Diabatic Mesomodel Initialization Using LAPS - an effort to generate accurate short term QPF By John...
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Diabatic Mesomodel Initialization Diabatic Mesomodel Initialization Using LAPS - an effort to generate Using LAPS - an effort to generate
accurate short term QPFaccurate short term QPFBy
John McGinley, NOAA Forecast Systems Lab
With contributors
Steve Albers, John Smart, Paul Schultz FSL BrentShaw, Weather News Intl
And
Gou-Ji Jian, Jen-Hsin Teng, Li-Hui Tai, CWB
OverviewOverview Brief review of LAPS and Hot Start Brief review of LAPS and Hot Start LAPS/ Model Deployments in Sub-tropicsLAPS/ Model Deployments in Sub-tropics
Brief Case StudiesBrief Case Studies VerificationVerification
Ensemble ApplicationsEnsemble Applications Probabilistic QPFProbabilistic QPF
Future WorkFuture Work Blending QPE/QPFBlending QPE/QPF
Our mission at FSL is short range high resolution forecasting. For many customers the first 12 hours is very critical. We needed an approach that allows rapid spin-up of clouds and precipitation.
Useful elements to work with:
1. LAPS cloud and moisture analysis (Water in All Phases: WIAP)2. Variational balancing scheme3. Coupled Mesoscale Models: RAMS, MM5, WRF
The LAPS Diabatic The LAPS Diabatic Initialization TechniqueInitialization Technique
LAPS includes:LAPS includes: Cloud analysisCloud analysis Dynamic balance using variational schemeDynamic balance using variational scheme Coupling to most mesoscale modelsCoupling to most mesoscale models Links to display devices (AWIPS, others)Links to display devices (AWIPS, others)
Sustain the “operational” tradition of LAPSSustain the “operational” tradition of LAPS Robust data ingest, QC, and fusionRobust data ingest, QC, and fusion Platform and model independencePlatform and model independence Computationally inexpensiveComputationally inexpensive
Key element: Water-In-All-Key element: Water-In-All-Phases AnalysisPhases Analysis
Cloud Analysis – satellite, radar, surface, aircraft Cloud Analysis – satellite, radar, surface, aircraft observationsobservations
Water Vapor Analysis – variational methodWater Vapor Analysis – variational method Recovery of microphysical variables using simple cloud Recovery of microphysical variables using simple cloud
model: model: Precipitation Total precipitable waterPrecipitation Total precipitable water Water vapor Integrated liquid water Water vapor Integrated liquid water Cloud water Cloud coverCloud water Cloud cover Cloud Ice Ice Crystals Cloud Ice Ice Crystals
Cloud Analysis SchemeCloud Analysis Scheme
Uses satellite Visible Uses satellite Visible and IRand IR
Aircraft observationsAircraft observations Surface observationsSurface observations RadarRadar Interpolates cloud obs Interpolates cloud obs
to grid with SCMto grid with SCM Cloud feeds back into Cloud feeds back into
water vapor analysiswater vapor analysis
Updated CWB/ FSL scheme (cloud derive subr)
CB
CS
Downdrafts in stratiform region
Less dependence on cloud type,Updraft goes to top of cloud
Randomness in broad convectiveregions
Strongest updraftsin regions of high reflectivity
LAPS Diabatic Initialization
CloudAnalysis
UnivariateData
Fusion
3DVARDynamic
Constraint
LAPSPREP
NWP SystemLAPSPOST
Surface RAOB Sat ACARS GPS Radar(Vr) Profilers Radar(Z) Sat Aircraft METAR
NWP FG
Data Ingest/Quality Control
National NWPLBC
LSM IC
NativeOutput
Forecaster
IsobaricOutput
T, , p, u, v, , RH
T,
qc qi, qr qs, qg
c
T, , p, u, v, RH
Constraints:Mass Continuityu/v Time TendenciesBackground ErrorObservation Error
Adjust for Model:Hydrometeor Concen.Saturation Condition
EquationsEquations
( ) b are background quantities; (^) are solution increments from background; ( )’ are observation differences from background
Dynamic Balancing and Continuity Formalism
Cloud, Wind and Mass Dynamic Adjustment
Cloud, Wind and Mass Dynamic Adjustment
wcFH
FL
T> 0^
q= qs
Figure 3. 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 onlyCold start
0-Hr 5 min forecast
Balanced Field Unbalanced Field
MODEL NOISE |dp/dt|
Difficult problem: Heavy Rain in the Subtropics Difficult problem: Heavy Rain in the Subtropics - Dominican Republic and Haiti, May 2004- Dominican Republic and Haiti, May 2004
LAPS GUI – Global locatibilityLAPS GUI – Global locatibility
Window MM5 Forecast for Dominican Republic/ Window MM5 Forecast for Dominican Republic/ Haiti Flash Floods 00Z May 22, 2004Haiti Flash Floods 00Z May 22, 2004
TRMM Rainfall EstimatesTRMM Rainfall EstimatesMay 18-25, 2004 May 18-25, 2004 (NASA Goddard)(NASA Goddard)
Taiwan CWB Short Term Forecast System
LAPS (Local Analysis and Prediction System)
Diabatic Initialization technique
Hot-Start MM5
Forecast domains & Computational requirement
