Sandra L. Castro Candidacy for Promotion to Associate Research Professor ASEN Research Seminar, November 30 th 2012

The Influence of Near-Surface

Oceanic Variability on SST Retrieval

Uncertainty

Sandra L. CastroCandidacy for Promotion to Associate Research

ProfessorASEN Research Seminar, November 30th 2012

There are so many SST products, how do I decide which one to use?

Outline

MotivationSpatial variability in SSTs at different

depths in the water columnVariability in SST due to the presence

of the near-surface diurnal thermocline (different from the seasonal thermocline)

Currently funded projects

Outline





The Physics of SST

Q net ~ ( - ) k x ( /r dT dz )

This skin layer is generally present, and is ~ 10 µm thick.

It is usually cooler than the water below by ~ 0.3 K

The Near-Surface Thermal Structure of the Ocean

IR Radiom

Buoys

Night Day

1mm

- 5 m

-1 m

Interpreting SST measurements

What do radiometers measure and why the near-surface thermal structure of the ocean matters

10μm

VOS or SOOdrifting or

moored buoyresearchvessel

Polar-orbitinginfrared radiometer

Polar-orbitingmicrowaveradiometer

Platforms for Measuring SST

Geostationary orbitInfrared radiometer

Argo Floats

Processes Affecting SST Measurement

Uncertainties

Sensor calibration

Atmospheric correction

AerosolsCloud detection

Water vaporPrecipitation

Skin-bulkmodel

eTSkin + (1-e)TSky

10 μm

5 m

?

Cloud

Processes

Detector, transducer,amplifier, digitiser

Surface emissivity effects

Scattering & absorptionby stratospheric dust

Absorption byWater vapor, etc.

Thermal microlayer

Diurnal thermocline

SST =c0 + Σcj Tb

Tb

Digital signal, SST

TOA radiance (brightness

temperatures), Tb

Bulk temperature, Tbulk

Water-leavingradiance

Skin temperature, TS

Temperature Measure

TS

TbulkDiurnal Warming

model

Tskin

Tbulk

5 cm

NASA Science Team: SST Error Budget

http://www.sstscienceteam.org/white_paper.html

NASA SST Science Team developed White Paper on contributions to the SST uncertainty budget

Diurnal and spatial variability identified as most critical physical processes contributing to the high resolution upper ocean variability

Outline





Do differences in the spatial variability of the thermocline structure of the ocean have an impact on the validation and interpretation of satellite-derived SST products ?

14

Skin, Bulk or Both?

• What do users require?– Tbulk for thermal capacity, deep

convection and for existing bulk flux parameterisations.

– TS for air-sea interaction processes,

better for fluxes

• In situ measurements:• Tbulk is conventionally observed

– But at what depth? Strictly we should record Tz and z.

– May be compromised by DTdiurnal

• Shipborne radiometry with sky correction can measure Tskin

• Satellite observations– All IR satellites “see” only TSkin

– TS is precisely defined at the surface.

– TS atmospheric algorithms are

fundamentally-based Independent of in situ calibration Require in situ TS for validation only

– Tbulk algorithms have hybrid function Sensitive to definition of Tbulk

Near-surface SST gradients introduce uncertainties in the SST error budget

DTcool represented as a globally applied bias correction

Need Physical models of DTdiurnal Require in situ calibration (buoy network)

Traditional Assumption

Since IR satellite retrievals sensitive to the skin temperature, it was believed improvements in regression algorithm accuracy possible through use of in situ skin measurements

Does direct regression of satellite IR brightness temperatures to in situ skin temperatures result in SST-product accuracy improvements over traditional regression retrievals to bulk temperatures?

Approach

We evaluated parallel skin and subsurface MCSST-type models using coincident in situ skin and SST-at-depth measurements from research-quality ship data.

RMS accuracy of skin and SST-at-depth regression equations directly compared

Data: 2003-2005 NOPP Skin SST

ROYAL CARIBBEAN EXPLORER OF THE SEAS

SKIN SST: M-AERI INTERFEROMETER (RSMAS)

SST-AT-DEPTH: SEA-BIRD THERMOMETER (SBE-38) @ 2M

NOAA R/V RONALD H. BROWN

SKIN SST: CIRIMS RADIOMETER (APL)

SST-AT-DEPTH: SEA-BIRD THERMOMETER (SBE-39) @ 2M

Satellite IR: 2003-2005 AVHRR/N17 GAC and LAC NAVOCEANO BTs

Satellite - In situ Matchup Criteria

Collocation window: 25 km and 4 hours

All Mean Min Time

Results

How much better were the SSTbulk?

