Please note, this dataset has been superseded by a newer version (see below). Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). Integrated Global Radiosonde Archive is a digital data set archived at the former National Climatic Data Center (NCDC), now National Centers for Environmental Information (NCEI). This dataset contains monthly means of geopotential height, temperature, zonal wind, and meridional wind derived from the Integrated Global Radiosonde Archive (IGRA). IGRA consists of radiosonde and pilot balloon observations at over 1500 globally distributed stations, and monthly means are available for the surface and mandatory levels at many of these stations. The period of record varies from station to station, with many extending from 1970 to 2016. Monthly means are computed separately for the nominal times of 0000 and 1200 UTC, considering data within two hours of each nominal time. A mean is provided, along with the number of values used to calculate it, whenever there are at least 10 values for a particular station, month, nominal time, and level.

Archived data for the 2016 KORUS-AQ study in South Korea. Data may include non-EPA data generated on the DC-8 aircraft and both EPA and non-EPA data generated to ground based sites in the study area. Portions of this dataset are inaccessible because: The data for this manuscript contains both EPA owned data and non-EPA generated data that are all stored in one _location on a publicly accessible data archive. As such it is not feasible to store just EPA owned data associated with this manuscript on ScienceHub as we do not have permission to store non-epa generated data on Sciencehub. It is far more practical to house the data on the NASA owned stdudy data archive. They can be accessed through the following means: Data sets are given in ICARTT format and stored on the -KORUS-AQ data archive managed by NASA: https://www-air.larc.nasa.gov/cgi-bin/ArcView/korusaq. Data sets can be downloaded from the archive by the user. Format: Data sets are given in ICARTT format and stored on the -KORUS-AQ data archive managed by NASA: https://www-air.larc.nasa.gov/cgi-bin/ArcView/korusaq. Data sets include both EPA owned data and non-EPA generated data. This dataset is associated with the following publications: Long, R. The roles of suburban forest in controlling vertical trace gas and OH reactivity distributions – a case study for Seoul Metropolitan Area.. Faraday Discuss. Royal Society of Chemistry, Cambridge, UK, 537-550, (2021). Long, R. Boundary layer versus free tropospheric submicron particle formation: A case study from NASA DC-8 observations in the Asian continental outflow during the KORUS-AQ campaign. Atmospheric Research. Elsevier Science BV, Amsterdam, NETHERLANDS, 264: na, (2021).

Abstract (en): Of the 14 nations included in the original study, these data cover the following ten: Brazil, Cuba, Dominican Republic, India, Israel, Nigeria, Panama, United States, West Germany, and Yugoslavia. (The data for Egypt, Japan, the Philippines, and Poland are not available through ICPSR.) In India and Israel the interviews were conducted in two waves, with different samples. Besides ascertaining the usual personal information, the study employed a "Self-Anchoring Striving Scale," an open-ended scale asking the respondent to define hopes and fears for self and the nation, to determine the two extremes of a self-defined spectrum on each of several variables. After these subjective ratings were obtained, the respondents indicated their perceptions of where they and their nations stood on a hypothetical ladder at three different points in time. Demographic variables include the respondents' age, gender, marital status, and level of education. For more information on the samples, coding, and the means of measurement, see the related publication listed below. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Adult population of Brazil, Cuba, Dominican Republic, India, Israel, Nigeria, Panama, United States, West Germany and Yugoslavia. Separate samples were drawn in each country. All samples were intended to be crossnational, except for the kibbutz sample in Israel. However, both India samples underrepresent females, and the sample from Cuba was drawn exclusively from urban areas. In addition, the samples from Brazil, Cuba, the Dominican Republic, India, Nigeria, Panama, and the United States were weighted to achieve the intended representation. 2006-01-12 All files were removed from dataset 13 and flagged as study-level files, so that they will accompany all downloads. (1) Because the original data format included some multiply punched variables, it is inappropriate to assume that the first response of a multiple response variable is more important than the rest: the current order of responses is an artifact of the technology used to record and recover them. It is even possible to have a missing data code followed by further substantive responses in some cases. (2) These data files were originally released separately, under ICPSR study numbers 7023-7031, 7085-7086, and 7258. They are now concatenated into one data collection as 7023. References in the codebooks to the old study numbers should be ignored. (3) The codebooks are also available together in one bound volume available upon request from ICPSR. 4) The codebook is provided by ICPSR as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

