Quantitative Observing System Assessment Program

The QOSAP program evaluates both new and proposed observing systems by conducting experiments to determine impact of observational data on models (existing or proposed). The QOSAP program has two primary evaluations, Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs). Together, these experiments can help managers make decisions about the effectiveness, impact, and viability of new and proposed observing systems.

Quantitative Observing System Assessment Program

The QOSAP program evaluates both new and proposed observing systems by conducting experiments to determine impact of observational data on models (existing or proposed). The QOSAP program has two primary evaluations, Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs). Together, these experiments can help managers make decisions about the effectiveness, impact, and viability of new and proposed observing systems.

Optimizing Observing Systems

The QOSAP program not only improves the effectiveness and integration of new instrumentation, it allows scientists and managers to allocate their resources in a way that maximizes the effectiveness of the observing system and its data.

Supporting the Weather Research & Forecasting Act of 2017

The Weather Research and Forecasting Innovation Act of 2017 specifically mandates NOAA to perform OSSEs to quantitatively assess the relative value and benefits of observing capabilities and systems.

  • AOML conducts Observing System Simulation Experiments (OSSE) to assess the impact of large and expensive observing systems prior to deployment.
  • AOML completed OSSE experiments required by the  Weather Research and Forecasting Innovation Act of 2017 on Radial Occultation Satellites.
  • AOML will conduct OSSEs for all proposed satellite systems in NOAA and optimize the use of NOAA’s reconnaissance aircraft.

 

The Weather Law H.R. 353 mandates NOAA to perform OSSEs to quantitatively assess the relative value and benefits of observing capabilities and systems. In particular, OSSEs shall be conducted prior to the acquisition of major Government-owned or Government-leased operational observing systems which a lifecycle cost of more than $500,000,000, and prior to the purchase of any major new commercially provided data with a lifecycle cost of more than $500,000,000.

About the QOSAP Program

QOSAP improves existing atmospheric, oceanic, and coupled models by performing simulation experiments to evaluate the tradeoffs and impacts of different observation types across NOAA Line Offices. These studies aid NOAA management by determining accuracy, cost-efficiency, and viability of observations within various model designs.

Objectives

Improve quantitative and objective assessment capabilities to evaluate operational and future observation system impacts and trade-offs to assess and to prioritize NOAA’s observing system architecture.

  • Increase NOAA’s capacity to conduct quantitative observing system assessments.
  • Develop and use appropriate quantitative assessment methodologies.
  • Inform major decisions on the design and implementation of optimal composite observing systems.

Economic Viability

Observing systems are more cost effective and allow for strategic evolution of emerging technology.

Quantitative Improvements

Effectiveness of new instrumentation is assessed. Integration response is predicted for various instrument configurations, and impacts of data assimilation are diagnosed.

Time Efficiency

Time lags between instrument deployment and operational integration are reduced with OSSE integration.

Key Differences Between OSSEs and OSEs

Which experiment is right for my project? When to use an OSE, an OSSE, or both. Rollover and Click the button on the image for more.

Observing System Experiments

Observing System Experiments (OSEs) evaluate the impact of real data from deployed instruments on a particular model. OSEs can help scientists determine the most effective way to use instrumentation for a model

Observing System Simulation Experiments

Observing System Simulation Experiments (OSSEs) assess the impact of a potential observing system by testing a potential dataset from the proposed instrumentation on a simulated natural environment (called a Nature Run).

Key Differences Between OSSEs and OSEs

Which experiment is right for my project? When to use an OSE, an OSSE, or both. Rollover and Click the button on the image for more.

OSSE

Observing System Simulation Experiments (OSSEs) assess the impact of a potential observing system by testing a potential dataset from the proposed instrumentation on a simulated natural environment (called a Nature Run).

Image: Methodology for an OSSE. First, Nature run with simulated truth, then synthetic observations, then analysis, then model forecast and further analysis. The model is verified by the nature run.

Image: Methodology for an OSSE. First, Nature run with simulated truth, then synthetic observations, then analysis, then model forecast and further analysis. The model is verified by the nature run.

