Whither the Constellation?

George J. HuffmanNASA/GSFC, Greenbelt, MD, USA

george.j.huffman@nasa.gov

What do we have now?What do we need?What is scheduled?What do we need?What do we do?

What do we have?

A diverse, changing, uncoordinated set of input precip estimates,with various• periods of record• regions of coverage• sensor-specific strengths and

limitations• 5 passive microwave imagers

• 3 SSMIS, AMSR-2, GMI• 5 passive microwave sounders

• 3 MHS (NOAA18 just died), 2 ATMS (SAPHIR precip still pending)

Seek the longest, most detailed record of �global� precip

< 3 hr interval 90% of the time

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What do we need?

Reed et al. (2015), Fig. 1

• extremes can be defined at any time scale, but weather and climate studies tend to focus on daily• this scale of accumulation requires few-hour snapshots to catch the short events

• the long time record of extremes is very important in global change science

Hydrological Analysis

Flash flood analysis is challenging• few-km-scale detail matters

• the corresponding time scale is order(1hr)

Reed et al. (2015) ran a large modeling study• temporal resolution required for satellite

precip to maintain acceptable model flood predictions

• how quickly do predictions of surface water runoff degrade as the satellite temporal resolution degrades?

• most areas require < 3 hrExtreme Events

Extreme events are loosely defined

In the early GPM era, the notional goal of sampling intervals < 3 hours is satisfied better than 90% of the time

• lack of coordination between programs and satellite drift create time-varying overlaps and gaps in time coverage

• requires “extra” satellites for coverage

• perennial gap for the 12/24 slot, except from precessingsatellites

• decrease in planned launches foretells longer data gaps• demise of ADEOS2,

MADRAS, DMSP-F19 demonstrate that assets can end early

• most new launches are sounders

What is scheduled?

[Spring 2018 data]

[courtesy Chris Kidd]

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030SMMR

SMMR

F08 SSM/I

F10 SSM/I

F11 SSM/I

F12 SSM/I

F13 SSM/I

F14 SSM/I

F15 SSM/I

TRMM TMI

F16 SSMIS

F17 SSMIS

F18 SSMIS

NOAA19 MHS

MetOpA MHS

NOAA18 MHS

AQUA AMSR-E

ADEOS-2 AMSR

NOAA17 AMSU-B

NOAA15 AMSU-B

NOAA16 AMSU-B

NPP ATMS

Megha-Tropiques MADRAS

Megha-Tropiques SAPHIR

GCOM-W1 ASMR2

MetOp-B MHS

GPM GMI

F19 SSMIS

EPS-SG MWS

EPS-SG MWI

JPSS-1 ATMS

MetOpC MHS

JPSS-2 ATMS

Conical

Cross-track

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What do we need?

C.4. The coverage and quality of satellite observations should be improved to a constellation providing three-hourly (or more frequent) revisit times over the entire globe by a combination of GMI/AMSR2-class multi-channel conically scanning microwave imagers and ATMS-class multi-channel cross-track microwave sounders. These instruments are identified because they provide input data for a wide range of applications.

Extract from The GEOSS Water Strategy: From Observations to Decisions

GEOSS (2014)

plus• satellite radar• geosynchronous VIS/IR/other

Current sensors are proven, big, and expensive

Are there sensors that are• better/faster/cheaper and• meet the requirements?

How long will it take to get these ready?

What do we do?

We need ideas!• what’s important?• to whom should we be talking?• are there other resources?• is technology going to save us?• how soon will models be useful at the fine scales?

How do we convince agencies to • spend money they’re not now spending • on satellites they’re not now planning?

