Summary of Joint OSSE meeting on December 4, 2008\
12/19/2008 draft

Attendee

David Emmitt(SWA), Lars Peter Riishojgaard(JCSDA-GMAO),Oreste
Reale(NASA/GLA), Bill Lapenta(EMC), Wayman Baker(JCSDA),Jack Woollen
(EMC), Yoshiaki Sato(EMC-JMA), Mozheng Wei(EMC), Michiko
Masutani(EMC-JCSDA)

Telecom

Gert-Jan Marseille(KNMI), Tom Schlatter(ESRL), Tong Zhu(NESDIS), Zhaoxia
Pu(Univ. Utah), Runhua Yang(NASA/GMAO), Raj Khanna(Noblis), Yuanfu Xie
(ESRL), Nikki Prive(ESRL), Dezso Devenyi(ESRL), Daniel L
Birkenheuer(ESRL)

Apology for absence received

Erik Andersson(ECMWF, other meeting), Ron Errico (GMAO)

The presentations for the meeting on 11/20 are being posted at\
<http://www.emc.ncep.noaa.gov/research/JointOSSEs/record/2008/Dec08/>

**Agenda:\
**1.  ADM-Aeolus simulation for J-OSSE KNMI plan (Gert-Jan Marseille)

KNMI_josse_telecon_dec2008.ppt\
\
2. Simulation of DWL. Plans at SWA (Dave Emmitt)

> NCEP Joint OSSE\_ Simulating DWL winds and CMVs for OSSEs.ppt
>
> NCEP Joint OSSE_Airborne Doppler wind lidar.ppt
>
> NCEP Joint OSSE_Seabloom.jul08.ppt
>
> NCEP Joint OSSE_Utility of Doppler Wind Lidars in cloudy
> conditions.ppt

-   A general overview of the DLSM as it currently is embedded in the
Sensor Web Simulator;\
-  A description of the way in which we use aerosols in the DWL
simulations;\
-  A description of how we handle clouds, including the use of GLAS and\
CALIPSO data to validate our simulated clouds.\
-  The role of temperature information in the DWL simulations.\
-  Our plans for simulating ADM (with input from the ADM team) products
for ROSES07 effort (just recently started).\
\
2.  The fundamental principle of Joint OSSEs. ( start around 11am)\
     (Tom Schlatter)

Schlatter_OSSEtalk04Dec08.x.ppt\
Reference:

Springer_OSSE_Chapter_PartF_Ch3.081117.draft.doc\
Manuscript prepared by leaders of Joint OSSEs for\
\"Data Assimilation: Making sense of observations\",   to be published\
from Springer in 2009.

**\[ADM-Aeolus simulation for J-OSSE KNMI plan\]**

**[Outline of the presentation]{.underline}**

-   Spring 2008: ADM Mission Advisory Group (ADMAG) advices ESA to
    participate in Joint OSSE

-   KNMI writes TOGETHER proposal to ESA

*TOGETHER*

*TOwards a Global obsErving sysTem tHrough collaborative simulation
ExpeRiments*

-   ESA budget limited to 20K euro

    -   Participation limited to simulation of ADM and verification

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-   ESA gives higher priority to other ADM related projects

    -   L1B/L2B processor

    -   ADM vertical sampling

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-   ESA budget available not before spring 2009

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-   DM OSSE heritage, for details see Stoffelen et al., 2006

*http://www.knmi.nl/\~marseill/publications/fulltexts/osse.pdf*

-   Tools for retrieving nature run fields from ECMWF archive

-   Orbit simulator

-   Interpolation of model fields to ADM location

    -   "True" (HLOS) wind

-   Instrument error: LIPAS (Lidar Performance Analysis Simulator)

    -   For details see Marseille and Stoffelen, 2003

*http://www.knmi.nl/\~marseill/publications/fulltexts/dwlsimul.pdf*

-   LIPAS is updated and compatible with L2B processor performance

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-   Representativeness error (next slide)

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-   ECMWF model spectra for T799 (25 km) model version

Atmospheric wind variability follows a *k*-5/3 power spectrum (Nastrom
and Gage, 1985)

ECMWF model (T799)does not well resolve scales below 250 km

Effective model resolution is 250 km

ADM partly samples 250 km grid box

-   Unresolved model scales in nature run and ADM sampling determines
    representativeness error to be added to ADM HLOS wind observation

Verification against SWA ADM simulation. Simulation consistency needed
on

-   Clouds

    -   Laser beam cloud hit from model grid box cloud cover. Random?

    -   Cloud backscatter and extinction from model clouds

    -   Maximum overlap between clouds in adjacent (vertical) levels

-   Aerosols

    -   Backscatter and extinction

    -   Horizontal variability

        -   along track over 50 km accumulation length

        -   between adjacent observations (separated by 150 km)

    -   Vertical variability (stratification)

-   Dynamics

    -   Wind variability over 50 km accumulation length

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-   ADM continuous mode

    -   ESA decision December 2008

    -   If continuous mode is selected then more funding will probably
        become available for additional simulation studies

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-   Simulation of post-ADM scenarios

    -   EUMETSAT funding?

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-   GJM will attend next LWG meeting, Destin, Fl., 27-29 Jan. 2009

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-   GJM will visit NCEP on 30 Jan. 2009 for further discussions

-   

**[Comments and discussion]{.underline}**

Spectral analysis

T511 or T159 may have better spectral characteristics.

Scale which show -3/5 is not clear. -3 is

Problem is T213 nature run the slope become -5 or even less in smaller
scale.

Aerozol

Aerosol in KNMI DWL simulation model is not clear.

Oreste said Arlindo Dasilva is trying to simulate aerosol but ofrecast
skill with aerosol was not better than without.

Michiko mentioned, Arlindo\'s aerosol may be good enough for sampling.

Emmitt

SWA and Winker are funded to inventigate estimation of realistic return
using CALIPSO data.

[Dead Line]{.underline}

We need to demostrate the results from OSSE other wise people will give
up linstining to OSSE.

We need to produce results early 2009.

LPR

Not much expectation for ESR

GDE:

Getting ready for the best use of ADM data.

We have to show with ADM data, CMV and scatterometer data increase the
qualiti significantly.

LPR

ADM October 2010

We need to show OSSE can help preparation for ADM.

Michiko

Nature Run will get obsolate quickly.

ESA fund for OSSE

ESA will not be interested in OSSE for ADM but possible interest for
OSSE for followup mission.

LRP said if we have ADM OSSE ready that will help OSSEs for follow up
mission.

Michiko

We need lidar data to test data assimilation codes for ADM. Only
available data are for 1993 old NR.

GDE

SWA has some test data.

**\[Plan and progress at SWA\]**

.

SWA is working on simulation of cloud motion vector DWL with 12
prospective

Simulating form SIVO from T213 NR and also working on T511 NR for Joint
OSSE

Four presentations were covered in this presentation.

Joint OSSE\_ Simulating DWL winds and CMVs for OSSEs.ppt

NCEP Joint OSSE_Seabloom.jul08.ppt

Adaptive targetting

NCEP Joint OSSE_Utility of Doppler Wind Lidars in cloudy conditions.ppt

Calibration using CALIPSO data

Role of temperature data

NCEP Joint OSSE_Airborne Doppler wind lidar.ppt

Post ADM misstion

**[Simulating Doppler Wind Lidar and Cloud motion vector (CMV)
Observations for Use in OSSEs and Technology Trade
Studies]{.underline}**

[Joint OSSE\_ Simulating DWL winds and CMVs for OSSEs.ppt]{.underline}

-   General discussion of DLSM (previous presentation and online
    session)
    [www.swa.com/ald/DLSM4.2/index.htm](http://www.swa.com/ald/DLSM4.2/index.htm)
    )

-   Use of DLSM in SIVO's Sensor Web Simulator (includes video of
    adaptive targeting)

-   Calibration of DLSM using GLAS and CALIPSO data (general description
    of ongoing work)

-   The role of temperature in DWL simulations

-   Simulating ADM in preparation for assimilating ADM data (ROSES07)

Back ground

-   Simulating Doppler Wind Lidar(DWL) data for use in OSSEs began in
    1989 (Emmitt and Wood).

    -   Used Nature Run T106 Nature Run to conduct first series of NWP
        impact studies.

    -   Followed with T213, MM5, GEOS (25km) and now T511 and T799.

-   Simulating Cloud Motion Vectors (CMV) began in 2003 (O'Handley,
    Emmitt and Greco) using Nature Run clouds.

The Doppler Lidar Simulation Model

-   Allows configuration of mission including orbits, DWL instruments
    and data processing

-   Allows choice of input Nature Runs and atmospheric optical
    properties

-   Subgrid scale turbulence and cloud properties are dealt with in ways
    best suited for sampling with a \<100m diameter beam.

Cloud Motion Vectors

-   Challenge was to determine subset of model clouds that would be
    suitable for use by CMV algorithms.

-   Simulation of navigation errors and height assignment errors
    produced CMVs with both the random (\~ 3 -5m/s) and bias (\~ 1.5 m/s
    slow) errors.

