Forecasting Boot Camp Major Steps in the

Forecasting Boot Camp

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

Data Collection • Satellite data is now the dominant data source (perhaps 90%) • Huge increases in the numbers of surface stations and aircraft reports.

Observation and Data Collection

Quality Control • Automated algorithms and manual intervention to detect, correct, and remove errors in observed data. • Examples: – Range check – Buddy check (comparison to nearby stations) – Comparison to first guess fields from previous model run – Hydrostatic and vertical consistency checks for soundings. • A very important issue for the forecaster-sometimes good data is rejected and vice versa.

Pacific Analysis At 4 PM 18 November 2003 Bad Observation

3 March 1999: Forecast a snowstorm … got a windstorm instead Eta 48 hr SLP Forecast valid 00 UTC 3 March 1999

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/Data Assimilation • Often starts with a “first guess”, usually 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.

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: Newton’s 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: Newton’s Second Law: F = ma

Simplified form of the primitive equations

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 lack the necessary physics – Lack sufficient resolution to resolve key processes. – Small scale physics has to be put in terms of larger scale variables

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) • Makes use of powerful computers • Keep in mind that a model with certain 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 operational 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 25 km grid model. Run out to 384 hr, four times per day. • Weather Research and Forecasting Model (WRF). WRF is a mesoscale modeling system that is used by the NWS and the university/research community. Two versions (different ways of representing the dynamics): WRF-NMM and WRFARW. Universities use WRF-ARW. The NWS runs WRF-NMM at 12 -km grid spacing, four times a day to 84 h. AFWA is also using WRF (ARW). Run here (36, 12, 4, 1. 3 km)

Major U. S. Models • MM 5 (Penn. State/NCAR Mesoscale Model Version 5). Had been the dominant model in the research community. Run here at the UW (36, 12 resolution). • COAMPS (Navy). The Navy mesoscale model. . similar to MM 5. • 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: GEM Model • Other lesser centers

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. http: //www. nco. ncep. noaa. gov/pmb/nwprod/analysis/ • The Department Regional Prediction Page gets to the department regional modeling output. http: //www. atmos. washington. edu/mm 5 rt/

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. Also helps deal with uncertainty.

Post-Processing • Numerical model output sometimes has systematic biases (e. g. , too warm or too cold in certain situations). Why not correct the biases? • Numerical models may not have the resolution or physics to deal with certain problems (e. g. , low level fog in a valley). Some information on local effects be derived from historical model performance. • The solution: post-processing of model forecasts.

MOS • In the 1960 s and 1970 s, the NWS developed and began using statistical post-processing of model output…known as Model Output Statistics…MOS • Based on linear regression: Y=a 0 + a 1 X 1 + a 2 X 2+ a 3 X 3 + … • MOS is available for many parameters and greatly improves the quality of many model predictions.

Post-Processing • There are other types of post-processing. • Here at the UW we have developed other ways 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, deterministic prediction has been the way forecasting was done, but this is changing.

A More Fundamental Issue • The work of Lorenz (1963, 1965, 1968) demonstrated that the atmosphere is a chaotic system, in which small differences in the initialization…well 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 • There are several ways to produce probabilistic information but the most viable and popular is ensemble prediction. • 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 can be used to estimate the uncertainty of the prediction. • The ensemble mean is on average more skillful than any individual member.

12 h forecast 24 h forecast 36 h forecast e a c u j T n An aly sis Re gi g on t M T 48 h forecast Region

The Thanksgiving Forecast 2001 42 h forecast (valid Thu 10 AM) SLP and winds 1: cent Verification - Reveals high uncertainty in storm track and intensity - Indicates low probability of Puget Sound wind event 2: eta 5: ngps 8: eta* 11: ngps* 3: ukmo 6: cmcg 9: ukmo* 12: cmcg* 4: tcwb 7: avn 10: tcwb* 13: avn*

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.

Human Interpretation • Once all the numerical simulations and postprocessing are done, humans still play an important role: – Evaluating the model output – Making adjustments if needed – Attempting to consider features the model can’t 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

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 -12 h 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. , WRF, NAM. 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 DT 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 increasingy 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.