GPM August 28, 2002 Eric A. Smith @pop900.gsfc.nasa.gov 1 Visit of Taiwanese Delegation Eric A....

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August 28, 2002Eric A. Smith http://[email protected]

Visit of Taiwanese DelegationEric A. Smith; NASA/Goddard Space Flight Center, Greenbelt, MD 20771

[tel: 301-286-5770; fax: 301-286-1626; [email protected]; http://gpmscience.gsfc.nasa.gov]

August 28, 2002; Greenbelt, MD

Global Precipitation Measurement (GPM) Mission

An International Partnership &Precipitation Satellite Constellation

for Researchon Global Water & Energy Cycle

Current GPM Mission Issues

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Today’s Focus?

I. Possible Taiwan Contributions to GPM MissionFor Example

1. Development & participation of science team.2. Use of ground measuring facilities (especially at NCU) to

participate in GPM validation program.3. Explore potential for space hardware contribution.

II. Possible GPM Mission Contributions to TaiwanFor Example

1. Provide near-realtime access to GPM global rain products.2. Inclusion in international GPM science team activity.3. Governance participation in GPM science policy.

Discuss Scientific Opportunities in GPM Mission

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GPM Reference ConceptGPM Reference Concept

Core Satellite•TRMM-like spacecraft (NASA)•H2-A rocket launch (NASDA)•Non-sun-synchronous orbit ~ 65° inclination ~400 km altitude•Dual frequency radar (NASDA) K-Ka Bands (13.6-35 GHz) ~ 4 km horizontal resolution ~250 m vertical resolution•Multifrequency radiometer (NASA) 10.7, 19, 22, 37, 85, (150/183 ?) GHz V&H

Constellation Satellites•Pre-existing operational-

experimental & dedicated satellites with PMW radiometers

•Revisit time 3-hour goal at ~90% of time•Sun-synch & non-sun- synch orbits 600-900 km altitudes

OBJECTIVES•Understand horizontal & vertical

structure of rainfall, its macro- & micro-physical nature, & its associated latent heating

•Train & calibrate retrieval algorithms for constellation radiometers

OBJECTIVES•Provide sufficient global

sampling to significantly reduce uncertainties in short-term rainfall accumulations

•Extend scientific and societal applications

Precipitation Processing Center

•Produces global precipitation products•Products defined by GPM partners

Precipitation Validation Sites for Error Characterization

•Select/globally distributed ground validation “Supersites” (research quality radar, up looking radiometer-radar-profiler system, raingage-disdrometer network, & T-q soundings)

• Dense & frequently reporting regional raingage networks

Core Constellation

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I. How is global Earth system changing? (Variability)1. How are global precip, evap, & water cycling changing?2. How is global ocean circulation varying on interannual, decadal, & longer time scales? 3. How are global ecosystems changing?4. How is stratospheric ozone changing, as abundance of ozone-destroying chemicals decreases & new substitutes increases?5. What changes are occurring in mass of Earth’s ice cover?6. What are motions of Earth & its interior, & what information can be inferred about its internal processes?

II. What are primary forcings of Earth system? (Forcing)1. What trends in atmospheric constituents & solar radiation are driving global climate?2. What changes are occurring in global land cover & land use, & what are their causes?3. How is Earth’s surface being transformed & how can such information be used to predict future changes?

III. How does Earth system respond to natural & human-induced changes? (Response)

1. What are effects of clouds & surf hydrology on climate?2. How do ecosystems respond to & affect global environmental change & carbon cycle?3. How can climate variations induce changes in global ocean circulation?4. How do stratospheric trace constituents respond to change in climate & atmospheric composition?5. How is global sea level affected by climate change?6. What are effects of regional pollution on global atmosphere, & effects of global chemical & climate

changes on regional air quality?

How is Earth changing and what are consequences for life on Earth?