Night Day Night & Day0

5

10

15

20

25

30

CiRIMS & MAERICIRIMSMAERI

RMSE differences equivalent to removing an independent error in the bulk SST measurements

% A

ccur

acy

Impr

ovem

ent

Night Day Night & Day

05

1015202530



05

1015202530



05

10152025


Satellite SST retrievals based on regression models calibrated with in situ bulk SSTs almost always resulted in better accuracies (lower RMSE) than direct skin SST regressions

Occam’s Razor

Can differences in measurement uncertainty and spatial variability explain the lack of accuracy improvement in the skin SST retrievals?

Spatio-Temporal Variability

The issue of the Point-to-Pixel Sampling Characteristics of Remotely Sensed SSTs

Satellite sensors see an

“average” value of the

radiation emanating from the

footprint, whereas in situ

instruments measure the

emission at single points on

the ground

Sub-pixel variability long

acknowledged as source of

uncertainty in satellite

validation, but magnitude

largely unquantified

Approach

To test this hypothesis, we added increasing levels of noise to the SST-at-depth, such that:

RMSE bulk SST = RMSE skin SST

Attempt to decompose the supplemental noise into individual contributions from the two effects using Variogram techniques.

Added discrete realizations of white noise processes to SST-at-depth:

Note that, for equal number of observations:

RMSE SST-at-depth < RMSE SST skin, but

RMSE SST skin < RMSE SST emulated buoy

Build RMSE curve for noise-degraded SST-at-depth.

Where the RMSE (skin) intercepts the curve, corresponds to the supplemental noise needed for equivalence in RMSE

Estimates of Required Noise

Added Noise: LAC SST: σ ~ O ( 0.09 – 0.14 K )GAC SST: σ ~ O ( 0.14 – 0.17 K )

% Measurement Error & % Spatial Variability?

Measurement Error? Spatial Variability?

Supplemental Noise: σ ~ O ( 0.1 – 0.2 K )

Instrument Measurement Error

From literature: M-AERI: 0.079°C and CIRIMS: 0.081°C

IR Radiometers: σ~ O(0.08°K) Thermometers: σ~ O(0.01 K) Added Noise: σ~ O(0.08°K)

From the data:

Empirical distributions for the measurement uncertainty of M-AERI skin SST and coincident SST-at-depth support the required supplemental noise obtained from values reported in the literature!!

Variogram Analysis

M-AERI CIRIMS

The variogram (Cressie, 1993, Kent et al., 1999) is a means by which it is possible to isolate the individual contributions from the 2 sources of variability, since the behavior at the origin yields an estimate of the measurement error variance, while the slope gives an indication of the changes in natural variability with separation distance.

Method

We fitted a linear variogram model by weighted least squares to both skin SST and SST-at-depth with separation distances up to 200 km, and extrapolated to the origin to obtain the variance at zero lag.

M-AERI

Measurement Uncertainty

Spatial Variability

SST Uncertainty Estimates

Method Variogram GraphicError

ContributionMeasurement

UncertaintySpatial

VariabilityTotal Variability Supplemental

NoiseM-AERI 0.10 0.07 0.12 0.15

CIRIMS 0.07 0.07 0.10 0.23

C & M 0.12 0.10 0.16 0.17

Variogram estimates provide strong support to the notion that the combined role of differences in measurement uncertainty and spatial variability between the skin and SST-at-depth account for the range of required subsurface supplemental noise found graphically

Measurement uncertainty estimates are consistent with the noise required to reconcile the accuracy differences between thermometers and IR radiometers (σ~O(0.08 K))

On spatial scales of O(25 km):

SST Uncertainty Estimates

Method Variogram GraphicError

ContributionMeasurement

UncertaintySpatial

VariabilityTotal Variability Supplemental

NoiseM-AERI 0.10 0.07 0.12 0.15

CIRIMS 0.07 0.07 0.10 0.23

C & M 0.12 0.10 0.16 0.17

Agreement between Variogram estimates and supplemental noise levels provide strong support to the notion that the combined role of differences in measurement uncertainty and spatial variability between the skin and SST-at-depth are responsible for the lack of accuracy improvement in skin-only regressions

Measurement uncertainty estimates are consistent with the noise required to reconcile the accuracy differences between thermometers and IR radiometers (σ~O(0.08 K))

Measurement uncertainty and spatial variability contribute in equal measure to the overall uncertainty budget

On spatial scales of O(25 km):

Even if technological advances allowed for better accuracies in the measurements of IR radiometer (say, σ~O(0.01 K)), we still have differences in spatial variability to worry about…

Better yet, we need higher accuracy contact thermometers (buoys) and improved atmospheric corrections

Implications The role of spatial variability in the uncertainty budget arises in

part because inadequacies in point-to-pixel comparisons

Sparse radiometric sampling along a single track does not

provide full coverage of the spatial variability within the

satellite footprint.

