1. 452 NWP 2016

2. 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

3. 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

4. Observation and Data Collection

8. Atmospheric Moisture Vectors

12. 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)

13. 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.

15. 3 March 1999: Forecast a snowstorm … got a windstorm instead
Eta 48 hr SLP Forecast valid 00 UTC 3 March 1999
3 March 1999: Forecast a snowstorm … got a windstorm instead

16. Pacific Analysis
At 4 PM
18 November
2003
Bad Observation

17. 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.

18. Objective Analysis/Data Assimilation
Observations are scattered in three dimensions
Numerical weather models are generally solved on a three-dimensional grid
Need to interpolate observations to grid points and to ensure that the various fields are consistent and physically plausible (e.g., most of the atmosphere in hydrostatic and gradient wind balance).

19. 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)

20. 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.
Often iterative

22. An early objective analysis scheme is the Cressman scheme

23. 3DVAR: 3D Variational Data Assimilation
Used by the National Weather Service today for the GFS and NAM (called GSI)
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.

24. 3DVAR Covariances: Spreads Error in Space

25. 4DVAR: Four Dimension Variational Data Assimilation
Tries to optimize analyses at MULTIPLE TIMES
Tries to duplicate the observed evolution over time as well as the situation at initialization time.
Uses the model itself as a data assimilation too.

26. 4DVAR Components Full non-linear model
Tangent linear version of the full model (linearized version of the forecast model)
Adjoint of the tangent linear model -which allows one to integrate the model backwards. Tells sensitivity of final state to the initial state.

27. 4DVAR
Typical runs the model back and forth during an initialization period (6-12 hr), roughly ten times.
Substantial computational cost.
Need to have adjoint and TL version of the model.
Currently used by ECMWF, CMC, UKMET, and US Navy. NOT NCEP.

28. Many of the next generation data assimilation approaches are ensemble based
Example: the Ensemble Kalman Filter (EnKF)

29. Mesoscale Covariances
12 Z January 24, 2004
Camano Island Radar
|V950|-qr covariance

30. Surface Pressure Covariance
Land
Ocean

31. An Attractive Option: EnKF
Temperature observation
3DVAR
EnKF

32. Hybrid Data Assimilation: Now Used in GFS
Uses both 3DVAR and EnkF
Uses EnkF covariances from GFS ensemble in 3DVAR.

34. Next Advance ENVAR
Use temporal covariances to spread impact of observations over TIME.
Will go operational NEXT WEEK.
Has some of the properties of 4DVAR (adjusts model evolution)

35. Vertical Coordinates and Nesting

36. Vertical Coordinate Systems
Originally p and z: but they had a problem…BC when the grid hit terrain!
Then eta, sigma p and sigma z, theta
Increasingly use of hybrids– e.g., sigma-theta

37. Sigma

38. Sigma-Theta

39. Nesting

40. 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 it…nesting is one approach.
In nesting, a small higher resolution domain is embedded with a larger, lower-resolution domain.

43. 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.

44. Next Generation Global Models Under Development!
Will use different geometries

45. MPAS: Hexagonal Shapes

46. MPAS

48. NOAA FIM Model

49. 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.

50. “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

51. One Form

52. 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.

53. 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.

54. 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.

55. 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

56. 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.

57. 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.

58. 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

59. 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

60. GFS
Vertical coordinates are hybrid sigma/pressure… sigma at low levels to pressure aloft.

62. Vertical coordinate comparison across North America

63. GFS Data Assimilation (GDAS)
Has a later data cut-off time than the mesoscale models…and 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.

64. GFS Hybrid Data Assimilation

65. GFS is not the only global model and is not the best

72. Higher Resolution Operational Models

73. 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

74. 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

75. WRF and NMM

76. 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

77. Standard Physics Interface
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

78. 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

79. The NCAR ARW Core Model: (See: www.wrf-model.org)
Terrain following vertical coordinate
two-way nesting, any ratio
Conserves mass, entropy and scalars using up to 6th 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

80. NWS NMM1—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

82. NMM-B Hybrid sigma-pressure vertical coordinate 60 levels
Betts-Miller-Janjic convective parameterization scheme
Mellor-Yamada-Janji boundary layer scheme

83. 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

84. 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 36hr

85. NMMB 4-km Conus

86. NAM Generally less skillful than GFS, even over U.S.
Generally inferior to WRF-ARW at same resolution (more diffusion and smoothing, worse numerics)

87. Navy COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System)
Sigma-Z
Atmosphere
And Ocean

88. 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.

90. 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.

