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Vahid Naeimi, Zoltan Bartalis, and Wolfgang Wagner

Abstract

This article presents a first comparison between remotely sensed surface soil moisture retrieved with the European Remote Sensing Satellite-2 (ERS-2) scatterometer (SCAT) and the corresponding product provided by the Advanced Scatterometer (ASCAT) on board Meteorological Operation satellite (MetOp), the first of a series of three satellites providing, among other things, continuity of global soil moisture observations using active microwave techniques for the next 15 yr. Three months of collocated 2007 data were used from the SCAT and ASCAT, limited to two study regions with different land cover composition. The result of the assessment is satisfactory and ensures consistency of migrating soil moisture retrieval from the long-term SCAT dataset to ASCAT measurements. The influence of a shift of observation incidence angle ranges between the two instrument generations was not found to be significant for the soil moisture retrieval. The correlation coefficients (R) between two relative soil moisture (normalized water content) datasets compared in different incidence angle ranges are around 0.90 with root-mean-square error (RMSE) values in the order of 8.5. Results are expected to improve slightly further once the calibration of the ASCAT instrument is finalized.

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Stefan Schneider, Yong Wang, Wolfgang Wagner, and Jean-Francois Mahfouf

Abstract

In this study, remotely sensed soil moisture data from the Advanced Scatterometer (ASCAT) on board the Meteorological Operational (MetOp) series of satellites are assimilated in the regional forecasting model, Aire Limitée Adaptation Dynamique Développement International (ALADIN-Austria), using a simplified extended Kalman filter. A pointwise bias correction method is applied to the ASCAT data as well as quality flags prepared by the data provider. The ASCAT assimilation case study is performed over central Europe during a 1-month period in July 2009. Forecasts of those assimilation experiments are compared to the control run provided by the operational ALADIN version of the Austrian Met Service, Zentralanstalt für Meteorologie und Geodynamik (ZAMG). Forecasts are furthermore verified versus in situ data. For a single-day case study the ability of the approach to improve precipitation forecast quality in the presence of high impact weather is demonstrated. Results show that 1) based on a one station in situ data evaluation, soil moisture analysis is improved, compared to the operational analysis, when ASCAT soil moisture data is assimilated; 2) pointwise bias correction of the satellite data is beneficial for forecast quality; 3) screen level parameter forecasts can be slightly improved as a result of this approach; and 4) convective precipitation forecast is improved over flatland for the investigation period while over mountainous regions the impact is neutral.

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Deming Zhao, Claudia Kuenzer, Congbin Fu, and Wolfgang Wagner

Abstract

In this paper, the capability of the European Remote Sensing Satellite (ERS) scatterometer-derived soil water index (SWI) data to disclose water availability and precipitation distribution in China is investigated. Monthly averaged SWI data for the years 1992–2000 are analyzed to evaluate the use of the SWI as an index to monitor water availability and water stress at three different scales in China and to investigate if it reflects general precipitation distribution characteristics in China. Monthly averaged in situ relative soil moisture from Chinese meteorological gauge stations, as well as monthly precipitation data from the Global Precipitation Climatology Centre (GPCC), are employed to perform comparisons with SWI on local, regional, and countrywide scales. First, since soil moisture is highly affected by the precipitation, area-averaged SWI is compared with in situ relative soil moisture and GPCC precipitation data in one local area. Second, area-averaged SWI and GPCC precipitation data are used to perform comparisons in three regions of China. Finally, the relationship between SWI and GPCC precipitation data in China is investigated on a countrywide scale. Such multiscale analyses with SWI data have not been performed before, and SWI has never been investigated in detail for China. ERS-derived SWI data in China for the years 1992–2000 are evaluated to be a good indicator for water availability on local, regional, and countrywide scales. SWI and SWI anomaly data correlate well with precipitation and in situ soil moisture data. SWI has furthermore been demonstrated to reflect extreme events such as droughts and floods in China, occurring during the investigated period between 1992 and 2000. Additionally, the SWI allows one to monitor increasing soil moisture resulting from snowmelt, which cannot be deduced from precipitation data. The freely available 15-yr (1992–2007) time series SWI data are thus a valuable tool to overcome the scarcity of in situ soil moisture observations, which are usually not available on regional and countrywide scales.