The overarching challenge is to mesh operational and research requirements to make this a win-win for all the agencies and users

Parts of the constellation are planned … we just have to get the rest of it assembled

Next week the Committee on Earth Observations from Space is holding a “Water from Space” workshop

Backup slides

Requirements for a Robust Precipitation Constellation

George J. Huffman*1, Ralph R. Ferraro2, Christopher Kidd1,3,Vincenzo Levizzani4, F. Joseph Turk5

1: NASA/GSFC, Greenbelt, MD, USA2: NESDIS/STAR, College Park, MD, USA3: Univ. of Maryland College Park, College Park, MD, USA4: CNR-ISAC, Bologna, Italy5: Cal. Tech. JPL, Pasadena, CA, USA

george.j.huffman@nasa.gov

[presented at CEOS-PVC, 2016]

C.4. The coverage and quality of satellite observations should be improved to a constellation providing three-hourly (or more frequent) revisit times over the entire globe by a combination of GMI/AMSR2-class multi-channel conically scanning microwave imagers and ATMS-class multi-channel cross-track microwave sounders. These instruments are identified because they provide input data for a wide range of applications.

Extract from The GEOSS Water Strategy: From Observations to Decisions

GEOSS (2014)

U.S. National Academies of Sciences, Engineering, MedicineDecadal Survey for Earth Science and Applications from Space

http://sites.nationalacademies.org/DEPS/esas2017/index.htm

“… help shape science priorities and guide agency investments into the next decade. The survey, sponsored by NASA, NOAA, and the USGS, is driven by input from the scientific community and policy experts.”

The first long-record microwave instrument, SMMR, is not currently useful, but might be

The microwave constellation developed as sensors were launched• DMSP F08 began the modern

imager record• NOAA15 AMSU-B began the

modern sounder record• both operational and research

satellites contribute openly available datasets

• at community urging, agencies have continued to operate satellites beyond their planned lives

1. A BRIEF HISTORY AND OUTLOOK

MetOp A will be allowed to drift after station-keeping fuel is used

At first, the 6 a.m./p.m. SSM/I precip estimates were best used to routinely calibrate more-approximate estimates based on geosynchronous- and low-orbit infrared data• GPCP SG monthly• GPCP 1-Deg. Daily

By the early 2000’s, multiple sensors allowed the first multi-satellite precip algorithms• TMPA• CMORPH• NRL• near-real-time production (8-18 hr

latency) demonstrates value for new classes of users

1. A BRIEF HISTORY AND OUTLOOK

In the early GPM era, the notional goal of sampling intervals no longer than 3 hours is satisfied better than 90% of the time (see below)

But• lack of coordination between

programs and satellite drift create time-varying overlaps and gaps in time coverage• requires “extra” satellites for

coverage• perennial gap for the 12/24

slot, except from precessingsatellites

• lack of planned new launches foretells an aging set of instruments• demise of ADEOS2,

MADRAS, DMSP-F19 demonstrate that assets can end early

1. A BRIEF HISTORY AND OUTLOOK

Courtesy C. Kidd (ESSIC; GSFC)+ GCOM-W2 AMSR2 ?

2. USER NEEDS

Horizontal Resolution

Time Resolution

Accuracy Latency

L:1km R:10km G:50 to 100km to 500km Also stated variably as 5km to 50km etc.

L:1hr R:3hr G:1d Also stated variably as:0.08hr to 0.5hr; 1h to 12h, or 1d to 3d

0.1mm/5% Also stated variably as: 0.1mm/h to 1mm/hr or 0.5mm/hr to 3mm/hr; 0.5mm/d to 5mm/d; 2mm/d to 10mm/d

0.1h to 6hr 3hr-24hr;1d-2d;7d-30d or Real- and Delayed-Time (application dependent)

GEO (2014), Table 9. L, R, G are Local, Regional, Global

GEO (2010), Fig. 2

2. USER NEEDS

Users across the GEO Societal Benefit Areas require precip data• a few examples: flash and

mainstream flood analysis; extreme event analysis (e.g., insurance); crop forecasting, water resource management; snowpack analysis; pathogen/disease vector forecasting

• drives a wide range of specifications• it is challenging to refine quantitative

values• unclarity remains about snapshot

versus accumulated precip data needs• accumulations require finer-scale

snapshots to adequately resolve the desired accumulation scale

In many cases satellites are the only practical way to satisfy user requirements• oceans and unpopulated areas• underdeveloped regions• conflict zones• areas with restrictive data policies

It is clear that the finest-scale needs are beyond the current or any planned system• what’s a “sweet spot” that satisfies

the most users?• is there a driving requirement from

one or more high-value application(s)?