Executive Statement To support analysis of the Landsat long-term data record that began in 1972, the USGS Landsat data archive was reorganized into a formal tiered data collection structure. This structure ensures all Landsat Level 1 products provide a consistent archive of known data quality to support time-series analysis and data “stacking”, while controlling continuous improvement of the archive, and access to all data as they are acquired. Collection 1 Level 1 processing began in August 2016 and continued until all archived data was processed, completing May 2018. Newly-acquired Landsat 8 and Landsat 7 data continue to be processed into Collection 1 shortly after data is downlinked to USGS EROS.Learn more: https://www.usgs.gov/media/files/landsat-collection-1-level-1-product-definition

Blu Ray Archive System Market Outlook

The global Blu Ray Archive System market size was estimated at USD 1.5 billion in 2023 and is expected to reach USD 2.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.9%. Key growth factors driving this market include the increasing need for cost-effective and reliable data storage solutions, the rise in data generation across various industries, and the proliferation of high-definition media content.

One of the primary drivers of the Blu Ray Archive System market is the exponential increase in data generation. With the digital transformation sweeping across industries, the volume of data generated globally has surged, necessitating reliable and scalable storage solutions. Blu Ray technology provides a cost-effective and durable solution for long-term data storage, making it an attractive option for businesses that need to archive large volumes of data securely.

Another significant growth factor is the increasing demand for high-definition content. The media and entertainment industry, in particular, requires high-capacity storage solutions to archive high-definition videos, movies, and other media content. Blu Ray discs, with their high storage capacity and longevity, offer an ideal solution for this industry. Furthermore, the growing adoption of 4K and 8K resolution in video content has only heightened the need for efficient and high-capacity archiving solutions.

The healthcare industry is also contributing to the growth of the Blu Ray Archive System market. With the increasing adoption of electronic health records (EHRs) and medical imaging technologies, the volume of data generated by healthcare providers is growing rapidly. Blu Ray Archive Systems provide a secure and durable solution for storing medical records, imaging data, and other critical healthcare information, ensuring compliance with data retention regulations and safeguarding patient privacy.

Regionally, North America is expected to dominate the Blu Ray Archive System market due to the high adoption rate of advanced technologies and the presence of major market players. The Asia Pacific region is projected to exhibit the highest growth rate, driven by the rapid digitalization of industries and the increasing demand for data storage solutions in emerging economies such as China and India. Europe, Latin America, and the Middle East & Africa are also expected to witness significant growth, fueled by the rising awareness of data storage needs and technological advancements in these regions.

Component Analysis

The Blu Ray Archive System market is segmented into three key components: hardware, software, and services. Each of these segments plays a crucial role in the overall functionality and effectiveness of Blu Ray Archive Systems. The hardware segment primarily includes Blu Ray drives and discs, which are essential for data storage and retrieval. The increasing demand for high-capacity storage devices is driving the growth of this segment. Blu Ray drives have evolved to support larger storage capacities and faster data transfer rates, making them a preferred choice for archiving purposes.

The software segment encompasses the various applications and solutions used to manage, organize, and retrieve data stored on Blu Ray discs. This includes archiving software, data management tools, and retrieval applications. The growth of this segment is driven by the need for efficient and user-friendly software solutions that can handle large volumes of data and provide quick access to archived information. Advances in software technology have enhanced the capabilities of Blu Ray Archive Systems, making them more versatile and efficient.

The services segment includes installation, maintenance, and support services provided by companies specializing in Blu Ray Archive Systems. These services are crucial for ensuring the smooth operation and longevity of the systems. The increasing adoption of Blu Ray Archive Systems across various industries has led to a growing demand for professional services to support these systems. Service providers offer a range of solutions, including system integration, troubleshooting, and regular maintenance, to ensure optimal performance and minimize downtime.

Overall, the component analysis reveals that the Blu Ray Archive System market is a comprehensive ecosystem that relies on the seamless integration of hardware, software, and services. Each component is interdependent, and advancements in one area often drive improvements in the others

Integrated Global Radiosonde Archive (IGRA) Version 2 consists of quality-controlled radiosonde observations of temperature, humidity, and wind at stations across all continents. Data are drawn from more than 30 different sources. The earliest year of data is 1905, and the data are updated on a daily basis. Record length, vertical extent and resolution, and availability of variables varies among stations and over time. In addition to the merged and quality-controlled set of soundings, several supplementary products are included: sounding-derived moisture and stability parameters for each suitable sounding; monthly means at mandatory pressure levels; the Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) in which post-1997 data are based on IGRA 2; and station history information derived from documented changes in instruments and observing practice as well as from instrument codes received along with the sounding data. The change to Version 2.2 includes two additional data streams which permits further updating of the IGRA data records that use the new BUFR format. Version 2.2 began in 2023.

At the request of the Senate Finance Committee, the Court of Auditors carried out an investigation into the national archives managed, within the Ministry of Culture and Communication, by three departments with national competence (National Archives proper, National Overseas Archives and World of Work Archives). The Court found that the tasks of collecting, classifying, preserving and communicating archives are carried out in an unequal and often failing manner, and that organisational and statutory changes are necessary. In addition, decisions are to be taken without delay to avoid a new impasse, especially with the announced closure of the Fontainebleau site. Finally, the Court considers that the inter-ministerial policy of archives should be strengthened in order to counter the breakdown of archival services. Despite their designation, the three SNAs of the National Archives do not have a monopoly on the archives of the State and the bodies attached to it: they only manage about 16 % of them. More than 60 % are by the Departmental Archives and the balance by the Ministries of Foreign Affairs and Defence and other archival services. The Court makes eight recommendations.

This report is available on the Court’s website.

The published files correspond to the data used in the preparation of the report.

In support of the COAPEC Thematic Programme the BADC has extracted seasonal forecast ensemble data from the ECMWF MARS (Meteorological Archive and Retrieval System) archive. These data are also known as "Hindcasts" as they are forecasts run retrospectively. Since the data is part of the ECMWF Operational system BADC users must successfully apply for access to this dataset before they can obtain the data. The ECMWF produced two sets of runs, System 1 and System 2. The data archived at the BADC are the System 2 runs which use the atmospheric component Cy23r4 of the IFS (Integrated Forecasting System) with a horizontal resolution of TL95 at 40 levels in the vertical. This is the same cycle of the IFS used for the ERA-40 re-analysis. A detailed description of the ECMWF Seasonal Forecasting system can be found on the ECMWF web site. Products: The BADC has extracted monthly means, maxima, minima and standard deviations for the available surface variables from 1987 to 6 months before the present date. Atmospheric variables are only currently available as monthly means. The data is held as part of the main BADC Operational ECMWF archive. For each month there are six forecast months archived, with 5 ensemble members for 10 months of the year, and 40 ensemble members in May and November of each year from 1987-2001. From 2002 onwards there are 40 ensemble members per month. There are 33 parameters held on surface or single levels and 6 parameters available on pressure levels. The data is held on a regular 1.875 x 1.875 degree grid in GRIB format.

No description is available. Visit https://dataone.org/datasets/a4e812fa40857da26bb2b65f6cc0022c for complete metadata about this dataset.

