Atmospheric and ocean modeling for the people whose decisions depend on it.
Ghosttree Technical Solutions provides research and development in numerical weather prediction, ocean and wave forecasting, electromagnetic propagation, and machine learning — from model configuration on HPC through verification and delivery to the end user.
What we do
Hands-on expertise across the full modeling workflow, from research configuration through operational deployment — the kind of capability that normally lives inside a national lab, available as a contract.
Weather, ocean & wave model deployment
Initialization, configuration, execution, and operational deployment of mesoscale and global atmospheric, ocean, and wave prediction systems on HPC and cloud infrastructure.
EM & RF propagation modeling
Deriving refractivity environments from NWP output, modeling electromagnetic and radio-frequency propagation, and developing ducting metrics for operational assessment.
Data assimilation & observation QC
Development and validation of assimilation methods, plus quality control and filtering of in-situ observations from radiosondes, small UAS, and surface stations.
Verification & skill assessment
Python-based skill assessment suites, systematic verification of model output against observations, and end-user dashboards that make the results legible to decision makers.
AI/ML for atmospheric applications
Training dataset development, model architecture design, autoencoder-based data compression, ML-enhanced prediction, and downscaling applied to NWP workflows.
Field campaign support
Hands-on collection and processing of atmospheric profiles from radiosondes, ship-deployed small UAS, and marine radar remote sensing platforms.
Who this work is for
The physics is the same whether the customer is a federal lab, a wind developer, or a marine contractor. What changes is the decision on the other end of it.
Offshore wind & marine energy
Met-ocean characterization and hindcasting, wind and wave resource assessment, extreme-value statistics of met-ocean conditions, and independent technical review of met-ocean analyses.
Maritime & coastal operations
Wave and weather forecasting for construction, survey, and dredging windows; weather-window and downtime analysis; nearshore and coastal modeling; site-specific forecast verification.
Defense & federal research
NWP system development, electromagnetic and RF propagation assessment, data assimilation, and task order support — as a prime or as a subcontractor to an existing team.
Research groups & scientific software teams
For organizations with models and data but no capacity to build around them: ML surrogates and compression, verification suites, processing pipelines, and dashboards that make output usable.
Ghosttree provides scientific analysis and modeling, not licensed professional engineering services.
A national-lab research background, available as a contract
Ghosttree Technical Solutions, LLC is an independent technical consultancy founded by Andrew (AJ) Kammerer, a research scientist with more than a decade in numerical weather prediction, ocean modeling, and remote sensing.
From 2017 to 2023, AJ was a federal physical scientist at the Naval Research Laboratory's Marine Meteorology Division, serving as principal investigator on multiple Office of Naval Research–sponsored projects. That work spanned mesoscale and global model execution, electromagnetic propagation prediction, machine-learning data compression, and field campaign data collection at sea.
Ghosttree takes on research and development on contract: model verification and skill assessment tooling, forecast system development and deployment, and applied AI/ML for prediction and propagation. The same methods carry over to commercial met-ocean problems — the models, the observations, and the verification approaches are the ones offshore energy and marine operations depend on.
Engagements range from short technical studies and independent review through sustained, multi-year task order support. Ghosttree is a one-person firm by design — you work directly with the person doing the analysis, and the work does not get handed to a junior team.
AJ holds an M.S. in Coastal Marine and Wetlands Studies and a B.S. in Marine Science from Coastal Carolina University, and maintains an active peer-reviewed publication record in remote sensing, propagation modeling, and marine boundary layer observation.
- Legal entity
- Ghosttree Technical Solutions, LLC
- State of formation
- North Carolina
- Founded
- 2021
- Business size
- Small business
- Location
- Hampstead, NC 28443
- Contract vehicles
- Prime & subcontract
Publication record
Published work in electromagnetic propagation, evaporation duct characterization, machine learning, ocean surface wave remote sensing, and marine atmospheric observation. Authored by AJ Kammerer; work performed at the U.S. Naval Research Laboratory and Coastal Carolina University except where noted.
Journal articles
- A Deep Learning Framework for 2-D, Multifrequency Propagation Factor Estimation Wessinger, S.E., L.N. Smith, J. Gull, J. Gehman, Z. Beever, A.J. Kammerer (2025). IEEE Transactions on Antennas and Propagation, 74(1), 1263–1268.
