Transcript Introduction to QPF

Introduction to QPF
Wes Junker
NCEP/HPC (ret.)
(with minor tweaks/modifications by SMR)
The COMET® Program
University Corporation For Atmospheric Research
Outline
Introduction
Where?
When?
How Much?
Other QPF Considerations
Summary
Quantitative Precipitation Forecasts
(QPF)
Focus of talk is liquid precipitation
Predicting how much precipitation will
fall during a specified time period
How hard can that be? 
Composing a QPF is Difficult
 A forecaster must determine:
– Where? (will precipitation fall)
– When? (will precipitation fall)
– How Much? (precipitation will fall)
 Requirements:
– Good analysis and forecasting skills
– Good pattern recognition skills
– Working knowledge of local climatology and
precipitation processes at multiple scales
(synoptic/mesoscale/microscale)
– Good understanding of numerical forecast models
(strengths/limitations/biases)
Where, When and How Much?
 Generically, precipitation is produced in regions
of combined moisture and lift
 Precipitation amount is determined by:
– Moisture availability
– Precipitation intensity (convective/stratiform/both?)
 Heaviest precipitation usually occurs in regions
of high moisture and best lift where the
atmosphere is most unstable
 Challenge to forecasters:
– find the regions of best moisture, lift and instability
– identify how fast the “MLI” regions will move
Questions to Ask
When Preparing a QPF
1. What is the time range and forecast period?
2. Which NWP model is ‘handling’ the mass/wind
fields best?
3. Is the present synoptic or mesoscale pattern
conducive to heavy precipitation?
4. Is a MCS likely to develop?
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Need to forecast initiation, mode, scale, and motion
NOT handled well by large-scale NWP models
5. What type of precipitation event is likely?
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Convective?
Stratiform?
Combination of both?
Questions to Ask
When Preparing a QPF (cont.)
6. How confident are you in your forecast?
 If you lack confidence, be conservative!
Where?
How Large of an Area?
 To help determine how large an area of rainfall
to expect, consider:
1. Scale of the synoptic forcing (size/strength)
2. Moisture availability, which depends upon:
 absolute/relative ambient moisture (PW/mixing ratio/RH)
 strength of moisture transport into the area
3. Anticipated system movement during its lifetime
(fast or slow?)
4. Pattern recognition
5. Model guidance
 A good first guess, especially for cool-season events
Importance of Pattern Recognition
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Pattern recognition is VERY useful to QPF
Patterns vary by:
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Patterns can be identified using
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Season
Geographic region
Scale
Conventional data
Model output
Satellite imagery
Radar imagery
Verify patterns by looking at previous events
Pattern Recognition (cont.)
 Heavy rainfall events can often be
identified by their patterns
– Must understand why pattern favors heavy
precipitation!
 Heavy rainfall events often share certain
characteristics
– Even in winter, heavy rain (and snow) usually
falls in mesoscale bands
Pattern Recognition Process:
Step 1: Synoptic Scale
 Well-established connection between shortwave troughs and convection
– ‘Primes’ area for convection
– However, synoptic-scale UVM rarely lifts parcels to
their LFCs (by itself)
 What does large-scale lift do, then?
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Steepens the lapse rate
Promotes moisture transport
Weakens convective inhibition (cap)
Changes vertical wind shear (more for severe wx)
Pattern Recognition Process:
Step 2: Mesoscale
 Do a mesoanalysis of surface and upper-air
features (if time allows)
 Perform a multi-sensor analysis to identify
surface boundaries
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Sources of lift and focus of convection
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Fronts, drylines, outflow boundaries, sea breeze fronts, etc.
 Examine satellite and radar imagery to
determine what is causing any current
precipitation
Pattern Recognition Process:
Step 3: Using NWP
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Look for favorable synoptic and mesoscale heavy
precipitation patterns in NWP output (large-scale and
mesoscale models)
Models can provide decent forecasts of low-level wind
and moisture fields for assessing moisture transport
(sfc/850/700 hPa)
Model products are useful for assessing areas of forcing
and may be useful for forecasting locations of larger
boundaries
Models also provide actual QPF numbers, but use these
with caution (esp. during convection)
Remember: be aware of model limitations when
performing these assessments!
When?
Will Convection Occur?
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Answering “When?” often requires determining if and
when convection will occur
Convection produces most heavy rainfall events
Three ingredients necessary to initiate deep moist
convection (DMC)
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1.
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Moisture
Instability
Upward vertical motion (lift)
Large-scale models have limitations forecasting
convection
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Parameterization of sub-grid scale processes
Some mesoscale models now predict convection explicitly
Assessing Instability
 Soundings are the best tools to assess
instability and wind profile:
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CAPE/CIN (don’t forget parcel choice!)
