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Federally approved OCSE Form 34A and instructions for child support professionals.

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The increasing availability of multi-scale remotely sensed data and global weather datasets is allowing the estimation of evapotranspiration (ET) at multiple scales. We present a simple but robust method that uses remotely sensed thermal data and model-assimilated weather fields to produce ET for the contiguous United States (CONUS) at monthly and seasonal time scales. The method is based on the Simplified Surface Energy Balance (SSEB) model which is now parameterized for operational applications, renamed as SSEBop. The innovative aspect of the SSEBop is that it uses pre-defined, boundary conditions that are unique to each pixel for the "hot" and "cold" reference conditions. The SSEBop model was used for computing ET for 12 years (2000-2011) using the MODIS and Global Data Assimilation System (GDAS) data streams. SSEPop ET results compared reasonably well with monthly eddy covariance ET data explaining 64% of the observed variability across diverse ecosystems in the CONUS during 2005. Twelve annual ET anomalies (2000-2011) depicted the spatial extent and severity of the commonly known drought years in the CONUS. More research is required to improve the representation of the pre-defined boundary conditions in complex terrain at small spatial scales. SSEBop model was found to be a promising approach to conduct water use studies in the CONUS, with a similar opportunity in other parts of the world. The approach can also be applied with other thermal sensors such as Landsat.

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Source data for forest stand age were obtained from the USDA Forest Inventory and Analysis (FIA) DataMart and were projected for future scenarios based on selected IPCC emission scenarios. The spatial extent is the conterminous United States and the temporal extent is from 2006 through 2050. The data of this variable are spatially gridded data in GeoTiff format and have been re-projected to Albers Equal Area in the NAD83 datum at a resolution of 2000 meters.

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This information is derived from inspections of restaurants and other food establishments in Chicago from January 1, 2010 to the present. Inspections are performed by staff from the Chicago Department of Public Health’s Food Protection Program using a standardized procedure. The results of the inspection are inputted into a database, then reviewed and approved by a State of Illinois Licensed Environmental Health Practitioner (LEHP). For descriptions of the data elements included in this set, go to http://bit.ly/tS9IE8 Disclaimer: Attempts have been made to minimize any and all duplicate inspection reports. However, the dataset may still contain such duplicates and the appropriate precautions should be exercised when viewing or analyzing these data. The result of the inspections (pass, pass with conditions or fail) as well as the violations noted are based on the findings identified and reported by the inspector at the time of the inspection, and may not reflect the findings noted at other times. For more information about Food Inspections, go to http://bit.ly/tD91Sb.

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The U.S. Climate Reference Network is designed specifically to monitor national climate change with best scientific practice and adherence to the accepted principles of climate observations. USCRN hourly soil moisture and soil temperature data are available in the Soilsip01 file set for all stations in the network which are equipped with soil sensors, and have completed an evaluation process currently lasting 240 days from installation.

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Critical Care Areas (CCAs) are nursing care areas that provide intensive observation, diagnosis, and therapeutic procedures for patients who are critically ill. These areas exclude step-down, intermediate, or telemetry care areas. This table shows the hospital-specific central line-associated bloodstream infection (CLABSI) data and central line insertion practices (CLIP) adherence percent by patient care locations in CCAs excluding neonatal critical care. The CLABSI data for each hospital include number of CLABSIs, central line-days and patient days, CLABSI rates and their 95% confidence intervals. We also performed statistical analysis to determine whether the rates are statistically higher, lower, or no different than California average rates by patient care locations. California average rates for 2013 can be found at http://www.cdph.ca.gov/programs/hai/Documents/2013-CLABSI-T1.pdf. CLABSI rates are affected by clinical and infection control practices related to central line insertion and maintenance practices, patient-based risk factors, and surveillance methods. While stratifying CLABSI rates by patient care location makes rates more comparable, it cannot control for all individual patient factors that can affect CLABSI rates. A low CLABSI rate may reflect greater diligence with infection prevention or may reflect less effective surveillance methods that detect fewer infections, including failure to appropriately apply standardized surveillance definitions and protocols. Similarly, a high rate may reflect failure to consistently implement all recommended infection prevention practices or more aggressive infection surveillance including more consistent application of standardized surveillance definitions and protocols. Finally, readers should consider comparisons between two time periods cautiously, as more time is needed to determine if changes will be sustained, and therefore, more meaningful. To link the CDPH facility IDs with those from other Departments, like OSHPD, please reference the "Licensed Facility Cross-Walk" Open Data table at https://chhs.data.ca.gov/Facilities-and-Services/Licensed-Facility-Cross.... A list of healthcare facilities can be found at: https://cdph.data.ca.gov/Facilities-and-Services/Healthcare-Facility-Loc....

