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

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; Suárez-Salas, Luis; Silva, Yamina

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

15 May 2018 to 30 April 2026

Geographic coverage

Huancayo Geophysical Observatory, Mantaro Valley, central Peruvian Andes

Latitude: [-12.04145]

Longitude: [-75.31875]

Elevation: [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

1. Raw 1-min tower observations were time-sorted, audited for duplicate timestamps, and regularized to a 1-min temporal grid when required.

2. 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.

3. Hard-fail values were removed under a conservative rule: RANGE or simultaneous STEP and SPIKE. Contextual flags were retained for diagnostic use.

4. Thermodynamic variables were derived after QC, including vapour-pressure variables, specific humidity, and virtual potential temperature.

5. Cleaned 1-min profiles were aggregated to 30-min profiles with availability diagnostics.

6. Flux-specific pre-calculation QC screened each 30-min profile before flux estimation.

7. MOST and BRN_ANC flux estimates were computed independently from the same eligible profiles.

8. Method-status flags recorded numerical success, non-convergence, invalid profile slopes, Richardson-number exceedance, and other execution outcomes.

9. Post-calculation QC retained physically plausible flux estimates and masked non-passing values in the final filtered output columns.

10. 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:

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.