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 [email protected] (301)763-8201.

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Quantitative Precipitation Forecasting at the Hydrometeorological Prediction Center (HPC)

www.hpc.ncep.noaa.gov

Dan Petersen

HPC Forecast Operations Branch

[email protected] (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 Hydrometeorological Prediction Center (HPC)

  • 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) Hydrometeorological Prediction Center (HPC)

  • 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)




Real Time Case Study-Short term QPF QPF 1724z Satellite June 14 2005Satellite Derived Convective Available Potential Energy- June 14 2005 16z


Real time case study short term qpf satellite derived lifted index june 14 2005 16z
Real Time Case Study-Short term QPF QPF 1724z Satellite June 14 2005Satellite Derived Lifted Index June 14 2005 16z


Real time case study short term qpf satellite derived convective inhibition june 14 2005 16z
Real Time Case Study-Short term QPF QPF 1724z Satellite June 14 2005Satellite Derived Convective Inhibition June 14 2005 16z


Real time case study short term qpf satellite derived precipitable water june 14 2005 16z
Real Time Case Study-Short term QPF QPF 1724z Satellite June 14 2005Satellite Derived Precipitable Water June 14 2005 16z


Oh valley case study short term qpf june 14 2005 storm total precipitation
OH Valley Case Study-Short term QPF QPF 1724z Satellite June 14 2005June 14 2005 Storm Total Precipitation


Oh valley case study short term qpf observed 06 hour amounts ending 00z june 15 2005
OH Valley Case Study-Short term QPF QPF 1724z Satellite June 14 2005Observed 06 hour amounts ending 00z June 15 2005


Case study results
Case Study Results QPF 1724z Satellite June 14 2005

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


Verification-24 Hour QPF vs. Models Satellite/Radar/Model diagnostics?


FY2005 Verification Satellite/Radar/Model diagnostics?


Short term qpf benefits from multi sensor analysis
Short Term QPF Benefits from Multi-sensor Analysis Satellite/Radar/Model diagnostics?

  • 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 Satellite/Radar/Model diagnostics?

  • 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) Satellite/Radar/Model diagnostics?

  • 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) Satellite/Radar/Model diagnostics?

  • 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) Satellite/Radar/Model diagnostics?

  • 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) Satellite/Radar/Model diagnostics?

  • 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 Satellite/Radar/Model diagnostics?

  • 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 Satellite/Radar/Model diagnostics?

  • 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 Satellite/Radar/Model diagnostics?

  • 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 propagation

  • 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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