Lecture at the UT-Austin, Austin, 17 October 2006.Land Surface Modeling inNumerical Weather Prediction ModelsFei ChenResearch Applications Laboratory (RAL)The Institute for Integrative and Multidisciplinary Earth Studies (TIIMES)NCARLecture at the UT-Austin, Austin, 17 October 2006.1900EvergreenShrubMarshCrops1993Effects of Land-use Change on RainfallAverage rain (238mm)213mmJune – July Rainfall (mm)0.0196600.1066370.2543390.5022390.7511661.006341.502162.0079 Valid 2 June 91 - 29 June 91ETA: 80-KM precipitation forecast skills at 36hOSU/Noah LSM Slab model 0.00.20.40.6Rainfall Threshold (In)OSU/Noah: explicit vegetation, time-varying soil moistureSlab model: simple soil model with fixed soil moisture Precipitation Equitable Threat ScoreImpact of Land Surface Model on NWPQuantitative Precipitation ForecastChen, Pielke, and Mitchell, 2001, chapter in “Observation and Modeling of the Land Surface Hydrological Processes”Lecture at the UT-Austin, Austin, 17 October 2006.Outline• Overview of land surface processes• Modeling of land surface inmesoscale numerical predictionmodels• Land data assimilation techniquesEarth’s Global Energy Budget• Incident solar flux normalized to “100 units”• Albedo ~ .30: (25 from clouds and 5 from ground)• 70 units still left to be absorbed and re-emitted– 45 units absorbed by the ground, 25 units by the atmosphere– Change of state of water takes a lot of energy: 24 of the 45 units absorbed by the surface used forevaporationSurface affect energy redistributionLecture at the UT-Austin, Austin, 17 October 2006.Aristotle (350 BC)• The term meteorology comes from Aristotle'sbook “Meteorology (or Meteorological)”• Hydrological cycle:“Now the sun, moving as it does, sets upprocesses of change and becoming anddecay, and by its agency the finest andsweetest water is every day carried up and isdissolved into vapour and rises to the upperregion, where it is condensed again by thecold and so returns to the earth”.Lecture at the UT-Austin, Austin, 17 October 2006.Global Water CycleWater Vapourover Land3Evapotranspiration75Glaciers andSnow 24,064BiologicalWater 1LakeMarsh11River2Water Vapourover Sea10Net Water VapourFlux Transport 40Precipitation115Precipitation391Evaporation441Runoff40Permafrost300Soil Moisture17Ground Water23,400Flux in1015 kg/yState Variablein 1015kgSea1,338,000Surface (ocean and land): source of water vapor to the atmosphereLecture at the UT-Austin, Austin, 17 October 2006.Classic Forms of Boundary Layer EvolutionHeightPotential TemperatureWhy and how BL over land is different from that over water? Residual layerStable boundary layerOceanLecture at the UT-Austin, Austin, 17 October 2006.The Boundary Layer Over Water• Surface energy balance of water– Low albedo: most shortwave radiation absorbed.– Large emissivity: `black bodies'– Large heat capacity and rapidconduction/convection: SST relatively constantover daily timescales– Large evaporation: source of latent heat for theatmosphere• Boundary layer– Humid and relatively cool– Small diurnal cycle– cooler by day (warmer at night) than BL overadjacent land– Land-sea breezeLecture at the UT-Austin, Austin, 17 October 2006.The Boundary Layer Over Land• Surface energy balance of land– Complex surface conditions: soil moisture and vegetation forpartitioning of latent and sensible heat (Bowen Ratio)– Small heat capacity and slow conduction/convection– Large diurnal cycle caused by daytime solar heating andnocturnal longwave cooling• Boundary layer– Daytime• shortwave radiation heat the ground• surface layer becomes warm and a slightly unstablelayer in the lowest tens of meters; generation ofturbulence• heat diffuses higher into the BL by turbulent mixing, andthe inversion is erodedLecture at the UT-Austin, Austin, 17 October 2006.The Boundary Layer Over Land (Cont.)• Nocturnal– Upper levels cool through long-wave radiation– Air slightly higher in BL cools by radiationand by conduction downwards (negativesensible heat flux)– Low levels become cold and very stable layernear the surface– Vertical motion and turbulence suppressed– Upward ground heat flux determine the rate atwhich the ground temperature fallsLecture at the UT-Austin, Austin, 17 October 2006.The Atmospheric Boundary Layer(ABL) growth is driven primarily by• Entrainment of warmer air from the freetroposphere• Surface sensible and latent fluxes (topicaddressing by this lecture).• Also be influenced by the presence ofmesoscale phenomena such as the sea-breeze orthe mountain valley circulation, due to surfacedifferential heating.Spatial Variability of PBL on 29 May 02Site 1 (western Track)Site 3 (western Track)drier, larger sensible heat fluxrainSoil moistureNet RadLatent heatSensible heatContrast between two IHOP-02 western Sites ~50 km apartLecture at the UT-Austin, Austin, 17 October 2006.Spatial Variability of PBLThe differences in surface sensible heating resulted in 200-300 m deeper boundary layer at Site 3Site 1Wet soilSite 3dry soilLecture at the UT-Austin, Austin, 17 October 2006.Outline• Overview of land surface processes• Modeling of land surface inmesoscale numerical predictionmodels• Land data assimilation techniquesLecture at the UT-Austin, Austin, 17 October 2006.General Flow of Numerical Weather Prediction (NWP)Quality ControlObjectiveAnalysisDataAssimilationPrediction ModelFirst GuessForecastValidationRadiosondeSatelliteWx stationRadarAircraft Buoy Rain gaugeObservation systemLecture at the UT-Austin, Austin, 17 October 2006.Examples of NWP Models• Global models– GFS, Global Forecast System (previously AVN), NOAA.– NOGAPS, US Navy.– GEM, Global Environmental Multiscale, Meteorological Service of Canada– ECMWF, model by the European Centre for Medium-Range WeatherForecasts– UKMET, UK Met Office– GME, German Weather Service• Regional models– WRF, Weather Research and Forecasting Model– NAM, North American Mesoscale (formerly Eta - renamed Jan 2005),NOAA– NMM-WRF, Weather Research and Forecasting NonhydrostaticMesoscale Model became the NAM at NCEP in June 2006.– ARW(RF), Advanced Research WRF developed primarily at NCAR)– MM5, Fifth Generation Penn State/NCAR Mesoscale Model, PSU/NCAR– HIRLAM, High Resolution Limited Area ModelLecture at the UT-Austin, Austin, 17 October 2006.Physics Parameterization in NWP Model• Dynamics• Physics– Computers are not yet powerfulenough to
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