Stability indices (LI, K, TT, Showalter)
Depth of moisture (K index useful)
Vertical wind profile (how much shear?)
Equilibrium level (warm top convection?)
CAPE
 CAPE is a better indicator of instability than
any index that only uses mandatory levels
(LI/SI/KI/TT)
 Most heavy precipitation events have some
instability, but they do not require high CAPE
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In fact, storms with high precipitation efficiencies
typically have moderate CAPE values
‘Shape’ of the CAPE important in +RA events
‘Skinny’ (distributed) CAPE vs. ‘fat’ CAPE (strong
UVM)
 Models generally do not forecast CAPE well
CIN
 The negative area (energy) on the sounding
mainly below the LFC (AKA cap)
 Often the key for determining “when/if”
convection will occur
 CIN can either:
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‘store’ energy, allowing it to be released explosively
later; or
inhibit convection completely
 Models generally do not predicted CIN well
CIN (cont.)
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Bluestein and Jain (1985) suggested that slightly stronger
upstream CIN may lead to backbuilding convection
Convection will fire in areas of weakest CIN first,
followed by areas with slightly higher CIN values
Changing Stability
 A forecaster needs to anticipate how the stability
is changing
 Lapse rate can change due to:
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Diabatic heating/cooling
Lapse rate ‘advection’
Differential temperature advection
(Differential) vertical motion
 Models often miss stability changes
How Much?
Rainfall Amount
 Amount of rainfall that falls over an area
depends on three factors:
1. INTENSITY of the rainfall
2. SIZE of the rainfall area
3. PROPAGATION of the rainfall area
Rainfall Amount (cont.)
 Additional rainfall total considerations:
1.
2.
3.
4.
How much moisture is available?
Will the precipitation be convective or not?
Will cells train over the same area or not?
Will new storms form or continue to form
upstream? (regeneration)
5. Will a boundary or local topography provide local
enhancement?
Precipitation Intensity
 Precipitation intensity is proportional to the
vertical moisture flux
 Thus, it is important to assess the following:
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How much moisture will be available (ambient or
transported)?
What proportion of the moisture entering the cloud
will fall as rain (precipitation efficiency)?
How strong is the UVM?
How much instability is present (if any)?
Precipitation Efficiency
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A (sometimes significant) portion of the water vapor
that enters a storm system passes through without
condensing (although most does condense)
Of the water vapor that does condense:
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Some will evaporate
Some will fall out as precipitation
Some will be carried away by the clouds
Two inhibitors of precipitation production:
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A dry layer in the storm system, and
Strong vertical wind shear
Precipitation Efficiency:
Favorable Factors
1.
A deep warm layer
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Increased residence time in clouds
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3.
Enhances collision-coalescence process
Strong UVM
A broad spectrum of cloud droplet sizes
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4.
Rainfall intensity will be greater if depth from the LCL to the
0°C isotherm is ~3-4 km (greater in SE U.S.)
Generally, low cloud base events are more efficient precipitators
(higher RH)
Also enhances the collision-coalescence process
Occurs when air masses have long over-ocean trajectories
Weak to moderate shear
System Movement
 Slow-moving systems usually produce the
heaviest rainfall (no way!)
 For very short-term (0-6 h) forecasts,
extrapolation based on radar and satellite
provide primary guidance
 For longer-term forecasts, models provide
decent guidance
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Some NWP models have ‘spin-up’ issues, so better in
long term
But you still need to consider model characteristics,
limitations, and biases
Problems with NWP
 Initialization and QC smoothes data fields
(important details can be lost)
 Lack of data over oceans/Mexico
 Terrain is (over)simplified
 Subgrid-scale processes are often parameterized
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convection*, boundary-layer processes, cloud
microphysics, radiation, etc.
*some models can now represent/forecast convection
explicitly
Problems with NWP (cont.)
 Atmospheric processes are nonlinear
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Small changes in initial conditions can lead to large
forecast variations (uncertainty)
Basis for ensemble forecasting (probabilistic)
Not the perfect solution
 What does a CPS do when it is ‘turned on?’
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It changes vertical stability profile
It generates and redistributes heat
It redistributes momentum
It makes clouds/precip
 Is CPS physically ‘accurate?’ Good question…
CPS Issues
 Several types with own strengths/weaknesses
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Kain-Fritsch
Betts-Miller-Janic
Grell
 If the CPS is not vigorous enough:
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Model will generate too much explicit (grid-scale)
precipitation
This will lead to erroneous latent heat feedback
 Most CPSs handle outflow poorly
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Model therefore cannot forecast propagation well
Explicit convection representation can help somewhat
An Example of a CPS Problem
Eta 6-h forecast of
convective precipitation
(< 1 inch)
Eta 6-h forecast of
grid-scale precipitation
(~5 inches!)
