1 ese science review [meeting on weather forecasting] eric a. smith; nasa/goddard space flight...
TRANSCRIPT
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• ESE Science Review[Meeting on Weather Forecasting]
• Eric A. Smith; NASA/Goddard Space Flight Center, Greenbelt, MD 20771[301-286-5770; 301-286-1626; [email protected]]
• July 20, 2001; NASA Headquarters, Washington, DC
GPM
A Limited Perspective on Weather ForecastScience Problems Confronting GPM
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Projected Satellite Data Streams for GPM Erafrom Passive Microwave Radiometers & Precipitation Radars
[at left are either actual (bold) or orthodox (paren) nodal crossing times (DN or AN) or non-sun-synch labels ]
CY 99-18 99 00 11 12 13 14 15 16 17 1803 08 09 1001 02 0704 05 06
AMSR-E CMR-1
F20
NPOESS C1
F18 SSM/I SSMIS CMIS
SSM/I SSMIS CMIS MSU AMSU-A
AMSR AMSR-FO
GPM-1 (e.g., N-GPM)
GPM Core (65∞ inc)
ADEOS II
PR/TMI DPR/ATMI
TRMM (35∞ inc)
DMSP F13DMSP F16
NPOESS C3
DMSP F15DMSP F17 F19
NPOESS LITE-CMIS
AQUA
GPM-3 (e.g., E-GPM)
GPM-2 (e.g., I-GPM)
GCOM-B1
MEGHA-TROPIQUES (22∞ inc)MADRAS
GPM-5 (partner needed)
GPM-4 (partner needed)
CMR-2
CMR-3
CMR -4
CMR-5
FY-3TBD
0530DN0530DN
0130DN
NSS
NSS
1030DN
TBD
NOAA-MNOAA-K CMIS MSU AMSU-A
0730DN
NOAA-L NOAA-N
Potential Gap
KEY carries preferred PMW frequencies carries alternate PMW frequenciesGPM-1,-2,-3,-4,-5 are dedicated GPM drone
satellites (e.g., N-GPM, I-GPM, E-GPM)
NOAA-J
(1430AN)
(1730AN)
(2030AN)
(2330AN)
(1030DN)
(0230DN)
NPOESS C2
NOAA-N’
Continuous Geosynchronous Satellite Coverage by GOES E/W, METEOSAT/MSG, & GMS
(0830DN)
(0530DN)
NPP-ATMS
Replacement Era
Replacement Era
Replacement Era
Replacement Era
Replacement Era
Replacement Era
0915DN0830DN
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GPM Mission is Being Formulatedwithin Context of GWEC
with Main Science Objectives Focusing On:_____________________________________________________________________________________________
Improving flood hazard & basin-scale hydrological predictions -- through more frequent sampling and full-earth coverage of precipitation measurements
Improving climate prediction -- through better understanding of water cycling and accompanying accelerations -- decelerations of atmospheric and surface branches of water cycle
Improving weather forecasting -- through better methods of rainfall data assimilation and more accurate & precise measurements of instantaneous rainrates
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GPM Era Coverage GPM Era Coverage with 3 Inclined
GPM Core, DMSP-F18, DMSP-F19, GCOM-B1, Megha-Tropiques,& Three 600-km DronesDrones @ 34∞, 84∞, 90∞
3-hour Ground Trace
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TRMM Impact on Mesoscale Simulation of Super Typhoon Paka
PAKA (8.9∞N, 161.8∞E)[13 Dec-1997 / 0911 UTC]
SSM/I 85 GHz TB
GEOS with TRMM RR & TPW + Bogus Vortex(adjoint-based 4-D VAR)[13 Dec-1997/ 0900 UTC]
RR(mm/3hr); LP; 850 hPa Wind
GEOS without TRMM [13 Dec-1997 / 0900 UTC]
RR(mm/3hr); SLP; 850 hPa Wind
GEOS with TRMM RR & TPW[13 Dec-1997 / 0900 UTC]
RR(mm/3hr); SLP; 850 hPa Wind
[Pu & Tao, 2001: GSFC]
33 hr forecasts using PSU/NCAR MM5 model at 5-km horizontal resolution testing different initial conditions for time 12 Dec-1997 / 0000 UTC
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Data Assimilation Experiments Based on Retrieved SSM/I &
TRMM Rainrates
Have Not Been Particularly Sensitive to Intensity of
Rainrates
Nor Have Made Use of Vertical Profile of
Rainrate or Latent Heating
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Impact of Rainfall Assimilation on GEOS Analysis
Assimilationwith
TMI+SSM/Irainfall & TPW
Precipitation
verified against
GPCP____________________
TPW
verified against
Wentz____________________
OLR
verified against
CERES/TRMM____________________
IR Cld Forcing
verified against
CERES/TRMM
Assimilation of satellite-based rainfall data improves clouds & TOA radiation,Assimilation of satellite-based rainfall data improves clouds & TOA radiation,
plus reduces state-dependent systematic errors in GEOS analysisplus reduces state-dependent systematic errors in GEOS analysis
