Composite Time Series Research Articles

Lagrangian analysis of high-speed turbulent premixed reacting flows: Thermochemical trajectories in hydrogen–air flames

A Lagrangian analysis approach is used to examine the effects of high-speed turbulence on thermochemical trajectories in unconfined, stoichiometric hydrogen–air (H2–air) premixed flames. Two different intensities of turbulence in the unburnt reactants are considered, giving premixed flames with Karlovitz numbers of roughly 150 and 450. These two cases are modeled using direct numerical simulations (DNS) with both multi- and single-step H2–air reaction kinetics. In each of the four resulting simulations, trajectories of fluid parcels are calculated using a high-order Runge–Kutta method, and time series of temperature and chemical composition within each parcel are recorded. The resulting thermochemical trajectories are used to examine the evolution of thermodynamic quantities and chemical composition, as well as measure fluid parcel residence times and path lengths during different phases of the combustion process. Fuel mass fraction and temperature within fluid parcels are shown to be frequently non-monotonic along fluid trajectories in both single- and multi-step H2–air simulations, and the prevalence of non-monotonic trajectories increases with increasing turbulence intensity. Using results from single-step simulations, it is shown that this non-monotonicity can be caused solely by molecular transport processes resulting from large gradients in temperature and species concentrations created by turbulent advection. As a related consequence of advection, fluid parcel residence times are found to be smaller than in a laminar flame and the ratio of turbulent to laminar residence times decreases from roughly 0.8 to 0.6 as the turbulence intensity increases. By contrast, fluid parcel path lengths in the present high-speed turbulent flames are found to be substantially greater than laminar path lengths, resulting in fluid parcels that travel 4 and 7 times further than in a laminar flame for the two different turbulence intensities considered here.

Updating Landsat time series of surface-reflectance composites and forest change products with new observations

The use of time series satellite data allows for the temporally dense, systematic, transparent, and synoptic capture of land dynamics over time. Subsequent to the opening of the Landsat archive, several time series approaches for characterizing landscape change have been developed, often representing a particular analytical time window. The information richness and widespread utility of these time series data have created a need to maintain the currency of time series information via the addition of new data, as it becomes available. When an existing time series is temporally extended, it is critical that previously generated change information remains consistent, thereby not altering reported change statistics or science outcomes based on that change information. In this research, we investigate the impacts and implications of adding additional years to an existing 29-year annual Landsat time series for forest change. To do so, we undertook a spatially explicit comparison of the 29 overlapping years of a time series representing 1984–2012, with a time series representing 1984–2016. Surface reflectance values, and presence, year, and type of change were compared. We found that the addition of years to extend the time series had minimal effect on the annual surface reflectance composites, with slight band-specific differences (r≥0.1) in the final years of the original time series being updated. The area of stand replacing disturbances and determination of change year are virtually unchanged for the overlapping period between the two time-series products. Over the overlapping temporal period (1984–2012), the total area of change differs by 0.53%, equating to an annual difference in change area of 0.019%. Overall, the spatial and temporal agreement of the changes detected by both time series was 96%. Further, our findings suggest that the entire pre-existing historic time series does not need to be re-processed during the update process. Critically, given the time series change detection and update approach followed here, science outcomes or reports representing one temporal epoch can be considered stable and will not be altered when a time series is updated with newly available data.

