Quantitative Precipitation Forecasting at the Hydrometeorological Prediction Center (HPC)
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Quantitative Precipitation Forecasting at the Hydrometeorological Prediction Center (HPC) www.hpc.ncep.noaa.gov. Dan Petersen HPC Forecast Operations Branch Dan.Petersen@noaa.gov (301)763-8201.

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Quantitative Precipitation Forecasting at the Hydrometeorological Prediction Center (HPC)

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Quantitative Precipitation Forecasting at the Hydrometeorological Prediction Center (HPC)

www.hpc.ncep.noaa.gov

Dan Petersen

HPC Forecast Operations Branch

Dan.Petersen@noaa.gov (301)763-8201


Quantitative Precipitation Forecasting at the Hydrometeorological Prediction Center (HPC)Goals of Presentation

  • Short Range QPF Methods

  • Short Range QPF Case Study

  • Verification


Composing a QPF

  • Short range ( <12 hours )

    • Forecast composed by blending the latest radar and satellite data with an analysis of Moisture/Lift/Instability and model output

  • Long range ( >12 hours )

    • Forecast increasingly relies on model output of QPF, Moisture/Lift/Instability

    • Adjustments are made for known model biases and latest model trends/verification/comparisons (including ensembles)


Composing a QPF ( <12 hours)

  • Radar

    • Looping can show areas of training and propagation

    • Review radar-estimated amounts-Be wary of beam blocking, bright bands, overshooting tops & attenuation

    • Compare observations to estimates (Z – R relationship impact)

  • Satellite

    • Rainfall estimates from NESDIS/Satellite Analysis Branch

    • Looping images can show areas of training/development

    • Derived Precipitable Water, Lifted Indices, soundings, etc.


OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF GFS 18z-00z QPF June 14 2005 from 12z Run


OH Valley Case Study-Using Models/Radar/Satellite to Compose QPFNAM 18z-00z QPF June 14 2005 from 12z Run


OH Valley Case Study-Using Models/Radar/Satellite to Compose QPFHPC Forecast qpf 18z-00z QPF Jun14-15 2005


OH Valley Case Study-Using Models/Radar/Satellite to Compose QPFNAM Forecast CAPE/CIN 18z June14 2005


OH Valley Case Study-Using Models/Radar/Satellite to Compose QPFNAM Forecast Precipitable Water 18z June14 2005


OH Valley Case Study-Using Models/Radar/Satellite to Compose QPFNAM Forecast Best Lifted Indices 18z June14 2005


OH Valley Case Study-Using Models/Radar/Satellite to Compose QPFNAM Forecast Boundary Layer Moisture Convergence 18z June14 2005 (none over OH River)


OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF 1719z Radar June 14 2005


OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF 1724z Satellite June 14 2005


Real Time Case Study-Short term QPFSatellite Derived Convective Available Potential Energy- June 14 2005 16z


Real Time Case Study-Short term QPFSatellite Derived Lifted Index June 14 2005 16z


Real Time Case Study-Short term QPFSatellite Derived Convective Inhibition June 14 2005 16z


Real Time Case Study-Short term QPFSatellite Derived Precipitable Water June 14 2005 16z


OH Valley Case Study-Short term QPFJune 14 2005 Storm Total Precipitation


OH Valley Case Study-Short term QPFObserved 06 hour amounts ending 00z June 15 2005


Case Study Results

  • NAM model diagnostics supported developing convection, but did not identify boundary to provide lift

  • Satellite derived products supported model prognostics favorable for convection plus (combined with radar) identified boundaries to provide lift


Verification-How much Improvement Can We Derive from Satellite/Radar/Model diagnostics?


Verification-24 Hour QPF vs. Models


FY2005 Verification


Short Term QPF Benefits from Multi-sensor Analysis

  • Improved real time multi-sensor analysis would

    • Reduce uncertainty of real time satellite/radar estimates

    • Reduce uncertainty of post-event rainfall and time spent on quality control (more reliable verification)

    • Lead to improvements in moisture/lift/instability-related diagnostics/prognostics, and thus confidence in qpf and excessive rainfall forecasts

  • Questions/needed clarifications?


