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Soil moisture (SM) is critical for various hydro-meteorological applications. Land surface models (LSMs) can produce global spatio-temporal continuous SM estimates. Recently, NASA and ECMWF released GLDAS-2.1 and ERA5-Land datasets, respectively, which contain newly produced LSM-based global SM products, and these have not been thoroughly evaluated in China. To better understand the two products, we decomposed them into SM climatology (i.e., mean seasonal cycle) and SM anomaly (i.e., short-term variability) components and evaluated them separately in China. In particular, the evaluation was conducted considering ground-based SM observations obtained from 1411 stations and two remotely sensed SM products. The following key results were obtained: (a) In the SM climatology evaluation, ERA5-Land showed a larger bias in (semi-) humid areas (0.06 m3/m3 on an average), while GLDAS-2.1 was generally unbiased. GLDAS-2.1 showed higher temporal precision (temporal mean R = 0.47 [-]) than ERA5-Land (temporal mean R = 0.17 [-]) in northern arid areas, while ERA5-Land exhibited better performance (temporal mean R = 0.64 [-]) than GLDAS-2.1 (temporal mean R = 0.34 [-]) in southern humid areas. (b) For the SM anomaly evaluation, ERA5-Land and GLDAS-2.1 performed similarly, and ERA5-Land (temporal mean R = 0.45 [-]) marginally outperformed GLDAS-2.1 (temporal mean R = 0.40 [-]). (c) For the raw SM, GLDAS-2.1 and ERA5-Land had higher temporal precision in the northern and southern areas, respectively, which are mostly determined by their SM climatology. Our findings highlight the important role of SM climatology and provide an important reference for improving the aforementioned SM products.

Data fusion can effectively improve the accuracy of remotely sensed (RS) soil moisture (SM) products. Understanding the error structures of RS SM products is beneficial for formulating a data fusion scheme. In this article, a data fusion scheme is examined on the Tibetan Plateau, and the Soil Moisture Active Passive mission, Soil Moisture and Ocean Salinity mission, and Advanced Microwave Scanning Radiometer 2 products are used as the experimental input datasets. The RS apparent thermal inertia (ATI) is transformed into SM values as the reference data with reliable systemic variability. The ATI-based SM, along with three RS SM products, is introduced into the triple collocation (TC) method to decompose the errors of the three RS SM products into systemic and random errors at each RS pixel. Due to the presence of systemic errors, the temporal mean values and amplitudes of the three RS SM products were calibrated by those of the ATI-based SM. The rescaled anomalies (including amplitude and random error) were merged according to their random errors estimated by the TC method, and then the merged anomalies were added to the temporal mean values of the ATI-based SM to obtain the final merged results. Compared with the merged European Space Agency Climate Change Initiative passive SM product and input SM datasets, the merged results in this article exhibit optimal accuracy. The scheme for merging RS SM products shows high data fusion performance and can be further considered a reliable way to obtain a high-quality merged RS SM dataset.

Here we show that the variance over time of all-India daily rainfall (AIR) can be related to the mean over time of AIR by a seasonally varying power law. Outside of the peak monsoon months of July and August, AIR variance increases in proportion to a positive power of mean daily rainfall. During July and August, monthly averages of AIR show little association with the corresponding variances. This power-law relationship of temporal variance to temporal mean is known in biological sciences as Taylor’s law (TL) and in physical sciences as fluctuation scaling. We explain the seasonal variation in TL qualitatively by independent and identically distributed random sampling. Accounting for day-to-day correlation in AIR is sufficient to match quantitatively the observed power-law behavior. Our findings provide a quantitative month-specific assessment of the variability of AIR that could prove useful for the design of crop insurance and social safety nets for the large fraction of the population of the Indian subcontinent that depends on rainfed agriculture.

The assessment of predictability for decadal predictions of quantities like temperature and precipitation typically focuses on regional and temporal mean values. However, changes in extremes can be different to changes in mean climate, and accordingly their predictability may be different. We use simulations from CSIRO-Mk3-6-0 (CMIP5 archive) to set up a ‘perfect model’ experiment to compare the predictability for mean and extreme temperature and precipitation on interannual to decadal time-scales. The results show that both the mean and likelihood of near-surface temperature extremes is potentially predictable in many regions in the first lead year, while the areas with precipitation predictability tend to be mostly in low-latitude regions during this period. On decadal time scales, significant potential skill for mean and extreme temperatures is found over the North Atlantic and Southern Ocean but also over some land areas including North Africa, Europe and North America. The general spatial patterns of predictability are very similar between the mean and extremes. However, indices of moderate temperature extremes in particular show a tendency towards higher predictability than the mean. The approach to studying predictability presented here uses international coordinated model intercomparison project simulations. However a larger number of different initializations would be required from more models to allow improved robustness of the results.

Historically stable areas across North Africa, known as pseudo invariant calibration sites (PICS), have been used as targets for the calibration and monitoring of optical satellite sensors. However, two major drawbacks exist for these sites: first is the dependency on a single location to be always invariant, and second is the limited amount of observation achieved using these sites. As a result, longer time periods are necessary to construct a dense dataset to assess the radiometric performance of on-orbit optical sensors and confirm that the change detected is sensor-specific rather than site-specific. This work presents a global land cover classification to obtain an extended pseudo invariant calibration site (EPICS) on a global scale using Landsat-8 Operational Land Imager (OLI) data. This technique provides multiple calibration sites across the globe, allowing for the building of richer datasets in a shorter time frame compared to the traditional approach (PICS), with the advantage of assessing the calibration and stability of the sensors faster, detecting possible changes sooner and correcting them accordingly. This work identified 23 World Reference System two (WRS-2) path/row locations around the globe as part of the global EPICS. These EPICS have the advantage of achieving multiple observations per day, with similar spectral characteristics compared to traditional PICS, while still producing a temporal coefficient of variation (ratio of temporal standard deviation and temporal mean) less than 4% for all bands, with some as low as 2.7%.

The spatial and temporal diversity provided by Sentinel-1 SAR images is used to detect archaeological structures. The temporal mean over a year suppresses speckle without reducing spatial resolution. The combined use of the ascending and descending orbits makes it possible to highlight man-made features.

In this paper, we consider a large-scale wireless network consisting of source-destination (SD) pairs where the source nodes frequently send status updates about some underlying physical processes (observed by them) to their corresponding destination nodes. For this setup, we employ age of information (AoI) as a performance metric to quantify freshness of the status updates when they reach the destination nodes. While most of the existing works are focused on the analysis of the temporal mean AoI in deterministic network topologies, we aim to characterize the spatial AoI performance disparity that is inherently present in wireless networks. In particular, we treat the temporal mean AoI as a random variable over space as the update delivery rate of a wireless link is a function of the interference field observed by its receiver. Our objective is to characterize the spatial distribution of the temporal mean AoI observed by the SD pairs by modeling them as a Poisson bipolar process. We first derive accurate bounds on the moments of the successful transmission probability of a status update which are then used to derive tight bounds on the moments as well as the spatial distribution of the temporal mean peak AoI. Our results provide useful design guidelines on the appropriate selection of different system parameters to minimize the mean peak AoI.

A depth-independent isotropic Gent-McWilliams (GM) transport parameter κ is diagnosed from an idealized eddy-resolving primitive equation simulation. The optimal depth-independent isotropic GM parameterization is only able to model less than 50% of the diagnosed total tendency of temperature induced by unresolved mesoscale eddies. A spatio-temporal stochastic model of the GM parameter is developed based on the diagnosed values; the graphical lasso is used to estimate the spatial correlation structure. The stochastic model is used as a stochastic parameterization in low-resolution model simulations. The low-resolution stochastic simulation does a poor job of reproducing the temporal mean of large-scale temperature. Deterministic GM parameterizations and multiplicative stochastic GM parameterizations with unrealistic structure result in significantly more-accurate large-scale temperature in the low-resolution simulations. These results suggest that either the depth-independence or the isotropy of the GM parameterization are unrealistic as models of the eddy tracer transport, or that a stochastic GM parameterization should include an additive component.