Influence of the El Niño/Southern Oscillation on South Korean streamflow variability

AbstractDeciphering the mechanisms through which the El Niño/Southern Oscillation (ENSO) affects hydrometeorological parameters in the tropics and extratropics is of great interest. We investigate climatic teleconnections between warm or cold phases of ENSO and streamflow patterns over South Korea using an empirical methodology designed to detect regions showing a strong and consistent hydroclimatic signal associated with ENSO. We calculate not only spatial coherence values by monthly streamflow composite formed over 2‐year ENSO cycle and the first harmonic fit to detect candidate regions but also temporal consistency rates by aggregate composite and index time series to determine core regions. As a result, the core regions, namely, the Han river basin and the Nakdong river basin, are detected with a high level of response of ENSO phenomena to streamflow patterns. The ENSO composites for both core regions indicate drier (wetter) conditions in early autumn of the warm (cold) episode years and wetter (drier) conditions from winter to spring of the following year. For both regions, the spatial coherences are over 92% (82%) and the temporal consistencies are 71% (75%) during the El Niño (La Niña) events. In addition, for the core regions identified by composite‐harmonic analysis for both extreme episodes, the results of comparative analyses by using correlation, annual cycle, and Wilcoxon rank sum test indicate that 2 opposite phases‐streamflow relationships have a tendency of sign reversal of the streamflow anomaly. Also, the positive departures during the El Niño years show more coherent and strong responses than the negative anomalies in the La Niña events. In conclusion, South Korea experiences climatic teleconnection between ENSO forcing and midlatitude streamflow patterns.

A method to predict PM2.5 resulting from compliance with national ambient air quality standards

Area-wide composite monitor time series of daily PM2.5 (particulate matter with aerodynamic diameter < 2.5 μm) that correspond to just meeting U.S. National Ambient Air Quality Standards (NAAQS) have been used in risk assessments conducted during periodic reviews of PM NAAQS by U.S. EPA. Such time series were developed by adjusting ambient PM2.5 over an area according to a prescribed spatial pattern. A new technique for this purpose based on photochemical grid modeling for the continental U.S. is demonstrated here. The method uses a spatial prediction model to impute missing data and PM2.5 relative response factors based on simulations with reductions in anthropogenic emissions of primary PM2.5 and PM2.5 precursors (i.e., NOx and SO2). Case study results indicate that relatively urban sites are generally more responsive to primary PM2.5 reductions, while outlying sites are more responsive to NOx and SO2 reductions. The method enables the sensitivity of outcomes to be examined by quickly implementing PM2.5 adjustments based on different combinations of primary PM2.5 and NOx and SO2 emission reductions for areas of the U.S.

Dirichlet ARMA models for compositional time series

A compositional time series is a multivariate time series in which the observation vector at each time point is a set of proportions that sum to 1. Traditionally, such time series are modeled by taking a log-ratio transformation of the observations and then modeling them with a Gaussian vector autoregressive moving average (ARMA) model. In this paper, a new class of models is proposed by assuming that the proportions follow a time-varying Dirichlet distribution, and that the corresponding time-varying parameters, after a proper transformation, assume an ARMA-type of dynamic structure. The new model is referred to as the Dirichlet autoregressive moving average (DARMA) model. Under this model, after a proper transformation, the original data follow a vector ARMA model with a martingale difference sequence as its noise series. Two specific transformations are studied under the DARMA framework. Estimation procedures are developed and their numerical properties are investigated. Simulation studies and real examples are presented to demonstrate the properties of the proposed models, and comparisons are made with the existing modeling approaches.

Forecasting compositional time series: A state space approach

A framework for the forecasting of composite time series, such as market shares, is proposed. Based on Gaussian multi-series innovations state space models, it relies on the log-ratio function to transform the observed shares (proportions) onto the real line. The models possess an unrestricted covariance matrix, but also have certain structural elements that are common to all series, which is proved to be both necessary and sufficient to ensure that the predictions of shares are invariant to the choice of base series. The framework includes a computationally efficient maximum likelihood approach to estimation, relying on exponential smoothing methods, which can be adapted to handle series that start late or finish early (new or withdrawn products). Simulated joint prediction distributions provide approximations to the required prediction distributions of individual shares and the associated quantities of interest. The approach is illustrated on US automobile market share data for the period 1961–2013.

