452 NWP 2015 Major Steps in the Forecast Process Data Collection Quality Control Data Assimilation Model Integration Post Processing of Model Forecasts Human Interpretation (sometimes) Product and graphics generation Data Collection Weather is observed throughout the world and the data is distributed in real time. Many types of data and networks, including: Surface observations from many sources Radiosondes and radar profilers Fixed and drifting buoys Ship observations Aircraft observations Satellite soundings Cloud and water vapor track winds Radar and satellite imagery Observation and Data Collection Weather Satellites Are Now 99% of the Data Assets Used for NWP Geostationary Satellites: Imagery, soundings, cloud and water vapor winds Polar Orbiter Satellites: Imagery, soundings, many wavelengths RO (GPS) satellites Scatterometers Active radars in space (GPM) Quality Control Automated algorithms and manual intervention to detect, correct, and remove errors in observed data. Examples: Range check Buddy check Comparison to first guess fields from previous model run Hydrostatic and vertical consistency checks for soundings. A very important issue for a forecaster--sometimes good data is rejected and vice versa. Eta 48 hr SLP Forecast valid 00 UTC 3 March March 1999: Forecast a snowstorm got a windstorm instead Pacific Analysis At 4 PM 18 November 2003 Bad Observation Forecaster Involvement A good forecast is on the lookout for NWP systems rejecting bad data, particularly in data sparse areas. Quality control systems can allow models to go off to never never land. Less of a problem today due to satellite data everywhere. Objective Analysis/Data Assimilation Numerical weather models are generally solved on a three-dimensional grid Observations are scattered in three dimensions Need to interpolate observations to grid points and to insure that the various fields are consistent and physically plausible (e.g., most of the atmosphere in hydrostatic and gradient wind balance). Objective Analysis Interpolation of observational data to either a grid (most often!) or some basis function (e.g., spectral components) Typically iterative (done in several passes) Typically starts with first guess (short-term forecast) Objective Analysis/Data Assimilation Often starts with a first guess, often the gridded forecast from an earlier run (frequently a run starting 6 hr earlier) This first guess is then modified by the observations. Adjustments are made to insure proper balance. Objective Analysis/Data Assimilation produces what is known as the model initialization, the starting point of the numerical simulation. An early objective analysis scheme is the Cressman scheme 3DVAR: 3D Variational Data Assimilation Used by the National Weather Service today for the GFS and NAM Tries to create an analysis that minimizes a cost function dependent on the difference between the analysis and (1) first guess and (2) observations Does this at a single time. 3DVAR Covariances 4DVAR: Four Dimension Variational Data Assimilation Tries to optimize analyses at MULTIPLE TIMES Uses the model itself as a data assimilation too. Many of the next generation data assimilation approaches are ensemble based Example: the Ensemble Kalman Filter (EnKF) An Attractive Option: EnKF Temperature observation 3DVAREnKF Mesoscale Covariances Camano Island Radar|V 950 |-q r covariance 12 Z January 24, 2004 Surface Pressure Covariance OceanLand Hybrid Data Assimilation: Now Used in GFS Uses both 3DVAR and EnkF Uses EnkF covariances from GFS ensemble in 3DVAR. Next Advance ENVAR Use temporal covariances to spread impact of observations over TIME. Vertical Coordinates and Nesting Vertical Coordinate Systems Originally p and z: but they had a problemBC when the grid hit terrain! Then eta, sigma p and sigma z, theta Increasingly use of hybrids e.g., sigma- theta Sigma Sigma-Theta Nesting Why Nesting? Could run a model over the whole globe, but that would require large amounts of computational resource, particularly if done at high resolution. Alternative is to only use high resolution where you need itnesting is one approach. In nesting, a small higher resolution domain is embedded with a larger, lower-resolution domain. Nesting Can be one-way or two way. In the future, there will be adaptive nests that will put more resolution where it is needed. And instead of rectangular grids, other shapes can be used. Next Generation Global Models Under Development! Will use different geometries MPAS: Hexagonal Shapes MPAS NOAA FIM Model Model Integration: Numerical Weather Prediction The initialization is used as the starting point for the atmospheric simulation. Numerical models consist of the basic dynamical equations (primitive equations) and physical parameterizations. Primitive Equations 3 Equations of Motion: Newtons Second Law First Law of Thermodynamics Conservation of mass Perfect Gas Law Conservation of water With sufficient data for initialization and a mean to integrate these equations, numerical weather prediction is possible. Example: Newtons Second Law: F = ma One Form Physics Parameterizations We need physics parameterizations to include key physical processes. Examples include radiation, cumulus convection, cloud microphysics, boundary layer physics, etc. Why? Primitive equations with lack the necessary physics or lack sufficient resolution to resolve key processes. Parameterization Example: Cumulus Parameterization Most numerical models (grid spacing of 12- km is the best available operationally) cannot resolve convection (scales of a few km or less). In parameterization, represent the effects of sub-grid scale cumulus on the larger scales. Numerical Weather Prediction A numerical model includes the primitive equations, physics parameterization, and a way to solve the equations (usually using finite differences on a grid) Make use of powerful computers Keep in mind that a model with a horizontal grid spacing is barely simulating phenomenon with a scale four times the grid spacing. So a 12-km model barely is getting 50 km scale features correct. Numerical Weather Prediction Most modeling systems are run four times a day (00, 06, 12, 18 UTC), although some run twice a day (00 and 12 UTC) The main numerical modeling centers in the U.S. are: Environmental Modeling Center (EMC) at the National Centers for Environmental Prediction (NCEP)--part of the NWS. Located near Washington, DC. Fleet Numerical Meteorology and Oceanography Center (FNMOC)-Monterey, CA Air Force Weather Agency (AFWA)-Offutt AFB, Nebraska Major U.S. Models Global Forecast System Model (GFS). Uses spectral representation rather than grids in the horizontal. Global, resolution equivalent to 13 km grid model. Run out to 384 hr, four times per day. Weather Research and Forecasting Model (WRF). Two versions: WRF-NMM and WRF- ARW(different ways of representing the dynamics). WRF is a new mesoscale modeling system system that is used by the NWS and the university/research community. AFWA also uses WRF. The NWS runs WRF-NMMB and WRF- ARW. WRF-NMMB is run at 12-km grid spacing, four times a day to 84h. Also smaller 4-km nests. Major U.S. Models MM5 (Penn. State/NCAR Mesoscale Model Version 5). Has been the dominant model in the research community. Run here at the UW (36, 12 and 4 km resolution). COAMPS (Navy). The Navy mesoscale model..similar to MM5 There are many others--you will hear more about this in 452. Forecasters often have 6-10 different models to look at. Such diversity can provide valuable information. Major International NWP Centers ECMWF: European Center for Medium- Range Weather Forecasting. The Gold standard. Their global model is considered the best. UK Met Office: An excellent global model similar to GFS Canadian Meteorological Center Other lesser centers Global Forecast System (GFS) Model Previous called the Aviation (AVN) and Medium Range Forecast (MRF) models. Spectral global model and 64 levels Relatively primitive microphysics. Sophisticated surface physics and radiation Run four times a day to 384 hr (16 days!). Major increase in skill during past decades derived from using direct satellite radiance in the 3DVAR analysis scheme and other satellite assets. 13 km grid spacing equivalent over the first 10 days of the model forecast and 35 km from 10 to 16 days (384 hours). Thus, it now essential a global mesoscale model GFS Vertical coordinates are hybrid sigma/pressure sigma at low levels to pressure aloft. Vertical coordinate comparison across North America GFS Data Assimilation (GDAS) Has a later data cut-off time than the mesoscale modelsand thus can get a higher percentage of data. Uses much more satellite assets..thus improve global analysis and forecasts. Major gains in southern hemisphere Hybrid Data assimilation based on 3DVAR (they call it GSI) and GFE ensemble (next slide) Every 6 hr. GFS Hybrid Data Assimilation GFS is not the only global model and is not the best Higher Resolution Operational Models Major U.S. High-Resolution Mesoscale Models (all non-hydrostatic ) WRF-ARW (developed at NCAR) NMM-B (developed at NCEP Environmental Modeling Center) COAMPS (U.S. Navy) MM5 (NCAR, old, replaced by WRF) RAMS (Regional Atmospheric Modeling System, Colorado State) ARPS (Advanced Regional Prediction System): Oklahoma Operational Mesoscale Model History in US Early: LFM, NGM (history) Eta (mainly history) MM5: Still used by some, but mainly phased out NMM- Main NWS mesoscale model, updated Eta model. Sometimes called WRF-NMM and NAM. WRF-ARW: Heavily