Composing a QPF

  • Must have knowledge of:

    • Climatology

    • Precipitation producing processes

    • Sources of lift (boundaries, topography too)

    • Forecasting Motion (propagation component vs. advection)

    • Identifying areas of moisture/lift/instability


Analysis (Synoptic/Mesoscale)

  • Perform a synoptic & mesoscale analysis

    • Upper air

      • Upper fronts, cold pools, jet streaks

    • Surface Data

      • Boundaries

    • Satellite Data

      • Moisture plumes, Upper jet streaks

    • Radar

      • Boundaries

  • Try to link ongoing precipitation with diagnostics


Analysis (Moisture)

  • Precipitable Water (PW)

  • Surface through 700 mb dew points

  • Mean layer RH

  • K indices

  • Loops of WV imagery/derived PWs

  • Consider changes in moisture

    • Upslope/Down slope

    • Veritical/Horizontal advection

    • Soil moisture

    • Nearby large bodies of water


Analysis (Lift)

  • Low/Mid level convergence

    • Lows, fronts, troughs

  • Synoptic scale lift

    • Isentropic

    • QG components (differential PVA & WAA)

  • Jet dynamics

    • Nose of LLJ

    • Left front/right rear quadrants of relatively straight upper jets with good along stream variation of speed

  • Mesoscale boundaries

    • Outflow, terrain, sea breeze

  • Orographic lift

  • Solar heating


Analysis (Instability)

  • Soundings are your best tool

  • CAPE/CIN is better than any single index

    • Beware!! Models forecast CAPE/CIN poorly

  • Equilibrium Level

  • Convective Instability

    • Mid-level drying over low-level moisture

    • Increasing low-level moisture under mid-level dry air

  • Changing Instability

    • Try to anticipate change from

      • Low level heating

      • Horizontal/Vertical temperature/moisture advection

      • Vertical Motion


Precipitation Efficiency Factors

  • Highest efficiency in deep warm layer

    • Rainfall intensity is greater if depth of warm layer from LCL to 0o isotherm is 3-4 km

    • Low cloud base

  • Collision-Coalescence processes are enhanced by increased residence time in cloud

  • Need a broad spectrum of cloud droplet sizes

    • present from long trajectories over oceans

  • Highest efficiency in weak to moderately sheared environments

  • Some inflowing water vapor passes through without condensing

  • Of the water vapor that does condense

    • Some evaporates

    • Some falls as precipitation

    • Some is carried (blown) downstream as clouds or precipitation

  • In deep convection, most of the water vapor input condenses


Low Level Jet

  • Nocturnal maximum in the plains

  • Inertial oscillation enhances the jet

  • Often develops in response to lee low development

  • LLJ can be enhanced by upper level jet streak

  • Barrier jets (near mountains) can play a role in focusing lift

  • Convection can induce very focused LLJs


LLJ Importance

  • Speed convergence max at nose of LLJ

  • Confluent flow along axis of the LLJ

  • Vertical/Horizontal Moisture Flux positively related to strength of LLJ

  • Differential moisture/temperature advection can lead to rapid destabilization

  • Quasi-Stationary LLJ can lead to cell regeneration/training

  • Often located on the SW flank of a backward propagating MCS


Movement of a system is dependent on cell movement and propagation

  • The vector describing the propagation is the vector anti-parallel to the LLJ Vprop = -VLLJ

  • The vector that describes the movement of the most active part of an MCS is represented by

    • V = Vcell + Vprop

  • Propagation is dependent on how fast new cells form along some flank of the system


Factors leading to training/regenerating convection

  • Slow moving low level boundary

  • Quasi-stationary low level jet

  • Quasi-stationary area of upper level divergence

  • Low level boundary (moisture/convergence) nearly parallel to the mean flow

  • Lack of strong vertical wind shear (speed & directional)


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