SRAT-Distribution Voltage Sags and Reliability Assessment Tool

Interruptions to supply and sags of distribution system voltage are the main aspects causing customer complaints. There is a need for analysis of supply reliability and voltage sag to relate system performance with network structure and equipment design parameters. This analysis can also give prediction of voltage dips, as well as relating traditional reliability and momentary outage measures to the properties of protection systems and to network impedances. Existing reliability analysis software often requires substantial training, lacks automated facilities, and suffers from data availability. Thus it requires time-consuming manual intervention for the study of large networks. A user-friendly sag and reliability assessment tool (SRAT) has been developed based on existing impedance data, protection characteristics, and a model of failure probability. The new features included in SRAT are a) efficient reliability and sag assessments for a radial network with limited loops, b) reliability evaluation associated with realistic protection and restoration schemes, c) inclusion of momentary outages in the same model as permanent outage evaluation, d) evaluation of the sag transfer through meshed subtransmission network, and e) simplified probability distribution model determined from available faults records. Examples of the application of the tools to an Australian distribution network are used to illustrate the application of this model

Hybrid cloud and error masking to improve the quality of deterministic satellite sea surface temperature retrieval and data coverage

AbstractIn the infrared region, the quality of sea surface temperature (SST) retrievals critically depends on the cloud detection scheme. More than 5 million matchups, where the surface and top of atmosphere measurements are available, have been carefully analyzed to understand clouds related errors and to develop the advanced cloud detection scheme for improvement of satellite SST quality. The effectiveness of a Bayesian cloud detection (BCD) scheme, operationally implemented at the NOAA Office of Satellite Product Operations (OSPO) for the GOES-Imager, has been examined using an experimental filter and it is found that this scheme is not optimal. Thus, a new algorithm for cloud and error masking (CEM) scheme is proposed for physical SST retrievals. This is based on a quasi-deterministic approach combined with an approximated radiative transfer model and the functional spectral differences at pixel level. Although, traditionally the validation of cloud detection algorithms have often been reported qualitatively using visual inspection of imagery, we have made a quantitative validation of the cloud algorithm for its intended purpose by determining the quality of satellite SST retrievals against in situ data. Results show that CEM can reduce the root mean square error in SST by an average of 22% while increasing the data coverage by an average of 38% compared to the operationally implemented BCD at OSPO, as assessed over a period of fifty months

A new high-resolution sea surface temperature blended analysis

The National Oceanic and Atmospheric Administration’s (NOAA) office of National Environmental Satellite, Data, and Information Service (NESDIS) now generates a daily 0.05° (∼5 km) global high-resolution satellite-based sea surface temperature (SST) analyses on an operational basis. The new analysis combines SST data from U.S., Japanese, and European geostationary infrared imagers, and low-Earth-orbiting infrared (United States and Europe) SST data, into a single high-resolution 5-km product. An earlier version produced a 0.1° (∼11 km) resolution, a resolution chosen to approximate the Nyquist sampling criterion for the midlatitude Rossby radius (∼20 km), in order to preserve mesoscale oceanographic features such as eddies and frontal meanders. Comparison between the two analyses illustrates that the higher-resolution grid spacing has more success in this regard. The analysis employs a rigorous multiscale optimum interpolation (OI) methodology that approximates the Kalman filter, together with a data-adaptive correlation length scale, to ensure a good balance between detail preservation and noise reduction. The product accuracy verified against globally distributed buoys is ∼0.02 K, with a robust standard deviation of ∼0.25 K. The new analysis has proven a significant success even when compared to other products that purport to have a similar resolution. This analysis forms the basis for other operational environmental products such as coral reef bleaching risk and ocean heat content for tropical cyclone prediction. Forthcoming enhancements include the incorporation of microwave SST products from low-Earth-orbiting platforms [e.g., Global Change Observation Mission for Water-1 (GCOM-W1)] in order to improve the resolution of SST features in areas of persistent cloud and correct for diurnal effects via a turbulence model of upper-ocean heating

Improved Quality of MODIS Sea Surface Temperature Retrieval and Data Coverage Using Physical Deterministic Methods

Sea surface temperature (SST) retrievals from satellite imager measurements are often performed using only two or three channels, and employ a regression methodology. As there are 16 thermal infrared (IR) channels available for MODIS, we demonstrate a new SST retrieval methodology using more channels and a physically deterministic method, the modified total least squares (MTLS), to improve the quality of SST. Since cloud detection is always a part of any parameter estimation from IR satellite measurements, we hereby extend our recently-published novel cloud detection technique, which is based on both functional spectral differences and radiative transfer modeling for GOES-13. We demonstrate that the cloud detection coefficients derived for GOES-13 are working well for MODIS, while further improvements are made possible by the extra channels replacing some of the previous tests. The results are compared with available operational MODIS SST through the Group for High Resolution SST website–the data themselves are originally processed by the NASA Goddard Ocean Biology Processing Group. It is observed the data coverage can be more than doubled compared to the currently-available operational product, and at the same time the quality can be improved significantly. Two other SST retrieval methods, offline-calculated coefficients using the same form of the operational regression equation, and radiative transfer based optimal estimation, are included for comparison purposes

Sea Surface Temperature Retrieval from MODIS Radiances Using Truncated Total Least Squares with Multiple Channels and Parameters

Global sea-surface temperatures (SST) from MODIS measured brightness temperatures generated using the regression methods, have been available to users for more than a decade, and are used extensively for a wide range of atmospheric and oceanic studies. However, as evidenced by a number of studies, there are indications that the retrieval quality and cloud detection are somewhat sub-optimal. To improve the performance of both of these aspects, we endorse a new physical deterministic algorithm, based on truncated total least squares (TTLS), using multiple channels and parameters, in conjunction with a hybrid cloud detection scheme using a radiative transfer model atop a functional spectral difference method. The TTLS method is a new addition that improves the information content of the retrieval compared to our previous work using modified total least squares (MTLS), which is feasible because more measurements are available, allowing a larger retrieval vector. A systematic study is conducted to ascertain the appropriate channel selection for SST retrieval from the 16 thermal infrared channels available from the MODIS instrument. Additionally, since atmospheric aerosol is a well-known source of degraded quality of SST retrieval, we include aerosol profiles from numerical weather prediction in the forward simulation and include the total column density of all aerosols in the retrieval vector of our deterministic inverse method. We used a slightly modified version of our earlier reported cloud detection algorithm, namely CEM (cloud and error mask), for this study. Time series analysis of more than a million match-ups shows that our new algorithm (TTLS+CEM) can reduce RMSE by ~50% while increasing data coverage by ~50% compared to the operationally available MODIS SST

Improved Quality of MODIS Sea Surface Temperature Retrieval and Data Coverage Using Physical Deterministic Methods

Sea surface temperature (SST) retrievals from satellite imager measurements are often performed using only two or three channels, and employ a regression methodology. As there are 16 thermal infrared (IR) channels available for MODIS, we demonstrate a new SST retrieval methodology using more channels and a physically deterministic method, the modified total least squares (MTLS), to improve the quality of SST. Since cloud detection is always a part of any parameter estimation from IR satellite measurements, we hereby extend our recently-published novel cloud detection technique, which is based on both functional spectral differences and radiative transfer modeling for GOES-13. We demonstrate that the cloud detection coefficients derived for GOES-13 are working well for MODIS, while further improvements are made possible by the extra channels replacing some of the previous tests. The results are compared with available operational MODIS SST through the Group for High Resolution SST website–the data themselves are originally processed by the NASA Goddard Ocean Biology Processing Group. It is observed the data coverage can be more than doubled compared to the currently-available operational product, and at the same time the quality can be improved significantly. Two other SST retrieval methods, offline-calculated coefficients using the same form of the operational regression equation, and radiative transfer based optimal estimation, are included for comparison purposes

Maximizing the Information Content of Ill-Posed Space-Based Measurements Using Deterministic Inverse Method

For several decades, operational retrievals from spaceborne hyperspectral infrared sounders have been dominated by stochastic approaches where many ambiguities are pervasive. One major drawback of such methods is their reliance on treating error as definitive information to the retrieval scheme. To overcome this drawback and obtain consistently unambiguous retrievals, we applied another approach from the class of deterministic inverse methods, namely regularized total least squares (RTLS). As a case study, simultaneous simulated retrieval of ozone (O3) profile and surface temperature (ST) for two different instruments, Cross-track Infrared Sounder (CrIS) and Tropospheric Emission Spectrometer (TES), are considered. To gain further confidence in our approach for real-world situations, a set of ozonesonde profile data are also used in this study. The role of simulation-based comparative assessment of algorithms before application on remotely sensed measurements is pivotal. Under identical simulation settings, RTLS results are compared to those of stochastic optimal estimation method (OEM), a very popular method for hyperspectral retrievals despite its aforementioned fundamental drawback. Different tweaking of error covariances for improving the OEM results, used commonly in operations, are also investigated under a simulated environment. Although this work is an extension of our previous work for H2O profile retrievals, several new concepts are introduced in this study: (a) the information content analysis using sub-space analysis to understand ill-posed inversion in depth; (b) comparison of different sensors for same gas profile retrieval under identical conditions; (c) extended capability for simultaneous retrievals using two classes of variables; (d) additional stabilizer of Laplacian second derivative operator; and (e) the representation of results using a new metric called “information gain”. Our findings highlight issues with OEM, such as loss of information as compared to a priori knowledge after using measurements. On the other hand, RTLS can produce “information gain” of ~40–50% deterministically from the same set of measurements.https://doi.org/10.3390/rs1007099