OSE

Observing System Experiments (OSEs) evaluate the impact of real data from deployed instruments on a particular model. OSEs can help scientists determine the most effective way to use instrumentation for a model.

Image: Methodology for an OSE. First, a real atmosphere environment is selected, then observations are assimilated. Next, analysis is conducted and a model forecast created with subsequent analysis on success. The model is verified through analysis and forecasting.

Image: Methodology for an OSE. First, a real atmosphere environment is selected, then observations are assimilated. Next, analysis is conducted and a model forecast created with subsequent analysis on success. The model is verified through analysis and forecasting.

Observing System Simulation Experiments (OSSEs)

Fiscal Year 2018 Accomplishments

  • Impact of GEOstationary hyperspectral sounders (GeoHSS) on numerical weather prediction
  • Radio Occultation observations (COSMIC-2, refractivity and bending angle retrievals)
  • Global and regional impacts of Cubesats
  • Ocean OSSEs related to the role of ocean observations in hurricane prediction
Key OSSE Results
  • Pioneer Technology

    Developed First Regional Ocean OSSE for the Gulf of Mexico.

  • Communications

    Utilized OSSEs for GNSS Radio Occultation and Geostationary Hyperspectral sounders.

  • Unmanned Aircraft

    Integration of both manned and unmanned aircraft improving hurricane track and intensity forecasts.

  • Global Possibilities

    Developed and validated a new global OSSE system.

Ocean & Ecosystem OSSE System Development
  • Initial development of a global ocean OSSE system
  • Progress on developing OSE and OSSE capabilities for ecosystem and fisheries monitoring, prediction and management
  • Progress on regional OSEs and OSSEs for evaluation of shelf to coastal observing system components
  • Progress on regional biophysical OSEs and OSSEs for improved management of locally and remotely stressed coastal habitats
Atmospheric OSSE System Development
  • Improvement of the simulation of conventional and satellite observations
  • Development and evaluation of new NOAA basin scale nature run
  • Acquisition and initial evaluation of new ECMWF global nature run
  • Initial development of the 4DEnVAR OSSE system
  • Implementation and initial testing of EFSO in OSSE framework
  • Revised verification metrics for atmospheric assessments
  • Initial discussions and research to develop capabilities to assess non-model applications

The OSSE Checklist

The Observing System Simulation Experiment or OSSE checklist presented here is meant to be a detailed guide to OSSE practitioners and OSSE consumers to evaluate the design of an OSSE system and OSSE experiments. It provides a step-by-step process to encourage the user to consider all aspects of good OSSE design principles. The current version of the OSSE checklist is directly applicable to weather-related observing, modeling and data assimilation (DA) systems. But very similar issues and concerns apply to OSSEs that would be used across a diverse range of geophysical domains and the user of the OSSE checklist should be able to translate the discussion presented here to these domains fairly directly.

Source

Thinking About Performing an OSSE?

  • Use this checklist to determine if an OSSE is right for your project.
  • Is the System Realistic?
  • Is the OSSE system Appropriate for the Impacts of Interest?
  • Are the Caveats Associated with the OSSE System Well-Known?
  • Will the OSSE be Completed Quickly?

These are just a few of the questions sampled from the comprehensive checklist for OSSEs. To visit the checklist and take the quiz, click the button below.

Featured Publication

Image of paper "Future Observing System Simulation Experiments"

Featured Publication

Future Observing System Simulation Experiments

Abstract: An additional set of challenges will arise when future DA systems strongly couple the different Earth system components. As operational forecast and data assimilation (DA) systems evolve, observing system simulation experiment (OSSE) systems must evolve in parallel. Expected development of operational systems—especially the use of data that are currently not used or are just beginning to be used, such as all-sky and surface-affected microwave radiances—will greatly challenge our ability to construct realistic OSSE systems.In response, future OSSE systems will require coupled models to simulate nature and coupled observation simulators. The requirements for future evolving OSSE systems and potential solutions to satisfy these requirements are discussed. It is anticipated that in the future the OSSE technique will be applied to diverse and coupled domains with the use of increasingly advanced and sophisticated simulations of nature and observations.

