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NVAP Annual Report March 2011.docx - Colorado State University

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									                    Annual Short Report:

Improvement of the NVAP Global Water Vapor Data Set for
       Climate, Hydrological and Weather Studies

                         March 15, 2011

                A NASA MEaSUREs Project

                 Award Number: MEAS-06-0023

                   Thomas H. Vonder Haar, PI

   With contributions from John M. Forsythe, Janice L. Bytheway

               Science and Technology Corporation

                        METSAT Division

                          P.O. Box 467

                       Laporte, CO 80535

                     vonderhaar@stcnet.com

                STC-METSAT Project Number 9008-000
Table of Contents
1.     Summary of Progress this Period .......................................................................................................... 3
2.     Milestones Completed in Last Reporting Period .................................................................................. 6
3.     Adjustments Needed to Milestones ..................................................................................................... 6
4.     Publications, Presentations, Meetings and Outreach........................................................................... 8
     4.1       Current Reporting Period .............................................................................................................. 8
     4.2       NVAP Citations .............................................................................................................................. 8
5.     List of Acronyms .................................................................................................................................... 9
6.     References .......................................................................................................................................... 10
7. Appendix A: Selection Rationale for Elsaesser and Kummerow (2008) Passive Microwave Retrieval
Algorithm .................................................................................................................................................... 11
  1. Summary of Progress this Period

      The NVAP-MEaSUREs (NVAP-M) project is creating a global water vapor dataset spanning 1987-
  2010, incorporating water vapor data from both satellite and in-situ measurements. The project will
  begin its third year of production in May, 2011.

      Shortly after submitting our annual report for 2010, STC purchased 18TB of computer storage to
support the project. This storage has allowed us to download and store all necessary datasets for
processing NVAP-M. Data through the end of 2010 is now available for the majority of our input
datasets and the radiosonde, AIRS, MERRA and SSM/I Level 1C have been downloaded through this
time for our use. Clear sky radiances for HIRS are not yet available for 2010 and we have contacted
the data producers to check on the status of this product. A DVD backup of all original data files has
been made in case something happens to the external hard-drives.

       In June of 2010 we traveled to NASA Jet Propulsion Lab (JPL) to meet with our MEaSUREs
colleagues working on the AIRS water vapor project. We discussed the creation of a single ATBD
relative to both projects and agreed that, barring concerns from program scientists, this would be
conducive to preparing the most unified and thorough water vapor dataset possible.

      We continued to perform validation tasks on results from our chosen retrieval algorithms. As
mentioned in our previous reports, we began to consider the Elsaesser and Kummerow (2008) optimal
estimation type retrieval for use with the intercalibrated SSM/I radiances. At this time we have
decided to forgo the Greenwald et al (1993) retrieval in favor of the Elsaesser and Kummerow
algorithm. This algorithm has many benefits, including improved precipitation screening and
diagnostic outputs that allow us to assess the retrieval’s dependence on a priori information and that
can be used to determine the weighting SSM/I retrievals will be given in the NVAP merging procedure.
Additionally, this algorithm is designed to be sensor independent, such that any biases found in
comparisons made between retrieved water vapor from different instruments using this algorithm will
not be due to differences in retrieval used, and the retrieval will remain valid in any future extensions
of the NVAP dataset. Further details justifying our selection of the Elsaesser and Kummerow
algorithm are given in Appendix 1.

      While we initially intended to follow through with use of the SSM/I Level 1C intercalibrated
radiance dataset (Kummerow et al.), recent communication with Dr. Kummerow has indicated that
the Level 1C dataset will not continue prior to 1997. Instead, intercalibrated radiances being produced
under a NOAA grant covering 1987-2010 will be produced, however not under the Level 1C format.
While the intercalibrated radiances will not be available in final form for over a year, a preliminary,
quality controlled version will be available in several weeks. Although not finalized, this
intercalibrated data represents an improvement over the radiances used in the heritage NVAP
dataset.
     PRELIMINARY RESULT, NOT                            SSM/I
     FOR DISTRIBUTION


                         HIRS




Figure 1. Time series of daily global average total precipitable water (TPW) from the HIRS (NOAA satellites), SSM/I (DMSP
satellites, ocean only) and the AIRS instrument (clear sky only). Generally, the results from intercalibrated datasets follow
closely, except for the switch from HIRS/2 to HIRS/3 in 1998 and known issues with the RADCAL beacon on DMSP F15
initially starting on August 14, 2006. HIRS and AIRS values are lower than those from SSM/I because data from these
instruments are being processed only for clear-sky and over both ocean and land, while SSM/I data is being processed over
ocean only for both clear and cloudy conditions.




           -10                                                  0                                                       10
                                                               mm
Figure 2. Hövmoller diagram showing the zonal average TPW anomalies for a preliminary merged NVAP-M climate product.
Only one time-dependent bias exists in late 1997 when SSM/I data is added to the product.
       Work is continuing to determine if the bias between HIRS/2 and HIRS/3 retrieved water vapor,
shown in figure 1, can be removed. If we are not able to remove this bias, we may need to release the
data with the understanding that this bias is present and make its presence well known to any
potential users. Meanwhile, we continue to produce preliminary results of merged NVAP-M total
precipitable water. One encouraging result, shown in figure 2, is a Hövmoller diagram of water vapor
anomalies from 1987-2009. This result indicates that the current processing of NVAP-M removes
time-dependent biases previously seen in the heritage NVAP data. The remaining time-dependent
bias is a result of the addition of the available SSM/I Level 1C data currently being used. Figure 3
shows a sample result of daily NVAP-M gridded TPW for July 1, 2003.

      A project website was created this past summer (http://nvap.stcnet.com/) and visitors to the
original NVAP dataset at the Langley ASDC are directed to this site to learn more about the reanalysis
and extension.

      As mentioned in our November, 2010 interim report, we plan to submit a proposal to add a fifth
year to the NVAP-M project. This would place our timeline more in sync with that of our MEaSUREs
colleagues at NASA JPL. We expect to submit this proposal in the next several weeks.

     The NVAP-M team has been busy in the past several months giving both oral and poster
presentations highlighting NVAP-M and its preliminary results at various large and small meetings,
which will be highlighted in section 4.




                   Figure 3. NVAP-M daily gridded total column water vapor for July 1, 2003.
 2. Milestones Completed in Last Reporting Period

       May 15th represents the end of month 36 of the project. Relevant milestones for this reporting
period covering months 24-36 are shown in table 1, and our progress with respect to these milestones
is discussed below. Adjustments needed to the remaining milestones will be addressed in section 3.

        Key Milestones
             o As mentioned above, a link to our project website is now found on the heritage
                 NVAP dataset’s webpage hosted at the Langley ASDC.
             o Some data has been made available to our colleagues at JPL, and was used in a
                 poster presentation given at the AGU Fall 2010 meeting. Progress with larger-scale
                 public distribution is delayed while we continue to work with the time dependent
                 bias found in the HIRS retrieved TPW as well as the availability issues with the SSM/I
                 intercalibrated radiances.
             o With regard to the Data Center User Working Group certification, we anticipate
                 hearing from our contact at the Langley ASDC in the next month or two.
        Reporting
             o Since we are not yet publicly distributing the data on a large scale, we have not
                 started entering metrics. We have been in contact with Greg Hunolt regarding this
                 delay and have discussed with him entering preliminary metrics early this summer.
        ESDSWG
             o John Forsythe participated in a teleconference for the Metrics Planning and
                 Reporting Working Group (MPARWG) in mid-August 2010.
             o Janice Bytheway participated in the ESDSWG annual meeting in New Orleans, LA in
                 October, 2010.
                      Best practices for MEaSUREs projects interacting with the DAACs were
                          discussed and recommendations were made regarding future calls for
                          proposals including a plan for project/DAAC interaction.
                      Potential project quality metrics were discussed and several possible
                          methods for implementing them were suggested. No formal
                          recommendations were made and the discussion was tabled to continue via
                          teleconferences and next year’s meeting.


 3. Adjustments Needed to Milestones

            Key Milestones
                 o We have been unable to submit our revised ATBD on schedule because we are
                     awaiting both program review of the draft version and an opinion from project
                     scientists regarding the potential joint-ATBD with JPL. An individual project
                     ATBD was submitted via E-Books in September, 2009. A trip was taken to JPL in
                     June 2010 to discuss this possibility, and another trip is being planned later this
                     spring to continue this work. The delay in ATBD review does not, however,
                     delay our progress producing the dataset.
                   o   We will continue to operate under the understanding that we need to begin
                       working more closely with both the ASDC and Project Scientists as our products
                       are processed and become available for distribution.
               Reporting Milestones
                   o Metrics reporting was originally scheduled to begin in month 21; however we
                       are now planning to begin reporting in month 36.


                          Table 1. NVAP-MEaSUREs project milestones months 24-36.

                       Item                         Due (months                     Comments
                                                     after start)
              KEY MILESTONES
Enter Directory Interchange Format (DIF)           27
document(s) into Global Change Master
Directory
Make data publicly available to users via          27
MEaSUREs project's web site
EOSDIS Data Center(s) show links to                27
MEaSUREs project's web site
Get data products certified by Data Center         24-36             DUWGs advise EOSDIS Data
User Working Groups (DUWGs) and validated                            Centers about relative priority of
by the relevant HQ program scientist and the                         the data to be archived and
PE for DS.                                                           distributed by them, and levels of
                                                                     service to be provided for the data
                                                                     commensurate with the Data
                                                                     Centers' budget.
Enter Directory Interchange Format (DIF)           27
document(s) into Global Change Master
Directory

REPORTING
Enter metrics                                      21, 22, 23, …     Awardees enter metrics monthly
                                                                     after production of ESDRs
                                                                     commences

ESDSWG
Participate in WG meetings/Telecons                As needed         Activities vary from WG to WG.
                                                                     Some WGs schedule ad hoc
                                                                     meetings/telecons in frequently.
                                                                     Others have biweekly telecons
Participate in annual Joint Working Group          12, 24, …         NASA organizes these meetings.
meetings                                                             They are generally held in October
                                                                     each year.
  4. Publications, Presentations, Meetings and Outreach

   4.1 Current Reporting Period


        The creation of our project website represents an important step in publicizing NVAP-M to
potential users of the heritage NVAP dataset. As a response to a common question regarding possible
climate trends in the heritage NVAP dataset, we released a “Trend Statement”
(http://nvap.stcnet.com/NVAP_Trend_Statement.pdf) cautioning the use of heritage NVAP for this
type of study.

         PI Vonder Haar presented a talk at the 17th American Meteorological Society (AMS)
Conference on Satellite Meteorology and Oceanography held September 27 – 30 in Annapolis, MD
titled “Variability, Trends and Challenges in the Long-Term Global Satellite Record of Water Vapor”.
Co-I John Forsythe presented a poster, “The NASA NVAP-MEaSUREs Project: A Reanalyzed and
Extended Record of Global Water Vapor from 1987-2010,” at the same meeting.

       Janice Bytheway attended the ESDSWG annual meeting in October 2010 and participated in
the Metrics Planning and Reporting Working Group (MPARWG). John Forsythe also participates in
MPARWG.

       Janice Bytheway presented a talk, “Integrating Past and Present: Satellite Observations and
the NVAP-M Water Vapor Dataset” at the 2010 Annual Meeting of the American Geophysical Union
(AGU)

        Under international GEWEX sponsorship, Janice Bytheway presented an invited talk, “The
NASA NVAP-MEaSURES 1987-2010 Global Water Vapor Data Set: Design Approach and Heritage
Science”, at the GEWEX/ESA DUE GlobVapour workshop on long term water vapour data sets and
their quality assessment held March 8-10, 2011 in Frascati, Italy.

        We continue to receive requests for help with the heritage NVAP data set and software from
users. In addition, we have received many requests for the reanalyzed NVAP-M products. These
requests help us to identify potential “test users” for the preliminary versions of NVAP-M.

   4.2 NVAP Citations


        Heritage NVAP continues to be widely used in the science community and has been cited over
100 times. In our 2010 annual report, we presented a list of publications citing the heritage NVAP
dataset, and we continue to monitor AMS, AGU and IEEE publications for its use. Listed below are the
most recent publications, as well as several that were not discovered during our previous searches.

Glecker, P. J., K. E. Tayler and C. Doutriaux, 2008: Performance metrics for climate models. J. Geophys.
Res., 113, D06104.
 Schlosser, C. A. and X. Gao, 2010: Assessing evapotranspiration estimates from the Global Soil
 Wetness Project Phase 2 (GSWP-2). J. Hydromet., 11, 880-897.

