The Local Analysis and Prediction System (LAPS) Local Analysis and Prediction Branch NOAA Forecast Systems Laboratory Paul Schultz

LAPS Mission A system designed to: Exploit all available data sources Exploit all available data sources Create analysis grids for nowcasting and “generic” model intialization Create analysis grids for nowcasting and “generic” model intialization Build products for specific forecast applications Build products for specific forecast applications Provide reliable forecast guidance Provide reliable forecast guidance Use advanced display technology Use advanced display technology …All within a local weather office, forward site, or in fully deployed mode

The LAPS team John McGinley, branch chief, variational methods John McGinley, branch chief, variational methods Paul Schultz, project manager, modeler, your speaker today Paul Schultz, project manager, modeler, your speaker today Brent Shaw, modeler Brent Shaw, modeler Steve Albers, cloud analysis, temp/wind analysis Steve Albers, cloud analysis, temp/wind analysis Dan Birkenheuer, humidity analysis Dan Birkenheuer, humidity analysis John Smart, everything John Smart, everything

LAPS GUI – Global localization

LAPS GUI – Grid refinement

Example LAPS/WRF 5km Domain

LAPS Diabatic Initialization Cloud Analysis Data Fusion 3DVAR Dynamic Constraint LAPSPREP NWP System LAPSPOST Surface RAOB Sat ACARS GPS Radar(Vr) Profilers Radar(Z) Sat Aircraft METAR NWP FG Data Ingest/Quality Control National NWP LBC LSM IC Native Output Forecaster Isobaric Output T, , p, u, v, , RH T,  q c q i, q r q s, q g cc T, , p, u, v, RH Constraints: Mass Continuity u/v Time Tendencies Background Error Observation Error Adjust for Model: Hydrometeor Concen. Saturation Condition

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

LAPS Dynamic Balance Adjustment FHFLFHFL Q > 0

“Hot Started” forecasts 00Hr Fcst, Valid 28 Mar 01/00Z01Hr Fcst, Valid 28 Mar 01/01Z Cloud fields realistically maintained

Illustration 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. Hot Start Cloud insertion Initialization5 min forecast

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

Fire Weather Home Page

LAPS Ventilation Index

Front Range 600m Domain Feb 9, 2004 Analyzed Surface Winds

Space Launch Operations Support USAF Space Launch Facilities USAF Space Launch Facilities Vandenberg and Cape Canaveral Vandenberg and Cape Canaveral LAPS and MM5 LAPS and MM5 10, 3.3, 1.1 km nests 10, 3.3, 1.1 km nests Critical for launch and range safety weather forecasting Critical for launch and range safety weather forecasting Utilizes local towers, profilers, miniSODARs, etc. Utilizes local towers, profilers, miniSODARs, etc. Operational “firsts” Operational “firsts” AWIPS Integration AWIPS Integration Linux cluster modeling Linux cluster modeling

Cape Canaveral 6-hour QPF on 1-km Grid and Radar Verification 9 Feb 04

FSL Support for USAF/ US Army Precision Air Drop

Typical Airdrop Events Treated in PADS PADS System Background DESCENT TRAJECTORY Fall or Glide Trajectory Model + 3D Atmospheric Wind/Density Field Complex 3D Atmospheric Flow over/through Mountainous Terrain Ballistic System or Guided System (Corrects to Planned Descent Trajectory) CARP Green Light Roll-Out Canopy- Opening/ Deceleration Drop Sonde Assim Time

Current PADS Features PADS Fly-Away Kit: Flight-Certified for the C-130 and the C-17

Results: Intermediate Altitude C- 130 Airdrops (10,000-15,000 ft)

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 Basis: Multiple equally-skillful forecasts can be combined into a single forecast that is better than any one of the ensemble members FSL’s first application: a road weather prediction project FSL’s first application: a road weather prediction project

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

Maintenance Decision Support System Sponsored by FHWA Sponsored by FHWA Cooperative 5-yr project with NCAR/RAP, CRREL, MIT/LL Cooperative 5-yr project with NCAR/RAP, CRREL, MIT/LL Help snowplow garage supervisors decide when/where to send trucks, chemical treatments Help snowplow garage supervisors decide when/where to send trucks, chemical treatments FSL: produce supplemental model runs and transmit them to NCAR FSL: produce supplemental model runs and transmit them to NCAR FSL model data NCAR Road Weather Forecast System CRREL Road temp/chemical module MIT/LL rules of treatment practice GUI in the field

MDSS modeling domain

Forecast point status display Place cursor over a forecast point

Bulk statistics State variables, 12-hr forecasts Feb 1 – Apr 8, 2003 Temperature (K) Wind speed (m/s) Dewpoint (K) MM5-AVN MM5-Eta RAMS-AVN RAMS-Eta WRF-AVN WRF-Eta

A closer look 9 pm model runs, verifying only Iowa stations, entire expt

MM5-Eta MM5-AVN WRF-AVN RAMS-EtaRAMS-AVNWRF-Eta

Conclusions from 2003 MDSS demonstration Lateral bounds not useful for adding diversity for this application Lateral bounds not useful for adding diversity for this application Good diversity Good diversity Models: MM5 and WRF Models: MM5 and WRF Initialization data Initialization data Considerable value to the client in earliest hours of forecasts (hot start) Considerable value to the client in earliest hours of forecasts (hot start)

Juggling act 6 model runs 6 model runs 4 sets per day (i.e., every 6 hrs) 4 sets per day (i.e., every 6 hrs) 27-hr forecasts 27-hr forecasts 3-hr temporal resolution 3-hr temporal resolution 2 model runs 24 sets per day (i.e., every hour) 15-hr forecasts 1-hr temporal resolution

Loops of the two different models initialized at the same time

Loops of the same model (WRF) initialized an hour apart

4 forecasts valid at the same time

Bulk statistics State variables, 12-hr forecasts Dec 29 – Mar 19, 2004 Temperature (K) Wind speed (m/s) Dewpoint (K) MM WRF Eta

Diurnal trend in temperature forecast errors Midnight model runs

3-h Precipitation verification

6-h Precipitation verification

Advances in numerical weather prediction via MDSS Practical diabatic initialization Practical diabatic initialization Models have useful, skillful precipitation forecasts in first few hours Models have useful, skillful precipitation forecasts in first few hours Reduced latency Reduced latency MDSS: forecasts available ~1 h after data valid time MDSS: forecasts available ~1 h after data valid time NCEP: forecasts available ~3 h after data valid time NCEP: forecasts available ~3 h after data valid time Increased frequency Increased frequency MDSS: updates every hour MDSS: updates every hour NCEP: updates every six hours NCEP: updates every six hours

Cycle :20 :35 :48 :00

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

Reflectivity Probabilities for Aviation The forecast-area specificity decreases as forecast lead times increases. 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. 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. Probability of level 3 echo with green 10%, yellow 30% and red 60%. 0-1 hr1-2 hr 2-3 hr 3-4 hr Slide courtesy C. Mueller, NCAR/RAP

Use of Mesoscale Model Ensembles - Transport Weather and Fire Weather Probabability generator Economic cost/loss models Yes or no Forecast for Springfield, MO: 79% chance of 1 mm 36% chance of 10 mm 100% chance T > 32F

Ensemble-Generated 1-Hr Probability of Smoke Concentration > 60% > 20%

Ensemble-Generated 2-Hr Probability of Smoke Concentration > 60% > 20%

Ensemble-Generated 3-Hr Probability of Smoke Concentration > 60% > 20%