**SUMMARY OF TELECONFERENCE DISCUSSION**

**July 29 2005, 11 am -- 1 pm EST**

**Present:** J. Whitaker (CDC), J. Anderson (NCAR), C. Bishop (NRL), M.
Zupanski (CIRA/CSU), I. Szunyogh, E. Kostelich (UM), Y. Song, and Z.
Toth (NCEP/EMC) (T. Hamill missed the call, M. Wei on travel)

**Agenda:**

1)  Brief description of activities in first year - Each group described
    their work and main results

Jeff Whitaker

Successfully compared their ensemble data assimilation results with T62
NCEP SSI benchmark, using same or less amount of data (in fact, only
75-150k pieces of data were used, compared with \~300k in SSI -- only
data from +/-1 hr window used, and even that was thinned). No radiance,
radar, or scatterometer data used. Results are very encouraging, 5-10%
rms error reduction compared with SSI results (see his slides).
Processing of remotely sensed data with same sequential algorithm is not
practical, looking for alternative solutions (ETKF?) Tested 3 types of
variance inflation methods, difference between successive archived
analysis fields may work best, simple inflation by a coefficient almost
as good

Istvan Szunyogh

Started work with simulated obs, worked well. Adapted ETKF formulation,
still applied locally (region by region), very efficient algorithm. When
switched to assimilation of real observations some bugs got into code,
working on clearing up software. Expects some results by end of summer
2005. Looking into use of additive inflation procedures. Analyzing
effect of imperfect model on DA results, using bias estimation ideas.
Discussed a slide indicating that a relatively small ensemble may be
able to well describe low dimensional dynamics for global circulation
(PECA-type analysis).

Craig Bishop

Was unable to hire post-doc, working with Master level student, had to
adjust research plans somewhat. Worked on producing large ensembles with
ETKF. In parallel, work on generating ensemble perturbations to be
centered around NAVDAS variational analysis, similar to M. Wei's
research at NCEP: use estimate of analysis error variance derived from
NAVDAS to constrain initial ensemble variance using ET algorithm.
Reports that successfully used ET technique to inflate covariance: uses
ET to transform old archived ensemble data tfor inflating variance in
tropics of current ensemble Toth points out link between this work and
that of D. Hou at EMC who plans to use similar technique to introduce
stochastic perturbations. Plans to experiment with combining ensembles
from different sources.

Milija Zupanski

Hired postdoc, secured accounts on NCEP IBM computers. Plans to test his
method, similar to ETKF, except solves for mode (instead of mean) of
distribution. Currently setting up software on NCEP machines, will start
testing with simulated obs soon, in couple of mos will start using real
obs. Plans using bos operators and other applicable software from NCEP
SSI code. This will enable quick technology transfer to NCEP operations
if research is successful.

Jeff Anderson (Unfunded collaborator)

Made an attempt to port GFS system to NCAR. Work is not complete, no
funding from NOAA. Worked on generic filters, looked at sampling error
in ensemble filters. Found a solution where no inflation is needed in
perfect model setup. Ran some experiments, without much tuning, with
NCAR T85 CAM climate model, real observations, January 2003 cases, using
radiosonde and other traditional data, but no radiances. Compared
results with GFS T254 operational system (p. 16 of his slides). Very
encouraging results, 5-10+% rms error reduction for temperature, even
larger reduction for low level wind errors. Problem with winds higher up
traced to use of inaccurate obs error variances with ACAR data.

Yucheng Song

Briefly described NCEP T62 SSI benchmark analysis/forecast data set that
he prepared for use by other groups (see below)

Zoltan Toth

Pointed out few links between external research and NCEP development
activities: Connection between model error studies of B. Hunt (UM) and
M. Pena (EMC); ET initialization by C. Bishop (NRL) and M. Wei (EMC);
Inflation with ET method by C. Bishop (NRL) and D. Hou (EMC).