The satellite measurement is a spatial average over the IFOV.

This integration might be smoothing out the enhanced

variability of the skin, making the variability across the pixel

more representative of the less variable point measurements

of the SST-at-depths To better understand and quantify these effects, we require

increased observations of sub-pixel satellite SST variability. In

particular, direct observations of spatial variability as a function

of measurement depth are needed.

Outline

MotivationVertical variability in SST associated

with the near-surface thermal structure of the ocean

Variability in SST associated with the presence of the diurnal thermocline

Current funded projects

40

The GHRSST Concept• Emphasis on synergy benefits of multi-sensor SST products• In principle, the merging and analysis of complementary satellite and in

situ measurements can deliver SST products with enhanced spatial and temporal coverage

• Many analyses currently available, but most ignore daytime obs

What is Foundation SST?

Skin SST

Foundation SST ΔT diurnal

ΔT cool

Predawn SST Previous night composite SST observations at winds greater than 6 m/s

Common definitions:

In recent years, improvements in the accuracy and sampling of geostationary satellites have enabled better characterization of diurnal warming from space

Average difference between MTSAT(skin) and RAMSSA (fnd) for the 1 degree box around 151.5E, 0.5S on 1 Jan 2009

Diurnal Warming Peak Amplitudes

• Monthly climatologies of maximum diurnal warming observed from Geostationary MSG/Seviri SSTs for February and June

• Previous night composite used as Foundation SST• Amplitudes are small on average but can be significant at low winds

ΔTdiurnal Average effect on fluxes

Clayson and Bogdanoff (2012)

Flux w/ diurnal correction – Flux w/o diurnal correction (W/m2)

Maximum effect on fluxes (1998 - 2007)

Clayson and Bogdanoff (2012)

What is the uncertainty in estimates of diurnal warming as a function of time and depth?

Diurnal Warming Model Validation

APPROACH

Detailed Physical Models• Wick’s Modified Kantha-Clayson• GOTM• COAREParametric Models• Castro LUT• CG03

HR Forcing from Cruises

HR Forcing from NWP and Satellite data

Physical Models of ΔTdiurnal

Wick Modified Kantha-Clayson Second moment turbulence closure model with enhanced treatment of mixing near the surface Run with 1-minute resolution and fine vertical grid

Generalized Ocean Turbulence Model (GOTM) Used 2 different included turbulence schemes

K-epsilon Mellor-Yamada

Run with 1-minute resolution on same vertical grid

COARE Warm-layer and cool skin portions of the COARE 3.0 model with included flux computations Forced with temporal resolution of available forcing data

Solar Penetration Models 3-band absorption model from COARE (Fairall et al., 1996)

9-band absorption model from Paulson and Simpson (1981)

Wick and Castro

Forcing: Cruise DataNOAA/ESRL Cruises (courtesy C. Fairall)

NOAA R/V R. H. Brown cruises from 1992-2000 Detailed eddy covariance flux measurements SST-at-depth from the Sea Snake (5-50 cm)

CIRIMS Cruises (Courtesy A. Jessup) NOAA R/V R. H. Brown cruises from 2003-2005 HR Skin SSTs from the CIRIMS Through-the-Hull SST-at-depth (2-3 m)

Over 300 diurnal warming events!

Real Simulations

Models demonstrate ability to reproduce observed warming, but their relative performance varies notably with environmental conditions

Wick and Castro

Skin Validation Sub-skin Validation

1D Models’ Absolute Accuracy

Mean bias can be reduced to small levels but RMS differences of O(1K) remain even when the models are run with high resolution forcing fields

Wick and Castro

Modified Kantha-Clayson

COARE

GOTM

Limitations of Physical Models

1D Models are computationally expensive

Require high resolution forcing fields to produce reliable predictions

Not currently practical to implement in an operational framework

Why Look-up Tables?

Easily integrated into operational applications and revised

Can be tuned for specific regionsLook-up tables, if trained with actual

observations of diurnal warming, can incorporate incomplete or unknown functional dependencies

What should be the optimal input parameters in a LUT approach for estimating satellite diurnal warming corrections in near real time?