This data collection consists of archived Geostationary Operational Environmental Satellite-R (GOES-R) Series Geostationary Lightning Mapper (GLM) Level 0 data from the GOES-East and GOES-West satellites in the operational (OPS) and the post-launch test (PLT) phases. The GOES-R Series provides continuity of the GOES mission through 2035 and improvements in geostationary satellite observational data. GOES-16, the first GOES-R satellite, began operating as GOES-East on December 18, 2017. GOES-17 began operating as GOES-West on February 12, 2019. GOES-T launched on March 1, 2022, and was renamed to GOES-18 on March 14, 2022. GOES-U, the final satellite in the series, is scheduled to launch in 2024. GLM is a near-infrared optical transient detector observing the Western Hemisphere. The GLM Level 0 data are composed of Consultative Committee for Space Data Systems (CCSDS) packets containing the science, housekeeping, engineering, and diagnostic telemetry data downlinked from the instrument. The Level 0 data files also contain orbit and attitude/angular rate packets generated by the GOES spacecraft. Each CCSDS packet contains a unique Application Process Identifier (APID) in the primary header that identifies the specific type of packet, and is used to support interpretation of its contents. Users may refer to the GOES-R Series Product Definition and Users’ Guide (PUG) Volume 1 (Main) and Volume 2 (Level 0 Products) for Level 0 data documentation. Related instrument calibration data and Level 1b processing information are archived and available for order at the NOAA CLASS website. The GLM Level 0 data files are delivered in a netCDF-4 file format, however, the constituent CCSDS packets are stored in a byte array making the data opaque for standard netCDF reader applications. The GLM Level 0 data files are packaged in hourly tar files (data bundles) by satellite for the archive. Recently ingested archive tar files are available for 14 days on an anonymous FTP server for users to download. Data archived on offline tape may be requested from NCEI.

ESA maintains an archive of IKONOS Geo Ortho Kit data previously requested through the TPM scheme and acquired between 2000 and 2008, over Europe, North Africa and the Middle East. The imagery products gathered from IKONOS are categorised according to positional accuracy, which is determined by the reliability of an object in the image to be within the specified accuracy of the actual location of the object on the ground. Within each IKONOS-derived product, location error is defined by a circular error at 90% confidence (CE90), which means that locations of objects are represented on the image within the stated accuracy 90% of the time. There are six levels of IKONOS imagery products, determined by the level of positional accuracy: Geo, Standard Ortho, Reference, Pro, Precision and PrecisionPlus. The product provided by ESA to Category-1 users is the Geo Ortho Kit, consisting of IKONOS Black-and-White images with radiometric and geometric corrections (1-metre pixels, CE90=15 metres) bundled with IKONOS multispectral images with absolute radiometry (4-metre pixels, CE90=50 metres). IKONOS collects 1m and 4m Geo Ortho Kit imagery (nominally at nadir 0.82m for panchromatic image, 3.28m for multispectral mode) at an elevation angle between 60 and 90 degrees. To increase the positional accuracy of the final orthorectified imagery, customers should select imagery with IKONOS elevation angle between 72 and 90 degrees. The Geo Ortho Kit is tailored for sophisticated users such as photogrammetrists who want to control the orthorectification process. Geo Ortho Kit images include the camera geometry obtained at the time of image collection. Applying Geo Ortho Kit imagery, customers can produce their own highly accurate orthorectified products by using commercial off the shelf software, digital elevation models (DEMs) and optional ground control. Spatial coverage: Check the spatial coverage of the collection on a map available on the Third Party Missions Dissemination Service.

This dataset tracks the updates made on the dataset "NHANES Select Mean Dietary Intake Estimates" as a repository for previous versions of the data and metadata.

Means of variables of interest.

The Japan Meteorological Agency (JMA) conducted JRA-55, the second Japanese global atmospheric reanalysis project. It covers 55 years, extending back to 1958, coinciding with the establishment of the global radiosonde observing system. Compared to its predecessor, JRA-25, JRA-55 is based on a new data assimilation and prediction system (DA) that improves many deficiencies found in the first Japanese reanalysis. These improvements have come about by implementing higher spatial resolution (TL319L60), a new radiation scheme, four-dimensional variational data assimilation (4D-Var) with Variational Bias Correction (VarBC) for satellite radiances, and introduction of greenhouse gases with time varying concentrations. The entire JRA-55 production was completed in 2013, and thereafter will be continued on a real time basis.

Specific early results of quality assessment of JRA-55 indicate that a large temperature bias in the lower stratosphere has been significantly reduced compared to JRA-25 through a combination of the new radiation scheme and application of VarBC (which also reduces unrealistic temperature variations). In addition, a dry land surface anomaly in the Amazon basin has been mitigated, and overall forecast scores are much improved over JRA-25.

Most of the observational data employed in JRA-55 are those used in JRA-25. Additionally, newly reprocessed METEOSAT and GMS data were supplied by EUMETSAT and MSC/JMA respectively. Snow depth data over the United States, Russia and Mongolia were supplied by UCAR, RIHMI and IMH respectively.