2. USER NEEDS

Reed et al. (2015), Fig. 1

Hydrological Analysis

Flash flood analysis presents a challenging application• few-km-scale detail matters

• the corresponding time scale is order(1hr)

Reed et al. (2015) ran a large modeling study• computed required temporal resolution for

satellite-based precip to maintain acceptable model-based flood predictions

• quantifies how quickly predictions of surface water runoff degrade as the satellite temporal resolution degrades

• most areas require < 3 hr

2. USER NEEDS

Extreme Events

Extreme events are loosely defined• extremes can be defined at any time scale, but

weather and climate studies tend to focus on daily• this scale of accumulation requires few-hour

snapshots to catch the short events• the long time record of extremes is very important

in global change science

Sampling

Individual microwave sensors have reasonable skill, but• it decays below the (low) skill of

IR within �90 min• NOAA Stage II over CONUS

for June–September 2007• IR observations are at the

shifted time

Frequent samples are key to getting accurate averages• subsample the 5 km, 15 min

European radar data (U.K. Met Office) over the period 2014-15

• evenly spaced samples per day• accumulation intervals:

3 hr 6 hr 12 hr 24 hr

Joyce and Xie (2011), Fig. 3

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SE

Norm

alize

d M

AEBi

as

samples/day

samples/day

samples/day

samples/dayCourtesy C. Kidd (ESSIC; GSFC)

3. DATA SAMPLING INTERVAL, LATENCY

3. DATA SAMPLING INTERVAL, LATENCY

The initial GPM constellation met the 3-hour sampling better than 90% of the time at all latitudes• DMSP-F16 SSMIS • Megha-Tropiques

SAPHIR• DMSP-F17 SSMIS • METOP-A MHS• DMSP-F18 SSMIS • METOP-B MHS• DMSP-F19 SSMIS • NOAA-18 MHS• GCOMW-1 AMSR • NOAA-19 MHS• GPM GMI • NPP ATMS• TRMM TMI• the local minima at specific latitudes are

driven by the turning latitudes of precessingsatellites (GPM, TRMM, Megha-Tropiques)

TRMM TMI ended 8 April 2015, reducing coverage 40�N-S

DMSP-F19 ended 11 Feb. 2016, affecting all latitudes

0 1 2 3 4 5 690N60N30N

Eq30S60S90S90N60N30N

Eq30S60S90S90N60N30N

Eq30S60S90S

0 10 20 30 40 50 60 70 80 90 +

1 Jan. 2015

1 June 2015

1 March 2016

%

Courtesy C. Kidd (ESSIC; GSFC)

3. DATA SAMPLING INTERVAL, LATENCY

The impact of losing another satellite from the 1 March 2016 constellation depends on how much the satellite overlaps other satellites• AMSR2’s (AM2) low impact is because ATMS

duplicates it• ATMS (ATM) has higher impact because its

swath is wider than AMSR2’s• F18 has more impact since it has less overlap

with others• SAPHIR (SAP) is key in low latitudes• GMI is low impact because it overlaps so many

other satellites, but• loss of both GMI and SAPHIR is more than the

sum of the individual losses in SAPHIR’s domain

Drift in the constellation will re-sort the impacts over the course of a year

(%)

Courtesy C. Kidd (ESSIC; GSFC)

3. DATA SAMPLING INTERVAL, LATENCY

0 1 2 3 4 5 6 7 8 9 10 11 12Latency (hours)

