nowcasting flash floods and heavy precipitation --- a satellite perspective
DESCRIPTION
Nowcasting Flash Floods and Heavy Precipitation --- A Satellite Perspective. --------------------------------------------- Jim Gurka National Weather Service Office of Meteorology Silver Spring, MD [email protected] 301-713-1970. Satmet 99-2 Wednesday, 28 April 1999. - PowerPoint PPT PresentationTRANSCRIPT
1
Nowcasting Flash Floods andHeavy Precipitation ---A Satellite Perspective
---------------------------------------------
Jim Gurka
National Weather Service
Office of Meteorology
Silver Spring, MD
301-713-1970Satmet 99-2Wednesday, 28 April 1999
2
3
4
Satellite Pictures
• GOES Water Vapor (6.7 m) day and night): detects moisture and weather systems between 700 - 300 mb
• GOES Infrared (10.5 - 12.6 m) day and night): detects cloud top temperature and surface temperature
• GOES Visible (0.55 - 0.75 m) (day only): what you can see: clouds, water, land
5
Satellite Pictures
• Polar Microwave (SSM/I and AMSU): detects precipitation, moisture, snow cover, ocean surface winds, surface wetness
6
7
Quantitative Precipitation
Forecasts (QPF)• The prediction of how much
precipitation (e.g., 1/2 inch, 1 inch, 2 inches, etc) will fall during a specified period of time (e.g., 3 hours, 6 hours, 12 hours, 24 hours, etc)
8
Quantitative Precipitation
Forecasts (QPF)• Assimilation• Analysis• Anticipation• Intuition• Experience
9
NCEP
• EMC• HPC SPC TPC MPC CPC AWC
SEC
10
Forecasting Pcpn Amounts from NWP
Models• Data Assimilation
– Sfc, Upr Air; Satellite; Radar/Profilers; ACARS•Into NWP Models
– Gives an approximate solution to wind, temperature & moisture fields in the atmosphere;
•QPF: Serves as guidance to how much precipitation will occur
11
NWP Model Output vs Observations
• 24 hours before a pcpn event:– Model output is about 17x more
useful than data;
• At time zero: Model output has virtually no value compared to observations;
• 9 hours before an event: Obs and model output have about equal value;
12
QPF: 2 Problems
•Interpreting the Numerical Guidance;
•Knowing a big rainfall event from a small one (in terms of scale and intensity)
13
Why Models Have Forecast Problems
• Lack of data over oceans/Mexico– Sparse upper air obs: lose details
• Initialization and QC…smooth data fields
• Smoothed terrain• Atmospheric process are non-
linear• Model Physics are
approximations
14
HPC Tips for Doing a QPF
• Look at precipitation history;• System evolution;• Basic ingredients & trends• Gridded data…be careful
– compare models for continuity and known biases
• Consider local effects;• Watch out for High Bias;• Verification• Warm Season: lower
expectations• Call NCEP for guidance/empathy
301-763-8343
15
QPF Improved with Resolution and Data
Assimilation• Meso-Eta, Cool Season:
– Performance better in cool season– Resolves intense thermal gradients
• Meso-Eta, Warm Season– A problem with MCS
•in the prediction of outlow boundaries
• Meso-Eta over High Terrain– low bias due to model resolution (32
km)– Betts Scheme needs adjustment for
shallow conv.
