Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model

As vegetation regulates water, carbon, and energy cycles from the local to the global scale, its accurate representation in land surface models is crucial. The assimilation of satellite-based vegetation observations in a land surface model has the potential to improve the estimation of global carbon and energy cycles, which in turn can enhance our ability to monitor and forecast extreme hydroclimatic events, ecosystem dynamics, and crop production. This work proposes the assimilation of a remotely sensed vegetation product (Leaf Area Index, LAI) within the Noah Multi-Parameterization land surface model using an Ensemble Kalman Filter technique. The impact of updating leaf mass along with LAI is also investigated. Results show that assimilating LAI data improves the estimation of transpiration and net ecosystem exchange, which is further enhanced by also updating the leaf mass. Specifically, transpiration anomaly correlation coefficients improve in about 77 and 66% of the global land area thanks to the assimilation of leaf area index with and without updating leaf mass, respectively. Random errors in transpiration are also reduced, with an improvement of the unbiased root mean square error in 70% (74%) of the total area without the update of leaf mass (with the update of leaf mass). Similarly, net ecosystem exchange anomaly correlation coefficients improve from 52 to 75% and random errors improve from 49 to 62% of the total pixels after the update of leaf mass. Better performances for both transpiration and net ecosystem exchange are observed across croplands, but the largest improvement is shown over forests and woodland. The global scope of this work makes it particularly important in data poor regions (e.g., Africa, South Asia), where ground observations are sparse or not available altogether but where an accurate estimation of carbon and energy variables can be critical to improve ecosystem and crop management.

ARRAY RADIATION PATTERN RECOVERY UNDER RANDOM ERRORS USING CLUSTERED LINEAR ARRAY

In practice, random errors in the excitations (amplitude and phase) of array elements cause undesired variations in the array patterns. In this paper, the clustered array elements with tapered amplitude excitations technique are introduced to reduce the impact of random weight errors and recover the desired patterns. The most beneficial feature of the suggested method is that it can be used in the design stage to count for any amplitude errors instantly. The cost function of the optimizer used is restricted to avoid any unwanted rises in sidelobe levels caused by unexpected perturbation errors. Furthermore, errors on element amplitude excitations are assumed to occur either randomly or sectionally (i.e., an error affecting only a subset of the array elements) through the entire array aperture. The validity of the proposed approach is entirely supported by simulation studies.

A close-loop verification approach for pedestrian stability based on machine vision

In public places, it is significant to analyze the stability of the crowd which can support the crowd management and control, and protect the evacuees safely and effectively. The numerical analysis method of system stability based on Lyapunov theory suffers problems that it is difficult to avoid random errors in the initialization of pedestrian density and velocity, as well as cumulative errors due to time increasing, limiting its application. This study adopts a complementary model of theoretical numerical analysis and machine vision with a parallel convolutional neural network (CNN) model. It proposes an approach of stability analysis and closed-loop verification for crowd merging systems. Thereby, this research provides theoretical and methodological support for planning of the functional layout of crowd flow in public crowd-gathering places and the control measures for stable crowd flow.

Area and Energy Efficient QCA Based Compact Serial Concatenated Convolutional Code Encoder

Quantum-dot Cellular Automata (QCA) is a transistor-less technology known for its low power consumption and higher clock rate. Serial Concatenated Convolutional Coding (SCCC) encoder is a class of forward error correction. This paper picturizes the implementation of the outer encoder as a (7, 4, 1) Bose Chaudhary Hocquenghem encoder that serves the purpose of burst error correction, a pseudo-random inter-leaver used for permuting of systematic code words and finally the inner encoder which is used for the correction of random errors in QCA. Two different architectures of the SCCC encoder have been proposed and discussed in this study. In the proposed two architectures, the first based on external clock signals whereas the second based on internal clock generation. The sub-blocks outer encoder, pseudo-random inter-leaver and inner encoder of the SCCC encoder are optimized, implemented and simulated using QCADesigner and then integrated to design a compact SCCC encoder. The energy dissipation is computed using QCADesigner-E. The proposed SCCC encoder reduced the total area by 46% and energy dissipation by 50% when compared to the reference SCCC encoder. The proposed encoders are more efficient in terms of cell count, energy dissipation and area occupancy respectively.

Assessing Satellite, Land Surface Model and Reanalysis Evapotranspiration Products in the Absence of In-Situ in Central Asia

Shortfalls in regular evapotranspiration (ET) monitoring and evaluation pose a huge challenge to agricultural water resource distribution in arid Central Asia (CA). In this study, a first detailed regional assessment of GLEAM, ERA5, MERRA2, CLSM and NOAH ET products in CA was performed by systematically implementing the triple collocation (TC) method, in which about 36936 grid cells for each ET data (within a six-triplet design) were collocated, at 0.25° and with monthly resolutions during 2003–2020. The reliability of the strategy adopted was confirmed in four arid biomes using standard evaluation metrics (R, RMSE and BIAS), and by spatiotemporal cross-validation of the six ET triplets across CA. Results show that the systematic TC method produced more robust ET product assessment metrics with reduced RMSEs compared to the initial ET product validation using in-situ, which showed weak-positive correlation and high negative bias-range (i.e., −21.02 ≤ BIAS < 16 mm) in the four arid biomes of CA. The spatial cross-validation by TC showed that the magnitude of ET random errors significantly varies, and confirms the systematic biases with site-scale measurements. The highest ET uncertainties by CLSM (27.43%), NOAH (29.16%), MERRA2 (38.28%), ERA5 (36.75), and GLEAM (41%) were more evident in the shrubland, cropland, grassland, cropland again, and desert biomes, respectively. Moreover, error magnitudes in high altitudes (Tianshan Mountain range) are generally lower than in plain-desert areas. All ET products spatially captured ET dynamics over CA, but none simultaneously outperformed the other. These findings are invaluable in the utilization of the assessed ET products in supporting regional water resource management, particularly in CA.

Median based algorithm for sub-pixel estimation of extrema positions of diffuse light reflection signal

It has been proposed to utilize the median algorithm for determination of the extrema positions of diffuse light reflectance intensity distribution by a discrete signal of a photodiode linear array. The algorithm formula has been deduced on the base of piecewise-linear interpolation for signal representation by cumulative function. It has been shown that this formula is much simpler for implementation than known centroid algorithm and the noise immune Blais and Rioux detector algorithm. Also, the methodical systematic errors for zero noise as well as the random errors for full common mode additive noises and uncorrelated noises have been estimated and compared for mentioned algorithms. In these terms, the proposed median algorithm is proportionate to Blais and Rioux algorithm and considerably better then centroid algorithm.

MT Evaluation in the Context of Language Complexity

The paper focuses on investigating the impact of artificial agent (machine translator) on human agent (posteditor) using a proposed methodology, which is based on language complexity measures, POS tags, frequent tagsets, association rules, and their summarization. We examine this impact from the point of view of language complexity in terms of word and sentence structure. By the proposed methodology, we analyzed 24 733 tags of English to Slovak translations of technical texts, corresponding to the output of two MT systems (Google Translate and the European Commission’s MT tool). We used both manual (adequacy and fluency) and semiautomatic (HTER metric) MT evaluation measures as the criteria for validity. We show that the proposed methodology is valid based on the evaluation of frequent tagsets and rules of MT outputs produced by Google Translate or of the European Commission’s MT tool, and both postedited MT (PEMT) outputs using baseline methods. Our results have also shown that PEMT output produced by Google Translate is characterized by more frequent tagsets such as verbs in the infinitive with modal verbs compared to its MT output, which is characterized by masculine, inanimate nouns in locative of singular. In the MT output, produced by the European Commission’s MT tool, the most frequent tagset was verbs in the infinitive compared to its postedited MT output, where verbs in imperative and the second person of plural occurred. These findings are also obtained from the use of the proposed methodology for MT evaluation. The contribution of the proposed methodology is an identification of systematic not random errors. Additionally, the study can also serve as information for optimizing the translation process using postediting.