This dataset is about books. It has 1 row and is filtered where the book is The weather business : observation, analysis, forecasting and modification. It features 7 columns including author, publication date, language, and book publisher.

Data from the content analysis of the observation grids, in the article "Empowerment through Participatory Game Creation: A Case Study with Adults with Intellectual Disability".

The national database of deep sea coral observations. Northeast version 1.0. * This database was developed by the NOAA NOS NCCOS CCMA Biogeography office as part of a New York Offshore Spatial Planning project.

The differences between the observations and the forecast background used for the analysis (the innovations or O-F for short) and those between the observations and the final analysis (O-A) are by-products of any assimilation system and provide information about the quality of the analysis and the impact of the observations. Innovations have been traditionally used to diagnose observation, background and analysis errors at observation locations (Hollingsworth and Lonnberg 1989; Dee and da Silva 1999). At the most simplistic level, innovation variances can be used as an upper bound on background errors, which are, in turn, an upper bound on the analysis errors. With more processing (and the assumption of optimality), the O-F and O-A statistics can be used to estimate observation, background and analysis errors (Desroziers et al. 2005). They can also be used to estimate the systematic and random errors in the analysis fields. Unfortunately, such data are usually not readily available with reanalysis products. With MERRA, however, a gridded version of the observations and innovations used in the assimilation process is being made available. The dataset allows the user to conveniently perform investigations related to the observing system and to calculate error estimates. Da Silva (2011) provides an overview and analysis of these datasets for MERRA.

    The innovations may be thought of as the correction to the background required by a given instrument, while the analysis increment (A-F) is the consolidated correction once all instruments, observation errors, and background errors have been taken into consideration. The extent to which the O-F statistics for the various instruments are similar to the A-F statistics reflects the degree of homogeneity of the observing system as a whole. Using the joint probability density function (PDF) of innovations and analysis increments, da Silva (2011) introduces the concepts of the effective gain (by analogy with the Kalman gain) and the contextual bias. In brief, the effective gain for an observation is a measure of how much the assimilation system has drawn to that type of observation, while the contextual bias is a measure of the degree of agreement between a given observation type and all other observations assimilated.

    With MERRAs gridded observation and innovation data sets, a wealth of information is available for examination of the quality of the analyses and how the different observations impact the analyses and interact with each other. Such examinations can be conducted regionally or globally and should provide useful information for the next generation of reanalyses.

This part of DS 781 presents video observations from cruise Z107SC for the Santa Barbara Channel region and beyond in southern California. The vector data file is included in "z107sc_video_observations.zip," which is accessible from http://pubs.usgs.gov/ds/781/video_observations/data_catalog_video_observations.html. Some of the video observations from cruise Z107SC are published in Scientific Investigations Map 3225, "California State Waters Map Series--Hueneme Canyon and Vicinity, California" (see sheet 6). In addition, some of the video observations will be published in three future California State Waters Map Series SIMs of the region (namely, the Mugu Canyon and Vicinity, Offshore of Coal Oil Point, and Offshore of Gaviota map areas). Between 2006 and 2007, the seafloor in the Mugu Canyon and Vicinity, Hueneme Canyon and Vicinity, Offshore of Coal Oil Point, and Offshore of Gaviota map areas in southern California was mapped by California State University, Monterey Bay, Seafloor Ma ...

This data set contains snow pit observations from the SnowEx 2020 Grand Mesa Intensive Observation Period. Data were collected from 154 snow pits on Grand Mesa, Colorado between 27 January and 12 February 2020. The main parameters for this data set are snow temperature, snow depth, snow density, snow stratigraphy, snow grain size, liquid water content, and snow water equivalent. In addition to data files, this data set also contains site photos from each snow pit.

Hourly observations taken by U.S. Air Force personnel at bases in the United States and around the world. Foreign observations concentrated in the Middle East and Japan. Stations assigned WBAN numbers. Original forms sent from the Air Force to NCDC by agreement and stored in the NCDC archives.

This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all temperature models. The model training period was from 2010-10-01 to 2014-09-30, and the test period was from 2014-10-01 to 2016-09-30.