0

20

40

60

80

100

Perc

ent o

f dat

a re

ceive

d

DMSP F16/SSMISDMSP F17/SSMISDMSP F18/SSMISDMSP F19/SSMISGPM/GMIGCOM-W1/AMSR2NOAA-18/MHSNOAA-19/MHSMETOP-A/MHSMETOP-B/MHSMeghaTropiques/SAPHIRCPC Merged 4-km Geo-IRGPM/2BCMB

Latency of IMERG component estimatesOctober 20-28, 2015At NASA/PPS only

internal data appear in the first two hours

The latency for the constellation is driven by the agencies• downlink schedules• Level 0 and 1

processing• orbitization and other

reformatting• internal/external

transmission

Reducing latency would require systematic acceleration by all agencies to be effective

Notional “Quantified Earth Science Objective”

Provide a 30-year record of daily and sub-daily extreme precipitation events with “modern” quality for water/energy cycle closure, global change detection, climate and numerical weather forecast model evaluations, and flood and landslide analysis.

4. SENSOR MIX AND SPECIFICATIONS Classes of Sensors

Radar• to date, Ku or shorter wavelengths to keep receivers “small”• providing swath data is key

• nadir-only sampling (e.g., CloudSat) requires years of observations to build up confident results

• the “narrow” swaths in PR and DPR still restrict sampling, but are useful as a basis for routine monthly/seasonal calibrations of estimates from wider-swath passive microwave

• additional frequencies and Doppler capability improve the calibration and support new research

Conical-scan microwave imager• channels covering 10-183 GHz with linear polarization• utility for precip estimation depends on surface type

• all channels useful over ocean• only frequencies above ~37 GHz useful over land (due to warm,

heterogeneous, time-varying background)• surface snow/ice prevents easy retrieval

• footprints are constant size for a given channel• supports many other geophysical parameters (e.g., soil moisture)

4. SENSOR MIX AND SPECIFICATIONS Classes of Sensors

Cross-track-scan microwave sounder• channels covering 23-183 GHz• channel information similar to conical-scan imagers over land• footprints vary in size with scan position• supports atmospheric parameters (e.g., temperature, humidity)

Geosynchronous VIS/IR imager/sounder• excellent time/space sampling• low instantaneous correlation with surface precipitation• additional improvement in using channels is likely

Geosynchronous lightning• new capability with GOES-R• potential for identifying some regions of convective activity

Sensor information content isn’t the same as a particular algorithm’s performance

4. SENSOR MIX AND SPECIFICATIONS What Technology Will Be Useful?

Current sensors are proven, big, and expensive

Are there sensors that are• better/faster/cheaper and• meet the requirements?

For example• COWVR (next-generation wind vector radiometer)• ICI (ice cloud imager on EPS-SG)• TROPICS (cubesat test)* … (a reason we’re here!)

How long will it take to get these ready?

5. What’s Next?

The scheduled launches in the next decade aren’t sufficient to maintain the constellation• but observations are necessary to maintain quality on the products that users

have come to expect

Response for the U.S. Earth Science Decadal Survey RFI#2• Huffman, G.J., V. Chandrasekar, G. De Amici, G. Skofronick-Jackson, C.

Kidd, W. Meier, J. Piepmeier, F. Joseph Turk, 2016: Global Precipitation Observations to Advance Water/Energy Cycle, Climate, Model, and Disaster Science. White Paper submitted for RFI #2 in the NASEM Decadal Survey for Earth Sci. and Appl. from Space, 13 pp. http://surveygizmoresponseuploads.s3.amazonaws.com/fileuploads/15647/2604456/191-99875a374c06b8bd3ecdd7741de551b9_HuffmanGeorgeJPrecipConst.pdf

5. What’s Next?

The constellation is also key for other Earth Science parameters• we need to combine forces, but it’s harder to make the broader case• “one parameter’s noise is another parameter’s signal”