D02D03
D01
1km (169*151)
D02
D01
1368 km ( 153 points)
1260
km
( 1
41 p
oint
s)
151
pts
151 pts
9km
3km
CPUs 42 compaq 833 MHz
Need 1.5hrs for 24hrs fcst
0.000.050.100.150.20
0.400.350.300.25
0.710.680.650.620.580.540.500.45
0.920.900.880.860.830.800.770.74
0.990.980.970.960.94
1.00
30 V
erti
cal l
ayer
s (σ
leve
ls)
WRF Run for Typhoon Mindulle using WRF Run for Typhoon Mindulle using
NCEP GFS/NF-15 backgroundsNCEP GFS/NF-15 backgrounds Cold -GFS Hot - NFS
TS .60, .50,.38,.35,.26
TS .53, .51,.44,.29,.26
Verification of Hot Start Forecasts for Tropical Storm Mindulle 0-6 hr forecast - Analyzed gauge data on left; forecast on right
Threat scores for precip categories 1, 10, 20,50, 100, 200mm
6-12 hr forecast - Analyzed gauge data on left; forecast on right
Threat scores for precip categories 1, 10, 20,50, 100, 200mm
6-H Precipitation verification for 4 6-H Precipitation verification for 4 tropical systems in 2003 over Taiwan tropical systems in 2003 over Taiwan
(Jian and McGinley, accepted by JMSJ)(Jian and McGinley, accepted by JMSJ)
○: HOTS
●: NCLDa
Hot Start Cold Start
12-H Precipitation verification for 4 12-H Precipitation verification for 4 tropical systems over Taiwan (Jian and tropical systems over Taiwan (Jian and
McGinley)McGinley)
○: HOTS
●: NCLDbHot Start
Cold Start
Advantage of EnsemblesAdvantage of Ensembles
•Ensembles produce probability forecasts that can be more useful than single deterministic forecasts
•Probabilistic output can be input into economic cost/lost models
• Customers get a “yes-no” forecast based upon skill and spread of ensemble
From WRF/MM5/ RAMS Ensembles, From WRF/MM5/ RAMS Ensembles, to Probabilities, to Probabilities,
to Yes/No Forecaststo Yes/No Forecasts
6-D model grids(variable,x,y,z,t,prob)
Decision Engine
Cost/LossThresholds
Yes or No
Ensemble
Location,Time
Customer
MM5-EtaMM5-Eta MM5-AVNMM5-AVN WRF-AVNWRF-AVN
RAMS-EtaRAMS-Eta RAMS-AVNRAMS-AVN WRF-EtaWRF-Eta
6-Member Ensemble: 3 models, 2 boundary conditions
MM5-EtaMM5-Eta
NN
WRF-EtaWRF-Eta
H
H+1
H+2
H+3
H+4
H+5
Time-Phased Ensemble: an efficient way to get many members in limited computing environments
Time
t0
Ensemble at time = t0 Time weighting is applied to each member
(Number of members) =
(Number of models) x(Length of Forecast) /(Start Interval)
Each pair of runsHas a unique Initial condition basedon LAPS
Radar-observed precipitation -Radar-observed precipitation -24 hrs ending 13 Oct 04 12GMT24 hrs ending 13 Oct 04 12GMT
New approach: time-phased ensemble - model run New approach: time-phased ensemble - model run every hour with new initial conditions - with two every hour with new initial conditions - with two
models, 24 members for every timemodels, 24 members for every time
WRF Time phased ensembleWRF Time phased ensembleModel Run vt 13 Oct 04 18GMT - 18 hr forecastModel Run vt 13 Oct 04 18GMT - 18 hr forecast
WRF Time phased ensembleWRF Time phased ensembleModel Run vt 13 Oct 04 12GMT - 15 hr forecastModel Run vt 13 Oct 04 12GMT - 15 hr forecast
WRF Time phased ensembleWRF Time phased ensembleModel Run vt 13 Oct 04 12GMT - 12 hr forecastModel Run vt 13 Oct 04 12GMT - 12 hr forecast
WRF Time phased ensembleWRF Time phased ensembleModel Run vt 13 Oct 04 12GMT - 09 hr forecastModel Run vt 13 Oct 04 12GMT - 09 hr forecast
WRF Time phased ensembleWRF Time phased ensembleModel Run vt 13 Oct 04 12GMT - 06 hr forecastModel Run vt 13 Oct 04 12GMT - 06 hr forecast
WRF Time phased ensembleWRF Time phased ensembleModel Run vt 13 Oct 04 12GMT - 03 hr forecastModel Run vt 13 Oct 04 12GMT - 03 hr forecast
Ensemble Probabilities:Ensemble Probabilities:Threshold: 5 mm/3 hrThreshold: 5 mm/3 hrat 12 GMT 13 Oct 04at 12 GMT 13 Oct 04
>20
>40
> 60
>80
PrecipitationProbabilities %
Future WorkFuture Work
Ensemble probabilistic post-processing Ensemble probabilistic post-processing and coupling ensembles to decision and coupling ensembles to decision algorithmsalgorithms
Merging QPE and QPF over the 0-6 HR Merging QPE and QPF over the 0-6 HR time frametime frame
Designing a Forecast/ Observation, QPE/ QPF Designing a Forecast/ Observation, QPE/ QPF Blending SchemeBlending Scheme
wi wi wiwi
Forecasts 0H 1H 2H 3H
Observations 0H 1H 2H 3H
CorrelationsCoefficients/
Weights from Training Set
Forecast SetFor New Event
Post-processor
Optimum Forecast
Set
ConclusionsConclusions
1. The LAPS Hot Start Method has shown improvement over operational NWP output In the 0-6 hour time frame.
2. It has demonstrated improved verification of precipitation in higher categories
3. The LAPS /NWP System is capable of running on small computers in local weather offices
4. It has run in winter and summer experiments5. It shows promise for tropical storm QPF when combined with bogussing
the initial position