This data set contains the DYNAMO areal averages of a number of surface and upper air parameters over the DYNAMO region at three hourly intervals. Areal averages are included for two regions, the DYNAMO Northern Sounding Array and the Southern Sounding Array. For each region there are two sets of averages included, one using just observations (DYNAMO radiosondes, satellite wind, TRMM precipitation, and COSMIC profiles) and a second analyses that also included the use of ECMWF operational analyses in data sparse regions. The data are in NetCDF format.

The hourly, daily, and monthly Pacific sea level heights in this dataset were analyzed at the TOGA Sea Level Data Center.

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.

This part of USGS Data Series 935 (Cochrane, 2014) presents observations from underwater video collected in the Offshore of Seattle, California, map area, a part of the Southern Salish Sea Habitat Map Series. To validate the interpretations of multibeam sonar data and turn it into geologically and biologically useful information, the U.S. Geological Survey (USGS) towed a camera sled over specific locations throughout the Seattle map area to collect video and photographic data that would “ground truth” the seafloor. The ground-truth survey conducted in the Offshore of Seattle map area occurred in 2011 on the R/V Karluk (USGS field activity K0111PS) and on the Washington State Department of Fish and Game R/V Molluscan (USGS field activity M0111PS). The underwater camera sled was towed 1 to 2 m above the seafloor at speeds of between 1 and 2 nautical miles/hour. The surveys for this map area include approximately 6 hours (9.1 trackline km) of video.

Water Observations from Space (WOfS) is a service that draws on satellite imagery to provide historical surface water observations of the whole African continent. WOfS allows users to understand the location and movement of inland and coastal water present in the African landscape. It shows where water is usually present; where it is seldom observed; and where inundation of the surface has been observed by satellite. WOfS annual summary shows the frequency of a pixel being classified as wet over an annual period (calendar year). This is calculated by looking at:Total number of clear observations for each pixel: the number of observations that were clear (no cloud, cloud shadow or terrain shadow) for the selected time period. The classification algorithm then assigns these as either wet, or dry.Total number of wet observation for each pixel: the number of observations that were clear and wet for the selected time period.Key PropertiesGeographic Coverage: Continental Africa - approximately 37° North to 35° SouthTemporal Coverage: 1984 - 2022Spatial Resolution: 30 x 30 meterUpdate frequency: Annual from 1984 - 2022Number of Bands: 3 BandsParent Dataset: Landsat Collection 2 Level-2 Surface Reflectance; WOfS Feature LayerSource Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)

Available BandsBand IDDescriptionValue rangeData typeNo data valuecount_wetHow many times a pixel was wet0 - 32767int16-999count_clearHow many times a pixel was clear0 - 32767int16-999frequencyFrequency of water detection at a location0 - 1float32NaN

Interpreting WOfSThe WOfS service should be interpreted with caveats in the following situations:Mixed pixels: Discretion should be used where a single pixel covers both water and land. These areas tend to occur on the edges of lakes, and in wetlands where there is a mix of water and vegetation.Turbid or dark water: The WOfS algorithm is developed to identify a diverse range of waterbodies. However, the classifier may miss dark water surfaces or water with high concentration of sediments. In some cases, the impact can be mitigated by using a temporal summary of WOfS, such as the Annual Summary or All-Time Summary. A waterbody may be missed in a single observation, but over the course of the year it is mapped as water in other dates and therefore mapped as a waterbody in the summary products.Other environmental factors: Sediment, floating vegetation and similar obstructions change the colour of water and can obfuscate water detection by WOfS.Inaccurate input data: Inaccurate input surface reflectance may lead to false classification in WOfS. To maximize coverage, all pixels within a valid surface reflectance range (0-1) from Landsat Collection 2 are used to generate the WOFLs. When creating WOfS summaries, only WOFLs processed from Landsat Tier 1 data with good geometric accuracy are used.Note that WOfS is not intended for studying ocean. Validation has been centred around inland and near-coastal waterbodies.

More details on this dataset can be found here.

