Hand-in-Hand has brought together over 20 FAO units across multiple domains, from Animal Health to Trade and Markets, integrating data from across FAO on Soil, Land, Water, Climate, Fisheries, Livestock, Crops, Forestry, Trade, Social and Economics, etc.Data has also been sourced from FAO partners and public data providers across the UN and NGOs, private sector and space agencies.

Tables showing the warmest, coldest, wettest, and typical weather on 22 holidays across Alabama, Florida, Georgia, North Carolina, South Carolina, Virginia, and Puerto Rico.

2015 GOES-R/JPSS PROVING GROUND ALL HANDS TELECONFERENCE Meeting Updates and presentation Internet Archive URL: https://web.archive.org/web/*/http://www.goes-r.gov/users/2015-07-13-PG-AH.html

This site is part of pilot effort at the US Department of Energy (DOE) - Office of NEPA Policy and Compliance to evaluate providing IT web services as a shared service, hosted on the cloud, and using only Free and Open Source Software (FOSS). The site is a collaborative data and document sharing platform, data is made publically available both as a downloadable file in multiple Open Standard formats or as a web service using Open Geospatial Construtium (OGC) Open Standard services (WMS/WFS/WCS).

Monthly US calculated soil moisture data total (mm), anomaly (mm), and percentile.

Radio occultation observations gridded to daily 2.5°x2.5° fields by a Gaussian weighted average over ±2 days and 15°x5° longitude-latitude, based on radio occultation profile data version OPSv5.6.2. A detailed description of this data is given in Brunner and Steiner (2017): https://doi.org/10.5194/amt-10-4727-2017

Radio occultation observations gridded to daily 2.5°x2.5° fields by a Gaussian weighted average over ±2 days and 15°x5° longitude-latitude, based on radio occultation profile data version OPSv5.6.2. A detailed description of this data is given in Brunner and Steiner (2017): https://doi.org/10.5194/amt-10-4727-2017.

Radio occultation observations gridded to daily 2.5°x2.5° fields by a Gaussian weighted average over ±2 days and 15°x5° longitude-latitude, based on radio occultation profile data version OPSv5.6.2. A detailed description of this data is given in Brunner and Steiner (2017): https://doi.org/10.5194/amt-10-4727-2017.

Radio occultation observations gridded to daily 2.5°x2.5° fields by a Gaussian weighted average over ±2 days and 15°x5° longitude-latitude, based on radio occultation profile data version OPSv5.6.2. A detailed description of this data is given in Brunner and Steiner (2017): https://doi.org/10.5194/amt-10-4727-2017