In the present study, we report the first comparison between the aerosol optical depth (AOD) and Ångström exponent (AE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra (AODt) and Aqua (AODa) satellites and those measured using a sun photometer (AODSP) at Camagüey, Cuba, for the period 2008 to 2014. The comparison of Terra and Aqua data includes AOD derived with both deep blue (DB) and dark target (DT) algorithms from MODIS Collection 6. Combined Terra and Aqua (AODta) data were also considered. Assuming an interval of ±30 min around the overpass time and an area of 25 km around the sun photometer site, two coincidence criteria were considered: individual pairs of observations and both spatial and temporal mean values, which we call collocated daily means. The usual statistics (root mean square error, RMSE; mean absolute error, MAE; median bias, BIAS), together with linear regression analysis, are used for this comparison. Results show very similar values for both coincidence criteria: the DT algorithm generally displays better statistics and higher homogeneity than the DB algorithm in the behaviour of AODt, AODa, AODta compared to AODSP. For collocated daily means, (a) RMSEs of 0.060 and 0.062 were obtained for Terra and Aqua with the DT algorithm and 0.084 and 0.065 for the DB algorithm, (b) MAE follows the same patterns, (c) BIAS for both Terra and Aqua presents positive and negative values but its absolute values are lower for the DT algorithm; (d) combined AODta data also give lower values of these three statistical indicators for the DT algorithm; (e) both algorithms present good correlations for comparing AODt, AODa, AODta vs. AODSP, with a slight overestimation of satellite data compared to AODSP, (f). The DT algorithm yields better figures with slopes of 0.96 (Terra), 0.96 (Aqua) and 0.96 (Terra + Aqua) compared to the DB algorithm (1.07, 0.90, 0.99), which displays greater variability. Multi-annual monthly means of AODta establish a first climatology that is more comparable to that given by the sun photometer and their statistical evaluation reveals better agreement with AODSP for the DT algorithm. Results of the AE comparison showed similar results to those reported in the literature concerning the two algorithms' capacity for retrieval. A comparison between broadband aerosol optical depth (BAOD), derived from broadband pyrheliometer observations at the Camagüey site and three other meteorological stations in Cuba, and AOD observations from MODIS on board Terra and Aqua show a poor correlation with slopes below 0.4 for both algorithms. Aqua (Terra) showed RMSE values of 0.073 (0.080) and 0.088 (0.087) for the DB and DT algorithms. As expected, RMSE values are higher than those from the MODIS–sun photometer comparison, but within the same order of magnitude. Results from the BAOD derived from solar radiation measurements demonstrate its reliability in describing climatological AOD series estimates.

This study aims to evaluate the impacts of the aerosol direct effect on simulated concentrations of fine particulate matter (PM2.5) over East Asia, which is controlled by heavy local air pollution and long-range transport. The online coupled Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) modeling system was applied to one-way and two-way simulations (without and with the aerosol direct effect) of a period from January till March of 2014. The differences between the two simulations showed that there were particularly large impacts of the aerosol direct effect on the eastern Asian continent with high aerosol loading. The temporal mean contributions of the direct effect averaged over the regions from Northeast to Central China were a 15% decrease in the surface shortwave radiation, a 9.0% decrease in the planetary boundary layer (PBL) height, and an 8.6% increase in the ground-level PM2.5 concentration. In addition, there were negative contributions of the direct effect to the PM2.5 concentration over the ocean from the Sea of Japan to the East China Sea (a 1.0% decrease on average throughout the period). The PM2.5 decrease over the ocean was likely attributable to a reduction in the secondary PM2.5 outflow from the continent to the downwind region, which was caused by the increased dry deposition of PM2.5 precursors from the increased ground-level concentrations within a more stable PBL over the continent. Overall, the substantial decrease in the surface shortwave radiation due to the aerosol direct effect led to enhanced atmospheric stability and therefore increased the ground-level PM2.5 in the heavily polluted region.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: PA1810045000

This paper considers a large-scale wireless network consisting of source-destination (SD) pairs, where the sources send time-sensitive information, termed status updates, to their corresponding destinations in a time-slotted fashion. We employ Age of information (AoI) for quantifying the freshness of the status updates measured at the destination nodes for two different queuing disciplines, namely Type I and II queues. Type I queue is assumed to transmit the status updates in a first-come-first-served (FCFS) fashion with no storage facility. However, Type I queue may not necessarily minimize AoI because a new update will not be allowed to enter a server until the current update has been successfully transmitted. To overcome this shortcoming, we consider Type II queue in which the most recent status update available at a given transmission slot is transmitted in order to minimize the AoI. As the update delivery rate for a given link is a function of the interference field seen from the receiver, the temporal mean AoI can be treated as a random variable over space. Our goal in this paper is to characterize the spatial distribution of the mean AoI observed by the SD pairs by modeling them as a Poisson bipolar process. Towards this objective, we first derive accurate bounds on the moments of success probability while efficiently capturing the interference-induced coupling in the activities of the SD pairs. Using this result, we then derive tight bounds on the moments as well as the spatial distribution of peak AoI. Our numerical results verify our analytical findings and demonstrate the impact of various system design parameters on the mean peak AoI.

The effects of soil and topography on the responses of soil respiration (Rs) to climatic variables must be investigated in the southeastern mountainous areas of China due to the rapid land-use change from forest to agriculture. In this study, we investigated the response of Rs to soil temperature (ST), precipitation over the previous seven days (AP7), and soil water content (SWC) across two hillslopes that had different land uses: a tea garden (TG) and a bamboo forest (BF). Meanwhile, the roles of soil properties including soil clay content and total nitrogen (TN), and topography including elevation, profile curvature (PRC), and slope on the different responses of Rs to these climatic variables were investigated. Results showed that mean Rs on the BF hillslope (2.21 umol C m&minus, 2 s&minus, 1) was 1.71 times of that on the TG hillslope (1.29 umol C m&minus, 1). Soil clay content, elevation, and PRC had negative correlations (p &lt, 0.05) with spatial variation of Rs, and ST was positively correlated (p &lt, 0.01) with temporal variation of Rs on both hillslopes. Across both hillslopes ST explained 33%&ndash, 73% and AP7 explained 24%&ndash, 38% of the temporal variations in Rs. The mean temperature sensitivities (Q10s) of Rs were 2.02 and 3.22, respectively, on the TG and BF hillslopes. The Q10 was positively correlated (p &lt, 0.05) with the temporal mean of SWC and TN, and negatively correlated (p &lt, 0.05) with clay and slope. The mean AP7 sensitivities (a concept similar to Q10) were greatly affected by clay and PRC. When Rs was normalized to that at 10 &deg, C, power or quadratic relationships between Rs and SWC were observed in different sites, and the SWC explained 12%&ndash, 32% of the temporal variation in Rs. When ST and SWC were integrated and considered, improved explanations (45%&ndash, 81%) were achieved for the Rs temporal variation. In addition, clay and elevation had vital influences on the responses of Rs to SWC. These results highlight the influences of soil, topographic features, and land use on the spatial variations of the Rs, as well as on the responses of Rs to different climatic variables, which will supplement the understanding of controlling mechanisms of Rs on tea and bamboo land-use types in Southeastern China.

International audience; List of abbreviations and Declarations are specified at the end of the main text (after Conclusion).BACKGROUND:Magnetic resonance (MR) thermometry allows visualization of lesion formation in real-time during cardiac radiofrequency (RF) ablation. The present study was performed to evaluate the precision of MR thermometry without RF heating in patients exhibiting cardiac arrhythmia in a clinical setting. The evaluation relied on quantification of changes in temperature measurements caused by noise and physiological motion.METHODS:Fourteen patients referred for cardiovascular magnetic resonance imaging underwent an extra sequence to test the temperature mapping stability during free-breathing acquisition. Phase images were acquired using a multi-slice, cardiac-triggered, single-shot echo planar imaging sequence. Temperature maps were calculated and displayed in real-time while the electrocardiogram (ECG) was recorded. The precision of temperature measurement was assessed by measuring the temporal standard deviation and temporal mean of consecutive temperature maps over a period of three minutes. The cardiac cycle was analyzed from ECG recordings to quantify the impact of arrhythmia events on the precision of temperature measurement. Finally, two retrospective strategies were tested to remove acquisition dynamics related either to arrhythmia events or sudden breathing motion.RESULTS:ECG synchronization allowed categorization of inter-beat intervals (RR) into distinct beat morphologies. Five patients were in stable sinus rhythm, while nine patients showed irregular RR intervals due to ectopic beats. An average temporal standard deviation of temperature of 1.6°C was observed in patients under sinus rhythm with a frame rate corresponding to the heart rate of the patient. The temporal standard deviation rose to 2.5°C in patients with arrhythmia. The retrospective rejection strategies increased the temperature precision measurement while maintaining a sufficient frame rate.CONCLUSIONS:Our results indicated that real-time cardiac MR thermometry shows good precision in patients under clinical conditions, even in the presence of arrhythmia. By providing real-time visualization of temperature distribution within the myocardium during RF delivery, MR thermometry could prevent insufficient or excessive heating and thus improve safety and efficacy.