 Sherwood, S. C., R. Roca, T. M. Weckwerth, and N. G. Andronova, 2010: Tropospheric water vapor,
 convection, and climate. Rev. Geophys., 48, RG2001.

 Vey, S., R. Dietrich, A. Rulke, M. Fritsche, P. Steigenberger, and M. Rothacher, 2010: Validation of
 precipitable water vapor within the NCEP/DOE reanalysis using global GPS observations from one
 decade. J. Climate, 23, 7, 1675 – 1695.

 Watterson, I. G., 1998: An analysis of the global water cycle of present and doubled CO2 climates
 simulated by the CSIRO general circulation model. J. Geophys. Res., 103, 23 113-23 129.

 _____, M. R. Dix, and R. A. Colman, 1999: A comparison of present and doubled CO2 climates and
 feedbacks simulated by three general circulation models. J. Geophys. Res., 104, 1943 – 1956.


   5. List of Acronyms

AIRS – Atmospheric Infrared Sounder
AGU – American Geophysical Union
AMS – American Meteorological Society
ASDC – Atmospheric Science Data Center
ATBD – Algorithm Theoretical Basis Document
CLW – Cloud Liquid Water
CSU – Colorado State University
DAAC – Distributed Active Archive Center
DIF – Directory Interchange Format
DMSP – Defense Meteorological Satellite Program
DUE – Data User Element
DUWG – Data Center User Working Group
ECMWF – European Center for Medium-Range Weather Forecasts
EOSDIS – Earth Observing System Data and Information System
ERA – ECMWF Re-Analysis
ESA – European Space Agency
ESDSWG – Earth Science Data System Working Group
GEWEX – Global Energy and Water Cycle Experiment
GPM – Global Precipitation Measurement
HIRS – High resolution Infrared Radiation Sounder
IEEE – Institute of Electrical and Electronics Engineers
JPL – Jet Propulsion Lab
LWP – Liquid Water Path
MEaSUREs – Making Earth Science data records for Use in Research Environments
MERRA – Modern Era Retrospective-analysis for Research and Applications
MPARWG – Metrics Planning And Reporting Working Group
NASA – National Aeronautics and Space Administration
NCDC – National Climatic Data Center
NOAA – National Oceanic and Atmospheric Administration
NVAP – NASA Water Vapor Project
OE – Optimal Estimation
RSS – Remote Sensing Systems
SSM/I – Special Sensor Microwave Imager
SST – Sea surface temperature
STC-METSAT – Science and Technology Corporation, METSAT Division
TPW – total precipitable water


    6. References

Deblonde, G. and S. J. English, 2001: Evaluation of the FASTEM-2 fast microwave ocean surface
       emissivity model. Tech. Proc. Int. TOVS Study Conf. XI, Budapest, Hungary, WMO, 67 - 78.

Elsaesser, G. S. and C. D. Kummerow, 2008: Toward a fully parametric retrieval of the nonraining
        parameters over the global oceans. J. App. Meteo. and Climatol., 47, 1599 - 1619.

Greenwald, T. J., G. L. Stephens, T. H. Vonder Haar, and D. L. Jackson, 1993: A physical retrieval of cloud
      liquid water over global oceans using Special Sensor Microwave/Imager (SSM/I) observations. J.
      Geophys. Res., 98, 18471 - 18488.

Kohn, D. J., 1995: Refinement of a semi=empirical model for the microwave emissivity of the sea surface
       as a function of wind speed. M.S. thesis, Dept. of Meteorology, Texas A&M University, 44pp.

Leibe, H. J., G. A. Hufford, and T. Manabe, 1991: A model for the complex permittivity of water at
        frequencies below 1THz. Int. J. Infrared Millimeter Waves, 12, 659 - 675.

_____, _____, and M. G. Cotton, 1993: Propagation modeling of moist air and suspended water particles
        at frequencies below 1000 GHz. Advisory Group for Aerospace Research and Development conf.
        Proc: Atmospheric Propagation Effects through Natural and Man-Made Obscurants for Visible to
        MM-Wave Radiation (AGARD-CP-542), AGARD, Neuilly sur Seine, France, 3-1-3-10.

Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and
       satellite SST analysis for climate. J. Climate, 15, 1609 – 1625.

Rosenkranz, P. W. 1998: Water vapor microwave continuum absorption: A comparison of
       measurements and models. Radio Sci., 33, 919-928.

Wentz, F. J., 1997: A well-calibrated ocean algorithm for special sensor microwave/imager. J. Geophys.
       Res., 102 (C4), 8703 – 8718.

Wilheit, T. T., 1979a: A model for the microwave emissivity of the ocean’s surface as a function of wind
        speed. IEEE Trans. Geosci. Electron., 17, 244 – 249.
Wilheit, T. T., 1979b: The effect of wind on the microwave emission from the ocean’s surface at 37 GHz.
        J. Geophys. Res., 84, 4921 – 4926.




    7. Appendix A: Selection Rationale for Elsaesser and Kummerow
       (2008) Passive Microwave Retrieval Algorithm

           The Elsaesser and Kummerow (2008) optimal estimation (OE) retrieval offers many
  advantages over the Greenwald et al. (1993) retrieval algorithm. The OE retrieval was designed for
  use with the Global Precipitation Measurement (GPM) mission, another large-scale climate study, and
  simultaneously retrieves surface wind, total precipitable water (TPW) and cloud liquid water path
  (CLW). It was designed to be sensor independent, and therefore valid with any microwave
  radiometer we chose to use. If future processing of NVAP opted to include additional microwave
  sensors, this retrieval could be used to obtain TPW from those observations. Sensor independence is
  also beneficial if we were to use the OE on a different microwave radiometer in order to perform
  validation of our product; any differences in retrieved TPW would not be a result of different retrievals
  being used.

          The OE retrieval provides several diagnostic variables that are not available from the
  Greenwald et al. retrieval and that can be used to eliminate precipitating scenes without the use of
  an arbitrary liquid water path threshold, which can lead to discrepancies in high LWP clouds. The
  diagnostics might also be used to form an error estimate for each pixel that can be used in a weighted
  merging technique.

          The OE retrieval also makes use of peer-reviewed radiative models, including the specular
  emissivity model of Deblonde and English (2001), rough sea-surface models of Kohn(1995) and Wilheit
  (1979), the atmospheric absorption model of Rosenkranz(1998) and cloud liquid water absorption
  model of Leibe (1991) and Leibe et al. (1993).

           A priori estimates of surface wind, TPW and CLW were acquired as the global annual mean
  derived from AMSR-E by Remote Sensing Systems (RSS). Ancillary data includes sea surface
  temperature (SST) obtained from Reynolds (2002) and zonal average temperature lapse rate and
  water vapor profiles derived from ECMWF ERA-40. Although these zonal averages and a priori values
  are not globally representative and lack a seasonal cycle, the authors note the retrieval’s low
  sensitivity to them. In order to maintain consistency with our IR retrievals, we are also investigating
  the use of AIRS climatology as a priori.

           A more physical approach to precipitation screening, sensor independence, and the presence
  of error diagnostics makes the optimal estimation retrieval a desirable alternative to the Greenwald et
  al (1993) algorithm. In keeping with the MEaSUREs philosophy, the Elsaesser and Kummerow
  algorithm is peer reviewed and well understood by the NVAP-M production team. When comparing
  results from both candidate retrievals to an independent TPW dataset (Wentz Version 6, 1997), results
  from the Elsaesser and Kummerow algorithm exhibit a much smaller bias than those from the
  Greenwald algorithm (figure 2). Additionally, the recently released Version 7 of the Remote Sensing
  Data increases TPW values above 50mm by a few percent, further reducing the bias.
Figure 2. Comparison of the retrieved TPW (in mm) from Wentz V6 (x-axis) and from the Greenwald (1993, black asterisks)
and Elsaesser and Kummerow (2008, blue dots) algorithms for January 2003 of SSM/I on the DMSP F13 satellite. For
                      2                                                                                 2
Greenwald vs. Wentz, r = 0.991,  = 17.08 mm and bias=2.2mm. For Elsaesser and Kummerow vs. Wentz, r = 0.993,  =
16.14 mm and bias = 0.28 mm.

								
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