2)  Preliminary discussion on plans for 2^nd^ year

ZT commented that the results by JW-TH & FA are very encouraging, and
warrant continuation of ensemble-based DA research work. There was
general agreement on this. JW and CB discussed potential for using
ensemble covariance information for improving variational schemes. They
pointed out the demonstrated ability of variational schemes to process
large amounts of data. JA made the point that ensemble-based DA is a new
field and there is no evidence that these methods could not be modified
to cope with heavy data volume, all agreed on this. MZ mentioned that
after working on 4DVAR for 10 yrs, he switched to ens-DA methods because
he believes they offer a theoretically more appealing approach. IS & ZT
pointed out that CPU limitations on current operational machines should
not constrain research aimed at 3-5 years into the future. Focus should
be on understanding whether and how much improvements can be gained by
using ens-DA methods compared to variational methods. Algorithms should
be built with resource limitations in mind, but that should not be the
primary consideration at this stage. Optimization of procedures can be
considered and will become more important as the research evolves. ZT
suggested each group to continue their work under their proposal, and
the project to keep focusing on ensemble-based data assimilation
methods. Work on hybrid methods (where information from a set of
ensemble members are used in variational DA) is encouraged but the
THORPEX ens-DA funds should support the development and testing of
ensemble-based DA schemes. This research has a horizon of 3-5 yrs, as
compared to hybrid applications that if funded through other mechanisms
may bring some benefits on shorter time scale.

3)  Collaborative work within the project

ZT discussed the possibility of JW-TH, beyond their own research,
playing a central role in trying to build prototype ens-DA system as
time goes that would include useful and new results from any of the
participating groups. This will be further discussed at next meeting.

Proposed dates/time for next meeting:

Sept 7, 1-3 pm eastern time

Sept 9, 11 am -- 1 pm eastern time

Proposed agenda:

1)  Review detailed plans for yr2 (each group present their plans)

2)  What should be our stated goal for yr2 as a group? Like for first
    year, we wanted to generate a benchmark, have initial comparison;
    what should we aim to accomplish by end of yr2?

3)  How to enhance collaboration?

**The NCEP T62 Benchmark run**

Yucheng Song

This document summarizes the benchmark experiment done at NCEP in
preparation for the inter-comparison of different ensemble-based data
assimilation schemes.

**MODEL VERSION**

To be comparable to the four independent groups that work on
ensemble-based data assimilation (EBDA), we used the executable of
global forecast model (named global_fcst6228, which is Triangular
truncation T62 with 28 levels) archived at NCEP high performance storage
system (HPSS). For the assimilation, we used the executable compiled on
March 2 (which is named as global_ssi). For interested users who have
accounts at NCEP, the source is

/hpssprod/runhistory/rh2004/200405/20040530/nwprod.tar

**TEST PERIOD**

From January 1 to the February 29 of 2004, the first \~15 days are also
archived, thought they might be excluded from the evaluation.

**EXPERIMENT PLATFORM**

The benchmark experiment is finished on NCEP IBM BLUE machines.

**OBSERVATIONS TO BE USED**

The post quality control (post-qc) files are used for the experiment. A
note here is that the data has been processed by comparing with the
high-resolution gdas guess files. The input data files used are archived
onto HPSS as well which is named
/hpssuser/g01/wx20ys/Benchmark/dump.tar. The file also contains SST, ICE
and SNOW data files used for the experiment.

**ARCHIVED FILES**

Every 6 hours, for the 00Z and 12Z cycle, pgb (pressure level grib)
files are archived on the NCEP HPSS system. Bias correction, satellite
angle, surface analysis as well as sigma analysis files are also
archived. There are 31 levels in the pgb file, they are:

1000 975 950 925 900 850 800 750 700 650 600 550 500 450 400 350 300 250
200 150 100 70 50 30 20 10 7 5 3 2 1mb

**HOW TO GET THE FILES**

Data are archived on HPSS by day, for example, to get the data file for
Feb 24, in your desired directory, you can issue command like --

hpsstar get /hpssuser/g01/wx20ys/Benchmark/20040224.tar

The command hpsstar is Mark Iredell's version of tar which is convenient
to use. Users can also use htar to get the files.

**QUESTIONS or COMMENTS**

If after going through this entire document, you still have questions,
please let me know. I can be reached at yucheng.song@noaa.gov.

