__Dieser Datensatz hat noch keinen Anspruch auf Vollständigkeit und dient zu momentan zu Tests von Funktionalitäten. Für den Download verwenden Sie bitte https://hdl.handle.net/20.500.11756/de7539e1 .__ Die vorliegende Studie untersucht das Phänomen Konvektion im alpinen Gelände und im Alpenvorland rund um die Gebiete Tirol, Südtirol, Veneto und Trentino, erstmals flächendeckend mit Hilfe eines automatisierten Monitoringverfahrens und der kombinierten Information aus Radar- und Blitzdaten. Sie ist keine Klimastudie im eigentlichen Sinne, da der Beobachtungszeitraum von fünf konvektiven Saisonen zu kurz ist. Wohl aber gibt sie interessante Einblicke in das Erscheinungsbild konvektiver Prozesse, die in nachfolgenden Studien erweitert, verfeinert und ergänzt werden können.

__Dieser Datensatz hat noch keinen Anspruch auf Vollständigkeit und dient zu momentan zu Tests von Funktionalitäten. Für den Download verwenden Sie bitte https://hdl.handle.net/20.500.11756/de7539e1 .__ Die vorliegende Studie untersucht das Phänomen Konvektion im alpinen Gelände und im Alpenvorland rund um die Gebiete Tirol, Südtirol, Veneto und Trentino, erstmals flächendeckend mit Hilfe eines automatisierten Monitoringverfahrens und der kombinierten Information aus Radar- und Blitzdaten. Sie ist keine Klimastudie im eigentlichen Sinne, da der Beobachtungszeitraum von fünf konvektiven Saisonen zu kurz ist. Wohl aber gibt sie interessante Einblicke in das Erscheinungsbild konvektiver Prozesse, die in nachfolgenden Studien erweitert, verfeinert und ergänzt werden können.

__Dieser Datensatz hat noch keinen Anspruch auf Vollständigkeit und dient zu momentan zu Tests von Funktionalitäten. Für den Download verwenden Sie bitte https://hdl.handle.net/20.500.11756/de7539e1 .__ Die vorliegende Studie untersucht das Phänomen Konvektion im alpinen Gelände und im Alpenvorland rund um die Gebiete Tirol, Südtirol, Veneto und Trentino, erstmals flächendeckend mit Hilfe eines automatisierten Monitoringverfahrens und der kombinierten Information aus Radar- und Blitzdaten. Sie ist keine Klimastudie im eigentlichen Sinne, da der Beobachtungszeitraum von fünf konvektiven Saisonen zu kurz ist. Wohl aber gibt sie interessante Einblicke in das Erscheinungsbild konvektiver Prozesse, die in nachfolgenden Studien erweitert, verfeinert und ergänzt werden können.

__Dieser Datensatz hat noch keinen Anspruch auf Vollständigkeit und dient zu momentan zu Tests von Funktionalitäten. Für den Download verwenden Sie bitte https://hdl.handle.net/20.500.11756/de7539e1 .__ Die vorliegende Studie untersucht das Phänomen Konvektion im alpinen Gelände und im Alpenvorland rund um die Gebiete Tirol, Südtirol, Veneto und Trentino, erstmals flächendeckend mit Hilfe eines automatisierten Monitoringverfahrens und der kombinierten Information aus Radar- und Blitzdaten. Sie ist keine Klimastudie im eigentlichen Sinne, da der Beobachtungszeitraum von fünf konvektiven Saisonen zu kurz ist. Wohl aber gibt sie interessante Einblicke in das Erscheinungsbild konvektiver Prozesse, die in nachfolgenden Studien erweitert, verfeinert und ergänzt werden können.

