An Outline for Global Precipitation Mission Ground Validation: Building on Lessons Learned from TRMM Sandra Yuter and Robert Houze University of Washington May 2001

Intrinsic uncertainty in GV monthly surface rain rate maps is unacceptably high Major Error Sources: Gaps in monthly data set must be filled artificially Radar data are not measured at the surface –Height of beam relative to ground increases with range –Vertical resolution of beam increases with range –Requires assumptions regarding changes in rain rate from height of radar beam to surface Error in calibration (sometimes expressed as Z-R adjustment) –Requires suitable independent measurements with less error than error trying to correct Uncertainties in DSD and Z-R relation at scale of radar data volumes.

TRMM GV has not physically validated satellite algorithms Comparison of satellite to GV monthly rain maps +Demonstrated that differences exist –Limited utility Magnitudes of errors associated with GV products are in many cases larger than the uncertainties in satellite products Yielded little diagnostic information as to the physical reasons why the differences exist Yielded little physical guidance on how to improve satellite algorithms

Most of the assumptions that underlie satellite algorithms relate to the vertical profile of hydrometeors TRMM GV does not include products well suited to evaluating these assumptions: –TRMM GV products primarily focus on horizontal structure (2A-52,2A-53,2A-54,3A-54) –TRMM GV products which address vertical structure are limited to reflectivity (2A-55,3A-55). –LWC, IWC, particle types, shapes, and size distributions are needed TRMM Field campaigns collected aircraft data to obtain direct measurements of vertical profile of hydrometeors. –These data will be helpful once processing and analysis is complete but are limited in time and space.

Cloud modeling is underutilized in TRMM GV TRMM GV has not included cloud modeling associated with monthly products or GV site overpasses. Direct measurements of microphysical quantities such as DSD and ice particle distributions needed for physical validation cannot be obtained over spatial resolutions comparable to satellite sensors by any current technology. Cloud modeling could be developed into a tool to assimilate ground site observations and physically constrain estimates of desired microphysical quantities.

Key Lessons for GPM Tighten the feedback loop –Between ground-based observations and cloud models by routinely comparing observed fields to model-derived fields of hydrometeor characteristics (e.g observed reflectivity to model- derived reflectivity) –Between satellite algorithms and cloud models by routinely comparing cloud model-derived hydrometeor profiles initialized with site input to those chosen by the satellite algorithm from the pre-computed database. Make routine measurements using wide variety of measurement types (radar, radiometer, in situ) to collect data on different characteristics of the vertical profile of hydrometeors to use for model input/comparison Use cloud modeling as a virtual instrument to obtain information on microphysical fields we cannot measure directly.

New paradigm for GPM Validation 1) Vertically pointing instruments can more directly address the needs of physical validation 2) LWC and IWC are potentially better variables than rain rate to compare among satellite algorithms, model output, and ground and aircraft observations. –More closely related to the observed satellite radiances –Assumptions about the fall speed of particles and vertical air velocity are not required 3) Horizontally scanning instruments primarily address the location of precipitation

GPM Validation (cont.) 4) Area-threshold method of precipitation estimation is potentially more accurate then pixel by pixel rain mapping for time-space averaged rain estimation in monthly rain maps. –Applies independently determined rain rate statistics to radar-detected rain area. –Takes advantage of radar’s strength to identify where it is raining. –Avoids errors associated with quantitative estimation of rain rate at each pixel. –Can be refined by use of different area average rain rates for physically distinct rain types classified by radar data (e.g. convective and stratiform precipitation)

GPM physical validation requirements and product error characteristics will differ among four distinct regimes RegimeMicrowave algorithms Surface Precip Types Tropical OceanEmission and Combined Emission/Scattering Rain,Graupel Tropical LandScattering onlyRain,Graupel, Hail Midlatitude OceanEmission and Combined Emission/Scattering Rain,Graupel, Snow Midlatitude LandScattering onlyRain,Graupel, Hail, Snow  the fours regimes are associated with four GPM Super Sites

GPM Super Sites Several types of data streams will be needed from each of the four super sites: –12 hrly upper-air soundings (for input to routine modeling) –Scanning S-band or C-band Doppler radar (identifying areas of precip, maybe assimilated into model) –Ground-based particle probes and disdrometers (rain DSD, ice particle size distributions, ice habit) –Upward looking microwave (characteristics of vertical profile of hydrometeors) –Vertically pointing radar (characteristics of vertical profile of hydrometeors) –Scanning polarization radar (LWC and hydrometeor type) –Rain gauges and snow gauges (surface precipitation) –Routine cloud modeling output (virtual instrument) Exact set of instrumentation will vary among sites depending on physical validation requirements, precipitation regime, and logistical constraints

SNOW GPM New Challenge: SNOW To date sparse observations/analysis of snow: –Shape, mass, size distribution, and radiative characteristics –Z-S relations Widely varying height of freezing level: –Near surface –Not present (surface T < 0°C) –Abruptly changing height across fronts Identifying surface rain/snow boundary in mountainous terrain Mixtures of rain and snow at surface Distinguishing snow from light rain Distinguishing snow from sea spray in strong winds Limits on snowfall rate estimates based on sensitivity of satellite sensors

Priority—Snow Physical Validation Satellite algorithm developers would like input regarding surface snow physical assumptions several years before launch A focused winter measurement campaign in a mid-latitude coastal region with low,varying freezing level could address many snow issues over land and ocean –Multi-winter baseline data set Vertically pointing radiometers and radars (coastal, inland, possibly on commercial fishing fleet) In situ instruments at varying altitudes on mountain sides –Aircraft program during heaviest precipitation weeks Simulate GPM sensor data (NASA DC-8 and/or NASA ER-2) In situ data (lower flying aircraft with deicing equipment)