# Presentation: 530531

## Quantitative Precipitation Forecasting at the Hydrometeorological Prediction Center (HPC)

- www.hpc.ncep.noaa.gov

- Dan Petersen
- HPC Forecast Operations Branch
- _[Dan.Petersen@noaa.gov](mailto: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 QPFSatellite 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Satellite Derived Convective Inhibition June 14 2005 16z

## Real Time Case Study-Short term QPFSatellite 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Observed 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)