91. Rapid Refresh NWP The Main Tool For Nowcasting

93. RUC: AKA-Rapid Refresh
A major issue is how to assimilate and use the rapidly increasing array of off-time or continuous observations (not a 00 and 12 UTC world anymore!
Want very good analyses and very good short-term forecasts (1-3-6 hr)
The RUC/RR ingests and assimilates data hourly, and then makes short-term forecasts
Uses the WRF model…which uses a hybrid sigma/isentropic vertical coordinate
Resolution: Rapid Refresh: 13 km and 50 levels, High Resolution Rapid Refresh (3 km)

94. NOAA hourly updated models 13km Rapid Refresh (RAP) (mesoscale)
Rapid Refresh and HRRR
NOAA hourly updated models
13km Rapid Refresh (RAP) (mesoscale)
Version 2 – scheduled
NCEP implementation
Q2 (currently 28 Jan)
RAP
3km HRRR (storm-scale)
HRRR
High-Resolution
Rapid Refresh
Scheduled NCEP
Implementation Q3 2014
NCEP Production Suite Review
Rapid Refresh / HRRR
3-4 December 2013

95. RAPv2 Prediction System Overview
Hourly updated mesoscale analyses / forecasts
WRF-ARW model (Grell-3 cumulus param, Thompson microphysics, RUC-Smirnova land-surface, MYNN PBL scheme)
GSI hybrid analysis using 80-member global ensemble
13-km, 50 levels, 24 cycles/day – each run out to 18 hours
6-hour catch-up “partial” cycle run twice per day from GFS
Output grids: 13, 20, and 40 km CONUS, 32 km full domain, km Alaska, 16 km Puerto Rico
Use and downstream dependencies
Used by SPC, AWC, WPC, NWS FOs, FAA, energy industry, and others for short-range forecasts and hourly analyses
Downscaled RAP serves as first guess for RTMA
RAP serves as initial condition for SREF members
RAP will be used to initialize Hi-Res Rapid Refresh (HRRR)

96. Rapid Refresh Hourly Update Cycle
Observations Used
Hourly Observations
RAP 2013 N. Amer
Rawinsonde (T,V,RH)
120
Profiler – NOAA Network (V) 21
Profiler – 915 MHz (V, Tv) 25
Radar – VAD (V)
125
Radar reflectivity - CONUS
1km
Lightning (proxy reflectivity)
NLDN, GLD360
Aircraft (V,T)
2-15K
Aircraft - WVSS (RH)
0-800
Surface/METAR
(T,Td,V,ps,cloud, vis, wx)
Buoys/ships (V, ps)
GOES AMVs (V)
AMSU/HIRS/MHS radiances
Used
GOES cloud-top press/temp
13km
GPS – Precipitable water
260
WindSat scatterometer
2-10K
Partial cycle atmospheric fields – introduce GFS information 2x/day
Cycle hydrometeors
Fully cycle all land-sfc fields
(soil temp, moisture, snow)
1-hr
fcst
Time
(UTC)
Analysis
Fields
3DVAR
Obs
Back-
ground

97. RAPv2 Hybrid Data Assimilation
13 km
RAP
Cycle
13z
14z
15z
ESRL/GSD RAP 2013
Uses GFS 80-member ensemble
Available four times per day
valid at 03z, 09z, 15z, 21z
80-member GFS EnKF Ensemble forecast valid at 15Z (9-hr fcst from 6Z)
Obs
Obs
Obs
GSI
Hybrid
GSI
Hybrid
GSI
Hybrid
1 hr fcst HM
Obs
1 hr fcst HM
Obs HM
Obs
GSI HM Anx
GSI HM Anx
GSI HM Anx
Refl
Obs
Refl
Obs
Refl
Obs
Digital
Filter
Digital
Filter
Digital
Filter
18 hr fcst
18 hr fcst
18 hr fcst

99. Rapid Refresh: 13 km and larger domain

100. High-Resolution Rapid Refresh: 3 km, 1 hr, smaller domain

106. RTMA (Real Time Mesoscale Analysis System)
NWS New Mesoscale Analysis System for verifying model output and human forecasts.

109. Real-Time Mesoscale Analysis RTMA
Downscales a short-term forecast to fine-resolution terrain and coastlines and then uses observations to produce a fine-resolution analysis.
Performs a 2-dimensional variational analysis (2d-var) using current surface observations, including mesonets, and scatterometer winds over water, using short-term forecast as first guess.
Provides estimates of the spatially-varying magnitude of analysis errors
Also includes hourly Stage II precipitation estimates and Effective Cloud Amount, a GOES derived product
Either a 5-km or 2.5 km analysis.

110. RTMA
The RTMA depends on a short-term model forecast for a first guess, thus the RTMA is affected by the quality of the model's analysis/forecast system
CONUS first guess is downscaled from a 1-hour RR forecast.
Because the RTMA uses mesonet data, which is of highly variable quality due to variations in sensor siting and sensor maintenance, observation quality control strongly affects the analysis.

111. Why does NWS want this?
Gridded verification of their gridded forecasts (NDFD)
Serve as a mesoscale Analysis of Record (AOR)
For mesoscale forecasting and studies.

112. TX 2 m Temperature Analysis