Klotzbach, P. J., E. C. J. Oliver, R. D. Leeper, and C. J. Schreck, III, 2016: The relationship between the Madden–Julian Oscillation (MJO) and southeastern New England snowfall. Monthly Weather Review, 144, 1355-1362. http://dx.doi.org/doi:10.1175/MWR-D-15-0434.1
Bell, J. E., S. C. Herring, L. Jantarasami, C. Adrianopoli, K. Benedict, K. Conlon, V. Escobar, J. Hess, J. Luvall, C. P. Garcia-Pando, D. Quattrochi, J. Runkle, and C. J. Schreck, III, 2016: Ch. 4: Impacts of Extreme Events on Human Health. The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment, U.S. Global Change Research Program, 99–128. http://dx.doi.org/10.7930/J0BZ63ZV
Schreck, C. J., III, S. Bennett, J. M. Cordeira, J. Crouch, J. Dissen, A. L. Lang, D. Margolin, A. O’Shay, J. Rennie, and M. J. Ventrice, 2015: Natural gas prices and the extreme winters of 2011/12 and 2013/14: Causes, indicators, and interactions. Bulletin of the American Meteorological Society, 96, S93-S96. http://dx.doi.org/10.1175/BAMS-D-13-00237.1
Hennon, C. C., K. R. Knapp, C. J. Schreck, III, S. E. Stevens, J. P. Kossin, P. W. Thorne, P. A. Hennon, M. C. Kruk, J. Rennie, J.-M. Gadéa, M. Striegl, and I. Carley, 2015: Cyclone Center: Can citizen scientists improve tropical cyclone intensity records? Bulletin of the American Meteorological Society, 96, 591-607. http://dx.doi.org/10.1175/BAMS-D-13-00152.1
Schreck, C. J., III
, K. R. Knapp, and J. P. Kossin, 2015: The impact of best track discrepancies on global tropical cyclone climatologies using IBTrACS. Monthly Weather Review
3881-3899, doi:10.1175/MWR-D-14-00021.1. Available online at: http://dx.doi.org/10.1175/MWR-D-14-00021.1
Kruk, M. C., C. J. Schreck, III
, and T. Evans, 2014: [The Tropics] Eastern North Pacific basin [in "State of the Climate in 2013"]. Bulletin of the American Meteorological Society
, J. Blunden, and D. S. Arndt, Eds., American Meteorological Society, S90-S92. Available online at: http://journals.ametsoc.org/doi/pdf/10.1175/2014BAMSStateoftheClimate.1
, J.-R. Bidlot, H. P. Freitag, and C. J. Schreck, III
, 2014: Directional bias of TAO daily buoy wind vectors in the Central Equatorial Pacific Ocean from November 2008 to January 2010. Data Science Journal
79-87, doi:10.2481/dsj.14-019. Available online at: https://www.jstage.jst.go.jp/article/dsj/13/0/13_14-019/_article
Gottschalck, J., P. E. Roundy, C. J. Schreck, III
, A. Vintzileos, and C. Zhang, 2013: Large-scale atmospheric and oceanic conditions during the 2011Ð12 DYNAMO field campaign. Monthly Weather Review
4173-4196, doi:10.1175/MWR-D-13-00022.1. Available online at: http://dx.doi.org/10.1175/MWR-D-13-00022.1
Kruk, M. C., C. J. Schreck, III
, and R. Tanabe, 2013: [The Tropics] Eastern North Pacific basin [in "State of the Climate in 2012"]. Bulletin of the American Meteorological Society
S89ÐS92, doi:10.1175/2013BAMSStateoftheClimate.1. Available online at: http://dx.doi.org/10.1175/2013BAMSStateoftheClimate.1
Schreck, C. J., III
, J. M. Cordeira, and D. Margolin, 2013: Which MJO events affect North American temperatures? Monthly Weather Review
3840-3850, doi:10.1175/MWR-D-13-00118.1. Available online at: http://dx.doi.org/10.1175/MWR-D-13-00118.1
Schreck, C. J., III
, L. Shi, J. P. Kossin, and J. J. Bates, 2013: Identifying the MJO, equatorial waves, and their impacts using 32 years of HIRS upper-tropospheric water vapor. Journal of Climate
1418-1431, doi:10.1175/JCLI-D-12-00034.1. Available online at: http://dx.doi.org/10.1175/JCLI-D-12-00034.1
Shi, L., C. J. Schreck, III
, and V. O. John, 2013: An improved HIRS upper tropospheric water vapor dataset and its correlations with major climate indices. Atmos. Chem. Phys. Discuss.
33411-33442, doi:10.5194/acpd-12-33411-2012. Available online at: http://www.atmos-chem-phys-discuss.net/12/33411/2012/
Ventrice, M. J., M. C. Wheeler, H. H. Hendon, C. J. Schreck, III
, C. D. Thorncroft, and G. N. Kiladis, 2013: A modified multivariate MaddenÐJulian Oscillation index using velocity potential. Monthly Weather Review
4197-4210, doi:10.1175/MWR-D-12-00327.1. Available online at: http://dx.doi.org/10.1175/MWR-D-12-00327.1
Ventrice, M. J., C. D. Thorncroft, and C. J. Schreck, 2012: Impacts of convectively coupled Kelvin waves on environmental conditions for Atlantic tropical cyclogenesis. Mon. Wea. Rev., 140, 2198–2214, doi:10.1175/MWR-D-11-00305.1.
Kruk, M. C., C. J. Schreck, and Hennon, Paula A., 2012: [The Tropics] Eastern North Pacific basin [in “State of the Climate in 2011”]. Bull. Amer. Meteor. Soc., 93, S105–S107, doi:10.1175/2012BAMSStateoftheClimate.1.
Aiyyer, A., A. Mekonnen, and C. J. Schreck, 2012: Projection of tropical cyclones on wavenumber-frequency filtered equatorial waves. J. Climate, 25, 3653–3658, doi:10.1175/JCLI-D-11-00451.1.
Schreck, C. J., J. Molinari, and A. Aiyyer, 2012: A global view of equatorial waves and tropical cyclogenesis. Mon. Wea. Rev., 140, 774-788, doi:10.1175/MWR-D-11-00110.1.
Schreck, C. J., and J. Molinari, 2011: Tropical cyclogenesis associated with Kelvin waves and the Madden–Julian oscillation, Mon. Wea. Rev., 139, 2723-2734, doi:10.1175/MWR-D-10-05060.1.
Schreck, C. J., J. Molinari, and K. I. Mohr, 2011: Attributing tropical cyclogenesis to equatorial waves in the western North Pacific. J. Atmos. Sci., 68, 195-209, doi:10.1175/2010JAS3396.1.
Schreck, C. J., and J. Molinari, 2009: A case study of an outbreak of twin tropical cyclones. Mon. Wea. Rev., 137, 863-875, doi:10.1175/2008MWR2541.1.
Roundy, P. E., and C. J. Schreck, 2009: A combined wave-number–frequency and time-extended EOF approach for tracking the progress of modes of large-scale organized tropical convection. Quart. J. Roy. Meteor. Soc., 135, 161-173, doi:10.1002/qj.356.
Roundy, P. E., C. J. Schreck, and M. A. Janiga, 2009: Contributions of convectively coupled equatorial Rossby waves and Kelvin waves to the real-time multivariate MJO Indices. Mon. Wea. Rev., 137, 469-478, doi:10.1175/2008MWR2595.1.
Schreck, C. J., and F. H. M. Semazzi, 2004: Variability of the recent climate of eastern Africa. Int. J. Climatol., 24, 681-701, doi:10.1002/joc.1019.
Identifying Tropical Variability with CDRs
The Madden-Julian Oscillation (MJO), equatorial Rossby waves, and Kelvin waves are the dominant sources of synoptic-to-subseasonal variability in the tropics. The divergent circulations from their convection can influence tropical cyclones and other weather patterns around the globe. Forecasters in the Energy Industry pay particular attention to these modes, harnessing their long time scales and global impacts to anticipate energy demand in the United States. Climate Data Records (CDRs) play a key role in the identification and forecasting of these modes. This project endeavors develop new diagnostics for tracking tropical modes using CDRs.
