Analysis of current validation practices in Europe for space-based climate data records of essential climate variables

Velde

et al. 2016

Remote Sensing

Abstract:The inter-comparison of different soil moisture (SM) products over the Tibetan Plateau (TP) reveals the inconsistency among different SM products, when compared to in situ measurement. It highlights the need to constrain the model simulated SM with the in situ measured data climatology. In this study, the in situ soil moisture networks, combined with the classification of climate zones over the TP, were used to produce the in situ measured SM climatology at the plateau scale. The generated TP scale in situ SM climatology was then used to scale the model-simulated SM data, which was subsequently used to scale the SM satellite observations. The climatology-scaled satellite and model-simulated SM were then blended objectively, by applying the triple collocation and least squares method. The final blended SM can replicate the SM dynamics across different climatic zones, from sub-humid regions to semi-arid and arid regions over the TP. This demonstrates the need to constrain the model-simulated SM estimates with the in situ measurements before their further applications in scaling climatology of SM satellite products.

Section: Recommendationsmentioning

confidence: 99%

Blending Satellite Observed, Model Simulated, and in Situ Measured Soil Moisture over Tibetan Plateau

Velde

et al. 2016

Remote Sensing

“…A similar approach may be applied to determine the optimal installation configuration for other monitoring networks, which can contribute to a homogenized validation strategy [38] for satellite soil moisture products. By using the newly developed Lv's scheme, it will be feasible to determine which layer of soil moisture can be detected by the radiometer.…”

Section: Discussionmentioning

confidence: 99%

Determination of the Optimal Mounting Depth for Calculating Effective Soil Temperature at L-Band: Maqu Case

Wen

et al. 2016

Remote Sensing

Self Cite

Effective soil temperature T e f f is one of the basic parameters in passive microwave remote sensing of soil moisture. At present, dedicated satellite soil moisture monitoring missions use the L-band as the operating frequency. However, T e f f at the L-band is strongly affected by soil moisture and temperature profiles. Recently, a two-layer scheme and a corresponding multilayer form have been developed to accommodate such influences. In this study, the soil moisture/temperature data collected and simulated by the Noah land surface model across the Maqu Network are used to verify the newly developed schemes. There are two key findings. Firstly, the new two-layer scheme is able to assess which site provides relatively higher accuracy when estimating T e f f . It is found that, on average, nearly 20% of the T e f f signal cannot be captured by the Maqu Network, in the currently assumed common installation configuration. This knowledge is important, since the spatial averaged brightness temperature (a function of T e f f ) is used to determine soil moisture. Secondly, the developed method has made it possible to identify that the optimal mounting depths for the observation pair are 5 cm and 20 cm for calculating T e f f at the center station in the Maqu Network. It has been suggested that the newly developed method can provide an objective way to configure an optimal soil moisture/temperature network and improve the representativeness of the existing networks regarding the calculation of T e f f .

“…To establish the traceability of a validation process, one first needs to document each step of the validation process. After an analysis of current validation practices in Europe for space-based ECV CDRs, a generic validation strategy was developed in CORE-CLIMAX (Zeng et al 2014a).…”

Section: A Harmonized Approach For Cdr Validationmentioning

confidence: 99%

“…The first type of (inter)comparisons can be implemented at point or grid scales, to look for product differences and their reasons (Zeng et al , 2016, whereas the second type of (inter)comparisons checks the physical consistency from a functional point of view. Note that coupled data assimilation systems (e.g., ocean and atmosphere) offer a third way to check for the physical consistency of ECVs across domains (i.e., the third type).…”

Section: A Harmonized Approach For Cdr Validationmentioning

confidence: 99%

“…To this end, the validation data (inputs, outputs, and ancillary data), tools, and documentation should be archived together and made publicly accessible to facilitate openness, repeatability, transparency, and scrutiny. An example of applying this generic validation strategy in the Yangtze River basin (Huang et al 2015;Zeng et al 2014b) is given in the sidebar.…”

Section: A Harmonized Approach For Cdr Validationmentioning

confidence: 99%

See 1 more Smart Citation

An Overview of European Efforts in Generating Climate Data Records

Timmermans

Bulletin of the American Meteorological Society

et al. 2018

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