Image of paper "Future Observing System Simulation Experiments"

Observing System Experiments (OSEs)

Fiscal Year 2018 Accomplishments

  • Global experiments to quantify impacts of Radio Occultation, MW, IR and AMV in current operational global NWP/DA
  • Regional experiments to evaluate impact of CYGNSS
  • Impact of airborne Doppler Wind Lidar on hurricane analysis and prediction
  • Impact of current Radio Occultation on hurricane prediction
  • Ocean experiments to quantify impact of ocean observations in hurricane prediction
  • Testing of new lightning data assimilation algorithms
Recent OSEs Conducted
  • Advancing In-Situ Observations

    Wind lidar onboard the P3 aircraft.

  • Continuing Improvements

    Radio Occultation from COSMIC.

  • Closing Data Gaps

    Data gap mitigation strategies.

  • Assimilation Strategies

    Advanced assimilation algorithms for Radio Occultation observations.

  • Assimilation Algorithms

    Advanced assimilation algorithms for GOES-R lightning data.

Publications

  • OSSE Related Refereed Publications

    2013

    Hoffman, R.N., J.V. Ardizzone, S.M. Leidner, D.K. Smith, and R.M. Atlas. Error estimates for ocean surface winds: Applying Desroziers diagnostics to the cross-calibrated, multiplatform analysis of wind speed. Journal of Oceanic and Atmospheric Technology, 30(11):2596-2603 (doi:10.1175/JTECH-D-13-00018.1) (2013).

    Nolan, D.S., R. Atlas, K.T. Bhatia, and L.R. Bucci. Development and validation of a hurricane nature run using the Joint OSSE nature run and the WRF model. Journal of Advances in Modeling Earth Systems, 5(2):382-405 (doi:10.1002/jame.20031) (2013).

    Privé, N.C., Y. Xie, J.S. Woollen, S.E. Koch, R. Atlas, and R.E. Hood. Evaluation of the Earth Systems Research Laboratory’s global Observing System Simulation Experiment (OSSE) system. Tellus A, 65:19011 (doi:10.3402/ tellusa.v65i0.19011), 22 pp. (2013).

    Ralph, F.M., J. Intrieri, D. Andra, R. ATLAS, S. Boukabara, D. Bright, P. Davidson, B. Entwistle, J. Gaynor, S. Goodman, J.-G. Jiing, A. Harless, J. Huang, G. Jedlovec, J. Kain, S. Koch, B. Kuo, J. Levit, S. MURILLO, L.P. Riishojgaard, T. Schneider, R. Schneider, T. Smith, and S. Weiss. The emergence of weather-related testbeds linking research and forecasting operations. Bulletin of the American Meteorological Society, 94(8):1187-1211 (doi:10.1175/BAMS-D-12-00080) (2013).

    2014

    Baker, W.E., R. Atlas, C. Cardinali, A. Clement, G.D. Emmitt, B.M. Gentry, R.M. Hardesty, E. Kallen, M.J. Kavaya, R. Langland, Z. Ma, M. Masutani, W. McCarty, R.B. Pierce, Z. Pu, L.P. Riishojgaard, J. Ryan, S. Tucker, M. Weissmann, and J.G. Yoe. Lidar-measured wind profiles: The missing link in the global observing system. Bulletin of the American Meteorological Society, 95(4):543-564 (doi:10.1175/BAMS-D-12-00164.1) (2014).

    Halliwell, G.R., A. Srinivasan, V. Kourafalou, H. Yang, D. Willey, M. Le Henaff, and R. Atlas. Rigorous evaluation of a fraternal twin ocean OSSE system for the open Gulf of Mexico. Journal of Oceanic and Atmospheric Technology, 31(1):105-130 (doi:10.1175/JTECH-D-13-00011.1) (2014).