Statistical Catch‐Free Age‐Structured Assessment to Estimate Year‐Class Strength for Inland Recreational Fisheries: A Largemouth Bass Example

AbstractStatistical catch‐at‐age analysis (SCAA) is commonly used to estimate recruitment and mortality for large‐scale, economically important fish stocks, but fishery‐dependent catch‐at‐age data are often unavailable for smaller‐scale inland fisheries, thus limiting the use of SCAA. In these systems, a catch‐free approach to age‐structure modeling may be useful for estimating recruitment and mortality rates solely from a time series of survey age composition and catch rate data by estimating recruitment as a relative index of year‐class strength (Rt). Here, we demonstrate a statistical catch‐free age‐structured assessment (SCFASA) model for small‐scale inland fish stocks as an alternative to traditional catch curve approaches by using Largemouth Bass Micropterus salmoides as an example. We fitted the SCFASA to fishery‐independent survey age composition and catch‐per‐effort data from Largemouth Bass stocks in three Alabama reservoirs to estimate Rt, annual fishing mortality rates, and age‐specific electrofishing survey vulnerability; we compared Rt to catch curve residual estimates and performed a sensitivity analysis under a variety of model assumptions. Model estimates of Rt and temporal trends in fishing mortality were robust to changes in assumptions about the effective sample size of multinomial age samples, natural mortality rate, and temporal variability in fishing mortality. However, the magnitude of instantaneous fishing mortality rates at two of the three reservoirs was somewhat higher than empirical estimates for similar stocks and was sensitive to model assumptions. Our application of the SCFASA to survey data sets routinely collected by management agencies for Largemouth Bass potentially provides a comprehensive approach to estimating Rt, mortality, and sampling vulnerability for these stocks without increasing sampling effort. Future investigations of SCFASA's utility for small‐scale inland stocks should address the potential for bias in the magnitude of fishing mortality estimates due to incorrectly specified vulnerability schedules.Received October 5, 2015; accepted September 19, 2016 Published online December 21, 2016

The evolution and sensitivity of katabatic flow dynamics to external influences through the evening transition

Data collected over an arid shallow slope (2–4°) during the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program are used to study the katabatic structure and onset of katabatic flow through the evening transition. An unprecedented suite of instrumentation, including a transect of five turbulence towers with 29 sonic anemometers, is used for the investigation. Fifteen transition periods with well‐defined katabatic flow and relatively little synoptic forcing are used in the study. The katabatic onset, jet velocity and jet height all show a large degree of interdiurnal and intersite variance. The slope‐aligned budgets of momentum and potential temperature are used to define time‐scales that describe the evolution of the katabatic flow. Composite wind velocity time series are used to show that ≈30 min elapses from the time when the katabatic flow initializes at 0.5 m to the point of initialization at 20 m. A simple katabatic model utilizing surface energy‐budget modelling is developed and used to model the interdiurnal katabatic variance. Finally, uni‐ and multi‐variate statistical analyses are used to diagnose the influence of specific external variables. Valley wind speed, turbulence structure, soil moisture, and shadow front speed are all found to influence the katabatic dynamics to varying degrees.

A new look at solar dimming and brightening in China

AbstractPublished estimates on the magnitude of solar dimming and brightening in China show large discrepancies and inconsistencies in its seasonal variations. A nationwide reorganization of the surface solar radiation (SSR) network during 1990–1993 contributed to these uncertainties, which is reflected in a sudden upward jump in the published composite SSR time series. This jump is found to be prevalent in 23 out of 130 stations due to both natural (7 stations) and operational factors (16 stations). After eliminating the stations containing artifacts and discontinuous records, a new magnitude of solar dimming and brightening was estimated for China, and seasonal trends were determined. A transition from dimming to brightening remains in the SSR trend in China after excluding the affected stations and associated jump, in line with the global SSR trend.