**SUMMARY OF TELECONFERENCE DISCUSSION**

**April 7, 2004, 2-3 pm EST**

**Present:** J. Whitaker, T. Hamill (CDC), J. Anderson, J. Tribbia
(NCAR), C. Bishop (NRL, joined later), M. Zupanski (CIRA/CSU), I.
Szunyogh, E. Kostelich (UM), M. Wei, R. Wobus, Y. Zhu, and Z. Toth
(NCEP/EMC)

**General issues:**

4)  Funding

ZT mentioned that two participating groups will likely receive more
funding than initially thought, restoring funding level to that
originally requested by one group.

2)  Use of real observations or simulated observations in a perfect
    model framework?

ZT emphasized the main focus should be on inter-comparison of different
methods using real data. Several participants pointed out science
advantages of carrying out perfect model data assimilation experiments
as well. It was agreed that if resources permit, the project would
include perfect model DA experiments as well. Rest of the discussion
focused on real obs experiments, since this is the primary interest from
NCEP's point of view, and this is the setup that should drive basic
experimental design etc. It was noted that the addition of perfect model
DA experiments would not double resources needed for the project. JA
noted that NCAR could generate data based on a model integration. ZT
pointed to the NCEP OSSE software that should preferably be exercised
for generating simulated data (with realistic observational error).
Participants (including EMC) are asked to assess whether the addition of
perfect model experiments is within their reach. In case it is, details
of the perfect model setup will be discussed after plans are fleshed
out, and work begins with real observations.

3)  Software infrastructure to be used.

ZT recalled that whenever possible, software available from NCEP (NWP
model, observation operators, file format, verification routines, etc)
should be used. In case new software needs to be developed in the
inter-comparison project, it should be compatible with existing NCEP
software and practices. These practices will insure that as the project
progresses, participants can easily exchange parts of their software,
can start working jointly on a prototype software, that can later be
tested in an operational environment.

**Experimental design:**

1)  Test period

After short discussion, participants agreed to use Jan-Feb 2004 as a
test period. The first \~15 days will be excluded from the evaluation.

2)  Observations to be used

*Data types.* After some discussion, participants agreed that in the
main experiment, the following data types will be used:

Surface observations, radiosondes, ACAR winds, cloud drift winds.
Participants can ignore some of the observation types as they wish.

*Data files.* The NCEP prebufr files from the final gdas analysis cycle
will be used. EMC is going to make the gdas1 prepbufr files (including
restricted access data, please confirm you have privileges to use that,
and whether you can all use blocked data format) available on the IBM
machine in a few days. There was some discussion about using CDAS data
files. These files use a much longer data cut-off time that does not
allow for their use in real time weather forecasting. Also, they may not
contain some new data types that in further analysis we may want to
consider.

In addition to the basic experiment, participating groups can also run a
second experiment where they include additional data types.

*Observational period.* Following the usual practice (also reflected in
+/-3 hrs time window for data included in prepbufr files) analyses
performed at the nominal 0000 UTC time, for example, will use data up to
3 hrs after the nominal analysis time (0300 UTC). It was noted that
unlike 3DVAR where through time interpolation, "future" data are used
(currently up to 3 hrs into the future), ensemble-based schemes are
filters that may use data only up to the time of the analysis.
Therefore, if participants desire, they can choose to perform an
analysis step at 0300 UTC (using data up to that valid time), for a
comparison with SSI forecasts initialized at 0000 UTC (that also use
data up to 0300 UTC).

Issues not discussed/settled yet:

*Observational error statistics.* This has not been discussed*.
Suggestion* -- use observational error statistics as used in operational
3DVAR, given in prepbufr files.

*Quality control.* This issue has not been discussed yet. *Suggestion*
-- use operational QC marks as given in prepbufr files. Agree about
cut-off value regarding QC mark below/above which data will/will not be
used.

3)  Data assimilation

*Cycling frequency:* Each group decide on their own. *Suggestion* -
required minimum analysis frequency every 6 hrs (available at 00, 06,
12, and 1800 UTC).

**Next meeting, tentative:** Monday, 11 April, 10 am Pacific, 11 am
Mountain, 1 pm Eastern time

Continue with discussion of remaining experimental design issues in
strawperson plan.

