NWP Requirements for Hyperspectral IR data; a WMO perspective

Lars Peter RiishojgaardDirector, JCSDA

Chair, OPAG-IOS, WMO Commission for Basic Systems

Hyperspectral Workshop, Miami

Overview• Numerical weather prediction and societal

benefits• Satellite data and numerical weather

prediction– Where does the skill come from, and how do we

assess that?– Impact of hyperspectral IR data

• WMO requirements– Rolling review of requirements– Requirements applicable to hyperspectral IR data

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Windhoek, Namibia

Weather Prediction and the US Economy; A Macroscopic View

• Department of Commerce: “20% of overall US economy is weather sensitive”: ~$3 trillion/year

– Impact to air and surface transportation, agriculture, construction, energy production and distribution, etc.

• Assume that half of this is “forecast sensitive”: $1.5 trillion/year

• Assume that the potential savings due to weather forecasting amount to 5% of the “forecast sensitive total”: ~$75B/year

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Windhoek, Namibia

… a Macroscopic View … (II)

• Potential gain of $75B if we had “perfect forecast information”; but what does that mean? – 0 h useful forecast range => $0 in savings– 336 h useful forecast (two weeks maximum

predictability) range => $75B in savings

• Assume that this potential economic gain is distributed linearly over the potential forecast range – This implies a value to the US economy of >200M

per hour of forecast range per year !

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Hyperspectral Workshop, Miami

NWP requirements for upper-air data coverage

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Hyperspectral Workshop, Miami

Conventional obs (u, v, T, q, vertically resolved)

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Hyperspectral Workshop, Miami

Example satellite data coverage (AMSU-A)

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Hyperspectral Workshop, Miami Slide 8

Combined impact of all satellite data

EUCOS Observing System Experiments (OSEs):

• 2007 ECMWF forecasting system,

• winter & summer seasons,• Three experiments:

1) no satellite data (NoSAT),2) NoSAT + 1 AMSU-A3) Control using all data

500 hPa geopotential height anomaly correlation

3/4 day

3 days

Slide courtesy of Erik Andersson, ECMWF

March 29-31 2011

NWP skill and the Global Observing System

• Global NWP skill of major centers routinely compared within WMO using common metrics and definitions

• Impact of individual components of the Global Observing System (GOS) on NWP skill is also assessed, albeit in more sporadic fashion– Data denial and adjoint sensitivity diagnostics – Progress and results reviewed annually by WMO Expert

Team on the Evolution of the Global Observing System (ET-EGOS)

– Community-wide WMO Impact Workshops (1997, 2000, 2004, 2008, 2012,…) used to synthesize experiments and develop official WMO statements of guidance

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4th WMO Impact Workshop, Geneva May 2008

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AIRS and IASI found to have similar impacts and were ranked among the top observing systems in all regions

An additional 2 to 6 hours of useful forecast range is what most individual components of the GOS can contribute in the NH

This is very significant in terms of socioeconomic impact and is strongly linked to other measures of skill!

Satellite data now account for most of the skill

Hyperspectral IR data ranked no. 1 (as a group) by ECMWF

Impact of GOS components on 24-h ECMWF Global Forecast skill(courtesy of Erik Andersson, ECMWF)

Importance of Satellite Data in NWPhttp://www.nrlmry.navy.mil/obsens/

Importance of Satellite Data in NWPhttp://www.nrlmry.navy.mil/obsens/

Satellite Data has become the single most important componentof the global observing network for NWP

Σ Sat Radiances = -143.9 Σ Sat Winds = -198.3

2 161

all

Σ Conv = -168.0

Observation Impact

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Different satellite data important for different systems

WMO OMM

WMO Requirements and the Rolling Requirements Review (RRR)

• Commission for Basic Systems; one of eight WMO Technical Commissions. President: Fred Branski, NOAA/NWS– OPAG for the Integrated Observing System; one of four

OPAGs under CBS. Chair: L. P. Riishojgaard, JCSDA• Expert Team on the Evolution of the Global Observing System; one of six Expert

Teams under OPAG-IOS. Chair: John Eyre, Met Office– Requirements database, by application area, for Global NWP,

Regional NWP, Nowcasting, Agrometeorology, etc. (14 total)– Capabilities database, by observing system, e.g. RAOBS, GEO

imagers, hyperspectral IR sounders, AMDAR, buoys, etc.– Gap analysis, Statements of Guidance– Implementation plan– Vision for the GOS in 2025

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WMO OMM

WMO requirements for hyperspectral IR data

• WMO requirements are “technology-free”; WMO captures and documents measurement requirements on geophysical variables– Application area (example; 14 total): Global NWP– Geophysical quantity (example): Atmospheric temperature– Requirements on: Vertical resolution, Horizontal resolution, horizontal coverage,

temporal resolution (revisit), accuracy, precision, data latency,…• Observational capabilities are listed by observing systems; database

contains entries for AIRS, IASI, CrIS,…• … (gap analysis, planning and coordination …)• Vision for the GOS in 2025:

– Three hyperspectral IR sensors in sun–synchronous polar LEO, orbital planes equally spaced, capabilities assumed to be similar to those of AIRS/IASI

– “At least six geostationary satellites, separated by no more than 70 deg of longitude”, carrying hyperspectral IR sensors as one of three core missions

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Hyperspectral IR and NWP/DA in the future

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• Additional spectral coverage (at most 10% of the spectral data currently used in operational practice)

• Additional data over land (emissivity modeling)

• Additional data over cloudy areas (cloud microphysics and/or radiative transfer modeling)

• Data assimilation methodology– Is radiance assimilation the best approach for hyperspectral

sensors?

Hyperspectral Workshop, Miami

Summary• NWP has a large (and growing) economic impact• Satellite data have a large (and growing) impact on

NWP skill• On a “per instrument” basis, hyperspectral IR sensors

have some of the largest impacts of all existing observing systems

• Current WMO official WMO Vision for the GOS in 2025:– Three hyperspectral IR sensors in equally

spaced sun-synchronous LEO; capabilities assumed to be similar to AIRS/IASI

– Six hyperspectral IR sensors in GEO

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