam 2 Cam 3 The multi-angle snow camera (MASC) provides highly detailed photographs and fall speeds of snow and ice particles from 3 different angles. Uncertainties: whats the 3-D structure? the mass? Even the best direct observations of clouds and precipitation processes have significant uncertainty, and this propagates (knowingly or unknowingly) into all other aspects of remote sensing, modeling/analysis, and prediction. Fine, whats the point? Integrated Observations Remote sensing: - Active sensors: directly sensitive to ensembles of cloud particles and ice particles: Radar, Lidar - Passive sensors: directly sensitive to the scattering, absorption/emission of incident radiation: Radiometers (IR/MW), cameras/photometers (vis) - Depends explicitly on the physical properties of the cloud, atmosphere, and surface Remote Sensing of Cloud Processes Radar: High temporal/spatial resolution Spotty coverage CONUS Radar coverage Sampling Issues Remote Sensing Uncertainties Example: Radar Z-R relationships Kirstetter, P. E., (2015). Probabilistic precipitation rate estimates with groundbased radar networks. Tian, L., Heymsfield, G. M., Li, L., & Srivastava, R. C. (2007). Properties of light stratiform rain derived from 10and 94GHz airborne Doppler radars measurements. Journal of Geophysical Research: Atmospheres (1984 2012), 112(D11). 10.65V 10.65H 18.7V 18.7H 23.8V 36.5V 36.5H GMI 1C-R VO3A TBs // orbit: 420 // 26-Mar :56:38 UTC 89.0V 89.0H 166V 166H 1833 1837 MRMS GPROF [K] [mm/hr] 37 RED: Radar (NMQ/MRMS) BLUE: Passive Microwave Retrieval (GPROF) PURPLE: Spatial & Temporal Overlap 38 Hydrometeor Modeling: - Models of the thermodynamic and microphysical properties of cloud and precipitation hydrometeors - Numerical computation of the scattering, absorption, emission properties as a function of the above physical parameters - Daddy, where do models come from? Simulation of Physical Processes Simulation Example: Onset of melting snowflakes Z Z 13.4 GHz Backscat. Eff GHz Backscat. Eff.94.0 GHz Backscat. Eff. Where does all of this fit in? Assimilation of Observations / Products Any observatable Any retrieved parameter / product Radiative Transfer Model (e.g., CRTM) hydrometeor physical properties (e.g., particle masses, sizes, shapes) hydrometeor radiative relationships (e.g., scattering, extinction, emission) Analysis system physical process assumptions (e.g., cloud formation, precipitation formation) Spatial / temporal correlation scales (i.e., where does interpolation / extrapolation fail?) Sensitivity/Uncertainty Analyses (e.g., Covariance / Jacobian / TL / AD models) Anywhere a physical parameterization is made Uncertainty in Analysis and Prediction Inputs Physical Models Algorithm Outputs Parameter Uncertainty Model Uncertainty Algorithm Uncertainty Experiment Uncertainty Total Output Uncertainty Propagation of Uncertainty Scale, scope, precision, and accuracy of physical-process measurements are determined by the abilities of the observing system Different systems have different uncertainty profiles, even if theyre measuring or simulating the same physical quantity. Uncertainly exists, whether its addressed or not (e.g., over-constraining, non-linearities, etc.) Uncertainty estimates should be generated for all measurements and parameterizations (even if its as simple as mean + standard deviation) Uncertainty should be propagated as part of the information content into subsequent models / analyses (i.e., it becomes part of the product) Final Thoughts Questions? Propagation of Uncertainty There are no complete unified formalisms for uncertainty propagation in non-linear models. Linearized methods exist, but rely on approximate local linearity over the distance between two observations / grid points. Want (output Covariance):Have: (Input Cov.) Can Make (Jacobian): Variance and Covariance: Observations Simulations Example: Bias Correction Dimension (e.g., time)