Geophysical Observables & Passive Microwave Bands

no planned replacement not fully approved approved/ongoing

METOP-SG/MWS (CTMW; 2021 launch)

Sea Salinity, SST

Soil moisture, LST, Vegetation

Precipitation, TPW, CLW, Temperature/Humidity Sounding

Snow (SWE, cover)

Ocean Winds*

Sea ice

Frozen soil

GPM/GMI

WindSat

DMSP/SSMIS

METOP-SG/MWI (i2022 launch)

SNPP/ATMS (CTMW) ATMS

GCOM-W/AMSR2

channels for Earth Science variables

channels flying on missions/ sensors

SMOS

SMAP

WCOM (not fully approved; ~2019 launch)

L 1.4

C 6

X 10

K 18

K 23

Ka 36

W 89

150/ 166

G 183

MW Band GHz

Huffman et al. (2016) [DS RFI # 2 constellation], Fig. 2

5. What’s Next?

Response for the U.S. Earth Science Decadal Survey RFI#2• Huffman, G.J., S.T. Brown, V. Chandrasekar, G. Skofronick-Jackson, C. Kidd,

E. Kim, W. Meier, J. Piepmeier, P. Sellers, F. Joseph Turk, N.-Y. Wang, 2016: Multidisciplinary Earth System Science: An Approach to Enhance Value from Passive Microwave Earth Observing Satellite Sensors. White Paper submitted for RFI #2 in the NASEM Decadal Survey for Earth Sci. and Appl. from Space, 12 pp. http://surveygizmoresponseuploads.s3.amazonaws.com/fileuploads/15647/2604456/107-083e64649aa44dbf093c93fbaa67ba30_HuffmanGeorgeJMicrowaveConst.pdf

5. What’s Next?

GEOSS Water Strategy recommendation C.1• “The feasibility of developing a Water-Train satellite constellation should be

assessed. This suite of satellites would be modelled after the A-Train, providing a space segment of an observation system that would capture all fluxes and stores of the water cycle using a diverse suite of platforms and instruments. This system would operate as a Virtual Water Cycle Constellation.”

Water Strategy Implementation Study Team (WSIST) feasibility study (FS)• focus on six high-priority water cycle variables

• precipitation • soil moisture• evaporation/evapotranspiration • river discharge• surface water storage • ground water

• gap analysis of individual observation systems for the parameters and their combined observation system

• goal of the FS• address all six parameters• optimize the integrated observation system

• CEOS WSIST, 2016: CEOS Water Constellation Feasibility Study. Version 1.0, October 2016. Contact Ishida Chu, ishida.chu@jaxa.jp. 100 pp.

5. What’s Next?

We need ideas!• what’s important?• to whom should we be talking?• are there other resources?• is technology going to save us?• how soon will models be useful at the fine scales?

What drives your users crazy about turning the microwave data into useable parameters?• resolution• varying footprint sizes• heterogeneous, discontinuous sensor history• lack of retrospective processing to modern standards

The overarching challenge is to mesh operational and research requirements to make this a win-win for all the agencies and users

Parts of the constellation are planned … we just have to get the rest of it assembled

george.j.huffman@nasa.gov

SSMI/SSMIS Imager EDRs

Rain Rate AllAPI/Soil Moisture LandTotal Precipitable Water OceanCloud Liquid Water AllSnow Cover LandSnow Edge LandSnow Depth LandSnow Water Content LandIce Cover OceanIce Edge OceanIce Concentration OceanIce Age OceanLand Surface Temperature LandLand Emissivity LandSurface Wind OceanSurface Type All

Notes

need to refresh the constellationsampling intervalChinese and Russian datageo microwave (good down to 50 GHz) w/ leo3-hr interval tied to 0.25� scale?try to tie uneven sampling to sampling interval and channels for variables channel

range, color code“anchor imager”

resolutionideal constellation