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AMSU/MSU Lowtroposphere Day/Month Temperature Anomalies and Annual Cycle V6

The AMSU/MSU Lowtroposphere Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the lower troposphere. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.

VIIRS/NPP Thermal Anomalies/Fire 6-Min L2 Swath 750m NRT

The VIIRS/NPP Thermal Anomalies/Fire 6-Min L2 Swath 750m NRT product is based on the MODIS C6 Fire algorithm. The input to the Active Fires production are Level-1B moderate-resolution reflective band M7, and emissive bands M13 and M15. The fire algorithm first calculates bands M13, M15 brightness temperature (BT) statistics for a group of background pixels adjacent to each potential fire pixel. These statistics are used to set thresholds for several contextual fire detection tests. There is also an absolute fire detection test based on a pre-set M13 BT threshold. If the results of the absolute and relative fire detection tests meet certain criteria, the pixel is labeled as fire. The designation of a pixel as fire from the results of the BT threshold tests may be overridden under sun glint conditions or if too few pixels were used to calculate the background statistics.The VNP14_NRT product contains several pieces of information for each fire pixel: pixel coordinates, latitude and longitude, pixel M7 reflectance, background M7 reflectance, pixel M13 and M15 BT, background M13 and M15 BT, mean background BT difference, background M13, M15, and BT difference mean absolute deviation, fire radiative power, number of adjacent cloud pixels, number of adjacent water pixels, background window size, number of valid background pixels, detection confidence, land pixel flag, background M7 reflectance, and reflectance mean absolute deviation.The product provides day and nighttime active fire detection over land and water (from gas flares). The VNP14 product provides fire data continuity with NASA's EOS MODIS 1 km fire product. For more information visit University of Maryland VIIRS Active Fire Web page at http://viirsfire.geog.umd.edu/

Anomaly Detection for Complex Systems

In performance maintenance in large, complex systems, sensor information from sub-components tends to be readily available, and can be used to make predictions about the system's health and diagnose possible anomalies. However, existing methods can only use predictions of individual component anomalies to guess at systemic problems, not accurately estimate the magnitude of the problem, nor prescribe good solutions. Since physical complex systems usually have well-defined semantics of operation, we here propose using anomaly detection techniques drawn from data mining in conjunction with an automated theorem prover working on a domain-specific knowledge base to perform systemic anomalydetection on complex systems. For clarity of presentation, the remaining content of this submission is presented compactly in Fig 1.

Solving a prisoner's dilemma in distributed anomaly detection

Anomaly detection has recently become an important problem in many industrial and financial applications. In several instances, the data to be analyzed for possible anomalies is located at multiple sites and cannot be merged due to practical constraints such as bandwidth limitations and proprietary concerns. At the same time, the size of data sets affects prediction quality in almost all data mining applications. In such circumstances, distributed data mining algorithms may be used to extract information from multiple data sites in order to make better predictions. In the absence of theoretical guarantees, however, the degree to which data decentralization affects the performance of these algorithms is not known, which reduces the data providing participants' incentive to cooperate.This creates a metaphorical 'prisoners' dilemma' in the context of data mining. In this work, we propose a novel general framework for distributed anomaly detection with theoretical performance guarantees. Our algorithmic approach combines existing anomaly detection procedures with a novel method for computing global statistics using local sufficient statistics. We show that the performance of such a distributed approach is indistinguishable from that of a centralized instantiation of the same anomaly detection algorithm, a condition that we call zero information loss. We further report experimental results on synthetic as well as real-world data to demonstrate the viability of our approach. The remaining content of this presentation is presented in Fig. 1.

MODIS/Terra Near Real Time (NRT) Coarse Thermal Anomalies/Fire 5-Min L2 Swath 5km

MODIS Near Real Time (NRT) Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of a fire (a fire of strength sufficient to detect), and on detection relative to its background (to account for variability of the surface temperature and reflection by sunlight). Numerous tests are employed to reject typical false alarm sources like sun glint or an unmasked coastline. MOD14CRS provides level-2 swath data daily at a 5-kilometer resolution. The Science Data Sets in this product include fire-mask, algorithm quality, radiative power, and numerous layers describing fire pixel attributes. The Terra MODIS instrument acquires data twice daily (10:30 AM and PM), as does the Aqua MODIS (2:30 PM and AM). These four daily MODIS fire observations serve to advance global monitoring of the fire process and its effects on ecosystems, the atmosphere, and climate. Data Set Characteristics: File Size: ~ 990 KB Resolution: 5 kilometer Projection: None (swath data) Data Type: Fire Mask 8-bit unsigned Data Format: HDF-EOS Science Data Sets (SDS HDF Layers): 23 Version-5 MODIS/Terra Coarse Thermal Anomalies/Fire products are validated, meaning that product uncertainties are well defined over a range of representative conditions. Although there may be later improved versions, these data are ready for use in scientific publications.

MODIS/Terra Near Real Time (NRT) Thermal Anomalies/Fire 5-Min L2 Swath 1km

JPL TELLUS GRACE Level-3 Monthly Land Water-Equivalent-Thickness Surface Mass Anomaly Release 6.0 version 04 in netCDF/ASCII/GeoTIFF Formats

The monthly land mass grids contain water mass anomalies given as equivalent water thickness derived from GRACE & GRACE-FO time-variable gravity observations during the specified timespan, and relative to the specified time-mean reference period. The Equivalent water thickness represents the total terrestrial water storage anomalies from soil moisture, snow, surface water (incl. rivers, lakes, reservoirs etc.), as well as groundwater and aquifers. A glacial isostatic adjustment (GIA) correction has been applied, and standard corrections for geocenter (degree-1), C20 (degree-20) and C30 (degree-30) are incorporated. Post-processing filters have been applied to reduce correlated errors. Version 04 (v04) of the terrestrial water storage data uses updated and consistent C20 and Geocenter corrections (i.e., Technical Notes TN-14 and TN-13), as well as an ellipsoidal correction to account for the non-spherical shape of the Earth when mapping gravity anomalies to surface mass change. Data grids are provided in ASCII/netCDF/GeoTIFF formats. For the RL06 version, all GRACE products in the ASCII format have adopted the YAML encoding header, which is in full compliance with the PODAAC metadata best practices.

MODIS/Aqua Near Real Time (NRT) Thermal Anomalies/Fire 5-Min L2 Swath 1km

HOMAGE Monthly Time series of global average steric height anomalies and ocean heat content estimates from gridded in-situ ocean observations version 01

The [HOMAGE_STERIC_OHC_TIME_SERIES_v01] dataset contains monthly global mean ocean heat content (OHC) anomalies as well as thermosteric, halosteric and total steric sea level anomalies computed from various gridded ocean data sets of sub-temperature and salinity profiles as provided by different institutions: Scripps Institution of Oceanography (SIO); Institute of Atmospheric Physics (IAP); Barnes objective analysis (BOA from CSIO, MNR); Jamstec / Ishii et al. 2017 (I17); and Met Office Hadley Centre: EN4_c13, EN4_c14, EN4_g10, and EN4_I09. The data are averaged over the quasi-global ocean domain (i.e., where valid values are defined; note that gaps exist, in particular towards polar latitudes), at monthly intervals. The input profiling data (i.e, temperature and salinity profiles at depth levels), editing, quality flags and processing schemes vary across the different gridded products, please refer to the documentation for each institution’s data product for details. Since 2005, the profiling data are dominated by the observations from the global Argo network (e.g., https://argo.ucsd.edu/), which comprises nearly 4000 active floats (as of 08/2022). Before 2005, non-Argo data such as XBT profilers were used, and the global ocean coverage was significantly more sparse. Data sets from SIO and BOA are Argo-only, while the others also include other observations, such as expendable bathythermographs (XBTs) and Conductivity-Temperature-Depth (CTD) observations. The data are active forward stream data files and will be frequently updated as new observations are acquired by Argo, and processed by the data centers.

AMSU/MSU Tropopause Day/Month Temperature Anomalies and Annual Cycle V6

The AMSU/MSU Tropopause Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the tropopause. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.

MODIS/Aqua Near Real Time (NRT) Coarse Thermal Anomalies/Fire 5-Min L2 Swath 5km

MODIS Near Real Time (NRT) Thermal Anomalies/Fire products are primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of a fire (a fire of strength sufficient to detect), and on detection relative to its background (to account for variability of the surface temperature and reflection by sunlight). Numerous tests are employed to reject typical false alarm sources like sun glint or an unmasked coastline. MYD14CRS provides level-2 swath data daily at a 5-kilometer resolution. The Science Data Sets in this product include fire-mask, algorithm quality, radiative power, and numerous layers describing fire pixel attributes. The Aqua MODIS instrument acquires data twice daily (10:30 AM and PM), as does the Aqua MODIS (2:30 PM and AM). These four daily MODIS fire observations serve to advance global monitoring of the fire process and its effects on ecosystems, the atmosphere, and climate. Data Set Characteristics: File Size: ~ 990 KB Resolution: 5 kilometer Projection: None (swath data) Data Type: Fire Mask 8-bit unsigned Data Format: HDF-EOS Science Data Sets (SDS HDF Layers): 23 Version-5 MODIS/Aqua Coarse Thermal Anomalies/Fire products are validated, meaning that product uncertainties are well defined over a range of representative conditions. Although there may be later improved versions, these data are ready for use in scientific publications.

MODIS/Aqua Thermal Anomalies/Fire 5-Min L2 Swath 1km NRT

The MODIS/Aqua Thermal Anomalies/Fire 5-Min L2 Swath 1km Near Real Time (NRT), short name MYD14, product is primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of a fire (when the fire strength is sufficient to detect), and on detection relative to its background (to account for variability of the surface temperature and reflection by sunlight). Numerous tests are employed to reject typical false alarm sources like sun glint or an unmasked coastline.MYD14 is level-2 swath data provided daily at 1-kilometer resolution. The Science Data Sets in this product include fire-mask, algorithm quality, radiative power, and numerous layers describing fire pixel attributes. The Terra MODIS instrument acquires data twice daily (10:30 AM and PM), as does the Aqua MODIS (1:30 PM and AM). These four daily MODIS fire observations serve to advance global monitoring of the fire process and its effects on ecosystems, the atmosphere, and climate.

AMSU/MSU Midtroposphere Day/Month Temperature Anomalies and Annual Cycle V6

The AMSU/MSU Midtroposphere Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the mid-troposphere. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.

IceBridge Geometrics 823A Cesium Magnetometer L2 Geolocated Magnetic Anomalies, Version 1

This data set contains magnetic anomaly measurements taken over Antarctica using the Geometrics 823A Cesium Magnetometer. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC), with additional support from NASA Operation IceBridge.

Land Ice: Greenland & Antarctic ice mass anomaly

Data from NASA's Grace satellites show that the land ice sheets in both Antarctica and Greenland are losing mass. The continent of Antarctica (left chart) has been losing more than 100 cubic kilometers (24 cubic miles) of ice per year since 2002.

Intelligent Temporal Data Driven World Actuation in Ambient Environments Case Study: Anomaly Recognition and Assistance Provision in Smart Home

Abstract — A possible resident of smart home is an old person or an Alzheimer patient that should be assisted continuously for the rest of his life; however, normally this person desires to live independently at home. Typically, this person may forget sometimes completion of the activities; may realize the activities of daily living incorrectly, and may enter to dangerous states. In this context smart home project is proposed as an ambient intelligent environment, in which on one hand the resident is observed continuously through the embedded sensors, and on the other hand the resident is assisted automatically through the embedded electronically controllable actuators. In this work, we propose an approach to interpret the sensors’ observations and how to automatically reason in the required assistance. The result is provision of automated assistance for the smart home resident.

AMSU/MSU Lowstratosphere Day/Month Temperature Anomalies and Annual Cycle

The AMSU/MSU Lowstratosphere Day/Month Temperature Anomalies and Annual Cycle V6 dataset consists of temperature anomalies and annual cycle temperatures derived from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) radiance data since January 1978. All products are derived for the lower stratosphere. The dataset begins on January 1, 1978 and is still currently ongoing. The data are available in netCDF-4 and ASCII formats.

MODIS/Terra Thermal Anomalies/Fire 5-Min L2 Swath 1km NRT

The MODIS/Terra Thermal Anomalies/Fire 5-Min L2 Swath 1km Near Real Time (NRT), short name MOD14, product is primarily derived from MODIS 4- and 11-micrometer radiances. The fire detection strategy is based on absolute detection of a fire (when the fire strength is sufficient to detect), and on detection relative to its background (to account for variability of the surface temperature and reflection by sunlight). Numerous tests are employed to reject typical false alarm sources like sun glint or an unmasked coastline.MOD14 is level-2 swath data provided daily at 1-kilometer resolution. The Science Data Sets in this product include fire-mask, algorithm quality, radiative power, and numerous layers describing fire pixel attributes. The Terra MODIS instrument acquires data twice daily (10:30 AM and PM), as does the Aqua MODIS (1:30 PM and AM). These four daily MODIS fire observations serve to advance global monitoring of the fire process and its effects on ecosystems, the atmosphere, and climate.