ECMWF Summer Seminars
September 2011
Task 3: Management Activities
•
•
•
•
Orial Kryeziu recruited started June 18th
September coupled DA workshop
Chris Old recruited starting 1st October
Weekly Skype meetings Reading and Edinburgh
started 1st October
• Outline
• Chris Old: Background on SST in Reanalyses and
Observations
• Orial Kryeziu: Progress on extending SST
matchups
• Chris Old: Uncertainty in SST products for
Coupled Data Assimilation
Task 3: Deliverables
• KO+ 9 months D6 (initial): A current reanalysis assessment
report identifying case study periods and showing areas
where coupled model improvements may be expected
• KO+15 months D7: Documentation for Opensource
Visualisation software and demonstrator presenting
comparisons between ocean and atmosphere reanalysis
products using the observation operator codes developed
for assimilation, and observations, particularly near ocean
surface, for scientific and outreach use
• KO+24 months D6 (final): An final impact assessment
report based on Task 1, WP3.3 and 3.4 studies, describing
improved representations of coupled phenomena and
improved fitting to observational data.
Satellite SST in Coupled Data Assimilation
Chris Old and Chris Merchant
School of GeoSciences, The University of Edinburgh
ESA Data Assimilation Projects, Progress Meeting 1
11th December 2012, The University of Reading
Satellite SST in Coupled Data Assimilation
OBSERVATION vs MODEL
INCONSISTENCIES
4
Satellite SST in coupled data assimilation
Task 3: Evaluating observation strategies
The key objective of this task is to review the correlated variability of surface oceanic
and atmospheric datasets, both within the coupled model systems and with the
observations themselves. These results will provide input to the development of a set
of test experiments (Task 4), by identifying suitable test periods and data sets.
WP3.1 Consistency of current surface ocean and atmospheric analysis products
Use satellite measurements as a baseline to examine any inconsistencies within the
products, e.g. regions where there are discrepancies in SSTs between atmosphere and
ocean products, such as regions where strong (or weak) SST diurnal cycles are
detected but where the analysed surface wind speeds seem too high (or low).
Introduction
5
Observed SST Diurnal Variability
Solar heating produces near surface thermal
stratification, while wind driven mixing erodes
diurnal stratification.
Modification of instantaneous air-sea heat flux
from warm-layer formation can be 50 Wm-2.
Amplitude of cycle often observed up to 4K, larger
events are observed (>6K).
dSST excursions spatially and temporally coherent.
dSST event magnitude and horizontal length scale
anti-correlated.
Observed dSST between 9AM and 2PM on 2nd June 2006.
Data from the SEVIRI instrument on MSG2.
Extreme dSST maxima arise where low wind
speed sustained from early morning to midafternoon.
Introduction
References: Gentemann et al., 2008; Merchant et al., 2008
6
Methodology
First stage in the process is to look for instances of discrepancy between observed
and simulated diurnal SSTs.
Use SST data retrieved from SEVIRI on MSG2 to calculate observed diurnal SST.
Simulate diurnal SST using a statistical model based on ERA wind and heat flux fields.
Use the difference between observed and simulated dSST to identify regions of
inconsistency between model fields and observations.
Apply this test to a long time series of model fields and observations to determine
current state of modelling capability.
Use the results to identify a set of suitable data periods that can be used test the
performance of the new coupled assimilation systems being developed.
Introduction
7
Statistical Model for dSST


at 



Dt   Qt  

c
t
2
 1  bt W t 

Dt 
Diurnal SST difference for a time t after start of warming
Qt 
Integrated heat flux during warming period
W t 
Maximum 10m wind speed during warming period
at  , bt  , ct 
Coefficient functions ( LUTs )
Constructed using ERA40 reanalysis atmosphere data and SST retrieved from SEVIRI
data.
