Alpha-Numeric Model Guidance - PowerPoint PPT Presentation

About This Presentation
Title:

Alpha-Numeric Model Guidance

Description:

Alpha-Numeric Model Guidance – PowerPoint PPT presentation

Number of Views:80
Avg rating:3.0/5.0
Slides: 29
Provided by: Preferr360
Category:

less

Transcript and Presenter's Notes

Title: Alpha-Numeric Model Guidance


1
Alpha-Numeric Model Guidance
2
Two Types of Alpha-Numeric Guidance
  • FOUS (Forecast Output United States) Raw model
    forecast data interpolated to specific geographic
    points from actual model grid points.
  • Example FOUS (NAM Model) bulletins
  • This raw model guidance is directly translated
    from the model forecast fields and will contain
    all the accuracies and biases inherent in the
    parent model.
  • MOS (Model Output Statistics)
  • Statistically-altered model forecast data for
    specific local points.
  • Examples MOS for WRF, NAM, and GFS models.
  • This model guidance is statistically-altered
    based on historical model performance and local
    climatologies.
  • We will look at FOUS first.
  • But, before we start, let me state a warning

3
Warning
  • Blindly following model statistics (MOS) can
    easily lead to large forecast errors, so do not
    get in the habit of MOScasting. If you have
    thoroughly analyzed the weather situation and
    came up with temperatures and precipitation
    values similar to the model output, you may have
    a higher degree of confidence in your forecast.
  • MOS is computer-derived and easy to obtain. As a
    professional meteorologist, if all you do is
    regurgitate MOS for your forecast, you are adding
    no human value to the forecast. In other words,
    you may MOS yourself right out of a job.

4
DECODING FOUS from the NAM
  • The NAM FOUS bulletins contain the initial
    numerical model analyses and forecasts for points
    in the United States, Canada, and over the
    adjacent waters. The NAM FOUS will be discussed
    below
  • The forecasts are provided in 6 hourly intervals
    from 6 to 48 hours after either 0000 or 1200 GMT
    (Z) model cycle times. The forecasts are for 6
    hour accumulated precipitation, relative
    humidity, vertical velocity, lifted index, sea
    level pressure, direction and speed of the mean
    wind in the boundary layer, 1000 to 500 millibar
    thickness, and 3 model-layer temperatures.

5
ExampleNAM FOUS for Birmingham
TTPTTR1R2R3 VVVLI PSDDFF HHT1T3T5 BHM//521556
-0114 312518 52050605 06000502155 00514 302508
54100605 12000502882 02612 272614 55120706
18000574587 00711 262916 55120806 24000666783
-0409 263014 53110705 30000606656 -0707 252809
53140704 36000614425 -1006 223015 54150704
42000653924 -0107 223514 53120604 48000714927
00408 220405 50060604
6
The general format
  • FOXXII KWBC DDTTTT
  • TTPTT R1R2R3 VVVLI PSDDFF HHT1T3T5
  • ----------------------------------
  • NNN// R1R2R3 VVVLI PSDDFF HHT1T3T5
  • 06PTT R1R2R3 VVVLI PSDDFF HHT1T3T5
  • 12PTT R1R2R3 VVVLI PSDDFF HHT1T3T5
  • 18PTT R1R2R3 VVVLI PSDDFF HHT1T3T5
  • 24PTT R1R2R3 VVVLI PSDDFF HHT1T3T5
  • 30PTT R1R2R3 VVVLI PSDDFF HHT1T3T5
  • 36PTT R1R2R3 VVVLI PSDDFF HHT1T3T5
  • 42PTT R1R2R3 VVVLI PSDDFF HHT1T3T5
  • 48PTT R1R2R3 VVVLI PSDDFF HHT1T3T5

7
CODE EXPLANATION
  • XX Region identifier.
  • II Station group number.
  • DD Day of the month forecast was issued.
  • TTTT Greenwich time of forecast cycle on which
    the data is based.
  • NNN Forecast station three letter identifier.
  • PTT 6 hour accumulated precipitation in
    hundredths of inches.

8
  • R1 Mean relative humidity of the lowest
    model layer (lowest 35 mb), in percent.
  • R2 Mean relative humidity of model layers 2
    through 9 (up to 500 mb), in percent.
  • R3 Mean relative humidity of model layers 10
    through 13 (500 to 200 mb), in percent.
  • VVV Vertical velocity at 700 mb, in tenths of
    a microbar per second, weighted average of three
    hourly values at forecast time, one hour before,
    and one hour after (double weighted at forecast
    time). Minus sign represents downward motion.

