The effect of orbit drift on the calibration of the 3.7 µm channel of the AVHRR onboard NOAA-14 and its impact on night-time sea surface temperature retrievals

The orbit drift of National Oceanic & Atmospheric Administration (NOAA)-14 towards the terminator has caused the deterioration of the radiometric calibration of the Advanced Very High Resolution Radiometer (AVHRR) 3.7?µm channel at night. This deterioration is a result of solar contamination of the radiometric calibration system when the sun strikes the instrument from the spacecraft horizon. The long-term trend and seasonal variation of the contamination are analysed in this study based on trending data from 1995 to 2000. The calibration bias is evaluated and its effect on the sea surface temperature retrievals is quantified. The solar contamination in late 2000 affected as much as 25% of an orbit of data, compared to an average of 7% in 1995. The NOAA/NESDIS operational calibration algorithm partially corrects for the bias but residual effects can still contribute bias on the order of 0.5?K in scene brightness temperature.     

Tracking the calibration stability and consistency of the 3.7, 11.0 and 12.0 μm channels of the NOAA-KLM AVHRR with MODIS

The NOAA-KLM satellites (NOAA-15 to 18) are the current polar-orbiting operational environmental satellites (POES) that carry the Advanced Very High Resolution Radiometer (AVHRR). This study examines the calibration stability and consistency of all three infrared channels (3.7, 11.0 and 12.0 μm) of AVHRR onboard NOAA-15 to 18. The short-term stability is examined from variations of the scan-by-scan gain response, while the long-term stability and calibration consistency are examined by tracking the trends of gain response and measured scene brightness temperatures. The relative differences of observed scene brightness temperatures among NOAA-15 to 18 AVHRR are determined using MODIS as a transfer radiometer based on observations from simultaneous nadir overpasses (SNO). Results show that variations of the scan-to-scan gain responses are within 0.10% under normal operational conditions, while long-term gain changes over six years from 2001 to 2006 vary from 2 to 4% depending on channel. Long-term trending results show that total six-year drifts in observed brightness temperature from NOAA-15 to 18 AVHRR are less than 0.5 K for a given scene temperature in the 250 to 270 K range for the 3.7, 11.0 and 12.0 μm channels, respectively. The calibration consistency is examined for a scene temperature range of 220 to 290 K. The temperature biases among NOAA-16 to 18 AVHRR are within ±0.5 K for the 11.0 and 12.0 μm channels. For NOAA-15 AVHRR, biases of –2.0 K at 11.0 μm and –1.5 K at 12.0 μm are found in comparison with others at the low end of the temperature range. For the 3.7 μm channel, relative biases up to a few degrees among NOAA-15 to 18 could be found at low brightness temperatures.     

Squeeze film effects on dynamic performance of MEMS μ-mirrors-consideration of gas rarefaction and surface roughness

The squeeze film behavior of MEMS torsion mirrors is modeled, analyzed and discussed. Effects of gas rarefaction (first-order slip-flow model with non-symmetric accommodation coefficients, ACs) and surface roughness are considered simultaneously by using the average Reynolds type equation (ARTE). Based on the operating conditions with small variations in film thickness and pressure, the ARTE is linearized. A coordinate transformation, by stretching or contracting the axes by referring to the roughness flow factors, is proposed to transform the linearized ARTE into a diffusion type modal equation. The dynamic coefficients (stiffness and damping coefficients) are then derived and expressed in analytical form. The results show that the tilting frequency (or Γ₀ squeeze number), roughness parameters (γ Peklenik numbers, σ standard deviation of composite roughness) and gas rarefaction parameters (D inverse Knudsen number, ACs) are all important parameters on analyzing the dynamic performance of MEMS torsion mirrors.     

Investigating the effect of training–testing data stratification on the performance of soft computing techniques: an experimental study

Abstract

Cross-validation of soft computing techniques needs to be done efficiently to avoid overfitting and underfitting. This is more important in petroleum reservoir characterisation applications where the often-limited training and testing data subsets represent Wells with known and unknown target properties, respectively. Existing data stratification strategies have been haphazardly chosen without any experimental basis. In this study, the optimal training–testing stratification proportions have been rigorously investigated using the prediction of porosity and permeability of petroleum reservoirs as an experimental case. The comparative performances of seven traditional and advanced machine learning techniques were considered. The overall results suggested a recommendable optimum training stratification that could serve as a good reference for researchers in similar applications.     

On the use of synthetic 12 μm data in a split-window retrieval of sea surface temperature from AVHRR measurements

A numerical model of the radiation transfer through the atmosphere is used with a set of mid-latitude atmospheric profiles to simulate split-window measurements of the AVHRR/2 on the NOAA-7 satellite. These are used to quantify the degradation in accuracy which results in going from a sea surface temperature retrieval in which the effect of the atmosphere is estimated using measurements at 11 μm and 12 μm (channels 4 and 5), to one in which it is estimated using synthetic channel 5 data, as advocated by Singh et al (1985).     

A climatological study of the spatio-temporal variability of land surface temperature and vegetation cover of Vadodara district of Gujarat using satellite data

In the present study, a comprehensive assessment of the spatio-temporal variation of day-time and night-time land surface temperature (LST) and normalized difference vegetation index (NDVI) of Vadodara district of Gujarat in India from 2001 to 2012 has been carried out using satellite data. A significant cooling trend was observed in the day-time LST, whereas the night-time LST showed a distinct warming trend. The entire geographical extent of Vadodara was classified into different night-time LST classes to quantify the extent of the hot pockets, and it showed a clear-cut warming pattern for all the months of the year with an increase in the geographical areas under higher temperature range. Further analysis of Diurnal Temperature Range (DTR) also revealed a strong impact of the urbanization process, with annual DTR showing a decreasing trend at the rate of 0.29°C year^?1. An analysis of the vegetation cover of the district showed that on an average, the NDVI of the district increased during the study period. However, a micro-level examination of NDVI values depicted that the type of vegetation cover had drastically changed. The maximum NDVI values for months from May to December for 2012 were much lower than those of 2001 and 2006, indicating a change in vegetation pattern of the district. An assessment of the area under different NDVI values exhibited that for all the months of the year (except September), the total area with NDVI values of higher range (i.e. +0.5 and above) had substantially decreased from 2001 to 2012. The analysis revealed that for some of the months like February, while in 2001, 45% of district exhibited NDVI values above +0.5, but by 2012, it had decreased to only 18%, showing a drastic change in vegetation type and deterioration of the extent of thick dense vegetation.     

The impact of perceived privacy risks on organizations’ willingness to share item-level event data across the supply chain

When information is available about the path, on which individual items move through the real world, many beneficial applications can be designed. The necessary data can be generated through attaching identifiers to items and deploying suitable readers all over the supply chain that capture the information on the identifiers. Organizations only have access to data about item movements within their organizational boundaries. Therefore sharing of data between organizations is required to gain full visibility. However, the willingness of organizations to share data is considered to be low. In this paper we present the results of a study that aimed at investigating the actual willingness of companies to share item-level data and at exploring the perceived privacy risks that may restrain companies from sharing item-level data. From the findings, requirements for the design of inter-organizational data sharing infrastructures are derived.     

Estimating and source analysis of surface PM2.5 concentration in the Beijing–Tianjin–Hebei region based on MODIS data and air trajectories

Particulate matter (PM) with an aerodynamic diameter of <2.5 μm (PM_2.5) has become the primary air pollutant in most major cities in China. Some studies have indicated that there is a positive correlation between the aerosol optical thickness (AOT) and surface-level PM_2.5 concentration. In order to estimate PM_2.5 concentration over large areas, a model relating the concentration of PM_2.5 and AOT has been established. The scale height of aerosol and relative humidity as well as the effect of surface temperature and wind velocity were introduced to enhance the model. 2013 full year Moderate Resolution Imaging Spectroradiometer (MODIS) AOT data and ground measurements of the PM_2.5 concentration in the Beijing–Tianjin–Hebei region were used to fit a seasonal multivariate linear equation relating PM_2.5 concentration and AOT, and the accuracy of the model has been determined. When comparing MODIS-estimated PM_2.5 with the measurements from ground monitoring stations during spring, summer, autumn and winter, we found the R² values were 0.45, 0.45, 0.37, and 0.31, respectively. Based on this model, the spatial distribution of PM_2.5 concentration during four typical haze events sampled by seasons was derived, and displayed with the backward air trajectories calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We undertook a preliminary analysis about the source of surface-level PMs and the process of its accumulation and dispersion during the haze episodes by analysing the effect of terrain and topography in the specific location of the Beijing–Tianjin–Hebei region. The spatial distribution of the PM_2.5 concentration showed that the high value region was generally in the southeast of the study area, which approximately overlapped an area of lower vegetation coverage, and the temporal variation of PM_2.5 concentration indicated that the air pollution was more severe during winter and spring than summer and autumn. The results of the analysis of backward air trajectories suggested that the hazy weather in the Beijing–Tianjin–Hebei region was mainly caused by unfavourable terrain and weather conditions.     

The immediate effect of the abdominal drawing-in maneuver technique on stature change in seated sedentary workers with chronic low back pain

Abstract

Many studies have measured stature change arising from loads imposed on the spine during sitting. To improve stature recovery, it is important to stabilise the lumbar spine and compensate forces from the upper body. The abdominal drawing-in maneuver (ADIM) technique has been found to mainly activate deep trunk muscles. The purpose of this study was to determine whether activation of deep trunk muscles by the ADIM technique could immediately improve stature recovery during prolonged sitting. Twenty-four patients with chronic low back pain (CLBP) were randomly allocated into different orders of experimental conditions: control (sitting without ADIM technique) and intervention conditions (sitting with ADIM technique). The latter condition required participants to complete ADIM technique for 1?min and repeat it three times throughout 41?min prolonged sitting time. Stature recovery was improved by 3.292?mm in the intervention condition compared with control condition (p-value = 0.001). Our finding demonstrated that ADIM technique improved stature recovery.

Practitioner Summary: Prolonged sitting seemingly harms sedentary workers’ health, particularly affecting the lower back. Activation of deep trunk muscles using abdominal drawing-in maneuver technique can promote spinal recovery. Clinicians can teach abdominal drawing-in maneuver technique to activate deep trunk muscles in chronic low back pain, thereby promoting self-management of seated stature recovery.

Abbrevations: ADIM: abdominal drawing-in maneuver; RA: rectus abdominis; ICLT: iliocostalis lumborum pars thoracis; LM: lumbar multifidus; TrA: transversus abdominis; IO: internal oblique; CLBP: chronic low back pain; LBP: low back pain; RMDQ: Roland Morris disability questionnaire; NRS: numerical rating scale.     

Intercomparison of microwave remote-sensing soil moisture data sets based on distributed eco-hydrological model simulation and in situ measurements over the North China Plain

Intercomparisons of microwave-based soil moisture products from active ASCAT (Advanced Scatterometer) and passive AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System) is conducted based on surface soil moisture (SSM) simulations from the eco-hydrological model, Vegetation Interface Processes (VIP), after it is carefully validated with in situ measurements over the North China Plain. Correlations with VIP SSM simulation are generally satisfactory with average values of 0.71 for ASCAT and 0.47 for AMSR-E during 2007–2009. ASCAT and AMSR-E present unbiased errors of 0.044 and 0.053 m³ m^?3 on average, with respect to model simulation. The empirical orthogonal functions (EOF) analysis results illustrate that AMSR-E provides more consistent SSM spatial structure with VIP than ASCAT; while ASCAT is more capable of capturing SSM temporal dynamics. This is supported by the facts that ASCAT has more consistent expansion coefficients corresponding to primary EOF mode with VIP (R = 0.825, p < 0.1). However, comparison based on SSM anomaly demonstrates that AMSR-E and ASCAT have similar skill in capturing SSM short-term variability. Temporal analysis of SSM anomaly time series shows that AMSR-E provides best performance in autumn, while ASCAT provides lower anomaly bias during highly-vegetated summer with vegetation optical depth of 0.61. Moreover, ASCAT retrieval accuracy is less influenced by vegetation cover, as it is in relatively better agreement with VIP simulation in forest than in other land-use types and exhibits smaller interannual fluctuation than AMSR-E. Identification of the error characteristics of these two microwave soil moisture data sets will be helpful for correctly interpreting the data products and also facilitate optimal specification of the error matrix in data assimilation at a regional scale.