THE TOTAL SOLAR IRRADIANCE CLIMATE DATA RECORD

ABSTRACT We present the composite measurements of total solar irradiance (TSI) as measured by an ensemble of space instruments. The measurements of the individual instruments are put on a common absolute scale, and their quality is assessed by intercomparison. The composite time series is the average of all available measurements. From 1984 April to the present the TSI shows a variation in phase with the 11 yr solar cycle and no significant changes of the quiet-Sun level in between the three covered solar minima.

Convective and large-scale mass flux profiles over tropical oceans determined from synergistic analysis of a suite of satellite observations.

A new, satellite‐based methodology is developed to evaluate convective mass flux and large‐scale total mass flux. To derive the convective mass flux, candidate profiles of in‐cloud vertical velocity are first constructed with a simple plume model under the constraint of ambient sounding and then narrowed down to the solution that matches satellite‐derived cloud top buoyancy. Meanwhile, the large‐scale total mass flux is provided separately from satellite soundings by a method developed previously. All satellite snapshots are sorted into a composite time series that delineates the evolution of a vigorous and organized convective system. Principal findings are the following. First, convective mass flux is modulated primarily by convective cloud cover, with the intensity of individual convection being less variable over time. Second, convective mass flux dominates the total mass flux only during the early hours of the convective evolution; as convective system matures, a residual mass flux builds up in the mass flux balance that is reminiscent of stratiform dynamics. The method developed in this study is expected to be of unique utility for future observational diagnosis of tropical convective dynamics and for evaluation of global climate model cumulus parameterizations in a global sense.

FORECASTING URBAN EXPANSION BASED ON NIGHT LIGHTS

Forecasting urban expansion models are a very powerful tool in the hands of urban planners in order to anticipate and mitigate future urbanization pressures. In this paper, a linear regression forecasting urban expansion model is implemented based on the annual composite night lights time series available from National Oceanic and Atmospheric Administration (NOAA). The product known as 'stable lights' is used in particular, after it has been corrected with a standard intercalibration process to reduce artificial year-to-year fluctuations as much as possible. Forecasting is done for ten years after the end of the time series. Because the method is spatially explicit the predicted expansion trends are relatively accurately mapped. Two metrics are used to validate the process. The first one is the year-to-year Sum of Lights (SoL) variation. The second is the year-to-year image correlation coefficient. Overall it is evident that the method is able to provide an insight on future urbanization pressures in order to be taken into account in planning. The trends are quantified in a clear spatial manner.

FORECASTING URBAN EXPANSION BASED ON NIGHT LIGHTS

Abstract. Forecasting urban expansion models are a very powerful tool in the hands of urban planners in order to anticipate and mitigate future urbanization pressures. In this paper, a linear regression forecasting urban expansion model is implemented based on the annual composite night lights time series available from National Oceanic and Atmospheric Administration (NOAA). The product known as 'stable lights' is used in particular, after it has been corrected with a standard intercalibration process to reduce artificial year-to-year fluctuations as much as possible. Forecasting is done for ten years after the end of the time series. Because the method is spatially explicit the predicted expansion trends are relatively accurately mapped. Two metrics are used to validate the process. The first one is the year-to-year Sum of Lights (SoL) variation. The second is the year-to-year image correlation coefficient. Overall it is evident that the method is able to provide an insight on future urbanization pressures in order to be taken into account in planning. The trends are quantified in a clear spatial manner.

Inverse Gaussian‐based composite channel model and time series generator for land mobile satellite systems under tree shadowing

In this study, a new composite channel model for land mobile satellite (LMS) systems under tree shadowing is proposed utilising the inverse Gaussian (IG) distribution to model the shadowing component. Using the experimental data of received power under tree shadowing at L-band, which were carried out at Stromovka Park in Prague, the Czech Republic, the assumption of the IG distribution for the description of the first-order statistics of large-scale effects is investigated and validated. A new LMS channel model is developed as a sum of a signal experiencing IG shadowing and a signal under Rayleigh multipath effects. Moreover, a methodology to generate accurate time series of the received signal envelope using the IG distribution is presented. Such methodology and generally time series generators can be used for the evaluation of system's performance and for end-to-end simulations. Finally, it is shown that the proposed time series generator reproduces the first-order and second-order statistics of the LMS channel model with a very good accuracy.