**INTERCOMPARISON OF DIFFERENT ENSEMBLE-BASED DATA ASSIMILATION SCHEMES
(Jan. 21, 2004)**

**GENERAL DESCRIPTION**

**BACKGROUND.** Currently there are four groups that work independently
with different ensemble-based data assimilation (EBDA), or ensemble
generation schemes using the NCEP global assimilation/forecasting
software:

*Jeff Anderson and David Parrish* -- developed EBDA software (Ensemble
Adjustment Filter, EAF) and are in the process of testing it with T62
GFS system

*Mozheng Wei et al*. -- developed/adapted ensemble perturbation software
based on EBDA concept using Ensemble Transform Kalman Filter (ETKF)
algorithm, in the process of testing initial perturbation scheme. This
scheme will first be tested only as an initial perturbation generation
tool. Later it can be adapted and tested for data assimilation
applications, too.

*Istvan Szunyogh et al.* -- developed EBDA software (regional variant of
ETKF, RETKF) using GFS model, tested in perfect model environment, in
the process of setting up software to assimilate actual observations

*Whitaker and Hamill* -- developed EBDA software (Ensemble Square Root
Filter, ESRF, related to EAF), and tested it using limited surface
observations

**OBJECTIVE.** Compare the performance of the different schemes in terms
of the quality of their data assimilation and ensemble initial
perturbations results. Identify the strengths and weaknesses of each
scheme.

**WORK SCHEDULE**. The inter-comparison part of the project will be
completed in 12 months, depending on available resources. Suggested
starting date is Febr. 1, 2004.

Febr. 04 Work plan finalized

March-May Prepare software

June-Oct. Run (and if necessary, rerun) experiments

Nov.-Dec. Verification/evaluation

Jan. 05 Short summary report, with recommendations for future work

**LONG TERM BENEFITS**. The most promising elements identified in the
different EBDA schemes will be used to construct an EBDA scheme to be
further developed and later tested in a quasi-operational environment.
It is anticipated that most or preferably all groups will contribute to
the design of a scheme for future development.

**PROJECT CREDO**. The four groups will seek agreement on the specifics
of an experimental design that each group can accept and adopt, allowing
for a fair comparison of the different techniques. The four groups will
agree to use a common set of values/procedures for several important
aspects of the experiments. The use of a *common experimental design*
will allow the identification and attribution of differences in
performance that arise due to the differences between the various *EBDA
schemes themselves*.

**WORK PLAN.** Each group will make the necessary modifications to their
software and will perform the experiments on their own computers.
Verification software and benchmark experiments will be provided by
NCEP.

**EXPERIMENTAL DESIGN**

**BACKGROUND.** The specifics below are based on initial discussions
with some project participants and are presented here only as a starting
point for discussions. The four groups are expected to develop and agree
on the details of the inter-comparison study.

**EXPERIMENTS** *Test period:* 2-month period from most recent winter
(Jan-Febr 2004?)

**OBSERVATIONS** *Data set:* NCEP operational prepbufr and satellite
data files, containing data in +/-3hr chunks

> *Data selection*: There are two options here. Each group uses (1) all
> data they can; (2) the largest set of observations that every group
> can assimilate (ie, model variables with height assignment)?
>
> *Error statistics* As given in operational data files

**DATA ASSIMILATION** *Output frequency:* 6 hourly?

> *Model error*: Common approach (eg, multiplicative noise), or allow
> different treatment by each group?

**FORECASTS** *Model version*: NCEP GFS, operational at start of project

> *Resolution:* T62L28

*Digital filter*: Turned off?

**ENSEMBLE** *Size*: 50-100 members (40?)

*Surface fields*: Same for all members?

*Frequency/length:* Once per day (00Z), output every 12 hrs out to 7.5
(or 16?) days

*Output format:* Enspost files

*Output variables:* 1000 & 500 hPa geop. height, 850 hPa temp, u&v winds
at 850 & 250 hPa, and precip

**VERIFICATION** *Data assimilation* Fit of mean of first guess
forecasts (in sigma or standard pressure GRIB file) to observations,
based on standard statistics used at NCEP

> *Initial perturbations* Reposition initial ensemble members to be
> centered around operational SSI analysis. Run set of ensemble
> forecasts and evaluate them using standard NCEP probabilistic
> verification measures

**BENCHMARK** *Data assimilation*: Operational SSI analysis, run at
equivalent resolution

> *Initial perturbations*: Operational bred perturbations (at equivalent
> resolution