The Advanced Weather Interactive Processing System (AWIPS) uses shapefiles for base maps in the system. These shapefiles contain boundaries of areas used by NWS for forecasts and warnings as well as map backgrounds.NWS BordersThe County Warning Area boundaries are the counties/zones for which each Weather Forecast Office (WFO) is responsible for issuing forecasts and warnings. The shapefile was created by aggregating public zones with the same CWA designation into a single polygon and manually adjusting the boundaries of the exceptions to the rule.The NWS county and state borders are background map used internally in NWS.Coastal Marine Zone ForecastThis map layer contains links to NWS marine weather forecasts for coastal or nearshore waters within 20nm of shore out to Day 5. It includes predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas or combined seas, and icing. Air temperature forecasts are optional. The forecasts will also include any marine weather advisories, watches, and/or warnings. The purpose of the forecasts is to support and promote safe transportation across the coastal waters. The forecasts are issued twice per day with updates as necessary by NWS Weather Forecast Offices (WFOs) along the coast and Great Lakes.Offshore Zone ForecastsThis map layer contains links to NWS marine weather forecasts for offshore waters beyond 20 or 30nm of shore out to Day 5. The forecast provides information to mariners who travel on the oceanic waters adjacent to the U.S., its territorial coastal waters and the Caribbean Sea. The forecasts include predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas and likelihood of icing out to Day 5 along with information about any warnings. The offshore forecasts for the Western North Atlantic and Eastern North Pacific Oceans are produced by NWS/NCEP's Ocean Prediction Center. The offshore forecasts for the Gulf of Mexico and Caribbean Sea are issued by the NWS/NCEP National Hurricane Center's Tropical Analysis and Forecast Branch (TAFB). OPC and NHC/TAFB issues the forecasts four times daily at regular intervals, with updates when necessary. The offshore forecast for the waters around Hawaii are issued by the NWS Weather Forecast Office in Honolulu, HI four times daily at regular intervals, with updates when necessary. The offshore forecasts for Alaska waters in the Bering Sea and Gulf of Alaska are issued by NWS Weather Forecast Offices in Alaska at least twice a day with updates as necessary. The WFOs in Alaska include WFO Anchorage, WFO Fairbanks, and WFO Juneau.Public Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS surface weather forecasts, a zone-type forecast providing the average forecast conditions across the zone, usually at the county-scale or sub-county scale. These text forecasts include predictions of weather, sky cover, maximum and minimum surface air temperatures, surface wind direction and speed, and probability of precipitation out to 7 days into the future. In addition, the forecast highlights at the top include any active weather advisories, watches, and/or warnings. These zone predictions are derived from gridded forecasts created by NWS Weather Forecast Offices throughout the U.S. The text weather forecasts are usually issued in the early morning (e.g. 4AM LT) and early evening (4PM LT). They are updated during late mornings and late night and during fast changing weather conditions.Fire Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS Fire Weather Planning Forecasts, a zone-type forecast providing the average fire weather conditions across the zone. According to the NWS, the forecast is "used by land management personnel primarily for input in decision-making related to pre-suppression and other planning." The forecast is valid from the time of issuance through day five and sometimes through day seven and usually has a minimum of three 12-hour time periods. The forecast will have included a discussion of weather patterns affecting the forecast zone or area, identification of any active fire weather watches/warnings and a table of predicted fire weather variables for the next two days: 1) sky/weather conditions, 2) max/min air temperatures, 3) max/min relative humidity, 4) 0-minute average wind direction/speed at 20 feet and sometimes at another height (e.g. 10,000, 15,000 ft), 5) precipitation amount, duration, and timing, 6) mixing height, 7) transport winds, 8) vent category, and 9) several fire weather indices such as Haines Index, Lightning Activity (LAL), Chance of Wetting Rainfall (CWR), Dispersion Index, Low Visibility Occurrence Risk Index (LVORI), and Max LVORI. In addition, it will usually have a forecast in plain text for days 3 to 7. Sometimes an optional outlook of expected conditions for day 6 or possibly for day 6 and 7 is expected. The forecasts are issued by NWS WFOs at least once daily during the local fire season.Metadata:CWA: https://www.weather.gov/gis/CWAmetadataCoastal Marine: https://www.weather.gov/gis/CoastalMarineMetadataOffshore: https://www.weather.gov/gis/OffshoreZoneMetadataPublic Zones: https://www.weather.gov/gis/PublicZoneMetadataFire Zones: https://www.weather.gov/gis/FireZoneMetadataCounties: https://www.weather.gov/gis/CountyMetadataStates: https://www.weather.gov/gis/StateMetadataLink to data download: https://www.weather.gov/gis/AWIPSShapefilesQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled

This study examines the effects of land use on microclimate along several commercial-to-rural land use transects in the greater Phoenix metropolitan area.

