ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course 3-7 April 2017

ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course The estimation and correction of systematic errors (with some examples from climate reanalysis) ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course

Why do we need to worry about biases ? errors should be random and gaussian Systematic errors must be removed otherwise biases will propagate in to the analysis (causing global damage in the case of satellites!). A bias in the radiances is defined as: bias = mean [ Yobs – H(Xtrue) ]

Why do we need to worry about biases ? ERA-40 Cosmic shower failure of MSU on NOAA-11 ERA-15 GLOBAL 200hPa temperature

The definition of a bias: What we would like to quantify is: mean [ Yobs – H(Xtrue) ] But in practice all we can compute is the mean innovation : mean [ Yobs – H(Xb) ] …or the mean analysis residual : mean [ Yobs – H(Xa) ]

Example of a persistent mean innovation suggesting a bias AMSUA channel 14:

What can cause biases ? Instrument calibration / anomalies Instrument characterisation Radiative transfer model / spectroscopy Surface emissivity model Observation QC / selection / scale NWP model used to diagnose bias

And what do they look like ? Simple constant offset Geographically / air-mass varying Scan dependent Time dependent Satellite dependent

Scan variation of the bias: NOAA-18 AMSUA temperature sounding channels limb limb limb limb nadir nadir

Time variation of the bias: diurnal dependence of bias (K) Seasonal dependence of bias (K) drifting dependence of bias (K) Dec 2004 date June 2004

Satellite dependence of the bias: HIRS channel 5 (peaking around 600hPa on NOAA-14 satellite has +2.0K radiance bias against model HIRS channel 5 (peaking around 600hPa on NOAA-16 satellite has no radiance bias against model.

Sources and Characteristics of the bias: INSTRUMENT AIR-MASS SCAN TIME SATELLITE RADIATIVE TRANSFER SURFACE EMISSIVITY QC DATA SELECTION NWP MODEL

Sources and Characteristics of the bias: INSTRUMENT AIR-MASS SCAN TIME SATELLITE RADIATIVE TRANSFER YES SURFACE EMISSIVITY NO QC DATA SELECTION NWP MODEL

How do we correct for biases ? The type of correction used must be suited to the types of bias we have in our system and what we wish to correct (or perhaps more importantly what we do not wish to correct). Simple constant offset C Static air-mass predicted correction C[p1,p2,p3…] Adaptive (in time) predicted correction C [p1,p2,p3….,t]

A predictor based bias correction: We pre- define a set of predictors [P1, P2, P3…] From a training sample of departures: [ Yobs – H(Xb/a)] we find the values of the predictor coefficients that best predict the mean component of the departures. Predictors might be: mean temperature, TCWV, ozone, scan position, surface temperature etc..

Adaptive predictor based bias correction: We pre- define a set of predictors [P1, P2, P3…] From a training sample of departures: [ Yobs – H(Xb/a)] we find the values of the predictor coefficients that best predict the mean component of the departures. The training sample will generally be the radiance departure statistics of the current assimilation window and the values of the predictor coefficients will be updated each analysis cycle (e.g. every 12 hours)

Adaptive predictor based bias correction: External adaptive bias correction Update bias coefficients Perform analysis Update bias coefficients Perform analysis Internal adaptive bias correction Perform analysis + update bias coefficients Perform analysis + update bias coefficients

Internal adaptive predictor based bias correction (VarBC) Internal adaptive bias correction Perform analysis + update bias coefficients Perform analysis + update bias coefficients

Bias corrections of MSU2 in ERA-Interim Jan 1989: Transition between two separate production streams NOAA-14 recorded warm-target temperature changes, due to orbital drift (Grody et al. 2004)

When bias corrections go wrong Correction of NWP model error Under adaptive (Pinatubo) Over adaptive Interaction feedback with QC

When bias corrections go wrong Correction of NWP model error Under adaptive (Pinatubo) Over adaptive Interaction feedback with QC

Correction of NWP model error Our training sample is mean [ Yobs – H(Xb/a) ] IASI channel 76

Correction of NWP model error Our training sample is mean [ Yobs – H(Xb/a) ] Bias correction anchored to zero in Nov-07 for cycle 35R1 T799/L91 VARBC NOAA-16 AMSUA channel 14

When bias corrections go wrong Correction of NWP model error Under adaptive (Cosmic rays and Pinatubo) Over adaptive Interaction feedback with QC

Under adaptive correction Our training sample is mean [ Yobs – H(Xb/a) ] ERA-40 Cosmic shower failure of MSU on NOAA-11 ERA-15 200hPa temperature

Under adaptive correction Our training sample is mean [ Yobs – H(Xb/a) ] NOAA-10 NOAA-12

Under adaptive correction Our training sample is mean [ Yobs – H(Xb/a) ]

When bias corrections go wrong Correction of NWP model error Under adaptive (Pinatubo) Over adaptive Interaction feedback with QC

Interaction with QC Our training sample is mean [ Yobs – H(Xb/a) ]

Interaction with QC Our training sample is mean [ Yobs – H(Xb/a) ]

Interaction with QC Our training sample is mean [ Yobs – H(Xb/a) ]

Interaction with QC Our training sample is mean [ Yobs – H(Xb/a) ]

How do we stop corrections going wrong : Restrict number of predictors Restrict values of predictors Use of intelligent pattern predictors Restrict time evolution of predictors Anchoring Use of the MODE

How do we stop corrections going wrong : Restrict number of predictors Restrict values of predictors Use of intelligent pattern predictors Restrict time evolution of predictors Anchoring Use of the MODE

A highly complex / adaptive correction of satellite temperature data has caused a strengthening of the N – S thermal gradient and degraded the U-component of wind, compared to a simple flat correction of the data. Flat bias Complex bias With too many predictors the satellite data produces a mean analysis wind fit similar to a NO-SAT system !

How do we stop corrections going wrong : Restrict number of predictors Restrict values of predictors Use of intelligent pattern predictors Restrict time evolution of predictors Anchoring Use of the MODE

Anchoring with zero bias correction AMSUA channel 14

Anchoring with zero bias correction

How do we stop corrections going wrong : Restrict number of predictors Restrict values of predictors Use of intelligent pattern predictors Restrict time evolution of predictors Anchoring Use of the MODE

Interaction with QC Our training sample is mean [ Yobs – H(Xb/a) ] MODE MEAN

ECMWF Data Monitoring and Automated Alarm System

Why do we need an automatic system ??

Various observation quantities Feedback info (ODB) Past Statistics Per Data type, channel Current Statistics Per Data type, channel Set and adjusted manually Hard limits Detect slow drifts Soft limits Detect sudden changes Anomaly detection Various observation quantities Ignore facility Warning message Web E-mail

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Interaction with QC Our training sample is mean [ Yobs – H(Xb/a) ]