Title: The Local Analysis and Prediction System (LAPS)
1The Local Analysis and Prediction System (LAPS)
-
- Local Analysis and Prediction Branch
- NOAA Forecast Systems Laboratory
- Paul Schultz
2LAPS Mission
- A system designed to
- Exploit all available data sources
- Create analysis grids for nowcasting and
generic model intialization - Build products for specific forecast applications
- Provide reliable forecast guidance
- Use advanced display technology
- All within a local weather office, forward site,
or in fully deployed mode
3The LAPS team
- John McGinley, branch chief, variational methods
- Paul Schultz, project manager, modeler, your
speaker today - Brent Shaw, modeler
- Steve Albers, cloud analysis, temp/wind analysis
- Dan Birkenheuer, humidity analysis
- John Smart, everything
4LAPS GUI Global localization
5LAPS GUI Grid refinement
6Example LAPS/WRF 5km Domain
7LAPS Diabatic Initialization
8Cloud Analysis Scheme
- Uses satellite Vis and IR
- Aircraft observations
- Surface observations
- Radar
- Interpolates cloud obs to grid with SCM
- Cloud feeds back into water vapor analysis
9LAPS Dynamic Balance Adjustment
FH FL
Q gt 0
10Hot Started forecasts
00Hr Fcst, Valid 28 Mar 01/00Z
01Hr Fcst, Valid 28 Mar 01/01Z
Cloud fields realistically maintained
11Illustration
Initialization
5 min forecast
Hot Start
Cloud insertion
Cloud liquid (shaded), vertical velocity
(contours) and cross-section streamlines for
analyses (right) and 5-min forecasts (left). The
top pair shows LAPS hot-start DI with upward
vertical motions where clouds are diagnosed and
properly sustained cloud and vertical motions in
the forecast the bottom pair demonstrates the
artificial downdraft that usually results from
simply injecting cloud liquid into a model
initialization without supporting updrafts or
saturation. Note that cloud liquid at the top of
the updraft shown in the hot-started forecast
(above right) has converted to cloud ice.
12Current LAPS Projects
- Fire Weather Support
- Highway Weather Support Ensemble Modeling
- Space Center Support System - KSC and Vandenberg
- Army Paradrop Project - laptop deployment
- Taiwan Central Weather Bureau
13Fire Weather Home Page
14LAPS Ventilation Index
15Front Range 600m DomainFeb 9, 2004Analyzed
Surface Winds
16Space Launch Operations Support
- USAF Space Launch Facilities
- Vandenberg and Cape Canaveral
- LAPS and MM5
- 10, 3.3, 1.1 km nests
- Critical for launch and range safety weather
forecasting - Utilizes local towers, profilers, miniSODARs,
etc. - Operational firsts
- AWIPS Integration
- Linux cluster modeling
17Cape Canaveral 6-hour QPF on 1-km Grid and Radar
Verification9 Feb 04
18FSL Support for USAF/ US Army Precision Air Drop
19Typical Airdrop Events Treated in PADS
PADS System Background
Canopy- Opening/ Deceleration
CARP Green Light
Drop Sonde
Roll-Out
DESCENT TRAJECTORY Fall or Glide Trajectory Model
3D Atmospheric Wind/Density Field
Assim Time
Complex 3D Atmospheric Flow over/through
Mountainous Terrain
Ballistic System or Guided System (Corrects to
Planned Descent Trajectory)
20Current PADS Features
PADS Fly-Away Kit Flight-Certified for the C-130
and the C-17
21Results Intermediate Altitude C-130 Airdrops
(10,000-15,000 ft)
22Local model ensembles
- Basis Multiple equally-skillful forecasts can
be combined into a single forecast that is better
than any one of the ensemble members - FSLs first application a road weather
prediction project
23FWHA Road Maintenance Decision Support Project-
Iowa 2003, 2004
RWIS tower, I-35 south of Ames
24Maintenance Decision Support System
- Sponsored by FHWA
- Cooperative 5-yr project with NCAR/RAP, CRREL,
MIT/LL - Help snowplow garage supervisors decide
when/where to send trucks, chemical treatments - FSL produce supplemental model runs and
transmit them to NCAR
25MDSS modeling domain
26Forecast point status display
Place cursor over a forecast point
27Bulk statisticsState variables, 12-hr
forecastsFeb 1 Apr 8, 2003
Temperature (K) Temperature (K) Wind speed (m/s) Wind speed (m/s) Dewpoint (K) Dewpoint (K)
MM5-AVN 3.1 -0.7 2.5 0.8 5.6 1.5
MM5-Eta 3.0 -0.5 2.5 0.8 5.5 1.6
RAMS-AVN 5.8 -1.1 2.6 1.6 6.5 -0.9
RAMS-Eta 5.9 -1.1 2.6 1.7 6.9 -1.0
WRF-AVN 3.1 -0.4 2.4 1.1 5.7 1.4
WRF-Eta 3.1 -0.4 2.4 1.0 5.7 1.3
28A closer look
9 pm model runs, verifying only Iowa stations,
entire expt
29MM5-Eta
WRF-AVN
MM5-AVN
RAMS-Eta
RAMS-AVN
WRF-Eta
30Conclusions from 2003 MDSS demonstration
- Lateral bounds not useful for adding diversity
for this application - Good diversity
- Models MM5 and WRF
- Initialization data
- Considerable value to the client in earliest
hours of forecasts (hot start)
31Juggling act
2003
2004
- 6 model runs
- 4 sets per day (i.e., every 6 hrs)
- 27-hr forecasts
- 3-hr temporal resolution
- 2 model runs
- 24 sets per day (i.e., every hour)
- 15-hr forecasts
- 1-hr temporal resolution
32Loops of the two different models initialized at
the same time
33Loops of the same model (WRF) initialized an hour
apart
344 forecasts valid at the same time
35Bulk statisticsState variables, 12-hr
forecastsDec 29 Mar 19, 2004
Temperature (K) Temperature (K) Wind speed (m/s) Wind speed (m/s) Dewpoint (K) Dewpoint (K)
MM5 3.2 0.2 2.4 1.6 3.7 1.5
WRF 3.0 1.3 2.3 1.3 3.7 2.2
Eta 2.7 0.5 2.7 -0.2 2.6 1.7
36Diurnal trend in temperature forecast errors
Midnight model runs
373-h Precipitation verification
386-h Precipitation verification
39Advances in numerical weather prediction via MDSS
- Practical diabatic initialization
- Models have useful, skillful precipitation
forecasts in first few hours - Reduced latency
- MDSS forecasts available 1 h after data valid
time - NCEP forecasts available 3 h after data valid
time - Increased frequency
- MDSS updates every hour
- NCEP updates every six hours
40Cycle
00
48
20
35
41Ensemble applications
- Ensembles produce probability forecasts that can
be more reliable - Probabilistic output can be input into economic
cost/lost models - Customers get a yes-no forecast based upon
skill and spread of ensemble
42Reflectivity Probabilities for Aviation
- The forecast-area specificity decreases as
forecast lead times increases. - Example probability forecast of level 3 or
greater reflectivity for various forecast lead
times are shown. The valid time is the same for
all images. The images illustrate the expected
degradation in forecast-area specificity with
time.
0-1 hr
1-2 hr
3-4 hr
2-3 hr
- Probability of level 3 echo with green 10,
yellow 30 and red 60.
Slide courtesy C. Mueller, NCAR/RAP
43Use of Mesoscale Model Ensembles - Transport
Weather and Fire Weather
Forecast for Springfield, MO 79 chance of 1
mm 36 chance of 10 mm 100 chance T gt 32F
Probabability generator
Economic cost/loss models
44Ensemble-Generated 1-Hr Probability of Smoke
Concentration
gt 60
gt 20
45Ensemble-Generated 2-Hr Probability of Smoke
Concentration
gt 60
gt 20
46Ensemble-Generated 3-Hr Probability of Smoke
Concentration
gt 60
gt 20