The overview table of the dataset containing 12 Human Thermal Stress Indices in Greece (HTSI-GR):

Heat indices names

Description

Units

Reference

Dataset file names

AT

Apparent Temperature

°C

Steadman, R. G. Norms of apparent temperature in Australia. Aust. Met. Mag. 43, 1–16 (1994).

AT_min_1998-01-01_2022-12-31.csv, AT_mean_1998-01-01_2022-12-31.csv, AT_max_1998-01-01_2022-12-31.csv

HI

Heat Index

°C

Rothfusz, L.P. Te heat index equation. National Weather Service Technical Attachment. Report No. SR 90–23 (1990).

HI_min_1998-01-01_2022-12-31.csv, HI_mean_1998-01-01_2022-12-31.csv, HI_max_1998-01-01_2022-12-31.csv

Humidex

Humidity Index

°C

Masterson, J. & Richardson, F.A. Humidex: a method of quantifying human discomfort due to excessive heat and humidity (Environment Canada, 1979).

Humidex_min_1998-01-01_2022-12-31.csv, Humidex_mean_1998-01-01_2022-12-31.csv, Humidex_max_1998-01-01_2022-12-31.csv

NET

Normal Effective Temperature

°C

Landsberg HE. The assessment of human bioclimate: a limited review of physical parameters. Technical Note No. 123, WMO-No. 331 (World Meteorological Organization, 1972).

NET_min_1998-01-01_2022-12-31.csv, NET_mean_1998-01-01_2022-12-31.csv, NET_max_1998-01-01_2022-12-31.csv

WBGT

Wet Bulb Globe Temperature (simple)

°C

Australian Bureau of Meteorology. Thermal comfort observations http://bom.gov.au/info/thermal_stress/ (2020).

WBGT_min_1998-01-01_2022-12-31.csv, WBGT_mean_1998-01-01_2022-12-31.csv, WBGT_max_1998-01-01_2022-12-31.csv

thermofeelWBGT

Wet Bulb Globe Temperature

°C

Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011).

thermofeelWBGT_min_1998-01-01_2022-12-31.csv, thermofeelWBGT_mean_1998-01-01_2022-12-31.csv, thermofeelWBGT_max_1998-01-01_2022-12-31.csv

WBT

Wet Bulb Temperature

°C

Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011).

WBT_min_1998-01-01_2022-12-31.csv, WBT_mean_1998-01-01_2022-12-31.csv, WBT_max_1998-01-01_2022-12-31.csv

WCT

Wind Chill Temperature

°C

Office of the Federal Coordinator for Meteorological services and supporting research (OFCM). Report on Wind Chill Temperature and extreme heat indices: evaluation and improvement projects. Report No. FCM-R19-2003 (U.S. Office of the Federal Coordinator for Meteorological Services and Supporting Research, 2003).

WCT_min_1998-01-01_2022-12-31.csv, WCT_mean_1998-01-01_2022-12-31.csv, WCT_max_1998-01-01_2022-12-31.csv

MRT

Mean Radiant Temperature

°C

Weihs, P. et al. The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from measured and observed meteorological data. Int. J. Biometeorol. 56, 537–555 (2012).

MRT_min_1998-01-01_2022-12-31.csv, MRT_mean_1998-01-01_2022-12-31.csv, MRT_max_1998-01-01_2022-12-31.csv

UTCI

Universal Thermal Climate Index (UTCI)

°C

Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494(2012).

UTCI_min_1998-01-01_2022-12-31.csv, UTCI_mean_1998-01-01_2022-12-31.csv, UTCI_max_1998-01-01_2022-12-31.csv

UTCI2

Indoor environment UTCI with 2 parameters (air temperature and humidity)

°C

Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494(2012).

UTCI2_min_1998-01-01_2022-12-31.csv, UTCI2_mean_1998-01-01_2022-12-31.csv, UTCI2_max_1998-01-01_2022-12-31.csv

UTCI3

Outdoor shaded space environment UTCI with 3 parameters (air temperature, humidity, and wind speed)

°C

Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494(2012).

UTCI3_min_1998-01-01_2022-12-31.csv, UTCI3_mean_1998-01-01_2022-12-31.csv, UTCI3_max_1998-01-01_2022-12-31.csv

The overview table of the HTSI-GR additional resources folder containing support files and instructions for dataset replication:

File names

Description

1. Calculate thermofeelWBGT.py

A python script that calculates the Wet Bulb Globe Temperature (WBGT) using the Thermofeel library. Processes NetCDF files containing daily meteorological data and outputs WBGT values in new NetCDF files for each day.

