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Global Precipitation Measurement (GPM) Mission
An International Partnership & Precipitation Satellite
Constellation for Research on Global Water & Energy Cycle
Anticipated Improvements in Precipitation Physics &
Understanding of Water Cycle from GPM Mission IGARSS03 -- IEEE
International Geoscience & Remote Sensing Symposium [Session on
TRMM and GPM] NASA/Goddard Space Flight Center, Greenbelt, MD [tel:
; fax: ; July 21-25, 2003; Centre des Congrs Pierre Baudis,
Toulouse, France Improving Precipitation Retrievals
Cloud Macrophysical & Microphysical Fundamentals Determination
of: drop size distribution [DSD(r)], mass mixing ratio
[q(z)hydro(r)], rain mass flux [Fr(z)], fall velocity [w(z)hydro(r)
], & latent heating [LH(z) ] q(z)hydro(r) = sw (4/3pr3) DSD(r)
w(z)hydro(r) = GFO [q(z)hydro(r)] Fr(z) = q(z)hydro(r) w(z)hydro(r)
dr LH(z) = C [ Fr(z)/z] RR (z) = Fr(z) / sw RFsur = RR(zsur) Dt
Fiorino, S., and E.A. Smith, 2003:Examination of Microphysical
Assumptions within TRMM Radiometers Rain Profile Algorithm (2a12)
using Satellite, Aircraft, & Surface Datasets from KWAJEX
Implementation of Fully Modular OPEN ACCESS Facility Algorithms
Accompanied by COMPREHENSIVE TESTING Capability within PPS TRMM
& GPM Rainrate Retrieval Simulations Under
Varying Mean Adj Drop Diameter Profiles [ simulations based on
Monte Carlo proliferation of Hurricane Bonnie observations ] TRMM
Single-Frequency Algorithm (bias due to irretrievable DSD
variability) GPM Dual-Frequency Algorithm (near-zero bias &
reduced scatter in mid-range) R e t r i v d (mm hr-1) Actual R (mm
hr-1) Actual R (mm hr-1) Standard Deviation of R (%) as Function of
R in mm hr-1 95% of LH spectra TRMM modal value of LH with R
approximately log-normal then sR proportional to R GPM exact
variability depends on DSD variability in altitude Percent ~1.5 mm
hr-1 ~12.5 mm hr-1 Latent Heating for Hurricane Bonnie from
Vertical Derivative of Rain Mass Flux
Droplet Fall Velocities Derived from TRMM Combined Radar-Radiometer
Algorithm 2b31 (S. Yang, Z. Haddad, & E. Smith) CAPI View mm
hr-1 Rainrate Cross-Section mm hr-1 Latent Heating Cross-Section
deg hr-1 Global Water Budget Water Vapor Residence Time
WV Residence Time (RT) = Ave Atmospheric WV Reservoir Path / Ave RR
For example, and keeping things simple: RT = 25 mm / 2.5 mm day-1 =
10 days Thus even at 5% retrieval accuracy, and climatic
fluctuations of RT on order of less than 10%, many years of global
precipitation data will be needed to assess water cycle
acceleration -- Dennis Lettenmaier and his colleagues estimate this
to be some 60 years. Precipitation Prediction: Key Objective of
Water Cycle Research
NOW State of Art Climate Model (CCSM) GOAL Next Generation Climate
Model Satellite-based Water Budget of Gulf of Mexico &
Caribbean Basins
[P. Santos & E.A. Smith, 2003] Gulf-Caribbean Basins &
Upper Air/Buoy Validation Data Sites Design of Algorithm System
GOES Combined TRMM-SSM/I & GOES SSM/I GOES Q Uncertainty (%) vs
Sample Count (N) Line Integral Validation Study Area,
GOES-SSM/I-TRMM Sectors, & ECMWF Grid SSM/I GOES GOES Gulf
Basin Caribbean Basin ECMWF Validation Jul Z Fully-Averaged Monthly
Framework
Atmosphere Q = 14% (PW + LWP)/t = 0% Vapor Transport to
Surroundings Q = (E - P) Vapor-Condensate Storage E = 50% P = 36%
Ocean Diurnally-Averaged Monthly Framework
Atmosphere Q = 32% (PW + LWP)/t = 32% P Vapor Transport to
Surroundings Vapor-Condensate Storage E = 22% P = 14% Ocean TRMM
Precipitation Observations for Improving Hurricane Track
Forecast
Case Study 1: Hurricane Bonnie (September 2001) Research from
NASA/GSFC Data Assimilation Office (DAO); courtesy of Arthur Hou
Assimilation of TRMM precipitation data in global models Improves
climate analysis Improves storm track forecast Improves
precipitation forecast 5 day forecast of Bonnie storm track
