Raffaele Crapolicchio

Analysis of two years of ASCAT- and SMOS-derived soil moisture estimates over Europe and North Africa

Abstract More than two years of soil moisture data derived from the Advanced SCATterometer (ASCAT) and from the Soil Moisture and Ocean Salinity (SMOS) radiometer are analysed and compared. The comparison has been performed within the framework of an activity aiming at validating the EUMETSAT Hydrology Satellite Application Facility (H-SAF) soil moisture product derived from ASCAT. The available database covers a large part of the SMOS mission lifetime (2010, 2011 and partially 2012) and both Europe and North Africa are considered. A specific strategy has been set up in order to enable the comparison between products representing a volumetric soil moisture content, as those derived from SMOS, and a relative saturation index, as those derived from ASCAT. Results demonstrate that the two products show a fairly good degree of correlation. Their consistency has some dependence on season, geographical zone and surface land cover. Additional factors, such as spatial property features, are also preliminary investigated.

Quadruple Collocation Analysis for Soil Moisture Product Assessment

For validating remotely sensed products, the triple collocation (TC) is often adopted, which is able to retrieve the independent error variances of three systems observing the same target parameter. In this letter, three years of soil moisture data derived from the Advanced SCATterometer (ASCAT) aboard the MetOp satellite and the Soil Moisture and Ocean Salinity (SMOS) radiometer are analyzed and compared with the ERA Interim/Land model outputs and the ground measurements available from the International Soil Moisture Network. As we have four sources, a novel quadruple collocation (QC) approach is developed, which is more precise than TC since it uses the sources jointly. The results of QC show that the ERA model has the lowest error variance, while ground measurements are likely to be affected by the difficulty to represent a mean soil moisture within the satellite field of view by a limited number of stations. Moreover, the ASCAT retrievals outperform the SMOS ones if only anomalies with respect to the seasonal trend are considered, while the opposite occurs when the whole dynamic of soil moisture variation is considered.

Uniqueness of the ERS scatterometer for nowcasting and typhoon forecasting

Since the launch of the European Remote Sensing Satellite ERS-1 in 1991, surface wind-vector observations derived from space-borne scatterometer measurements have been available over the global oceans continuously. Currently, space- borne scatterometer wind products are based on the QSCAT-1 model function for the Ku-band radar frequency (QuikSCAT) and the CMOD5 model function, recently been developed at ECMWF and KNMI, for C-band (ERS-2 and the future ASCAT series). The dynamic range of ERS derived winds now extends to at least 35 m/s. The accuracy of the wind retrieval in combination with the minimal sensitivity of the C-band frequency for rain contamination makes the ERS scatterometer an unique instrument for weather research, and the improved ambiguity removal increases the usefulness in dynamical and extreme weather conditions. The reprocessing of the entire ERS mission, combined with an anticipated overlap with the forthcoming ASCAT era will provide a 30-year long continuous and high- quality surface wind data set unique for climate research. The ERS-2 Scatterometer mission is currently operated on regional basis. This results in an unprecedented data timeliness of about 30 minutes. The short timeliness makes the current ERS-2 wind product suitable for operational weather now-casting and short-range weather forecasting. This offers advantages in analyzing and forecasting extreme weather events, which leads to improved predictions of these events using ERS scatterometer surface winds in numerical weather prediction (NWP).

Error Characterization of Soil Moisture Satellite Products: Retrieving Error Cross-Correlation Through Extended Quadruple Collocation

The triple collocation (TC) technique is being increasingly used to validate soil moisture retrievals derived from different systems, like satellites, hydrological models, or in situ probes. In recent years, several extensions of this method were proposed in order to evaluate the error standard deviations of more than three systems and to soften the TC hypothesis. In this paper, a novel extended quadruple collocation (E-QC) method is proposed, in order to consider the possibility of a cross correlation between product errors, identifying automatically the couple of error cross-correlated systems. The method is applicable even to a larger number of collocated datasets, although it may be unfeasible to collect them in practice. A synthetic experiment showed promising results, concluding that the E-QC is able to individuate (if any) the pair of systems with cross-correlated errors. It correctly compensates for the latter contribution and accurately retrieves error standard deviations of each system, otherwise biased if cross correlation is not taken into account. The E-QC was applied to soil moisture retrievals provided by satellite (SMOS, ASCAT, and SMAP), model (ERA Interim), and in situ probes (ISMN). The E-QC method identified the presence of error cross-correlation between the satellite products. This was also confirmed by analyzing the five datasets all together. E-QC showed fair performances of satellite products, especially of SMAP, although not as good as in case the presence of error correlation is not correctly taken into account.

Lessons learnt from SMOS after 7 years in orbit

ESAs Soil Moisture and Ocean Salinity (SMOS) mission has been in orbit for over 7 years, with its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) functioning well. This 7 year period has provided a wealth of information which has enabled us to understand and consolidate the performance of the payload in great detail. More importantly, we know now the things that work well, those that need improvement, and how the instrument could be enhanced if we were to build it again. This paper presents the lessons learnt from SMOS after 7 years in orbit.

Tracking a tropical cyclone with ERS-SCAT: a CMOD4 model review

The spaceborne scatterometer is a microwave radar that provides high precision radiometric measures of the normalized radar cross section σ0 of the ocean surface. The backscatter is affected by the superficial roughness that is in turn related to the local wind. Since microwave wavelengths are used the scatterometer, at first order, can be meant as an instrument which provides measurements independent of clouds and sun illumination therefore it is able to observe the internal structure of a Tropical Cyclone (TC). The relationship between the σ0 and the surface wind filed is described by a geophysical model function (GMF). The model used in the ERS scatterometer processing is the well-known semi-empirical model CMOD4. Unfortunately this model is not tailored for high wind speeds, such as the case of TCs. This fact causes a poor quality in the wind field estimated through the scatterometer data acquired over a TC. In this paper we describe a study in view of a possible extension of the CMOD4 for high wind speeds. The study has been based on the ERS-2 σ0 measurements relevant to six selected TCs and the corresponding wind speeds obtained by employing the Holland model. We have selected six TCs and for each one we have developed a 3D wind speed pattern making use of the wind speed available through the NHC (National Hurricane Center) warnings. The obtained wind speeds are then correlated to the σ0’s acquired over these six TCs. The results obtained in this work support the need to extend the CMOD4 model.