Jimy Dudhia NCAR MWFR-WG Major Physical Processes that Influence the Forecast Accuracy for the Very Short Term Jimy Dudhia NCAR MWFR-WG

Use of NWP Models in Nowcasting Cloud-resolving models are increasingly being used to extend nowcasting from 0-1 hrs out to 6 hours Initialization techniques are specialized for this application to minimize spin-up, e.g., Rapid-update cycling (minutes to one hour cycle) Warm or hot start for convection Cloud analysis Dynamical spin-up (e.g. via digital filter)

NWP Models and Nowcasting Linear development is well handled by non-modeling nowcasting techniques Diurnal cycle Advection-dominated processes Linearity breaks down for a variety of reasons Convective triggering and development Surface-gradient effects (sea-breezes, topography) Cloud-cover changes and gradients Value of NWP models potentially increases as development becomes more nonlinear

Outline Physical processes associated with Convection and Heavy Precipitation Surface and Topography Cloud Cover

Convection Probably the highest priority in improving nowcasting Convective systems may be self-organized (squall lines or supercells) or associated with fronts or tropical storms Nowcasting involves individual storms but deterministicness is limited by cloud life cycle and generation of new clouds often requiring merging to probabilistic methods by 6 hours

Convection Supercell storms have a coherent dynamical structure – major challenge for initialization

Convection Prediction Supercells are associated with the most severe weather (hail, wind, tornadoes, lightning) Predicting exact formation location often depends on surface heterogeneities (see next topic) or pre-existing storm outflows Storm motion vector is not with mean wind but is well understood (e.g. right-movers) Propagation of existing storms is quite predictable with nowcasting techniques

Convection Modeling Cloud-resolving models (1-3 km) capture supercell structures and motions realistically

Convection Simulation However, success in cloud modeling is not sufficient to assume that such models will forecast convective systems well Depends on two major challenges Initialization of the correct structure at time zero via data assimilation (far from a solved problem) imbedded in correct large-scale sounding (CAPE, shear) Even with perfect initial conditions, physics parameterizations especially microphysics are not (and cannot be) perfect within the bulk parameterization framework

Microphysics Parameterization

Microphysics Parameterization Bulk schemes Limited mass variables Cloud water Rain Snow Ice crystals Graupel/hail Some also predict N Double moment

Microphysics Parameterization Because of feedback to the dynamics via latent heating and drag effects, storm simulations require accurate precipitation production mechanisms particle sizes and types (fall-speeds) evaporation of precipitation Studies have shown large sensitivity of storm motion and development to variations in parameters Second-generation storms developing on pre-existing storm outflows will have even greater uncertainty However, models show some skill in storm type (multicells versus supercells) and consequent propagation direction

Microphysics Parameterization Uncertainties in all aspects Ice nucleation (dust aerosols) Rain-formation (cloud condensation nuclei) Size distributions have to be assumed, affecting microphysical growth/evaporation rates fall speeds collision rates and efficiencies These uncertainties will remain in any foreseeable microphysics scheme that is to be run in real-time cloud-resolving models Tuning can mitigate errors for specific purposes (e.g. supercells in midwest) but aerosol amounts vary day-to-day adding inherent uncertainty in the microphysics parameters

Microphysics Parameterization also may subtly affect location and consequent prediction of other high-impact events such as Flash floods requiring pin-pointing convection’s effect on specific rivers Heavy local rainfall or snowfall depending on slow-moving systems or multiple systems affecting one location

Surface and Topography Several nowcasting problems relate to the surface Convective initiation Sea-breeze development Wind-storms related to orography Flash floods from watershed basins Wildland fire forecasting

Surface and Topography There is potential for models to add value in complex interactions between the large scale flow and Coastal gradients Mountains Also in weak-forcing situations Convection often evolves from local heterogeneities in fairly flat terrain Mountain-valley circulations dominate in complex terrain

Surface Physics Primary outputs of surface physics are Sensible heat flux Latent heat flux Surface drag Necessary resolved inputs at initial time of forecast Surface vegetation characteristics Vegetation fraction, height, evapotranspiration properties Soil moisture and temperature Affected by prior rainfall and soil characteristics Snow cover

Typical Land Surface Model

Surface Physics Uncertainties come from both initial state and parameterization of vegetation effects and soil water and heat budgets Initial state Example, soil moisture affects partitioning of heating into sensible and latent heat fluxes which impacts PBL growth and development of convection possibly leads to gradients due to heterogeneity in vegetation or soil moisture

Surface Physics Even with corrected initial state using a land data assimilation procedure, the physics has uncertainties in Vegetation and soil parameters Albedo, root zone, soil texture Treatment of snow cover in forests or urban areas Coupling strength of land to atmosphere Exchange coefficients, roughness lengths, stability functions

Surface Physics Effects of uncertainties Impact on convective triggering location and timing in weak forcing Bias in diurnal cycle magnitude

Complex Terrain

Complex Terrain Complex terrain often challenges model resolution, weakening or not resolving drainage flows in valleys, for example Additional heterogeneity effects due to solar radiation on slopes may be included in model, but only for well resolved slopes Drag effects due to unresolved terrain may not be accounted for leading to wind bias

Cloud Cover Initialization and development of the non-convective cloud field is a challenge for nowcasting and short-range prediction Initialization of clouds is tied to the data assimilation but also relies on consistency with the microphysics and dynamics to maintain them

Cloud Cover

Cloud Cover Four types of error in short-range forecasting Maintain initial clouds too long Dissipate initial clouds too soon Fail to form clouds during forecast Form spurious clouds during forecast All may be spatially dependent errors and affect the surface temperature and visibility and ceiling forecasts Related problem is the effect of dust and other aerosols on radiation

Cloud Cover Non-convective clouds interact directly with the radiation in the model having both local and nonlocal (shading) effects Cloud-radiation interaction depends on how radiation physics interacts with microphysics information Uncertainties come from assumed ice or water particle properties to provide optical depth, etc. The radiative heating may directly impact cloud lifetime (dissipation or maintenance)

Cloud Cover Short-range prediction of cloud cover is not often verified in models Skill depends heavily on initial analysis (e.g. relative humidity layer structure) and on model’s ability to vertically resolve thin cloud layers Cloud-resolving models do not often use cloud-fraction concepts which may be a drawback for representing fine-scale clouds such as cumulus and their radiative impact

Concluding Remarks 1 Physical processes that affect short-range forecasts Convective storms sensitive to microphysics in models and to land-surface for triggering in weakly forced conditions Surface properties in land-surface model affect evolution of boundary layer Orographic effects may not be completely resolved Large-scale cloud prediction relies on initialization of humidity and cloud-radiation interaction

Concluding Remarks 2 Physics is limited by its simplicity in real-time forecast models making specialized tuned physics necessary for certain applications/seasons/regions Spin-up issues occur where physics interacts with dynamics Convective initialization Land-surface initialization Orographic flow initialization Cloud initialization These can be addressed by using cycled forecasts or prior initialization and/or digital filtering in combination with data assimilation Models may help in nonlinearly developing situations but these are often not deterministically predictable and so ensemble methods should be considered to provide probabilities