Electric scooter trips taken under the City of Chicago ongoing program. Similar datasets from the 2019 and 2020 pilot programs are at:

https://data.cityofchicago.org/d/2kfw-zvte (2019) https://data.cityofchicago.org/d/3rse-fbp6 (2020)

Summary counts of trips between Census Tracts are provided in a separate dataset to allow for providing more data while still protecting privacy. See "E-Scooter Trips - Census Tract Summary" dataset (https://data.cityofchicago.org/d/cini-k95q).

Acknowlegement

Foto von Ernest Ojeh auf Unsplash

This data set is a daily gridded terrestrial snow water equivalent (SWE) dataset based on five component SWE products:GlobSnow combined SWE product (passive microwave/ground-based weather station, version 2)ERA-Interim/Land reanalysis SWE productMERRA reanalysis SWE product Crocus SWE data set: output from the Crocus snowpack model, driven by ERA-Interim meteorology (Brun et al. 2013)GLDAS SWE product (version 2) (Rodell et al. 2004; Rodell and Beaudoing 2013)

Daily Summary of Sri Lanka's Weather

Five Years of Observations

By Humanitarian Data Exchange [source]

About this dataset

This dataset contains five years of daily summaries of precipitation indicators in Sri Lanka. Data is compiled by the National Centers for Environmental Information (NCEI) in partnership with the United States government's National Oceanic and Atmospheric Administration (NOAA). These four indicators measure data collected from several stations across the country: Total Precipitation (TPCP), Maximum Snow Depth (MXSD), Total Snow Fall (TSNW), and Extreme Maximum Daily Precipitation (EMXP). Despite this dataset being comprehensive, it is important to recognize that due to late-arriving data, the number of recent records may be underestimated. Whether you are a researcher or climatologist, this dataset provides valuable insight into trends in Sri Lanka's weather patterns over the last five years

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How to use the dataset

This dataset contains the daily summaries on base stations across Sri Lanka for the past 5 years. It includes four indicators including: TPCP (Total Precipitation), MXSD (Max Snow Depth), TSNW (Total Snow Fall) and EMXP (Extreme Maximum Daily Precipitation). In this guide, we will show you how to use this dataset for your own purposes.

Research Ideas

• Analyzing the trend of maximum snow depth over the years in Sri Lanka using monthly, quarterly and yearly aggregations.
• Estimating extreme maximum daily precipitation in different regions of Sri Lanka to understand the changing patterns over time.
• Visualizing average total snowfall fields across various base stations and comparing these outcomes with climate simulations to identify potential climate change impacts on extreme weather events in Sri Lanka

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: precipitation-lka-csv-1.csv | Column name | Description | |:--------------|:--------------------------------------------------------------------| | date | Date when data was collected. (Date) | | datatype | Type of data that has been collected. (String) | | station | Location where data was recorded. (String) | | value | Measurement value for each indicator for each day. (Float) | | fl_miss | Tells if any observations are missing from that day. (Boolean) | | fl_cmiss | Tells whether all observations are complete. (Boolean) | | country | Country from where the observed values have been recorded. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Humanitarian Data Exchange.

Geological Attitude Observation Points, Guam

This tabular dataset represents monthly flow at 160 stream gages used for surface-water flow and flow-difference observations for the Rio Grande Transboundary Integrated Hydrologic Model (RGTIHM). Flow in this tabular dataset is specified monthly, when available, at each observation point as a rate (volume per time) in units of cubic feet per day. See the Site_Name attribute in the RGTIHM_Surface_Water_Obs feature class for the location of the stream gages.

Hatikka.fi observation database. Data quality: Content is not systematically verified. Users are mostly expert amateurs.

The Hadley Centre at the U.K. Met Office has created a global sub-daily dataset of several station-observed climatological variables which is derived from and is a subset of the NCDC's ... Integrated Surface Database. Stations were selected for inclusion into the dataset based on length of the data reporting period and the frequency with which observations were reported. The data were then passed through a suite of automated quality-control tests to remove bad data. See the HadISD web page for more details and access to previous versions of the dataset.