    Nolan, D.S., J.A. Zhang, and E.W. Uhlhorn. On the limits of estimating the maximum wind speeds in hurricanes. Monthly Weather Review, 142(8):2814-2837 (doi:10.1175/MWR-D-13-00337.1) (2014). [first sentence of abstract states: ‟This study uses an observing system simulation experiment (OSSE) approach to test the limitations…”

    Privé, N.C., Y. Xie, S. Koch, R. Atlas, S.J. Majumdar, and R.N. Hoffman. An observing system simulation experiment for the unmanned aircraft system data impact on tropical cyclone track forecasts. Monthly Weather Review, 142(11):4357-4363 (doi:10.1175/MWR-D-14-00197.1) (2014).

    2015

    Atlas, R., V. Tallapragada, and S. Gopalakrishnan. Advances in tropical cyclone intensity forecasts. Marine Technology Society Journal, 49(6):149-160 (doi:10.4031/MTSJ.49.6.2) (2015).

    Atlas, R., L. Bucci, B. Annane, R. Hoffman, and S. Murillo. Observing System Simulation Experiments to assess the potential impact of new observing systems on hurricane forecasting. Marine Technology Society Journal, 49(6):140-148 (doi:10.4031/MTSJ.49.6.3) (2015).

    Atlas, R., R.N. Hoffman, Z. Ma, G.D. Emmitt, S.A. Wood, S. Greco, S. Tucker, L. Bucci, B. Annane, R.M. Hardesty, and S. Murillo. Observing system simulation experiments (OSSEs) to evaluate the potential impact of an optical autocovariance wind lidar (OAWL) on numerical weather prediction. Journal of Atmospheric and Oceanic Technology, 32(9):1593-1613 (doi:10.1175/JTECH-D-15-0038.1) (2015).

    Cucurull, L., and R.A. Anthes. Impact of loss of U.S. microwave and radio occultation observations in operational numerical weather prediction in support of the U.S. data gap mitigation activities. Weather and Forecasting, 30(2):255-269 (doi:10.1175/WAF-D-14-00077.1) (2015).

    Halliwell, G.R., V. Kourafalou, M. Le Henaff, L.K. Shay, and R. Atlas. OSSE impact analysis of airborne ocean surveys for improving upper-ocean dynamical and thermodynamical forecasts in the Gulf of Mexico. Progress in Oceanography, 130:32-46 (doi:10.1016/j.pocean.2014.09.004) (2015).

    Oke, P.R., G. Larnicol, E.M. Jones, V. Kourafalou, A.K. Sperrevik, F. Carse, C.A.S. Tanajura, B. Mourre, M. Tonani, G.B. Brassington, M. Le Hénaff, G.R. Halliwell, R. Atlas, A.M. Moore, C.A. Edwards, M.J. Martin, A.A. Sellar, A. Alvarez, P. De Mey, and M. Iskandarani. Assessing the impact of observations on ocean forecasts and reanalyses: Part 2, Regional applications. Journal of Operational Oceanography, 8(S1):s63-s79 (doi:10.1080/ 1755876X.2015.1022080) (2015).

    2016

    Androulidakis, Y.S., V.H. Kourafalou, G.R. Halliwell, M. Le Henaff, H.S. Kang, M. Mehari, and R. Atlas. Hurricane interaction with the upper ocean in the Amazon-Orinoco plume region. Ocean Dynamics, 66(12):1559-1588 (doi:10.1007/s10236-016-0997-0) (2016).

    Boukabara, S.A., I. Moradi, R. Atlas, S.P.F. Casey, L. Cucurull, R.N. Hoffman, K. Ide, V.K. Kumar, R. Li, Z. Li, M. Masutani, N. Shahroudi, J. Woollen, and Y. Zhou. Community global Observing System Simulation Experiment (OSSE) package: CGOP—Description and usage. Journal of Atmospheric and Oceanic Technology, 33(8):1759-1777 (doi:10.1175/JTECH-D-16-0012.1) (2016).

    Boukabara, S.A., T. Zhu, H.L. Tolman, S. Lord, S. Goodman, R. Atlas, M. Goldberg, T. Auligne, B. Pierce, L. Cucurull, M. Zupanski, M. Zhang, I. Moradi, J. Otkin, D. Santek, B. Hoover, Z. Pu, X. Zhan, C. Hain, E. Kalnay, D. Hotta, S. Nolin, E. Bayler, A. Mehra, S.P.F. Casey, D. Lindsey, L. Grasso, K. Kumar, A. Powell, J. Xu, T. Greenwald, J. Zajic, J. Li, J. Li, B. Li, J. Liu, L. Fang, P. Wang, and T.-C. Chen. S4: An O2R/R2O infrastructure for optimizing satellite data utilization in NOAA numerical modeling systems: A step toward bridging the gap between research and operations. Bulletin of the American Meteorological Society, 97(12):2359-2378 (doi:10.1175/BAMS-D-14-00188.1) (2016).

    Hoffman, R.N., and R. Atlas. Future observing system simulation experiments. Bulletin of the American Meteorological Society, 97(9):1601-1616 (doi:10.1175/BAMS-D-15-00200.1) (2016).

    Kourafalou, V.H., Y.S. Androulidakis, G.R. Halliwell, H.-S. Kang, M. Mehari, M. Le Henaff, R. Atlas, and R. Lumpkin. North Atlantic Ocean OSSE system development: Nature Run evaluation and application to hurricane interaction with the Gulf Stream. Progress in Oceanography, 148:1-25 (doi:10.1016/ j.pocean.2016.09.001) (2016).

    Lee, P., R. Atlas, G. Carmichael, Y. Tang, B. Pierce, A.P. Biazar, L. Pan, H. Kim, D. Tong, and W. Chen. Observing System Simulation Experiments (OSSEs) using a regional air quality application for evaluation. In Air Pollution Modeling and its Application XXIV, D.G. Steyn and N. Chaumerliac (eds.). Springer International Publishing, 599-605 (doi:10.1007/978-3-319-24478-5_97) (2016).

    Ruf, C.S., R. Atlas, P.S. Chang, M.P. Clarizia, J.L. Garrison, S. Gleason, S.J. Katzberg, Z. Jelenak, J.T. Johnson, S.J. Majumdar, A. O’Brien, D.J. Posselt, A.J. Ridley, R.J. Rose, and V.U. Zavorotny. New ocean winds satellite mission to probe hurricanes and tropical convection. Bulletin of the American Meteorological Society, 97(3):385-395 (doi:10.1175/BAMS-D-14-00218.1) (2016).

    2017

    Cucurull, L., R. Li, and T.R. Peevey. Assessment of radio occultation observations from the COSMIC-2 mission with a simplified Observing System Simulation Experiment configuration. Monthly Weather Review, 145(9):3581-3597 (doi:10.1175/MWR-D-16-0475.1) (2017).

    Halliwell, G.R., M. Mehari, M. Le Henaff, V. Kourafalou, I. Androulidakis, H. Kang, and R. Atlas. North Atlantic Ocean OSSE system: Evaluation of operational ocean observing system components and supplemental seasonal observations for improving coupled tropical cyclone prediction. Journal of Operational Oceanography, 10(2):154-175 (doi:10.1080/1755876X.2017. 1322770) (2017).

    Halliwell, G.R., M. Mehari, L.K. Shay, V.H. Kourafalou, H. Kang, H.-S. Kim, J. Dong, and R. Atlas. OSSE quantitative assessment of rapid-response prestorm ocean surveys to improve coupled tropical cyclone prediction. Journal of Geophysical Research-Oceans, 122(7):5729-5748 (doi:10.1002/ 2017JC012760) (2017).

    Hoffman, R.N., N. Prive, and M. Bourassa. Comments on ‟Reanalysis and observations: What’s the difference?” Bulletin of the American Meteorological Society, 98(11):2455-2459 (doi:10.1175/BAMS-D-17-0008.1) (2017).

    Hoffman, R.N., S.-A. Boukabara, V.K. Kumar, K. Garrett, S.P.F. Casey, and R. Atlas. An empirical cumulative density function approach to defining summary NWP forecast assessment metrics. Monthly Weather Review, 145(4):1427-1435 (doi:10.1175/MWR-D-16-0271.1) (2017).