1. Merge_HI_by_max-mean-min.py

A python script that merges daily NetCDF files containing heat index (HI) data into three separate files based on mean, maximum and minimum values for further processing.

1. QGIS_zonal_statistics.py

A python script that calculates zonal statistics for heat indices using QGIS python console. Uses a shapefile of Greek communes and a raster NetCDF file containing daily index values, and outputs daily CSV files with computed statistics.

1. Zonal_format.py

A python script that formats the zonal statistics results into a comprehensive dataset. Combines daily CSV files into a single CSV, fills in missing data using nearest neighbour values, and produces a final formatted dataset.

Greek Communes.ZIP

Contains the shapefile of Greek communes derived from the Hellenic Statistical Authority (ELSTAT) required for zonal statistics calculations. KALCODE and Commune names are linked in the .shp.

Nearest Neighbour data table.csv

A support table to script 3.Zonal_format.py that lists communes with missing data and their nearest neighbour with data.

Read me.txt

Provides an overview and instructions for using the scripts. Describes the purpose of each script, lists prerequisites, and provides step-by-step instructions for replicating the dataset.

Mesoscale boundaries have an important influence on mesoscale weather. They can trigger, enhance or inhibit convections and severe weather. They are also indicators of shifts in wind speed and direction, temperature and relative humidity, and can affect air quality and heat indices. Around the Great Lakes, it has been observed that mesoscale boundaries are prevalent and can have complex interactions between each other. A mesoscale boundary is the interface between two air masses for weather phenomenon on a scale of 5km to 100km. Mesoscale boundaries are generally associated with wind and differences in temperature, pressure and relative humidity. During the 2015 Pan Am and Parapan Am Games periods, boundary information were collected from July 10 to August 15, 2015. These included lake and land breeze fronts, outflow boundaries, as well as merged and other boundaries. Each mesoscale boundary is represented as a curve on the surface of the Earth with a list of coordinates. The data are stored in GeoJSON format and Shapefiles. A CSV file summarizes all the properties of the boundaries, but does not include any geometric information.

Charlottesville participated in the 2021 NIHHIS-CAPA Urban Heat Island Mapping Campaign, a nationwide citizen-science based effort to collect local data on temperatures and humidity levels across the city. How urban environments and neighborhoods are built affects the amount of heat absorbed and retained, which can increase or reduce the impact of extreme heat events. Increases in extreme heat are one of the top projected impacts Charlottesville will experience from climate change.

Data was collected by ~30 volunteers on August 24, 2021 along 7 pre-set routes (5 driving routes and 2 bicycle routes) during a heat wave on a day with high heat and low precipitation. The collected data was processed by CAPA Strategies and is now available through the City of Charlottesville's Open Data Portal. A PDF Report, including generated maps and explaining the data collection methodology, is also available.

More information can be found on the City's Urban Heat Island Mapping Campaign webpage.

File Notations:

traverses (.shp, vector shapefile) = data collected by campaign participants rasters (.tif, geotiff) = heat surface models am = morning; af = afternoon; pm = evening t = temperature; hi = heat index f = fahrenheit e.g. pm_hi_f.tif = evening heat index geotiff raster with fahrenheit values.

Geological data were provided by China Geological Survey in shapefile with lithology and lithological age in the attribute table. The "class" means the lithology class, where "1" denotes a classification dominated by Dolomite; "2" is a classification dominated by Limestone; "3" represents a classification dominated by Clastic; "4" means water and "5" denotes a classification dominated by Carbonate rocks. "symbol" represents the lithological age.

Hydrological data is available at https://www.webmap.cn/commres.do?method=result25W in shapefile with 3 elements: rivers, lakes, springs and so on. "HYDA" represents the lakes, "HYDL" is rivers, and "HYDP" is springs and wells.

Climate data include mean annual precipitation (MAP, mm), mean annual temperature (MAT, °C), aridity index (aridity), humidity index (im), >0°C accumulated temperature (aat0dem, °C-days) and >10°C accumulated temperature (aat10dem, °C-days). MAP and MAT are at a resolution of 1km x 1km from 2000-2015. Aridity index (aridity), humidity index (im), >0°C accumulated temperature (aat0dem, °C-days) and >10°C accumulated temperature (aat10dem, °C-days) are at a resolution of 500m x 500m.

