GET

Observation Instance

Get a list of Observation objects.

### Available end points:

- `/observations/` - Will list all Results in the database
- `/observations.json` - Will return all Results in json format. This can
also be achieved by using the accept header. `application/json`
- `/observations/<object_id>/` - Returns Results object with that id

### Available Methods:

- `GET`
- `HEAD`

### Available filters:

- `title`
- `uuid`
- `keywords`
- `status`
- `result_field`
- `discoveryKeywords`
- `updateFrequency`
- `nonGeographicFlag`
- `publicationState`
- `permissions`

### How to use filters:

These filters can be used like django query filters using __ for related model relationships.

- `/observations/?uuid=d594d53df2612bbd89c2e0e770b5c1a0`
- `/observations/?status=completed`
- `/observations/?results_field__dataPath__startswith=/neodc/esacci`
- `/observations/?discoveryKeywords__name=ESACCI`
- `/observations/?nonGeographicFlag=True`

Filters can be stacked to build an 'AND' relationship. E.g.

- `/observations/?publicationState__in=published,citable&nonGeographicFlag=True`
- `/observations/?publicationState__in=published,citable&discoveryKeyword__name=NDGO0003`

GET /api/v2/observations/34626/?format=api
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "ob_id": 34626,
    "uuid": "d4eccbbd51db4ab7a8ad05a6f2f6a98a",
    "title": "Chapter 10 of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure 10.10 (v20220622)",
    "abstract": "Data for Figure 10.10 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).\r\n\r\n\r\nFigure 10.10 shows observed and projected changes in austral summer (December to February) mean precipitation in Global Precipitation Climatology Centre (GPCC), Climatic Research Unit Time-Series (CRU TS) and 100 members of the Max-Planck-Institut für Meteorologie Earth-System Model (MPI-ESM).\r\n\r\n\r\n---------------------------------------------------\r\n How to cite this dataset\r\n ---------------------------------------------------\r\n When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:\r\nDoblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012.\r\n\r\n---------------------------------------------------\r\n Figure subpanels\r\n ---------------------------------------------------\r\n The figure has two panels, with data provided for both panels. Panel (a) consists of two maps, panel (b) shows multiple timeseries and boxplots.\r\n\r\n\r\n---------------------------------------------------\r\n List of data provided\r\n ---------------------------------------------------\r\n The dataset contains data of relative precipitation anomalies over 1950-2100 with respect to 1995-2014 average for global, S.E.South-America, Sao Paulo and Buenos Aires for:\r\n \r\n - Observational data (GPCC and CRU TS)\r\n - Model data (100 runs of MPI-ESM)\r\n\r\n---------------------------------------------------\r\n Data provided in relation to figure\r\n ---------------------------------------------------\r\n Panel (a):\r\n - Data files: \r\nModelled precipitation rate OLS linear trends between 2015-2070 with respect to 1995-2014 average over S.E. South America region, from left to right (MPI-ESM member with min (driest) and max (wettest) trends):\r\nFig_10_10_panel-a_mapplot_trend_SES_DJF_MPI-GE_min_single-MultiModelMean_trend-min-median-max.nc, \r\nFig_10_10_panel-a_mapplot_trend_SES_DJF_MPI-GE_max_single-MultiModelMean_trend-min-median-max.nc\r\n \r\n Panel (b):\r\n - Data files: \r\nPrecipitation rate anomalies 1950-2100 with respect to 1995-2014 average for the global mean, S.E.South-America mean, Sao Paulo mean and Buenos Aires mean of GPCC (dark blue), CRU (dark brown), members of the MPI-ESM (grey), the MPI-ESM member with the driest (brown) and wettest (green) trend:\r\nFig_10_10_panel-b_timeseries_global.csv, \r\nFig_10_10_panel-b_timeseries_SES.csv, \r\nFig_10_10_panel-b_timeseries_SaoPaulo.csv, \r\nFig_10_10_panel-b_timeseries_BuenosAires.csv\r\n\r\n - Data files: \r\nUnderlying data points of the boxplot showing MPI-ESM modelled precipitation rate OLS linear trends over all members between 2015-2070 with respect to 1995-2014 average for the global mean, S.E.South-America mean, Sao Paulo mean and Buenos Aires mean:\r\nFig_10_10_panel-b_boxplot_BuenosAires.csv, \r\nFig_10_10_panel-b_boxplot_global.csv, \r\nFig_10_10_panel-b_boxplot_SaoPaulo.csv, \r\nFig_10_10_panel-b_boxplot_SES.csv; \r\n\r\nOLS - ordinary least squares regression.\r\n\r\n---------------------------------------------------\r\n Notes on reproducing the figure from the provided data\r\n ---------------------------------------------------\r\n The code for ESMValTool is provided.\r\n\r\n---------------------------------------------------\r\n Sources of additional information\r\n ---------------------------------------------------\r\n The following weblinks are provided in the Related Documents section of this catalogue record:\r\n - Link to the figure on the IPCC AR6 website\r\n - Link to the report component containing the figure (Chapter 10)\r\n - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11\r\n - Link to the code for the figure, archived on Zenodo.",
    "keywords": "IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group I, Physical Science Basis, Chapter 10, teleconnections, drivers, feedbacks, Linking global to regional, Regional scale, internal variability, forced change, model improvements, Figure 10.10",
    "publicationState": "citable",
    "dataPublishedTime": "2022-06-29T09:06:51",
    "doiPublishedTime": "2023-05-17T11:30:42.414570",
    "updateFrequency": "notPlanned",
    "status": "completed",
    "result_field": {
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        "dataPath": "/badc/ar6_wg1/data/ch_10/ch10_fig10/v20220622",
        "oldDataPath": [],
        "storageLocation": "internal",
        "storageStatus": "online",
        "volume": 652574,
        "numberOfFiles": 13,
        "fileFormat": "txt, netCDF, csv"
    },
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    "dataLineage": "Data produced by Intergovernmental Panel on Climate Change (IPCC) authors and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by the Technical Support Unit (TSU) for IPCC Working Group I (WGI).\r\n Data curated on behalf of the IPCC Data Distribution Centre (IPCC-DDC).",
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