from08/20/98 Red:best track(NOAA HRD) Green: forecast from analysis
without TRMM data Blue: forecast from analysis with TRMM data
Forecast Number TRMM Precipitation Observations for Improving
Hurricane Track Forecast
Case Study 2: Cyclone Zoe (December 2002) Research from ECMWF;
courtesy of E. Moreau, A. Benedetti, & P. Bauer ECMWF 4D-Var
Model Assimilation Reference control run uses wind profilers, ships
and buoys, radiosoundings, HIRS data, geostationary satellite data,
QuickSCAT wind data, SSM/I radiances in clear-sky areas, and AMSU
data. TRMM Radiometer (TMI) and Precipitation Radar (PR) data are
included in subsequent runs to assess impact on track prediction.
Forecast Time (hour) Research from Fla. State Univ.; courtesy of M.
Ou & E.A. Smith
GMS-IR Precipitation Observations for Improving Convective Storm
Forecasts Case Study 3: Future Time Data Assimilation over Korean
Peninsula using UW-NMS Mesoscale Model (1998, 1999, 2000) Research
from Fla. State Univ.; courtesy of M. Ou & E.A. Smith
Concerning Data Assimilation
Urban Legend Assimilation of SSM/I & TRMM satellite
precipitation observations have improved numerical weather
forecasting. Reality NCEP is only operational center producing
routine weather forecasts using satellite precipitation data
assimilation; impact has been marginal because they use 3d-VAR
system which does not exploit time-accumulation effect. ECMWF has
not achieved overall positive impact with their 4d-VAR system,
because their software is not yet on-line. GSFC-DAO (Hou et al),
FSU (Krishnamurti et al., Ou & Smith), ECMWF (Mahfouf and
Bauer), and JMA (Nakazawa et al) have shown substantive overall
positive impact with satellite precipitation data assimilation
schemes that exploit time-accumulation effect -- but none of these
assimilation schemes are based on optimal 4d-VAR technique. No
operational or research-based numerical weather prediction center
has used vertical rainrate profile information; only surface
rainrates are being used, and then only to adjust vertical moisture
profile, not to generate latent heating. No operational or
research-based numerical weather prediction center (with one
possible dubious exception) has used either vertically-integrated
or vertically-distributed diabatic (latent) heating information
from TRMM for data assimilation purposes. No operational or
research-based numerical weather prediction center has used
independent, objectively- acquired retrieval error characterization
information; those few modeling centers using error information in
their data assimilation schemes have been limited to constant or
conditional error variances fixed in space-time, applied in single
column data assimilation mode. Space-time error covariance
information has yet to be used anywhere -- this representing the
underlying quantitative property under which data can be
objectively leveraged and ingested into forecastswithin formal
mathematical framework of data assimilation. Operational numerical
weather prediction centers have not assimilated precipitation
observations wherever model grid predictions are zero, and have
resisted making what they view as ad hoc adjustments to allow such
calculations to take place -- this is tantamount to defining a
models background bias as perfect. NASA Objectives for GPM Missions
GV Program
Living -- dynamic research and operations program: generate
retrieval error characteristics from independent GV measurements
coupled to satellite retrievals. Deploy small network of GV
Supersites: sites which operate under data exchange protocol
between each sites Supersite Science-Center (SSC) and GPMs
Precipitation Processing System (PPS) -- in which PPS-generated
satellite retrievals (high bandwidth data packets) relative to
given Supersite are passed to SSC and SSC-generated retrieval error
characteristics (low bandwidth data packets) are passed to to PPS.
