Relative humidity at 15h (local time) at a height of 2 metres above the surface. This variable describes the amount of water vapour present in air expressed as a percentage of the amount needed for saturation at the same temperature. Unit: %. The Relative humidity variable is part of the Agrometeorological indicators dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). The Agrometeorological indicators dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. References: https://doi.org/10.24381/cds.6c68c9bb The Copernicus Climate Change Service (C3S) aims to combine observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate in Europe and worldwide. ECMWF operates the Copernicus Climate Change Service on behalf of the European Union and will bring together expertise from across Europe to deliver the service.

Marcus Weather Mapping (MWM) is an online, global weather / data mapping, visualization application that offers some unique features that no other current weather mapping system provides.

Below we highlight some features of MWM:

• Weather forecast and observational information updated every 6 hours • Non-static mapping - the ability to pan and zoom (to expose the highest level of station detail), a globally unique feature to Marcus Weather Mapping • Display preset areas OR build your own custom regions – again a feature unique to Marcus Weather Mapping
• Mapping variables include total precipitation, % normal precipitation, precipitation climatology, average/maximum/minimum temperature/temperature departures, GDDs, HDDs and CDDs (and departures) + others • Custom or pre-selected calendar dates (such as 5/10 days forward or 60/30 days back) up to a 180 day window • Historical Data selection - currently available from 2010, but will soon be adding data back to 2000 • The Yearly Comparison Tool, the ability to compare a weather variable for a user selected time period, against the same time period from a selected year – showing the difference between years • The Forecast Comparison Tool, the ability to compare forecast data from a previous forecast, to the current forecast, showing how the forecast has changed • Other mapping options include, map build speed, display density, choice of unit designation, coloring options, map contours, weather overlay opacity and map base layer options • A screenshot button for the current map created, weather fixed or zoomed • Satellite Imagery, Including: Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Thermal Condition Index (TCI) and Moisture Condition Index (VCI). Map Satellite Images for both preset AND user defined mapping areas. • Global Surface Soil Moisture, Root Zone Soil Moisture, Surface Soil Temperature, 10cm Subsurface Soil Temperature, 20cm Subsurface Soil Temperature. • Satellite Imagery Comparison Tool (SICT) – Compare any satellite image to another from a different time period, assessing change between the two satellite images. The SICT comes in two presentation modes, color change and Improve/Deteriorate View • MWM twitter, keeping users up to date of changes, improvements, bugs and other announcements – the twitter feedback be found here: MWM Twitter - https://twitter.com/MWMapping

Technological advancements in the Temperature and Humidity Chart Recorder industry are shaping the future market landscape. The report evaluates innovation-driven growth and how emerging technologies are transforming industry practices, offering a comprehensive outlook on future opportunities and market potential.

Abstract: The soil moisture time series data of the extension of the EUMETSAT H-SAF product H119: H120 based on MetOp-B, and -C ASCAT data, processing version v7 (https://doi.org/10.15770/EUM_SAF_H_0009), are converted into geographic maps (cartesian grid) of daily running 5-day average/composite soil moisture (SM) distribution separately for ascending and descending overpasses. Two different 5-day SM distributions are given: one is based solely on nominally computed SM, the other one includes also those SM values which were negative (down to -25%, correction flag = 1) or positive (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. All data are interpolated into a cartesian grid of x- and y-dimensions of original grid. For more information see the respective global attribute in the netCDF file.

TableOfContents: soil moisture; soil moisture noise; soil moisture extended; soil moisture extended noise; soil moisture status flag; number of overpasses per grid cell; historic probability of snow cover; historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD

Technical Info: dimensons: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2022-01-01; temporalExtent_endDate: 2022-12-31; temporalResolution: daily; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.1125; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-B, MetOp-C); instrumentProvider: EUMETSAT, ESA; License: The following applies to the original product: All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products.

Methods: For a description of the methods used to obtain the 5-day average / composite data we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi:10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.2, 2022; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.1, 2021; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v1.1, 2022.

