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Slide 1
Model Assimilation of Satellite Observations and Retrievals
Andrew Collard
IMSG@NOAA/NCEP/EMC
Slide 2
Talk Outline
Introduction
The Global Model Cycle
Data Assimilation
Evaluating Impact
Data Impact Results
GOES-R and JPSS
Summary
Slide 3
Introduction
This talk will summarize how we make use of satellite data in operational forecast models
We will discuss how satellite observations and derived products are used to produce our best estimate of the current state of the atmosphere
This can be considered a data fusion process, where the information from current and historical observations are combined in an optimal manner.
Slide 4
The global model cycle
NOAA Satellite Proving Ground Slide 4
Slide 5
Slide 6
Slide 7
Data Assimilation
NOAA Satellite Proving Ground Slide 7
Slide 8
Data Assimilation
Data assimilation for NWP updates the atmospheric state which is used as a starting point for the forecast run. This state is the analysis.
It is a retrieval of the entire atmospheric state using multiple data sources simultaneously.
It is also, therefore, an efficient data fusion process.
The a priori or first-guess data used in the data assimilation process is a short range forecast from a previous cycle.
- The first guess, and therefore the analysis, contain information from all previous observations, propagated forward using the atmospheric model.
NOAA Satellite Proving Ground Slide 8
Slide 9
Atmospheric Analysis ProblemJ = Jb + Jo + Jc
J = (x-xb)TBx-1(x-xb) + (y-K(x))T(E+F)-1(y-K(x)) + JC
J = Fit to background + Fit to observations + constraints
x = Analysis
xb = Background (usually a short-range forecast from the previous cycle)
Bx = Background error covarianceK = Forward model (nonlinear)O = ObservationsE+F = R = Instrument error + Representativeness error
JC = Constraint term
Slide 10
Atmospheric Analysis ProblemJ = Jb + Jo + Jc
J = (x-xb)TBx-1(x-xb) + (y-K(x))T(E+F)-1(y-K(x)) + JC
J = Fit to background + Fit to observations + constraints
x = Analysis
xb = Background (usually a short-range forecast from the previous cycle)
Bx = Background error covarianceK = Forward model (nonlinear)O = ObservationsE+F = R = Instrument error + Representativeness error
JC = Constraint term
Slide 11
The ensembles are run to give the data assimilation system, flow-dependent background errors
Slide 12
Atmospheric Analysis ProblemJ = Jb + Jo + Jc
J = (x-xb)TBx-1(x-xb) + (y-K(x))T(E+F)-1(y-K(x)) + JC
J = Fit to background + Fit to observations + constraints
x = Analysis
xb = Background (usually a short-range forecast from the previous cycle)
Bx = Background error covarianceK = Forward model (nonlinear)O = ObservationsE+F = R = Instrument error + Representativeness error
JC = Constraint term
Slide 13
Radiance Data Assimilated at NCEP
GOES-15 Sounders Channels 1-15
Meteosat-10 SEVIRI Channels 5-6
AMSU-A
NOAA-15 Channels 1-10, 12-13, 15
NOAA-18 Channels 1-8, 10-13, 15
NOAA-19 Channels 1-7, 9-13, 15
METOP-A Channels 1-6, 8-13, 15
METOP-B Channels 1-13, 15
AQUA Channels 6, 8-13
MHS
NOAA-18 Channels 1-5
NOAA-19 Channels 1-5
METOP-A Channels 1-5
METOP-B Channels 1-5
NOAA Satellite Proving Ground Slide 13
SSMIS
DMSP-17 Channels 1-7, 24
DMSP-18 Channel 1-7, 24
HIRS METOP-A Channels 2-15
AIRS AQUA 120 Channels
IASI
METOP-A 165 Channels
METOP-B 165 Channels
CrIS SNPP 84 Channels
Slide 14
Derived Products Assimilated at NCEP
Atmospheric Motion Vectors
- MODIS Aqua and Terra
- GOES-E, GOES-W
- MSG
- MTSAT
- VIIRS (pre-operational)
Ozone
- NOAA-19 SBUV Profiles
- OMI Total Column
GPSRO
NOAA Satellite Proving Ground Slide 14
Slide 15
Global Satellite Observing System
Slide 16
Data assimilation requires
Accurate and fast observation operators
- For radiances we use CRTMQuality control
- To handle situations where the observation cannot be used
Bias-correctionData quality monitoring
NOAA Satellite Proving Ground Slide 16
Slide 17
Application of NWP Bias Correction for SSMIS F18
Ascending Node Descending Node
Latitude
Unbias & Bias Corrected O-B
O-B Before Bias Correction
Global
Dsc
Asc
O-B After Bias Correction
Global
Dsc
Asc
O-B Before Bias Correction
O-B After Bias Correction
Using Met Office SSMIS Bias Correction Predictors
T (K)
T (K)
T (
K)
17
Slide 18
Quality Monitoring of Satellite Data
AIRS Channel 453 26 March 2007
Increase in SDFits to Guess
Slide 19
Data assimilation is statistical
NOAA Satellite Proving Ground Slide 19
A significant positive impact at day 8 still has a number of cases with negative impact.
Slide 20
Data assimilation is statistical (2)
NOAA Satellite Proving Ground Slide 20
Histogram of the individualimpact of each NOAA-19AMSU-A Ch 8 field-of-view
Slide 21
Verification
NOAA Satellite Proving Ground Slide 21
Slide 22
Verification
• Obviously the ultimate purpose of assimilating any kind of observation into NWP models is to improve the model forecast.
• Therefore robust methods for determining the impact of the observations are required. Operational centers typically have a wide range of tools available for this.
• The initial aim in any data assimilation system is to use the observations in such a way as to improve the accuracy in the analysis. Sometimes these improvements are not directly translated into increases in forecast skill due to the characteristics of the forecast model itself.
• In general this presentation is considering aircraft observations of temperature, humidity and wind vector together.
• But first, we need to define what we can use as the “truth” when verifying our models…
Slide 22
Slide 23
What is truth?
Observations?
Normally this means radiosondes (but also surface observations and aircraft are used). This means the statistics are biased towards densely populated regions in the northern temperate latitudes.
We should also (but usually don’t) take account of the errors in the observations themselves
Slide 23
Potentially we could also use satellite observations (e.g., radiances) for verificationas this would give more global sampling.However, comparisons in radiance spacewould be less intuitive.
Slide 24
What is truth?
Analyses?
The analysis should the best estimate of the atmospheric state through the combination of the information from the observations in the current and (through the forecast model) previous model cycles.
Also, given that the analysis does not suffer from the spatial sampling issues of conventional observations, the analysis seems to be the ideal “truth”.
However, for forecast ranges of approximately three days or less it is found that forecast skill (and even the sign of that skill) is highly dependent on the verifying analysis (control, test, independent) used.
Also, for certain types of changes, if additional structure is added to the analysis fields this can be penalized in the usual forecast skill measures as it is easier to obtain a good fit to smooth rather than complex fields.
Slide 24
Slide 25
What is truth?
In practice, both observations and analyses are used in verifying forecast skill
The 2014 NOAA Aircraft Workshop, ARINC Slide 25
Slide 26
Data Denial
Data denial or Observation System Experiments (OSEs) are simply a way of
investigating the impact of an observation or change by running full forecast
experiments with and without the element to be tested.
OSEs are expensive to run, particularly at full operational resolution, and they
need to be run for many forecast cycles (60 days is a typical number for global
forecast systems) before statistically significant results are obtained.
Individual case studies are generally not trusted as a way of demonstrating
forecast impact because of the dominance of statistical fluctuations.
Forecast impact scores are generally presented with error bars indicating
statistical significance.
Scores are normally given in terms of differences between forecasts and
“truth” in terms of RMS error or anomaly correlation coefficients (see next
slide)
The 2014 NOAA Aircraft Workshop, ARINC Slide 26
Slide 27
Anomaly correlation coefficients
The 2014 NOAA Aircraft Workshop, ARINC Slide 27
Forecast skill is often given in terms of anomaly correlation coefficients (ACC scores). This is a measure of the correlation between the forecast (f) and the analysis (a) while removing the climatological average field (c) to mitigate the seasonal signal:
Scores of 100% are perfect forecasts, 60% is considered the lower limit for useful skill.
Slide 28
xb
xg
t= -6 hrs
eg
xt
xf
xa
t= 24 hrst=0
ef
6 hr assimilation window
Observations move the forecast from the background trajectory to the trajectory starting from the new
analysis
In this context, “OBSERVATION IMPACT” is the effect of observations on the difference in forecast error norms C(ef-eg)
Forecast Sensitivity to Observations (FSO)
The 2014 NOAA Aircraft Workshop, ARINC 28
“Truth”“Background”
“Analysis”
30-hr fcst
24-hr fcst
Langland and Baker (Tellus, 2004), Gelaro et al (2007), Morneau et al. (2006)
Slide 29
xb
xg
t= -6 hrs
eg
xt
xf
xa
t= 24 hrst=0
ef
6 hr assimilation window
Observations move the forecast from the background trajectory to the trajectory starting from the new
analysis
In this context, “OBSERVATION IMPACT” is the effect of observations on the difference in forecast error norms C(ef-eg)
Forecast Sensitivity to Observations (FSO)
The 2014 NOAA Aircraft Workshop, ARINC 29
“Truth”“Background”
“Analysis”
30-hr fcst
24-hr fcst
(ef-eg)=Mkδy
δy = observation innovations (y-H(xb))
K=Kalman gain (transforms observation innovations to analysis increments)
M=Forecast model (transforms analysis to forecast)
Langland and Baker (Tellus, 2004), Gelaro et al (2007), Morneau et al. (2006)
Slide 30
Forecast Sensitivity to Observations (FSO)We want to get the sensitivity of the forecast to the observation increments so we apply the tangent linear model to C(ef-eg)=CMKδy:
δef-g = ½[MKδy]TC(ef-eg) = ½ δyTMTKTC(ef-eg)
The 2014 NOAA Aircraft Workshop, ARINC Slide 30
Slide 31
Forecast Sensitivity to Observations (FSO)We want to get the sensitivity of the forecast to the observation increments so we apply the tangent linear model to C(ef-eg)=CMKδy:
δef-g = ½[MKδy]TC(ef-eg) = ½ δyTMTKTC(ef-eg)
The 2014 NOAA Aircraft Workshop, ARINC Slide 31
Adjoint of data assimilation scheme
Adjoint of linearized forecast model*
Usually these both require approximations to be made (including linearity) and so this method is limited to forecast ranges of less than ~48 hours.
*Already required if running 4DVar
Slide 32
Forecast Sensitivity to Observations (FSO)
The 2014 NOAA Aircraft Workshop, ARINC Slide 32
Slide 33
Advantages and Disadvantages of FSO• Advantages
• Can infer the impact of observations to whatever level of detail is required (e.g. ob by ob, channel by channel) without having to re-run the full system repeatedly.• Useful for determining relative impact of observations and
for quality control of bad observations.• Allows the impact of observations on the forecast to be
monitored on a daily basis.• Disadvantages
• Limited to short-range forecasts • So there is sensitivity to the accuracy of the verifying
analysis• Impact is always in the context of the total observing system as
used• Forecast impacts of an observation type may change as
other observations are added/removed.
The 2014 NOAA Aircraft Workshop, ARINC Slide 33
Slide 34
Data denial experiments
At NCEP we are still developing an FSO capability based on the EnKF methodology.
So we will present an overview of observation impact based on the data denial method.