- Characterizing Range-Dependent Variations of the Evaporation Duct: A Meteorological Perspective Greenway, D.P., A.E. Vaughan, A.J. Kammerer, E.E. Hackett (2024). IEEE Transactions on Antennas and Propagation, 72(9), 7239–7251.
- Assimilating ASAPS Sensor Observations to Improve Atmospheric Refractivity Forecasts Amerault, C., C. Harten, M. Lockhart, P. Pauley, D. Tyndall, A.J. Kammerer, et al. (2023). Journal of DoD Research and Engineering, 6(2), 42–52.
- Group Line Energy in Phase-Resolved Ocean Surface Wave Orbital Velocity Reconstructions from X-band Doppler Radar Measurements of the Sea Surface Kammerer, A.J., E.E. Hackett (2019). Remote Sensing, 11(1), 71.
- Use of Proper Orthogonal Decomposition for Extraction of Ocean Surface Wave Fields from X-Band Radar Measurements of the Sea Surface Kammerer, A.J., E.E. Hackett (2017). Remote Sensing, 9(9), 881.
Conference papers & proceedings
- Impact of Surface Temperature Representation in a Mesoscale Numerical Weather Prediction Model on Electromagnetic Propagation Modeling over an Inland Water Body Kammerer, A.J., S.E. Wessinger, J. Yung, D.D. Flagg (2025). USNC-URSI National Radio Science Meeting, Boulder, CO.
- Statistical Distributions of Evaporation Duct Height and Strength Over Range and the Diurnal Cycle Wessinger, S.E., A.J. Kammerer, D.D. Flagg, Q. Jiang (2025). USNC-URSI National Radio Science Meeting, Boulder, CO.
- Evaluating the Impact of Sea State Variation on Multi-Frequency Electromagnetic Propagation in the Marine Atmospheric Boundary Layer Vaughan, A.E., A.J. Kammerer, S.E. Wessinger, M.A. Sletten, D.M. Pastore (2024). IEEE INC-USNC-URSI Radio Science Meeting, Florence, Italy, 273–274.
- Evaluation of COAMPS Driven Electromagnetic Propagation Modeling Using Field Campaign Measurements Wessinger, S.E., Q. Wang, H. Hansen, T. de Paolo, P. Rogowski, A.J. Kammerer (2024). USNC-URSI National Radio Science Meeting, Boulder, CO.
- Performance Assessments of High-Resolution Marine Atmospheric Boundary Layer Observations from a Ship-Deployed Small Unmanned Aerial Vehicle Rogowski, P., D.D. Flagg, A.J. Kammerer, M. Jackson, T. de Paolo, J. McCammon, E. Terrill (2021). OCEANS 2021: San Diego – Porto, 1–7.
- Investigating Correlation Dropouts of NWP Forecast EM Propagation for the TAPS Field Campaign Kammerer, A.J., T. Haack, H. Hansen (2019). Conference presentation.
- Predicting RF Propagation with Numerical Models Kammerer, A.J., T. Haack, R. Norris, H. Hansen, A. Kulessa, A. Barrios (2018). IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, Boston, MA, 1927–1928.
- Proper Orthogonal Decomposition Applied to Numerically Modeled and Measured Ocean Surface Wave Fields Remotely Sensed by Marine Radar Kammerer, A.J., G. Farquharson, E.E. Hackett (2016). AGU/ASLO/TOS Ocean Sciences Meeting, New Orleans, LA.
- Spatiotemporal Modulation and Analysis of High-Resolution Backscatter and Doppler X-band Radar Measurements of Ocean Surface Waves in Low Sea States Hackett, E.E., A.J. Kammerer, C.F. Merrill, A.M. Fullerton, T.C. Fu (2015). Sensing the Ocean with Marine Radar 3, Seattle, WA.
Thesis
- The Application of Proper Orthogonal Decomposition to Numerically Modeled and Measured Ocean Surface Wave Fields Remotely Sensed by Radar Kammerer, A.J. (2017). M.S. thesis, Coastal Marine and Wetland Studies, Coastal Carolina University.
Let's talk about your project
Available for commercial engagements, prime and subcontract work, task order support, independent technical review, and short studies. Inquiries are answered directly by the principal.