Pseudo Invariant Calibration Sites (PICS) have been increasingly used as an independent data source for on-orbit radiometric calibration and stability monitoring of optical satellite sensors. Generally, this would be a small region of land that is extremely stable in time and space, predominantly found in North Africa. Use of these small regions, referred to as traditional PICS, can be limited by: (i) the spatial extent of an individual Region of Interest (ROI) and/or site; (ii) and the frequency of how often the site can be acquired, based on orbital patterns and cloud cover at the site, both impacting the time required to construct a richly populated temporal dataset. This paper uses a new class of continental scaled PICS clusters (also known as Extended PICS or EPICS), to demonstrate their capability in increasing temporal frequency of the calibration time series which ultimately allows calibration and stability assessment at a much finer scale compared to the traditional PICS-based method while also reducing any single location’s potential impact to the overall assessment. The use of EPICS as a calibration site was evaluated using data from Landsat-8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Sentinel-2A&B Multispectral Instrument (MSI) images at their full spatial resolutions. Initial analysis suggests that EPICS, at its full potential and with nominal cloud consideration, can significantly decrease the temporal revisit interval of moderate resolution sensors to as much as of 0.33 day (3 collects/day). A traditional PICS is expected to have a temporal uncertainty (defined as the ratio of temporal standard deviation and temporal mean) of 2–5% for TOA reflectance. Over the same time period EPICS produced a temporal uncertainty of 3%. But the advantage to be leveraged is the ability to detect sensor change quicker due to the denser dataset and reduce the impact of any potential ‘local’ changes. Moreover, this approach can be extended to any on-orbit sensor. An initial attempt to quantify the minimum detectable change (a threshold slope value which must be exceeded by the reflectance trend to be considered statistically significant) suggests that the use of EPICS can decrease the time period up to approximately half of that found using traditional PICS-based approach.

Extended-range runoff forecasting is important for water resources management and energy planning. Experimental extended-range runoff was hindcasted, based on an extended-range climate model, developed by National Climate Center of the China Meteorological Administration, and semi-distributed hydrological model HBV-D. The skill of the runoff forecasts was explored using mean square skill score (MSSS), anomaly correlation coefficient (ACC), and areas under the relative operating characteristics curve (AUC) for three terciles for three experimental 51-day periods during flood season (June 1 to July 21, July 1 to August 20 and August 1 to September 20) for two rivers in China. The results revealed decreasing trends of the five indices, and varying length of the continuous longest skilful time slice from 3 days to 6 weeks depending on index, period and river location. In most cases, skilful abnormal terciles forecast occurred more often or with similar frequency to deterministic forecasts. It suggests that ensemble probability forecasting is a method with potential for extended-range river runoff forecast. Further, abnormal terciles are more skillful than normal terciles, and above normal are more skillful than below normal. In terms of a temporal mean of the MSSS and ACC, deterministic forecasts are skillful for both rivers in all three periods, but more skillful for the Beijiang River than for the Yiluo River in most cases.

Knowledge about spatiotemporal error characteristics of remotely sensed soil moisture (SM) products is essential for correctly interpreting observational information and optimally assimilating them into hydrological models. This work aims to (i) investigate the relative difference between time-invariant and time-variant daily SM errors of Advanced Scatterometer (ASCAT) and Soil Moisture Active Passive (SMAP) products and (ii) analyze correlation of the daily SM errors with surface biomass quantified by the Leaf Area Index (LAI) and rainfall. The time-invariant error denotes an aggregate error magnitude during the whole investigation period and the time-variant error at daily time scale also refers to an aggregate error magnitude but for the period of the 100-day time window centered at the day to be processed. The time-invariant and daily SM errors are estimated using the Triple Collocation Analysis (TCA) applied to ASCAT and SMAP SM retrievals along with the Global Land Data Assimilation System version 2.1 (GLDAS2) SM product for the period of April 2015 – January 2020. Results indicate the relative difference between time-invariant and time-variant daily errors is notable for both ASCAT and SMAP SM products. The relative difference in units percentage denotes the ratio of difference value between time-invariant and daily errors to time-invariant error itself. The daily TCA error fluctuated at 43% value from time-invariant error for ASCAT SM and at 47% value for SMAP SM on a global average. When averaged globally, temporal mean of time-variant daily errors was relatively smaller than time-invariant error by -27% for ASCAT SM and -18% for SMAP SM, respectively. In tropical areas, the relative difference between time-invariant and daily errors is large during the dry season and becomes small when rainy season comes. When the peak lagged correlation is used, daily errors exhibit a stronger correlation with rainfall than they do with LAI in 61% of landmass pixels for ASCAT SM and 66% of landmass pixels for SMAP SM. LAI cannot be used to predict temporal variability of time-variant SM errors in barren land. Lagged correlation analysis reveals rainfall peak coincides with SM error peak in areas featured with low vegetation cover, including barren land, grasslands, and open shrublands. By contrast, the LAI peak comes after the SM error peak in all cases. Savannas and woody savannas are a special case as SM error peak comes first, followed by rainfall then LAI peaks. In summary, ASCAT and SMAP time-variant error varies with a large deviation from time-invariant errors and its temporal variation shows a stronger association with rainfall than changing LAI.

Taylor’s power law (TPL) describes the variance of population abundance as a power-law function of the mean abundance for a single or a group of species. Using consistently sampled long-term (1958–2001) multimesh capture data of Lake Kariba in Africa, we showed that TPL robustly described the relationship between the temporal mean and the temporal variance of the captured fish assemblage abundance (regardless of species), separately when abundance was measured by numbers of individuals and by aggregate weight. The strong correlation between the mean of abundance and the variance of abundance was not altered after adding other abiotic or biotic variables into the TPL model. We analytically connected the parameters of TPL when abundance was measured separately by the aggregate weight and by the aggregate number, using a weight–number scaling relationship. We utilized TPL to find the number of samples required for fixed-precision sampling and compared the number of samples when sampling was performed with a single gillnet mesh size and with multiple mesh sizes. These results facilitate optimizing the sampling design to estimate fish assemblage abundance with specified precision, as needed in stock management and conservation.

A nonlinear filtering approach to imaging the dynamics of microbubble ultrasound contrast agents (UCAs) in microvessels is presented. The approach is based on the adaptive third-order Volterra filter (TVF), which separates the linear, quadratic, and cubic components from beamformed pulse-echo ultrasound data. The TVF captures polynomial nonlinearities utilizing the full spectral components of the echo data and not from prespecified bands, e.g., second or third harmonics. This allows for imaging using broadband pulse transmission to preserve the axial resolution and the SNR. In this paper, we present the results from imaging the UCA activity in a 200- $\mu \text{m}$ cellulose tube embedded in a tissue-mimicking phantom using a linear array diagnostic probe. The contrast enhancement was quantified by computing the contrast-to-tissue ratio (CTR) for the different imaging components, i.e., B-mode, pulse inversion (PI), and the TVF components. The temporal mean and standard deviation of the CTR values were computed for all frames in a given data set. Quadratic and cubic images, referred to as QB-mode and CB-mode, produced higher mean CTR values than B-mode, which showed improved sensitivity. Compared with PI, they produced similar or higher mean CTR values with greater spatial specificity. We also report in vivo results from imaging UCA activity in an implanted LNCaP tumor with heterogeneous perfusion. The temporal means and standard deviations of the echogenicity were evaluated in small regions with different perfusion levels in the presence and absence of UCA. The in vivo measurements behaved consistently with the corresponding calculations obtained under microflow conditions in vitro . Specifically, the nonlinear VF components produced larger increases in the temporal mean and standard deviation values compared with B-mode in regions with low to relatively high perfusion. These results showed that polynomial filters such as the TVF can provide an important tool for imaging UCA activity in regions with heterogeneous perfusion as is the case in some tumors and ischemic tissues.

Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash–Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

The study was aimed to introduce a design of a DICOM‐RT‐based tool box to facilitate 4D dose calculation based on deformable voxel‐dose registration. The computational structure and the calculation algorithm of the tool box were explicitly discussed in the study. The tool box was written in MATLAB in conjunction with CERR. It consists of five main functions which allow a) importation of DICOM‐RT‐based 3D dose plan, b) deformable image registration, c) tracking voxel doses along breathing cycle, d) presentation of temporal dose distribution at different time phase, and e) derivation of 4D dose. The efficacy of using the tool box for clinical application had been verified with nine clinical cases on retrospective‐study basis. The logistic and the robustness of the tool box were tested with 27 applications and the results were shown successful with no computational errors encountered. In the study, the accumulated dose coverage as a function of planning CT taken at end‐inhale, end‐exhale, and mean tumor position were assessed. The results indicated that the majority of the cases (67%) achieved maximum target coverage, while the planning CT was taken at the temporal mean tumor position and 56% at the end‐exhale position. The comparable results to the literature imply that the studied tool box can be reliable for 4D dose calculation. The authors suggest that, with proper application, 4D dose calculation using deformable registration can provide better dose evaluation for treatment with moving target. PACS number(s): 87.55.kh

Water vapor is the most variable constituent in the atmosphere and plays an important role in climate studies, mesoscale meteorology modeling and numerical weather forecasting. Being able to penetrate clouds, interferometric synthetic aperture radar (InSAR) shows great potential in atmospheric water vapor mapping. But InSAR can only measure differential water vapor between two acquisitions. In this paper, we formulate a general framework by constructing the Gauss-Markov model and developing the estimation method to retrieve the non-differential water vapor from Small BAseline Subset InSAR (SBAS-InSAR). To address the rank-deficiency in the Gauss-Markov model, we propose a new constraint, i.e., the temporal mean of water vapor being invariant. Simulated and real data experiments are conducted to validate the effectiveness of the framework and the advantages of the proposed constraint. The results show that the new constraint can offer an estimation more robust than the two traditional ones, i.e., the temporal mean of water vapor being zero and single or multiple epoch water vapor referencing. In addition, we found that there exists a constant bias, which equals to the temporal mean of water vapors, between the solutions under the new constraint and that under the constraint of the temporal mean of water vapor being zero. Finally, the possible methods to evaluate the temporal mean of water vapor are discussed.

Soil moisture dynamics in response to rainfall and irrigation events were examined using data obtained from continuous point measurements carried out under rice and wheat crops for an agricultural plot located within the Ganga Basin, India. Soil moisture data were collected using SM100 and SMEC300 sensors at 18 subplots and at four different depths (0−80 cm) during the period from 5 August 2018 to 31 March 2019. Soil moisture was decomposed into temporal mean and temporal anomalies components, and its variability was characterized considering both absolute soil moisture and temporal anomalies. They exhibited similar patterns at all the depths under rice crop cover. However, it varies with depth under wheat crop cover due to periodic wetting and drying conditions and temporally variable atmospheric demand. Similarly, the spatial variance of absolute soil moisture was decomposed into time-invariant and time-variant components. The results revealed that the time-invariant component contribution was dominant at all the depths (72.49–101.46 %) and the contribution of each component varies with soil wetness and land cover. In addition, temporal stability analysis of soil moisture was carried out. It was observed that the spatial pattern at surface depth cannot be preserved for subsurface depths, and similar subplots were found to be temporally stable at the surface and bottom depths under different crop covers. The results are expected to help improve the understanding of the nature of soil water dynamics in agricultural fields.

In this paper, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well preserved thanks to the multitemporal mean. The proposed approach can be divided into three steps: 1) estimation of a “superimage” by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the “superimage”; and 3) computation of the denoised image by remultiplying the denoised ratio by the “superimage.” Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising images from the original multitemporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the “superimage” that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio and structure similarity index) as well as visually on simulated and synthetic aperture radar (SAR) time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures.

Soil moisture plays a fundamental role in the mass and energy balance between the land surface and the atmosphere, making its knowledge essential for several hydrological and climatic applications. The aim of this study is to extend the current knowledge of soil moisture spatial-temporal variability at the catchment scale (up to 500 km2). The main implication is to provide guidelines to obtain soil moisture values representative of the mean behaviour at the medium-sized river basin scale, which is useful for remote sensing validation analysis and rainfall-runoff modeling. To this end, 23 measurements campaigns were carried out during a time span of 14 months at 20 sites located within the Upper Chiascio River Basin, a catchment with a drainage area of about 460 km2 in the Umbria Region (central Italy). The data set allowed the analysis of both soil moisture temporal stability and its dynamics. On the basis of statistical and temporal stability approaches, it was investigated how factors such as climatic regime and geomorphology influence soil moisture behaviour. For the investigated area, the spatial variability of soil moisture was higher in dry periods with respect to wet periods, mainly due to the rainfall pattern characteristics during different periods of the year. Soil moisture values recorded during wet periods showed a better correlation than those recorded during dry periods. The maximum number of required samples, to obtain the mean areal soil moisture with an absolute error of 3% vol/vol, was found equal to 12. The temporal stability analysis showed that during wet periods just one “optimal” measurement point can provide values of soil moisture representative of the catchment-mean behaviour, while during dry periods the number of “optimal” measurement points became equal to two. Therefore, at the adopted spatial scale the use of a single measurement point can lead to significant errors. From the perspective of soil moisture dynamics, the decomposition of the spatial variance showed that the contribution of the time-invariant component (temporal mean of each site) was predominant on respect to the total spatial variance of absolute soil moisture data, for almost the whole observation period. Results provided guidance to optimize soil moisture sampling by performing targeted measurements at a few selected points representative of the catchment-mean behaviour.

Background: Taylor's law (TL) states a linear relationship on logarithmic scales between the variance and the mean of a nonnegative quantity. TL has been observed in spatiotemporal contexts for the population density of hundreds of species including humans. TL also describes temporal variation in human mortality in developed countries, but no explanation has been proposed. Objective: To understand why and to what extent TL describes temporal variation in human mortality, we examine whether the mortality models of Gompertz, Makeham, and Siler are consistent with TL. We also examine how strongly TL differs between observed and modeled mortality, between women and men, and among countries. Methods: We analyze how well each mortality model explains TL fitted to observed occurrence-exposure death rates by comparing three features: the log-log linearity of the temporal variance as a function of the temporal mean, the age profile, and the slope of TL. We support some empirical findings from the Human Mortality Database with mathematical proofs. Results: TL describes modeled mortality better than observed mortality and describes Gompertz mortality best. The age profile of TL is closest between observed and Siler mortality. The slope of TL is closest between observed and Makeham mortality. The Gompertz model predicts TL with a slope of exactly 2 if the modal age at death increases linearly with time and the parameter that specifies the growth rate of mortality with age is constant in time. Observed mortality obeys TL with a slope generally less than 2. An explanation is that, when the parameters of the Gompertz model are estimated from observed mortality year by year, both the modal age at death and the growth rate of mortality with age change over time. Conclusions: TL describes human mortality well in developed countries because their mortality schedules are approximated well by classical mortality models, which we have shown to obey TL. Contribution: We provide the first theoretical linkage between three classical demographic models of mortality and TL

High-precision satellite altimeters help in measuring the variations in sea level since the early 1990s. After a number of such successful altimetry missions such as Topex/Poseidon, Jason-1, Jason-2, and Envisat, SARAL/AltiKa, a high resolution altimetry mission based on the Ka frequency band that can also cover high latitudinal zones, was launched in February 2013. Even though the data set available from this recent mission is not yet suitable for climate research owing to its short duration, in this study we perform a preliminary validation of SARAL/AltiKa sea-level data. The first part of the validation is the comparison of SARAL/AltiKa and Jason-2 sea-level data between March 2013 and August 2014 in terms of temporal mean spatial pattern. Comparisons in terms of global mean sea-level time series and latitudinal band-based mean time series are also performed. The second part of the validation is the comparison of the SARAL/AltiKa sea-level based time series with several tide gauge records covering the ...