Tracibility to ESE's Strategic Plan

IV. What are consequences of change in Earth system for civilization? (Consequences)

1. How are variations in local weather, precipitation & water resources related to global climate variation?

2. What are consequences of land cover & use change for sustainability of ecosystems & economic productivity?

3. What are consequences of climate & sea level changes & increased human activities on coastal regions?

V. How well can we predict future changes in the Earth system? (Prediction)

1. How can weather forecast duration & reliability be improved by new space obs, data assim, & modeling?

2. How well can transient climate variations be understood & predicted?

3. How well can long-term climatic trends be assessed & predicted?

4. How well can future atmospheric chemical impacts on ozone and climate be predicted?5. How well can cycling of carbon through Earth system be modeled, & how reliable are predictions

of future atmospheric concentrations of carbon dioxide & methane by these models?

Asrar, G., J.A. Kaye, & P. Morel, 2001: NASA Research Strategy for Earth System Science: Climate Component. Bull. Amer. Meteorol. Soc., 82, 1309-1329.

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Improved Climate Predictions: through quantifying trends & space-time variations in rainfall with associated error bars and improvements in achieving water budget closure from low to high latitudes -- plus focused GCM research on understanding relationship between rain microphysics/latent heating/DSD properties & climate variations as mediated by accompanying accelerations in global water cycle (both atmosphere & surface branches). Improved Weather Predictions: through accurate, precise, frequent & globally distributed measurements of instantaneous rainrate & latent heat release -- plus focused NWP research on advanced techniques in satellite precipitation data assimilation & error characterization of precipitation retrievals. Improved Hydrometeorological Predictions: through frequent sampling & complete continental coverage of high resolution rainfall measurements including snowfall -- plus focused research on innovative designs in hydrometeorological modeling encompassing hazardous flood forecasting, seasonal draught-flood outlooks, & fresh water resources prediction.

GPM Mission is Being Formulated within Context of Global Water & Energy Cycle

with Foremost Science Goals Focusing On

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Validation should be treated as important as retrieval because improved prediction depends on it.

Error characterization of satellite precipitation retrievals is needed to support:

(a) Algorithm Improvement -- for reducing bias & precision errors in retrieved precipitation estimates;

(b) Climate Diagnostic Analysis -- for assessing physical significance of trends/variations in observed precipitation time series;

(c) Data Assimilation -- for improving climate reanalyses, numerical weather prediction, & hydrometeorological forecasting.

(d) Validation Research -- for advancing validation techniques, validation measuring systems, & space instrumentation.

Validation Expectations fromResearch &Operations End Users

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TBD

b

(GMI / DPR)

GPM Core

DMSP-F18/20

DMSP-F19

GCOM-B1

(SSMIS)

(SSMIS)

(AMSR-FO)

N-GPM

E-GPM

FY-3

(EPMR / NPR)

Reference

Co-O

p D

ron

e P

art

ners

NPOESS-1

NPOESS-2

NPOESS-3

(CMIS)

(CMIS)

(CMIS)

MEGHATROPIQUES

(MADRAS)

Pote

ntia

l New

Dro

nes/P

artn

ers

Currently Conceived Constellation Architecture

NPOESS-Lite

(CMIS)

(CPMR)

(NPMR)

TRMM

AQUA

ADEOS-IIDMSP-F17

DMSP-F16

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GPM: Constellation Mission of Opportunity& Good Citizenship

Satellite Main Purpose Critical Rolein Constellation

1. GPM Core GPM rain reference calibration,[NASA/NASDA] system rain radar physics,

liquid/frozen precip,tropical-midlatitude sampling

2 & 3. DMSP US: NOAA/DOD met-ops & res liquid/frozen precip,[IPO] full global sampling

4. NPOESS-Lite US: NOAA/DOD met-ops & res liquid/frozen precip,[IPO] full global sampling

5. GCOM-B1 Japan: environ/hydro res liquid/frozen precip,[NASDA] & JMA met-ops full global sampling

6. E-GPM EU: cold seasons/flash flood/ rain radar physics,[ESA] data-assim res & EU met-ops liquid/frozen precip,

full global sampling

7. N-GPM US: MW radiometer technology testbed liquid precip,[NASA] full global sampling

8. Megha Tropiques India/France: tropical water cycle res liquid/frozen precip,[ISRO/CNES] intensive tropical sampling

9. FY-3 China: CMA met-ops & res liquid/frozen precip,[CSM] full global sampling

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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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Potential GPM Validation Sites

