Abstract This work focuses on the reconstruction of Sea Surface Temperature (SST) diurnal cycle through combination of numerical model analyses and geostationary satellite measurements. The approach takes advantage of geostationary satellite observations as the diurnal signal source to produce gap-free optimally interpolated (OI) hourly \{SST\} fields using model analyses as first-guess. The resulting \{SST\} anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing one to interpolate \{SST\} anomalies using satellite data acquired at different times of the day. The method is applied to reconstruct the hourly Mediterranean \{SST\} field during summer 2011 using \{SEVIRI\} data and Mediterranean Forecasting System analyses. A synthetic cloud reconstruction experiment demonstrated that the \{OI\} \{SST\} method is able to reconstruct an unbiased \{SST\} field with a RMS = 0.16 °C with respect to \{SEVIRI\} observations. The \{OI\} interpolation estimate, the model first guess and the \{SEVIRI\} data are evaluated using drifter and mooring measurements. Special attention is devoted to the analysis of diurnal warming (DW) events that are particularly frequent in the Mediterranean Sea. The model reproduces quite well the Mediterranean \{SST\} diurnal cycle, except for the \{DW\} events. Due to the thickness of the model surface layer, the amplitude of the model diurnal cycle is often less intense than the corresponding \{SEVIRI\} and drifter observations. The Diurnal \{OI\} \{SST\} (DOISST) field, resulting from the blending of model and \{SEVIRI\} data via optimal interpolation, reproduces well the diurnal cycle including extreme \{DW\} events. The evaluation of \{DOISST\} products against drifter measurements results in a mean bias of − 0.07 °C and a \{RMS\} of 0.56 °C over interpolated pixels. These values are very close to the corresponding statistical parameters estimated from \{SEVIRI\} data (bias = − 0.16 °C, RMS = 0.47 °C). Results also confirm that part of the mean bias between temperature measured by moorings at 1 m depth and the satellite observations can be ascribed to the different nature of the measurements (bulk versus skin).
Combining model and geostationary satellite data to reconstruct hourly \{SST\} field over the Mediterranean Sea / S. Marullo; R. Santoleri; D. Ciani; P. Le Borgne; S. Péré; N. Pinardi; M. Tonani; G. Nardone. - In: REMOTE SENSING OF ENVIRONMENT. - ISSN 0034-4257. - STAMPA. - 146:(2014), pp. 11-23. [10.1016/j.rse.2013.11.001]
Combining model and geostationary satellite data to reconstruct hourly \{SST\} field over the Mediterranean Sea
PINARDI, NADIA;
2014
Abstract
Abstract This work focuses on the reconstruction of Sea Surface Temperature (SST) diurnal cycle through combination of numerical model analyses and geostationary satellite measurements. The approach takes advantage of geostationary satellite observations as the diurnal signal source to produce gap-free optimally interpolated (OI) hourly \{SST\} fields using model analyses as first-guess. The resulting \{SST\} anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing one to interpolate \{SST\} anomalies using satellite data acquired at different times of the day. The method is applied to reconstruct the hourly Mediterranean \{SST\} field during summer 2011 using \{SEVIRI\} data and Mediterranean Forecasting System analyses. A synthetic cloud reconstruction experiment demonstrated that the \{OI\} \{SST\} method is able to reconstruct an unbiased \{SST\} field with a RMS = 0.16 °C with respect to \{SEVIRI\} observations. The \{OI\} interpolation estimate, the model first guess and the \{SEVIRI\} data are evaluated using drifter and mooring measurements. Special attention is devoted to the analysis of diurnal warming (DW) events that are particularly frequent in the Mediterranean Sea. The model reproduces quite well the Mediterranean \{SST\} diurnal cycle, except for the \{DW\} events. Due to the thickness of the model surface layer, the amplitude of the model diurnal cycle is often less intense than the corresponding \{SEVIRI\} and drifter observations. The Diurnal \{OI\} \{SST\} (DOISST) field, resulting from the blending of model and \{SEVIRI\} data via optimal interpolation, reproduces well the diurnal cycle including extreme \{DW\} events. The evaluation of \{DOISST\} products against drifter measurements results in a mean bias of − 0.07 °C and a \{RMS\} of 0.56 °C over interpolated pixels. These values are very close to the corresponding statistical parameters estimated from \{SEVIRI\} data (bias = − 0.16 °C, RMS = 0.47 °C). Results also confirm that part of the mean bias between temperature measured by moorings at 1 m depth and the satellite observations can be ascribed to the different nature of the measurements (bulk versus skin).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