Does this look reasonable?
So what happened here?
 CPS was not vigorous enough
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Not enough convective precipitation generated
Momentum/moisture/heat not redistributed properly
 As a result, the pressure and wind fields were
erroneous
 This lead to anomalously strong grid-scale
UVM/precipitation
Echo Training or Regeneration
 Factors that can lead to echo training or
regeneration:
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Slow-moving low-level boundary or front
Quasi-stationary LLJ
Quasi-stationary area of upper-level
divergence
Low-level boundary almost parallel to the
mean flow (source of moisture convergence)
Lack of strong vertical wind shear
Cell Movement and Propagation
 System movement depends on cell movement
and propagation
 Individual convective cells usually move at
around 90% of the mean wind with a slight
deviation to the right
 Propagation is affected by how fast new cells
form along some flank of the system
MCS Propagation
 Early Stage of MCS
– Individual cells move approximately with
850-300 hPa mean wind
– Much like ordinary thunderstorms
 Mature Stage of MCS
– Preferred cell motion slightly to right of mean
wind
– Active portion of MCS moves slightly to right
of mean flow
MCS Movement and Propagation
 MCS movement is dependent upon:
1. 850-300 hPa mean flow (Corfidi 1994); and
2. Rate/location at which new cells are growing
(propagation)
 MCS propagation is dependent upon:
– Location of the most unstable air
– Axis/orientation/relative speed of the LLJ
 Stronger LLJ  greater deviant motion
– Cold pool strength
– Location of strongest LL moisture convergence
MCS Propagation
MCS Propagation and
Thickness Patterns
 MCCs often track along the 1000-500 hPa (or
850-300 hPa) thickness contours
 The amount of moisture needed to produce
a large MCS/MCC appears to be dependent
on 1000-500 hPa thickness and precipitable
water
– Look for preferred thickness values and high
PWs/% of normal values
 Watch for MCC development and heavy
rain in areas of diffluent thickness
MCS Propagation: Stationary Case
What features do you see that support a heavy precipitation event?
Note E-W frontal zone
and high PWs
Note southerly 35-40 kt winds
and high instability
MCS Propagation: Stationary Case
Satellite Imagery
This MCS remained stationary for about 9 hours!
MCS Propagation:
Importance of Shape and Motion
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Vector C represents motion of storm complex
Note the difference in the rain received at circled point!
Other QPF
Considerations
Short-range Forecasts
 Short-term (0-6 h) QPF forecasts rely
primarily on current observations and
trends
Radar and satellite imagery provide
excellent information on intensity, size,
and propagation of precipitation systems
Still have to consider non-linear changes,
such as new cells forming upstream
Using Radar Imagery
Modern radars provide estimates of
rainfall rates and accumulations
– High temporal resolution
– Precipitation estimates in between rain gauges
and observation points
– Not a perfect system
Using Radar Imagery (cont.)
 Animating imagery is
useful for:
– Determining
cell/system movement
– Determining if/where
cells are training
Discussion Question
 How do the following radar ‘issues’ act as
limitations that can adversely impact your
decision making during a heavy precipitation
event?
– Elevation angle:
 beam can overshoot maximum reflectivity
 compare to composite reflectivity to account for this error
– Terrain:
 beam blockage
– Melting ice:
 bright-banding and hail contamination
– Z-R relationship variations/applicability:
 precipitation character/time of year/geography
Using Satellite Imagery
Modern satellites provide:
– Means to monitor pre-storm environment
(e.g., cloudy vs. clear, stable vs. unstable)
– Measurements of environmental
moisture/stability variables (with some
success)
– Precipitation estimates for areas in between
rain gauges and lacking radar information
– Cloud-top temperature information
Using Satellite Imagery (cont.)
 Animating imagery
is useful for:
– Locating forcing
features (short
waves, jet streaks,
etc.)
– Assessing moisture
transport
– Determining
cell/system
movement
– Monitoring system
growth and decay
Jet Streak Impacts
Jet streaks have been associated with:
– Variations in strength of the LLJ
– Cyclogenesis and major snowstorms
– Frontogenesis
Remember velocity and curvature
changes in the upper-level jet are both
important
Jet Streaks and Cyclogenesis
 Most cyclones to the lee of N-S mountain ranges
form under the left exit region of a jet streak
 The LLJ is enhanced due to the isallobaric winds
associated with the pressure falls
 The low-level winds also strengthen in response
to the increase in pressure gradient
 Differential temperature and moisture advection
destabilize the air mass
Example from Summer 1993
250 mb Isotachs
Divergence
The heaviest rainfall was usually located at the southern edge of the divergence
(revealing the sloping nature of the DTC’s ascending branch).
