CHARACTERIZATION OF INTER-SATELLITE DIFFERENCES IN RETRIEVED RAINFALL Dr. F. Joseph (Joe) Turk

CHARACTERIZATION OF INTER-SATELLITE DIFFERENCES IN RETRIEVED RAINFALL

Dr. F. Joseph (Joe) TurkNaval Research Laboratory, Marine Meteorology Division

Monterey, CA 93943(831)-656-4888 [email protected]

Third IPWG WorkshopMelbourne, Australia

23-27 October 2006

In light of program changes, launch delays, etc, the future microwave (MW) sensor suite likely to be composed of different sensor types (as it is today)

Radar/radiometer, different channels on MW radiometers, conical & cross track scanning instruments, spatial resolutions, etc.

Retrieved rainfall characteristics likely to be different between sensors – varying as a function of rainfall rate, latitude, season, background, etc.

Combining MW sensors is an ongoing research topic for GPM (in radiance space and rainfall space)

Data assimilation techniques may assimilate “rainy radiances” from a suite of inter-calibrated MW sensors; however,

High Resolution Precipitation Products (HRPP) will need to blend/merge these sets of disparate observations

Inter-Sensor Rainfall Characteristics

PEHRPP Suite 4: "Big picture" comparisonsPEHRPP Suite 4: "Big picture" comparisons (coordinator: ?)(coordinator: ?) Catch any artifacts not noticed in detailed statistics of above suitesCatch any artifacts not noticed in detailed statistics of above suites

• obvious systematic changes on a latitude line, related to availability of obvious systematic changes on a latitude line, related to availability of certain data typescertain data types

• changes in time series, related to data availabilitychanges in time series, related to data availability Validation of large-scale quantities and characteristics against bulk Validation of large-scale quantities and characteristics against bulk

quantities, existing products (GPCP, CMAP, etc.), streamflow data quantities, existing products (GPCP, CMAP, etc.), streamflow data sets, water budgets, and subjective judgment sets, water budgets, and subjective judgment

Focus on thousands of kilometers and monthly time scalesFocus on thousands of kilometers and monthly time scales

LOCAL OBSERVATION TIMES OF DMSP and NOAA SATELLITES

NOAA Satellites as of Late 2006Ascending Descending

0

6

12

18NOAA-15

NOAA-16

NOAA-18

NOAA-17

DMSP Satellites as of Late 2006Ascending Descending

0

6

12

18F-14

F-13

F-16

F-15

Dataset Preparation

Nearly three years (2004-current) of DMSP, NOAA, TRMM (WindSat since 6/06, Aqua since 4/05, F-16 since 2/06, NOAA-18 since 11/05)

From each orbit file, rain histogram is binned by date, latitude and surface (0.2 mm hr-1 steps)

First step is to analyze up-front rain/no-rain screening differences amongst various sensors

Second step is to analyze (non-zero) light rain differences

Northern Hemisphere30N-40N

Southern Hemisphere40S-30S

Middle Latitudes Rain/No-Rain Discrimination

Rain Detection - Middle Latitudes - Over OceanDMSP F-13, F-14, F-15 (SSMI), F-16 (SSMIS)



COLOR LEGENDF-13 (SSMI)F-14 (SSMI)F15 (SSMI)

F-16 (SSMIS)

winter summer winter summer

More “no-rain” events in summer seasons

SSMI’s well matched

SSMIS flags more no-rain values in both hemispheres

F-15 RADCAL issue apparent after mid-August 2006

summer winter summer winter

Rain Detection - Middle Latitudes - Over OceanTRMM-TMI/PR, Aqua-AMSR-E, Coriolis-WindSat



COLOR LEGENDTRMM (PR)TRMM (TMI)

Aqua (AMSR-E)Coriolis (WindSat)


PR has about 2-3% more no-rain events than TMI

TMI & AMSR-E well matched

WindSat lacks 85 GHz capability which improves

rain screening


Rain Detection - Middle Latitudes - Over OceanNOAA-15, NOAA-16, NOAA-17 (AMSU), NOAA-18 (MHS)



COLOR LEGENDNOAA-15 (AMSU)NOAA-16 (AMSU)NOAA-17 (AMSU)NOAA-18 (MHS)


Seasonality signal not well represented at high latitudes

AMSU and MHS well-matched

Slight differences likely due to AM and PM crossing time

difference


The Problem

• At the direction of US Strategic Command (USSTRATCOM) and in coordination with the NPOESS IPO-ADO for DMSP operations, the satellite operations control center activated the radar calibration (RADCAL) suite on DMSP F15, August 14, 2006.

• The RADCAL beacon operates at 150MHz & 400 MHz. On-orbit testing conducted in August 2005, confirmed that transmissions from the RADCAL 150Mhz Beacon produced interference in the SSMI 22GHz vertical polarization (22V) channel and that the 400Mhz Beacon interfered with SSMT-2 channel 4 performance.

• The SSMT-2 on F15 has since been declared "red" or non-operational due to an unrelated component failure.

• Users of the SSMI data must be aware that the 22V channel used in, ocean surface wind speeds, snow classification and depth, and rain rate calculations, etc., will be dramatically degraded during RADCAL Beacon transmission

Channel F-15 SSMI Mean TB

F-16 SSMIS Mean TB

F-15 SSMI Std Dev

F-16 SSMIS Std Dev

19V 198.7 199.4 13.3 13.2

19H 137.4 136.9 20.5 20.3

22V 237.5 225.9 22.2 21.8

37V 217.2 217.7 10.0 9.9

37H 162.6 162.3 18.3 18.0

85V 91V* 258.8 259.1 12.8 12.8

85H 91H* 231.3 230.7 24.0 23.7

F-15 and F-16 Intercomparison (Post-RADCAL)16 September 2006 Ocean-Only Center Beam Position

Data courtesy of Gene Poe, NRL

F-15 and F-16 synchronization:

F-13: 1833 localF-14: 1758 localF-15: 2010 localF-16: 2012 local

Good agreement at non-22 channels, max 0.7K difference

However, the statistics are dominated by no-rain pixels (~ 95%)

*85 GHz on SSMI, 91 GHz on SSMIS

DMSP F15-F1622V Channel Statistics

Pre-RADCAL01-13 August 2006

DMSP F15-F1622V Channel Statistics

Post-RADCAL15-28 August 2006

+/-70 Latitudes Over-Water

F-15RADCAL Beacon

Activated 14 Aug 2006

Impacts of Radcal Beacon Interference on F15 SSM/I Products

Saturated water vapor Missing clouds and precipitation

Scattering Index Conceptualized

scattering materials: Tv(22) > Tv(85)

precipitation, dry snow, aged sea ice, glacial ice, deserts

absorbing materials: Tv(22) < Tv(85)

clouds, melting snow, new sea ice, vegetation, wet soil

19 GHz 22 85

shading denotes water vapor

thermal emission 19 GHz 22 85

(typical values)

Material 19V 22V 85VPrecip over water 240 270 < 240

Precip over land 260 260 < 240

Dry Snow 250 240 210

Clouds over land 275 275 280

New sea ice 250 245 255

Wet soil 265 270 275Warmer TB as

frequency increasesColder TB as

frequency increases

91

91 GHz scatters a little more

than 85 GHz

91

Scattering Index Computation

scattering materials: Tv(22) > Tv(85)

precipitation, dry snow, aged sea ice, glacial ice, deserts

absorbing materials: Tv(22) < Tv(85)

clouds, melting snow, new sea ice, vegetation, wet soil

Oceanic:SI85= (-174.4 + 0.715*TB19v + 2.439*TB22v - 0.00504*TB22v*TB22v) - TB85v

Land:SI85= (451.9 - 0.44*TB19v - 1.775*TB22v + 0.00574*TB22v*TB22v) - TB85v

Estimation of the non-scattering contribution of the

85 GHz measurements

If SI85 > 10 then Rain ~ log(SI85)(after screens for ice, deserts, etc using polarization checks)