OLR Error Std Dev OLR Error Std Dev
verified against CERESverified against CERES
for 1-, 5-, & 30-dayfor 1-, 5-, & 30-day
Averaging PeriodsAveraging Periods
Control Run
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Reverse Schemes for Convective Parameterizations
Carrying Updrafts & Downdrafts
Do Not Yield Unique Solutionfor Adjusting (nudging)
Water Vapor Field
Once Model-ObservationSurface Rainfall Departures
Are Determined
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Control variables at time t0
Control variablesat time t
Surface rainrate
Forecast Model
(PHYSICS)
Interpolation at observation
location
MOIST PHYSICS(convection+
grid scalecondensation)
Increments ofcontrol variables
at time t0
Incrementsof control variables
at time t
Surface rainrate
departure
Adjoint ofForecast
Model(PHYSICS)
Adjoint of spatial
interpolation
Adjoint of MOIST PHYSICS
4DVAR Rainfall Data Assimilation
Forward
Backward
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GPM Validation StrategyTropical Continental
Confidencesanity checks
GPMSatellite
DataStreams
ContinuousSynthesis
∑ error variances∑ precip trends
Calibration
Mid-Lat Continental
Tropical Oceanic
Extratropical Baroclinic
High Latitude Snow
ResearchQuality
Data
Algorith
mIm
provem
ents
Research∑ cloud macrophysics∑ cloud microphysics∑ cloud-radiation modeling
FC Data
Supersite Products
II. GPM Supersites Basic Rainfall Validationhi-lo res gauge/disdrometer networkspolarametric Radar system
Accurate Physical Validationscientists & technicians staffdata acquisition & computer facilitymeteorological sensor systemupfacing multifreq radiometer systemDo/DSD variability/vertical structureconvective/stratiform partitioning
III. GPM Field Campaigns GPM Supersitescloud/ precip/radiation/dynamics processes GPM Alg Problem/Bias Regionstargeted to specific problems
I. Basic Rainfall Validation∑ Raingauges/Radars new/existing gauge networks new/existing radar networks
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Focused Field Campaigns
Meteorology-Microphysics Aircraft
GPM Core Satellite Radar/Radiometer
Prototype Instruments
Piloted
UAVs
150 km
Retrieval Error
Synthesis
AlgorithmImprovement
Guidance
Validation Analysis
Triple Gage Site(3 economy scientific gages)
Single Disdrometer/Triple Gage Site(1 high quality-Large Aperture/2 economy scientific gages)
150 km
100-Gage Site Lo-Res DomainCentered on Multi-parm-Radar
5 km
50-Gage Site Hi-Res DomainCenter-Displaced with
∑ Uplooking Radiom/Radar System[10.7,19,22,37,85,150 GHz/14,35,95 GHz]∑ 915 or 2835 MHz Doppler Radar Profiler
∑ Portable X-band Radar
Data Acquisition-Analysis Facility
DELIVERY
Legend
Multiparameter Radar
Uplk Radiom/Radar940 MHz Profiler
Port X-band Radar
Meteorological Tower
Supersite Template
Site Scientist (3)
Technician (3)
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ECMWF Requirements for GPMfrom Rainfall Assimilation Experience
Spatial Resolution:• Well-defined rain product spatial resolution (ECMWF-model
will be going to 15 km forecast / 30 km assimilation resolutions)Sampling:• Prefer “less often but more accurate”Error Considerations:• Quantification of error in rain detection • Quantification of retrieval errors/time-space biases• Removal of inter-satellite retrieval errors• Assessment of errors due to spatial/temporal sampling mismatch
Plans at ECMWF:• Evaluation of rainrate vs simplified radiance assimilation• Improved estimation of humidity profile forecast errors
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Final Comments
Currently, NASA is only organization with wherewithal to bring online global observing system of precipitation & closely related data assimilation variables which could significantly improve weather forecasting through improvements in water-related components of numerical prediction models & associated data assimilation schemes.
However:
Better observations by themselves do not solve or resolve all standing problems in predictive modeling and thus ESE plan must resolve which groups and through what mechanisms model & data assimilation technique development will proceed to take advantage of current and future space measurements NASA intends to provide.