Assimilation of TCvitals -- still an problem in 2014?
Mike Fiorino, NOAA ESRL, Boulder CO
23 November 2014
IWTC VIII is fast approaching and the head of the data assimilation group, Prof. Sharan Majumdar University of Miami asked me:
1) With the advancement of ensemble Kalman filters and 4-d schemes (and
various hybrids thereof), is it still essential to assimilate TCvitals?
If so, why? Are there limitations in the DA methodology that make it
necessary to assimilate position, central pressure etc?
2) Have some quantitative studies been done to show the impact of
including TCvitals data in the assimilation? Do you have a figure or
reference that illustrates this?
3) Are there better ways to assimilate TCvitals than is being done now?
probably: 1) better field surgery to relocate the model background storm to the TCvitals position; 2) assimilation of 1-h TCvitals (through interpolation); and 3) direct assimilation of motion. and in all cases: LESS is MORE.
4) Are we missing the appropriate observations that would make TCvitals
unnecessary, and if so, what types of observations would we need?
[For example, would ocean surface wind vectors in convective systems
help?]
observations that support the assimilation of TCvitals, e.g., IR imagery wind retrievals (Slocum and Fiorino 2011) and other retrievals from MTCSWA (Knaff et al. 20??)
This blog gives some preliminary answers; from what I consider a fairly unique view point.
A quick inspection of the fields showed no TC and RH that went to 0 at the equator. I remember calling Dr. Hovermale and saying (tongue-in-cheek), "Dr. Hovermale, did you know the relative humidity is almost 0 in the tropics?" He said, "we have to do that to prevent the PE (the baroclinic operational model of the day) from blowing up with grid point storms. NB: the issue of MRFocanes has a long history at NCEP...
To forecast Eloise I had to do a lot of 'data improvement' which now would be correctly called a kind of 'Data Assimilation.' My DA scheme was a far cry from current technology, but the first problem was getting a vortex into the large-scale analyses. There truly were very few obs to analyze Eloise, unlike today, but I did know the TCvitals -- best track position and intensity (TCvitals now include more variables such as radius of 34/50/65 kt winds).
It should not be controversial that TCvitals are the 0th-order TC observation. The standard common complaint is that they are not objective, yet if one considers what a meteorological observation really is, I argue they are as valid as any other observation. The problem with TCvitals is not what they are, but their error.
others TC obs in that they have a more uncertainty and a more variable error.
To not make a first-order objective of a TC DA system the correct analysis of TCvitals is to me strange. Even if not assimilated, the TC vortex analysis will certainly be judged by the model-analyzed TCvitals.
Question 1 assumes there are sufficient non-TCvitals obs to correctly analyze the model TC vortex 'close enough' to the TCvitals position. In the observing system of 2014, there will be situations where sufficient obs do exist, but what is 'close enough?' An initial position error
(IPE) that is within the uncertainty of the (working) best track? Initial intensity error (IIE, btw this means 'no' in Japanese)? How about the wind radii? The issue of 'close enough' begs the question of
how do we assess the quality of any TC vortex analysis.
Even if TCvital assimilation were unnecessary, TCvitals are the key TC obs used to verify both the DA and forecast.
The bottom-line metric is position error (PE) and intensity error (IE) which come from a comparison of a model tracker to a best track - either 'working' (real-time, operational) or the final, reanalyzed best track. These 'three numbers' are a bulk/gross measure of the entire model forecast field and thus cannot assess any individual process (e.g., precipitation) involved with TC motion and intensification. However, the 3-numbers do measure net quality and are obviously the most important output for forecasting.
of schemes to assimilate TC vitals, specifically:
What is interesting about the current state of TCvital assimilation is that they all use elements from my M.S. thesis (Fiorino 1978 and Fiorino and Warner 1981) and/or 'bogussing' as originally proposed by Anderson and Hollingsworth (1978) at ECMWF -- ECMWF never implemented because the method was considered too artificial. And this sentiment would prevail during my time at ECMWF where I developed a reduced retrieval scheme to assimilate TCvitals in the ERA-40 reanalysi. Whilst ECMWF did you not use my reduced TC wind retievals, JMA did in their first global reanalysis JRA-25 (Katsushika et al., 2009) and in their second JRA-55.