Units: units for all variables (see TableOfContents): percent, percent, percent, percent, 1, 1, percent, percent, percent, percent, m³/m³, m³/m³

geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLongitude: -90.0 degrees North; northBoundLongitude: 90.0 degrees North; geoLocationPlace: global over land

Size: 730 files per year [note: there are 2 files per day, one for the ascending, one for the descending overpasses]; ~61.569 MegaByte per file; ~43.892 GigaByte per year (provided as two zip-files per year)

Format: netCDF

DataSources:

Original Data as time series on a 12.5 km DGG Grid: https://hsaf.meteoam.it/Products/Detail?prod=H120 (last access: 2023-07-04); this original product comes with the following notion: "All intellectual property rights of the HSAF products belong to EUMETSAT. The use of these products is granted to every user, free of charge. If users wish to use these products, EUMETSAT's copyright credit must be shown by displaying the words "Copyright EUMETSAT" under each of the products shown. EUMETSAT offers no warranty and accepts no liability in respect of the HSAF products. EUMETSAT neither commits to nor guarantees the continuity, availability, or quality or suitability for any purpose of, the HSAF products."

See also: http://hsaf.meteoam.it; https://hsaf.meteoam.it/Products/Detail?prod=H120

Contact: stefan.kern (at) uni-hamburg.de

Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html

Meteomatics calculate our soil moisture index by using data from ECMWF’s atmospheric model, which brings a number of benefits, including a high spatial resolution (10km), the frequency of data updates, with high temporal resolution; four model runs with hourly timesteps. Plus, the advanced soil type scheme derived from the root zone data (30-100 cm below the surface) of the FAO/UNESCO Digital Soil Map of the World, DSMW (FAO, 2003), which exists at a resolution of 5' X 5' (about 10 km).

The SMI can be very helpful in several situations, such as estimating the drought risk, so an extensive history of soil moisture conditions in the upper soil layers is essential. Plus it can also be used in hydrological applications such as flooding and runoff forecasts, where a detailed knowledge of both upper and lower soil layers is indispensable. Meteomatics soil moisture index includes four discrete layers (-150cm / -50cm / -15cm / -5cm below ground level) based on the model data within ECMWF atmospheric model.

The index is 0 if the permanent wilting point is reached and 1 at field capacity. Note that the index can exceed 1 after rainfall events. The soil moisture index is available for 4 depth levels.

This product is an aggregation of daily maps of soil moisture, over a 3 day moving window, a decade or a month. Ascending and descending orbits are processed separately.

This product is the daily product of soil moisture, and contains filtered data. The retrievals are based on a multi-orbit retrieval algorithm. A detection of freeze and snow is performed and added to the flags. Ascending and descending orbits are processed separately.

Soils and soil moisture greatly influence the water cycle and have impacts on runoff, flooding and agriculture. Soil type and soil particle composition (sand, clay, silt) affect soil moisture and the ability of the soil to retain water. Soil moisture is also affected by levels of evaporation and plant transpiration, potentially leading to near dryness and eventual drought.Measuring and monitoring soil moisture can ensure the fitness of your crops and help predict or prepare for flash floods and drought. The GLDAS soil moisture data is useful for modeling these scenarios and others, but only at global scales. Dataset SummaryThe GLDAS Soil Moisture layer is a time-enabled image service that shows average monthly soil moisture from 2000 to the present at four different depth levels. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. The GLDAS soil moisture data is useful for modeling, but only at global scales. Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.Depth: This layer has four depth levels. By default they are summed, but you can view each using the multidimensional filter. You must disable time animation on the layer before using its multidimensional filter. It is also possible to toggle between depth layers using raster functions, accessed through the Image Display tab.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.This layer has query, identify, and export image services available. This layer is part of a larger collection of earth observation maps that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about earth observations layers and the Living Atlas of the World. Follow the Living Atlas on GeoNet.

Soils and soil moisture greatly influence the water cycle and have impacts on runoff, flooding and agriculture. Soil type and soil particle composition (sand, clay, silt) affect soil moisture and the ability of the soil to retain water. Soil moisture is also affected by levels of evaporation and plant transpiration, potentially leading to near dryness and eventual drought.Measuring and monitoring soil moisture can ensure the fitness of your crops and help predict or prepare for flash floods and drought. The GLDAS soil moisture data is useful for modeling these scenarios and others, but only at global scales. Dataset SummaryThe GLDAS Soil Moisture layer is a time-enabled image service that shows average monthly soil moisture from 2000 to the present at four different depth levels. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. The GLDAS soil moisture data is useful for modeling, but only at global scales. Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.Depth: This layer has four depth levels. By default they are summed, but you can view each using the multidimensional filter. You must disable time animation on the layer before using its multidimensional filter. It is also possible to toggle between depth layers using raster functions, accessed through the Image Display tab.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.This layer has query, identify, and export image services available. This layer is part of a larger collection of earth observation maps that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about earth observations layers and the Living Atlas of the World. Follow the Living Atlas on GeoNet.

