Giovanna De Chiara

Scientific Developments and the EPS-SG Scatterometer

The second-generation exploitation of meteorological satellite polar system (EPS-SG) C-band-wavelength scatterometer instrument (called SCA), planned for launch in 2022, has a direct heritage from the successful advanced scatterometer (ASCAT) flown on the current EPS satellites. In addition, SCA will represent three major innovations with respect to ASCAT, namely: 1) Cross polarization and horizontal copolarization; 2) a nominal spatial resolution of 25 km; and 3) 20% greater spatial coverage than ASCAT. The associated expected science and application benefits that led the SCA design are discussed with respect to ocean, land, and sea ice applications for near-real time, climate monitoring, and research purposes. Moreover, an option to implement an ocean Doppler capability to retrieve the ocean motion vector is briefly discussed as well. In conclusion, the SCA instrument innovations are well set to provide timely benefits in all the main application areas of the scatterometer (winds, soil moisture, sea ice) and can be expected to contribute to new and more sophisticated meteorological, oceanographic, land, sea ice, and climate services in the forthcoming SCA era.

Estimating Random Errors of Scatterometer, Altimeter, and Model Wind Speed Data

Scatterometer and altimeter wind data are very important for data assimilation and verification of numerical weather prediction models. Standard deviation of absolute random errors can be estimated using the triple collocation technique. However, error correlations between various wind sources (e.g., due to data assimilation) complicate the error estimation. A method is used to alleviate the impact of error correlations between the scatterometer and the model that assimilates such data. Using twenty-two datasets of triplet composed of Jason-2 altimeter, Metop-A/B scatterometers (ASCAT-A/B, respectively), and ECMWF model analysis and forecasts (1 altimeter × 2 scatterometers × 11 model analysis and forecasts = 22 datasets) covering a period of two years from August 2013 to July 2015, the correlation coefficient between the errors of scatterometers and the model analysis was found to be about 0.33 for those datasets. This correlation reduces with forecast lead time until it almost vanishes at day seven. Altimeter and scatterometer errors are not correlated. The standard deviation of wind speed random errors of Jason-2, ASCAT-A/B, and the IFS analysis are estimated as 0.7, 0.8, and 0.9 m/s, respectively. As expected, there was no difference between ASCAT-A and ASCAT-B results.

Improving the Assimilation of Scatterometer Wind Observations in Global NWP

This study aims at improving the assimilation of scatterometer wind observations in global Numerical Weather Prediction (NWP) model by refining the background quality control and optimizing the observation sampling strategy. To improve the background quality control, different Huber Norm distribution implementations are tested and compared against the current “Gaussian plus flat” distribution. Sensitivity experiments show that the usage of the Huber Norm distribution improves the analysis and forecasts. The benefit is mainly seen in the lower model levels in the tropics and extra-tropical Southern Hemisphere. The optimal wind sampling is investigated by testing several combinations of thinning scheme and observation error standard deviation. The impact is demonstrated with a large sample and illustrated by a case study. The case study shows the impact of different settings on the analysis and forecast of a tropical cyclone. A revised wind sampling setting, where four times more observations and a higher observation error than the current operational one are used, showed slightly positive impact on the European Centre for Medium-Range Weather Forecasts (ECMWF) global NWP analyses and forecasts.

On the improvement of ASCAT wind data assimilation in global NWP

The assimilation of Advanced Scatterometer (ASCAT) winds has proven to be beneficial for the European Center for Medium Range Weather Forecasting (ECMWF) system, particularly over the Tropics. In this study, several important aspects of the ASCAT data are addressed in order to further test and improve the impact of scatterometer wind data assimilation into ECMWF Integrated Forecasting System (IFS). First, an improved wind quality control (QC) is proposed and used to remove unrepresentative ASCAT winds. Second, a new ASCAT wind product, more representative of the ECMWF model resolved scales, is produced by averaging the relatively-high resolution ASCAT wind vector cells to lower resolution in an aggregation process. Two months of ASCAT low resolution data are then used to evaluate the impact of the refined QC and the aggregation technique on the IFS data assimilation.

On the assimilation of ASCAT winds

In contrast with scatterometer wind data, Numerical Weather Prediction (NWP) models do not well resolve the mesoscale sea surface wind flow under increased wind variability conditions, such as in the vicinity of low-pressure centers, frontal lines, and moist convection. In this paper, several important issues are addressed in order to improve the impact of scatterometer data assimilation into global and regional NWP models, including model error structure functions, situation-dependent Observation/ Background error estimation, and improved scatterometer wind quality control.

Cross-calibration of ERS-1 and ERS-2 wind scatterometers; Towards a homogeneous 20-year-long wind vector monitoring of the earth

The importance of long-term, continuous, and homogenous time-series of satellite data is widely accepted and strongly fostered by the international scientific community. The various global projects and initiatives undertaken in the last few years are evidences of that effort. Among those are: the Long Term Data Preservation Working Group [1], the Permanent Access to the Records of Science in Europe (PARSE) [2], or the Global Climate Observing System (GCOS) [3]. One of the examples of long-term monitored variable is the wind vector. Since the European Remote-sensing Satellite (ERS)-1 launch in July 1991 and until ERS-2 decommissioning in July 2011, a continuous and consistent database of backscattering signal from the Earth surface has been built, and is now available. The Active Microwave Instrument (AMI) [4], which was one of the ERS-1 and ERS-2 payloads, provided radar backscattering coefficient measurements during the last 20 years by using its three nominal operational acquisition modes: Synthetic Aperture mode (SAR mode), Scatterometer mode (wind mode) and a special combination of the two over ocean where SAR and Scatterometer mode are interleaved (wind/wave mode). The main applications for data acquired in Scatterometer mode is related to the estimation of the wind vector over the sea surface. In that field the ERS-2 Scatterometer measurements give a very valuable contribution to the accuracy of the numerical weather forecast models, being assimilated in several meteorological weather forecast centers since the beginning of the mission. After the decommissioning of ERS-2, effort has been devoted to achieve a complete reprocessed database, including both ERS-1 and ERS-2 acquisitions [5]. The cross-calibration between these two satellites is a crucial task to obtain the homogeneousness of the wind vector database, and allow its long-term characterization. The approach followed by ESA in term of team organization, cross-calibration strategy and validation methodology towards this goal is presented in this paper as well as the preliminary results of the long-term characterization of the wind vector.

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.