Reference: Filipiak et al., 2012
Methodology
8
Application to ALADIN DW database
Calculate observed dSSTobs between 9am and 2pm
Exclude pixels with in 0.2° of land
(local solar time)
(land surface temperature contamination)
Exclude pixels where SDI > 0.5 at any point during the day
Exclude pixels where dSSTobs < -1.4K
(dust contamination)
(cloud contamination)
Calculate dSSTsim using statistical model
(based on NWP reanalysis fields)
Calculate Δ(dSST) = dSSTobs- dSSTsim
Locate peak values where |Δ(dSST)| > 1.5K
(threshold of significant difference)
Locate all pixels around peak where |Δ(dSST)| > 1.0K
Eliminate regions that are too small
(identify coherent regions)
(fewer than 40 points)
Δ(dSST) > 1.5K modelled wind fields too large
Δ(dSST) < -1.5K modelled wind fields too weak
Methodology
9
Example of method – 2nd June 2006
SEVIRI
Statistical Model
(ERA Interim Fields)
Methodology
10
Observed - Modelled dSST – 2nd June 2006
Case 1: Wind speed too large
Observed – Modelled dSST difference ( K )
Case 2: Wind speed too low
Case3: Wind field offset
Methodology
11
Inconsistent regions – 2nd June 2006
Methodology
12
ERA 40 compared with ERA Interim
Using ERA40 data
Using ERA Interim data
Methodology
13
Sea Surface Diurnal Warming
Orial Kryeziu, Keith Haines
Diurnal Warming: sub-daily variations in sea surface temperature (SST) defined relative
to the temperature prior to diurnal stratification (foundation temperature).
Achievements:
Implemented diurnal warming observation operator codes for ERA40, ERAInterim and
ALADIN and compared with SEVIRI data
Extended search area to include full SEVERI disk data (June 2006), data obtained Apr 2006
– Sept 2008
Obtained ERAInterim 3 hourly wind data based on interleaved forecasts, to test sensitivity
of diurnal SST Observation operator code
Initiated dialogue with ECMWF on incorporating surface wave data into diurnal model
- The SST data are hourly observations from SEVIRI spanning -100°W to 45°E and -60°S to 60°N mapped
on a 0.05° resolution grid. Currently, data is available for one month: June 2006. Also available from SEVIRI is
the “Saharan dust index” (SDI)- non-dimensional index based on infra-red wavelengths.
- Peak-to-peak mean amplitude in dSST for the ocean as whole is 0.25 K. Largest dSSTs exceed 6K, and affect
0.01% of the surface (Filipiak et al., 2012).
16
Winds fields (ERA-interim, 3 hourly) derived from a numerical prediction model (NWF) are obtained
from ECMWF. The winds in the western Mediterranean and European Seas are heavily defined by landsea contrasts and orographic effects. Diurnal warming cases occur frequently in this region.
The picture shows the mean of the reciprocal of the wind speed between 0900 and 1500 h UTC.
17
Mean maximum value of dSST here shows correlation with the mean reciprocal of the wind
strength. It is useful to consider knowledge of frequent local wind fields to see orographic
influences on diurnal variability. For example:
- Surface winds accelerated through the Corsica and Sardinia passage (Merchant et al. 2008).
- Mistral winds are often responsible for the clear, sunny weather in the Golfe du Lion.
Mistral Wind
18
●
●
●
Extreme cases (>4K) arise under conditions with persistent light winds and strong
sunlight.
Sustained low winds in the morning are rare.
In the North and Baltic Seas a prevalent factor in diurnal warming is the optical
attenuation coefficient of water (Merchant et al. 2008).
19
There is an anti-correlation between the magnitude and the length scale of dSST events. Also the
scales of areas of sustained low winds are smaller than those of instantaneously low winds. A
few examples of dSST>4 (14pm - 9am):
18th of June 2006
20
14th of June 2006
21
3rd of June 2006
22
Conclusions:
●
●
●
Extreme diurnal events (peak dSST > 4K) are observed by SEVIRI.
In the Mediterranean sea orographic influence is an important factor. In
the North and Baltic Seas, optical attenuation coefficient of water is a
(significant ) driving factor.
Sustained low winds are required for extreme warming events to be
observed.