The mangrove ecosystem of Sundarbans region plays an important ecological and socioeconomical role in both India and Bangladesh. The satellite-based approach has been adopted for assessing the ecological disturbance caused by major cyclones. Moderate Resolution Imaging Spectroradiometer (MODIS) time-series (2001–2011) Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) products were used to compute the MODIS Global Disturbance Index (MGDI). The pre- and post-cyclonic changes in the EVI as well as LST were analyzed to study the impact of cyclones on the vegetation and surface temperature, respectively. In the present study, time-series MGDI was transformed to the percentage change in MGDI using temporal mean for each pixel, and its response toward cyclonic events was studied. The pre- and post-cyclonic Landsat data were analyzed to identify the affected areas discernible on the image and a threshold was decided to mark the “disturbed pixels.” The threshold for the instantaneous disturbance caused by cyclones was found to be more than 11 % of the temporal mean MGDI. The selected threshold was applied on the time-series MGDI images to delineate the disturbed pixels, and percent-disturbed area of each Island was computed. The classified disturbed area maps were able to characterize the impact of cyclones and effectively represent the distribution of the spatial intensity of the ecological disturbance in Sundarbans region.

Space-time variability of SSS in the Atlantic Ocean (33°S–80°N) is analyzed using near-surface salinity observations from the period 1980–2013 jointly with the output from an eddy-resolving numerical ocean simulation. Results show a good agreement between in situ and model results in terms of spatial and temporal mean SSS patterns, geographically varying SSS variability, and spatial and temporal scales of SSS variability. A good agreement exists also for estimates of the amplitude and phase of the annual cycle of SSS with the model providing more spatial details of SSS variability, which cannot be resolved by observations, especially near ocean margins and in shelf areas. Dominant spatial and temporal scales of SSS variability are, respectively, between 100 and 250 km and between 30 and 70 days in most of the Atlantic when the annual cycle of the SSS is included. However, smaller-scale salinity features are also present, which show temporal decorrelation scales of only 3–5 days throughout the Atlantic. This fast variability must be considered when producing weekly averaged salinity products from satellite measurements.

The quality of depth images is important in the 3D video system to represent complete 3D contents. However, the original depth image from a depth camera has a low resolution and a flickering problem which shows vibrating depth values in terms of temporal meaning. This problem causes an uncomfortable feeling when we look 3D contents. In order to solve a low resolution problem, we employ 3D warping and a depth weighted joint bilateral filter. A temporal mean filter can be applied to solve the flickering problem while we encounter a residual spectrum problem in the depth image. Thus, after classifying foreground and background regions, we use an upsampled depth image for a foreground region and temporal mean image for background region. Test results shows that the proposed method generates a time consistent depth video with a high resolution.

Land surface albedo (LSA) is an important component of the surface radiation budget. For calculation of the surface shortwave net radiation budget, temporal mean albedo is more important than instantaneous albedo. Although Moderate Resolution Imaging Spectroradiometer (MODIS) albedo products have been extensively validated, little effort has been made to evaluate the accuracy of daily mean albedo from MODIS. In this study, we calculate daily mean albedo from MODIS data using a direct method and a look-up table (LUT) method. Comparison with in situ albedo measured at 27 field stations shows that both methods can estimate daily mean albedo with high accuracy. The root-mean-square error (RMSE) of snow-free daily mean albedo retrieved by the LUT method and the direct method is 0.033 and 0.034, respectively. Over the 12 spatially representative stations, RMSE of daily mean albedo is 0.022 and 0.023 by the LUT and direct approach, respectively. Simply using the local noon albedo value as a surrogate of daily mean albedo leads to overestimation of daily shortwave net radiation. By using the data of daily mean albedo, the bias in estimating daily shortwave net radiation can be reduced by 2.8 W/m2 with the direct method and 2.6 W/m2 with the LUT method, compared to the use of local noon albedo.

The time series of the net meridional heat transport (MHT) at 15 degrees N in the Pacific Ocean from 2003 to 2012 is estimated by combining the Argo profiles with the satellite altimeter and scatterometer data. The estimate is validated against the climatological ocean data and the ECCO2 products, and is demonstrated to be reasonable. The MHT has a high degree of variability with a temporal mean of 0.700.31 PW, which is concentrated in the upper 800 dbar. The time series of the MHT and Ekman temperature transport have a significant annual cycle which peaks near April and December, whereas the time series of the geostrophic temperature transport have a subannual cycle. The results are consistent with previous estimates and lower than the ECCO2 estimate, which may mainly be caused by the different data sources and processings. The correlation between the air-sea flux and MHT is 0.50 with a 3 month delay. This report describes the first such attempt at a continuous transport of heat at 15 degrees N in the Pacific Ocean from in situ observations.

Estimation of cerebral perfusion pressure during elective shoulder surgery in the beach chair position is regularly performed by noninvasive brachial blood pressure (NIBP) measurements. The relationship between brachial mean arterial pressure and estimated temporal mean arterial pressure (eTMAP) is not well established and may vary with patient positioning. Establishing a ratio between eTMAP and NIBP at varying positions may provide a more accurate estimation of cerebral perfusion using noninvasive measurements.This prospective study included 57 patients undergoing elective shoulder surgery in the beach chair position. All patients received an interscalene block and general anesthesia. After the induction of general anesthesia, values for eTMAP and NIBP were recorded at 0°, 30°, and 70° of incline.A statistically significant, strong, and direct correlation between NIBP and eTMAP was found at 0° (r = 0.909, P ≤ .001), 30° (r = 0.874, P .001), and 70° (r = 0.819, P .001) of incline. The mean ratios of eTMAP to NIBP at 0°, 30°, and 70° of incline were 0.939 (95% confidence interval [CI], 0.915-0.964), 0.738 (95% CI, 0.704-0.771), and 0.629 (95% CI, 0.584-0.673), respectively. There was a statistically significant decrease in the eTMAP/NIBP ratio as patient incline increased from 0° to 30° (P .001) and from 30° to 70° (P .001).The eTMAP-to-NIBP ratio decreases as an anesthetized patient is placed into the beach chair position. Awareness of this phenomenon is important to ensure adequate cerebral perfusion and prevent hypoxic-related injuries.

Agricultural production is one of the most important Brazilian economic activities accounting for about 21,5% of total Gross Domestic Product. In this scenario, the use of satellite images for estimating biophysical parameters along the phenological development of agricultural crops allows the conclusion about the sanity of planting and helps the projection on design production trends. The objective of this study is to analyze the temporal patterns and variation of six vegetion indexes obtained from the bands of Sentinel 2A satellite, associated with greenness (NDVI and ClRE), senescence (mARI and PSRI) and water content (DSWI and NDWI) to estimate maize production. The temporal pattern of the indices was analyzed in function of productivity data collected in-situ. The results obtained evidenced the importance of the SWIR and Red Edge ranges with Pearson correlation values of the temporal mean for NDWI 0.88 and 0.76 for CLRE.

Agricultural land use is essential for prosperity and sustainability of a city. However, the performance of detecting agricultural land use based on vegetation parameters will be substantially reduced when the vegetation changed in type, quantity and condition. This study believed that soil moisture, a fundamental soil parameter, is able to detect agricultural land use. A method was proposed to evaluate city-scale agricultural land use detection capability based on three temporal statistics of soil moisture in both local and global perspectives. Using GF-1 images as a data source, an experiment in Wuhan city was performed to discuss the city-scale agricultural land use detection capability based on soil moisture. Soil moisture were inverted by the modified perpendicular drought index derived from GF-1 images. Under the visual and quantitative comparisons, it was found that the temporal mean of soil moisture performed much better than the temporal standard deviation of soil moisture and the temporal coefficient of variance of soil moisture. The overall accuracy and Kappa coefficient of the temporal mean of soil moisture reach 91.7% and 0.81, respectively, demonstrating its great capability of detecting city-scale agricultural land use. A global analysis is implemented by regression analyses with true zonal agricultural statistics, showing a high global accuracy in agricultural land use detection.

International audience; In this study, we evaluated a new SMOS (Soil Moisture and Ocean Salinity) soil moisture (SM) product, developed by the collaboration of INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d'Etudes Spatiales de la BIOsphere), against the operational SMOS level 3 SM product (SMOSL3). This new product (hereinafter referred to as SMOS-INRA-CESBIO, i.e. SMOSIC in short) differs from SMOSL3 three ways: (i) the SMOSIC algorithm considers the pixel as homogeneous and does not take into account the heterogeneity of the pixel; (ii) uses a new calibration of the effective scattering albedo and soil roughness parameters; (iii) no time correlation is applied on the optical depth. The evaluation was done over North America using the (European Center for Medium range Weather Forecasting) ECMWF SM simulation as a reference, using data for 2011. A better performance of the SMOSIC SM product with respect to ECMWF was found: (i) SMOSIC had higher correlation coefficients (temporal dynamics) and lower unbiased RMSD (absolute values) values with ECMWF over most of the study area and (ii) the spatial patterns of the SMOSIC temporal mean SM maps were in a better agreement with ECMWF.

Recent studies have revealed that the contrast-enhanced ultrasound (CEUS) correlates to the presence and degree of intraplaque neovascularization as determined histologically. However, most studies used a qualitative system to visually grade CEUS images. A computer-aided method is proposed for objective and convenient quantification of contrast agent spatial distribution within plaques in CEUS image sequences. It consists of three algorithms including cardiac cycle retrieval and sub-sequence selection, temporal mean image segmentation, and texture feature extraction. The first algorithm automatically retrieves and selects cardiac cycles from CEUS frames without electrocardiogram gating. The second is composed of three steps, i.e., temporal averaging, interactive plaque delineation, and automatic neovascularization segmentation. The third extracts eight texture features from the grayscale temporal mean images and the binary segmented images. The capability of the quantitative features in discriminating between qualitative grades is examined via the t -tests, analysis of variance (ANOVA), Fisher criterion of inter-intra class variance ratio and logistic regression classification with leave-one-out cross-validation. Experimental results on 33 carotid plaques demonstrated that the optimal feature, namely the combined area ratio, exhibited significant difference among three qualitative grades ( P

Understanding the spatio-temporal changes of the terrestrial water mass behavior together with Enhanced models of natural water storage variation are imperative for better assessing, adapting better evaluable quantified hydrologic catchments. This study provides a reliable impact of the GRACE “gravity recovery and climate experiment” data to gain an independent water mass change on large regional scale. Using GRACE data sets, Terrestrial total Water Storage (TWS) are alternative index to investigate spatio-temporal water variation responses. This study focuses on the Nile basin and utilizes the integration of TWS extracted from GRACE data solutions and Tropical Rainfall Measuring Mission (TRMM) data sets to interpret the average annual/seasonal variation of Terrestrial Total Water Mass. Terrestrial total Water Storage (TWS) was obtained from GRACE data on monthly basis over the period of January 2003–Jul 2016.156 gravity field solutions (RL06 unconstrained solutions) were analyzed and processed as follows: (1) Removing of temporal mean, destriping, smoothing with 250 km radius, (2) Generation of Trend and Standard deviation (SD) images over the entire period, (3) All mass anomalies derived from GRACE data were explicated as an indicator for water mass changes, (4) Topographic, geological and hydrological data were compared to the preformed SDGRACE spatial distribution anomalies utilizing GIS workspace data pool to recognize areas addressing major and significant temporal variations in mass considering the affecting parameters that lead to such observed rise in those variations. Generally, both the analyzed, processed GRACE and TRMM data sets as well as the resulted TWS, SD and trend images displayed a statistically increasing trend in the seasonal water mass over the entire Nile basin as an average in the investigated period (2003–2016). The results show a significant increasing trend during 2013–2016-time segment especially over the northern region.

Knowledge of the spatial snow distribution and its interannual persistence is of interest for a broad spectrum of issues in cryospheric sciences. In this study, snow depths derived from airborne laser scanning are analyzed for interannual persistence of the seasonal snow cover in a partly glacierized mountain area (~36 km2). At the end of five accumulation periods, the snow-covered area varied by 16% of its temporal mean. Mean snow depth of the total area ranged by a factor of two (1.31–2.58 m), with a standard deviation of 0.42 m. Interannual correlation coefficients of snow depth distribution were in the range 0.68–0.84. Of the investigated area, 75% was found to be interannually persistent. The remaining area showed variable snow cover from year to year, caused by occasional avalanches and changes in surface topography as a result of glacier retreat. Snow cover underwent a change from a homogeneous distribution on the former glacier surface to a more heterogeneous snow cover in the recently deglaciated terrain. A geostatistical analysis shows interannual persistence in scaling behavior of snow depth in ice-free terrain with scale break distances at 20 m. Scale-invariant behavior of snow depth is indicated over >100 m on smooth glacier surfaces.

A PM2.5 pollution episode over Hangzhou, China during 8 to 16 December 2011 was simulated using the Models-3 Community Multiscale Air Quality (CMAQ). Relative contributions from local and regional emission sources to the pollution event were also investigated through numerical sensitivity tests. Comparisons between simulations and measurements at six meteorological sites over the Yangtze River Delta Region (YRDR) and four air monitoring stations at Hangzhou were satisfactory. The temporal mean of the PM2.5 mass concentration in Hangzhou was lower than those at most areas of Jiangsu province and Shanghai during the episode. Process analysis of the four air monitoring stations at Hangzhou shows that emissions and aerosol processes contributed to the primary and secondary PM2.5 concentrations, with the mean accumulated rates of 1.2–25.5 µg/m 3 /hr and 0.5–1.2 µg/m 3 /hr, respectively. The process of advection also increased the PM2.5 mass concentration (1.2–3.4 µg/m 3 /hr). Diffusion was the dominant removal process at most air monitoring stations, with the removal rates of 4.1–20.7 µg/m 3 /hr. The dry (–3.0 to –3.6 µg/m 3 /hr) and wet deposition and heterogeneous processes (–0.4 to –1.8 µg/m 3 /hr) contributed to the loss of PM2.5. Process analysis also indicates that the maximum concentrations of PM2.5 that occurred during 13–14 December were mainly due to ineffective removal through diffusion. Results of the sensitivity tests suggest that non-Hangzhou pollutants made significant contributions to the PM2.5 pollution in Hangzhou, reaching up to 70% during the focal episode. Under certain meteorological conditions, pollutants transported from outside of Hangzhou not only increased the PM2.5 concentration, but also extended the pollution episode period in Hangzhou by one day. Nevertheless Hangzhou’s local emissions were not negligible, because they had important impacts on PM2.5 peak values.

[1] An ocean state estimate has been developed for the Gulf of Mexico (GoM) using the MIT general circulation model and its adjoint. The estimate has been tested by forecasting loop current (LC) evolution and eddy shedding in the GoM. The adjoint (or four-dimensional variational) method was used to match the model evolution to observations by adjusting model temperature and salinity initial conditions, open boundary conditions, and atmospheric forcing fields. The model was fit to satellite-derived along-track sea surface height, separated into temporal mean and anomalies, and gridded sea surface temperature for 2 month periods. The optimized state at the end of the assimilation period was used to initialize the forecast for 2 months. Forecasts explore practical LC predictability and provide a cross-validation test of the state estimate by comparing it to independent future observations. The model forecast was tested for several LC eddy separation events, including Eddy Franklin in May 2010 during the deepwater horizon oil spill disaster in the GoM. The forecast used monthly climatological open boundary conditions, atmospheric forcing, and run-off fluxes. The model performance was evaluated by computing model-observation root-mean-square difference (rmsd) during both the hindcast and forecast periods. The rmsd metrics for the forecast generally outperformed persistence (keeping the initial state fixed) and reference (forecast initialized using assimilated Hybrid Coordinate Ocean Model 1/12° global analysis) model simulations during LC eddy separation events for a period of 12 months.