The ECMWF HRES model (hi-resolution deterministic run) is the obvious to control for comparison of the current quality of TCvital assimilation.
We now look at the performance of the above models for 2014 in the atLANTic (LANT), Eastern north PACific (EPAC) and Western north PACific (WPAC).
First consider Position Error (PE - sometimes erroneously called track error) in the three basins.
2014 LANT activity was ~35% below normal (see http://ruc.noaa.gov/hfip/tcact) -- broad generalizations cannot be made. However, the results are based on the latest versions of the models and while the growth curves in earlier years are not the same, the relationship between IPE and IIE and 12-36 h errors are similar.
Figure 1 gives the heterogeneous mean PE for the 00/12 UTC forecasts only. Since the focus is on how a model's mean error changes and not model intercomparison, we do not make the cases homogeneous between the models. The number of cases is not very dissimilar (except for ECMWF and UKMO which long intergrations are made only at 00/12 UTC), but homogeneous comparisons can make surprising changes in the mean error -- the means are sensitive to case selection.
Models with strong assimilation of TCvitals (HWRF and GFDL) have the lowest IPE. ECMWF has higher IPE than the GFS, but the GFS relocates the background vortex to the TCvitals position. The large IPE for NAVGEM is surprising to me, ditto for the UKMO. Despite the 'good' assimilation of TCvitals in HWRF/GFDL (lowest IPE), the PE grows quickly whereas ECMWF shows a much slower growth. To emphasize error growth we calculate the % improvement relative to GFS as this global model provides initial and lateral boundary conditions for all the limited-area models in the LANT in Figure 2 below.
HWRF and GFDL have much lower IPE v GFS, but by 12h the errors are higher than the GFS. More significantly ECMWF had similar IPE to the GFS but its error grows slower. From tau 12-36 h the ECMWF errors are 12-28 % lower than the GFS.
This simple comparison shows that the assimilation of TCvitals does not improve the early forecasts despite low IPE.
Let's continue to EPAC/WPAC and only the % improvement as in Fig. 2
The results are similar to the LANT: 1) the USN models are poor performers; 2) HWRF/GFDL have better IPE compared to GFS but the early forecasts are not better; and 3) ECMWF has much higher IPE than the GFS but as soon as 12 h its track forecasts are superior.
#2: From the comparisons above, we could conclude that TCvital assimilation is not good idea for track prediction. Alternatively, we could say that too much TCvital assimilation (HWRF/GFDL) is adding error in the initial TC vortex on scales (> 500 km) much larger than the inner core (~50 km) and thereby distorting the motion (Fiorino and Elsberry, 1989).
Next consider the third number of the three-number metric -- intensity or the maximum surface wind speed.
Fir
1) With the advancement of ensemble Kalman filters and 4-d schemes (and
various hybrids thereof), is it still essential to assimilate TCvitals?
If so, why? Are there limitations in the DA methodology that make it
necessary to assimilate position, central pressure etc?
2) Have some quantitative studies been done to show the impact of
including TCvitals data in the assimilation? Do you have a figure or
reference that illustrates this?
3) Are there better ways to assimilate TCvitals than is being done now?
probably: 1) better field surgery to relocate the model background storm to the TCvitals position; 2) assimilation of 1-h TCvitals (through interpolation); and 3) direct assimilation of motion. and in all cases: LESS is MORE.
4) Are we missing the appropriate observations that would make TCvitals
unnecessary, and if so, what types of observations would we need?
[For example, would ocean surface wind vectors in convective systems
help?]
observations that support the assimilation of TCvitals, e.g., IR imagery wind retrievals (Slocum and Fiorino 2011) and other retrievals from MTCSWA (Knaff et al. 20??)
This blog gives some preliminary answers; from what I consider a fairly unique view point.
