1. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 1 Global Precipitation Measurement Mission An International Partnership & Precipitation Satellite Constellation for Research on Global Water & Energy Cycle GPM L2 Ground Validation Preliminary Requirements Review Eric A. Smith; NASA/Goddard Space Flight Center, Greenbelt, MD 20771 [tel: 301-286-5770; eric.a.smith@nasa.gov; fax: 301-286-1626; http://gpmscience.gsfc.nasa.gov] 11 March 2004; NASA/Goddard Space Flight Center, Greenbelt, MD GPM Scientific Agenda for GPM Mission’s Ground Validation Research Program

2. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 2 Why does GPM Mission need Ground Validation Research Program?  To scientifically verify credibility of satellite retrievals.  To establish uncertainties of retrievals. - - - - - - - - - - -  To serve scientific clients who depend upon GV information in their research and applications.

3. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 3 Who does GPM Mission’s GV Research Program Serve?  PMM Science Team members -- and scientists at large -- who are concerned with accuracy and precision of GPM precipitation retrievals, as well as other kinds of satellite-retrieved precipitation measurements -- in their research and applications programs and projects. Examples include: water cycle & climate diagnosticians, (2) weather climate & forecast modelers, (3) climatologists, (3) hydrometeorologists and hydrometeorological forecasters, and (4) air sea interaction specialists involved with fresh water flux problems.  GV scientists (both PMM and at large) who are involved with assessing and improving GV methodologies and measuring technologies. - - - - - - - - - - -  Data assimilation specialists at experimental and operational forecast centers needing near-realtime error characterization of retrieved precipitation products as part of their forecast procedures.  Algorithm creators needing ongoing information concerning their algorithm’s performance (some 3-5 groups) to enable continued algorithm improvement.

4. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 4 How do we plan to establish and accomplish effective GV Research Program?  Galvanize scientific working group within PMM Science Team and other similar international science teams to work cooperatively and under formal partnership arrangements using mission supported Measurement Infrastructure and Technical Support Groups.  Conduct GV operations at international network of GV Supersites (some 5-10) whose main responsibilities will be to provide: routine and specialized GV data acquisition from suite of high quality measuring systems computation, archive, and dissemination of standard and specialized GV products for GV research and applications ----------------------------------------------------------------------------------------------------------------------------------- generation and dissemination of near-realtime, low bandwidth “error characterization” factors, consisting of: (1) bias, (2) bias uncertainty, and (3) error covariance matrices detect and report to standard algorithm support groups, “instantaneous” and “significant” retrieval errors  Carry out effort within international framework -- much of this organization structure has been defined in preliminary fashion, but awaits final documents and signatures.

5. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 5

6. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 6 GPM’s GV Strategy Requires Sampling Selection of Global Rainfall Regimes Extratropical Baroclinic & High Latitude Snow Regime Mid-latitude Continental & Mountain Semi-arid Mediterranean Tropical Continental Tropical Oceanic

7. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 7 Actual and Potential GPM Ground Validation Sites Radar GV Site or GV Supersite Regional Raingage Site Both Australia U.S. -- NASA-Ocean Kawajalein/RMI Japan -- CRL-Southern Okinawa South Korea India France Italy Germany Brazil Spain U.S. -- NASA-Gauge KSC Canada Taiwan U.S. -- NASA-Land DOE/ARM-SGP South Africa U.K. Austria Netherlands C China Japan -- CRL-Northern Wakkanai Israel Finland Greece West Africa (AMMA) Switzerland

8. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 8 Existing or Potential GV Supersite Existing or Potential Standard GV Site U.S. -- NASA-Ocean Kawajalein/RMI Japan -- CRL-South Okinawa South Korea U.S. -- NASA-Land DOE/ARM-SGP U.K. Netherlands Current Status of GPM Ground Validation Site Network France Germany Brazil U.S. -- NASA-KSC Canada South Africa Austria Israel Finland Greece Italy Spain-Catalunya Australia India Taiwan C China Japan -- CRL-North Wakkanai West Africa (AMMA) Switzerland

9. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 9 What does GPM GV Supersite look like and what are error characteristics?

10. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 10 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

11. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 11 Focused Field Campaigns Meteorology-Microphysics Aircraft GPM Core Satellite Radar/Radiometer Prototype Instruments Piloted UAVs 150 km Retrieval Error Detection Algorithm Improvement Guidance Error Characterization Triple Gage Site Single Disdrometer/Triple Gage Site 150 km Multiple-Gage Lo-Res Domain Site centered on main Multiparmeter Radar 5 km  Multi-Gage Hi-Res Domain Site center-displaced with  Uplooking Matched Radiom/Radar [10.7,19,22,37 GHz/14,35 GHz]  Upward dual-freq Doppler Radar Profilers Data Acquisition & Analysis Facility (SSC) DELIVERY Legend S/X-band Multiparameter Radars Uplk Mtchd Radiom/Radar & Dual-Frequency Doppler Profiler Meteorological Tower & Atmospheric Sounding System Supersite Template Site Scientists Engineers & Technicians

12. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 12 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 R ) 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 }

13. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 13 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 }

14. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 14 Where are NASA’s Supersites going to be located? Oceanic:Kwajalein Atoll, RMI Continental:DOE ARM-CART Site, Lamont, OK

15. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 15 Have we quantified our scientific goals vis-à-vis GV measurements themselves? YES -- through Level 2 GV Requirements Document

16. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 16

17. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 17 Do we have evidence that our requirements are rationale and achievable? YES -- through current and ongoing analysis of TRMM and other GV analyses

18. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 18 Kwajalein Monthly Mean Rain Rates

19. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 19 Kwajalein Total Rain Rate PDFs and CDFs

20. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 20 Kwajalein Summary Statistics

21. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 21 Melbourne Monthly Mean Rain RatesMelbourne Total PDFs & CDFs

22. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 22 Melbourne Bias & Precision Summaries by Month OCN LND

23. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 23 Comparison between GSFC & U-WASH GV Retrievals at Kwajalein

24. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 24 Comparison between GSFC & U-WASH GV Retrievals at Kwajalein

25. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 25 StatisticGSFCUWASH mean211198 stn-dev10187 median198186 min1010.5 max667601 Comparison between GSFC & UWASH GV Retrievals at Kwajalein [Jul-Dec 1999 period (6 mo): 103,927 monthly accumulations for 2 x 2 km grids]

26. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 26 What are our problems, what are we missing -- and what are we doing about it?  Some Degree of Institutional and Science Team Resistance to Paradigm Shift  Lack of Physical Error Model

27. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 27 Proposed Physical Error Model (PEM) (E.A. Smith & K-S. Kuo, 2003) Use Ground Eyeball Radar-Radiometer Z-TB measurements in conjunction with matched Core Satellite DPR-GMI measurements to observe both ends of reflectance-radiance tube between satellite and eyeball instruments. Use dual-frequency ground Doppler Radar Profiler measurements (either VHF-UHF or UHF-SBand) to provide initial guess DSD profile to Radiative Transfer Model (RTE model), variationally adjusting DSD profile to within standard error of estimates to optimally match observed Z-TB observations, in which residual mismatch objectively defines bias uncertainty. Take time-average of realization differences vis-à-vis satellite rainrate algorithm estimates with modeled estimates (diagnosed from resultant model- adjusted DSD profiles) to define conditional bias errors. Based on TRMM analyses, monthly zonally-averaged accuracies are expected to be approximately 5%.

28. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 28 Matching two-point measurements with radiative transfer simulation by perturbing hydrometeor profile and physical parameters in RTE model. 1.Use hydrometeor profile retrieved from 2- frequency ground-based Doppler profiler (radar) as starting input to RTE model. 2.Perturb model hydrometeor input, to within standard error of measurement, until there is optimal match between simulated reflectances-radiances and those from spaceborne and ground radar-radiometer measurements. 3.Hydrometeor profile determined from best agreement between observed and simulated reflectances-radiances -- result taken as truth for purpose of accuracy assessment of spacecraft retrievals. Note: Effects of small perturbations can be efficiently calculated without running time-consuming model again by first solving adjoint form of RTE (Box et al., 1988, 1989; Polonski and Box, 2002). Application to GV Error Characterization

29. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 29

30. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 30 Graupel and aggregate hydrometeors are assumed to be clusters of multi-layered spherical particles. Single-scattering properties of multi- layered sphere is obtained using multi- layered Mie solution (Johnson, 1996) capable of ~7000 layers. One of six canonical configurations is assumed for each hydrometeor, whose single-scattering properties are calculated using consummate solution (interacting dipoles) for ensemble of spheres (Fuller and Kattawar, 1988a-b). Population of hydrometeors is assumed to be composed of such particles with various sizes in specified orientations (e.g., random or oriented). Bulk scattering properties of population are then derived accordingly. Formulation for Single Scattering Properties

31. http://gpmscience.gsfc.nasa.govEric.A.Smith@nasa.gov 31 3-Dimensional, Time-Dependent, Deterministic Radiative Transfer Model used to simulate multiple scattering within absorbing gaseous medium containing 3-dimensional heterogeneous mix of hydrometeors Model Description: 3-dimensional geometry with heterogeneous composition. Deterministic solution, as opposed to reverse Monte Carlo solution. Picard iterative solution, akin to successive- order-of-scattering solution. Capable of simulating responses to time- dependent sources such as radar pulses. Additional Notes: Picard iteration is arguably most efficient 3-D deterministic RTE solution. Time-dependent solution is obtained by succession of steady-state simulations under momentarily constant medium conditions. Reverse Monte Carlo Plane-Parallel 3-D Deterministic