1. Numerical Weather Prediction (NWP) Usage, Strengths, Limitations & Strategies
Don Day, DayWeather, Inc.

2. Making Sausage

3. Weather Forecast Models
History of Numerical Weather Prediction
How they are built and run
Examples
Strengths
Limitations/weaknesses
Other weather forecasting models & tools you may not know about

4. History of NWP
The history of numerical weather prediction began in the 1920s through the efforts of Lewis Fry Richardson, who used procedures originally developed by Vilhelm Bjerknes to produce by hand a six-hour forecast for the state of the atmosphere over two points in central Europe, taking at least six weeks to do so.

5. History of NWP
Numerical weather prediction uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions.

6. History of NWP
The atmosphere is a fluid. As such, the idea of numerical weather prediction is to sample the state of the fluid at a given time and use the equations of fluid dynamics and thermodynamics to estimate the state of the fluid at some time in the future.

7. NWP - Initialization
The process of entering observation data into the model to generate initial conditions is called initialization.

8. NWP - Initialization Surface observations Radiosonde data (big one)
Terrain
Oceans
Soil moisture, etc.

9. USA Radiosonde Network

10. Global Radiosonde Network

11. Global Surface Network

12. Parameterization
Parameterization in a weather or climate model within numerical weather prediction refers to the method of replacing processes that are too small-scale or complex to be physically represented in the model by a simplified process.

13. Parameterization
Clouds
Precipitation microphysics
Terrain
Many more

14. Differential Equations

15. Differential Equations
A fundamental problem lies in the chaotic nature of the partial differential equations used to simulate the atmosphere. It is impossible to solve these equations exactly, and small errors grow with time (doubling about every five days).

16. Differential Equations
Therefore, numerical weather prediction methods only obtain approximate solutions.

17. Time Stepping
The equations are initialized from the analysis data and rates of change are determined. These rates of change predict the state of the atmosphere a short time into the future; the time increment for this prediction is called a time step.

18. Time Stepping
The equations are then applied to this new atmospheric state to find new rates of change, and these new rates of change predict the atmosphere at a yet further time step into the future. This time stepping is repeated until the solution reaches the desired forecast time.

19. Time Stepping 1 5

20. Time Stepping Like the game of telephone
“Cabin fever is contagious in many parts of the Southeast United States.”

21. Time Stepping
Time steps for global models are on the order of tens of minutes, while time steps for regional models are between one and four minutes.

22. Amplified Errors
Extremely small errors in temperature, winds, or other initial inputs given to numerical models will amplify and double every five days

23. Time to Make the Sausage

24. Numerical Weather Prediction
Manipulating the vast datasets and performing the complex calculations necessary to modern numerical weather prediction requires some of the most powerful supercomputers in the world.

25. Computer Power - ENIAC

26. Moore’s Law

27. Computer Power - Present

28. Numerical Weather Prediction
Factors affecting the accuracy of numerical predictions include the density and quality of observations used as input to the forecasts, along with deficiencies in the numerical models themselves.

29. Numerical Weather Prediction
Some meteorological processes are too small-scale or too complex to be explicitly included in numerical weather prediction models.
For example, cumulus clouds, small convective systems, etc.
Terrain has to be smoothed

30. Grid used by Richardson in 1922 to calculate the pressure change in central Germany.

31. Grid System

32. Grid System

33. Interpolation
Interpolation is a method of constructing new data points within the range of a discrete set of known data points.

34. Model Output

36. Improving NWP Output
Because forecast models based upon the equations for atmospheric dynamics do not perfectly determine weather conditions, statistical methods have been developed to attempt to correct the forecasts.

37. Model Output Statistics (MOS)
MOS can correct for local effects that cannot be resolved by the model due to insufficient grid resolution, as well as model biases. Because MOS is run after its respective global or regional model, its production is known as post-processing.

38. Global Grid vs Regional Grid

39. Model Output KROW ROSWELL GFS LAMP GUIDANCE 2/05/2015 2100 UTC
TMP
DPT
WDR
WSP
WGS NG NG NG NG NG NG NG NG NG NG NG NG NG NG NG NG NG NG NG NG NG NG NG NG NG
PPO
PCO N N N N N N N N N N N N N N N N N N N N N N N N N P LP
LC N N N N N N N N N N N N N N CP
CC N N N N N N N N N N N N N N
POZ
POS
TYP R R R R R R R R R S S S S S S S S S R R R R R R R
CLD CL CL CL CL CL CL CL SC BK SC FW CL CL CL FW CL CL CL CL CL CL CL CL CL CL
CIG
CCG
VIS
CVS
OBV N N N N N N N N N N N N N N N N N N N N N N N N N

40. Ensemble Forecasts
Since the 1990s, ensemble forecasts have been used operationally (as routine forecasts) to account for the stochastic nature of weather processes – that is, to resolve their inherent uncertainty. This method involves analyzing multiple forecasts created with an individual forecast model by using different physical parametrizations or varying initial conditions.

41. Ensemble Forecasts

42. Ensemble Forecasts -72hr

43. Ensemble Forecasts -120hr

44. Ensemble Forecasts – 240hr

45. Ensemble Forecasts – 384hr

46. Model Accuracy

47. Most Commonly Used Models on Weather Websites
GFS
NAM
MOS

48. Lipstick on a Pig

49. How are Internet Weather Forecasts Made?
Almost all are automated, NO HUMAN INTERFACE
Almost all use the SAME data, but present it differently through different GUI – Mostly GFS
Database driven – zip code, not specific to your location
Updated when computer models are completed (many different models 00z,06z,12z,18z)
Interpolation

50. How are Internet Weather Forecasts Made?
Remember
All forecast models come from the same dataset of observed weather (radiosondes, surface, etc.)

51. Global Models
GFS*
ECMWF
UKMET
Environment Canada
JMA
Others

52. Examples - GFS

53. Examples – Environment Canada

54. Regional Models
RUC
RAP
NDFD
MM5
WRF
Others

55. Examples - RUC

56. Model Strengths & Weaknesses – Global Models
Long Range weather available
Global coverage
Easily accessible (GFS)
Myriad of weather products available
Updated 4x day
Weaknesses
Coarse resolution
Susceptible to feedback errors (convection)
Best used to get an overall picture of the weather, not the best for the finer details

57. Model Strengths & Weaknesses – Regional Models
Smaller grids, better resolution
Better terrain
1 hour forecast increments
Higher resolution
Better suited for ballooning applications
Weaknesses
No long range forecasts
Can be negatively impacted if base model is initialized poorly
Not entirely global

58. Weather Risk Management
Risk management is the identification, assessment, and prioritization of risks.

59. Weather Risk Management
Identify, characterize threats
Assess the vulnerability of critical assets to specific threats
Determine the risk (i.e. the expected likelihood and consequences of specific types of attacks on specific assets)
Identify ways to reduce those risks
Prioritize risk reduction measures based on a strategy

60. Weather Risk Management
Worst Case Scenario - Worst possible environment or outcome out of the several possibilities in planning or simulation.

61. Avoid WishCasting WU says light winds till 10 a.m.
RUC says winds to 15 mph by
10 a.m.