n What has happened? n Why has it happened? n What is happening? n Why is it happening? n What will happen? n Why will it happen?
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1 ATMOS 5010: Weather Forecastig Forecastig Tools ad Techiques Forecast Techiques NOAA/weather.gov Jim Steeburgh Departmet of Atmospheric Scieces Uiversity of Utah Successful Forecastig Requires Kowledgeable, well-traied, & egaged forecasters Meteorological kowledge ad experiece Local weather & climate kowledge User eed recogitio Model stregth, weakess, ad bias assessmet Huma cogitio ad iterpretatio AKA: The Huma-Machie Mix Skillful & reliable NWP guidace, forecast tools, ad other aids Image: whistlerdiaries.com Huma Cogitio Automated Systems The Forecast Process Critical Forecast Questios What has happeed? Why has it happeed? What is happeig? Why is it happeig? What will happe? Why will it happe? Easy to cocetrate oly o this = NWP, tools, aids, etc. Morss ad Ralph (2007) = Kowledgeable, well traied, & egaged forecasters Source: Bosart (2003) 1
2 Critical Forecast Questios The Forecast Methodology What has happeed? Why has it happeed? What is happeig? Why is it happeig? What will happe? Why will it happe? Importat whe NWP goes awry or caot resolve local orographic effects To aswer these questios, use the forecast fuel Begi at plaetary scale Focus attetio o progressively smaller scales I complex terrai, build i orographic effects Plaetary Scale Syoptic Scale Mesoscale Local Scale Source: Bosart (2003) The Forecast Methodology Aswer the what ad the why i the past, preset, ad future Forecast Fuel i Practice Avoid meso-myopia Uderstad larger scales before progressig to smaller scales Whe usig high-resolutio models, evaluate cofidece i large-scale forecast before progressig to smaller scales Expect limited local skill if largescale is ot well forecast Beware whe the atmosphere is i outlier mode Geeralizatios break dow Plaetary Scale Syoptic Scale Mesoscale Local Scale Evaluate past, curret, ad future plaetary scale settig The Forecast Fuel i Practice The Forecast Fuel i Practice Evaluate cofidece i syoptic-scale forecast Fuel to syoptic scale 2
![advatage [idicates] that dedicated ad traied](/docs-images/77/74676934/images/3-3.jpg "forecasters ca extract maximum advatage from")
3 The Forecast Fuel i Practice The Forecast Fuel i Practice Fuel to mesoscale Cosider mesoscale, orographic, ad lad-surface processes???? Adjust for local effects? Real-Time Example Usig IDV Humas Make a Differece Source: NCEP/HPC This cotiuig skill advatage [idicates] that dedicated ad traied forecasters ca extract maximum advatage from improvemets i operatioal weather predictio models -Bosart (2003) O the other Had. Do t be o Autopilot Forecasters who grow accustomed to lettig MOS ad the models do their thikig o a regular basis are at high risk of goig dow i flames whe the atmosphere is i a outlier mode - Bosart (2003) Although NWP is importat, basic uderstadig, patter recogitio ad climatology cotiue to play a essetial role because of limitatios i curret NWP systems, icludig iadequate terrai represetatio, iitial coditio ucertaity, ad parameterizatio ucertaity 3
4 Bottom Lie Forecast Tools Forecasters have a clear role i the forecast process, by cotributig a wealth of kowledge, tools ad techiques that caot be duplicated by computers or NWP McCarthy et al. (2007) But forecasters eed to be egaged ad icreasigly eed a advaced educatio to extract maximum beefit from today s sophisticated forecast tools This class begis that educatio A meteorologist kows their tools, icludig their stregths ad weakesses All observatios are bad, but some are useful All models are wrog, but some are useful Forecast Tools Climatology Persistece Observatios Your eyes I-situ surface ad upper-air Wid profiler/rass Satellite Radar Weather cameras Maual aalysis NWP Models Numerical aalyses Global ad mesoscale models Esemble forecast systems Model Output Statistics (MOS) Scietific aalysis ad visualizatio systems Forecast Tools Source: Gary Larso, The Far Side Source: Gary Larso, The Far Side Forecast Tools: Climatology The statistics of weather Forecast Tools: Climatology More tha just log-term mea Mea, variace, extremes, probabilities Impacts of ENSO ad modes of climate variability PDO, NAO, etc. Local ad mesoscale effects Complex terrai results i large climatological gradiets Ofte poorly resolved by computer models Climatology to used dowscale or bias correct model forecasts for local effects Ca be overused e.g., Not all storms have the climatological precipitatio-altitude relatioship Is this useful???? Meas ad Probabilities for Forecast Practicum Variables 4
5 Forecast Tools: Climatology Forecast Tools: Persistece Persistece: What has happeed recetly Icludig treds Provides cotext for forecast Relevace for forecast varies from high to low High durig slowly evolvig patters Low durig major patter shifts Thik Beyod the Mea Forecast Tools: Persistece Forecast Tools: Your Eyes Never uderestimate the value of lookig out the widow or goig outside to feel the weather Cotext for forecast Durig this period is Differet at LGU & WBB Source: cartoostick.com, collaborativejoureys.com Forecast Tools: Sfc/Upper-Air Data Forecast Tools: Satellite ASOS, Sprigfield, IL (NWS) Weather Balloo (NWS) Wid Profiler (Wikipedia) Wid profilers provide more tha wid! Visible Imagery Visible radiatio reflected back to space by clouds, aerosols, sow, lad surface, etc. 5