Supersite Regional Raingage Site Supersite & Regional Raingage Site

Australia

NASA Ocean

Japan

South Korea

IndiaFrance (Niger & Benin)

Italy

Germany

Brazil

England

Spain

NASA KSC

NASA Land

Canada

Taiwan

ARM/UOK

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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 Matched Radiom/Radar [10.7,19,22,37,85,150 GHz/14,35 GHz] Upward S-/X-band Doppler Radar Profilers & 90 GHz Cloud Radar

Data Acquisition-Analysis Facility

DELIVERY

Legend

Multiparameter Radar

Uplk Mtchd Radiom/RadarS-/X-Band Profilers90 GHz Cloud Radar

Meteorological Tower &Sounding System

Supersite Template

Site Scientist (3)

Technician (3)

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Radiance T

ube

TOAmeteorology & microphysics

characterization volume observed by independent instruments

Doppler Profilers (2) for precip/cloud hydrometeor profiles Radiosonde for T(z)/q(z) profiles Downward-pointing Radiometer for surface emissivity Raingage & Disdrometer Network for cross-checking

TOA

Surface

Principles of Physical Error Modeling

matched core satellite radiometer/Radar ground instrument

“Eyeball”[with active target calibration]

difference between retrieved & RTE modeled hydrometeor profiles yields retrieval "bias"

mismatch in 2-ended RTE model solution based on absorption-scattering properties assigned to characterization volume yields retrieval "bias uncertainty"

Note: ground Radars & regional raingage networks in conjunction with coincident satellite retrievals can generate "space-time correlation structure functions" & "space-time error covariance matrices".

Surface

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Error Characterization (Accuracy)

based on: physical error model ( passive-active RTE model ) matched satellite radiometer/radar instrument on ground with continuous calibration ( eyeball ) independent measurements of observational inputs needed for error model (DSD profile, T-q profile, surface)

Bias (B) & Bias Uncertainty (B)

All retrievals from constellation radiometers & other satellite instruments are bias- adjusted according to bias estimate from reference algorithm for core satellite.

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May 17, 2002Eric A. Smith

Radiance Tube

TOAmeteorology & microphysics

characterization volume observed byindependent instruments

•DopplerProfilers(2)forprecip/cloudhydrometeorprofiles•RadiosondeforT(z)/q(z)profiles•Downward-pointingRadiometerforsurfaceemissivity•Raingage&DisdrometerNetworkforcross-checking

TOA

Surface

Principles of Physical ErrorModeling

matched core satellite/radiometer Radar

ground instrument“Eyeball”

[ ]with active target calibration

•differencebetweenretrieved&RTEmodeledhydrometeorprofilesyieldsretrieval" bias"

•mismatchin2-endedRTEmodelsolutionbased on absorption-scattering propertiesassignedtocharacterizationvolumeyieldsretrieval" biasuncertainty"

Note: ground Radars & regionalraingage networks in conjunctionwith coincident satellite retrievalscangenerate" space-timecorrelationstructurefunctions" &" space-timeerrorcovariancematrices" .

Surface

Based on Physical Error ModelAt SupersiteB(RRi)tk

= ∑ j = -NT/2,+NT/2 [1/(NT+1)]

[ RRiSR(tj,RRi)RRi

PEM(tj,RRi]

B(RRi)tk end-to-end uncertainties in PEM

{for i = 1 , L rainrate intervals (~5) and time period tk}

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Space-Time Autocorrelation Structure Given By volume scanning ground radars ( dual-polarization enables DPR calibration cross-checks ) research-quality, uniformly distributed, dense, & hi-frequency sampled raingage networks

Space-Time Observational Error Covariance (O)

Error Characterization (Precision)

J(x) = (xb – x)T F-1 (xb – x) + ( yo – H(x))T ( O + P )-1 ( yo – H(x))

F, O, & P are error covariance matrices associated with forecast model, observations, & forward model (precip parameterization),

where yo , H, & x are observation, forward model, & control variable.

At Supersite (regional expansion rule based on DPR)

O(rrj,j,tj)tk = ∑ rj = 0,100 ∑ ri = 0,100.∑ j = 0,360 ∑ i = 0,360

∑ j = -NT/2,+NT/2 ∑ i = -NT/2,+NT/2 [1/NT] [ SR(rrj,j,tj) GV(rMOD(ri+rj,100),MOD(i+j,360),ti+j) ]2

{given polar coordinates (r,) for r out to 100 km and time period tk}

RR

RR

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Correlation Coefficient(AWS time window = + 10min )

Grid Size (deg)0.1 0.2 0.3 0.4 0.5 1.0 1.5 2.0 2.5 3.0

Averaging Period

1hr2hr3hr6hr12hr24hr3day5day10day15day30day 0.9

0.8

0.8

0.80.8

0.7

0.7

0.7

0.7

0.6

0.6

0.6

0.5

0.5

0.4

0.4

0.6

0.30.2

Mean Bias(AWS time window = + 10min )

Grid Size (deg)0.1 0.2 0.3 0.4 0.5 1.0 1.5 2.0 2.5 3.0

Averaging Period

1hr2hr3hr6hr12hr24hr3day5day10day15day30day

-0.10-0.15

-0.15 -0.15-0.20

-0.10-0.10

-0.10

-0.20

-0.20

-0.25

-0.25

-0.30

-0.30-0.35-0.40-0.45-0.50

RMS Error(AWS time window = + 10min )

Grid Size (deg)0.1 0.2 0.3 0.4 0.5 1.0 1.5 2.0 2.5 3.0

Averaging Period

1hr2hr3hr6hr12hr24hr3day5day10day15day30day

0.5

0.51.0

1.0

1.52.02.5

3.53.04.05.55.04.56.56.0

Use of Korean AWS Raingage Data to DetermineSatellite Validation Properties in Space-Time

[Jun - Aug 2000]

Space Dimension (deg) Space Dimension (deg)0.1 0.2 0.3 0.4 0.5 1.0 1.5 2.0 2.5 3.0 0.1 0.2 0.3 0.4 0.5 1.0 1.5 2.0 2.5 3.0

Mean Bias (±10 min time window) RMSE (±10 min time window)

Corr Coef (±10 min time window)

0.1 0.2 0.3 0.4 0.5 1.0 1.5 2.0 2.5 3.0

Tim

e D

imen

sion

(h

r or

day

)AWS Network

30 dy

15 dy

10 dy

5 dy

3 dy

1 dy

12 hr

6 hr

3 hr

2 hr

1 hr

Tim

e D

imen

sion

(h

r or

day

)

30 dy

15 dy

10 dy

5 dy

3 dy

1 dy

12 hr

6 hr

3 hr

2 hr

1 hr

Tim

e D

imen

sion

(h

r or

day

)

30 dy

15 dy

10 dy

5 dy

3 dy

1 dy

12 hr

6 hr

3 hr

2 hr

1 hr

125E 126E 127E 128E 129E 130E 131E

39N

38N

37N

36N

35N

34N

33N

mm hr-1 mm hr-1

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Comparisons of Various GMS Retrival Algorithmswith KMA-AWS Raingage Measurements

Oh, H.J., B.J. Sohn, E.A. Smith, J. Turk, A.S. Seo,and H.S. Chung, 2002: Meteorol. Atmos. Phys., in press.

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QPF Data Assimilation Experiments Over Korea

Ou, M., and E.A. Smith, 2002

GPM August 28, 2002 Eric A. Smith @pop900.gsfc.nasa.gov 1 Visit of Taiwanese Delegation Eric A....

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