• RUC: 3 hour pcpn forecasts more accurate than from Eta
16
Model Resolution
•AVN: 110 km / 28 layers•NGM: 80 km / 16 layers•RUC: 60 km / 26 layers•Early Eta: 48 km / 38 layers•Meso-Eta: 32 km / 45 layers •Meso-Eta: 17 layers below
850 mb vs.•NGM: 3 layers
17
Data Assimilation: Goals
•Reduce “spin up” problem• Improve model QPF• Improve soil moisture
initialization• Include mesoscale
information found in precipitation data
18
Pattern Recognition
•Heavy rainfall events can often be identified with particular patterns
•Patterns are identified by:–conventional data–model output–satellite data– radar
19
Pattern Recognition
•Thickness patterns•70% saturation thickness•Preferred thickness
20
Rules of Thumb for Hvy Rain
•Max: Strongest inflow + Boundary
•Max: Just NE of Theta-E Ridge
•Along outflow S of Wmfnt (summer)
• Inverted isobars along a front
•MCS track alng or Rt of K lines
•Convection Srn edge of Westerlies
•MCSs often form near upr ridge
21
Rules of Thumb for Hvy Rain
•Beware slow moving synoptic systems (SHARS) …often have warm tops
•MCSs often form near upr ridge
•Strong hgt falls / fast movg systems usually preclude very heavy rafl
•Models usually predict axis of heaviest rain too far north (outflows)
22
Rules of Thumb for Hvy Rain
•K index: good measure of deep moisture–beware Ks in upper 30s
•Tropical Systems Max rainfall–usually in core at night–usually along periphery in daytime
23
To Release CAPE:
•Syn scale forcing does not act quickly enough (but moistens air & wkns cap)
•Need meso source of lift to reach LFC– low level boundaries/ fronts–find boundaries in T, DP, Theta-E & wind
– low level convergence
24
Importance of CIN
•Steep lapse rate above inversion–classic loaded gun sounding
•Cap stores energy leading to higher potential buoyant energy later in the day or evening
•Slightly stronger CIN upstream can sometimes lead to backbuilding
25
Eta Characteristics
•Most srn bias for cyclone track–esp east of Appalachians
•Too slow and too far west with strng surface cyclones coming up East coast
•Sometimes overdevelops LLJ•Too much Pcpn Gulf and Atl
coasts–due to diff in convective parameterization
•Vort centers too strong (with convct)
26
Eta Characteristics
•Significantly underforecasts monsoonal convct over SW states
•Best at fcstg frntl psn (using LI grdnt) in damming events ovr E. U.S.
•Often slightly overforecasts strength of Arctic anticyclones
•Not enough cnvctn ovr plains (summ)
•Too wet in Florida
27
AVN Characteristics
•Cold bias (mainly E. U.S.)•Genly 1st short range model
to fcst cyclogenesis–can be too slow deepening
•Best with filling cyclones•Best psn fcst cyclones and
anticy (days 2 & 3)– too far N & W sfc cycl formg E of Rockies
–once upr trof…lee Rockies…OK
28
AVN Characteristics
•Often weakens Srn stream system or phases the 2 separate systems;
•Too slow…Arctic airmass in the plains
•Overdoes upslp pcpn Cntrl and Srn Rockies and strength of upslope flow
•Oct-April Avn QPF quite good–competitive with Eta and btr than NGM
29
AVN Characteristics
•With strong wrapped up systems, forecasts hvy pcpn too far N & W
•Tracks tropical systems too far W
•Overdeepens weak tropical waves
•Overpredicts the # and strength of anticyclones ovr Grt Basin (cool ssn)
•Significantly underpredicts monsoonal precipitation
30
HPC QPF Summary
•Start with an accurate assessment of the atmosphere
•Pattern Recognition
•Know the model biases
31
32
Questions to ask when preparing Nowcasts and
QPF“this involves”
•Satellite interpretations / signatures
•Satellite conceptual models
•satellite products
33
Global to Synoptic Scale
•Wetness of ground?•Pattern recognition?
34
Soil (surface) Wetness Index (85 GHz - 19 GHz) (H)for January 1995
35
Soil (surface) Wetness Index (85 GHz - 19 GHz) (H)5 day moving averages ending August 7, 1998
36
PATTERN RECOGNITION: 6.7 micron water vapor imageryfor 11-26-97 0000 UTC; 300 mb winds (mps) are superimposed
37
PATTERN RECOGNITION: 6.7 micron water vaporimagery for 6-11-98 1200 UTC; 300 mb winds (mps)are superimposed
38PATTERN RECOGNITION: 6.7 Micron for 2-3-98 1200 UTC;300 mb winds (mps) are superimposed
39
Synoptic to Mesoscale
• Available moisture and stability?• Presence of lifting or lid?• presence of boundaries?
(synoptic of mesoscale)
40
Use of Water Vapor(6.7 m) Imagery
• middle - upper level flow fields and circulations
• lifting mechanisms- jet streaks
- cyclonic circulations/lobes - trough axes - mid-level cold air advection
• excellent data for pattern recognition
41
6.7 m Water Vapor Plumes
• Makes environment more efficient?