Abstract: The globally gridded daily 5-day running mean surface soil moisture product derived at ICDC (https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html , https://doi.org/10.25592/uhhfdm.16509) from soil moisture time series data of the extension of the EUMETSAT H-SAF product H119: H120 based on MetOp-B, and -C ASCAT data, processing version v7 (https://doi.org/10.15570/EUM_SAF_H_0009), are averaged to obtain monthly means of the surface soil moisture (SM) distribution separately for ascending and descending overpasses. The monthly mean SM values include the nominally computed SM values as well as those SM values which were negative (down to -25%, correction flag = 1) or larger than 100% (up to 125%, correction flag = 2) but set to 0% and 100%, respectively. The threshold for the monthly average is (the number of days per Month) 10. If there are fewer values per month, the value is set to the missing_value. For more information see the respective global attribute in the netCDF file.

TableOfContents: mean soil moisture extended; mean soil moisture extended noise; number of valid soil moisture extended values per month; mean number of overpasses per grid cell; mean historic probability of snow cover; mean historic probability of frozen land; inundation and wetland fraction; topographic complexity; soil porosity LDAS; soil porosity HWSD; soil moisture status flag

Technical Info: dimensions: 3207 columns x 1599 rows x unlimited; temporalExtent_startDate: 2022-01-01; temporalExtent_endDate: 2024-07-31; temporalResolution: monthly; spatialResolution: 0.1125; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.11225; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.1125; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: Advanced SCATterometer (ASCAT); instrumentType: C-band microwave_scatterometer; instrumentLocation: Meteorological Operational Satellite (MetOp-B, MetOp-C); instrumentProvider: EUMETSAT, ESA

Methods: For a description of the methods used to obtain the daily 5-day running mean / composite data on which these monthly data are based, we refer to the global attributes of the netCDF files. For the methods used for the native soil moisture time series please see: [1] Wagner, W., et al.: A method for estimating soil moisture from ERS scatterometer and soil data, Rem. Sens. Environ., 70(2), 191-207, 1999. doi: 10.1016/S0034-4257(99)00036-X; [2] Naeimi, V., et al.: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Rem. Sens., 47(7), 1999-2013, 2009. doi: 10.1109/TGRS.2008.2011617; [3] Naeimi, V., et al.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Trans. Geosci. Rem. Sens., 50(7), 2566-2582, 2012. doi: 10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.2, 2022; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.1, 2021; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v1.1, 2022.

Units: Units for all variables (see TableOfContents): percent, percent, 1, 1, percent, percent, percent, percent, m³/m³, m³/m³, 1

geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land

Size: 24 files per year [12 for ascending, 12 for descending overpasses]; ~56.439 MegaByte per file; ~1.3228 GigaByte in total (data are packed into two zip-archives per year, one for the ascending, one for the descending data)

Format: netCDF

DataSources:

Gridded daily 5-day running mean surface soil moisture maps: https://doi.org/10.25592/uhhfdm.16509; see also https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html

Original time-series of the surface soil moisture: https://hsaf.meteoam.it/Products/Detail?prod=H120