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Simon Zwieback, Chun-Hsu Su, Alexander Gruber, Wouter A. Dorigo, and Wolfgang Wagner

Abstract

The error characterization of soil moisture products, for example, obtained from microwave remote sensing data, is a key requirement for using these products in applications like numerical weather prediction. The error variance and root-mean-square error are among the most popular metrics: they can be estimated consistently for three datasets using triple collocation (TC) without assuming any dataset to be free of errors. This technique can account for additive and multiplicative biases; that is, it assumes that the three products are linearly related. However, its susceptibility to nonlinear relations (e.g., due to sensor saturation and scale mismatch) has not been addressed. Here, a simulation study investigates the impact of quadratic relations on the TC error estimates [also when the products are first rescaled using the nonlinear cumulative distribution function (CDF) matching technique] and on those by two novel methods. These methods—based on error-in-variables regression and probabilistic factor analysis—extend standard TC by also accounting for nonlinear relations using quadratic polynomials. The relative differences between the error estimates of the ASCAT remotely sensed product by the quadratic and the linear methods are predominantly smaller than 10% in a case study based on remotely sensed, reanalysis, and in situ measured soil moisture over the contiguous United States. Exceptions with larger discrepancies indicate that nonlinear relations can pose a challenge to traditional TC analyses, as the simulations show they can introduce biases of either sign. In such cases, the use of nonlinear methods may complement traditional approaches for the error characterization of soil moisture products.

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Bernhard Bauer-Marschallinger, Wouter A. Dorigo, Wolfgang Wagner, and Albert I. J. M. van Dijk

Abstract

Australia is frequently subject to droughts and floods. Its hydrology is strongly connected to oceanic and atmospheric oscillations (climate modes) such as the El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and southern annular mode (SAM). A global 32-yr dataset of remotely sensed surface soil moisture (SSM) was used to examine hydrological variations in mainland Australia for the period 1978–2010. Complex empirical orthogonal function (CEOF) analysis was applied to extract independent signals and to investigate their relationships to climate modes. The annual cycle signal represented 46.3% of the total variance and a low but highly significant connection with SAM was found. Two multiannual signals with a lesser share in total variance (6.3% and 4.2%) were identified. The first one had an unstable period of 2–5 yr and reflected an east–west pattern that can be associated with ENSO and SAM but not with IOD. The second one, a 1- to 5-yr oscillation, formed a dipole pattern between the west and north and can be linked to ENSO and IOD. As expected, relationships with ENSO were found throughout the year and are especially strong during southern spring and summer in the east and north. Somewhat unexpectedly, SAM impacts strongest in the north and east during summer and is proposed as the key driver of the annual SSM signal. The IOD explains SSM variations in the north, east, and southeast during spring and also in the west during winter.

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Luca Ciabatta, Luca Brocca, Christian Massari, Tommaso Moramarco, Silvia Puca, Angelo Rinollo, Simone Gabellani, and Wolfgang Wagner

Abstract

State-of-the-art rainfall products obtained by satellites are often the only way of measuring rainfall in remote areas of the world. However, it is well known that they may fail in properly reproducing the amount of precipitation reaching the ground, which is of paramount importance for hydrological applications. To address this issue, an integration between satellite rainfall and soil moisture SM products is proposed here by using an algorithm, SM2RAIN, which estimates rainfall from SM observations. A nudging scheme is used for integrating SM-derived and state-of-the-art rainfall products. Two satellite rainfall products are considered: H05 provided by EUMESAT and the real-time (3B42-RT) TMPA product provided by NASA. The rainfall dataset obtained through SM2RAIN, SM2RASC, considers SM retrievals from the Advanced Scatterometer (ASCAT). The rainfall datasets are compared with quality-checked daily rainfall observations throughout the Italian territory in the period 2010–13. In the validation period 2012–13, the integrated products show improved performances in terms of correlation with an increase in median values, for 5-day rainfall accumulations, of 26% (18%) when SM2RASC is integrated with the H05 (3B42-RT) product. Also, the median root-mean-square error of the integrated products is reduced by 18% and 17% with respect to H05 and 3B42-RT, respectively. The integration of the products is found to improve the threat score for medium–high rainfall accumulations. Since SM2RASC, H05, and 3B42-RT datasets are provided in near–real time, their integration might provide more reliable rainfall products for operational applications, for example, for flood and landslide early warning systems.

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Christoph Rüdiger, Jean-Christophe Calvet, Claire Gruhier, Thomas R. H. Holmes, Richard A. M. de Jeu, and Wolfgang Wagner

Abstract

This paper presents a study undertaken in preparation of the work leading up to the assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the land surface model (LSM) Interaction Soil Biosphere Atmosphere (ISBA) at Météo-France. This study consists of an intercomparison experiment of different space-borne platforms providing surface soil moisture information [Advanced Microwave Scanning Radiometer for Earth Observing (AMSR-E) and European Remote Sensing Satellite Scatterometer (ERS-Scat)] with the reanalysis soil moisture predictions over France from the model suite of Système d’analyse fournissant des renseignements atmosphériques à la neige (SAFRAN), ISBA, and coupled model (MODCOU; SIM) of Météo-France for the years of 2003–05. Both modeled and remotely sensed data are initially validated against in situ observations obtained at the experimental soil moisture monitoring site Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) in southwestern France. Two different AMSR-E soil moisture products are compared in the course of this study—the official AMSR-E product from the National Snow and Ice Data Center (NSIDC) and a new product developed at the Vrije Universiteit Amsterdam and NASA (VUA–NASA)—which were obtained using two different retrieval algorithms. This allows for an additional assessment of the different algorithms while using identical brightness temperature datasets. This study shows that a good correlation generally exists between AMSR-E (VUA–NASA), ERS-Scat, and SIM for low altitudes and low-to-moderate vegetation covers (1.5–3 kg m−2 vegetation water content), with a reduction in the correlation in mountainous regions. It also shows that the AMSR-E (NSIDC) soil moisture product has significant differences when compared to the other datasets.

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Wouter Dorigo, Stephan Dietrich, Filipe Aires, Luca Brocca, Sarah Carter, Jean-François Cretaux, David Dunkerley, Hiroyuki Enomoto, René Forsberg, Andreas Güntner, Michaela I. Hegglin, Rainer Hollmann, Dale F. Hurst, Johnny A. Johannessen, Chris Kummerow, Tong Lee, Kari Luojus, Ulrich Looser, Diego G. Miralles, Victor Pellet, Thomas Recknagel, Claudia Ruz Vargas, Udo Schneider, Philippe Schoeneich, Marc Schröder, Nigel Tapper, Valery Vuglinsky, Wolfgang Wagner, Lisan Yu, Luca Zappa, Michael Zemp, and Valentin Aich

Abstract

Life on Earth vitally depends on the availability of water. Human pressure on freshwater resources is increasing, as is human exposure to weather-related extremes (droughts, storms, floods) caused by climate change. Understanding these changes is pivotal for developing mitigation and adaptation strategies. The Global Climate Observing System (GCOS) defines a suite of Essential Climate Variables (ECVs), many related to the water cycle, required to systematically monitor the Earth's climate system. Since long-term observations of these ECVs are derived from different observation techniques, platforms, instruments, and retrieval algorithms, they often lack the accuracy, completeness, resolution, to consistently to characterize water cycle variability at multiple spatial and temporal scales.

Here, we review the capability of ground-based and remotely sensed observations of water cycle ECVs to consistently observe the hydrological cycle. We evaluate the relevant land, atmosphere, and ocean water storages and the fluxes between them, including anthropogenic water use. Particularly, we assess how well they close on multiple temporal and spatial scales. On this basis, we discuss gaps in observation systems and formulate guidelines for future water cycle observation strategies. We conclude that, while long-term water-cycle monitoring has greatly advanced in the past, many observational gaps still need to be overcome to close the water budget and enable a comprehensive and consistent assessment across scales. Trends in water cycle components can only be observed with great uncertainty, mainly due to insufficient length and homogeneity. An advanced closure of the water cycle requires improved model-data synthesis capabilities, particularly at regional to local scales.

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Will Pozzi, Justin Sheffield, Robert Stefanski, Douglas Cripe, Roger Pulwarty, Jürgen V. Vogt, Richard R. Heim Jr., Michael J. Brewer, Mark Svoboda, Rogier Westerhoff, Albert I. J. M. van Dijk, Benjamin Lloyd-Hughes, Florian Pappenberger, Micha Werner, Emanuel Dutra, Fredrik Wetterhall, Wolfgang Wagner, Siegfried Schubert, Kingtse Mo, Margaret Nicholson, Lynette Bettio, Liliana Nunez, Rens van Beek, Marc Bierkens, Luis Gustavo Goncalves de Goncalves, João Gerd Zell de Mattos, and Richard Lawford

Drought is a global problem that has far-reaching impacts, especially on vulnerable populations in developing regions. This paper highlights the need for a Global Drought Early Warning System (GDEWS), the elements that constitute its underlying framework (GDEWF), and the recent progress made toward its development. Many countries lack drought monitoring systems, as well as the capacity to respond via appropriate political, institutional, and technological frameworks, and these have inhibited the development of integrated drought management plans or early warning systems. The GDEWS will provide a source of drought tools and products via the GDEWF for countries and regions to develop tailored drought early warning systems for their own users. A key goal of a GDEWS is to maximize the lead time for early warning, allowing drought managers and disaster coordinators more time to put mitigation measures in place to reduce the vulnerability to drought. To address this, the GDEWF will take both a top-down approach to provide global realtime drought monitoring and seasonal forecasting, and a bottom-up approach that builds upon existing national and regional systems to provide continental-to-global coverage. A number of challenges must be overcome, however, before a GDEWS can become a reality, including the lack of in situ measurement networks and modest seasonal forecast skill in many regions, and the lack of infrastructure to translate data into useable information. A set of international partners, through a series of recent workshops and evolving collaborations, has made progress toward meeting these challenges and developing a global system.

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