Post on 12-Jan-2016
description
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)