1. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 1 Visit of Taiwanese Delegation Eric A. Smith; NASA/Goddard Space Flight Center, Greenbelt, MD 20771 [tel: 301-286-5770; fax: 301-286-1626; easmith@pop900.gsfc.nasa.gov; http://gpmscience.gsfc.nasa.gov] August 28, 2002; Greenbelt, MD Global Precipitation Measurement (GPM) Mission An International Partnership & Precipitation Satellite Constellation for Research on Global Water & Energy Cycle Current GPM Mission Issues

2. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 2 Today’s Focus? I. Possible Taiwan Contributions to GPM Mission For 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 Taiwan For 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

3. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 3 GPM 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 CoreConstellation

4. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 4 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.

5. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 5 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

6. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 6  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 from Research &Operations End Users

7. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 7 TBD b (GMI / DPR) GPM Core DMSP-F18/20 DMSP-F19 GCOM-B1 (SSMIS) (AMSR-FO) N-GPM E-GPM FY-3 (EPMR / NPR) Reference Co-Op Drone Partners NPOESS-1 NPOESS-2 NPOESS-3 (CMIS) MEGHA TROPIQUES (MADRAS) Potential New Drones/Partners Currently Conceived Constellation Architecture NPOESS-Lite (CMIS) (CPMR) (NPMR) TRMM AQUA ADEOS-IIDMSP-F17 DMSP-F16

8. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 8 GPM: Constellation Mission of Opportunity & Good Citizenship SatelliteMain PurposeCritical Role in Constellation 1.GPM CoreGPM rain referencecalibration, [NASA/NASDA]systemrain radar physics, liquid/frozen precip, tropical-midlatitude sampling 2 & 3.DMSPUS: NOAA/DOD met-ops & resliquid/frozen precip, [IPO]full global sampling 4.NPOESS-LiteUS: NOAA/DOD met-ops & resliquid/frozen precip, [IPO]full global sampling 5.GCOM-B1Japan: environ/hydro resliquid/frozen precip, [NASDA]& JMA met-opsfull global sampling 6.E-GPMEU: cold seasons/flash flood/rain radar physics, [ESA] data-assim res & EU met-opsliquid/frozen precip, full global sampling 7.N-GPMUS: MW radiometer technology testbedliquid precip, [NASA]full global sampling 8.Megha TropiquesIndia/France: tropical water cycle resliquid/frozen precip, [ISRO/CNES]intensive tropical sampling 9.FY-3China: CMA met-ops & resliquid/frozen precip, [CSM]full global sampling

9. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 9 GPM Validation Strategy Tropical Continental Confidence sanity checks GPM Satellite Data Streams Continuous Synthesis  error variances  precip trends Calibration Mid-Lat Continental Tropical Oceanic Extratropical Baroclinic High Latitude Snow Research Quality Data Algorithm Improvements Research  cloud macrophysics  cloud microphysics  cloud-radiation modeling FC Data Supersite Products II. GPM Supersites  Basic Rainfall Validation hi-lo res gauge/disdrometer networks polarametric Radar system  Accurate Physical Validation scientists & technicians staff data acquisition & computer facility meteorological sensor system upfacing multifreq radiometer system D o /DSD variability/vertical structure convective/stratiform partitioning III. GPM Field Campaigns  GPM Supersites cloud/ precip/radiation/dynamics processes  GPM Alg Problem/Bias Regions targeted to specific problems I. Basic Rainfall Validation  Raingauges/Radars new/existing gauge networks new/existing radar networks

10. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 10 Potential GPM Validation Sites Supersite Regional Raingage Site Supersite & Regional Raingage Site Australia NASA Ocean Japan South Korea India France (Niger & Benin) Italy Germany Brazil England Spain NASA KSC NASA Land Canada Taiwan ARM/UOK

11. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 11 Focused Field Campaigns Meteorology-Microphysics Aircraft GPM Core Satellite Radar/Radiometer Prototype Instruments Piloted UAVs 150 km Retrieval Error Synthesis Algorithm Improvement 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 Domain Centered on Multi-parm Radar 5 km  50-Gage Site Hi-Res Domain Center-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/Radar S-/X-Band Profilers 90 GHz Cloud Radar Meteorological Tower & Sounding System Supersite Template Site Scientist (3) Technician (3)

12. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 12 Radiance Tube TOA meteorology & 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

13. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 13 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. Based on Physical Error Model At Supersite B (  RR i )  t k = ∑ j = -NT/2,+NT/2 [1/(NT+1)] [ RR i SR (t j,  RR i )  RR i PEM (t j,  RR i ]  B (  RR i )  t k  end-to-end uncertainties in PEM { for i = 1, L rainrate intervals (~5) and time period  t k }

14. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 14 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) = (x b – x) T F -1 (x b – x) + ( y o – H (x)) T ( O + P ) -1 ( y o – H (x)) F, O, & P are error covariance matrices associated with forecast model, observations, & forward model (precip parameterization), where y o, H, & x are observation, forward model, & control variable. At Supersite (regional expansion rule based on DPR) O ( r rj,   j,t j )  t k = ∑ 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 ( r rj,   j,t j )  GV ( r MOD(ri+rj,100),  MOD(  i+  j,360),t i+j ) ] 2 { given polar coordinates ( r,  ) for r out to 100 km and time period  t k }

15. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 15 Use of Korean AWS Raingage Data to Determine Satellite Validation Properties in Space-Time [Jun - Aug 2000] Space Dimension (deg) 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 Time Dimension (hr 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 Time Dimension (hr or day) 30 dy 15 dy 10 dy 5 dy 3 dy 1 dy 12 hr 6 hr 3 hr 2 hr 1 hr Time Dimension (hr 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

16. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 16 Comparisons of Various GMS Retrival Algorithms with 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.

17. GPM August 28, 2002 Eric A. Smith http://gpmscience.gsfc.nasa.goveasmith@pop900.gsfc.nasa.gov 17 QPF Data Assimilation Experiments Over Korea Ou, M., and E.A. Smith, 2002