Abstract:Rain on Snow is a statewide coverage of rain-on-snow zones.  Rain-on-snow zones are based on average amounts of snow on the ground in early January, relative to the amount of snow that could reasonably be melted during a model storm event.  Five Rain on Snow zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation.  Rain on Snow was digitized from 1:250,000 USGS quads.Purpose:The Rain-on-snow coverage was created as a screening tool to identify forest practice applications that may be in a significant rain-on-snow zone (WAC 222-22-100).Description:Five ROS zones are defined in Washington State and are based on climate, elevation, latitude, and vegetation. Rain on snow is a process that exhibits spatial and temporal variation under natural conditions, with the effects of vegetation on snow accumulation and melt adding additional complications in prediction. There is no map that shows the magnitude and frequency of water inputs to be expected from rain on snow events, so we have attempted to create an index map based on what we know about the process controls and their effects in the various climatic zones. If we assume that, averaged over many years, the seasonal storm tracks that bring warm, wet cyclonic storms to the Northwest have access to all parts of Washington , then the main factors controlling and/or reflecting the occurrence and magnitude of a R/S event in any particular place are: 1) Climatic region: especially the differences between windward and leeward sides of major mountain ranges, which control seasonal climatic patterns;2) Elevation: controls temperature, thus the likelihood and amount of snow on the ground, and affects orographic enhancement of storm precipitation; 3) Latitude: affects temperature, thus snow;4) Aspect: affects insolation and temperature (especially in winter), thus melting of snow; 5) Vegetation: the species composing forest communities can reflect the climate of an area (tolerance of warmth or cold, wet or dry conditions, deep and/or long lived snowpacks); the height and density of vegetation also partly controls the amount of snow on the ground. As natural vegetation integrates the effects of all of these controls, we tried to find or adapt floral indicators of the various zones of water input. We designed the precipitation zones to reflect the amount of snow likely to be on the ground at the beginning of a storm. We assumed that some middle elevation area would experience the greatest water input due to Rain on Snow, because the amount of snow available would be likely to be approximately the amount that could be melted. Higher and lower elevation zones would bear diminished effects, but for opposite reasons (no snow to melt, vs too cold to melt much). These considerations suggested a three or five zone system. We chose to designate five zones because a larger number of classes reduces the importance of the dividing lines, and thus of the inherent uncertainties of those lines. The average snow water equivalents (SWE) for the early January measurements at about 100 snow courses and snow pillows were compiled; snow depths for the first week in January at about 85 weather stations were converted into SWE. For each region (western North Cascades, Blue Mountains, etc.), the snow amounts were sorted by station elevation to derive a rough indicator of the relationship between snow accumulation and elevation. (Sub regional differences in snow accumulation patterns were also recognized.) After trying various combinations of ratios for areas where the snow hydrology is relatively well known, we adopted the following designations: 5. Highlands: >4 5 times ideal SWE; high elevation, with little likelihood of significant water input to the ground during storms (precipitation likely to be snow, and liquid water probably refreezes in a deep snow pack); effects of harvest on snow accumulation are minor; 4. Snow dominated zone: from "1.25 1.5 ideal SWE, up to "4; melt occurs during R/S (especially during early season storms), but effects can be mitigated by the lag time of percolation through the snowpack; 3. Peak rain on snow zone: "0.5 0.75 up to "1.25 ideal SWE; middle elevations: shallow snow packs are common in winter, so likelihood and effects of R/S in heavy rainstorms are greatest; typically more snow accumulation in clearings than in forest; 2. Rain dominated zone: "0.1 0.5 ideal SWE; areas at lower elevations, where rain occasionally falls on small amounts of snow; 1. Lowlands: <0.1 ideal SWE; coastal, low elevation, and rain shadow areas; lower rainfall intensities, and significant snow depths are rare. Precipitation zones were mapped on mylar overlays on 1:250,000 scale topographic maps. Because snow depth is affected by many factors, the correlation between snow and elevation is crude, and it was not possible to simply pick out contour markers for the boundaries. Ranges of elevations were chosen for each region, but allowance was made for the effects of sub regional climates, aspect, vegetative indicators of snow depth, etc. Thus, a particular boundary would be mapped somewhat lower on the north side of a ridge or in a cool valley (e.g. below a glacier), reflecting greater snow accumulations in such places. The same boundary would be mapped higher on the south side of the ridge, where inter-storm sunshine could reduce snow accumulation. Conditions at the weather stations and snow courses were used to check the mapping; but in areas where measurements are scarce, interpolation had to be performed. The boundaries of the precipitation zones were entered in the DNR's GIS. Because of the small scale of the original mapping and the imprecision of the digitizing process, some errors were introduced. It should not be expected that GIS images can be projected to large scales to define knife edge zone boundaries (which don't exist, anyway), but they are good enough to locate areas tens of acres in size. Some apparent anomalies in the map require explanation. Much of western Washington is mapped in the lowland or highland zones. This does not mean that R/S does not occur in those areas; it does, but on average with less frequency and hydrologic significance than in the middle three zones. Most of central and eastern Washington is mapped in the rain dominated zone, despite meager precipitation there; this means only that the amount of snow likely to be on the ground is small, and storm water inputs are composed dominantly of the rain itself, without much contribution from snow melt. Much of northeastern Washington is mapped in the peak Rain Snow zone, despite the fact that such events are less common there than in western Washington. This is due to the fact that there is less increase in snow depth with elevation (i.e. the snow wedge is less steep), so a wider elevation band has appropriate snow amounts; plus, much of that region lies within that elevation band where the 'ideal' amount of snow is liable to be on the ground when a model Rain Snow event occurs. This does not reflect the lower frequency of such storms in that area.

Radio occultation observations gridded to daily 2.5°x2.5° fields by a Gaussian weighted average over ±2 days and 15°x5° longitude-latitude, based on radio occultation profile data version OPSv5.6.2. A detailed description of this data is given in Brunner and Steiner (2017): https://doi.org/10.5194/amt-10-4727-2017

Radio occultation observations gridded to daily 2.5°x2.5° fields by a Gaussian weighted average over ±2 days and 15°x5° longitude-latitude, based on radio occultation profile data version OPSv5.6.2. A detailed description of this data is given in Brunner and Steiner (2017): https://doi.org/10.5194/amt-10-4727-2017