Early work with TCvitals
In 1977 I was working on my M.S. at Penn State under Tom Warner and Rick Anthes. They gave me a tape from NCEP (NMC at the time) with operational analyses for the time period of hurricane Eloise of 1975. To say these analyses were dreadful, compared to today (hard to believe but the Eloise case is almost 40 y old), would be a gross understatement, yet I was charged with using MM0 (version 0.? of MM5 at the time) to make forecasts for Eloise.A quick inspection of the fields showed no TC and RH that went to 0 at the equator. I remember calling Dr. Hovermale and saying (tongue-in-cheek), "Dr. Hovermale, did you know the relative humidity is almost 0 in the tropics?" He said, "we have to do that to prevent the PE (the baroclinic operational model of the day) from blowing up with grid point storms. NB: the issue of MRFocanes has a long history at NCEP...
To forecast Eloise I had to do a lot of 'data improvement' which now would be correctly called a kind of 'Data Assimilation.' My DA scheme was a far cry from current technology, but the first problem was getting a vortex into the large-scale analyses. There truly were very few obs to analyze Eloise, unlike today, but I did know the TCvitals -- best track position and intensity (TCvitals now include more variables such as radius of 34/50/65 kt winds).
It should not be controversial that TCvitals are the 0th-order TC observation. The standard common complaint is that they are not objective, yet if one considers what a meteorological observation really is, I argue they are as valid as any other observation. The problem with TCvitals is not what they are, but their error.
TCvital Observations
Let's go back to basics. What is an observation? My definition is that it is the result of an analysis process of *measurements*. Consider satellite wind vectors. The measurement are images, the analysis process is a computer code written by humans (i.e., has errors) that compares a sequence of images, and produces a meteorological observation of wind. For TCvitals, the measurements are images, synop...and the analysis process is the forecaster. While one can complain that the TCvitals analysis process (the human) is variable in quality, the process is constrained by things like Dvorak and forecaster training. Thus, I maintain that TCvitals are valid observations that differ fromothers TC obs in that they have a more uncertainty and a more variable error.
To not make a first-order objective of a TC DA system the correct analysis of TCvitals is to me strange. Even if not assimilated, the TC vortex analysis will certainly be judged by the model-analyzed TCvitals.
Question 1 assumes there are sufficient non-TCvitals obs to correctly analyze the model TC vortex 'close enough' to the TCvitals position. In the observing system of 2014, there will be situations where sufficient obs do exist, but what is 'close enough?' An initial position error
(IPE) that is within the uncertainty of the (working) best track? Initial intensity error (IIE, btw this means 'no' in Japanese)? How about the wind radii? The issue of 'close enough' begs the question of
how do we assess the quality of any TC vortex analysis.
Even if TCvital assimilation were unnecessary, TCvitals are the key TC obs used to verify both the DA and forecast.
Answer to #1
TCvital assimilation is not a technical DA issue -- 4DVAR, EnKF etc are *not* sufficient, nor are the non-TCvital obs. Current DA schemes are adequate, what is not adequate are the obs that can sufficiently analyze the entire TC vortex. And so we come to issue of verification...and a proposal for assessing the quality/skill of any TC DA and forecast model.The bottom-line metric is position error (PE) and intensity error (IE) which come from a comparison of a model tracker to a best track - either 'working' (real-time, operational) or the final, reanalyzed best track. These 'three numbers' are a bulk/gross measure of the entire model forecast field and thus cannot assess any individual process (e.g., precipitation) involved with TC motion and intensification. However, the 3-numbers do measure net quality and are obviously the most important output for forecasting.