The Data Support Section (DSS) at NCAR has processed the 1.25 degree version of JRA-55 with the RDA (Research Data Archive) archiving and metadata system. The model resolution data has also been acquired, archived and processed as well, including transformation of the TL319L60 grid to a regular latitude-longitude Gaussian grid (320 latitudes by 640 longitudes, nominally 0.5625 degree). All RDA JRA-55 data is available for internet download, including complete subsetting and data format conversion services.

No description is available. Visit https://dataone.org/datasets/c14789bd6f4cc42e10cf93f382518d27 for complete metadata about this dataset.

This archive contains model forcing and output for the Shyft model, along with scripts of the related data processing. The structure of the archive (folders) is as follows:

1. "lalm" and "elverum: contain the meteorological forcing data for the two catchments Lalm and Elverum, respectively.
2. "model forcing for historical periods": contains the bias corrected climate model data representing the three historical periods.
3. "model simulation output": contains the model output data for the simulations of the three historical periods.
4. "shyft workspace": contains the data processing scripts.

1. This dataset consists of the folders: "senorge", "era5" and "hysn5". The included data variables are: temperature, precipitation, wind speed, relative humidity and radiation. Temperature and precipitation are found in "senorge". Wind speed is found in "era5". Lastly, relative humidity and radiation are found in "hysn5". The dataset is of the netCDF-format. The folders contain data that was downloaded from the sources: SeNorge2018 (The Norwegian Meteorological institute, 2022), ERA5-land (Muñoz, 2019; Muñoz, 2021) and HYSN5 (Haddeland, 2022). The data is described as follows:

temperature:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

precipitation:

• Description: daily mean precipitation
• Unit: mm/day
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

wind speed:

• Description: daily mean wind speed
• Unit: m/s
• Spatial resolution: 0.1x0.1 degree (native resolution of 9 km)
• Grid mapping: EPSG:4326
• Dimension: time, latitude and longitude

relative humidity:

• Description: daily mean near-surface relative humidity
• Unit: %
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

radiation:

• Description: daily mean surface downwelling shortwave radiation
• Unit: W/m²
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

2. This dataset contains climate model data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD). The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: temperature (temp), precipitation (prec), wind speed (wind), relative humidity (humi) and radiation (radi). The dataset is of the netCDF-format. The related source data is not published here, due to licences. Contact Lu Li at the NORCE research centre regarding data accessibility. The data is described as follows:

temperature:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

precipitation:

• Description: daily mean precipitation
• Unit: mm/hour
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

wind speed:

• Description: daily mean wind speed
• Unit: m/s
• Spatial resolution: 0.1x0.1 degree (native resolution of 9 km)
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

relative humidity:

• Description: daily mean near-surface relative humidity
• Unit: -
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

radiation:

• Description: daily mean surface downwelling shortwave radiation
• Unit: W/m²
• Spatial resolution: 1x1 km
• Grid mapping: UTM Zone 33
• Dimension: time, latitude and longitude

3. This dataset contains time series data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD), which are output from the Shyft model. The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: discharge, temperature, precipitation, wind_speed, relative_humidity and radiation, snow water equivalent (SWE) and snow covered area (SCA). The dataset is of the csv-format.

NB: the datetime index of the data suggests that the data covers the period of 1700-1850, however this is only true for IT. This inconsistency is caused by a limitation of datetime64 in pandas, which does not handle dates prior to the year 1678.

The data is described as follows:

discharge:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Dimension: time

temperature:

• Description: daily mean air temperature
• Unit: degrees Celsius
• Dimension: time

precipitation:

• Description: daily mean precipitation
• Unit: mm/hour
• Dimension: time

wind_speed:

• Description: daily mean wind speed
• Unit: m/s
• Dimension: time

relative_humidity:

• Description: daily mean near-surface relative humidity
• Unit: -
• Dimension: time

radiation:

• Description: daily mean surface downwelling shortwave radiation
• Unit: W/m²
• Dimension: time

SWE:

• Description: daily mean snow water equivalent
• Unit: mm
• Dimension: time

SCA:

• Description: daily mean snow covered area (% of total catchment area)
• Unit: -
• Dimension: time