LUT Basis

• Used detailed 2nd moment closure turbulent mixed layer model based on Kantha and Clayson (1994) with added skin layer (Wick, 1995) – Wick’s Modified KC Model

• Forced with research cruise observations from the NOAA R/V Ronald H. Brown: ▪ Eddy covariance flux measurements from

the NOAA/ESRL flux campaigns (C. Fairall)▪ SST Observations from the Calibrated

Infrared In Situ Measurement System (CIRIMS) radiometer (A. Jessup)

Development of Look-up Tables

Used previous DW estimates to generate look-up tables as a function of wind speed, insolation, and elapsed time of day since sunrise (6 am)

LUT available for warming at skin and subskin

Three versions: Instantaneous wind and insolation Integrated insolation and instantaneous

wind Integrated wind and insolation

LUT Parameter Space

Fine Resolution LUT

Coarse Resolution LUT

Evaluation of LUT Accuracy

LUT Accuracy wrt 1D Model LUT Accuracy wrt Cruise Obs

LUT approach has minimal bias wrt to model from which they were derived

and RMS up to ~ 0.6 ºC at peak LUT have biases wrt to observations from which they were derived of ~ 0.2

ºC in the morning hours leading to peak and ~ -0.2 ºC after the peak. RMS

varies from ~ 0.4 ºC at nighttime to ~ 1.0 ºC for daytime.

Satellite Application in TWP+ ACCESS-R NWP Hourly forecasts of winds and fluxes at 0.375° res MTSAT-1R Hourly Skin SSTs at 0.05° res RAMSSA Skin and RAMSSA SSTfnd at 1/12° res

Instantaneous Wind

Integrated Wind

Insolation

Peak Insolation

Derivation of SSTfnd from MTSAT-1R

Hour Bias St Dev Hour Bias St Dev

22 -0.101 0.456 8 -0.349 0.761

23 -0.191 0.445 9 -0.293 0.681

0 -0.247 0.449 10 -0.243 0.699

1 -0.275 0.442 11 -0.193 0.658

2 -0.289 0.456 12 -0.238 0.712

3 -0.247 0.460 13 -0.174 0.673

4 -0.174 0.464 14 -0.151 0.704

5 -0.015 0.463 15 -0.172 0.720

16 -0.227 0.791

17 -0.380 0.925

MTSAT Accuracy evaluation wrt drifting buoys

Distribution of DW Events in the TWP+

Distribution of winds < 3 m/s

Not all low winds resulted in DW events, probably due to clouds

Coastal events on the western coast not captured by low wind probability distribution

Other factors: air-sea temperature differences?

Joint distribution of winds < 3 m/s and ΔTdiurnal > 0 in MTSAT

Observed Warming in MTSATFebruary 12 – 15, 2010

MTSAT-1R Observed DW

CG03

ACCESS-R NWP Wind

1D Wick MODKC

LUT Integ Wind, Integ Qs

3D Coupled Model HR CLAM

Model Accuracy wrt MTSAT

Results stratified by wind speed

Model Accuracy wrt MTSAT

Results stratified by diurnal warming amplitude

Satellite Application in ALADIN

ALADIN NWP Hourly forecasts of winds and fluxes at 0.1° res SEVIRI Hourly Skin SSTs at 0.1° res

Instantaneous Wind

SST Analysis

Insolation

Peak Insolation

ΔTdiurnal Model Comparison

Seviri Δtdiurnal Obs Wick MODKC Δtdiurnal COARE Δtdiurnal

Instant Wnd-Instant Qs Instant Wnd-Integrated Qs Integrated Wnd-Integrated QsLUT Δtdiurnal

Diurnal Conclusions

Extreme diurnal warming events are not uncommon

Current models do have skill in predicting diurnal warming in an average sense

Different approaches best capture different aspects of observed diurnal warming

At low wind speeds random errors ~O(1 K) A limiting factor is availability of time

history of forcing data (fluxes, winds) from satellites

Conclusions: DW from Satellites

For NWP-derived forcing data, simplified DW models produce as good or better results than more detailed approaches

More model improvements needed before we can use them obtained improve SST analyses by explicitly accounting for diurnal warming

Overall Conclusions

Near-surface SST variability one of biggest issues in constructing multi-sensor SST analyses

Variations in near-surface spatial variability impacts validation and interpretation of IR SST products Greater spatial variability in the skin layer IR spatial averages more representative of point

subsurface measurements than skin Diurnal variability potentially a significant source

of uncertainty in referencing observations to a common time Models work well in the mean but random errors

approach 1 K

Outline

MotivationVertical variability in SST associated

with the near-surface thermal structure of the ocean

Variability in SST associated with the presence of the diurnal thermocline


Currently Funded Projects

Evaluation of Diurnal Warming models in conjunction with the Australian BoM and NOAA NESDIS for application to their different operational SST products

Use of Argo Floats for Validation of Satellite-derived Foundation SST products (NOPP)

NASA PO: Climatologies of ΔTdiurnal

MIZOPEX (NASA Airborne Science)

Demonstration of UAS capabilities to monitor Arctic Ice/SST conditions

Data Coordinator for the project

SST Spatial variability studies from airborne IR Radiometers flying at different elevations

Evaluation of SST analyses at high latitudes

ISSI: Satellite SST CDR

Documents

Sandra L. Castro Candidacy for Promotion to Associate Research Professor ASEN Research Seminar, November 30 th 2012