Answer to #2 to Answer #1
An old friend Bob Kistler, the father of the first NCEP reanalysis, wisely said that 'forecasting is the acid test of an analysis.' In the next section we'll take a look at the current models that use a varietyof schemes to assimilate TC vitals, specifically:
Table 1. Main operational NWP models circa 2014 and TCvitals assimilation
model
|
ATCF ID
|
domain
|
TCvital Assimilation
|
ECMWF HRES
|
EDET
|
global
|
none
|
NCEP GFS
|
AVNO
|
global
|
'relocation' of the TC in the background (first quess) to the TCvital position
|
NCEP HWRF
|
HWRF
|
limited-area
|
relocation as in the GFS + adjustment of the *analyzed* TC vortex so the tracker intensity matches the TCvitals
|
NCEP GFDL
|
GFDL
|
limited-area
|
spinup of a symmetric version of model, add beta-gyre asymmetries to the 2-D vortex to make a 3-D vortex, then blend into the total initial fields
|
UKMO
|
EGRR
|
global
|
retrieve 1-D wind profiles from the TCvitals (synthetic obs) and assimilate
|
USN NAVGEM
|
NVGM/NGX
|
global
|
similar to UKMO except more retrievals and spectral smoothing of the background near the vortex
|
USN COAMPS
|
COTC
|
limited-area
|
similar to NAVGEM, but experimenting with vortex blending as in the GFDL and nudging
|
What is interesting about the current state of TCvital assimilation is that they all use elements from my M.S. thesis (Fiorino 1978 and Fiorino and Warner 1981) and/or 'bogussing' as originally proposed by Anderson and Hollingsworth (1978) at ECMWF -- ECMWF never implemented because the method was considered too artificial. And this sentiment would prevail during my time at ECMWF where I developed a reduced retrieval scheme to assimilate TCvitals in the ERA-40 reanalysi. Whilst ECMWF did you not use my reduced TC wind retievals, JMA did in their first global reanalysis JRA-25 (Katsushika et al., 2009) and in their second JRA-55.
The ECMWF HRES model (hi-resolution deterministic run) is the obvious to control for comparison of the current quality of TCvital assimilation.
We now look at the performance of the above models for 2014 in the atLANTic (LANT), Eastern north PACific (EPAC) and Western north PACific (WPAC).
First consider Position Error (PE - sometimes erroneously called track error) in the three basins.
2014 LANT activity was ~35% below normal (see http://ruc.noaa.gov/hfip/tcact) -- broad generalizations cannot be made. However, the results are based on the latest versions of the models and while the growth curves in earlier years are not the same, the relationship between IPE and IIE and 12-36 h errors are similar.
Figure 1 gives the heterogeneous mean PE for the 00/12 UTC forecasts only. Since the focus is on how a model's mean error changes and not model intercomparison, we do not make the cases homogeneous between the models. The number of cases is not very dissimilar (except for ECMWF and UKMO which long intergrations are made only at 00/12 UTC), but homogeneous comparisons can make surprising changes in the mean error -- the means are sensitive to case selection.
 |
| Figure 1. LANT mean FE for the models in table 1for taus 0-36 h. Cases are heterogeneous to emphasize a model's change in error from t=0 and the early forecasts where the initial vortex would be expected to affect the forecasts. |
Models with strong assimilation of TCvitals (HWRF and GFDL) have the lowest IPE. ECMWF has higher IPE than the GFS, but the GFS relocates the background vortex to the TCvitals position. The large IPE for NAVGEM is surprising to me, ditto for the UKMO. Despite the 'good' assimilation of TCvitals in HWRF/GFDL (lowest IPE), the PE grows quickly whereas ECMWF shows a much slower growth. To emphasize error growth we calculate the % improvement relative to GFS as this global model provides initial and lateral boundary conditions for all the limited-area models in the LANT in Figure 2 below.
 |
| Figure 2. LANT 2014 % improvement relative to the GFS |
This simple comparison shows that the assimilation of TCvitals does not improve the early forecasts despite low IPE.
Let's continue to EPAC/WPAC and only the % improvement as in Fig. 2
 | ||
| Figure 3. As in Fig. 2 except for EPAC |
 |
| Figure 4. As in Fig. 2 except for WPAC |
#2: From the comparisons above, we could conclude that TCvital assimilation is not good idea for track prediction. Alternatively, we could say that too much TCvital assimilation (HWRF/GFDL) is adding error in the initial TC vortex on scales (> 500 km) much larger than the inner core (~50 km) and thereby distorting the motion (Fiorino and Elsberry, 1989).
Next consider the third number of the three-number metric -- intensity or the maximum surface wind speed.
Fir