used by research and some operational communities. NMM replaced by NMM-B WRF and NMM History of WRF model An attempt to create a national mesoscale prediction system to be used by both operational and research communities. A new, state-of-the-art model that has good conservation characteristics (e.g., conservation of mass) and good numerics (so not too much numerical diffusion) A model that could parallelize well on many processors and easy to modify. Plug-compatible physics to foster improvements in model physics. Designed for grid spacings of 1-10 km WRF Modeling System Obs Data, Analyses Post Processors, Verification WRF Software Infrastructure Dynamic Cores Mass Core NMM Core Standard Physics Interface Physics Packages Static Initialization 3DVAR Data Assimilation Two WRF Cores ARW (Advanced Research WRF) developed at NCAR Non-hydrostatic Numerical Model (NMM) Core developed at NCEP Both work under the WRF IO Infrastructure NMM ARW The NCAR ARW Core Model: (See: Terrain following vertical coordinate two-way nesting, any ratio Conserves mass, entropy and scalars using up to 6 th order spatial differencing equ for fluxes. Very good numerics, less implicit smoothing in numerics. NCAR physics package ( converted from MM5 and Eta ), NOAH unified land-surface model, NCEP physics adapted too NWS NMM 1 The NAM RUN Run every six hours over N. American and adjacent ocean Run to 84 hours at 12-km grid spacing. Uses the Grid-Point Statistical Interpolation (GSI) data assimilation system (3DVAR) Start with GDAS (GFS analysis) as initial first guess at t-12 hour (the start of the analysis cycle) Runs an intermittent data assimilation cycle every three hours until the initialization time. 1-Non-hydrostatic mesoscale model, NAM: North American Mesoscale run NMM-B Hybrid sigma-pressure vertical coordinate 60 levels Betts-Miller-Janjic convective parameterization scheme Mellor-Yamada-Janji boundary layer scheme NMM-B Details One-way nested forecasts computed concurrently with the 12-km NMM-B parent run for CONUS (4 km to 60 hours) Alaska (6 km to 60 hours) Hawaii (3 km to 60 hours) Puerto Rico (3 km to 60 hours) For fire weather, moveable 1.33-km CONUS and 1.5-km Alaska nests are also run concurrently (to 36 hours). A change in horizontal grid from Arakawa-E to Arakawa- B grid, which speeds up computations without degrading the forecast 74 September 2011 NAM-B Upgrade New NAM NEMS based NMMB B-grid replaces E-grid Parent remains 12 km to 84 hr Four Fixed Nests Run to 60 hr 4 km CONUS nest 6 km Alaska nest 3 km HI & PR nests Single placeable 1.33km or 1.5 km FireWeather/IMET/DHS run to 36hrSingle placeable 1.33km or 1.5 km FireWeather/IMET/DHS run to 36hr NMMB 4-km Conus NAM Generally less skillful than GFS, even over U.S. Generally inferior to WRF-ARW at same resolution (more diffusion and smoothing, worse numerics) Navy COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) Sigma-Z Atmosphere And Ocean Accessing NWP Models The department web site (go to weather loops or weather discussion) provides easy access to many model forecasts. The NCEP web site is good place to start for NWS models. The Department Regional Prediction Page gets to the department regional modeling output. A Palette of Models Forecasters thus have a palette of model forecasts. They vary by: Region simulated Resolution Model Physics Data used in the assimilation/initialization process The diversity of models can be a very useful tool to a forecaster. Post-Processing Numerical model output sometimes has systematic biases (e.g., too warm or too cold in certain situations). Why not remove it? Numerical models may not have the resolution of physics to deal with certain problems (e.g., low level fog in a valley). Some information be derived from historical model performance. The solution: post-processing of model forecasts. MOS In the 1960s and 1970s, the NWS developed and began using statistical post-processing of model outputknown as Model Output StatisticsMOS Based on linear regression: Y=a0 + a1X1 + a2X2+ a3X3 + MOS is available for many parameters and greatly improves the quality of most model predictions. Post-Processing There are other types of post-processing. Here at the UW we have developed a way of removing systematic bias. Others have used neural nets as an approach. Another approach is to combine several models, weighing them by previous performance (called Bayesian Model Averaging). Ensemble Forecasting All of the model forecasts I have talked about reflect a deterministic approach. This means that we do the best job we can for a single forecast and do not consider uncertainties in the model, initial conditions, or the very nature of the atmosphere. These uncertainties are often very significant. Traditionally, this