6 Forecast Tools: Satellite Forecast Tools: Satellite Widow IR Imagery Log-wave radiatio emitted primarily by clouds, lad-surface, etc. Cloud-top temperature ad lad-surface temperature Water Vapor Chael (IR) Imagery Log-wave radiatio emitted primarily by upper-tropospheric clouds ad water vapor Upper-level flow, troughs, etc. Forecast Tools: Satellite Forecast Tools: Radar Precipitable Water from Polar-orbitig microwave sesors NEXRAD Doppler Radar NEXRAD vs. TDWR Now: Polarimetric NEXRAD GOES Fog Detectio Logwave IR (10.7 micro)-shortwave IR (3.9 micro) MODIS Sources: SSEC, NESDIS Sources: NOAA/SPC Forecast Tools: Weather Cameras Forecast Tools: Maual Aalysis Click for Aimatio Sources: Bosart ad Seimo (1988); Neima et al. (1988) A maual surface aalysis helps you feel the weather i your veis 6
7 Useful Sites for Observatios Surface observatios & time series Radar overlays Satellite, radar, surface maps, upper-air maps Upper-air soudigs, upper-air maps, surface mesoaalysis You ame it Satellite ad radar Useful IDV Budles for Obs Real-Time-WX>Radar> KMTX-3DTopo KMTX-2D-Obs+Aal Real-Time-WX>Aalyses> Global-10day Global-2day SuperNatioal Cous-East Cous-West Global Forecast System (GFS) Medium rage (out to 384 hours) global aalyses ad forecasts every 6 h Effective grid spacig of ~13 km to 192 h (lower resolutio thereafter) Available o lower-resolutio grids Stregths relative to other NCEP models Accuracy of large-scale forecast Terrai represetatio Precip structure North America Mesoscale Model (NAM) Based o the WRF-NMM Short-rage (out to 84 hours) forecasts for North America every 6 h Grid spacig of ~12 km Higher resolutio 4-km CONUS est available Supposed to be upgraded to 3-km soo Available o lower-resolutio grids Stregths relative to other NCEP models Terrai represetatio, mesoscale detail Limited area, large-scale accuracy Rapid Refresh (RAP) Aalyses for CONUS every hour Very-Short-rage (out to 18 hours) forecasts for CONUS every 3 h Grid spacig of ~13 km Available o lower-resolutio grids Stregths relative to other NCEP models High frequecy aalyses ad forecasts Resolutio, terrai represetatio, mesoscale details Limited area, large-scale accuracy High Resolutio Rapid Refresh (HRRR) Aalyses for CONUS every hour Short-rage (out to 18 hours) forecasts for CONUS every hour Grid spacig of ~3 km Stregths relative to other NCEP models High frequecy aalyses ad forecasts Resolutio, terrai represetatio, mesoscale details Limited area, large-scale accuracy 7
8 Weather Research ad Forecast Model (WRF) Ru i various cofiguratios at NCEP ad other locatios Some cofiguratios provide high resolutio (<10 km) short-rage (48 h or less) forecasts Stregths Resolutio ad terrai represetatio Limited area, ofte lousy iitial coditio geeratio Short Rage Esemble Forecast System (SREF) km grid spacig based o differig models, model cofiguratios, ad iitial coditios Forecasts out to 87 h every 6-h (0300 UTC, etc.) Stregths Probabalistic iformatio, allows assessmet of cofidece i large-scale forecast Not calibrated, mea ad spread of esemble may be biased Global Esemble Forecast System (GEFS) 20 a effective grid spacig of 55 km based o differet iitial coditios Forecasts out to 384 h every 12-h Stregths Probabalistic iformatio, allows assessmet of cofidece i large-scale forecast Not calibrated, mea ad spread of esemble may be biased Spread slow to develop Low resolutio Useful Sites for Model Data GFS/NAM GFS/NAM/RAP SREF ap/ ECMWF Dyamic Tropopause (Jet Stream) Dyamic Tropopause & Near SFC (Jet Stream) Dyamic Tropopause (Jet Stream) 500 mb ~5500 m/18000 ft MSL Syoptic Diagostic 700 mb ~3000 m/10000 ft MSL Surface 8
9 PW, Shear, Lapse Rate CAPE/CIN Surface Covective Diagostic Lower-Mid Trop Surface Theta-E Time Height Model Output Statistics (MOS) Based o statistical relatioships betwee model forecast variables ad actual weather i the past Relatioships the applied to latest model ru Usually based o stepwise multiple liear regressio Performs better tha NWP or Statistics aloe Bleds the best of both worlds Available for NAM ad GFS Model Output Statistics (MOS) Model Output Statistics Advatages Cheap ad easy Corrects for systematic model biases Bleds best of NWP ad Statistics Does well i geeric weather patters Disadvatage Does t hadle model chages well i.e., shifts i biases Does t hadle outlier or uusual evets well Forecaster overreliace o MOS leads to rigormortis of skill Sources: NOAA/MDL, weather.uisys.com 9
10 Forecast Tools: Scietific Visualizatio Cocludig Thoughts Lear to sip from the firehouse Fid what sites/products you like, bookmark them, ad develop a system AWIPS/AWIPS-II IDV Use IDV or similar software to itegrate products whe possible Advatage: Itegratio of Diverse Atmospheric/Earth Data Time maagemet is a critical aspect of forecastig Sources: Wikipedia Commos, Uidata 10
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