Temperature
The dataset offers a concise summary of monthly surface normal temperatures from 1969 to 2020 for stations across India, including those in Meghalaya. It also maps these stations to their respective districts and states, along with the LGD State and District Codes, providing valuable insights into long-term temperature patterns in the region.
Data Dictionary
| Column | Type | Label | Description |
|---|---|---|---|
| id | int4 | index | |
| date | date | Date | |
| station_name | text | Name of the Station | |
| station_code | text | Station Code | |
| parameter | text | Evaluation Parameter | |
| average_temperature | numeric | Average Value | |
| standard_deviation | numeric | Standard Deviation Value | |
| highest_temperature | numeric | Highest Value | |
| lowest_temperature | numeric | Lowest Value | |
| state_name | text | Name of the State | |
| district_name | text | Name of the District | |
| state_code | text | LGD State Code | |
| district_code | text | LGD District Code |
Additional Information
| Field | Value |
|---|---|
| Data last updated | June 5, 2024 |
| Metadata last updated | June 5, 2024 |
| Created | unknown |
| Format | CSV |
| License | Open Data Commons Attribution License |
| Created | 4 months ago |
| Frequency | Monthly |
| Media type | text/csv |
| Size | 445,268 |
| Additional info | nan |
| Data extraction page | https://cdsp.imdpune.gov.in/home_riturang_sn.php#snormals, later |
| Data insights | 1. Trend Analysis: The expansive dataset covering the period from 1969 to 2020 enables analysts to discern long-term temperature trends in Meghalaya, aiding in the prediction of year-on-year variations and potential anomalies in temperature patterns. 2. Geospatial Mapping: By associating temperature stations with their respective districts and states, the data empowers researchers to gain a comprehensive understanding of regional temperature variations and predict month-on-month temperature trends, facilitating informed decision-making in various sectors such as agriculture, public health, and urban planning. |
| Data last updated | 01-12-2020 |
| Data retreival date | 08-08-2023 |
| Datastore active | True |
| District no | 174 |
| Gp no | 0 |
| Granularity | Point |
| Has views | True |
| Id | 96e8fed6-a8e8-4958-ab14-72748ad76f49 |
| Idp ready | False |
| Methodology | The minimum and maximum temperatures for various stations are recorded and consolidated on the Climate Data Service Portal by the Indian Meteorlogical Department. |
| No indicators | 4 |
| Package id | 1c615683-c98f-4b7f-a008-f4aed1b68af2 |
| Position | 1 |
| Sku | moes-imd_cdsp_temperature-pl-mn-meg |
| State | active |
| States uts no | 33 |
| Tags | ['Minimum Temperature', 'Maximum Temperature', 'Temperature Trends\n Weather'] |
| Tehsil no | 0 |
| Url type | upload |
| Village no | 0 |
| Years covered | 1969 - 2020 |
| Methodology | The minimum and maximum temperatures for various stations are recorded and consolidated on the Climate Data Service Portal by the Indian Meteorlogical Department. |
| Similar Resources | |
| Granularity Level | Point |
| Data Extraction Page | https://cdsp.imdpune.gov.in/home_riturang_sn.php#snormals, later |
| Data Retreival Date | 08-08-2023 |
| Data Last Updated | 01-12-2020 |
| Sku | moes-imd_cdsp_temperature-pl-mn-meg |
| Dataset Frequency | |
| Years Covered | 1969 - 2020 |
| No of States/UT(s) | 33 |
| No of Districts | 174 |
| No of Tehsils/blocks | 0 |
| No of Gram Panchayats | 0 |
| Additional Information | nan |
| Number of Indicators | 4 |
| Insights from the dataset | 1. Trend Analysis: The expansive dataset covering the period from 1969 to 2020 enables analysts to discern long-term temperature trends in Meghalaya, aiding in the prediction of year-on-year variations and potential anomalies in temperature patterns. 2. Geospatial Mapping: By associating temperature stations with their respective districts and states, the data empowers researchers to gain a comprehensive understanding of regional temperature variations and predict month-on-month temperature trends, facilitating informed decision-making in various sectors such as agriculture, public health, and urban planning. |
| IDP Ready | No |