Future work:
1) Extend Case criteria to using other near-surface variables:
●
Sea state information: Wave data from ERAInterim; Scatterometer and
Altimetric satellite data
2) Develop uncertainty model for SST L2 coupled reanalysis.
Satellite SST in Coupled Data Assimilation
SSTobs ERROR COVARIANCES
24
Uncertainties for SST assimilation
WP3.1 Consistency of current surface ocean and atmospheric analysis products
The space-time sampling of satellite data will be assessed against spatial and
temporal variability of the ocean and atmospheric reanalysis fields. This sampling
uncertainty will be used in combination with uncertainty components from other
sources developed in the ARC SST and SST CCI projects.
Coupled Data Assimilation Workshop :
An understanding and quantification of satellite SST error covariance length scales in
time and space can be used to constrain the coupled assimilation of surface fields.
Little work has been done to date to quantify satellite based SST error covariance
length scales.
Observation Error Covariance
25
Components of SST uncertainty
• Radiometric noise
• Usually random (but variable)
• 1/√n averaging over n pixels
• Algorithmic
• Geographically systematic component
• Variable component usually correlated to synoptic scales ( average ≠ 1/√n )
• Sampling
• Spatial sub-sampling (only clear sky) - representativity
• Time within diurnal cycle of SST
• Outliers
• cloud, aerosol problems
Observation Error Covariance
26
Radiometric noise
• Noise equivalent differential temperature (NEdT)
 y  SD yc 
NEdT = SD of errors in brightness temperatures
Propagates simply to SST random uncertainty
c
• Can depend on
Variations in coefficients
Channel set
Scene temperature
Instrument properties and state
Time (degradation)
 xˆ 
a  y
c
channels , c
c
• Random uncertainty in SST varies in calculable way between SSTs
 sst  random 
 a  
c
2
yc
channels , c
Observation Error Covariance
27
Algorithmic Errors
Algorithmic limitations in coping with varying atmospheric conditions.
Synoptically correlated in time and space.
Error distributions can be simulated.
BTs, y
Least squares
regression
+
Radiative Transfer
SSTs, x
Coefficients, a
 a lg o  SD xˆ  x 
x̂
 a lg o  avg  xˆ  x 
Observation Error Covariance
28
Instantaneous
simulation
of retrieval
error
Observation Error Covariance
29
Instantaneous
simulation
of retrieval
error
Observation Error Covariance
30
Instantaneous
simulation
of retrieval
error
Observation Error Covariance
31
Instantaneous
simulation
of retrieval
error
Observation Error Covariance
32
Instantaneous
simulation
of retrieval
error
Observation Error Covariance
33
Instantaneous
simulation
of retrieval
error
Observation Error Covariance
34
Instantaneous
simulation
of retrieval
error
Observation Error Covariance
35
Observation Error Covariance
36
Temporal correlation scales
r ~ ( 1/e time scale ) / days
Observation Error Covariance
37
Spatial correlation scales
Zonal
Meridional
Approximation to r ~ ( 1/e length scales ) / km
Observation Error Covariance
38
Error Covariance Work Plan
Proposed approach:
Choice of case study: new AATSR L2P*
( full resolution product )
• Simulate retrieval error fields
• Calculate covariance information
• Re-grid onto an assimilation grid
( ECMWF input required )
• Can also flag “trusted, independent obs” set for model testing
* Currently being developed at CEMS under NCEO and contains ARC/SST CCI uncertainty
information
Observation Error Covariance
39
References:
Gentemann, C. L., P. J. Minnett, P. Le Borgne, C. J. Merchant. 2008. Multi-satellite measurements of large
diurnal warming events. Geophysical Research Letter, 35, L22602
Merchant, C. J., M. J. Filipiak, P. Le Borgne, H. Roquet, E. Autret, J. -F. Piollé, S. Lavender. 2008. Diurnal warmlayer events in the western Mediterranean and European shelf. Geophysical Research Letter, 35, L04601
Filipiak, M. J., C. J. Merchant, H. Kettle, P. Le Borgne. 2012. An empirical model for the statistics of sea surface
diurnal warming. Ocean Science, 8, 197-209
40