Assessments of future tree species’ distributions should account for time lags in the adaptation of their external range limits to climatic changes. In simulation experiments it is therefore necessary to capture processes that influence such time lags, in particular tree species’ migration. We hypothesise that directional processes such as migration are sensitive to the exact sequence of simulated climate influences, and that the uncertainty associated with a given interannual climate variability has to be accounted for when simulating migration explicitly. In this paper we used the intermediate-complexity multi-species model TreeMig to examine whether different realisations of future climate influences with the same temporal mean and the same interannual variability cause fundamental differences in simulated migration. We assume that the impact of interannual climate variability becomes most apparent in situations which critically influence regeneration and survival. Such situations arise, for example, when species’ sensitivities to climate, competition and spatial fragmentation interact. We therefore developed an illustrative and realistic simulation setup representing this situation. We simulated the northwards migration of the sub-Mediterranean tree species Ostrya carpinifolia Scop. (European Hop Hornbeam) through the highly fragmented and climatically heterogeneous landscape of the Swiss Alps. Situations critically influencing regeneration and survival can lead to low species’ abundances. Before investigating effects of interannual climate variability, we therefore tested whether the continuous representation of species’ cell populations in TreeMig, which allows for infinitesimal population densities, can have side effects on simulated migration. Specifically, we tested for effects of minimum density thresholds, i.e. thresholds below which a species is treated as absent. We found that small thresholds in the magnitude of one individual per km2 cell have a considerable impact on simulated migration, and can even impede migration in situations critical for regeneration and survival. To test for effects of interannual climate variability, we compared simulation results from multiple repetitions driven by different annual climate time series generated stochastically from the same probability distribution. Results from these repetitions were additionally compared to results from simulations driven by cyclically repeated climate and steadily applied mean climate, respectively. These comparisons were conducted for different species parameter sets within the plausible parameter range of O. carpinifolia to account for potential interactions between species’ sensitivities and the environment. Simulated tree species’ migration was highly dependent on the species parameters applied and markedly influenced by interannual climate variability. Notable divergence in species’ spread resulted amongst multiple realisations of annual climate time series stochastically sampled from the same probability distribution. We conclude that uncertainty associated with interannual climate variability has to be accounted for. Single realisations can be insufficient and mean value simulations as well as averages of output results can be too simplistic to reflect possible outcomes of tree species’ migration.

Atmospheric CO in situ measurements are carried out at the Izaña (Tenerife) global GAW (Global Atmosphere Watch Programme of the World Meteorological Organization – WMO) mountain station using a Reduction Gas Analyser (RGA). In situ measurements at Izaña are representative of the subtropical Northeast Atlantic free troposphere, especially during nighttime. We present the measurement system configuration, the response function, the calibration scheme, the data processing, the Izaña 2008–2011 CO nocturnal time series, and the mean diurnal cycle by months. We have developed a rigorous uncertainty analysis for carbon monoxide measurements carried out at the Izaña station, which could be applied to other GAW stations. We determine the combined standard measurement uncertainty taking into consideration four contributing components: uncertainty of the WMO standard gases interpolated over the range of measurement, the uncertainty that takes into account the agreement between the standard gases and the response function used, the uncertainty due to the repeatability of the injections, and the propagated uncertainty related to the temporal consistency of the response function parameters (which also takes into account the covariance between the parameters). The mean value of the combined standard uncertainty decreased significantly after March 2009, from 2.37 nmol mol−1 to 1.66 nmol mol−1, due to improvements in the measurement system. A fifth type of uncertainty we call representation uncertainty is considered when some of the data necessary to compute the temporal mean are absent. Any computed mean has also a propagated uncertainty arising from the uncertainties of the data used to compute the mean. The law of propagation depends on the type of uncertainty component (random or systematic). In situ hourly means are compared with simultaneous and collocated NOAA flask samples. The uncertainty of the differences is computed and used to determine whether the differences are significant. For 2009–2011, only 24.5% of the differences are significant, and 68% of the differences are between −2.39 and 2.5 nmol mol−1. Total and annual mean differences are computed using conventional expressions but also expressions with weights based on the minimum variance method. The annual mean differences for 2009–2011 are well within the ±2 nmol mol−1 compatibility goal of GAW.

The temporal dynamics of complex networks such as the Internet are characterized by a power scaling between the temporal mean and dispersion of signals at each network node. Here we tested the hypothesis that the temporal dynamics of the brain networks are characterized by a similar power law. This realization could be useful to assess the effects of randomness and external modulators on the brain network dynamics. Simulated data using a well-stablished random diffusion model allowed us to predict that the temporal dispersion of the amplitude of low frequency fluctuations (ALFF) and that of the local functional connectivity density (lFCD) scale with their temporal means. We tested this hypothesis in open-access resting-state functional magnetic resonance imaging datasets from 66 healthy subjects. A robust power law emerged from the temporal dynamics of ALFF and lFCD metrics, which was insensitive to the methods used for the computation of the metrics. The scaling exponents (ALFF: 0.8 ± 0.1; lFCD: 1.1 ± 0.1; mean ± SD) decreased with age and varied significantly across brain regions; multimodal cortical areas exhibited lower scaling exponents, consistent with a stronger influence of external inputs, than limbic and subcortical regions, which exhibited higher scaling exponents, consistent with a stronger influence of internal randomness. Findings are consistent with the notion that external inputs govern neuronal communication in the brain and that their relative influence differs between brain regions. Further studies will assess the potential of this metric as biomarker to characterize neuropathology.

The goal of this study was to evaluate the ability of dynamically downscaled reanalysis data to reproduce local-scale spatiotemporal precipitation and temperature data needed to accurately predict streamflow in the Tampa Bay region of west central Florida. In particular, the Florida State University Center for Ocean-Atmospheric Prediction Studies CLARReS10 data (NCEP DOE 2 reanalysis data (R2) downscaled to 10-km over the Southeast USA using the Regional Spectral Model (RSM) were evaluated against locally available observed precipitation and temperature data and then used to drive an integrated hydrologic model that was previously calibrated for the Tampa Bay region. Resulting streamflow simulations were evaluated against observed data and previously calibrated model results. Results showed that the raw downscaled reanalysis predictions accurately reproduced the seasonal trends of mean daily minimum temperature, maximum temperature and precipitation, but generally overestimated the monthly mean and standard deviation of daily precipitation. Biases in the temporal mean and standard deviation of daily precipitation and temperature predictions were effectively removed using a CDF-mapping approach; however, errors in monthly precipitation totals remained after bias correction. Monthly streamflow simulation error statistics indicated that the accuracy of the streamflow produced by the bias-corrected downscaled reanalysis data was satisfactory (i.e., sufficient for seasonal to decadal planning), but that the accuracy of the streamflow produced by the raw downscaled reanalysis data was unsatisfactory for water resource planning purposes. The findings of this study thus indicate that further improvement in large-scale reanalysis data and regional climate models is needed before dynamically downscaled reanalysis data can be used directly (i.e. without bias correction with local data) to drive hydrologic models. However, bias-corrected dynamically downscaled data show promise for extending local historic climate observation records for hydrologic simulations. Furthermore, results of this study indicate that similarly bias-corrected dynamically downscaled retrospective and future GCM projections should be suitable for assessing potential hydrologic impacts of future climate change in the Tampa Bay region.

Knowledge about the spatio-temporal variability of soil moisture is essential to understand and predict processes in climate science and hydrology. A significant body of literature exists on the characterization of the spatial variability and the rank stability (also called temporal stability) of absolute soil moisture. Yet previous studies were generally based on short-term measurement campaigns and did not distinguish the respective contributions of time-varying and time-invariant components to these quantities. In this study, we investigate this issue using measurements from 14 grassland sites of the SwissSMEX soil moisture network (spatial extent of approx. 150 × 210 km) over the time period May 2010 to July 2011. We thereby decompose the spatial variance of absolute soil moisture over time in contributions from the spatial variance of the mean soil moisture at all sites (which is time-invariant), and components that vary over time and are related to soil moisture dynamics. These include the spatial variance of the temporal soil moisture anomalies at all sites and the covariance between the site anomalies to the spatial mean at a given time step and those for the temporal mean values. The analysis demonstrates that the time-invariant term contributes 50–160% (on average 94%) of the spatial soil moisture variance at any point in time, while the covariance term generally contributes negatively to the spatial variance. On the other hand, the spatial variance of the temporal anomalies, which is overall most relevant for climate and hydrological applications because it is related to soil moisture dynamics, is relatively limited and constitutes at most 2–30% (on average 9%) of the total variance. Nonetheless, this term is not negligible compared to the temporal anomalies of the spatial mean. These results suggest that a large fraction of the spatial variability of soil moisture assessed from short-term campaign may be time-invariant if other regions present a similar behavior. Moreover, we find that the rank (or temporal) stability concept, when applied to absolute soil moisture at the sites, mostly characterizes the time-invariant patterns. Indeed, sites that best represent the mean soil moisture dynamics of the network are not the same as those that best reflect mean soil moisture at any point in time. Overall, this study shows that conclusions derived from the analysis of the spatio-temporal variability of absolute soil moisture need not generally apply to temporal soil moisture anomalies, and hence to soil moisture dynamics.