What are satellite retrieval error characteristics? Conditional
Rainrate Bias Uncertainty: i.e., bias as function of rainrate
(accuracy). Error Covariance Structure: pixel-relative, local
space-time precision error distribution matrices based on using
imperfectly calibrated but high time-space resolution volume-scan
radar rainrates as truth data for local space-time auto-correlation
structure. Error Characterization (Accuracy)
Bias (B) & Bias Uncertainty (DB) At Supersite B(RRi)tk = j =
-NT/2,+NT/2 [1/(NT+1)] [ RRiSR(tj,RRi)RRiPEM(tj,RRi] B(RRi)tk
end-to-end uncertainties in PEM {for i = 1 , L rainrate intervals
(~5) and time period tk} Based on Physical Error Model 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) All
retrievals from constellation radiometers & other satellite
instruments are bias- adjusted according to bias estimate from
reference algorithm for core satellite. 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. Space-Time Observational Error Covariance (O) 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} 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 GPM Validation Strategy
Tropical Continental Tropical Oceanic I. Basic Rainfall Validation
Raingauges/Radars new/existing gauge networks new/existing radar
networks Research Quality Data Confidence sanity checks 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 Do/DSD variability/vertical structure convective/stratiform
partitioning GPM Satellite Data Streams Mid-Lat Continental
Continuous Synthesis error variances precip trends Calibration
Improvements Algorithm Supersite Products Extratropical Baroclinic
III. GPM Field Campaigns GPM Supersites cloud/
precip/radiation/dynamicsprocesses GPM Alg Problem/Bias Regions
targeted to specific problems Research cloud macrophysics cloud
microphysics cloud-radiation modeling FC Data High Latitude Snow
Actual and Potential GPM Ground Validation Sites
Finland U.K. Canada Germany Japan -- CRL-Northern Wakkanai
Netherlands Austria U.S. -- NASA-Land DOE/ARM-SGP South Korea
France Italy Greece China Spain C Japan -- CRL-Southern Okinawa
Israel U.S. -- NASA-Gauge KSC Taiwan India U.S. -- NASA-Ocean
Kawajalein/RMI Brazil South Africa Australia Radar GV Site or GV
Supersite Regional Raingage Site Both Supersite Template DELIVERY
Focused Field Campaigns Legend
Data Acquisition- Analysis Facility Focused Field Campaigns
Multiparameter Radar GPM Core Satellite Radar/Radiometer Prototype
Instruments Uplk Mtchd Radiom/Radar S-/X-Band Profilers 90 GHz
Cloud Radar Meteorological Tower & Sounding System Piloted Site
Scientist (3) Technician (3) UAVs Retrieval Error Synthesis
DELIVERY Meteorology-Microphysics Aircraft Algorithm Improvement
Guidance Validation Analysis 150 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 150 km 5 km Triple Gage Site (3
economy scientific gages) Single Disdrometer/ Triple Gage Site (1
high quality-Large Aperture/ 2 economy scientific gages) 100-Gage
Site Lo-Res Domain Centered on Multi-parm Radar 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-radiancetube 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--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-averagedaccuracies are
expected to be approximately 5%. Formulation for Single Scattering
Properties
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. 3-Dimensional,
Time-Dependent, Deterministic Radiative Transfer Model
used to simulate multiple scattering within absorbing gaseous
medium containing 3-dimensional heterogeneous mix of hydrometeors
Reverse Monte Carlo Plane-Parallel 3-D Deterministic 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.
Comparison of Radiative Transfer Solution Methods
3D Deterministic Reverse Monte Carlo Plane-Parallel Generality High
Low Spatial Heterogeneity 3D 1D Intensity Output In all quadrature
directions at all grid points In few directions at few positions
layers Computational Demand O(NxNyNzAq2Af2) Moderate S.I.B.
