Major Physical Processes that Influence the Forecast Accuracy for the Very Short Term

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

2. 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)

3. 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

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

5. 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

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

7. 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

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

9. 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

10. Microphysics Parameterization

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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. Typical Land Surface Model

19. 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

20. 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

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

22. Complex Terrain

23. 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

24. 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

25. Cloud Cover

26. 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

27. 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)

28. 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

29. 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

30. 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