• Enhances precipitation through cloud seeding?
• associated with favorable synoptic patterns for low-level moisture and instability
42
WATER VAPOR PLUME: 6.7 micron water vaporimagery for 10-16-98 2345 UTC; 300 mb winds (mps)are superimposed
43
WATER VAPOR PLUME: 6.7 micron water vaporimagery for 10-17-98 1215 UTC
446.7 micron water vapor imagery for 6-22-97, 1745 UTC
45
46
Precipitation Efficiency Factors
• Precipitable Water (PW) values ---- higher than 1.0 inch, enhances Precipitation Efficiency
• Mean environmental Relative Humidity (RH) ---- higher than 65 % results in less dry air entrainment into cloud masses
47
Precipitation Efficiency Factors
• Depth of cloud with temperatures warmer than 0 degress C enhances the collision-coalescence process by increasing residence time of droplets in clouds --- this increases rainfall intensity and improves precipitation efficiency
48
Precipitation Efficiency Factors
• Vertical wind shear ---- produces entrainment and reduces Precipitation Efficiency, especially if environmental air is dry
• Cloud-scale vertical motion function of “CAPE” (Convective Available Potential Energy) related to condensate production and residence time of droplets
49
Additional Precipitation
Efficiency Factors• Storm-relative mean inflow and
moisture transport into storm• Duration of precipitation
50
Use of Precipitable Water (PW) and
Relative Humidity (RH) Data
• magnitude• transport (plumes)• trends• relation to equivalent potential
temperature
51
PW Plume: Composited PW (mm) (SSM/I + GOES + ETA model)for 10-17-98 1200 UTC; 850 mb winds are superimposed
52PW Plume: Composited Precipitable Water Product (mm) for6-22-97 1800 UTC; 850 mb winds superimposed
53
Precipitable Water Available
• SSM/I (polar microwave)- water only
• GOES 8/9/10- clear (cloud free) areas
• National Center for Environmental Prediction (NCEP) “ETA” and “AVN” models
• Rawinsondes• Composites (SSM/I + GOES 8/9/10
+ ETA/AVN)
54
Defense MeteorologicalSatellite
Program/SpecialSensor Microwave
Imager(SSM/I) Channel most
sensitive to Total Precipitable Water
22.235 GHz
55
GOES 8 Sounding Channels Sensitive to Precipitable Water (m) (over land and water, cannot calculate when clouds are present)
14.37; 14.06; 13.96; 13.37; 12.66; 12:02; 11.03; 7.43; 7.02; 6.51; 4.57; 4.52; 4.13
Cloud Detection
3.74 and 0.94
56GOES Precipitable Water Products (mm) for October 22 - 23, 1995
57
Precipitable Water X Relative Humidity
• Adjusts satellite-derived Quantitative Precipitation Estimates (QPE)
• Used in Quantitative Precipitation Forecasts (QPF) Techniques
58
Precipitable Water (mm) X Relative Humidity for10-17-98 1200 UTC from the ETA Model; 850 mb winds (mps)are superimposed
59
Equivalent Potential Temperature Ridge
Axis• Represents a potential energy
axis for convective development• Conservative (similar to vorticity)• “Maximum areas” are often
associated with afternoon to evening convection
60
Equivalent Potential Temperature Ridge
Axis• “Gradient areas” (especially
north of the ridge axis) are often associated with “overrunning” nocturnal convection
61850 mb equivalent potential temperature (degrees K) for 10-17-98 1200 UTC
62
6.7 micron water vapor imagery for 6-20-96 0515 UTC
65
GOES 8/9 Precipitable Water Product (mm) for 6-20-96 0546 UTC
66850 mb winds (kts) for 6-20-96 0000 UTC
67850 mb equivalent potential temperature (degrees K)for 6-20-96 0000 UTC
68
69
Short Waves(lifting mechanisms)
• Dark areas detected in the 6.7 m water vapor imagery:
- mid-level cold air advection - jet streaks
- positive vorticity advection
- trough axes- height fall (rise)
centers
706.7 micron water vapor imagery for 6-26-95 1915 UTC