Soil properties are also available at the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (https://www.resdc.cn/) at a resolution of 1km x 1km. Where names of the soil order codes represent as follows: 10: Alfisols. 11: Semi-alfisol. 13: Xerosol. 15: Primitive soil. 16: Semi-hydric soil. 17: Hydric soil. 18: Saline-alkali soil. 19: Anthrosols. 20: Alpine soil. 21: Ferralsols. 22: Cities. 23: Rocks. 24: Lakes and reservoirs. 25: Rivers. 26: Sand bars and islands in rivers. 27: Glacier and snow cover. 28: Coral reefs and sea islands. 30: Coastal salt farm/aquaculture farm. Soil texture includes clay content (%), silt content (%), and sand content (%).

DEM (ASTGTM2_dem) is also available at the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (https://www.resdc.cn/) at a resolution of 30m x 30m.

This dataset is the percentage of above-ground biomass carbon carrying capacity reached in the eight provinces of southern China and in different forest types from 2002 to 2017 at the resolution of 500m x 500m, with the urban and water areas, cropland, and the southeast margin of the Tibet Plateau masked. The dataset takes values ranging from 0%-100%. 0% represents the highest carbon sequestration potential, while 100% represents carbon sequestration has reached saturation. The dataset can locate areas where vegetation has not yet reached its full potential, which is significant for the implementation and adjustment of ecological engineering. The dataset is publicly available.

Conventional adsorbents, namely zeolites and silica gel, are often used to control humidity by adsorbing water; however, adsorbents capable of the dual functionality of humidification and dehumidification, offering the desired control of the moisture level at room temperature, have yet to be explored. Here we report Y-shp-MOF-5, a hybrid microporous highly connected rare-earth-based metal–organic framework (MOF), with dual functionality for moisture control within the recommended range of relative humidity (45%–65% RH) set by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). Y-shp-MOF-5 exhibits exceptional structural integrity, robustness, and unique humidity-control performance, as confirmed by the large number (thousand) of conducted water vapor adsorption–desorption cycles. The retained structural integrity and the mechanism of water sorption were corroborated using in situ single-crystal X-ray diffraction (SCXRD) studies. The resultant working water uptake of 0.45 g·g–1 is solely regulated by a simple adjustment of the relative humidity, positioning this hydrolytically stable MOF as a prospective adsorbent for humidity control in confined spaces, such as space shuttles, aircraft cabins, and air-conditioned buildings.

This dataset shows the projected current and future (2070) climatic suitability for the invasive plant species Clidemia hirta, Hiptage benghalensis, Miconia calvescen, Miconia nervosa, Miconia racemose, Stevia ovata,and Turbina corymbosa across North Queensland. Modelled using CLIMEX.

Method:

CLIMEX (Sutherst & Maywald 1985; Sutherst et al. 2007) is a modelling package that enables users to model the climatic potential distribution of organisms based primarily on their current distribution, through taking into consideration climate response information from other knowledge domains if this is available.

CLIMEX is a dynamic model that integrates the weekly responses of a population to climate using a series of annual indices. CLIMEX uses an annual growth index (GIA) to describe the potential for population growth as a function of soil moisture and temperature during favourable conditions, and up to eight stress indices (cold, wet, hot, dry, cold-wet, cold-dry, hot-wet and hot-dry) to determine the probability that the population can survive unfavourable conditions. The growth and stress indices are calculated weekly and are then combined into an overall annual index of climatic suitability, the Ecoclimatic Index (EI), which gives an overall measure of the potential of a given location to support a permanent population of the species. The Ecoclimatic Index (EI), ranges from 0 for locations at which the species is not able to persist to 100 for locations that are optimal for the species year round.

CLIMEX is a bioclimatic model, relying on a database of climatic variables of long-term monthly precipitation totals, averages of minimum and maximum temperatures, and averages of relative humidity at 09:00 and 15:00 hours. The historical climate dataset used for these analyses was the CliMond dataset (www.climond.org), with a spatial resolution of 10’, using station records centred on 1975 (Kriticos et al. 2012).

The impacts of climate change on the potential for each species to grow or pose an invasion risk were explored using a climate scenario model for 2070 taken from the CliMond dataset (Kriticos et al. 2012). The selected climate datasets were developed using the A1B emission scenario applied to the CSIRO Mk 3.0 global climate model.

For each species, we used parameter sets that were either published or which we have developed.

The CLIMEX parameters used in the model for Clidemia hirta are published in: Breadon R. C., Brooks S. J. & Murphy H. T. (2012) Biology of Australian Weeds: Clidemia hirta L.D.Don. Plant Protection Quarterly 27, 3-18.

The CLIMEX parameters used in the model for Hiptage benghalensis, Miconia calvescens, Miconia nervosa, Miconia racemose, Stevia ovata and Turbina corymbosa can be obtained by contacting the author.

Other references:

Kriticos, D. J., B. L. Webber, A. Leriche, N. Ota, J. Bathols, I. Macadam, and J. K. Scott. 2012. CliMond: global high resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution 3:53-64. doi: 10.1111/j.2041-210X.2011.00134.x

Sutherst, R. W., G. F. Maywald, and D. J. Kriticos. 2007. CLIMEX Version 3: User's Guide. Hearne Scientific Software Pty Ltd, www.Hearne.com.au.

Sutherst, R. W., G. F. Maywald, T. Yonow, and P. M. Stevens. 1999. CLIMEX. Predicting the Effects of Climate on Plants and Animals. User Guide. CSIRO Publishing, Melbourne, Australia.

Format:

14 shapefiles in polygon format using the spatial reference of GCS_WGS_1984.

For each of the invasive species investigated there are 2 shapefiles, one for its projected climate suitability as at 1975 and another for its projected climate suitability in 2070. The shapefiles are: • C_hirta_1975.* • C_hirta_2070.* • H_benghal_1975.* • H_benghal_2070.* • M_calvescens_1975.* • M_calvescens_2070.* • M_nervosa_1975.* • M_nervosa_2070.* • M_racemosa_1975.* • M_racemosa_2070.* • S_ovata_1975.* • S_ovata_2070.* • T_corymbosa_1975.* • T_corymbosa_2070.*

Data Dictionary:

Each shapefile has the same attributes. - Longitude: - Latitude: - GI: unknown - EI: range (0-100), Ecoclimatic Index, gives an overall measure of the potential of a given location to support a permanent population of the species.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\NERP-TE\7.2_Invasive-species

The dataset contains all the data used in the associated article “Contribution of standardized indexes to understand groundwater level fluctuations in response to meteorological conditions in cold and humid climates” by Dubois and Larocque (2024). The dataset contains (1) the shapefile of the study area, (2) the metadata of the 152 wells used in the analyses, (3) the pretreated monthly time series of groundwater levels, (4) the aggregated monthly precipitation for each geological region, (5) the aggregated daily temperature for each geological region. The period covered corresponds to the 2000-2022 period and the geological regions corresponds to the Appalachians north, Appalachians south, St. Lawrence Platform, Canadian Shield north, and Canadian Shield south. Groundwater time series and interpolated climate data were provided by the Quebec Ministry of the Environment (Ministère de l’Environnement et de la Lutte contre les changements climatiques, de la Faune et des Parcs - MELCCFP). The study area is located between 46°N and 52°N in the province of Quebec (Canada; 980 000 km2). The study area includes three geological provinces, the metasedimentary Appalachian Province, the sedimentary basin of the St. Lawrence Platform, and the metamorphic Grenville Province (named the Canadian Shield, its overlying region). The three geological units were further subdivided using the 47°N line as a subjective and approximative line dividing the warmer, southern Quebec (temperature>4°C) and the colder, northern Quebec (average temperature<4 °C). The resulting geological regions and sub-regions are the Appalachians North, the Appalachians South, the St. Lawrence Platform, the Canadian Shield North, and the Canadian Shield South.

Ash dieback is induced by Hymenoscyphus fraxineus, an invasive pathogenic fungus. It is causing severe damage to European ash populations. However, the local environment, such as climate or site conditions, is known to affects ash dieback. We studied the landscape epidemiology of the disease on a 22 km² area in north-eastern France at two stages of the invasion process using Bayesian spatio-temporal models fitted with integrated nested Laplace approximation (INLA). Several features characterizing disease severity, crown dieback, frequency of collar canker, and density of infected leaf debris in the litter were determined on a regular grid over a 3.5 x 6.5 km area. We first analysed the effect of landscape features on the disease establishment stage in 2012, two years after the first report of the disease in the area, and then on further disease development, in 2016–2018. Landscape features had little impact on the disease at the establishment stage but strongly determined its further development. Local fragmentation of tree cover was the most important factor, with trees that are isolated or in hedges far less affected than trees in a forest environment. We showed that they were subjected to different microclimates, with higher crown temperatures unfavourable to pathogen development. Low host density strongly reduced disease development. The presence of large ash populations in the vicinity affected local disease severity up to several hundred meters.

Synthesis. We showed that the landscape characteristics strongly affect the development and spread of ash dieback. The disease is far less severe in forest conditions when ash density is low or in open canopies such as hedges and isolated trees. Ash trees are often in these types of landscapes, which should strongly limit the overall impact of Ash dieback.