Organizational process improvement often emphasizes reductions in variation; mean performance may remain unchanged if the effect is primarily a reduction in extreme values (high quantiles). Such data may be best analyzed by modeling the temporal regression relationship at the 0.95th quantile or 95th percentile (cases that exceed 95 percent of the distribution) controlling for the independent variables. A conventional linear regression analysis assumes the relationship observed for the mean holds at the different quantiles of the response variable. The estimated temporal mean trend basically determines the shape of temporal trends at individual quantiles. The resulting trend estimates cannot be necessarily true or close to the true temporal trend at a particular quantile of interest. Least-squares analysis is not valid when addressing a performance improvement goal stated as “95 percent of patients have less than 10 days between first contact and first treatment.” Quantile regression, on the other hand, estimates the 95th percentile directly and allows us to investigate the question without constraining the trend at 95 percent to be of a certain shape. System-level studies of the performance measure “wait time to treatment” are rare in the U.S. health care system despite the importance of this measure. The need for systematically monitored and analyzed process measures is particularly pressing within the substance abuse treatment field (Garnick et al. 2002). Patients seeking treatment for alcohol and drug use disorders often encounter waiting lists and experience delays between the day they request care and the day they enter care. The delays reflect a fixed capacity for new patients and limited financing for expanding access to care (Hoffman and McCarty 2012). The organization and delivery of care, however, can also inhibit access to care and discourage patients from attending addiction treatment appointments (Stark, Campbell, and Brinkerhoff 1990; Hser et al. 1998; Ford et al. 2007). NIATx was the first widespread application of process improvement techniques to substance abuse treatment (http://www.niatx.net) (Capoccia et al. 2007). Participating agencies were trained to use a simplified version of the Institute for Healthcare Improvement's hospital improvement support system (http://www.ihi.org). A cross-site evaluation of NIATx participants examined: (1) change in days between first contact and first treatment and (2) the percentage of clients that began treatment and completed the first four units of care. Thirteen agencies that began participation in NIATx in August 2003 reduced days to treatment 37 percent—from 19.6 to 12.4 days across all levels of care. Retention in care also improved; the proportion of clients who completed a first session of care and returned for a second and third session of care increased 18 percent between the first and second session (72–85 percent) and 17 percent between the first and third session of care (62–73 percent) (McCarty et al. 2007). A subsequent analysis of 14 outpatient and intensive outpatient treatment programs within the second NIATx cohort replicated the reduction in wait time and the improvement in retention and noted that the first cohort sustained the gains during a 20-month follow-up (Hoffman et al. 2008). Previous analyses of wait time to treatment within the Network for the Improvement of Addiction Treatment (NIATx) employed a conventional least-squares regression analysis and estimated rates of change in the mean of the monthly averaged outcome variables (McCarty et al. 2007; Hoffman et al. 2008). Analysis of mean function is useful, but other aspects of the response distribution can also be of interest. Patient wait times may have unequal variation due to complex interactions between variables or unobserved exogenous noises that are not accounted for in the regression model. Figure 1 shows distributions of patient wait time in a NIATx program. The distribution of all wait times across the 15-month study period appears in Figure 1a. Figure 1b presents wait time distributions for month 1 (first), month 8 (mid), and month 15 (last) and illustrates change in wait time distributions over time. This variation in the distributions implies that more than one single slope describes the temporal trends in wait time from the first contact to the first treatment. In the presence of such heterogeneity, conventional least-squares regression models may underestimate, overestimate, or fail to detect important changes occurring locally at a certain quantile of data, because it focuses on changes in the means (Terrell et al. 1996; Cade, Terrell, and Schroeder 1999). Figure 2 provides expository guidelines where quantile regression is desirable over the conventional least-squares regression analysis. Figure 1 The Distributions of Wait Time to Treatment in Program 8. (A) Density Plot of All Wait Time. (B) Density Plot of Wait Time in the First (n = 39), Mid (n = 49) and Last Months (n = 33) Figure 2 Guidelines How to Choose between Least-Squares Regression and Quantile Regression Quantile regression (Koenker and Bassett 1978) extends the concept of percentile or quantile in the univariate analysis to regression and estimates regression relationships specific to a certain percentile of the response variable controlling for the independent variables. In its simplest application, quantile regression examines median regression and describes changes in the center of the distribution. As the median is robust to outliers in the univariate analysis, median regression is also robust to outliers. Median, however, is just one of the many quantiles that can be examined. A series of quantile regression for different percentiles estimates multiple rates of change, providing a more robust and detailed description of the regression relationships that can be overlooked by other regression methods. Quantile regression is well established and available in common statistical software (e.g., proc quantreg procedure in SAS; quantreg package in R). Applications include economics (Hendricks and Koenker 1992; Buchinsky 1994; Manning, Blunmberg, and Moutlton 1995; Poterba and Rueben 1995); biology, especially for growth curve and duration models (Cade, Terrell, and Schroeder 1999; Koenker and Geling 2001); medicine, especially for reference charts (Cole 1988; Cole and Green 1992; Wei et al. 2006); environmental modeling (Pandey and Nguyen 1999); and infrastructure studies (He, Simpson, and Wang 2000). More examples are available in Koenker (2005).

A time series of surface geostrophic velocity is developed using satellite altimetry data during 1992–2010 for a track across the Kuroshio southeast of Kyushu, Japan. The temporal mean geostrophic velocity is estimated by combining the along-track sea level anomaly and shipboard ADCP data. This approximately 6-km resolution dataset is successful in representing the Kuroshio cross-current structure and temporal variation of the Kuroshio current-axis position during 2000–2010. The authors use this dataset to examine the winter Kuroshio path destabilization phenomenon. Its seasonal features are characterized as follows: the velocity shear on the inshore side of the Kuroshio becomes stronger and the Kuroshio path state becomes unstable from the summer to winter. This evidence is consistent with the hypothetical mechanism governing the destabilization phenomenon discussed in a previous study. Furthermore, the interannual amplitude modulation of the seasonality is examined in relation to interannual variations in the winter northerly wind over the northern Okinawa Trough and the Pacific Decadal Oscillation (PDO) index. The destabilization phenomenon appears 15 times in the period 2000–2010. Ten cases are related to local wind effects, and 7 of these are also connected with the PDO index. This is probably because the winter northerly wind over the northern Okinawa Trough is regulated by the PDO signal in interannual time-scales. Only 4 cases are related to the PDO index, but their driving mechanism remains uncertain.

The sources of the rivers that form six of the main Brazilian hydrographic regions, Parnaiba, Parana, Paraguay, Araguaia-Tocantins, Sao Francisco and the Amazon are in the Cerrado Biome. Because of its hydric potential, the Cerrado Biome is called the Cradle of Waters.. Aiming to analyze the spatial and seasonal variation of pluvial precipitation in Mato Grosso do Sul , the temporal and spatial variability of rainfall was studied, and the results were analyzed using the Rainfall Anomaly Index (RAI). The interpolation method used was the Topo to Raster. The temporal mean precipitation for the 30 year period, needed for the RAI calculation, which is used to classify periods of years as dry or wet according to the local mean was calculated in the statistical treatment. The results presented are the spatial and temporal seasonal distribution of the rainfall. The RAI showed 3.61% of extremely dry months, 22.22% of very dry months, 30.56% of dry months, 1.94% of months without anomaly, 25.83% of rainy months, 10.56% of very rainy months, and 5.28% of extremely rainy months in the studied period

Observations of stratification and currents between June 2007 and March 2009 reveal a strong overflow between 400- and 570-m depth from the Panay Strait into the Sulu Sea. The overflow water is derived from approximately 400 m deep in the South China Sea. Temporal mean velocity is greater than 0.75 m s−1 at 50 m above the 570-m Panay Sill. Empirical orthogonal function analysis of a mooring time series shows that the flow is dominated by the bottom overflow current with little seasonal variance. The overflow does not descend below 1250 m in the Sulu Sea but rather settles above high-salinity deep water derived from the Sulawesi Sea. The mean observed overflow transport at the sill is 0.32 × 106 m3 s−1. The observed transport was used to calculate a bulk diapycnal diffusivity of 4.4 × 10−4 m2 s−1 within the Sulu Sea slab (∼575–1250 m) ventilated from Panay Strait. Analysis of Froude number variation across the sill shows that the flow is hydraulically controlled. A suitable hydraulic control model shows overflow transport equivalent to the observed overflow. Thorpe-scale estimates show turbulent dissipation rates up to 5 × 10−7 W kg−1 just downstream of the supercritical to subcritical flow transition, suggesting a hydraulic jump downstream of the sill.