Holocene climate changes in eastern Beringia (NW North America) – A systematic review of multi-proxy evidence

Reconstructing climates of the past relies on a variety of evidence from a large number of sites to capture the varied features of climate and the spatial heterogeneity of climate change. This review summarizes available information from diverse Holocene paleoenvironmental records across eastern Beringia (Alaska, westernmost Canada and adjacent seas), and it quantifies the primary trends of temperature- and moisture-sensitive records based in part on midges, pollen, and biogeochemical indicators (compiled in the recently published Arctic Holocene database, and updated here to v2.1). The composite time series from these proxy records are compared with new summaries of mountain-glacier and lake-level fluctuations, terrestrial water-isotope records, sea-ice and sea-surface-temperature analyses, and peatland and thaw-lake initiation frequencies to clarify multi-centennial- to millennial-scale trends in Holocene climate change. To focus the synthesis, the paleo data are used to frame specific questions that can be addressed with simulations by Earth system models to investigate the causes and dynamics of past and future climate change. This systematic review shows that, during the early Holocene (11.7–8.2 ka; 1 ka = 1000 cal yr BP), rather than a prominent thermal maximum as suggested previously, temperatures were highly variable, at times both higher and lower than present (approximate mid-20th-century average), with no clear spatial pattern. Composited pollen, midge and other proxy records average out the variability and show the overall lowest summer and mean-annual temperatures across the study region during the earliest Holocene, followed by warming over the early Holocene. The sparse data available on early Holocene glaciation show that glaciers in southern Alaska were as extensive then as they were during the late Holocene. Early Holocene lake levels were low in interior Alaska, but moisture indicators show pronounced differences across the region. The highest frequency of both peatland and thaw-lake initiation ages also occurred during the early Holocene. During the middle Holocene (8.2–4.2 ka), glaciers retreated as the regional average temperature increased to a maximum between 7 and 5 ka, as reflected in most proxy types. Following the middle Holocene thermal maximum , temperatures decreased starting between 4 and 3 ka, signaling the onset of Neoglacial cooling. Glaciers in the Brooks and Alaska Ranges advanced to their maximum Holocene extent as lakes generally rose to modern levels. Temperature differences for averaged 500-year time steps typically ranged by 1–2 °C for individual records in the Arctic Holocene database, with a transition to a cooler late Holocene that was neither abrupt nor spatially coherent. The longest and highest-resolution terrestrial water isotope records previously interpreted to represent changes in the Aleutian low-pressure system around this time are here shown to be largely contradictory. Furthermore, there are too few records with sufficient resolution to identify sub-centennial-scale climate anomalies, such as the 8.2 ka event. The review concludes by suggesting some priorities for future paleoclimate research in the region. • We summarize diverse Holocene paleoclimate records from Alaska and NW Canada. • Proxy types include midges, pollen, biogeochemistry, glaciers, isotopes, lake levels. • Early Holocene (11.5–8.0 ka) temperatures were highly variable, but cold on average. • Regional average temperature increased to a maximum between 7 and 5 ka. • Priorities for future paleoclimate research in the region are suggested.

A Self-Organizing State Space Type Microstructure Model for Financial Asset Allocation

Among the models that describe the dynamic behaviors of financial market, the discrete time microstructure model stands out because of its efficiency in considering the relationship between the price, excess demand, and liquidity of a market. However, the estimation problem of such a microstructure model is challenging, because the model is essentially a nonlinear state space model. A decent solution is to define a self-organizing state-space model by combining the unknown parameters and the state vector of the original model into a new state vector. Then, the sequential Monte Carlo method can be used to simultaneously estimate the parameters and states. To handle the difficulty in setting the initial distributions of parameters for the self-organizing state space model, we propose to use the results obtained by the Kalman filter on the original microstructure model. Finally, a dynamic asset allocation strategy is designed based on estimated excess demand using the self-organizing state space model. The proposed methodology is evaluated by the China SZSE (ShenZhen Stock Exchange) Composite Index time series, and the results show its effectiveness.

