Data-set of graditent tower for quality-controlled and surface-flux estimations in the Peruvian central Andes

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:For boundary-layer and micrometeorology researchers, this dataset offers quality-controlled flux estimates in complex terrain, useful for model validation and method development.

Title Quality-controlled gradient-tower meteorological profiles and surface-flux estimates from the Huancayo Geophysical Observatory, Peruvian central Andes Alternative short title HYGO gradient-tower surface-flux dataset Resource type Dataset Version v1.0 Creators Flores-Rojas, José Luis; Pérez Tello, María; Fashé-Raymundo, Octavio; Pareja Quispe, David; Eche Llenque, José Carlos; Silva, Yamina; Zuñiga Huaman, Gerson Description This dataset contains quality-controlled gradient-tower meteorological profiles and surface-flux estimates from the Huancayo Geophysical Observatory (HYGO) of the Geophysical Institute of Peru (IGP), located in the Mantaro Valley of the Peruvian central Andes. HYGO is a high-altitude agricultural and atmospheric observatory representative of complex Andean terrain, strong diurnal forcing, seasonal moisture contrasts, and mountain–valley circulations. The dataset was developed to support reproducible analysis of near-surface atmospheric structure and turbulent exchange in complex terrain. Native 1-min observations of air temperature, relative humidity, wind speed, and wind direction were processed through a documented workflow that includes timestamp auditing, primary meteorological quality control, conservative bit-mask flagging, thermodynamic derivation, 30-min aggregation, flux-specific pre-calculation quality control, dual-method turbulent-flux estimation, method-status diagnostics, and post-calculation plausibility filtering. The released products include cleaned 1-min tower observations, per-sample QC flags, derived thermodynamic variables, 30-min aggregated profiles, and surface-flux estimates obtained with two aerodynamic approaches: Monin–Obukhov Similarity Theory (MOST) and an anchored multi-layer Bulk Richardson Number method (BRN_ANC). The flux products include friction velocity, sensible heat flux, latent heat flux, Obukhov length, bulk Richardson number, method-status flags, post-calculation QC flags, raw method outputs, and post-QC-filtered outputs. This structure allows users to distinguish between input-profile limitations, numerical method failures, and physically implausible flux estimates. The gradient-tower system includes measurements at multiple levels between 2 and 29 m above ground level. For the flux-gradient calculations, the 2, 6, 12, and 24 m levels were used to construct vertical profiles of wind speed, temperature, humidity, and virtual potential temperature. The 18 and 29 m levels were used for wind-direction information where available but were not included in the main flux-gradient calculations. The dataset is intended for boundary-layer research, land–atmosphere interaction studies, evaluation of surface-layer parameterizations, comparison of MOST and Richardson-number methods, model validation, agricultural micrometeorology, frost-risk assessment, drought-related studies, and development of reproducible workflows for high-frequency meteorological tower data. Dataset period [Insert final period, e.g., 15 May 2018 to 30 April 2026] Geographic coverage Huancayo Geophysical Observatory, Mantaro Valley, central Peruvian Andes Latitude: [insert final latitude, e.g., -12.04145] Longitude: [insert final longitude, e.g., -75.31875] Elevation: [insert final elevation, e.g., 3315 m a.s.l.] Temporal resolution 1 min for native and cleaned meteorological observations. 30 min for aggregated profiles and turbulent-flux products. Main variables Air temperature Relative humidity Wind speed Wind direction Atmospheric pressure Saturation vapour pressure Actual vapour pressure Water-vapour mixing ratio Specific humidity Virtual potential temperature Friction velocity Sensible heat flux Latent heat flux Obukhov length Bulk Richardson number Input QC flags Method-status flags Post-calculation QC flags 30-min availability diagnostics Processing summary Raw 1-min tower observations were time-sorted, audited for duplicate timestamps, and regularized to a 1-min temporal grid when required. A primary meteorological QC system generated per-sample bit-mask flags for missing values, range violations, step changes, persistence, spikes, calm wind, humidity inconsistency, and resample-inserted timestamps. Hard-fail values were removed under a conservative rule: RANGE or simultaneous STEP and SPIKE. Contextual flags were retained for diagnostic use. Thermodynamic variables were derived after QC, including vapour-pressure variables, specific humidity, and virtual potential temperature. Cleaned 1-min profiles were aggregated to 30-min profiles with availability diagnostics. Flux-specific pre-calculation QC screened each 30-min profile before flux estimation. MOST and BRN_ANC flux estimates were computed independently from the same eligible profiles. Method-status flags recorded numerical success, non-convergence, invalid profile slopes, Richardson-number exceedance, and other execution outcomes. Post-calculation QC retained physically plausible flux estimates and masked non-passing values in the final filtered output columns. Raw method outputs were preserved separately to support diagnostic audits and sensitivity analyses. File contents [Edit this list to match the final Zenodo upload.] cleaned_1min_tower_data.[nc/csv] Cleaned 1-min meteorological observations and primary QC flags. derived_thermodynamic_variables.[nc/csv] Pressure, vapour-pressure variables, mixing ratio, specific humidity, and virtual potential temperature. aggregated_30min_profiles.[nc/csv] Thirty-minute mean profiles and data-availability diagnostics. surface_fluxes_MOST_BRN_ANC_30min.[nc/csv] MOST and BRN_ANC flux estimates, method-status flags, post-QC flags, raw outputs, and filtered outputs. qc_flag_dictionary.[csv/json] Definitions of primary QC bit masks, pre-calculation QC flags, post-calculation QC flags, and method-status flags. processing_scripts.[zip] Python scripts used for QC, thermodynamic derivation, aggregation, MOST, BRN_ANC, post-QC, diagnostics, and figures. environment.[yml/txt] Software environment and package dependencies required to reproduce the workflow. README.md Dataset description, file structure, variable names, units, QC interpretation, and recommended use. Recommended citation Flores-Rojas, J. L., Pérez Tello, M., Fashé-Raymundo, O., Pareja Quispe, D., Eche Llenque, L. Suárez Salas, J. C., Silva, Y., and Zuñiga Huaman, G. ([year]). Quality-controlled gradient-tower meteorological profiles and surface-flux estimates from the Huancayo Geophysical Observatory, Peruvian central Andes (Version v1.0)  Keywords gradient tower; surface energy fluxes; quality control; Monin–Obukhov Similarity Theory; MOST; Bulk Richardson number; BRN_ANC; atmospheric surface layer; boundary layer; turbulent fluxes; sensible heat flux; latent heat flux; friction velocity; tropical Andes; Mantaro Valley; Huancayo Geophysical Observatory; HYGO; Peru; micrometeorology; land–atmosphere interactions; reproducible workflow License [Recommended: Creative Commons Attribution 4.0 International, CC BY 4.0, if allowed by your institution and funder.] Related identifiers Is supplement to: [insert article DOI after publication] Is documented by: [insert manuscript/preprint DOI if available] Is supplemented by: [insert software DOI if scripts are archived separately] Is version of: [insert previous Zenodo DOI if this is an updated version] Funding Instituto Geofísico del Perú; PROCIENCIA project “Fortalecimiento del Laboratorio de Microfísica Atmosférica y Radiación para el estudio de la interacción superficie–atmósfera en una zona agrícola de los Andes Centrales del Perú, en el contexto de cambio climático” (LAMAR), Contract No. PE501086050-2023-PROCIENCIA-BM. Notes Users should treat the flux estimates as gradient-based products, not as direct eddy-covariance measurements. MOST and BRN_ANC estimates are provided together to support method comparison and uncertainty assessment. Strongly stable, weak-wind, transition-period, and horizontally heterogeneous conditions may increase uncertainty. Users are encouraged to use the QC flags, method-status flags, and raw-output variables when performing sensitivity analyses or applying stricter filters.

• Zenodo https://zenodo.org/records/20632379first seen 2026-06-14 04:15:04 · last seen 2026-06-16 04:15:42