Loren Data's SAM Daily™

FBO DAILY ISSUE OF JUNE 12, 2008 FBO #2390
SOLICITATION NOTICE

R -- Climate/2H8X21

Notice Date

6/10/2008
 

Notice Type

Presolicitation
 

Contracting Office

Environmental Protection Agency, Office of Acquisition Management, EPA/Headquarters, Environmental Protection Agency, Program Contract Service Center, 1200 Pennsylvania Avenue, Nw, Washington, DC 20460
 

ZIP Code

20460
 

Solicitation Number

RFQ-DC-08-00188
 

Response Due

6/25/2008
 

Archive Date

7/25/2008
 

Point of Contact

Point of Contact, Thomas Martinez, Purchasing Agent, Phone (202) 564-1418
 

Small Business Set-Aside

N/A
 

Description

NAICS Code: 541620 NAICS code 541620: The U.S. Environmental Protection Agency (EPA) plans to negotiate on a sole source basis with Dr.Semazzi of the University of North Carolina State. Negotiations shall be conducted pursuant to the authority of 41 U.S.C. 253 (c)(1), which allows fornegotiations without full and open competition if there is only one responsible source. PART I ? BACKGROUNDThe US EPA ORD Global Change Research Program researches and assesses the impacts of changing climate, as well as shifts in the drivers of global change, including land-use, national/regional energy and transportation choices on US air quality. The body of climate change research indicates that a warming climate will manifest, not as a simple, across-the-board increase in temperature, but as changing synoptic-scale meteorological patterns. Modeling studies supported by the EPA GCRP have shown that the primary impacts of a warming climate on air quality will be tied to changing synoptic-scale weather patterns. Meteorology has a well understood role in determining air quality. For example, high ambient O3 concentrations are typically associated with low wind speeds (stagnation), high solar radiation levels and warm temperatures. In the case of PM, precipitation frequency is closely tied to ambient concentration. For both PM and O3, inversion layer heights play a large role in local pollutant concentrations. State-level policy analysts and decision-makers concerned with air quality are in need of policy evaluation tools that consider information about projected local and regional air quality responses to climate change. (cf. NRMRL Adaptation Workshop report, 2008) Such a policy evaluation tool should be constructed on the basis of a general framework (or platform) that can be tuned to fit a particular state or region by the incorporation of AQ sensitivity details characteristic of the area, along projected changes in regional meteorological patterns. Such a framework will allow all users to benefit from future improvements. The purpose of this sole source purchase order is the acquisition analysis services for EPA?s Global Change Research Program in the Office of Research and Development. The analyses to be obtained will support of the development of an air quality management planning tool that estimates the future impacts on air quality of changing climate at urban (MSA) scales. The State of North Carolina will serve as the test case for developing the methodology that will serve as the foundation for the air quality planning tool. The first phase in the development of the methodology involves identification of the meteorological patterns to which AQ is most sensitive in the test case region. These patterns can serve as a "basis set" from which future meteorological dynamics can be projected using regional climate modeling outputs. This first phase will yield necessary insight into the best methodologies for use in the design of the generalized tool framework. To construct the meteorological and air quality baseline, the following relationships will be analyzed and linked to a geospatial grid that will be compatible with those used in meteorology and air quality modeling: Current and historical statistical relationships of local air quality observations to local climate observations, including individual meteorological variablesIdentification of current and historical meteorological patterns characteristic of the test case regionConstruction of a geophysical/numerical model linking site-specific North Carolina air quality responses to the meteorological patterns characteristic of the North Carolina region The work proposed here will address the second need ? identification of the primary climate/meteorological patterns present in the North Carolina region. The Empirical Orthogonal Functions (EOF) method as implemented by Bowden & Semazzi (2008 & references therein) on the North American Regional Reanalysis (NARR, 1979- present; www.emc.ncep.noaa.gov/mmb/rreanl) dataset to derive time series and homogeneous patterns of the climate indices that summarize the behavior of the climate system of North Carolina. The region of analysis will encompass sufficient land surface and ocean area to ensure an accurate resulting description of North Carolina?s climatology.Each EOF comprises a spatial geographical pattern (fixed in time) and the corresponding time series. However, typically there are only about 3 or 4 EOFs for each analysis that are statistically significant. The rest are considered to be associated with incoherent noise due to observational and data analysis deficiencies. So in practice we will generate three or four maps and time series for each meteorological variable. The interpretation of a specific EOF is that its contribution to the original value of the analysis variable (e.g. surface temperature at a grid point near Raleigh) is obtained by multiplying the value of the EOF on the map at the location of interest by the value of the corresponding EOF time series for a particular year, say 1985. The original value of the NARR data at the grid point may be recovered by summing the contributions of all the EOFs. PART II - JUSTIFICATION The work to be performed under this Purchase Request requires expert knowledge of the application of statistical analysis techniques to climatological/meteorological data, and substantial experience in the application of these techniques to the data and modeling outputs for North Carolina and the surrounding region. In particular, the contractor secured to complete the project described below must have specific expertise in the Empirical Orthogonal Function (EOF) analytical technique. EPA has determined that the EOF methodology is the statistical approach best suited for identifying the characteristic climatological/meteorological patterns of importance to air quality, as described above. FAR 6.302-1 authorizes contract actions when the agency?s need for the supplies or services required by the Agency are available from only one responsible source and that no other type of supplies or services will satisfy the Government?s needs. The statutory authority is 41 U.S.C. 253 (c) (1). The responsible source identified for this task order, Dr. Fredrick Semazzi, has a unique set of capabilities and resources which make him well-suited to meet the requirements of this contract. Dr. Semazzi and his research group have made extensive use of Empirical Orthogonal Functions (EOF) methods and observational data from multiple data sources, including NOAA NCEP, gridded data, and satellite data over the eastern region of the US, in their studies of Atlantic storm systems impacting North Carolina and the US Southeast. Their statistical and modeling results are being used to run a flooding model for North Carolina and the US Southeast. This research requires a unique combination of statistical and regional climate modeling expertise for the eastern region of US and close working relations with the regional weather/climate organizations and programs, including North Carolina Climate Office (NCCO). The following list of peer-reviewed publications supports the conclusion that Dr. Semazzi and his research group have a broad base of experience in regional climate modeling and analysis, in addition to their specific experience with the techniques and data required to complete the project:1. Semazzi, F.H.M., J. Bowden, and N. Davis, 2007: Baseline Information for the Adaptation to Climate Change in Arid Lands of Kenya, pp 35. World Bank Assessment Report. Available on request.2. Onol, B., and F. H. M. Semazzi, 2007: Regionalization of Climate Change Simulations over Eastern Mediterranean. (Journal of Climate, accepted; copy available at[http://climlab.meas.ncsu.edu/rev_sub_prep_manuscripts.html]).3. The IPCC Assessment; Climate Change; Cambridge Press, (2007); Chapter 9 (WGII).Review editor: Fredrick Semazzi.4. Bowden, J.H. and F.H.M. Semazzi, 2007: Empirical Analysis of the Intraseasonal Climate Variability for the Greater Horn of Africa. J. Clim., 23, 5715-5731.5. Anyah, R.O., F.H.M. Semazzi and Lian Xie, 2006: Simulated physical mechanisms associated with the climate variability over Lake Victoria Basin in East Africa; MonthlyWeather Review, 134, 3588-3609.6. Anyah. R.O. and F.H.M. Semazzi, 2004: Simulation of the sensitivity of Lake Victoria basin climate to lake surface temperatures. Theor. Appl. Climatol., 79, 55-69;DOI:10.1007/s00704-004-0057-4.7. Anyah. R.O. and F.H.M. Semazzi, 2006: Climate variability over the Greater Horn ofAfrica based on NCAR AGCM ensemble. Theor. Appl. Climatol., 86, 39-62;DOI:10.1007/s00704-005-0203-7.8. Anyah, R.O., F.H.M. Semazzi, and L. Xie, 2006a: Simulated physical mechanisms associated with climate variability over Lake Victoria Basin in East Africa. MWR, 134,3588-3609; DOI: 10.1175/MWR3266.1.9. Anyah, R.O. and F.H.M. Semazzi, 2007: Variability of East African rainfall based on multiyear RegCM3 simulations. Int. J. Climatol.; DOI:10.1002/joc1401.10. Semazzi, F. H. M., Scroggs, J., Pouliot, G., Analemma Leia Mckee-Burrows., Norman, M., Poojary, V., and Tsai, Y., 2005: On the accuracy of semi-Lagrangian numerical simulation of internal gravity wave motion in the atmosphere. Journal of Meteorological Society of Japan, Vol. 83, No. 5, No. 5, 851-869.11. Fall, S., D. Niyogi, F. Semazzi 2006: Analysis of mean climate conditions in Senegal (1971 - 1998). AMS Earth Interactions Volume 10, issue 5, pg 1-40.12. Song, Y., H. F. M Semazzi, L. Xie, and L. Ogallo, 2004: A coupled regional climate model for the Lake Victoria basin of East Africa. International Journal of Climatology, 24, 57-75.13. Schreck, C. J. III, and F. H. M. Semazzi, 2004: Variability of the Recent Climate ofEastern Africa. International Journal of Climatology, 24, 681 ? 701.14. Anyah R.O., and F.H.M. Semazzi, 2004: Simulation of the sensitivity of Lake Victoria basin climate to lake surface temperatures. Theoretical and Applied Climatology, 79, 55-69.15. Song, Y., and H. F. M Semazzi, 2002: Chapter#4: Development of a coupled regional climate simulation model for the Lake Victoria basin [Book title: The East African Great lakes, Limnology, Paleolimnology and Biodiversity]. Kluwer Academic Publishers B. V. In press.16. The IPCC Assessment; Climate Change; Cambridge Press, edited by Sir J. T. Houghton et al (2001); Chapter 12 on Detection and Attribution of causes. Review editors: Fredrick Semazzi. and John Zillman. ISBN 0521 80767.17. Semazzi F. H. M., and Y., Song, 2001: A GCM study of deforestation induced climate change in Africa. Climate Research, Vol. 17: 169-182.18. Indeje, M., F. H. M. Semazzi, and L. J. Ogallo, 2001: A Mechanistic Model Simulation of East African Turkana Jet. Mon. Wea. Rev., vol., 12.19. Indeje, M., F. H. M. Semazzi, and L. J. Ogallo, 2000: ENSO signals in East African rainfall and their prediction potentials. Int. J. Climatol. 20, 19-46.20. Indeje, M., and F. H. M. Semazzi, 2000: Relationships between QBO in the lower equatorial stratospheric zonal winds and East African seasonal rainfall. Meteorol. Atmos.Phys. 73, 227-244.21. Semazzi, F. H. M. and M. Indeje, 1999: Inter-seasonal variability of ENSO rainfall signal over Africa. J. Afri. Meteorol. Soc. 4, 81-94.22. Sun, L., F. H. M. Semazzi, F. Giorgi, and L. Ogallo 1999: Application of the NCAR Regional Climate Model to Eastern Africa. Part I: Simulations of Autumn Rains of 1988.J. Geoph. Res., 104, NO. D6, 6529-6548.23. Sun, L., F. H. M. Semazzi, F. Giorgi, and L. Ogallo 1999: Application of the NCAR Regional Climate Model to Eastern Africa. Part II: Simulations of Interannual Variability. J. Geoph. Res., 104, NO. D6, 6549-6562.24. Qian, J., F. H. M. Semazzi, and J. S. Scroggs, 1998: A global Semi-Lagrangian Semi- Implicit Atmospheric Model with Orography. Mon. Wea. Rev., 126, No. 3, 747-771.25. Semazzi, H. F. M., B. Burns, N. H. Lin and J. E. Schemm: 1996: A GCM Study of the Teleconnections Between the Continental Climate of Africa and Global Sea-Surface Temperature Anomalies. Journal of Climate, 9, 2480-2497.26. Intergovernmental panel on Climate Change (IPCC), 1996: The science of climate change. (F. H. M. Semazzi, Lead Author, chapter 5 on model evaluation), edited by J. T. Houghton, L. G. Meira Filho, B. A. Callander, N. Harris, A. Kattenberg and K. Maskell. Cambridge University Press, Cambridge and New York, 572 pp.27. Lin, Y-L, T-A Wang, and F. H. M. Semazzi, 1996: Response of stably stratified atmosphere to large-scale diabatic forcing with applications to wind patterns in Brazil and the Sahel. Journal of Geophysical Research 101, # D3, 7049-7073.28. Semazzi, H. F. M., and L. Sun, 1996: The Role of Orography in Determining theSahelian Climate. International Journal of Climatology, Vol. 17, No. 6, 381-396.29. Semazzi, F. H. M., D. Webb and G. Pouliot, 1996: A study of trajectory uncentering in semi-Lagrangian models. Journal of the Meteorological Society of Japan, vol. 74, No. 5, 1-13.30. Semazzi, F.H.M., 1996: Review of recent advances in the development of semi- Lagrangian numerical models in meteorology. World Congress of Non-Linear Analysts. Editor, Prof. V. Lakshmikantham. Walter de Gruyter. Berlin. New York 1996.31. Semazzi, H.F.M., J-H Qian, and J. Scroggs, 1995: A global Semi-Lagrangian Semi- Implicit Atmospheric Model. Mon. Wea. Rev., 123, 2534-2550.32. Semazzi, F. H. M., and P. Dekker, 1994: Optimal accuracy in semi-Lagrangian models. Mon. Wea. Rev., 122, 2139-2159.33. Semazzi, H F M, Lin, N H, Lin, Y L, and Giorgi, F, 1993, A GCM nested model study of the influence of sea surface temperature anomalies on Sahelian Climate, Geophys Res Lett, 20, 2897-2900.34. Bates, J. R., F. H. M. Semazzi, R. W. Higgins, and R. M. Barros 1990: Integration of the shallow water equations on the sphere using a vector semi-Lagrangian scheme with amultigrid solver. Mon. Wea. Rev., 118, 1615-1627.35. Semazzi, H. F. M., Y. C. Sud, and V. Mehta, 1989: Reply to comments on, 'An investigation of the relationship between sub-Saharan rainfall and global sea surface temperatures'. Atmosphere-Ocean, 27, 601-605.36. Semazzi, H. F. M., Y. C. Sud, and V. Mehta, 1988: An investigation of the relationship between sub-Saharan rainfall and global sea surface temperatures. Atmosphere-Ocean., 26, 118-138.37. Semazzi, F. H. M., and I. M. Navon, 1986: A comparison between the bounded derivative and normal mode initialization methods using real data. Mon. Wea. Rev., 114,2106-2212.38. Semazzi, H. F. M., 1985: An investigation of the equatorial orographic dynamic mechanism. J. Atmos. Sci., 42, 28-83.39. Semazzi, H. F. M., 1980: Numerical experiments on the orographic dynamic phenomenon over a tropical belt. Arch. Met. Geoph. Biokl., Ser. A., 29, 55-65.40. Semazzi, H. F. M., 1980: Stationary barotropic flow induced by a mountain over a tropical belt. Mon. Mea. Rev., 108, 922-880.PART III ? SCOPE OF WORKThe contractor shall employ the EOF methodology to analyze the meteorological fields present in the North American Regional Reanalysis (NARR) that are of relevance to North Carolina air quality. These fields will include the following:PBL temperatureAtmospheric water vaporCloud coverPrecipitation amounts and frequencyWind speed and directionAtmospheric pressure Deliverable #1: The contractor shall develop a quality assurance project plan (QAPP) for this project for the project manager?s and quality assurance coordinator?s approval. The QAPP shall address the quality assurance/quality control for data analysis for this project. (See Appendix A: Joint Quality Management/Quality Assurance Project Plan (JQM/QAPP) for Data Analysis for: An Urban-scale AQ Planning Tool for Global Change Adaptation -- NARR EOF Analysis)Deliverable #2: The contractor shall produce a report containing maps and graphs, with accompanying narrative, representing: The EOF modes identified in the NARR data subset as described, above.Composite maps for the variables, listed above, including those which will not be used in the actual construction of the EOFS. The report shall explain, in detail, the justification for all analytical judgments made by the contractor, for example, choices for EOF threshold designations. The included explanations must be understandable to a physical scientist without expert knowledge in meteorology. Deliverable #3: The contractor shall supply EPA with an archive containing the following three primary, and any necessary supporting, datasets: A. NARR data subset used in the EOF analyses, e.g. 1979-present for North Carolina and the surrounding region. B. NARR EOF analysis results, including the spatial patterns & corresponding time series for the time period, 1979-present. C. NARR EOF composite analysis results, as above. D. Electronic copies of all maps, figures and tables used in the project report.Project TimelineDeliverableDeadline for Receipt by EPA1. Quality Assurance Project Plan15 days following contract award2. Project ReportSeptember 30, 20083. Archived information as describedSeptember 30, 2008This notice of intent is not a request for competitive proposals. However, interested parties may identify their interest and capability to respond to the requirement by submitting documentation, to martinez.thomas@epa.gov, phone number (202)564-1418, which establishes that their specifications meet EPA's requirement. Documentation must be received within fifteen (15) days after the date of publication of this synopsis to be considered by EPA. A determination not to compete this proposed contract based upon responses to this notice is solely within the discretion of the government. Information received will normally be considered solely for the purpose of determining whether to conduct a competitive procurement.
 

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