Historical forest management in Romania is imposing strong legacies on contemporary forests and their management

Historical forest management can heavily affect contemporary forest management and conservation. Yet, relatively little is known about century-long changes in forests, and that limits the understanding of how past management and land tenure affect current forestry practice and ecosystem conservation. Our goal here was to examine the relationship between historical forest management (as depicted by historical forest cover, species composition, age structure and harvesting data) and contemporary forest patterns in Romania. Romania represents an ideal case-study to examine the effects of historical forest management, because it experienced multiple shifts in forest management regimes since the 1800s due to Austro-Hungarian, Ottoman, Romanian, Soviet and later EU policy influences, and because it is both a conservation hotspot harboring some of the largest old-growth forest in Europe, and an important source of timber for international markets. We reviewed forestry literature and statistics since the 19th century to reconstruct a time-series of forest cover, composition, disturbance patterns, and ownership patterns and interpreted these data in light of institutional changes. We further assessed changes in forest cover, forest harvest, species composition and age structure between two points in time (1920s and 2010s) at the county level, using a combination of historical forest statistics, remote sensing data and modeled forest composition. We complemented our national data with three case studies for which we had stand-level historical and contemporary forest management data. We found that forest area increased in Romania since 1924 by 5% and that the annual rate of forest harvest between 2000 and 2013 was half of the annual rate between 1912 and 1922, which indicates high potential for forest biodiversity conservation. However, the composition, distribution, and age structure of contemporary forests is also substantially different from historical forests. We found an overall increase in coniferous species and several deciduous species (such as Tilia, Populus, Betula, Alnus sp.), a spatial homogenization of species composition, and more even-aged stands. We also observed a drop from 14% to 9% in the relative abundance of old forests (>100years). Spikes in forest harvest coincided with times of widespread forest privatization, and drastic institutional changes, such as agrarian reforms, or the onset and collapse of the Soviet Regime. Overall, our results suggest that effects of past management, land ownership and institutional changes can persist for centuries, and affect forest ecosystem composition, health and structure, and consequently ecosystem services and habitat availability. Our findings are scientifically important because they provide evidence for legacies of past management and for the effects of forest privatization on harvesting rates. Our findings are also relevant to forest management and conservation practice, because they highlight that environmentally sound management over long time periods is essential for sustainable forestry and old-growth forest protection in Europe and elsewhere.

Coast-to-interior gradient in recent northwest Greenland precipitation trends (1952–2012)

The spatial and temporal variability of precipitation on the Greenland ice sheet is an essential component of surface mass balance, which has been declining in recent years with rising temperatures. We present an analysis of precipitation trends in northwest (NW) Greenland (1952–2012) using instrumental (coastal meteorological station) and proxy records (snow pits and ice cores) to characterize the precipitation gradient from the coast to the ice sheet interior. Snow-pit-derived precipitation near the coast (1950–2000) has increased (∼7% decade−1, p < 0.01) whereas there is no significant change observed in interior snow pits. This trend holds for 1981–2012, where calculated precipitation changes decrease in magnitude with increasing distance from the coast: 13% decade−1 (2.4 mm water equivalent (w.e.) decade−2) at coastal Thule air base (AB), 8.6% decade−1 (4.7 mm w.e. decade−2) at the 2Barrel ice core site 150 km from Thule AB, −5.2% decade−1 (1.7 mm w.e. decade−2) at Camp Century located 205 km from Thule AB, and 4.4% decade−1 (1.0 mm w.e. decade−2) at B26 located 500 km from Thule AB. In general, annually averaged precipitation and annually and seasonally averaged mean air temperatures observed at Thule AB follow trends observed in composite coastal Greenland time series, with both notably indicating winter as the fastest warming season in recent periods (1981–2012). Trends (1961–2012) in seasonal precipitation differ, specifically with NW Greenland summer precipitation increasing (∼0.6 mm w.e. decade−2) in contrast with decreasing summer precipitation in the coastal composite time series (3.8 mm w.e. decade−2). Differences in precipitation trends between NW Greenland and coastal composite Greenland underscore the heterogeneity in climate influences affecting precipitation. In particular, recent (1981–2012) changes in NW Greenland annual precipitation are likely a response to a weakening North Atlantic oscillation.

Reassessment and update of long‐term trends in downward surface shortwave radiation over Europe (1939–2012)

AbstractThis paper presents trends in downward surface shortwave radiation (SSR) over Europe, which are based on the 56 longest series available from the Global Energy Balance Archive that are mainly concentrated in central Europe. Special emphasis has been placed on both ensuring the temporal homogeneity and including the most recent years in the data set. We have generated, for the first time, composite time series for Europe covering the period 1939–2012, which have been studied by means of running trend analysis. The mean annual SSR series shows an increase from the late 1930s to the early 1950s (i.e., early brightening), followed by a reduction until mid‐1980s (i.e., global dimming) and a subsequent increase up to the early 2000s (i.e., global brightening). The series ends with a tendency of stabilization since the early 21st century, but the short time period is insufficient with regard to establishing whether a change in the trend is actually emerging over Europe. Seasonal and regional series are also presented, which highlight that similar variations are obtained for most of the seasons and regions across Europe. In fact, due to the strong spatial correlation in the SSR series, few series are enough to capture almost the same interannual and decadal variability as using a dense network of stations. Decadal variations of the SSR are expected to have an impact on the modulation of the temperatures and other processes over Europe linked with changes in the hydrological cycle, agriculture production, or natural ecosystems. For a better dissemination of the time series developed in this study, the data set is freely available for scientific purposes.

风云三号C星太阳总辐照度观测结果分析

Solar Irradiance Monitor-II (SIM-II) onboard FengYun-3C (FY-3C), the third satellite of the second generation of Chinese meteorological polar orbit series, is a successive instrument from FY-3A/B SIMs with improved capabilities of solar tracking and temperature controlling. SIM-II is aimed to observe incoming solar energy over 0.2-50 mm band at top of atmosphere and convert it to total solar irradiance (TSI) at the average sun-earth distance. The instrument has a measuring requirement of 0.1% absolute accuracy and long-term relative precision of 0.02% during its 4-year design lifetime. The TSI observed by SIM-II is analyzed and validated by comparing with SORCE/TIM and the composite time series from Royal Meteorological Institute of Belgium (RMIB) daily mean products. It shows that there is a good consistency on variation trend in TSI among SIM-II, TIM and RMIB, from March 5, 2014 to September 22, 2014. The quantitative comparison shows SIM-II daily TSI product has an 87×10-6 rms relative error compared with SORCE/TIM data and 140×10-6 rms relative error with RMIB composite data. The sunspot data including sunspot number and area is also analyzed with SIM-II TSI product. The sunspot number is negative correlation with SIM-II TSI daily product at solar activities. The TSI variation at the strongest solar activity during the October 2014 shows a good correlation with the sunspot area data. When sunspot area is increased by 2470, the variation in TSI from SIM-II is about -2.94 and -2.98 W/m2 for TIM, which is very close to each other. It can be concluded that the SIM-II TSI observations can quantitatively reflect the variations of the solar emission during solar activities. These studies show that FY-3C TSI product had achieved the same accuracy as those of SORCE/TIM and RMIB data, and can be used in the researches of solar activities and climate changes.