Significance of the Low-Level Jet
 Speed convergence maximized at the nose of the
LLJ
 Confluent low-level flow is often present along
the axis of the LLJ
 Strength of vertical moisture flux related to the
strength of the LLJ
 Differential moisture and temperature advection
can lead to rapid destabilization
 A quasi-stationary LLJ supports the cell
regeneration and/or echo training
 The LLJ is often located on southwest/western
flank of a backward-propagating MCS
Subtle Heavy Rainfall Signatures
(SHARS)
Plan View
• Note strong vorticity max
• Comma-shaped cloud signature
• Slow-moving
• U/L lows in moist environments
Vertical Profile
• Note significant veering
• Weak speed shear
• High absolute/relative moisture
•low DD values
•high RH/PW values
Heavy Rainfall in Tropical Systems
• South of ~35o latitude
– Typically from slow movement/terrain influence
– Typically associated with eyewall convection (especially where
wind is perpendicular to coastline) and feeder bands
– Often located along the region of max inflow just to the east of
the center
• North of ~35o latitude and Western U.S.
– Often associated with mid-latitude interaction (most intense
rainfall north & west of track)
– Terrain can provide focus
More on Rainfall with Tropical Systems
 Max rainfall=100/storm speed (rule of thumb, doesn’t always work)
 Amount of pre-existing moisture important in governing rainfall
potential
 As storm decays, the heaviest precipitation often shifts to the
northwest side of storm, especially if it interacts with westerlies
 Watch for nighttime "core" rains near center (may be deceptively
inactive during the day)
 Tropical moisture associated with storm sometimes interacts with
fronts north and east of the system (even if the system is hundreds of
km away)
 Pacific systems moving northeastward from Mexico can cause heavy
rains well ahead of the center (can focus on a front in the Southern
Plains)
+RA Forecasting Rules of Thumb 1
 Max rainfall often occurs where center of
strongest inflow intersects a boundary
 Max rainfall often occurs to NE of e ridge
– LL moisture convergence center
– Best LL thermal forcing
 Summertime +RA often forms along outflow
boundaries south of warm fronts
 Inverted isobars (i.e. inverted trough) along a
front can indicate +RA potential
– Usually associated with low/mid-level WAA
– Lower pressure upstream
+RA Forecasting Rules of Thumb 2
 +RA often falls in areas of thickness diffluence
(often implies exit region of ULJ)
 Beware of thickness contours that remain
stationary or sink southward in LL southerly flow
– Forms via adiabatic cooling from UVM
– Works during cool and warm seasons
 MCSs/MCCs often follow 1000-500 hPa thickness
contours (or just to the right of them)
 MCCs often form under UL ridge axes
– Weak inertial stability and WAA
– Beware of ULJs moving into ridge crests
+RA Forecasting Rules of Thumb 3
 +RA possible behind weak mid-level vort
max/near vort min
– If LL thermal ridging/MCON are present
– +RA possible despite lack of mid/upper-level support
 Watch for favorable ULJ structures
– Cyclonically-curved left exit regions
– Anticyclonically-curved right entrance regions
 K indices good measures of deep moisture
– KI > 35C indicate high +RA potential
– Look for ‘high’ values even during cool season
+RA Forecasting Rules of Thumb 4
 Max rainfall from tropical systems found in center
core at night, due to organized moisture
convergence
 Watch for tropical connections in WV imagery
– Increases precip efficiency via enhanced CC processes
– Reduces need for moistening of mid/upper levels
 Subtle heavy rainfall signatures (SHARS)
– Slow-moving comma-shaped clouds in satellite
– Warm-topped convection (> -58C)
+RA Forecasting Rules of Thumb 5
 Fast-moving systems and/or strong height falls
not conducive to +RA
– Larger area of moderate RA (1-2”)
– Slow-moving/weak falls better for +RA
 Large-scale NWP models usually place axis of
+RA too far north
– Poor handling of outflow boundaries and other
mesoscale features/details
– Better with synoptic-scale pattern, LLJ, and moisture
distribution
– Not applicable to mesoscale models
Summary
Summary
Composing a QPF is challenging
It requires answering three difficult
questions:
– When (or if) precipitation will fall?
– Where will precipitation fall?
– How much precipitation will fall?
Summary (cont.)
 Meteorologists must use their analysis and
forecasting skills, along with their local
climatology knowledge to:
– Recognize scenarios known to create regions of
instability, moisture, and lift
– Apply their knowledge of cloud and precipitation
processes
– Use observations from multiple platforms and NWP
forecast products intelligently (with limitations in
mind) to monitor and anticipate system evolution