O(NxAq2) Application to GV Error Characterization
Matching two-point measurements with radiative transfer simulation
by perturbing hydrometeor profile and physical parameters in RTE
model. Use hydrometeor profile retrieved from 2-frequency
ground-based Doppler profiler (radar) as starting input to RTE
model. 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. 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). 1st International
GPM GV Research Programme Workshop
November 3-6, Abingdon, England Hosted by Rutherford-Appleton
Laboratory & Chilbolten Radar Facility (c/o Dr. John Goddard)
Program Committee Eric Smith, Kenji Nakamura, Alberto Mugnai, John
Goddard, Carron Wilson, Paul Hwang Main Workshop Objectives
1.Present and share opinions on interests, perspectives, and
concerns. 2.Examine conceptual and/or planned GV site templates
from NASA, NASDA, ESA, and Other partners. 3.Define scope of
international GV research programme. 4.Identify provisional, basic
set of GV programme requirements. GPM Mission Design OBJECTIVES
Core Satellite Constellation Satellites
Understand horizontal & vertical structure of rainfall, its
macro- & micro-physical nature, & its associated latent
heating Train & calibrate retrieval algorithms for
constellation radiometers Provide sufficient global sampling to
significantly reduce uncertainties in short-term rainfall
accumulations Extend scientific and societal applications Core
Constellation Core Satellite TRMM-like spacecraft (NASA) H2-A
rocket launch (JAXA) Non-sun-synchronous orbit ~ 65 inclination
~400 km altitude Dual frequency radar(JAXA/CRL) K-Ka Bands ( GHz) ~
4 km horizontal resolution ~250 m vertical resolution
Multifrequency radiometer (NASA) 10.7,19,22,37,85, (166/1833/7) 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 km altitudes Precipitation Validation
Sites for Error Characterization Globally distributed ground
validation Sites & Supersites (research quality radars,
up-looking GMI/DPR-like radiometer-radar systems, dual-frequency
Doppler profiler systems, raingage-disdrometer networks, & T-q
soundings) Dense & frequently reporting regional raingage
networks Precipitation Processing Center Produces global
precipitation products Products defined by GPM partners
Optimization and Compromise Potential New Drones/Partners
GPM International Constellation Architecture NPOESS-1 Reference GPM
Core (CMIS) NASA-Partner CS (GMI , DPR-Ku/Ka) DMSP-F18/20 b AQUA
(GMI) (SSMIS) (AMSR-E) NPOESS-2 TRMM EGPM Co-Op Drone Partners
DMSP-F19 (CMIS) DMSP-F16 AQUA Optimization and Compromise (SSMIS)
Potential New Drones/Partners (EGPM-PMR , NPR-Ka) DMSP-F17 ADEOS-II
NPOESS-Lite FY-3D CORIOLIS FY-3C NPOESS-3 (CMIS) ADEOS-II (PRC-PMR)
(CMIS) (PRC-PMR) MEGHA TROPIQUES GCOM-B1 (AMSR) (MADRAS) NPP (back
up) (AMSR-FO) TBD (ATMS) Pixel Frequency (log scale)
Assessment of ATMS/WindSat as Rain Instruments TMI (5-frequency)
SSM/I (4-frequency) Mean (retr) = 1.33 Bias = 0.04 rms = 0.85 r =
0.92 Mean (retr) = 1.41 Bias = -0.02 rms = 1.13 r = 0.84 ATMS total
power radiometer sun-synchronous orbit cross-track scanned 824 km
altitude 2300 km swath 0.25 m antenna 23.8 GHz (5.2 deg B/W) 31.4
GHz (5.2 deg B/W) 90.0 GHz (2.2 deg B/W) 33% degradation Retrieved
Rainrate (mm hr-1) Retrieved Rainrate (mm hr-1) True Rainrate (mm
hr-1) True Rainrate (mm hr-1) ATMS (3-frequency) WindSat
(5-frequency) Mean (retr) = 1.55 Bias = 0.02 rms = 1.69 r = 0.66
WINDSAT total power radiometer sun-synchronous orbit conically
scanned 830 km altitude 1025 km swath 1.8 m antenna 6.8 GHz (40 x
60 km res) 10.7 GHz (25 x 38 ) 18.7 GHz (16 x 27 ) 23.8 GHz (12 x
20 ) 37.0 GHz (8 x 13 ) Mean (retr) = 1.35 Bias = -0.04 rms = 0.83
r = 0.92 99% degradation 2% improvement Retrieved Rainrate (mm
hr-1) Retrieved Rainrate (mm hr-1) Pixel Frequency (log scale)
International Unified Physical Validation Program