SearcharxivSearch

arXiv · 1603.08698

Validation of the multi-mission altimeter wave height data for the Baltic Sea region

Abstract

We present a complete cross-validation of significant wave heights (SWH) extracted from altimetry data from all ten existing satellites with available in situ (buoy and echosounder) wave measurements for the Baltic Sea basin. The main purpose is to select an adequate altimetry data subset for a subsequent evaluation of the wave climate. The satellite measurements with the backscatter coefficients > 13.5 cdb, errors in the SWH normalized standard deviation > 0.5 m and snapshots with centroids closer than 0.2 degrees to the land are not reliable. The ice flag usually denotes the ice concentration of > 50%. The presence of ice affects the SWH data starting from concentrations 10%, but substantial effects are only evident for concentrations > 30%. The altimetry data selected based on these criteria have very good correspondence with in situ data, except for GEOSAT Phase 1 data (1985-1989) that could not be validated. The root-mean-square difference of altimetry and in situ data is in the range of 0.23-0.37, which is significant for the Baltic Sea, compared with an average wave height of ~1 m. The bias for CRYOSAT-2, ERS-2, JASON-1/2 and SARAL data is below 0.06 m. The ENVISAT, ERS-1, GEOSAT and TOPEX satellites revealed larger biases up to 0.23 m. The SWH time series from several satellite pairs (ENVISAT/JASON-1, SARAL/JASON-2, ERS-1/TOPEX) exhibit substantial mutual temporal drift and part of them evidently are not homogeneous in time. A new high-resolution SWH data set from the SARAL satellite reveals a very good correspondence with the in situ data and with the data stream from previous satellites.

Explore related subjects

Keep this discovery

BibTeXRIS

N. A. Kudryavtseva, T. Soomere. 2016-03-29. Validation of the multi-mission altimeter wave height data for the Baltic Sea region. https://doi.org/10.3176/earth.2016.13

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph