The Local Analysis and Prediction System (LAPS)

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The Local Analysis and Prediction System (LAPS)

• Local Analysis and Prediction Branch
• NOAA Forecast Systems Laboratory
• Paul Schultz

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LAPS 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

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The 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

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LAPS GUI Global localization
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LAPS GUI Grid refinement
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Example LAPS/WRF 5km Domain
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LAPS Diabatic Initialization
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Cloud 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

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LAPS Dynamic Balance Adjustment
FH FL
Q gt 0
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Hot Started forecasts
00Hr Fcst, Valid 28 Mar 01/00Z
01Hr Fcst, Valid 28 Mar 01/01Z
Cloud fields realistically maintained
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Illustration
                 
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.
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Current 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

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Fire Weather Home Page
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LAPS Ventilation Index
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Front Range 600m DomainFeb 9, 2004Analyzed
Surface Winds
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Space 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

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Cape Canaveral 6-hour QPF on 1-km Grid and Radar
Verification9 Feb 04
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FSL Support for USAF/ US Army Precision Air Drop
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Typical 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)
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Current PADS Features
PADS Fly-Away Kit Flight-Certified for the C-130
and the C-17
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Results Intermediate Altitude C-130 Airdrops
(10,000-15,000 ft)
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Local 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

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FWHA Road Maintenance Decision Support Project-
Iowa 2003, 2004
RWIS tower, I-35 south of Ames
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Maintenance 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

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MDSS modeling domain
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Forecast point status display
Place cursor over a forecast point
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Bulk 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
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A closer look
9 pm model runs, verifying only Iowa stations,
entire expt
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MM5-Eta
WRF-AVN
MM5-AVN
RAMS-Eta
RAMS-AVN
WRF-Eta
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Conclusions 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)

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Juggling 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

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Loops of the two different models initialized at
the same time
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Loops of the same model (WRF) initialized an hour
apart
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4 forecasts valid at the same time
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Bulk 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
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Diurnal trend in temperature forecast errors
Midnight model runs
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3-h Precipitation verification
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6-h Precipitation verification
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Advances 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

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Cycle
00
48
20
35
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Ensemble 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

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Reflectivity 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
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Use 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
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Ensemble-Generated 1-Hr Probability of Smoke
Concentration
gt 60
gt 20
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Ensemble-Generated 2-Hr Probability of Smoke
Concentration
gt 60
gt 20
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Ensemble-Generated 3-Hr Probability of Smoke
Concentration
gt 60
gt 20