Contact: stefan.kern (at) uni-hamburg.de

Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/ascat-soilmoisture.html

Summary:Land Information System (LIS) 0-200 cm layer Soil Moisture Percentile generated by the NASA SPoRT Center over a Contiguous United States domain.The NASA Land Information System (LIS) is a high-performance land surface modeling and data assimilation system used to characterize land surface states and fluxes by integrating satellite-derived datasets, ground-based observations, and model re-analyses. The NASA SPoRT Center at MSFC developed a real-time configuration of the LIS (“SPoRT-LIS”), which is designed for use in experimental operations by domestic and international users. SPoRT-LIS is an observations-driven, historical and real-time modeling setup that runs the Noah land surface model over a full CONUS domain. It provides soil moisture estimates at approximately 3-km horizontal grid spacing over a 2-meter-deep soil column and has been validated for regional applications and against U.S. Drought Monitor products.SPoRT-LIS consists of a 33-year soil moisture climatology spanning from 1981 to 2013, which is extended to the present time and forced by atmospheric analyses from the operational North American Land Data Assimilation System-Phase 2 through 4 days prior to the current time, and by the National Centers for Environmental Prediction Global Data Assimilation System in combination with hourly Multi-Radar Multi-Sensor precipitation estimates from 4 days ago to the present time. A unique feature of SPoRT-LIS is the incorporation of daily, real-time satellite retrievals of VIIRS Green Vegetation Fraction since 2012, which results in more representative evapotranspiration and ultimately soil moisture estimates than using a fixed seasonal depiction of vegetation in the model.The 33-year soil moisture climatology also provides the database for real-time soil moisture percentiles evaluated for all U.S. counties and at each modeled grid point. The present-day soil moisture analyses are compared to daily historical distributions to determine the soil wet/dry anomalies for the specific day of the year. Soil moisture percentile maps are constructed for the model layers, and these data are frequently referenced by scientists and operational agencies contributing to the weekly U.S. Drought Monitor product.Suggested Use:This product can be used for drought assessment, fire risk assessment, potential for flooding hazards associated with heavy precipitation and high percentiles; contextualizing soil moisture content to historical values.Soil moisture percentiles are shown using a Classified Color Ramp (Multi-Color, 11-classes) that colorize the low percentile categories (≤ 30th) as shown in the U.S. Drought Monitor weekly products, ranging from yellow to dark red. The high percentile categories (≥ 70th) are colorized with increasing blue intensity. Intermediate percentiles in the 30th to 70th range are assigned a nominal gray shade.The 0-200 cm layer combines SPoRT-LIS soil moisture analyses from all four model layers 0-10 cm, 10-40 cm, 40-100 cm, and 100-200 cm depths. The 0-200 cm cumulative layer adjusts slowly to precipitation episodes or the lack thereof compared to the other cumulative layered percentile products. It takes considerably longer time periods for intercepted rainfall and snowmelt to infiltrate from the upper layers into the lower layers at 40-100 cm and 100-200 cm, or conversely for the deeper soil layer to dry from evapotranspiration processes. Expect anomalies of soil moisture percentiles in the total column 0-200 cm layer to respond to meteorological features on the order of months to years (especially for drying periods), depending on the soil classification and soil responsiveness.Data Caveats:The SPoRT-LIS is as good as the input forcing analyses, so occasional soil moisture artifacts may appear in the horizontal maps related to quality-control issues of the input datasets. These can be manifested with unusually low or high percentiles, especially along international borders, coastlines, and isolated dry “bulls-eyes” at rain gauge with quality issues.Data Visualization:The Soil Moisture Percentile is the histogram rank of the current day’s soil moisture value compared to the 33-year climatology for the present day. The percentile places into historical context the soil moisture to determine how unusually wet or dry, or typical the conditions are. Percentile thresholds as established by the drought community are used to categorize soil moisture dry anomalies can be found here.

1501 Global import shipment records of Humidity Test Chamber with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

7065 Global export shipment records of Measuring Humidity with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

These data were compiled as a supplement to a previously published journal article (Bradford et al., 2019), that employed a ecosystem water balance model to characterize current and future patterns in soil temperature and moisture conditions in dryland areas of western North America. Also, these data are associated with a published USGS data release (Bradford and Schlaepfer, 2019). The objectives of our study were to (1) characterize current and future patterns in soil temperature and moisture conditions in dryland areas of western North America, (2) evaluate the impact of these changes on estimation of resilience and resistance among a representative set of climate scenarios. These data represent geographic patterns in simulated soil temperature and soil moisture conditions and underlying variables based on SOILWAT2 simulations under climate conditions representing historical (current) time period (1980-2010) and two future projected time periods (2020-2050, d40yrs) and (2070-2100, d90yrs) for two representative concentration pathways (RCP4.5, RCP8.5) as medians across simulation runs based on output from each of the available downscaled global circulation models that participated in CMIP5 (RCP4.5, 37 GCMs; RCP8.5, 35 GCMs; Maurer et al. 2007). Additional information about the SOILWAT2 simulation experiments can be found in Bradford et al. 2019. These data were created in 2018, 2019, and 2021 for the area of the sagebrush region in the western North America. These data were created by a collaborative research project between the U.S. Geological Survey, Marshall University and Yale University. These data can be used with the high-resolution matching as defined by Renne et al. (in prep.), and within the scope of Bradford et al. 2019. These data may also be used to evaluate the potential impact of changing climate conditions on geographic patterns in simulated soil temperature and soil moisture conditions.

The Atlas of the Biosphere is a product of the Center for Sustainability and the Global Environment (SAGE), part of the Gaylord Nelson Institute for Environmental Studies at the University of Wisconsin - Madison. The goal is to provide more information about the environment, and human interactions with the environment, than any other source.