Methods Data was collected by ground survey during a period of 7 years on a network of plots located on a systematic grid design. Plots were established on grid nodes with ash (100–111 depending on the year). Data were collected: - Ash basal area: measure of ash tree circumference on plots with fixed surface (128, 800 and 1250 m² depending on their diameter at breast height) sum of tree basal area weighted by the surface of the plot on which they were measured This was used to compute the Ash density in the neighbourhood (HAN, host abundance in the neighbourhood) - rPA100 is a tree cover fragmentation index computed from a polygon of tree cover within 100 m from plot center. The polygon is derived from the tree cover IGN shape file VEGETATION.shp (http://professionnels.ign.fr). rPA100 is the perimeter/area ratio for that polygon - Ash health status: visual assessment of crown condition and of collar canker presence - Hymenoscyphus fraxineus presence: qPCR test in laboratory on samples taken from the field - Amount of H. fraxineus infected ash rachides in the litter: collection of ash rachides on fix surface transect, visual assessment of their infection level measure of either their total length or weight after 24h at 50°C per infection category (infected / not infected) - Fructification of H. fraxineus: collection of infected ash rachides on fix surface transect, counts of apothecia in the laboratory, measure of total infected rachis length - Data on microclimate in crown of ash trees (temperature, relative humidity) EL-USB-2 data loggers (Lascar Electronics Ltd UK, Wiltshire, United Kingdom)

The Shiyang River Basin Information System thematic data set is one of the results of the technical assistance project “Optimization of Desertification Control in Gansu Province” assisted by the Asian Development Bank, including 5 folders including document, investigation_point, maps, photo, and spatial. Each file The folder contains several files. The document folder includes the target design, data processing, thematic summary report, and projection information.The gpspoint folder includes files recorded in shapefile point format sampled by gps according to different purposes.The maps folder contains Chinese, english, and fonts files. Folder, the first two folders represent 14 Chinese and English maps stored in A4 format and pdf format, and fonts contain some special fonts: the photo folder contains field survey digital photos stored in bmp format: spatial The folder contains the dem folder of the digital elevation model, the gansu folder of the outline map of Gansu Province and the Hexi Corridor, the generate folder of the site data file shapefile, the grid folder of the raster data of various geographic features, and the remote sensing image. image folder, meteoHydro folder for original site text data, and vector folder for vector data for various geographic features. The data includes: 1. DEM folder: 100m dem, hillshade, divided into GRID and geotif formats 2. Gansu folder: Gansu border, Hexi border 3. Grid folder: NDVI (vegetation index), lndchange (land transfer matrix), landscape86 (land landscape map in 86 years), landscape2k (land landscape map in 2000), Desertiftype (desert type landscape map), Desersevrt (desert type map ), Annprecip 4. Meteohydro folder: Minqin, Wuwei, Yongchang meteorological data (1) daily daily observation items: Airpress (humidity), Precipitation (radiation), Sunlight (sunlight), Temperature (temperature) ), Wind (wind speed) (2) Months (monthly): Airpress (air pressure), Humidity (humidity), Rain (precipitation), Sunlight (sunlight), Temperature (temperature), Wind (wind speed) (3) tendays: Airpress, Humidity, Rain, Sunlight, Temperature, Wind (4) years (year by year): Precipitation, Temperature 5. Vectro folder: (1) Admwhole (county boundary map), (2) Lake (lake), (3) Hydrasta (hydrological site), (4) Basin (watershed boundary), (5) Landscape2000 (land use 200 (Year), (6) landscape86 (land use 1986), (7) Meteosta (meteorological station), (8) Lakep (reservoir point), (9) Place (residential point), (10) Rainfallcontour (railway), ( 11) Rainfallcontour (rainfall contour map), (12) Road (highway), (13) Stream (water system map), (14) Town (county name), (15) Township (county township boundary), (16) Vegetation (vegetation map) Data projection information: PROJCS ["Albers", GEOGCS ["GCS_Krasovsky_1940", DATUM ["Not_specified_based_on_Krassowsky_1940_ellipsoid", SPHEROID ["Krasovsky_1940", 6378245.0,298.3]], PRIMEM ["Greenwich", 0.0], UNIT ["Degree", 0.0174532925199433]], PROJECTION ["Albers_Conic_Equal_Area"], PARAMETER ["False_Easting", 0.0], PARAMETER ["False_Northing", 0.0], PARAMETER ["longitude_of_center", 105.0], PARAMETER ["Standard_Parallel_1", 25.0], PARAMETER ["Standard_Parallel_2", 47.0], PARAMETER ["latitude_of_center", 0.0], UNIT ["Meter", 1.0]] For detailed data description, please refer to the data file

