Ethiopia climate and weather data

These datasets provide Ethiopia’s Temperature Condition Index (TCI) values for the past five years, focusing specifically on the growing season (June–September). The Temperature Condition Index is a remote sensing-based indicator used to evaluate vegetation stress resulting from temperature extremes, primarily heat stress. TCI is calculated using Land Surface Temperature (LST) data sourced from the MODIS platform. Higher TCI values reflect cooler and more favorable conditions for plant growth, while lower values are associated with increased heat stress and potential vegetation damage. For long-term seasonal analysis, MODIS LST data from 2000 to the present were utilized, with an emphasis on the June–September period. These time series datasets highlight TCI values from the recent five growing seasons, enabling direct comparison with current year conditions.

Rainfall anomaly refers to the difference between the observed rainfall and the long-term average for a specific location and time period. It indicates whether rainfall conditions are above or below normal. Positive anomalies represent higher-than-average rainfall, while negative anomalies indicate below-average rainfall. These anomalies are essential for monitoring climate variability, including droughts and floods, and for assessing their impacts on agriculture, water resources, and other sectors. For this analysis, CHIRPS daily rainfall data for Ethiopia were utilized, covering the month of March 2024, 2025 and 2025. Rainfall anomalies for month of March were calculated by subtracting the long-term average (2000–2020) from the observed rainfall values for the same period.

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Aridity index (AI) is an effective estimator of drought status, and spatiotemporally continuous long-term AI dataset is critical for drought assessment and applications.This dataset offers valuable support for research on dryland ecosystems, agriculture, and climate change, offering critical insights to address global environmental and sustainability challenges. Resolution ;0.05 × 0.05   in the resolution  ( referenced year 2022)

This dataset provides Palmer Drought Severity Index (PDSI) values for Ethiopia from 2020 to 2024, offering insights into drought conditions across the country. The PDSI is a widely used drought index that measures deviations in soil moisture conditions from the long-term average, helping to identify both drought and wet conditions. Unlike traditional PDSI calculations that rely on the Thornthwaite method for estimating potential evapotranspiration (ET), this dataset utilizes a more physically based Penman-Monteith reference ET, which accounts for: * Solar radiation * Temperature * Humidity * Windspeed ## Interpretation of PDSI Values: * PDSI between -0.5 and 0.5 → Near-normal soil moisture conditions * PDSI 4 → Very wet conditions

Precipitation minus actual evapotranspiration (P-ET) calculated over the Awash River basin area in Ethiopia. The P-ET is the difference between WaPOR precipitation (based on CHIRPS) and the actual evapotranspiration and interception. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The evapotranspiration from precipitation (ETRAIN) is the portion of the actual evapotranspiration and Interception (ETIa) that is satified from precipitation. It is computed from precipitation, actual evapotranspiration and soil moisture and calculated by spliting ETIa to rainfall and incremental ET using pixel based Soil Moisture Water Balance Model. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The incremental evapotranspiration is part of the actual evapotranspiration and Interception (ETIa) that is not satified from precipitation but from supply or runoff from other cells or from groundwater. It is calculated by spliting ETIa to rainfall and incremental ET using pixel based soil moisture water balance model. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: mm Conversion factor: the pixel value in the downloaded data must be multiplied by 0.1 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 3. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [Solomon Seyoum](mailto:s.seyoum@un-ihe.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage