SIF.v2.sm

Processing math: 100%

Contribution to COPERNICUS Atmosphere Monitoring Service

Institut Pierre Simon Laplace / Laboratoire des Sciences du Climat et de l'Environnement

CTESSEL developments

orchidas : cams / CAMS41-CO2 / SIF.v2.sm

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Project CAMS41
Optimization of the relationship between CHTESSEL GPP and SIF data [per REGION and per SEASON]

All input data (SIF, GPP Jung and GPP CTESSEL) are smoothed with the 3-month running averaging window

PFT1 (Crops, mixed farming)
PFT2 (Short grass)
PFT3 (Evergreen needleleaf)
PFT4 (Deciduous needleleaf)
PFT5 (Deciduous broadleaf)
PFT6 (Evergreen broadleaf)
PFT7 (Tall grass)
PFT9 (Tundra)
PFT10 (Irrigated crops)
PFT11 (Semidesert)
PFT13 (Bogs and marshes)
PFT16 (Evergreen shrubs)
PFT17 (Deciduous shrubs)
PFT18 (Mixed forest - Wood)
PFT19 (Interrupted forest)

Optimize $(a,b)$ per CTESSEL PFT from the relationship
$SIF_\mathsf{SAT} = a \cdot GPP_\mathsf{Jung} + b$

Calculate inverse coefficients $(\alpha,\beta)$
$\alpha = \frac{1}{a}$
$\beta = -\frac{b}{a}$

Compute CTESSEL SIF
$SIF_\mathsf{CTESSEL} = a \cdot GPP_\mathsf{CTESSEL} + b$

Compare SIF for CTESSEL vs SAT
$\Delta SIF = SIF_\mathsf{SAT} - SIF_\mathsf{CTESSEL}$

Convert difference from SIF to GPP
$\Delta GPP_\mathsf{SAT} = \alpha \cdot \Delta SIF$

Compare with GPP difference
$\Delta GPP = GPP_\mathsf{Jung} - GPP_\mathsf{CTESSEL}$

PFT maps

SIF data

Posterior data for GOSAT

a_post
REGION	YEAR	WINTER	SPRING	SUMMER	AUTUMN
01	0.114	0.132	0.116	0.107	0.127
01_N	0.108	0.158	0.108	0.106	0.132
01_T	0.108	0.112	0.11	0.118	0.114
01_S	0.122	0.133	0.135	0.14	0.116

b_post
REGION	YEAR	WINTER	SPRING	SUMMER	AUTUMN
01	0.0377	0.0308	0.029	0.0392	0.0457
01_N	0.0326	0.0218	0.0211	0.0321	0.0415
01_T	0.0698	0.0831	0.0548	0.0413	0.0712
01_S	0.0483	0.00859	0.0573	0.0375	0.058

n_points
REGION	YEAR	WINTER	SPRING	SUMMER	AUTUMN
01	36322	8750	9232	9552	8788
01_N	25082	5936	6293	6770	6083
01_T	6377	1657	1689	1492	1539
01_S	4863	1157	1250	1290	1166

RMSD (GOSAT vs Jung)
REGION	MODE	YEAR	WINTER	SPRING	SUMMER	AUTUMN
01	all data	0.162	0.171	0.16	0.161	0.156
01	seasonal	0.162	0.171	0.16	0.161	0.155
01_N	all data	0.155	0.168	0.154	0.151	0.144
01_N	seasonal	0.154	0.168	0.154	0.151	0.143
01_T	all data	0.182	0.171	0.173	0.208	0.178
01_T	seasonal	0.182	0.17	0.172	0.208	0.177
01_S	all data	0.167	0.181	0.165	0.143	0.181
01_S	seasonal	0.167	0.179	0.163	0.143	0.181

Posterior data for GOME2-Joiner

a_post
REGION	YEAR	WINTER	SPRING	SUMMER	AUTUMN
01	0.156	0.149	0.153	0.147	0.154
01_N	0.185	0.247	0.188	0.159	0.205
01_T	0.115	0.0947	0.103	0.125	0.121
01_S	0.146	0.15	0.164	0.149	0.131

b_post
REGION	YEAR	WINTER	SPRING	SUMMER	AUTUMN
01	0.07	0.0529	0.0586	0.102	0.0833
01_N	0.0526	0.0292	0.0357	0.102	0.0606
01_T	0.109	0.119	0.1	0.0949	0.129
01_S	0.084	0.0643	0.0599	0.0831	0.124

n_points
REGION	YEAR	WINTER	SPRING	SUMMER	AUTUMN
01	257615	56294	67002	67163	67156
01_N	161581	32294	42990	43152	43145
01_T	54083	13512	13524	13524	13523
01_S	41951	10488	10488	10487	10488

RMSD (GOME2-Joiner vs Jung)
REGION	MODE	YEAR	WINTER	SPRING	SUMMER	AUTUMN
01	all data	0.102	0.116	0.0943	0.104	0.0941
01	seasonal	0.1	0.114	0.0933	0.101	0.0933
01_N	all data	0.0928	0.114	0.0861	0.095	0.078
01_N	seasonal	0.0908	0.112	0.0848	0.0921	0.076
01_T	all data	0.109	0.108	0.103	0.116	0.111
01_T	seasonal	0.107	0.106	0.0995	0.116	0.107
01_S	all data	0.104	0.109	0.0917	0.092	0.12
01_S	seasonal	0.102	0.108	0.0904	0.092	0.117

GPP

SIF (all data optimization)

SIF (seasonal optimization)

Delta GPP

Last modified: 21/02/2019 07:33:42

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