FORECAST MODELS: DYNAMIC(PHYSICAL) VS. STATISTICAL

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Presentation transcript:

FORECAST MODELS: DYNAMIC(PHYSICAL) VS. STATISTICAL DYNAMICAL MODEL STATISTICAL MODEL Physical equations! - 7 fund. + few more Statistical equations! - Pure mathematical

Statistical models … examples

STATISTICAL MODELS … examples MOS “Model Output Statistics” How are MOS equations made ? Relate dynamical model output to observations of past weather

MOS Partially Removes Model Biases!

STATISTICAL MODELS (MOS) Why have MOS ? Partially removes biases! Predicts parameters that dynamical models don’t Visibility, Cloud ceilings ….. Predicts some parameters better than dynamical models (averaged over all cases) Surface temperature, Td, wind

AVN MOS BUF EC AVN MOS GUIDANCE 9/26/01 1200 UTC DAY /SEPT 26 /SEPT 27 /SEPT 28 / HOUR 18 21 00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00 MN/MX 44 54 45 59 TEMP 51 52 49 48 47 46 46 51 52 51 49 48 48 48 48 53 57 56 53 DEWPT 40 41 42 43 43 43 44 44 44 45 45 44 44 44 44 44 43 43 44 CLDS OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV OV BK SC WDIR 22 23 25 24 26 28 25 27 27 26 25 28 30 33 36 02 36 35 30 WSPD 16 13 09 07 07 07 07 08 09 10 09 08 05 05 06 06 07 08 07 POP06 70 61 61 72 60 55 47 23 24 POP12 82 79 64 35 QPF 1/ 1/ 1/1 2/ 1/2 1/ 1/1 0/ 0/0 TSV06 10/ 0 7/ 0 9/ 0 3/ 1 26/ 0 21/ 0 10/ 0 10/ 0 9/ 0 TSV12 14/ 0 9/ 1 33/ 0 16/ 0 PTYPE R R R R R R R R R R R R R R R POZP 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 POSN 0 0 0 1 5 9 2 2 4 1 0 5 0 0 0 SNOW 0/ 0/ 0/0 0/ 0/0 0/ 0/0 0/ 0/0 CIG 4 4 4 4 5 4 3 4 4 4 5 5 5 VIS 5 5 5 5 4 4 4 5 4 5 4 5 5 OBVIS N N N N N F F N N N F N N

AVN MODEL Station: BUF Lat: 42.93 Lon: -78.73 Elev: 217 Closest grid pt: 13.5 km. Initialization Time: 01-09-26 1200 UTC PARAMETER/TIME 000 006 012 018 024 030 036 042 048 ----------------------- ------ ------ ------ ------ ------ ------ ------ ------ DAY / HOUR 26/12 26/18 27/00 27/06 27/12 27/18 28/00 28/06 28/12 TEMPS 1000 MB (C) 7 12 13 11 9 12 14 15 13 950 MB (C) 4 9 10 8 6 9 11 12 10 900 MB (C) 1 4 6 4 3 4 6 7 7 850 MB (C) -2 0 1 0 -1 1 2 3 3 800 MB (C) -9 -10 -9 -10 -11 -12 -10 -8 -6 1000-500 THCK 5405 5418 5419 5404 5381 5386 5411 5439 5459 MOISTURE 1000 MB DP(C)/RH 6/93 4/5 5/57 6/75 9/95 7/73 7/63 7/60 8/68 850 MB DP(C)/RH -3/92 -1/91 0/91 0/97 -1/97 0/97 1/93 1/89 2/91 700 MB DP(C)/RH-10/93 -10/98 -10/97 -11/95 -12/90 -12/97 -11/94 -10/82 -9/78 500 MB DP(C)/RH-22/76 -25/64 -28/60 -24/77 -24/92 -24/99 -25/94 -24/86 -26/61 PRCPABLE WTR (I 0.64 0.59 0.64 0.64 0.61 0.64 0.68 0.68 0.69 CONV PRECIP (IN 0.02 0.07 0.01 0.00 0.00 0.01 0.00 0.00 TOTAL PRECIP (I 0.02 0.07 0.01 0.00 0.00 0.01 0.00 0.00 WIND DD/FFF (Kts) 1000 MB 21/014 21/009 26/011 30/009 30/009 28/007 30/008 36/011 01/016 850 MB 23/025 23/021 25/017 28/011 30/011 31/013 32/011 36/014 01/017 700 MB 23/036 24/029 25/028 26/022 29/013 34/017 35/020 01/024 02/026 500 MB 22/049 23/045 25/039 25/034 27/021 33/016 00/027 02/038 03/044 250 MB 22/050 23/054 23/048 24/044 25/027 29/011 00/026 02/039 03/050

FORECAST MODELS: DYNAMIC(PHYSICAL) VS. STATISTICAL dynamic models statistical models- MOS

CONCEPTUAL MODELS Dynamic Ascent: Shortwaves Aloft: 500mb

What is a conceptual model? a mental model of how things in our surrounding environment work based on information received through scientific data and observations important diagnostic tool, widely used in meteorology

CONCEPTUAL MODELS: The Norwegian cyclone model

Forecasting Technique: Pattern Recognition Associating a “weather pattern” to a “weather event” Significant weather events have patterns surface features upper level features 4-panel maps useful

Pattern Recognition: Severe Weather Dynamics (winds) Shortwaves/vortmax “JET STREAM DISTURBANCE” Thermodynamics (stability) High theta-e air! WARM, HUMID AIR “MATCH” “FUEL”