4. The scripts make up the workflow of the thesis. In order to reproduce the results, the first script has to be run firstly, then the second script is applied on the output from the first etc. Keep in mind that manual adjustments inside the scripts are required in order to obtain some of the results. The scripts are described as follows:

1. "Subsetting_data.ipynb": This script subsets forcing data (temperature, precipitation, wind speed, relative humidity and radiation) from the sources (SeNorge2018, ERA5-Land and HySN5) to the catchments of Lalm and Elverum.
2. "convert_netcdf.ipynb": This script converts netCDF-files of temperature, precipitation, wind speed, relative humidity and radiation to fit as model forcing to the Shyft modeling framework. It also creates a cell data file containing information about the catchments (Lalm and Elverum) forest, lake and glacier fraction, which are required in Shyft.
3. "QDM_lalm.ipynb" and "QDM_elverum.ipynb": These scripts perform the bias correction approach, Quantile Delta Mapping (QDM), on the climate model data (temperature, precipitation, wind speed, relative humidity and radiation).
4. "extract_historical_periods.ipynb": This script extracts the three historical periods of 1000-1150 (Medieval Warm Period), 1600-1750 (Little Ice Age) and 1800-1950 (Industrial Time) from the climate model data (temperature, precipitation, wind speed, relative humidity and radiation).
5. "calibration_lalm.ipynb" and "calibration_elverum.ipynb": Scripts that runs the calibration of Lalm and Elverum catchment using the Shyft model, respectively.*
6. "simulation_lalm.ipynb" and "simulation_elverum.ipynb": Scripts that runs the simulation of Lalm and Elverum catchment using the Shyft model, respectively.*
7. "data_analysis.ipynb": This script contains the data analysis performed on the Shyft model simulation output. The analysis includes: calculations of mean monthly values of the climate variables (discharge, temperature, precipitation, snow water equivalent and snow covered area), decadal time series of the climate variables, calculations of mean floods and 100-year floods, flood and extreme precipitation frequency analysis, calculation of season index, estimation of flood generating processes, plotting of flood roses and estimation of Standardised Precipitation Index.

*For the Shyft model configuration, simulation and calibration files (yaml-files) are included in the folder "yaml_lalm" and "yaml_elverum" for the two catchments. These yaml-files are described as follows:

• simulation.yaml: is used for configuration of the model simulation
• calibration.yaml: is used for configuration of the model calibration
• calibrated_model.yaml: contains the calibrated model parameters
• datasets.yaml: contains the paths to the data variables
• interpolation.yaml: contains the interpolation methods and parameters
• region.yaml: contains the modeling domain

References:

Haddeland, I. (2022). HySN2018v2005ERA5 (Version 1) [Data set]. Zenodo. (Accessed on: 19-09-2022). doi: https://doi.org/10.5281/zenodo.5947547.

Muñoz Sabater, J. (2019). ERA5-Land hourly data from 1981 to present [Dataset]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on: 19-09-2022). doi: https://doi.org/10.24381/cds.e2161bac.

Muñoz Sabater, J. (2021).

On 6/28/2023, data on cases by vaccination status will be archived and will no longer update.

A. SUMMARY This dataset represents San Francisco COVID-19 positive confirmed cases by vaccination status over time, starting January 1, 2021. Cases are included on the date the positive test was collected (the specimen collection date). Cases are counted in three categories: (1) all cases; (2) unvaccinated cases; and (3) completed primary series cases.

1. All cases: Includes cases among all San Francisco residents regardless of vaccination status.

2. Unvaccinated cases: Cases are considered unvaccinated if their positive COVID-19 test was before receiving any vaccine. Cases that are not matched to a COVID-19 vaccination record are considered unvaccinated.

3. Completed primary series cases: Cases are considered completed primary series if their positive COVID-19 test was 14 days or more after they received their 2nd dose in a 2-dose COVID-19 series or the single dose of a 1-dose vaccine. These are also called “breakthrough cases.”

On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

Data is lagged by eight days, meaning the most recent specimen collection date included is eight days prior to today. All data updates daily as more information becomes available.

B. HOW THE DATASET IS CREATED Case information is based on confirmed positive laboratory tests reported to the City. The City then completes quality assurance and other data verification processes. Vaccination data comes from the California Immunization Registry (CAIR2). The California Department of Public Health runs CAIR2. Individual-level case and vaccination data are matched to identify cases by vaccination status in this dataset. Case records are matched to vaccine records using first name, last name, date of birth, phone number, and email address.

We include vaccination records from all nine Bay Area counties in order to improve matching rates. This allows us to identify breakthrough cases among people who moved to the City from other Bay Area counties after completing their vaccine series. Only cases among San Francisco residents are included.

C. UPDATE PROCESS Updates automatically at 08:00 AM Pacific Time each day.

D. HOW TO USE THIS DATASET Total San Francisco population estimates can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). To identify total San Francisco population estimates, filter the view on “demographic_category_label” = “all ages”.

Population estimates by vaccination status are derived from our publicly reported vaccination counts, which can be found at COVID-19 Vaccinations Given to SF Residents Over Time.

The dataset includes new cases, 7-day average new cases, new case rates, 7-day average new case rates, percent of total cases, and 7-day average percent of total cases for each vaccination category.

New cases are the count of cases where the positive tests were collected on that specific specimen collection date. The 7-day rolling average shows the trend in new cases. The rolling average is calculated by averaging the new cases for a particular day with the prior 6 days.

New case rates are the count of new cases per 100,000 residents in each vaccination status group. The 7-day rolling average shows the trend in case rates. The rolling average is calculated by averaging the case rate for a particular day with the prior six days. Percent of total new cases shows the percent of all cases on each day that were among a particular vaccination status.

Here is more information on how each case rate is calculated:

1. The case rate for all cases is equal to the number of new cases among all residents divided by the estimated total resident population.

2. Unvaccinated case rates are equal to the number of new cases among unvaccinated residents divided by the estimated number of unvaccinated residents. The estimated number of unvaccinated residents is calculated by subtracting the number of residents that have received at least one dose of a vaccine from the total estimated resident population.

3. Completed primary series case rates are equal to the number of new cases among completed primary series residents divided by the estimated number of completed primary series residents. The estimated number of completed primary series residents is calculated by taking the number of residents who have completed their primary series over time and adding a 14-day delay to the “date_administered” column, to align with the definition of “Completed primary series cases” above.

E. CHANGE LOG

• 6/28/2023 - data on cases by vaccination status are no longer being updated. This data is currently through 6/20/2023 (as of 6/28/2023) and will not include any new data after this date.
  
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “sf_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.
• 7/15/2022 - references to “fully vaccinated” replaced with “completed primary series” in column “vaccination_status".
• 7/15/2022 - rows with “partially vaccinated” in column “vaccination_status” removed from dataset.

Map showing sea surface temperature quarterly and overall means in the Northern Marine region. The CSIRO Marine Research Remote Sensing facility automatically receives and archives data from the USA's National Oceanographic and Atmospheric Administration (NOAA) satellites. Up to 18 passes per day are tracked to receive data. The Advanced Very High Resolution Radiometer (AVHRR) data is received on the High Resolution Picture Transmission (HRPT) signal. Within an hour of reception, these data are automatically processed into full resolution sea surface temperature (SST) images. Raw data originate from the AVHRR sensor on various NOAA polar orbiting satellites, received at various stations around Australia and consolidated ("stitched") by the CSIRO Earth Observation Centre. The stitching removes redundancy and minimises data corruption. Processing from the stitched archive to produce SST is carried out in the CMR Remote Sensing Facility in Hobart using the split window algorithm of McMillin for NOAA9 and NOAA12 satellites and the NLSST (NOAA non-linear SST) algorithm for the other satellites. Cloud-clearing is performed based on the algorithm of Saunders and Kriebel. Each map is made by combining the estimates over the composite period using a time and spatial neighbourhood median filtering method. Each pixel of the images is the 65 percentile of all cloud-cleared SST estimates during the composite period and within a 4x4 km region. The compositing process also removes most residual cloud contamination. This map has been produced by CSIRO for the National Oceans Office, as part of an ongoing commitment to natural resource planning and management through the 'National Marine Bioregionalisation' project.