The Atlas provides maps of an ever-growing number of environmental variables, under the following categories:

Human Impacts (Humans and the environment from a socio-economic perspective; i.e., Population, Life Expectancy, Literacy Rates);

Land Use (How humans are using the land; i.e., Croplands, Pastures, Urban Lands);

Ecosystems (The natural ecosystems of the world; i.e., Potential Vegetation, Temperature, Soil Texture); and

Water Resources (Water in the biosphere; i.e., Runoff, Precipitation, Lakes and Wetlands).

Map coverages are global and regional in spatial extent. Users can download map images (jpg) and data (a GIS grid of the data in ESRI ArcView Format), and can view metadata online.

The NCEP operational Global Forecast System analysis and forecast grids are on a 0.25 by 0.25 global latitude longitude grid. Grids include analysis and forecast time steps at a 3 hourly interval from 0 to 240, and a 12 hourly interval from 240 to 384. Model forecast runs occur at 00, 06, 12, and 18 UTC daily. For real-time data access please use the NCEP data server [http://www.nco.ncep.noaa.gov/pmb/products/gfs/].

The global market for Temperature and Humidity Chart Recorders is experiencing robust growth, driven by increasing demand across various sectors. The rising need for precise environmental monitoring in industries like pharmaceuticals, food processing, and healthcare is a key factor fueling this expansion. Furthermore, stringent regulatory compliance requirements regarding data logging and traceability are compelling businesses to adopt sophisticated temperature and humidity monitoring solutions. Technological advancements, including the integration of digital data logging and remote monitoring capabilities, are enhancing the functionality and appeal of these recorders. The market is segmented by recorder type (analog, digital), application (industrial, laboratory, healthcare), and region. While the precise market size and CAGR are unavailable, a reasonable estimate, based on market reports for similar monitoring equipment and considering a healthy growth trajectory, would place the 2025 market size around $800 million, with a CAGR of approximately 6% projected through 2033. This growth will likely be influenced by the ongoing adoption of smart manufacturing practices and the Internet of Things (IoT) in various industries. Competition in this market is moderately high, with established players like OMEGA Engineering, Yokogawa, and Honeywell alongside smaller, specialized manufacturers. The market's future depends on continued technological innovation, including the development of more accurate, reliable, and user-friendly devices. The increasing focus on data analytics and predictive maintenance further presents opportunities for companies to offer integrated solutions that go beyond simple data logging. However, the market also faces constraints such as the high initial investment cost for advanced systems and the potential for disruptions due to economic downturns or changes in regulatory landscapes. The growing demand for data security and robust cloud-based data storage solutions also presents both challenges and opportunities for companies in this market.

This data set consists of daily, global grayscale TIFF images measured in the 6.7 µm window (6.5 µm - 7.0 µm) and the 11.5 µm window (10.5 µm - 12.5 µm) by the Temperature-Humidity Infrared Radiometer (THIR) on board the Nimbus 7 satellite. Each data granule is a daytime or nighttime global composite of all the swaths in a day. Note: This data set is not georeferenced and there are some gaps in the temporal coverage because of missing data.

Groundwater (GW) impacts water, energy, and carbon cycles by providing additional moisture to the root zone. Although the interactions of shallow GW and the terrestrial land surface are widely recognized, incorporating shallow GW into the land surface, climate, and agroecosystem models as a lower boundary condition is not yet possible due to the lack of groundwater data. Here, we provide global maps of the terrestrial land surface areas influenced by shallow GW at daily timesteps. We derived this data using spaceborne soil moisture observations from NASA's SMAP satellite. We used the Level-2 enhanced passive soil moisture (L2_SM_P_E) product to detect shallow GW signals. The presence of shallow GW is obtained using an ensemble machine learning model. The model is trained using results from global simulations. We published the details of our approach in a separate research paper (Soylu and Bras, 2022 - https://ieeexplore.ieee.org/document/9601254). Our data covers the period from mid-2015 to 2021 (a separate NetCDF file for each year) with a 9 km spatial resolution, the same as the SMAP "Equal Area Scalable Earth" (EASE) grids.

Reference: Soylu, M.E, and Bras, R.L. "Global Shallow Groundwater Patterns From Soil Moisture Satellite Retrievals." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15 (2022): 89-101

This product is a daily product of root zone soil moisture representative of the 0-1 m depth of the soil.