__Dieser Datensatz hat noch keinen Anspruch auf Vollständigkeit und dient zu momentan zu Tests von Funktionalitäten. Für den Download verwenden Sie bitte https://hdl.handle.net/20.500.11756/de7539e1 .__ Die vorliegende Studie untersucht das Phänomen Konvektion im alpinen Gelände und im Alpenvorland rund um die Gebiete Tirol, Südtirol, Veneto und Trentino, erstmals flächendeckend mit Hilfe eines automatisierten Monitoringverfahrens und der kombinierten Information aus Radar- und Blitzdaten. Sie ist keine Klimastudie im eigentlichen Sinne, da der Beobachtungszeitraum von fünf konvektiven Saisonen zu kurz ist. Wohl aber gibt sie interessante Einblicke in das Erscheinungsbild konvektiver Prozesse, die in nachfolgenden Studien erweitert, verfeinert und ergänzt werden können.

__Dieser Datensatz hat noch keinen Anspruch auf Vollständigkeit und dient zu momentan zu Tests von Funktionalitäten. Für den Download verwenden Sie bitte https://hdl.handle.net/20.500.11756/de7539e1 .__ Die vorliegende Studie untersucht das Phänomen Konvektion im alpinen Gelände und im Alpenvorland rund um die Gebiete Tirol, Südtirol, Veneto und Trentino, erstmals flächendeckend mit Hilfe eines automatisierten Monitoringverfahrens und der kombinierten Information aus Radar- und Blitzdaten. Sie ist keine Klimastudie im eigentlichen Sinne, da der Beobachtungszeitraum von fünf konvektiven Saisonen zu kurz ist. Wohl aber gibt sie interessante Einblicke in das Erscheinungsbild konvektiver Prozesse, die in nachfolgenden Studien erweitert, verfeinert und ergänzt werden können.

__Dieser Datensatz hat noch keinen Anspruch auf Vollständigkeit und dient zu momentan zu Tests von Funktionalitäten. Für den Download verwenden Sie bitte https://hdl.handle.net/20.500.11756/de7539e1 .__ Die vorliegende Studie untersucht das Phänomen Konvektion im alpinen Gelände und im Alpenvorland rund um die Gebiete Tirol, Südtirol, Veneto und Trentino, erstmals flächendeckend mit Hilfe eines automatisierten Monitoringverfahrens und der kombinierten Information aus Radar- und Blitzdaten. Sie ist keine Klimastudie im eigentlichen Sinne, da der Beobachtungszeitraum von fünf konvektiven Saisonen zu kurz ist. Wohl aber gibt sie interessante Einblicke in das Erscheinungsbild konvektiver Prozesse, die in nachfolgenden Studien erweitert, verfeinert und ergänzt werden können.

Attributes of synthetic aperture radar (SAR) data acquired by TerraSAR-X and TanDEM-X satellite missions and archived at WInSAR facility.

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports. The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.

Note: This service is deprecated and will be shutdown on June 29th, 2023.  Link to Service Change NoticeMapping of old Web Service to the new Web Service can be found here: https://www.weather.gov/media/notification/ref/On-premise__Mapping_To_AWS_Cloud_GIS%20Services_Links.pdf

Note: This service is deprecated and will be shutdown on June 29th, 2023.  Link to Service Change NoticeMapping of old Web Service to the new Web Service can be found here: https://www.weather.gov/media/notification/ref/On-premise__Mapping_To_AWS_Cloud_GIS%20Services_Links.pdf

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

List of Sentinel-1A InSAR images acquired between 2014-11-01 and 2016-10-31, and archived at the link below. NOTE: The user must create an account in order to access the data

List of synthetic aperture radar (SAR) images acquired by TerraSAR-X and TanDEM-X satellite missions and archived at UNAVCO's WINSAR facility. See file "Bradys TSX Holdings.csv" for individual links. NOTE: The user must create an account in order to access the data (See "Instructions for Creating an Account" below).

These files contain the geodatabases related to the Desert Peak Geothermal Field. It includes all input and output files used in the project. The files include data categories of raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs including Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files for the Desert Peak Geothermal Site are used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Desert Peak Geothermal Field.

ERMA Gulf Response, powered by Environmental Response Management Application (ERMA), is a web-based Geographic Information System (GIS) tool designed to assist both emergency responders and environmental resource managers who deal with incidents that may adversely impact the environment. This application is currently assisting with response operations for the Deepwater Horizon spill and data regarding this incident is displayed here and updated daily. ERMA is also assisting in resource management decisions in support of Natural Resource Damage Assessment.

This submission contains tarred pair directories for interferometric synthetic aperture radar (InSAR) data covering Coso Geothermal Field in California, USA. Explanation of pair subdirectories: Pairs are formed using the InSAR processing software GMT5SAR (Sandwell et al., 2011). Pair subdirectories are named by starting and ending epochs in YYYYMMDD_YYYYMMDD format. Each tarred pair directory contains GRD files for phase data (radians), unwrapped range change (drhomaskd, in m), and unwrapped range change rate (drange, in m/yr). The data are given in both latitude/longitude and Universal Transverse Mercator (Zone 11N). Raw Synthetic Aperture Radar (SAR) data from the Envisat satellite mission operated by the European Space Agency (ESA) are copyrighted by ESA and were provided through the WInSAR consortium at the UNAVCO facility. Raw Synthetic Aperture Radar (SAR) data from the Sentinel-1A satellite mission operated by ESA were available free of charge through the Distributed Active Archive Center (DAAC) at the Alaska Satellite Facility (ASF) and through the Sentinels Scientific Data Hub. References: Farr, T.G.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L.; Seal, D.; Shaffer, S.; Shimada, J.; Umland, J.; Werner, M.; Oskin, M.; Burbank, D.; Alsdorf, D. The Shuttle Radar Topography Mission. Reviews of Geophysics 2007, 45, RG2004. doi:10.1029/2005RG000183. Sandwell, D.; Mellors, R.; Tong, X.; Wei, M.; Wessel, P. Open radar interferometry software for mapping surface deformation. Eos, Transactions American Geophysical Union 2011, 92, 234?234. http://dx.doi.org/10.1029/2011EO280002. doi:10.1029/2011EO280002.

Graph theory is useful for estimating time-dependent model parameters via weighted least-squares using interferometric synthetic aperture radar (InSAR) data. Plotting acquisition dates (epochs) as vertices and pair-wise interferometric combinations as edges defines an incidence graph. The edge-vertex incidence matrix and the normalized edge Laplacian matrix are factors in the covariance matrix for the pair-wise data. Using empirical measures of residual scatter in the pair-wise observations, we estimate the variance at each epoch by inverting the covariance of the pair-wise data. We evaluate the rank deficiency of the corresponding least-squares problem via the edge-vertex incidence matrix. We implement our method in a MATLAB software package called GraphTreeTA available on GitHub (https://github.com/feigl/gipht). We apply temporal adjustment to the data set described in Lu et al. (2005) at Okmok volcano, Alaska, which erupted most recently in 1997 and 2008. The data set contains 44 differential volumetric changes and uncertainties estimated from interferograms between 1997 and 2004. Estimates show that approximately half of the magma volume lost during the 1997 eruption was recovered by the summer of 2003. Between June 2002 and September 2003, the estimated rate of volumetric increase is (6.2 +/- 0.6) x 10^6 m^3/yr. Our preferred model provides a reasonable fit that is compatible with viscoelastic relaxation in the five years following the 1997 eruption. Although we demonstrate the approach using volumetric rates of change, our formulation in terms of incidence graphs applies to any quantity derived from pair-wise differences, such as wrapped phase or wrapped residuals. Date of final oral examination: 05/19/2016 This thesis is approved by the following members of the Final Oral Committee: Kurt L. Feigl, Professor, Geoscience Michael Cardiff, Assistant Professor, Geoscience Clifford H. Thurber, Vilas Distinguished Professor, Geoscience

Note: This service is deprecated and will be shutdown on June 29th, 2023.  Link to Service Change NoticeMapping of old Web Service to the new Web Service can be found here: https://www.weather.gov/media/notification/ref/On-premise__Mapping_To_AWS_Cloud_GIS%20Services_Links.pdf