-   Number of CMVs available to DA is controlled by simple random
    thinning.

Objectives

-   Generate simulated Cloud Motion Winds (CMW) using a "Nature Run"
    from a global numerical model

-   Provide CMW where the model indicates "trackable" cloud targets

-   Produce velocity errors (e.g. slow speed bias) similar to those
    experienced with real CMWs

-   Apply similar approach to simulating WVMW

Speed Bias

-   Identify trackable clouds

-   Determine physical thickness of cloud

-   Assign wind speed from middle of cloud to the height of the cloud
    top (limited to 300 mb thickness)

Current DWL/CMV effort at SWA

-   Simulating realistic and proposed DWL instrument concept(s) for use
    in SIVO (NASA/GSFC) experiments and NCEP OSSEs.

    -   Currently producing data products from a hybrid DWL as endorsed
        in the NRC Decadal Survey and various DWL advisory groups

-   Simulating CMVs for SIVO and NCEP.

-   Simulating various ADM follow-on missions.

**[SIVO OSSE]{.underline}**

[NCEP Joint OSSE_Seabloom.jul08.ppt]{.underline}

A model-driven sensor web is an Earth observing system that uses
information derived from data assimilation systems and numerical weather
prediction models to drive targeted observations made from
earth-orbiting spacecraft as well as from atmospheric- and ground-based
observing systems.

Demonstrate the value of implementing sensor web concepts for
meteorological use cases

Quantify cost savings to missions

Quantify improvement in achieving science goals

Design and Build an integrated simulator with functional elements that
will allow multiple "what if" scenarios in which different
configurations of sensors, communication networks, numerical models,
data analysis systems, and targeting techniques may be tested

-   Sensor Web Simulator Design

-   During 2007 most elements of the lidar use case (1-5) were executed
    "by hand" to help aid in the design of the simulator prototype

-   Five separate Observing System Simulation Experiments (OSSEs) were
    conducted that concluded:

    -   Under certain situations1, the lidar duty cycle may be reduced
        30% without impacting forecast skill

    -   Under certain situations, having the model task the lidar to
        perform a roll maneuver improves detection of features of
        interest 30% (tropical cyclones, jet streaks, rapidly changing
        atmospheric conditions)

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-   SIVO Workflow Tool ("NASA Experiment Design")

-   Selected as the "glueware" to sequentially execute components 1-6
    and manage data flow

1 The OSSEs performed were based upon a 20 day assimilation cycle during
September 1999. Although the use cases have been examined by GMAO
scientists they have not undergone a rigorous scientific review and the
results should not be considered scientifically valid. OSSEs presented
here are to validate engineering processes of the simulator.

Latest Results

In Spring, 2008 Simpson Weather Associates, Inc. established the Doppler
Lidar Simulation Model version 4.2 onto an Apple dual quad processor
computer for the SensorWeb project. SSH, the network protocol that
allows data to be exchanged over a secure channel between two computers,
was installed and tested. SWA and SIVO were able to test the push/pull
and communications functionality successfully. SIVO was able to push
DLSM inputs to SWA and request model simulations. The DLSM was
successfully executed and SIVO was able to retrieve DWL coverage and DWL
line-of-sight wind products for a six hour simulation in less than 2
minutes.

Near Future Plan

-   Line of Sight wind operator for the assimlation models

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-   Integrate Satellite Toolkit into the workflow tool to

provide satellite location and attitude inputs

-   Establish the T511 and T799 nature runs into DLSM

database format including generating aerosol, molecular

and cloud optical property databases

-   Build the slewing capability into the scanner model

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-   Integrate into the Sensor Web the SWA cloud motion

wind model

-   Global OSSEs (maybe mesoscale OSSEs - hurricanes)

**[Airborne Doppler wind lidar: typhoon research and long flight-leg
data sets for data impact studies related to a future space-based
Doppler wind lidar]{.underline}**

[NCEP Joint OSSE_Airborne Doppler wind lidar.ppt]{.underline}

An airborne Doppler wind lidar (1.6 micron coherent) was flown on a Navy
P3 in ONR/NSF typhoon research missions during August/October 2008.

In addition to full vector wind profiles in clear and partly cloudy
conditions, a primary research target unique to the Doppler lidar will
be the Organized Large Eddies (OLE) that are theorized to have
significant impacts on air sea fluxes controlling the evolution of
tropical cyclones.

Measurements of the vertical velocity of the water surface allows wave
spectra to be computed.

Efforts are underway to process and subset the thousands of lidar wind
profiles taken to simulate the coverage expected from a future
space-based DWLs such as GWOS and NWOS.

**Out Line**

-   Overview of the P3DWL activity in TPARC/TCS08

    -   Wind profiles to augment dropsondes

    -   Prospecting for OLEs as major air/sea interaction factor in
        developing TCs

    -   MBL aerosol structures and wave spectra

-   Example of P3DWL data from Typhoon Nuri flights

-   Examples of DWL data for OLEs

-   Examples of DWL data for extracting wave data

**Summary**

-   Wind lidar data taken with a 2 um coherent Doppler wind lidar on
    board the NRL P3 aircraft is now going through final processing.

-   Preliminary inspection of the data suggests that \> 85% useful data
    collected between surface and 3km; this appears to be the case even
    when observing near TCs.

-   Long curtains of wind profiles are available for simulating future
    space-based DWLs and data impact studies at modeling centers.

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-   Dan Carre', Simpson Weather Associates

    -   26- 30 July ferry flight Pax river to Guam

    -   1 -17 August in Guam

-   Michael Riemer, Naval Post Graduate School

    -   15 -- 31 August

-   Brian Tang, Massachusetts Institute of Technology

    -   28 -- 15 September

-   CDR Dan Eleuterio, NRL/ONR

    -   13 September -- 3 October

**[Utility of Doppler Wind Lidars in cloudy conditions]{.underline}**

[NCEP Joint OSSE_Utility of Doppler Wind Lidars in cloudy
conditions.ppt]{.underline}

-   There are concerns expressed that airborne lidars may be marginally
    useful in cloudy conditions (same for space-based lidars)

-   Airborne and space-based data suggests otherwise.

-   An airborne wind lidar combined with dropsondes makes a powerful
    combination

    -   The DWL can provide direct measure of representativeness of the
        dropsonde observations enabling dynamic assignment of
        representativeness in the total observation error (σo) assigned
        for DA.

    -   

Evidence of lidar utility in cloudy conditions

-   Emmitt and Seze (1991): CLOS for space based lidars using SPOT
    imagery

-   Airborne Coherent Lidars

    -   TODWL (Emmitt, SWA/NPOESS) 2002,3,4,6 &7 flights

    -   HRDL (Hardesty, NOAA)

    -   WINDS (DLR, Germany)

-   Airborne Direct Lidars (no Doppler wind lidars yet)

    -   Cloud Lidar System (Spinhirne, NASA)

    -   DIAL over hurricanes (Browell, NASA)

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-   Space-based aerosol lidars (non-Doppler)

    -   LITE based cloud penetration statistics (Winker and Emmitt,
        1997)

    -   GLAS cloud penetration statistics(Emmitt and Greco, 2006)

    -   CALIPSO (NASA funded research in progress, Emmitt and Winker)

Summary of current expectations

-   From space, current laser technology will detect cloud on 80% of
    individual laser shots; however, 80% of all laser shots will provide
    a ground return (i.e. 75% of the shots that intercept cloud will
    also provide a ground return (based upon analyses of GLAS data).

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-   Difficult to generalize for airborne lidars since mission objectives
    could be targeting cloudy phenomena. However, experience by those
    flying airborne lidars is that laser shots penetrate clouds far more
    frequently than initially expected. Thus clouds are seen as
    optically porous at near infrared wavelengths.

Recommendations

-   Recommend hybrid wind lidar for research from high altitude aircraft
    such as ER2, WB-57, Proteus, Global Hawk..

    -   Hybrid Doppler wind lidar (DWL) includes a molecular subsystem
        for aerosol weak regions

    -   A coherent sub system for cloudy situations and aerosol rich
        regions (PBL, elevated dust layers..)

-   Co-fly hybrid DWL with dropsondes as will be done in TPARC using the
    NAVY P3 and the DLR Falcon.

Definisions

-   SPOT: Satellite Pour l\'Observation de la Terre

-   CFLOS: Cloud Free Line of Sight

-   TODWL: Twin Otter Doppler Wind Lidar

-   HRDL: High Resolution Doppler Lidar

-   DIAL: Differential Absorption Lidar

-   LITE: Lidar Technology Experiment

-   GLAS: Geoscience Laser Altimeter System

**[Comments and discussion]{.underline}**

It is not clear the role of DWL in sensor web at SIVO and GMAO interest.

> Adaptive targeting is tested and interaction with other observing
> systems is investigated by sensor web
>
> Objective of sensor web is to study how the instruments communicate
> each other.
>
> DAS system for OSSE at SIVO is provided by GMAO.
>
> GMAO is more interested in detail data impact in data analysis.

Scanning (Sympetric coverage)

Advantage of symmetric coverage was very clear.