has been the way forecasting has been done, but that is changing now. A More Fundamental Issue The work of Lorenz (1963, 965, 1968) demonstrated that the atmosphere is a chaotic system, in which small differences in the initializationwell within observational error can have large impacts on the forecasts, particularly for longer forecasts. Similarly, uncertainty in model physics can result in large forecast differences..and errors. Not unlike a pinball game. Often referred to as the butterfly effect Probabilistic-Ensemble NWP Instead of running one forecast, run a collection (ensemble) of forecasts, each starting from a different initial state or with different physics. The variations in the resulting forecasts could be used to estimate the uncertainty of the prediction. Ensemble Prediction Can use ensembles to provide a new generation of products that give the probabilities that some weather feature will occur. Can also predict forecast skill! It appears that when forecasts are similar, forecast skill is higher. When forecasts differ greatly, forecast skill is less. Ensemble Prediction During the past decade the size and sophistication of the NCEP and ECMWF ensemble systems have grown considerably, with the medium-range, global ensemble system becoming an integral tool for many forecasters. Also during this period, NCEP has constructed a higher resolution, short-range ensemble system (SREF) that uses breeding to create initial condition variations. The Thanksgiving Forecast h forecast (valid Thu 10AM) 13: avn* 11: ngps* 12: cmcg* 10: tcwb* 9: ukmo* 8: eta* Verification 1: cent 7: avn 5: ngps 6: cmcg 4: tcwb 3: ukmo 2: eta - Reveals high uncertainty in storm track and intensity - Indicates low probability of Puget Sound wind event SLP and winds Human Interpretation Once all the numerical simulations and post-processing are done, humans still play an important role: Evaluating the model output Making adjustments if needed Attempting to consider features the model cant handle Communicating to the public and other users. Product Generation Some completely objective and automated. Others depend on human intervention Example: the National Weather Service IFPS system Interactive Forecast Preparation System (IFPS) and National Digital Forecast Database (NDFD) The Forecast Process Step 1: What is climatology for the location in question? What are the record and average maxima and minima? You always need very good reasons to equal or break records. Step 2: Acquaint yourself with the weather evolution of the past several days. How has the circulation evolved? Why did past forecasts go wrong or right? Step 3: The Forecast Funnel. Start with the synoptic scale and then downscale to the meso and local scales. Major steps: I. Synoptic Model Evaluation Which synoptic models have been the most skillful during the past season and last few days? Has there been a trend in model solutions? Have they been stable? Are all the model solutions on the same page? If so, you can more confidence in your forecast. Evaluate synoptic ensemble forecasts. Are there large or small spread of the solutions? Which model appears to most skillful today based on initializations and short-term (6-12h forecasts)? Satellite imagery and surface data are crucial for this latter step II. Decide on the synoptic evolution you believe to be most probable. Attempt to compensate for apparent flaws in the best model. III: Downscaling to the mesoscale. What mesoscale evolution will accompany the most probable synoptic evolution? This done in a variety of ways: a. Subjective rules and experience: e.g., the PSCZ occurs when the winds on the WA coast are from the W to NW? Onshore push occurs when HQM-SEA gets to 3.5 mb. Knowledge of these rules is a major component of forecast experience. Typical diurnal wind fields in the summer. b. High resolution mesoscale modeling: e.g., MM5. Clearly becoming more and more important c. Model Output Statistics (MOS, for some fields) IV. Downscaling to the microscale for point forecasts. Subjective approach using knowledge of terrain and other local characteristics. For subjective forecasts remember the approach: It is nearly impossible to forecast a parameter value from first principles--so consider what has changed. STEP 4. The Homestretch Combine the most probable synoptic, mesoscale, and microscale evolution in your mind to produce a predicted scenario Attempt to qualify the uncertainty in the forecast. Synoptic and mesoscale (SREF) ensemble systems are becoming increasingly important for this task. Ask yourself: am a missing something? Am I being objective? Overcompensating for a previous error? Check forecast discussions from other forecasters to insure you are not missing something. Todays Forecast