Copernicus Atmosphere Monitoring Service

CAMS General Assembly, Athens, 14-16 June 2016

Johannes Kaiser (Max Planck Institute for Chemistry)

What are the main development lines of the

global system, with a focus on FIRE ASPECTS?

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Collaborators• Max Planck Institute for Chemistry (MPIC)

• Johannes Kaiser• Angelika Heil• Imke Hüser

• King’s College London (KCL)• Martin Wooster

• VU University Amsterdam (VUA)• Guido van der Werf

• Instituto Português do Mar e da Atmosfera (IPMA)• Isabel Trigo

• European Centre for Medium-range Weather Forecasts (ECMWF)• Mark Parrington et al.

Global Atmosphere Modellingin CAMS (up to now “MACC-I/-II/-III”)

5-day forecast of atmospheric composition

here:CO8-12/7/2013

GFAS in MACC & Canadian smoke over Europe (July 2013)

Global Fire Assimilation System (GFAS) in CAMS [Kaiser et al. 2012]

satellite products of CO, AOD, etc.

daily global gap-filled FRP fields

here:2013

MODIS Fire Radiative Power (FRP)SEVIRI, GOES, VIIRS, SLSTR in preparation

0.1º fluxes of 40 smoke constituents

here:COQuebec, 7/7/2013

Validation of 1-day forecast over Paris:observationsmodel with GFASmodel with GFAS & satellite CO assim.

=> good location and timing, source strength to be improved further

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Regionalair quality forecasts

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GFAS now and in the future

1. satellite monitoring

2. assimilation of GEOs

3. assimilation of more LEOs

4. FRP forecasting

5. generate more GEO FRP

main rational:Use more satellite observationsto improve accuracy andstability and represent diurnal cyclein GFAS.

6. dynamic emission factors

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1. Satellite Monitoring• Characterise systematic and random

differences between FRP products from different satellites for:• operational monitoring of input

data• bias correction to

• enable assimilation of FRP products from various satellites

• correct spurious oscillation with period of 2 days in current GFAS

(GEOs miss about halve of the FRP due to high detection thresholds and low channel saturation temperatures.)

[Xu et al. 2010]

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2. Assimilation of GEOs• SEVIRI and the GOES imagers have observation

frequencies up to 1/15minutes over Africa, the Mediterranean and the higher latitude Americas.

• Benefits:• representation of the fire evolution at ~1 hour

resolution• increased coverage in partly cloudy situations

• Caveats:• GEO FRP products are biased low.• processing at 1h resolution even without GEO

products required.• Approach:

• bias correction derived from monitoring• parameterised diurnal cycle model

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3. Assimilation of more LEOs• FRP products from NPP-VIIRS and Sentinel-3-SLSTR will be

available in real time from NASA and ESA, respectively.• suitable addition/replacement of MODIS observations

• improved spatial resolution• reduced detection threshold (able to detect lower FRP fires)

• errors not yet characterised

need to monitor before assimilation in GFAS

courtesy Tianran Zhang & Martin Wooster KCL)

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4. FRP Forcasting • Current recommendation for emission during forecast:

• persistence for 5 days (in Europe 3 days)• shown to lead to false alarms when weather

changes (e.g. Huijnen et al. 2012)

• approaches to be investigated:• calculate FRP forecast from current situation and

meteorological forecast, by means of fire weather indices

• calculate termination of high FRP from met. forecast

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5. Generate FRP from more Geostationary Satellites

• Fire satellite products differ greatly, depending on algorithm, settings and platform.

• EUMETSAT LSA SAF produces SEVIRI FRP-pixel with “Wooster” algorithm, which is also used by NASA for MODIS Col. 6.

• MACC/CAMS produce consistent FRP product from GOES-E/-W.

Applicability for Himarawi-8 and GOES-R will be investigated. coverage of all (sub-)tropics

with high temporal resolution

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one day over southern Africa

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MODIS snapshot of same agricultural burning area in Eastern China.

Geostationary H8 data should allow assessment of FRP every 10 mins over Asia – for fires with FRP > ~30 MW.

Himawari-8 First Test

E. China, 11/6/15Agricultural Fires

Biomass burning peak on 11 June 2015 – crop residue burning

[Courtesy Weidong Xu and Martin Wooster]

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6. Dynamic Emission Factors

• Emission factors (trace species emitted per unit biomass burnt) depend of fire type, e.g.• flaming vs. smouldering• fire “temperature” (intensity)• fuel moisture

• temporal variability currently not accounted for despite potentially large effects

to be included in GFAS

[van Leeuwen et al. 2013]

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Summary• The main development line of GFAS in CAMS will focus on

producing the most accurate representation of the global FRP distribution• using information from all satellite observations of FRP

(that are available in real time)• increased resilience w.r.t. satellite failures

• true temporal resolution of 1 hour in most regions

• Secondary development lines will focus on• FRP forecasting• dynamic emission factors• flexible response to user requests

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Example UsersReal time• all global and regional CAMS

services• Met Services of Japan and

Singapore• NASA

Reserach• KIT• NOAA• various others

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[Kaiser et al. EGU 2016]

Pyrogenic carbon flux in Maritime SE Asia

GFAS1.2 [Huijnen et al. 2016]