This project produced several peer-reviewed papers in FY13 that demonstrated the value of CDRs of monitoring and predicting tropical variability:
- Schreck et al. (2013) demonstrated the use of a new index, the Multivariate Pacific–North American (MVP) index, that uses Outgoing Longwave Radiation (OLR) in conjunction with dynamical fields to identify which MJO events affect temperatures over North America and which do not.
- Gottschalk et al. (2013) used OLR to summarize the large-scale conditions during the international DYNAMO (Dynamics of the MJO) field campaign during 2011/12.
- Kruk et al. (2013) used OLR and NOAA’s Optimum Interpolation Sea Surface Temperature (OI SST) analysis to identify the conditions for hurricanes in the eastern Pacific during 2012.
- Shi et al. (2013) evaluated the interactions between the CDR of HIRS above-cloud water vapor and various climate modes.
- Ventrice et al. (2013) compared two indices for tracking the MJO to identifying the strengths and weaknesses of including OLR.
Figure 1. shows an example from Schreck et al. (2013) of how the MVP can be used to anticipate weather patterns over the United States. Tropical convection is identified by the shaded OLR anomalies, while the extratropical response is shown by the contours of 200-hPa streamfunction. Both composites are for the same phase of the MJO, as indicated by the large area of convection near the Maritime Continent. However, they are subdivided by the phase of the MVP.
When the MVP is negative (bottom), a secondary maximum in convection appears near Hawaii. This convection is associated with an extratropical wave train (contours) that leads to warm temperatures over the eastern United States. Such a pattern is expected for this phase of the MJO. However, this pattern is strikingly absent when the MVP is positive (top). Even though the forcing from the MJO is similar in both patterns, the MVP identifies which set of dates produces the expected extratropical response and which does not.
Combinations of the MJO and the MVP can influence North American temperature patterns for as long as 20 days. For this reason, the MVP has been added to a suite of other CDR-based diagnostics on monitor.cicsnc.org/mjo, which serves hundreds of unique users and the public and private sectors every month.
Figure 1: Composites of OLR (shading) and 200-hPa streamfunction anomalies. Both composites show the same phase of the MJO but different phases of the MVP.
- Evaluate new CDR of daily OLR
- Revise monitor.cicsnc.org/mjo to leverage the new interim CDR of daily OLR
- Schreck, C. J., J. M. Cordeira, and D. Margolin, 2013: Which MJO events affect North American temperatures? Mon. Wea. Rev., 141, 3840–3850, doi:10.1175/MWR-D-13-00118.1.
- Gottschalck, J., P. E. Roundy, C. J. Schreck III, A. Vintzileos, and C. Zhang, 2013: Large-scale atmospheric and oceanic conditions during the 2011–12 DYNAMO field campaign. Mon. Wea. Rev., 141, 4173–4196, doi:10.1175/MWR-D-13-00022.1.
- Kruk, M. C., C. J. Schreck, and R. Tanabe, 2013: [The Tropics] Eastern North Pacific basin [in “State of the Climate in 2012”]. Bull. Amer. Meteor. Soc., 94, S89–S92, doi:10.1175/2013BAMSStateoftheClimate.1.
- Shi, L., C. J. Schreck, and V. O. John, 2013: An improved HIRS upper tropospheric water vapor dataset and its correlations with major climate indices. Atmos. Chem. Phys, 12, 33411–33442, doi:10.5194/acpd-12-33411-2012.
- Ventrice, M. J., M. C. Wheeler, H. H. Hendon, C. J. Schreck, C. D. Thorncroft, and G. N. Kiladis, 2013: A modified multivariate Madden–Julian Oscillation index using velocity potential. Mon. Wea. Rev., 141, 4197–4210, doi:10.1175/MWR-D-12-00327.1.
- monitor.cicsnc.org/mjo served CDR-based diagnostics to an average of 270 unique visitors every month
- Developed a business case study for the CICS-NC Executive Forum on Business and Climate on CDR use by the energy industry
- Schreck, C. J., 2013: The latest on the MJO. MDA Weather Services’ 12th Annual Energy Conference for Traders and Analysts, Las Vegas, NV, 17 October 2013.
- Schreck, C. J., 2013: Overview of hurricanes and seasonal hurricane prediction. Statistical and Applied Mathematical Sciences Institute Undergraduate Modeling Workshop. Raleigh, NC, 13 May 2013.
- Schreck, C. J., 2013: Scale interactions: Bridging the gap between the tropics and extratropics. Third Workshop on Medium/Long Range Weather Forecasting and Subseasonal Atmospheric Drivers, Albany, NY, 15 May 2013.
- Schreck, C. J., 2013: Use of NOAA satellite products by the energy sector. NOAA Satellite Conference for Direct Readout, GOES/POES, and GOES-R/JPSS Users, College Park, MD, 11 April 2013.
- Schreck, C. J., 2013: Comparing MJO diagnostics during DYNAMO. MJO Field Data and Science Workshop, 4-8 March 2013, Kohala Coast, HI.
Climate Data Record (CDR) Integrated Product Team (IPT) Support
Climate Data Record (CDR) IPTs are multi-disciplinary teams comprised of members from offices and organizations supporting the transition of research-grade CDRs into an initial operational capability (IOC) status. The IPTs are formed for the purpose of efficient and effective collaboration, coordination, and execution and reporting of member’s office/organization tasks required to transition the CDR to an IOC state.
Figure 1. A sample image for the Outgoing Longwave Radiation-Monthly CDR.
CICS-NC has participated in the IPTs of the following CDRs during this reporting period:
- Atmospheric Temperature Bundle-MSU and AMSU FCDR (Burden)
- Atmospheric Temperature Bundle-MSU and AMSU TCDR (Burden)
- Cryosphere Bundle-APP and APP-x (Burden)
- Cloud/Moisture Bundle-Cloud Top Pressure, TPW (Burden)
- Cloud Bundle-AVHRR Radiances, Cloud properties (Burden)
- Total Solar Irradiance-Observational (Inamdar)
- Total Solar Irradiance-Composite (Inamdar)
- Calibration of MSRI using the Moon (Inamdar)
- Land Surface Bundle (Matthews)
- Global Surface Albedo (Matthews)
- Sea Ice Concentration-Annual (Peng)
- Ocean Surface Bundle (Peng)
- Cryosphere Bundle-Snow cover fraction (Peng)
- Precipitation-PERSIANN-CDR (Prat)
- Outgoing Longwave Radiation-Monthly (Schreck)
- Outgoing Longwave Radiation-Daily (Schreck)
Products Branch representative IPT responsibilities include:
- Leading and scheduling IPT meetings needed for resolving technical issues on the products with PI
- Conducting initial assessment of CDR readiness for transition from scientific perspective
- Reviewing PI-submitted draft products against IOC requirements
- Providing feedback to PI on draft products
- Verifying PI-submitted final products conform to IOC requirements
- Participating in management and technical meetings as required
- Working with PI, IPT and O&M Project Manager to complete each CR and route for signatures
- Attending Change Control Board meetings, when needed
- Reviewing PI-submitted documents delivered as part of the WA (C-ATBD, Maturity Matrix, Data Flow Diagram, Implementation Plan) and providing feedback
- Reviewing PI-submitted documents delivered as part of the WA (QA procedure, QA results, VDD, annual reports) for information only
Operations Branch representative IPT responsibilities include:
- Leading and scheduling IPT meetings needed for resolving technical issues related to operations
- Assisting with initial assessment of CDR readiness for transition from an operational perspective
- Entering the source code into the NCEI version control system
- Requesting an initial security review
- Verifying source code and Readme packages for compliance with IOC standards
- Informal source code reviewing with feedback for CDRP
- Porting code for implementation on NCEI systems as requested by CDRP, PI, and/or other IPT members
- Assisting with ingest and archival of the CDR source code, documentation, and data packages
- Participating in management and technical meetings as required
- Working with PI, IPT, and O&M Project Manager to complete each CR.
Figure 2: An example image of Normalized Difference Vegetation Index (NDVI) for the Land Surface Bundle CDR.
- Continue participating on CDR IPTs as requested to transition CDRs to initial operational capability status
- Ashouri, H, K. Hsu, S. Sorooshian, D. Braithwaite, K.R. Knapp, L.D. Cecil, B.R. Nelson, and O.P. Prat, 2014. PERSIANN-CDR: Daily precipitation climate data record from multi-satellite observations for hydrological and climate studies. Bull. Am. Meteorol. Soc., conditionally accepted.
- Prat, O.P., B.R. Nelson, and L. Vasquez, 2014. Characterization of CONUS rainfall using a multi-sensor approach: Evaluation of radar-based, satellite-based, and ground-based QPE products. Abstract submitted to the International Weather Radar and Hydrology symposium, 7-9 April 2014, Washington, DC, USA.
- Prat, O.P., and B.R. Nelson, 2014. Toward the development of an evaluation framework of Climate Data Records for precipitation: A characterization of CONUS rainfall using a suite of satellite, radar, and rain gauge QPE products. 94th annual meeting of the American Meteorological Society, 2-6 February 2014, Atlanta, GA, USA.
Reanalyzing Tropical Cyclones Imagery with Citizen Scientists
The global record of tropical cyclones contains uncertainties caused by differences in analysis procedures around the world and through time. The human eye best recognizes patterns in storm imagery, so we enlisted the public. Interested volunteers are shown one of nearly 300,000 satellite images. They answer questions about that image as part of a simplified technique for estimating the maximum surface wind speed of tropical cyclones.
During Year 2 of Cyclone Center, classifications from volunteers continued to be collected and the site has now obtained nearly 300,000 analyses from more than 5,000 scientists. In addition, the site underwent a major overhaul and the way in which storms were selected and questions asked has changed considerably.
Preliminary analysis was performed, comparing output from Cyclone Center with that from both the best track data for cyclone intensity and objective techniques including the Advanced Dvorak Technique (ADT). Figure 1 shows an example for two storms, Typhoons Ivan (1997) and Yvette (1992). The Cyclone Center estimates reproduce the major features of each storm’s lifecycle, including the weakening on day 9 of Yvette (bottom). They also avoid a known issue of ADT, which limits the intensification of a storm until the eye is clearly visible (around days 5–6 for both storms).
The panels on the right in Fig. 1 show another unique advantage of the Cyclone Center estimates. Since at least 10 citizen scientists analyze each image, we can analyze the uncertainty in those estimates. In this case, we have used a Monte Carlo technique to stitch together randomly selected estimates for each time step.
We presented this initial research at the annual meetings of both the American Geophysical Union and the American Meteorological Society. Addtionally, a manuscript has been submitted to the latter for publication in the Bulletin of the American Meteorological Society (BAMS).
CICS-NC intern, Brady Blackburn, is a recent graduate of Asheville High School with an interest in pursuing a degree in environmental science. During FY13, CICS-NC helped Mr. Blackburn explore that interest by contributing to tropical cyclone research through the Cyclone Center. He examined the classifications provided by Cyclone Center’s volunteer users and he compared them with the actual satellite images. In particular, Mr. Blackburn sought to understand why a volunteer might mistakenly classify a storm as having an eye when it does not. His work will help us develop an objective method for evaluating the classifications, which will improve our future analysis.
Figure 1: Time series of intensities from (a,b) Typhoon Ivan (1997) and (c,d) Typhoon Yvette (1992). a,c) Comparisons between best tracks, ADT-HURSAT, and Cyclone Center (CC Consensus). b,d) Spread in the Cyclone Center estimates.
- Continue promoting this project through a variety of media outlets, particularly during the 2014 Atlantic Hurricane Season
- Publish the initial results in the Bulletin of the American Meteorological Society (BAMS)
- Develop method for applying the full Dvorak analysis to the data collected, including the “Data T-Number”
- Hennon, C.C., K.R. Knapp, C.J. Schreck, S.E. Stevens, and J.P. Kossin, 2013: Cyclone Center: Using crowdsourcing to determine tropical cyclone intensity. AGU Fall Meeting, 9-13 December 2013, San Francisco, CA.
- Thorne, P.W., C.C. Hennon, K.R. Knapp, C.J. Schreck III, S.E. Stevens, P.A. Hennon, J.P. Kossin, M.C. Kruk, J. Rennie, and L.E. Stevens, 2014: Cyclonecenter: Crowdsourcing insights into historical tropical cyclone intensities. 26th Conference on Climate Variability and Change, 2-6 February 2014, Atlanta, GA.