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 QPF

NAM 18z-00z QPF June 14 2005 from 12z Run

OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF

HPC Forecast qpf 18z-00z QPF Jun14-15 2005

OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF

NAM Forecast CAPE/CIN 18z June14 2005

OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF

NAM Forecast Precipitable Water 18z June14 2005

OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF

NAM Forecast Best Lifted Indices 18z June14 2005

OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF

NAM 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)