The impact may not be clear in global but clear in hurricane scale

Oreste:Tested with AIRS data. Advantage of uniform coverage was very
clear.

We really need LOS operator tested on GSI. The codes were tested for
idealized wind.

Using Aircraft data to preparation for space based DWL and evaluate
representativeness error.

DWL will enhance the quality of CMV and scatterometer. This aspect must
be emphasized.

**\[Parting Shots: Global OSSEs\]**

Tom Schlatter

[Schlatter_OSSEtalk04Dec08.x.ppt]{.underline}

Diagram for OSSE

-   The European Centre for Medium Range Weather Forecasts generated a
    13-month nature run at very high resolution (T599 L91) with model
    output every three hours.

-   ECMWF also ran two 35-day high-resolution nature runs at T799 L91
    with model output every hour. The first was for the hurricane
    season; the second was for the spring convective season.

Major computational effort using the most accurate global model
available.

-   Assess the realism of nature run by comparison with known
    atmospheric behavior (various statistics describing the general
    circulation and climate).

Joint effort by investigators.

**Why not analysis**

A succession of analyses is a poor choice for a nature run because:

-   They are too close to the real atmosphere and do not depart from it
    with time. A proper nature run should generate an independent,
    alternative atmosphere.

-   They are not connected by a smooth evolution of states.

-   The observations contributing to the analyses are unevenly
    distributed; thus the analysis error varies from place to place. In
    a model-generated nature run, the truth is known everywhere.

**Extraction Observation from N R**

Include all of the observation sources currently assimilated in
operational models, e.g.,

-   surface: land and ocean,

-   sounders: radiosondes, profilers, aircraft, etc.,

-   satellites: geosynchronous and in low-earth orbit,

plus proposed new sources.

his is a labor intensive effort involving many decisions about forward
models, how to account for clouds, and how to specify instrument errors
(random and correlated) and representativeness errors. Careful
documentation is essential.

**Handicaps of Regional OSSEs**

-   Lateral boundary conditions eventually dominate the forecast inside
    the regional domain, obscuring any effect of the observation mix on
    forecast accuracy. This must be considered when evaluating the OSSE.

-   The size of the geographic region controls the length of forecasts
    that can be considered....shorter forecasts for smaller regions.

-   Ideally, the same observation mix should be used in the regional
    model as in the global model that supplies the boundary conditions.

-   One is forced to execute two nature runs and coordinate two data
    assimilation and prediction systems.

**Major points to take away....**

-   The joint effect to conduct global OSSEs is a productive sharing of
    the strengths and resources of the participants.

-   Even full OSSEs, as described here, are subject to many compromises.
    Anything less further clouds the interpretation of results.

-   Even 1% of the cost of an expensive observing system would
    constitute unprecedented funding for global OSSEs and would almost
    surely be cost-effective.

**Comments and discussion**

Challenge to realism of the nture run

We can calibrate against

Some discrepancy is uncovered but in general ECMWF NR we have is
realistic

Short cuts have to be taken carefully

LPR said that it would be important this slide to be written as a
document

TWS said it is also good have as presentation but he is happy to help
for documentation.

Michiko reminded that Springer OSSE chapter will serve as introduction
to OSSE but it may be too long for some people.

TWS said Springer is good summary that went through more than 30
version.

LRP he wanted some thing to reference. Springer would be good.

After the meeting Ron Errico commented that even a few pages article in
BAMS will be important.

Discussion about representativeness error.

Dave Emmitt brough up the strategies of adding representativeness error.

Michiko mentioned about the software provided by GMAO. That add random
error based on error table.

TWS: representativeness error which represent error due to grid space
and observation.

Emmitt: T-PARK experiments.DWL measuerment every 3/4 KM Drop sonde
25-31km. This experiments will help to study representativeness error.

DWL target point moves 120m/sec. DWL data is area averaged data.

Drop sonde have 3m/s obs error.

The data with random error only will be very optimistic.

Michiko: That even still the results presented by Ron Errico was very
impressive although the results are very premature initial results. That
means large amount of error comes from model error. This shows that
identical twin OSSE is very misrepresenting the error.

Michiko: The argument for not using analysis is not strong.

Jack Woollen: Analysis give observation in right place. Aircraft data in
jet area cloud in right place.

Michiko: In that case ideally flight pass must be simulated in OSSE.

Real problem is analyses are forced by observation. For example if
observation changed analysis change. If one satellite is missing on one
day, that caused the analysis changes

Zoltan suggested to force Nature run with Analysis to make it more
realistic MJO but many model started to producing realistic MJO.