NOAA Satellite Proving Ground Slide 34
Slide 35
Data Denial Experiments(Jim Jung and Lars-Peter Riisshojgaard)
NOAA Satellite Proving Ground Slide 35
Slide 36
Background
NCEP Operational GDAS/GFS May 2011 versionT574L64 operational resolutionTwo Seasons
- Aug-Sept 2010
- Dec 2010-Jan 2011
Cycled experiments 7 Day forecast at 00ZControl late analysis (GDAS) used for
verificationNot NCEP operations computer
36
Slide 37
500 hPa Anomaly Correlations15 Aug – 30 Sep 2010
37
No Satellite / No Conventional Data
Northern Hemisphere Southern Hemisphere
Slide 38
500 hPa Anomaly Correlations 15 Aug – 30 Sep 2010
38
No AMSU-A / No MHS
Northern Hemisphere Southern Hemisphere
Slide 39
500 hPa Anomaly Correlations 15 Aug – 30 Sep 2010
39
No GPS-RO / No AMV
Northern Hemisphere Southern Hemisphere
Slide 40
500 hPa Anomaly Correlations 15 Aug – 30 Sep 2010
40
No Rawinsondes / No Aircraft
Northern Hemisphere Southern Hemisphere
Slide 41
500 hPa Anomaly Correlations 15 Aug – 30 Sep 2010
41
No Hyperspectral Infrared
Northern Hemisphere Southern Hemisphere
Slide 42
500 hPa, Day 5, Instrument Average AC scores
42
Slide 43
Data Addition Experiments(Jim Jung and Mitch Goldberg)
NOAA Satellite Proving Ground Slide 43
Slide 44
Background
44 April 21, 2023
• Pre 2015 version of the GDAS/GFS Hybrid (80 ensembles, T254) T670 Semi-Lagrangian
• Winter and summer seasons 2014• Baseline is conventional data and GPS-RO• Add single instruments
ATMS (SNPP), CrIS (SNPP) AMSUA, MHS (Metop-b), IASI (Metop-a) SSMIS (F18), AIRS (Aqua)
• Verified against the late control analysis with all operational data
Slide 45
250 hPa Northern Hemisphere AC scores for 20140801 – 20140831 00Z
45 April 21, 2023
Slide 46
250 hPa Southern Hemisphere AC scores for 20140801 – 20140831 00Z
46 April 21, 2023
Slide 47
500 hPa Northern Hemisphere AC scores for 20140801 – 20140831 00Z
47 April 21, 2023
Slide 48
500 hPa Southern Hemisphere AC scores for 20140801 – 20140831 00Z
48 April 21, 2023
Slide 49
1000 hPa Northern Hemisphere AC scores for 20140801 – 20140831 00Z
49 April 21, 2023
Slide 50
1000 hPa Southern Hemisphere AC scores for 20140801 – 20140831 00Z
50 April 21, 2023
Slide 51
200 hPa Tropical Vector Wind RMSE for 20140801 – 20140831 00Z
51 April 21, 2023
Slide 52
850 hPa Tropical Vector Wind RMSE for 20140801 – 20140831 00Z
52 April 21, 2023
Slide 53
JPSS and GOES-R
NOAA Satellite Proving Ground Slide 53
Slide 54
So how will JPSS/GOES-R fit in?ATMS and CrIS will be assimilated closely following our
experience with the NPP instruments
- We will continue to move towards greater use of cloudy radiances
- We will make more aggressive use of CrIS channels – both in terms of observation error and the number used.
VIIRS will be used primarily through the winds and SST analysis.
OMPS ozone
GOES-R will provide an initial impact through the AMVs
There are significant challenges still with using the full GOES-R radiance data stream, so averaged products will be useful.
NOAA Satellite Proving Ground Slide 54
Slide 55
Summary
NOAA Satellite Proving Ground Slide 55
Slide 56
Summary
Data assimilation is an efficient form of data fusion.
The global observing system combined with the data assimilation system is very robust.
- Individual data types have an incremental impact on forecast skill but the combined effect is very large.
Data addition experiments can give greater insight into the impact of each individual data type
We can expect rapid implementation (dependent on model upgrade implementation cycles) of CrIS, ATMS, VIIRS AMVs and GOES-R AMVs based on current experience with existing data.
NOAA Satellite Proving Ground Slide 56
Slide 57
NOAA Satellite Proving Ground Slide 57