The Advanced Weather Interactive Processing System (AWIPS) uses shapefiles for base maps in the system. These shapefiles contain boundaries of areas used by NWS for forecasts and warnings as well as map backgrounds.NWS BordersThe County Warning Area boundaries are the counties/zones for which each Weather Forecast Office (WFO) is responsible for issuing forecasts and warnings. The shapefile was created by aggregating public zones with the same CWA designation into a single polygon and manually adjusting the boundaries of the exceptions to the rule.The NWS county and state borders are background map used internally in NWS.Coastal Marine Zone ForecastThis map layer contains links to NWS marine weather forecasts for coastal or nearshore waters within 20nm of shore out to Day 5. It includes predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas or combined seas, and icing. Air temperature forecasts are optional. The forecasts will also include any marine weather advisories, watches, and/or warnings. The purpose of the forecasts is to support and promote safe transportation across the coastal waters. The forecasts are issued twice per day with updates as necessary by NWS Weather Forecast Offices (WFOs) along the coast and Great Lakes.Offshore Zone ForecastsThis map layer contains links to NWS marine weather forecasts for offshore waters beyond 20 or 30nm of shore out to Day 5. The forecast provides information to mariners who travel on the oceanic waters adjacent to the U.S., its territorial coastal waters and the Caribbean Sea. The forecasts include predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas and likelihood of icing out to Day 5 along with information about any warnings. The offshore forecasts for the Western North Atlantic and Eastern North Pacific Oceans are produced by NWS/NCEP's Ocean Prediction Center. The offshore forecasts for the Gulf of Mexico and Caribbean Sea are issued by the NWS/NCEP National Hurricane Center's Tropical Analysis and Forecast Branch (TAFB). OPC and NHC/TAFB issues the forecasts four times daily at regular intervals, with updates when necessary. The offshore forecast for the waters around Hawaii are issued by the NWS Weather Forecast Office in Honolulu, HI four times daily at regular intervals, with updates when necessary. The offshore forecasts for Alaska waters in the Bering Sea and Gulf of Alaska are issued by NWS Weather Forecast Offices in Alaska at least twice a day with updates as necessary. The WFOs in Alaska include WFO Anchorage, WFO Fairbanks, and WFO Juneau.Public Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS surface weather forecasts, a zone-type forecast providing the average forecast conditions across the zone, usually at the county-scale or sub-county scale. These text forecasts include predictions of weather, sky cover, maximum and minimum surface air temperatures, surface wind direction and speed, and probability of precipitation out to 7 days into the future. In addition, the forecast highlights at the top include any active weather advisories, watches, and/or warnings. These zone predictions are derived from gridded forecasts created by NWS Weather Forecast Offices throughout the U.S. The text weather forecasts are usually issued in the early morning (e.g. 4AM LT) and early evening (4PM LT). They are updated during late mornings and late night and during fast changing weather conditions.Fire Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS Fire Weather Planning Forecasts, a zone-type forecast providing the average fire weather conditions across the zone. According to the NWS, the forecast is "used by land management personnel primarily for input in decision-making related to pre-suppression and other planning." The forecast is valid from the time of issuance through day five and sometimes through day seven and usually has a minimum of three 12-hour time periods. The forecast will have included a discussion of weather patterns affecting the forecast zone or area, identification of any active fire weather watches/warnings and a table of predicted fire weather variables for the next two days: 1) sky/weather conditions, 2) max/min air temperatures, 3) max/min relative humidity, 4) 0-minute average wind direction/speed at 20 feet and sometimes at another height (e.g. 10,000, 15,000 ft), 5) precipitation amount, duration, and timing, 6) mixing height, 7) transport winds, 8) vent category, and 9) several fire weather indices such as Haines Index, Lightning Activity (LAL), Chance of Wetting Rainfall (CWR), Dispersion Index, Low Visibility Occurrence Risk Index (LVORI), and Max LVORI. In addition, it will usually have a forecast in plain text for days 3 to 7. Sometimes an optional outlook of expected conditions for day 6 or possibly for day 6 and 7 is expected. The forecasts are issued by NWS WFOs at least once daily during the local fire season.Metadata:CWA: https://www.weather.gov/gis/CWAmetadataCoastal Marine: https://www.weather.gov/gis/CoastalMarineMetadataOffshore: https://www.weather.gov/gis/OffshoreZoneMetadataPublic Zones: https://www.weather.gov/gis/PublicZoneMetadataFire Zones: https://www.weather.gov/gis/FireZoneMetadataCounties: https://www.weather.gov/gis/CountyMetadataStates: https://www.weather.gov/gis/StateMetadataLink to data download: https://www.weather.gov/gis/AWIPSShapefilesQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled

The Advanced Weather Interactive Processing System (AWIPS) uses shapefiles for base maps in the system. These shapefiles contain boundaries of areas used by NWS for forecasts and warnings as well as map backgrounds.NWS BordersThe County Warning Area boundaries are the counties/zones for which each Weather Forecast Office (WFO) is responsible for issuing forecasts and warnings. The shapefile was created by aggregating public zones with the same CWA designation into a single polygon and manually adjusting the boundaries of the exceptions to the rule.The NWS county and state borders are background map used internally in NWS.Coastal Marine Zone ForecastThis map layer contains links to NWS marine weather forecasts for coastal or nearshore waters within 20nm of shore out to Day 5. It includes predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas or combined seas, and icing. Air temperature forecasts are optional. The forecasts will also include any marine weather advisories, watches, and/or warnings. The purpose of the forecasts is to support and promote safe transportation across the coastal waters. The forecasts are issued twice per day with updates as necessary by NWS Weather Forecast Offices (WFOs) along the coast and Great Lakes.Offshore Zone ForecastsThis map layer contains links to NWS marine weather forecasts for offshore waters beyond 20 or 30nm of shore out to Day 5. The forecast provides information to mariners who travel on the oceanic waters adjacent to the U.S., its territorial coastal waters and the Caribbean Sea. The forecasts include predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas and likelihood of icing out to Day 5 along with information about any warnings. The offshore forecasts for the Western North Atlantic and Eastern North Pacific Oceans are produced by NWS/NCEP's Ocean Prediction Center. The offshore forecasts for the Gulf of Mexico and Caribbean Sea are issued by the NWS/NCEP National Hurricane Center's Tropical Analysis and Forecast Branch (TAFB). OPC and NHC/TAFB issues the forecasts four times daily at regular intervals, with updates when necessary. The offshore forecast for the waters around Hawaii are issued by the NWS Weather Forecast Office in Honolulu, HI four times daily at regular intervals, with updates when necessary. The offshore forecasts for Alaska waters in the Bering Sea and Gulf of Alaska are issued by NWS Weather Forecast Offices in Alaska at least twice a day with updates as necessary. The WFOs in Alaska include WFO Anchorage, WFO Fairbanks, and WFO Juneau.Public Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS surface weather forecasts, a zone-type forecast providing the average forecast conditions across the zone, usually at the county-scale or sub-county scale. These text forecasts include predictions of weather, sky cover, maximum and minimum surface air temperatures, surface wind direction and speed, and probability of precipitation out to 7 days into the future. In addition, the forecast highlights at the top include any active weather advisories, watches, and/or warnings. These zone predictions are derived from gridded forecasts created by NWS Weather Forecast Offices throughout the U.S. The text weather forecasts are usually issued in the early morning (e.g. 4AM LT) and early evening (4PM LT). They are updated during late mornings and late night and during fast changing weather conditions.Fire Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS Fire Weather Planning Forecasts, a zone-type forecast providing the average fire weather conditions across the zone. According to the NWS, the forecast is "used by land management personnel primarily for input in decision-making related to pre-suppression and other planning." The forecast is valid from the time of issuance through day five and sometimes through day seven and usually has a minimum of three 12-hour time periods. The forecast will have included a discussion of weather patterns affecting the forecast zone or area, identification of any active fire weather watches/warnings and a table of predicted fire weather variables for the next two days: 1) sky/weather conditions, 2) max/min air temperatures, 3) max/min relative humidity, 4) 0-minute average wind direction/speed at 20 feet and sometimes at another height (e.g. 10,000, 15,000 ft), 5) precipitation amount, duration, and timing, 6) mixing height, 7) transport winds, 8) vent category, and 9) several fire weather indices such as Haines Index, Lightning Activity (LAL), Chance of Wetting Rainfall (CWR), Dispersion Index, Low Visibility Occurrence Risk Index (LVORI), and Max LVORI. In addition, it will usually have a forecast in plain text for days 3 to 7. Sometimes an optional outlook of expected conditions for day 6 or possibly for day 6 and 7 is expected. The forecasts are issued by NWS WFOs at least once daily during the local fire season.Metadata:CWA: https://www.weather.gov/gis/CWAmetadataCoastal Marine: https://www.weather.gov/gis/CoastalMarineMetadataOffshore: https://www.weather.gov/gis/OffshoreZoneMetadataPublic Zones: https://www.weather.gov/gis/PublicZoneMetadataFire Zones: https://www.weather.gov/gis/FireZoneMetadataCounties: https://www.weather.gov/gis/CountyMetadataStates: https://www.weather.gov/gis/StateMetadataLink to data download: https://www.weather.gov/gis/AWIPSShapefilesQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled