Quantitative Precipitation Forecasting at the Hydrometeorological Prediction Center (HPC) Dan Petersen HPC Forecast Operations Branch (301)

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 from 12z Run

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

OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF HPC Forecast qpf 18z-00z QPF Jun

OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF NAM Forecast CAPE/CIN 18z June

OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF NAM Forecast Precipitable Water 18z June

OH Valley Case Study-Using Models/Radar/Satellite to Compose QPF NAM Forecast Best Lifted Indices 18z June

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

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

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

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

Real Time Case Study-Short term QPF Satellite Derived Lifted Index June z

Real Time Case Study-Short term QPF Satellite Derived Convective Inhibition June z

Real Time Case Study-Short term QPF Satellite Derived Precipitable Water June z

OH Valley Case Study-Short term QPF June Storm Total Precipitation

OH Valley Case Study-Short term QPF Observed 06 hour amounts ending 00z June

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 0 o 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)