SSMIS vs. SSMI:For scattering materials, 91 GHz scatters a bit more than 85 GHzFor absorbing materials, 91 GHz emits (absorbs) more than at 85 GHz

Cyclone XangsaneF-15 SSMI 85H

25 minutes time separation Cyclone Xangsane

F-16 SSMIS 91H

SSMI-SSMIS High Frequency Channel Differences Most Pronounced Over Heavy Convection

Cyclone XangsaneF-15 SSMI 85H

34 minutes time separation Cyclone Xangsane

F-16 SSMIS 91H

SSMI-SSMIS High Frequency Channel Differences Most Pronounced Over Heavy Convection

Rain Detection - Middle Latitudes - Over LandDMSP F-13, F-14, F-15 (SSMI), F-16 (SSMIS)




F-16 (SSMIS)


More “no-rain” events in winter seasons


SSMI and SSMIS similar



Rain Detection - Middle Latitudes - Over LandTRMM-TMI/PR, Aqua-AMSR-E, Coriolis-WindSat






Very similar to DMSP

TMI “oscillation” likely due to sampling repeat cycle


Rain Detection - Middle Latitudes - Over LandNOAA-15, NOAA-16, NOAA-17 (AMSU), NOAA-18 (MHS)





2-3% less no-rain events than DMSP shows

NOAA-17 crossing time difference evident

NOAA-15: 1736 localNOAA-16: 1526 localNOAA-17: 2219 localNOAA-18: 1343 local




Sub-Tropical Latitudes Rain/No-Rain Discrimination

Rain Detection – Sub Tropics - Over OceanDMSP F-13, F-14, F-15 (SSMI), F-16 (SSMIS)




F-16 (SSMIS)


More “no-rain” events in winter-spring seasons

SSMI’s well matched except F-15 post-RADCAL



Rain Detection – Sub Tropics - Over OceanTRMM-TMI/PR, Aqua-AMSR-E, Coriolis-WindSat






PR and TMI are closer than they are in mid latitudes


WindSat screening better in sub-tropics than in mid-

latitudes


Rain Detection – Sub Tropics - Over OceanNOAA-15, NOAA-16, NOAA-17 (AMSU), NOAA-18 (MHS)




NOAA datasets begin to show some seasonality and

are well-matched

About 3% more no-rain pixels flagged relative to TMI



Northern and Southern Hemispheres

5S-5N

Tropical Latitudes Rain/No-Rain Discrimination

Rain Detection – Tropics - Over OceanDMSP F-13, F-14, F-15 (SSMI), F-16 (SSMIS)


F-16 (SSMIS)




Rain Detection – Tropics - Over OceanTRMM-TMI/PR, Aqua-AMSR-E, Coriolis-WindSat



PR and TMI are closer than they are in mid latitudes


WindSat screening gradually improves moving from mid-

latitudes to the tropics

Rain Detection – Tropics - Over OceanNOAA-15, NOAA-16, NOAA-17 (AMSU), NOAA-18 (MHS)

About 3% more no-rain pixels flagged relative to TMI




Middle Latitudes Light Rain Detection(Non-zero rain < 2 mm hr-1)

Light Rain Detection - Middle Latitudes - Over OceanDMSP F-13, F-14, F-15 (SSMI), F-16 (SSMIS)




F-16 (SSMIS)


SSMIS picks up about half as much “light rain” relative to

the SSMI’s, in both hemispheres


Light Rain Detection - Middle Latitudes - Over OceanTRMM-TMI/PR, Aqua-AMSR-E, Coriolis-WindSat






TMI and AMSR-E detect about twice as much rain in this interval relative to PR

WindSat detects much more owing to a looser rain/no-rain

screen

Not sure about N-S hemisphere differences


Light Rain Detection - Middle Latitudes - Over OceanNOAA-15, NOAA-16, NOAA-17 (AMSU), NOAA-18 (MHS)





Little to no sensitivity to light rain over ocean at these

latitudes (over land better)


Light Rain Detection - Middle Latitudes - Over LandDMSP F-13, F-14, F-15 (SSMI), F-16 (SSMIS)




F-16 (SSMIS)



SSMI-SSMIS difference much smaller

F-15 RADCAL issue more apparent over-land than over-

ocean


Light Rain Detection - Middle Latitudes - Over LandTRMM-TMI/PR, Aqua-AMSR-E, Coriolis-WindSat






Slightly more light rain detected by PR than TMI

No WindSat data over land


Light Rain Detection - Middle Latitudes - Over LandNOAA-15, NOAA-16, NOAA-17 (AMSU), NOAA-18 (MHS)





Over land and at middle latitudes, AMSU/MHS light

rain detection is very similar to DMSP


Northern and Southern Hemispheres

5S-5N

Tropical Latitudes Heavy Rain Detection(Non-zero rain > 5 mm hr-1)

Heavy Rain Detection - Tropics - Over OceanDMSP F-13, F-14, F-15 (SSMI), F-16 (SSMIS)




F-16 (SSMIS)


Difficult to assess- since even with a 30-day running

average there are relatively few pixels > 5 mm hr-1


Heavy Rain Detection - Tropics - Over OceanTRMM-TMI/PR, Aqua-AMSR-E, Coriolis-WindSat






PR has about 2-3% more no-rain events than TMI


WindSat lacks 85 GHz capability which improves

rain screening


Heavy Rain Detection - Tropics - Over OceanNOAA-15, NOAA-16, NOAA-17 (AMSU), NOAA-18 (MHS)





Seasonality signal not picked up at these latitudes

AMSU and MHS well-matched

Slight differences likely due to AM and PM crossing time

difference


Summary

A rich “GPM” constellation exists today – employ current systems to address issues related to merging MW datasets

NESDIS algorithm for over-ocean high latitudes: Issues known and being improved – also should examine other AMSU-based algorithms

DMSP F-15 RADCAL issue: Radiance-level offset being characterized and a “fix” may be possible for precipitation datasets

Bring in light rain statistics from CloudSat - averaged across long timescales

May develop on improved SSMIS EDR suite after F-17 launch

All of these topics are relevant to the GPM

F15 – F14 Differences For Two-Week Periods Before and After RADCAL Activation on 14 August 2006

Pre-RADCAL SDR differences being fixed

Passive Microwave Constellation Local Observation TimesJakarta, Indonesia (6.1S 106.8E)

Passive Microwave Constellation Revisit Times(Including NOAA Cross-Track Sounders)

Jakarta, Indonesia (6.1S 106.8E)

6-Hour Level 6 (97%)5 (94%)4 (81%)3 (52%)

Percent of Year That

Revisit Is Less Than (Hours):

NOTE: Percent of year, not percent of total points

Passive Microwave Constellation Observation TimesSan Francisco, California (37.8N 122.4W)

Passive Microwave Constellation Revisit Times(Including NOAA Cross-Track Sounders)San Francisco, California (37.8N 122.4W)

6 (100%)5 (98%)4 (89%)3 (69%)

6-Hour Level



Aqua and NOAA-18

to the rescue

Passive Microwave Constellation Observation TimesHelsinki, Finland (60.1N 25.0E) No TRMM Coverage

Passive Microwave Constellation Revisit Times(Including NOAA Cross-Track Sounders)

Helsinki, Finland (60.1N 25.0E) No TRMM Coverage

6 (100%)5 (97%)4 (88%)3 (81%)

6-Hour Level



Documents

CHARACTERIZATION OF INTER-SATELLITE DIFFERENCES IN RETRIEVED RAINFALL Dr. F. Joseph (Joe) Turk