9
  • LI Lifted index in degrees Celsius.
    Negative values are designated by subtracting
    from 100 e.g. -4 96. Taken from the lowest
    (most unstable) of four possible values. The
    values derived from lifting parcels from the four
    lowest model layers up to 500 mb.
  • PS Sea level pressure calculated from lowest
    sigma level (based on the contour base map).
  • DD Direction in tens of degrees of the mean
    wind in the lowest model layer (35 mb).

10
  • FF Wind speed in knots of the lowest model
    layer (lowest 35 mb).
  • HH 1000-500 mb thickness in decameters with
    the first digit omitted.
  • T1 Temperature in model layer 1 (lowest 35
    mb) in degrees Celsius.
  • T3 Temperature in model layer 3
    (approximately 900 mb).
  • T5 Temperature in model layer 5
    (approximately 800 mb).

11
Numerical Models
  • FOUS data for a particular location should agree
    very closely with that models raw graphics
    output for similar variables.
  • For instance, Sea Level Pressure from the current
    NAM FOUS forecast should agree with the current
    NAM Sea Level Pressure forecast graphics from the
    NAM for particular locations.

12
Model Output StatisticsI. What is MOS?
  • MOS - Model Output Statistics - a statistical
    interpretation of output from numerical weather
    prediction models
  • MOS techniques develop optimal statistical
    relationships (using multiple linear regression
    and other techniques) between past model
    forecasts and "verifying" weather elements (e.g.,
    wind speed, temperature, etc...). For example,
    statistics could relate model 850 mb temperatures
    to maximum surface temperatures associated with
    previously forecast 850 mb temperatures.

13
II. What MOS does
  • Accounts for systematic model bias
  • Accounts for systematic model timing errors
  • Accounts for some regional or local effects
  • Accounts for regional climatic conditions
  • Predicts weather elements that are not explicitly
    simulated by numerical models
  • Provides reliable probability forecasts

14
III. What MOS doesn't do
  • Doesn't account for random numerical model errors
  • Doesn't handle extreme climatic conditions well
    (e.g., record highs and lows)
  • Only accounts for some numerical model biases
    that occur with specific synoptic situations
  • Doesn't account for changes in the modeling
    system
  • (This is important for NAM and GFS MOS
    construction)
  • Doesn't account for the full impact of local
    circulations and orographic effects

15
IV. Using MOS for weather forecasting
  • Can be used for time management Forecaster can
    focus on "mission critical" forecast issues.
  • Can be used as a forecast "check
  • Can be used as a first guess
  • Remember that NAM, WRF, and GFS MOS come from
    their respective models and may disagree with the
    picture presented by other models.
  • Forecasters can improve significantly on MOS by
  • identifying rare or extreme events, accurately
    choosing the model of the day
  • considering mesoscale or local effects not
    accounted for by MOS
  • looking for dynamical consistency in graphical
    model forecast panels
  • being aware of common model biases and weaknesses

16
  • Keep in mind these things
  • Often, MOS and FOUS forecasts from the same model
    will be different.
  • Keep in mind that MOS approaches continue to
    outperform raw model forecasts or "Perfect Prog"
    techniques that relate model variables to
    observed weather. Such raw model forecast
    techniques thus don't include the effects of
    systematic timing errors, biases, etc...

17
INTERPRNAMTION OF THE NAM BASED MOS FORECAST
MESSAGE (FWC - FOUS14)
  • FOUS14 KWBC 060357
  • - Bulletin Header.
  • DCA ESC NAM MOS GUIDANCE 3/06/91 0000 UTC
    - Message ID Station, Routing,
    Model, Initial Date and Time
    (UTC).
  • DAY /MAR 6 /MAR 7
    /MAR 8
  • - Valid Month and Day (UTC).
  • HOUR 06 09 12 15 18 21 00 03 06 09 12 15 18 21
    00 03 06 09 12 - Valid Hour (UTC).

18
  • MX/MN 59 39
    54 24
  • - Maximum or minimum temperature for
    daytime/nighttime period (F).
  • TEMP 37 34 33 38 45 53 52 49 46 43 40 42 47 51
    42 39 35 30 24
  • - Temperature at specified time (F).
  • DEWPT 27 28 28 30 32 36 40 38 41 41 37 33 28 27
    25 21 20 19 19
  • - Dew point temperature at specified
    time (F).

19
  • CLDS OV OV OV OV OV OV OV OV OV OV BK BK BK SC
    SC SC CL CL CL
  • - Opaque cloud cover forecast for
    specified time (see below).
  • CLDS
  • CL - Clear
  • SC - Scattered
  • BK - Broken
  • OV - Overcast

20
  • WDIR 26 18 08 12 14 14 15 18 24 27 28 29 29 29
    29 33 01 02 00
  • - Wind direction ( x 10 compass
    direction) for specified time.
  • WSPD 01 04 06 10 11 12 16 18 13 15 12 20 24 22
    14 12 14 08 00
  • - Wind speed (kts) for specified
    time.
  • POP06 4 9 46 85 62 3
    7 12 8
  • - Probability of precipitation for
    6-h period ending at specified time.
  • POP12 49 91
    8 19
  • - Probability of precipitation for
    12-h period ending at specified time.

21
  • QPF 0/ 0/ 1/1 3/ 2/4 0/
    0/0 0/ 0/0
  • - Precipitation amount forecast for
    6- and 12-h periods (see below).
  • QPF format "A/B"
  • A - Value for 6-h period / B - Value
    for 12-h period
  • 0 no precipitation 0 no
    precipitation
  • 1 0.01 - 0.09 inches 1 0.01 -
    0.09 inches
  • 2 0.10 - 0.24 inches 2 0.10 -
    0.24 inches
  • 3 0.25 - 0.49 inches 3 0.25 -
    0.49 inches
  • 4 0.50 - 0.99 inches 4 0.50 - 0.99
    inches
  • 5 gt 1.00 inches 5 1.00 - 1.99 inches
  • 6 gt 2.00 inches

22
  • TSV06 2/ 0 3/ 0 4/ 1 5/ 1 6/ 2 16/ 3 11/
    1 8/ 0 0/ 0
  • - Thunderstorm/conditional severe
    T-storm probabilities for 6-h periods ending
    at time.
  • TSV12 4/ 0 8/ 1 21/ 4
    9/
  • - Thunderstorm/conditional severe
    T-storm probabilities for 12-h periods ending
    at time.

23
  • PTYPE S S S S S R R R R R R R
    R S Z
  • - Conditional precipitation type
    forecast for specified time (see below).
  • PTYPE.
  • R - Liquid
  • Z - Freezing or Ice pellets
  • S - Snow

24
  • POZP 8 10 12 6 0 0 0 0 0 1 3 0
    2 24 35
  • - Conditional probability of
    freezing precip. or ice pellet, if
    precipitating.
  • POSN 65 67 70 48 41 14 11 13 15 16 20 9
    16 50 42
  • - Conditional probability of snow
    for specified time, if precipitating.

25
  • SNOW 0/ 0/ 0/1 0/ 0/0 0/
    0/0 0/ 0/0
  • - Snow amount for 6- and 12-h
    periods (see below).
  • SNOW format "A/B"
  • A - Value for 6-h period B - Value for
    12-h period
  • 0 no snow 0 no snow
  • 1 trace - 2 inches 1 trace - lt 2
    inches
  • 2 gt 2 Inches 2 2 - lt 4 inches
  • 4 4 - lt 6 inches
  • 6 gt 6 inches

26
  • CIG 4 5 4 4 5 6 7 6 3 2 1 5
    6
  • - Ceiling height forecast for
    specified time (see below).
  • CIG
  • 1 lt 200 ft.
  • 2 200 - 400 ft.
  • 3 500 - 900 ft.
  • 4 1000 - 3000 ft.
  • 5 3100 - 6500 ft.
  • 6 6600 - 12000 ft.
  • 7 gt 12000 ft.

27
  • VIS 3 4 3 5 5 5 5 4 2 2 1 3
    4
  • - Visibility forecast for specified
    time (see below).
  • VIS
  • 1 lt 1/2 mile
  • 2 1/2 - 7/8 mlle
  • 3 1 - 2 3/4 miles
  • 4 3 - 5 miles
  • 5 gt 5 miles

28
  • OBVIS H H H N N N N F F F F H
    N
  • - Obstruction to vision forecast for
    specified time (see below).
  • OBVIS
  • H - Haze
  • F - Fog
  • N - No haze, or fog
Write a Comment
User Comments (0)
About PowerShow.com