J. Muñoz-Sabater

Skill and global trend analysis of soil moisture from reanalysis and microwave remote sensing

AbstractIn situ soil moisture measurements from 2007 to 2010 for 196 stations from five networks across the world (United States, France, Spain, China, and Australia) are used to determine the reliability of three soil moisture products: (i) a revised version of the ECMWF Interim Re-Analysis (ERA-Interim; ERA-Land); (ii) a revised version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis from NASA (MERRA-Land); and (iii) a new, microwave-based multisatellite surface soil moisture dataset (SM-MW). Evaluation of the time series and anomalies from a moving monthly mean shows a good performance of the three products in capturing the annual cycle of surface soil moisture and its short-term variability. On average, correlations (95% confidence interval) are 0.66 (±0.038), 0.69 (±0.038), and 0.60 (±0.061) for ERA-Land, MERRA-Land, and SM-MW. The two reanalysis products also capture the root-zone soil moisture well; on average, correlations are 0.68 (±0.035) and 0.73 (±0.03...

Soil Moisture Analyses at ECMWF: Evaluation Using Global Ground-Based In Situ Observations

AbstractIn situ soil moisture from 117 stations across the world and under different biome and climate conditions are used to evaluate two soil moisture products from the European Centre for Medium-Range Weather Forecasts (ECMWF)—namely, the operational analysis and the interim reanalysis [ECMWF Re-Analysis Interim (ERA-Interim)]. ECMWF’s operational Integrated Forecasting System (IFS) is based on a continuous effort to improve the analysis and modeling systems, resulting in frequent updates (a few times a year). The ERA-Interim reanalysis is produced by a fixed IFS version (for the main component of the atmospheric model and data assimilation). It has the advantage of being consistent over the whole period from 1979 onward and by design, reanalysis products are more suitable than their operational counterparts for use in climate studies. Although the two analyses show good skills in capturing surface soil moisture variability, they tend to overestimate soil moisture, particularly for dry land. Over the 2...

Initialisation of Land Surface Variables for Numerical Weather Prediction

Land surface processes and their initialisation are of crucial importance for Numerical Weather Prediction (NWP). Current land data assimilation systems used to initialise NWP models include snow depth analysis, soil moisture analysis, soil temperature and snow temperature analysis. This paper gives a review of different approaches used in NWP to initialise land surface variables. It discusses the observation availability and quality, and it addresses the combined use of conventional observations and satellite data. Based on results from the European Centre for Medium-Range Weather Forecasts (ECMWF), results from different soil moisture and snow depth data assimilation schemes are shown. Both surface fields and low-level atmospheric variables are highly sensitive to the soil moisture and snow initialisation methods. Recent developments of ECMWF in soil moisture and snow data assimilation improved surface and atmospheric forecast performance.

A comparison of ASCAT and SMOS soil moisture retrievals over Europe and Northern Africa from 2010 to 2013

Abstract A comparison between ASCAT/H-SAF and SMOS soil moisture products was performed in the frame of the EUMETSAT H-SAF project. The analysis was extended to the whole H-SAF region of interest, including Europe and North Africa, and the period between January 2010 and November 2013 was considered. Since SMOS and ASCAT soil moisture data are expressed in terms of absolute and relative values, respectively, different approaches were adopted to scale ASCAT data to use the same volumetric soil moisture unit. Effects of land cover, quality index filtering, season and geographical area on the matching between the two products were also analyzed. The two satellite retrievals were also compared with other independent datasets, namely the NCEP/NCAR volumetric soil moisture content reanalysis developed by NOAA and the ERA-Interim/Land soil moisture produced by ECMWF. In situ data, available through the International Soil Moisture Network, were also considered as benchmark. The results turned out to be influenced by the way ASCAT data was scaled. Correlation between the two products exceeded 0.6, while the root mean square difference did not decrease below 8%. ASCAT generally showed a fairly good degree of correlation with ERA, while, as expected considering the different kinds of measurement, the discrepancies with respect to local in situ data were large for both satellite products.

Soil temperature at ECMWF: An assessment using ground-based observations

Soil temperature is an important variable for the representation of many physical processes in numerical weather prediction (NWP). It is the key driver for all surface emissions of energy, carbon dioxide, and water and forward operator for all satellite sensors sensitive to land. Yet the forecast quality of this variable in NWP is largely unknown. In this study, in situ soil temperature measurements from nearly 700 stations belonging to four networks across the United States and Europe are used to assess the European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts of soil temperature during 2012. Evaluation of the time series shows a good performance of the short-range forecasts (day one) in capturing both soil temperature annual and diurnal cycles with very high level of correlation (0.92 and over), averaged root-mean-square differences ranging from 2.54°C to 3.89°C and averaged biases ranging from −0.52°C to 0.94°C. The orography data set used in the forecast system was found to have a strong impact on the outcomes of the evaluation. The difference between elevation of a station and that of the corresponding grid cell in the ECMWF model may lead to large temperature differences linked to linear processes resulting in a constant bias, as well as nonlinear processes (e.g., to snow melt in spring). This verification study aims to contribute to a better understanding of the near-surface forecasts performance highlighting land-atmosphere processes that need to be better represented in future model development such as snow pack melting and heat diffusion in the soil.

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.

Soil Moisture Retrievals Based on Active and Passive Microwave Data: State-of-the-Art and Operational Applications

This chapter provides a description of current operational applications using state-of-the-art soil moisture retrievals from active and passive remote sensing data. Both types of observations have demonstrated to contain useful information of shallow land variables. In particular, this chapter focuses on how soil moisture retrievals are used at the operational level to derive flood, drought, and landslides warnings, as well as its use in coupled land-atmospheric models. An overview of the caveats in the production and applications of the retrievals is also provided. The chapter ends by providing perspectives of these novel applications.