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Neonatal Critical Care (NCC) Areas specialize in Level II/III and/or Level III critical care provided to newborns and infants. This table shows the hospital-specific central line-associated bloodstream infection (CLABSI) data and central line insertion practices (CLIP) adherence percent by patient care locations in Neonatal Critical Care Areas. The CLABSI data for each hospital neonatal care location include number of CLABSIs, central line-days and patient days, CLABSI rates per 1000 line days and their 95% confidence intervals. We also performed statistical analysis to determine whether the rates are statistically higher, lower, or no different than California average rates. California average rates for 2013 can be found at http://www.cdph.ca.gov/programs/hai/Documents/2013-CLABSI-T1.pdf. CLABSI rates are affected by clinical and infection control practices related to central line insertion and maintenance practices, patient-based risk factors, and surveillance methods. While stratifying CLABSI rates by patient care location makes rates more comparable, it cannot control for all individual patient factors that can affect CLABSI rates. A low CLABSI rate may reflect greater diligence with infection prevention or may reflect less effective surveillance methods that detect fewer infections, including failure to appropriately apply standardized surveillance definitions and protocols. Similarly, a high rate may reflect failure to consistently implement all recommended infection prevention practices or more aggressive infection surveillance including more consistent application of standardized surveillance definitions and protocols. Finally, readers should consider comparisons between two time periods cautiously, as more time is needed to determine if changes will be sustained, and therefore, more meaningful. To link the CDPH facility IDs with those from other Departments, like OSHPD, please reference the "Licensed Facility Cross-Walk" Open Data table at https://chhs.data.ca.gov/Facilities-and-Services/Licensed-Facility-Cross.... A list of healthcare facilities can be found at: https://cdph.data.ca.gov/Facilities-and-Services/Healthcare-Facility-Loc...

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This NOAA Climate Data Record (CDR) of cloud products was produced by the University of Wisconsin using the AVHRR Pathfinder Atmospheres - Extended (PATMOS-X) Version 5.3 processing system. The CDR spans from 1979 to the present with daily, global coverage generated from between two and ten NOAA and MetOp satellite passes per day. The source AVHRR data points, with a sensor resolution of 1.09 km near nadir, have been fitted to a 0.1 x 0.1 degree equal-angle grid in ascending and descending files. The four fundamental cloud variables in the CDR include: 1) Cloud Optical Thickness; 2) Cloud Particle Size; 3) Cloud Temperature; and 4) Cloud Emissivity. These cloud products are derived using the ABI (Advanced Baseline Imager) Cloud Height Algorithm (ACHA) and the Daytime Cloud Optical Properties (DCOMP) algorithm. Other variables in this dataset were derived from the four primary variables, or generated through error/quality control assessment in the algorithms. Not counting the coordinate variables, there are 48 data and ancillary data variables in the netCDF data file to facilitate proper data usage. The file format was converted from HDF to netCDF-4 with metadata following the Climate and Forecast (CF) Conventions and Attribute Convention for Dataset Discovery (ACDD). The dataset is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program.

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The American Association for Motor Vehicle Administrators (AAMVA), acting as an information conduit between state Motor Vehicle Administrations (MVAs) and SSA, submits a verification request electronically to SSOLV when a person applies for a driver's license. In response, SSA provides a verification response to AAMVA. AAMVA then forwards the results to the requesting MVAs. Verification requests include applicant's name, date of birth, and SSN. AAMVA reimburses SSA for the costs incurred for these services. In kind, AAMVA passes these costs on to the individual state MVAs. DEB is responsible for technical and analytical support of the SSOLV application, as well as providing management information. The Office of Data Exchanges is responsible for agreement liaison support and billing services.

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The 2011 Toxics Release Inventory (TRI) dataset contains the most current TRI data available and reflects toxic chemical releases and pollution prevention activities that occurred at TRI facilities during the 2011 calendar year. You can use this dataset to find out what TRI-covered toxic chemicals are being produced and used at industrial facilities in your local area and how they are being managed. Please note that this dataset will change as the TRI Program continues to process TRI submissions. The TRI Program provides this dataset annually in late July to give the public an opportunity to see the most recent TRI information prior to the publication of the TRI National Analysis report in December. To view National Analysis reports from previous years, please consult TRI's archive of National Analysis data at http://www.epa.gov/tri/.

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The Floodplain Mapping/Redelineation study deliverables depict and quantify the flood risks for the study area. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The Floodplain Mapping/Redelineation flood risk boundaries are derived from the engineering information Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).

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The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The projection is State Plane Coordinate System 1983. The horizontal accuracy meets Guidelines and specifications for DFIRM production.

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This geodatabase contains water density raster images for the Gulf of Maine that were interpolated from water density (sigma t or kilograms/ meters cubed) point data acquired from the Canadian Fisheries and Oceans/ Bedford Institute of Oceanography's Hydrographic database.The naming convention of the raster includes the year (or years) included in the the interpolation, the season, and the depth (in meters). So for example, the name: density_1997_2004_Fall_0m would indicate that this water density raster was interpolated using data from 1997-2004 for the fall at 0 meters (or the surface). The seasons were defined using the same months as the remote sensing data--namely, Fall = September, October, November; Winter = December, January, February; Spring = March, April, May; and Summer = June, July, August. The accuracies of these interpolated surfaces were calculated by validating with 25% of the water density points that were previously set aside. These control points are located in the personal geodatabase named Water_Stratification_Points.

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The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).

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The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12000.

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Database used to store client data both Identity and customer relationship management.

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The map layer of Color-Sliced Elevation of the Conterminous United States is a 100-meter resolution elevation image of the United States, in an Albers Equal-Area Conic projection. Each color tint represents a range of elevation values. There are 32 elevation classes and colors required to map all 50 of the United States, Puerto Rico, and the U.S. Virgin Islands, plus an additional class and color to represent non-U.S. land. The conterminous United States map layer contains elevation classes 1 to 31, as well as class 33 (non-U.S. land). The color-sliced elevation data were derived from National Elevation Dataset (NED) data and show the terrain of the conterminous United States at a resolution of 100 meters. The NED is a raster product assembled by the U.S. Geological Survey, designed to provide national elevation data in a seamless form with a consistent datum, elevation unit, and projection. Data corrections made in the NED assembly process minimize artifacts, permit edge matching, and fill sliver areas of missing data. More information on NED can be found at .

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The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). In addition to the preceding, required text, the Abstract should also describe the projection and coordinate system as well as a general statement about horizontal accuracy.

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Biome-BGC is a computer program that estimates fluxes and storage of energy, water, carbon, and nitrogen for the vegetation and soil components of terrestrial ecosystems. The primary model purpose is to study global and regional interactions between climate, disturbance, and biogeochemical cycles. Biome-BGC represents physical and biological processes that control fluxes of energy and mass. These processes include new leaf growth and old leaf litterfall, sunlight interception by leaves and penetration to the ground, precipitation routing to leaves and soil, snow accumulation and melting, drainage and runoff of soil water, evaporation of water from soil and wet leaves, transpiration of soil water through leaf stomata, photosynthetic fixation of carbon from CO2 in the air, uptake of nitrogen from the soil, distribution of carbon and nitrogen to growing plant parts, decomposition of fresh plant litter and old soil organic matter, plant mortality, and fire. The model uses a daily time-step, meaning that each flux is estimated for a one-day period. Between days, the program updates its memory of the mass stored in different components of the vegetation, litter, and soil. Weather is the most important control on vegetation processes. Flux estimates in Biome-BGC depend strongly on daily weather conditions. Model behavior over time depends on climate--the history of these weather conditions. A companion file with more information about Biome-BGC and its components is available. Biome-BGC, Version 4.1.1, was developed and is maintained by the Numerical Terradynamic Simulation Group, School of Forestry, the University of Montana, Missoula, Montana, U.S.A. Additional information can be found on their web site at: http://www.ntsg.umt.edu/.

