Data assimilation of polar orbiting satellites at ECMWF

ECMWFUse of Satellite Data at ECMWF – Tony McNally

Data assimilation of polar orbiting satellites at

ECMWF

Tony McNally

ECMWF


1. Data assimilation

2. Radiance observations from polar orbiting satellites

3. scientific challenges

Overview


Numerical Weather

Prediction (NWP)

Historical reanalysis for

climate research

Environmentalmonitoring and

modelling

Main areas of activity at ECMWF


Numerical Weather

Prediction (NWP)

Deterministic Monthly Seasonal



modelling

Estimating greenhouse gas concentration and

flux inversion

Monitoring and forecasting trajectory

of dust events

Monitoring and forecasting trajectory

of volcanic events


Re-analysis for climate

research

Trend analysis of climate parameters

Improved climatology for process studies

Cleansed historical observation data sets


Numerical Weather

Prediction (NWP)


climate research


modelling


DATA ASSIMILATION

Numerical Weather

Prediction (NWP)


climate research


modelling


What is data assimilation ?…in essence data assimilation is the combination of information from a model and observations to produce a best estimate of the state of the atmosphere (the analysis) ….


Forecast model

Observations

Assimilation algorithm

Super-computer

Key elements of the assimilation system:

])H[(])H[(

)()()(1

1

xyxy

xxxxxJT

bT

b

R

B


The forecast model

Xt=0 Xt=t


The forecast model

91 vertical levels from the surface to 0.01hPa (approx: 80Km)

Global T1279 spectral resolution(16km grid point spacing)

Physical and dynamical processes updated every 10 minutes


The forecast model

91 vertical levels from the surface to 0.01hPa (approx: 80Km)

Global T1279 spectral resolution(16km grid point spacing)

6,300,000,000,000,000 floating point operations

for a single 10 day forecast


The Observations

Yobs


Operational Global Observing Network


Operational Global Observing Network

~ 60,000,000 observations used every 12 hours


The Algorithm

4D-Var

(four dimensional variational analysis)


The 4D-Var Algorithm Jb

])H[(])H[(

)()()(1

1

xyxy

xxxxxJT

bT

b

R

B


The 4D-Var Algorithm Jo

])H[(])H[(

)()()(1

1

xyxy

xxxxxJT

bT

b

R

B


The Super-computer


Super computer configuration

June 2010


The assimilation of polar satellite observations


On NOAA / NASA / EUMETSAT polar orbiting spacecraftHigh resolution IR Sounder (HIRS), Advanced Microwave Sounding Unit (AMSU), Atmospheric IR Sounder (AIRS), Infrared Atmospheric Sounding Interferometer (IASI), Advanced Microwave Scanning Radiometer (AMSR), TRMM (TMI), Cross-track Infrared Sounder (CrIS)

On DMSP polar orbiting spacecraftSpecial Sensor Microwave Imager (SSMI,SSMI/S)

Note: the vast majority of data comes from near-nadir passive sounders

Some of the most important satellite instruments for NWP…


8461 infra-red radiances measured by the IASI instrument

Example of a modern satellite sounding instrument… IASI


What benefits do polar satellite observations bring

to NWP ?


Evolution of ECMWF NWP forecast skill


Evolution of NWP forecast skill

1987*


Anomaly correlationgeopotential height

500 hPa

Anomaly correlationgeopotential height

500 hPa

Forecast skill without polar satellites ?

S.H.: ~3 days at day 5

N.H.: ~2/3 to 3/4 of a day at day 5


Snowfall forecasts over North Eastern USA, 3 days in advance of the 19th December 2009 at 12z. The assimilation system with NO POLAR SATELLITES fails to predict the snow storm that caused widespread disruption to the US east coast. Contours start at 5cm and are at 5cm intervals. Red indicates more than 20cm.

NO POLAR

ECMWF OPS VERIFICATION



Forecasts of Mean Sea Level Pressure, 5 days in advance of the 30th October 2012 for the landfall of Hurricane Sandy. Forecasts from an assimilation system with no polar satellites fails to predict the correct landfall of the storm that caused widespread damage and loss of life to the US east coast.

NO POLAR SATECMWF OPS VERIFICATION


5 day forecast: Base time 2012-10-25-00z Valid Time: 2012-10-30-00z


What challenges do polar satellite observations

present ?


They DO NOT measure TEMPERATUREThey DO NOT measure HUMIDITY or OZONEThey DO NOT measure WIND

…these instruments measure the radiance L that reaches the top of the atmosphere at given frequency v …

…ECMWF assimilates these radiances directly(not retrievals of temperature, humidity etc…)

What do these instruments measure ?


dzdz

dzTBL

0

)())(,()(

+ Surfaceemission

+Surface

reflection/scattering

+ Cloud/raincontribution

+ ...

Planck source term* depending on temperature of the atmosphere

Absorption in theatmosphere

Other contributions to themeasured radiances

Our description of the atmospheremeasured by the satellite

The radiative transfer equation

])H[(])H[(

)()()(1

1

xyxy

xxxxxJT

bT

b

R

BThe RT equation is part of the 4DVar operator that maps the model state X vector into the observation space Y


1. Limited vertical resolution

2. Sensitivity to cloud and rain

3. Systematic error

Specific Science Challenges


o1 2

Ab

so

rpti

on

Frequency

Transmission Weighting function

Pre

ss

ure


dzdz

dzTBL

0

)())(,()(


AMSUA 15 channels IASI 8461 channels



1. Limited vertical resolutionIf we consider the assimilation of these radiances as correcting errors in the background state, the success will depend crucially on the size and vertical structure of the background errors (EDA / EnKF etc…)


dzdz

dzTBL

0

)())(,()(

+ Surfaceemission

+Surface

reflection/scattering

+ Cloud/raincontribution

+ ...

Our description of the atmospheremeasured by the satellite


The cloud uncertainty in radiance terms may be an order of magnitude larger than the T and Q signal (i.e. 10s of kelvin compared to 0.1s of Kelvin!


surfacesurface

full cloud at 500hPa

dR/dT500 = 0

dR/dT* = 1

dR/dT500 = 1

dR/dT* = 0

Weighting function non-linearity


Sensitive areas and cloud cover

Location of sensitive regions

Summer-2001(no clouds)

monthly mean high cloud cover

monthly mean low cloud cover

sensitivity surviving high cloud cover

sensitivity surviving low cloud cover

From McNally (2002) QJRMS 128


Cloud obscured singular vector ?

Some extra overcast observations are used – leading to some possibly important analysis differences in a sensitive area …

500hPa analysis difference (K)

Forecast impact from cloudy data!


3. Systematic error

(global influence)


Globally averaged bias correction estimates for MSU channel 2

Warm-target temperatures for MSU on NOAA-14

3. Systematic error … data


Shifts in upper-stratospheric temperature reanalysis

The transition from SSU Ch3 to AMSU-A Ch14 is clearly visible in global mean temperatures at 5hPa and above

The use of weak-constraint 4D-Var can (only) partially address this problem

This problem cannot be completely solved unless the forecast model is free of bias

ERA-Interim

Global mean temperature anomalies in the upper stratosphere

ERA-40

JRA-25

NCEP

3. Systematic error … model


Response to Pinatubo: HIRS Ch11 bias corrections

Volcanic aerosols in the lower stratosphere:

• Cooling effect on radiances • Not represented in radiative transfer model• ERA-Interim: Change the bias correction • ERA-40: Change the humidity increments

Bias corrections for HIRS Ch11 (tropical averages)

Bias corrections for NOAA-12:

• In ERA-Interim, correct initialisation followed by a gradual recovery • In ERA-40, bias held fixed

3.Systematic error..atmosphere


1. Data assimilation lies at the centre of NWP, climate re-analysis and environmental monitoring

2. Radiance observations from polar orbiting satellites are the single most influential component of the global observing system

3. Great progress has been made, but significant scientific challenges remain to advance the use of these observations

Summary


)(][][ 1b

TTba xyxx HRHBHHB

])H[(])H[()()()( 11 xyxyxxxxxJ Tb

Tb RB

)(][][ 1b

TTba xyxx HRHBHHB

Cost function:

Solution:

Sa = B - HB

Solution error covariance:

The 4D-Var Algorithm

Correction term

Data assimilation of polar orbiting satellites at ECMWF

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