Alpha-Numeric Model Guidance

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:

*** 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.

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.

Example: NAM FOUS for Birmingham TTPTTR1R2R3 VVVLI PSDDFF HHT1T3T5 BHM//

The general format: FOXXII KWBC DDTTTT TTPTT R1R2R3 VVVLI PSDDFF HHT1T3T 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

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.

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.

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).

FF Wind speed in knots of the lowest model layer (lowest 35 mb). HH 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).

Numerical Models FOUS data for a particular location should agree very closely with that model ’ s 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.

Model Output Statistics I. 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.

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

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

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

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...

INTERPRNAMTION OF THE NAM BASED MOS FORECAST MESSAGE (FWC - FOUS14) FOUS14 KWBC Bulletin Header. DCA ESC NAM MOS GUIDANCE 3/06/ UTC - Message ID [Station, Routing, Model, Initial Date and Time (UTC)]. DAY /MAR 6 /MAR 7 /MAR 8 - Valid Month and Day (UTC). HOUR Valid Hour (UTC).

MX/MN Maximum or minimum temperature for daytime/nighttime period (F). TEMP Temperature at specified time (F). DEWPT Dew point temperature at specified time (F).

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

WDIR Wind direction ( x 10 = compass direction) for specified time. WSPD Wind speed (kts) for specified time. POP Probability of precipitation for 6-h period ending at specified time. POP Probability of precipitation for 12-h period ending at specified time.

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 = inches 1 = inches 2 = inches 2 = inches 3 = inches 3 = inches 4 = inches 4 = inches 5 = > 1.00 inches 5 = inches 6 = > 2.00 inches

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.

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

POZP Conditional probability of freezing precip. or ice pellet, if precipitating. POSN Conditional probability of snow for specified time, if precipitating.

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 snow0 = no snow 1 = trace - 2 inches 1 = trace - < 2 inches 2 = > 2 Inches 2 = 2 - < 4 inches 4 = 4 - < 6 inches 6 = > 6 inches

CIG Ceiling height forecast for specified time (see below). CIG 1 = < 200 ft. 2 = ft. 3 = ft. 4 = ft. 5 = ft. 6 = ft. 7 = > ft.

VIS Visibility forecast for specified time (see below). VIS 1 = < 1/2 mile 2 = 1/2 - 7/8 mlle 3 = /4 miles 4 = miles 5 = > 5 miles

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