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An agreement for assistance with forest fire detection, suppression and presuppression services on wildlife refuge lands by the Florida Division of Forestry. The Division of Forestry agrees to provide this assistance by means of time and personnel. In turn, the Fish and Wildlife Service agrees to comply with all applicable forest fire and smoke management laws as well as reimbursement to the Division of Forestry.

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Storage of information used to manage the pending redetermination workload in the field.

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The Affordable Care Act is bringing an unprecedented level of scrutiny and transparency to health insurance rate increases. The Act ensures that, in any State, any proposed rate increase by individual or small group market insurers at or above 10 percent will be scrutinized by independent experts to make sure it is justified. This analysis will help moderate premium hikes and lower costs for individuals, families, and businesses that buy insurance in these markets. Additionally, insurance companies must provide easy to understand information to their customers about their reasons for unreasonable rate increases, as well as publicly justify and post on their website any unreasonable rate increases. These steps allow consumers to know why they are paying higher rates.

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The dataset includes: 1) physical survey attributes of 415 non-federally regulated dams from the Androscoggin River in Southern Maine to the Dennys River in Eastern Maine; and 2) survey of the owners of these dams. The physical survey attributes include dam height, length, condition, identification of building structures such as a power house or fishway, the type and condition of any fishway. The dam owner survey attributes include dam owner information, dam purpose, dam owner concerns and interest in respect to their dam, and dam owner interest in respect to dam removal, fish passage, or selling of the dam.

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USSOILS is an Arc 7.0 coverage containing hydrology-relevant information for 10,498 map units covering the entire conterminous United States. The coverage was compiled from individual State coverages contained in the October 1994 State Soil Geographic (STATSGO) Data Base produced on CD-ROM. The geo-dataset USSOILS.PAT relates (on the basis of a map unit identifier) the 10,498 map units to 78,518 polygons. The scale of the geo-dataset is 1:250,000. The INFO attribute table USSOILS.MUID_ATTS contains selected variables from the STATSGO data set for 10,501 map units (an extra 3 map units are contained in the attribute table that are not in the geo-dataset - see the 'Procedures' section below), including: the map unit identifier, a 2-character state abbreviation, available water capacity of the soil, percent clay in the soil, the actual k-factor used in the water erosion component of the universal soil loss equation, the organic material in soil, soil permeability, cumulative thickness of all soil layers, hydrologic characteristics of the soil, quality of drainage, surface slope, liquid limit of the soil, share of a map unit having hydric soils, and the annual frequency of flooding. To facilitate mapping the attribute data, the narrative section below contains instructions for transferring the information contained in the attribute table USSOILS.MUID_ATTS to the polygon attribute table USSOILS.PAT. STATSGO United States Soil Water Capacity Clay Organic material Permeability Infiltration Drainage Hydric Flood frequency Slope

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The Rapid Refresh (RAP) numerical weather model took the place of the Rapid Update Cycle (RUC) on May 1, 2012. Run by the National Centers for Environmental Prediction (NCEP), RAP runs with two versions. The first generates weather data on a 13-km (8-mile) resolution horizontal grid and the second, the High-Resolution Rapid Refresh (HRRR), generates data down to a 3-km (2-mile) resolution grid for smaller regions of interest. RAP forecasts are generated every hour with forecast lengths going out 18 hours with a 1 hour temporal resolution. Multiple data sources go into the generation of RAP forecasts: commercial aircraft weather data, balloon data, radar data, surface observations, and satellite data. This dataset contains a 20 km horizontal resolution Lambert Conformal grid covering the Continental United States (CONUS) domain.