Note: This service is deprecated and will be shutdown on June 29th, 2023.  Link to Service Change NoticeMapping of old Web Service to the new Web Service can be found here: https://www.weather.gov/media/notification/ref/On-premise__Mapping_To_AWS_Cloud_GIS%20Services_Links.pdf

Note: This service is deprecated and will be shutdown on June 29th, 2023.  Link to Service Change NoticeMapping of old Web Service to the new Web Service can be found here: https://www.weather.gov/media/notification/ref/On-premise__Mapping_To_AWS_Cloud_GIS%20Services_Links.pdf

**Overview** Monitor real-time profiles of virtual temperature (C), wind speed (ms-1), and direction (deg) few km above ground level. **Data Details** Raw files contain radial velocity (ms-1), signal-to-noise ratio (dB), signal power (dB), spectral width (ms-1), and noise amplitude (dB). "W" files contain hourly profiles of wind speed (ms-1) and direction (deg). "T" files contain hourly profiles of virtual temperature (C).

**Overview** Monitor real-time profiles of virtual temperature (C), wind speed (ms-1), and direction (deg) few km above ground level. **Data Details** Raw files contain radial velocity (ms-1), signal-to-noise ratio (dB), signal power (dB), spectral width (ms-1), and noise amplitude (dB). "W" files contain hourly profiles of wind speed (ms-1) and direction (deg). "T" files contain hourly profiles of virtual temperature (C).

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The temporal coverage for this dataset is as follows: Begin datetime: 1997-06-01 00:00:00, End datetime: 1997-07-31 23:59:59. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program operates a Baseline Surface Radiation Network (BSRN) station at the Central Facility (located near Lamont, in north-central Oklahoma) of its Southern Great Plains site. BSRN provides 1-min observations of direct-beam normal solar irradiance, downwelling hemispheric diffuse solar irradiance, downwelling hemispheric solar irradiance and downwelling hemispheric infrared irradiance.

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The temporal coverage for this dataset is as follows: Begin datetime: 1997-06-01 00:00:00, End datetime: 1997-07-31 23:59:59. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program operates a network of 12 Energy Balance Bowen Ration (EBBR) stations at its Southern Great Plains site. EBBR provides 30-min observations of latent and sensible heat flux along with net radiation, atmospheric pressure, bowen ratio, wind speed and direction, and the following parameters at five locations surrounding the sites: soil moisture, soil temperature, soil heat flow, soil heat capacity and soil heat flow at the surface. The Energy Balance Bowen Ratio (EBBR) system is a ground-based system using in situ sensors to estimate the vertical fluxes of sensible and latent heat at the local surface. EBBR systems will be installed at up to 15 grassland locations within the SGP CART Site. Flux estimates are made from observations of net radiation, soil heat flow, and the vertical gradients of temperature and relative humidity; these data are used in the Bowen ratio energy balance technique.

The Southern Great Plains 1997 (SGP97) Hydrology Experiment originated from an interdisciplinary investigation, "Soil Moisture Mapping at Satellite Temporal and Spatial Scales" (PI: Thomas J. Jackson, USDA Agricultural Research Service, Beltsville, MD) selected under the NASA Research Announcement 95-MTPE-03. The temporal coverage for this dataset is as follows: Begin datetime: 1997-05-31 00:00:00, End datetime: 1997-08-09 23:59:59. NOAA/ATDD (Tilden Meyers) started operation of a long term flux monitoring site near the Little Washita watershed in Oklahoma in 1996. Half-hourly observations of wind speed and direction, air temperature, relative humidity, pressure, incoming global radiation, incoming and outgoing visible radiation, net radiation, ground heat flux, precipitation, wetness, skin temperature, soil temperature (at 2, 4, 8, 16, 32 and 64 cm), average wind vector speed, kinematic shear stress, streamwise velocity variance, crosswind velocity variance, vertical velocity variance, sensible heat flux, latent energy flux, CO2 flux and soil moisture at 20 cm (started 5 June 1997).