    Leidner, S.M., T. Nehrkorn, J. Henderson, M. Mountain, T. Yunck, and R.N. Hoffman. A severe weather quick observing system simulation experiment (QuickOSSE) of global navigation satellite system (GNSS) radio occultation (RO) superconstellations. Monthly Weather Review, 145(2):637-651 (doi:10.1175/MWR-D-16-0212.1) (2017).

    Li, J., Z. Li, P. Wang, T.J. Schmit, W. Bai, and R. Atlas. An efficient radiative transfer model for hyperspectral IR radiance simulation and applications under cloudy-sky conditions. Journal of Geophysical Research-Atmospheres, 122(14):7600-7613 (doi:10.1002/2016JD026273) (2017).

    McNoldy, B., Annane, S. Majumdar, J. Delgado L. Bucci, and R. Atlas. Impact of assimilating CYGNSS data on tropical cyclone analyses and forecasts in a regional OSSE framework. Marine Technology Society Journal, 51(1):7-15 (doi:10.4031/MTSJ.51.1.1) (2017).

    Pu, Z., L. Zhang, S. Zhang, B. Gentry, D. Emmitt, B. Demoz, and R. Atlas. The impact of Doppler wind lidar measurements on high-impact weather forecasting: Regional OSSE and data assimilation studies. In Data Assimilation for Atmospheric, Oceanic and Hydrological Applications, Volume 3, S.K. Park and L. Xu (eds.). Springer International (doi:10.1007/978-3-319-43415-5) (2017).

    Wentz, F.J., L. Ricciardulli, E. Rodriguez, B.W. Stiles, M.A. Bourassa, D.G. Long, R.N. Hoffman, A. Stoffelen, A. Verhoef, L.W. O’Neill, J.T. Farrar, D. Vandemark, A.G. Fore, S.M. Hristova-Veleva, F.J. Turk, R. Gaston, and D. Tyler. Evaluating and extending the ocean wind climate data record. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5):2165-2185 (doi:10.1109/JSTARS. 2016.2643641) (2017).

    Zhang, S., Z. Pu, D.J. Posselt, and R. Atlas. Impact of CYGNSS ocean surface wind speeds on numerical simulations of a hurricane in observing system simulation experiments. Journal of Atmospheric and Oceanic Technology, 34(2):375-383 (doi:10.1175/JTECH-D-16-0144.1) (2017).

    2018

    Annane, B., B. McNoldy, S.M. Leidner, R. Hoffman, R. Atlas, and S.J. Majumdar. A study of the HWRF analysis and forecast impact of realistically simulated CYGNSS observations assimilated as scalar wind speeds and as VAM wind vectors. Monthly Weather Review (doi:10.1175/MWR-D-17-0240.1), in press.

    Blackwell, W.J., S. Braun, R. Bennartz, C. Velden, M. DeMaria, R. Atlas, J. Dunion, F. Marks, R. Rogers, B. Annane, and R.V. Leslie. An overview of the TROPICS NASA Earth Venture mission. Quarterly Journal of the Royal Meteorological Society (doi:10.1002/qj.3290), in press.

    Boukabara, S.-A., K. Ide, N. Shahroudi, Y. Zhou, T. Zhu, R. Li, L. Cucurull, R. Atlas, S.P.F. Casey, and R.N. Hoffman. Community global Observing System Simulation Experiment (OSSE) package (CGOP): Perfect observations simulation validation. Journal of Atmospheric and Oceanic Technology, 35(1):207-226 (doi:10.1175/JTECH-D-17-00771) (2018).

    Boukabara, S.-A., K. Ide, Y. Zhou, N. Shahroudi, R.N. Hoffman, K. Garrett, V. Krishna Kumar, T. Zhu, and R. Atlas. Community global OSSE package (CGOP). Part III: Calibration assessment and validation using an OSSE/OSE intercomparison of summary assessment metrics. Journal of Atmospheric and Oceanic Technology, submitted.

    Christophersen, H., R. Atlas, A. Aksoy, and J. Dunion. Combined use of satellite observations and Global Hawk unmanned aircraft dropwindsondes for improved tropical cyclone analyses and forecasts. Journal of Atmospheric and Oceanic Technology, accepted.

    Cucurull, L., R. Atlas, R. Li, M.J. Mueller, and R.N. Hoffman. An observing system simulation experiment with a constellation of radio occultation satellites. Monthly Weather Review, submitted.

    Haddad, Z., R. Atlas, G.S. Bhat, D. Bouniol, S. Brown, L. Callahan, P. Chambon, T. Fiolleau, K. Furukawa, C. Goldstein, G. Heymsfield, S. Hristova-Veleva, E. Im, R. Kakar, M.C. Kalapureddy, H. Kim, C. Kishtawal, T. L’Ecuyer, L. Li, Z.J. Luo, G. Mace, P. Mukhopadhyay, T.N. Rao, D. Posselt, A. Protat, R. Roca, G. Skofronick-Jackson, R. Storer, O. Sy, P. Tabary, S. Tanelli, W.-K. Tao, F.J. Turk, S. van den Heever, D. Vane, D. Waliser, D. Wu, and G. Stephens. Distributed satellite microwave observation strategies for cloud and precipitation dynamics. Journal of Earth System Science, submitted.

    Hoffman, R.N. The effect of thinning and superobservations in a simple one-dimensional data analysis with mischaracterized error. Monthly Weather Review, 146(4):1181-1195 (doi:10.1175/MWR-D-17-0363.1) (2018).

    Leidner, S.M., B. Annane, B. McNoldy, R. Hoffman, and R. Atlas. Variational analysis of simulated ocean surface winds from the Cyclone Global Navigation Satellite System (CYGNSS) and evaluation using a regional OSSE. Journal of Atmospheric and Oceanic Technology, accepted.

    Li, Z., W.P. Menzel, J. Li, T. Schmit, and R.M. Atlas. What is an infrared cloud top height? Geophysical Research Letters, submitted.

    Peevey, T.R., J.M. English, L. CucurulL, H. Wang, and A.C. Kren. Improving winter storm forecasts with Observing System Simulation Experiment (OSSEs). Part 1: An idealized case study of three US storms. Monthly Weather Review, 146(5):1341-1366 (doi:10.1175/MWR-D-17-0160.1) (2018).

    Tratt, D.M., J.A. Hackwell, B.L. Valant-Spaight, R.L. Walterscheid, L.J. Gelinas, J.H. Hecht, C.M. Swenson, C.P. Lampen, M.J. Alexander, L. Hoffman, D.S. Nolan, S.D. Miller, J.L. Hall, R. Atlas, F.D. Marks, and P.T. Partain. GHOST: A satellite mission concept for persistent monitoring of stratospheric gravity waves induced by severe storms. Bulletin of the American Meteorological Society (doi:10.1175/BAMS-D-17-0064.1), in press.

    Weatherhead, E.C., B.A. Wielicki, V. Ramaswamy, M. Abbott, T.P. Ackerman, R. Atlas, G. Brasseur, L. Bruhwiler, A.J. Busalacchi, J.H. Butler, C.T.M. Clack, R. Cooke, L. Cucurull, S.M. Davis, J.M. English, D.W. Fahey, S.S. Fine, J.K. Lazo, S. Liang, N.G. Loeb, E. Rignot, B. Soden, D. Stanitski, G. Stephens, B.D. Tapley, A.M. Thompson, K.E. Trenberth, and D. Wuebbles. Designing the climate observing system of the future. Earth’s Future, 6(1):80-102 (doi:10.1002/2017EF000627) (2018).

    Zhang, J.A., R. Atlas, G.D. Emmitt, L. Bucci, and K. Ryan. Airborne Doppler wind lidar observations of the tropical cyclone boundary layer. Remote Sensing, 10(6):825 (doi:10.3390/rs10060825) (2018).

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Contact

Lidia Cucurull, Chief Scientist and Deputy Director for the QOSAP Program. Photo Credit, NOAA.

Lidia Cucurull
303.497.6496

| Lidia Cucurull

Chief Scientist and Deputy Director, QOSAP Program

If you would like more information on the Quantitative Observing System Assessment Program, please contact Lidia Cucurull, Chief Scientist and Deputy Director of the Program.

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