ClimateSERV Datasets¶
Climate Hazards group IR Precipitation with Stations (CHIRPS)¶
Scientists at Famine and Early Warning System (FEWS NET) who are members of the SERVIR Applied Sciences Team used 30 years’ (1982- present) worth of multiple satellite data sources and ground observations to produce an unprecedented, global, spatially and temporally consistent and continuous 30-year record of satellite-derived rainfall data. This CHIRPS global dataset makes it possible to accurately assess and monitor large-scale rainfall patterns and analyze how they may be affected by climate change. The data are updated to the latest available rainfall estimates.
North American Multi-Model Ensemble (NMME) dataset¶
Forecasts of future precipitation are also critical to decision-makers. The NMME dataset, a compilation by National Oceanic and Atmospheric Administration (NOAA), reflects cutting edge work on seasonal forecasting. A SERVIR AST project has taken the NMME data and performed bias correction and spatial disaggregation using standard, well-accepted techniques to generate daily, 180-day temperature and precipitation forecasts for the entire globe. These seasonal forecasts, along with the CHIRPS historical rainfall data, provide an overall perspective to connect rainfall patterns from the past to future rainfall (up to 180 days out) as projected by NMME.
MODIS-derived Normalized Difference Vegetation Index (eMODIS NDVI)¶
SERVIR is also piloting the USGS pentadal eMODIS NDVI dataset at 250m spatial resolution over West Africa. NDVI, a measure of vegetation condition, provides a proxy for agricultural productivity by showing photosynthetic activity. By providing this 15 year dataset, SERVIR is enabling Ministries of Agriculture and the international donor community to explore how CHIRPS and NMME data link to vegetation growth and health. (The NDVI dataset is being expanded to other parts of Africa and beyond.) ClimateSERV enables decision-makers to link historical precipitation trends (CHIRPS) to past vegetation trends (NDVI) to gain insight into potential vegetation growth and health based on seasonal (up to 180 days) precipitation and temperature forecasts (NMME).
Evaporative Stress Index (ESI)¶
ESI reveals regions of drought where vegetation is stressed due to lack of water, enabling agriculture ministries to provide farmers with actionable advice about irrigation. The ESI can capture early signals of “flash drought,” a condition brought on by extended periods of hot, dry, and windy conditions leading to rapid soil moisture depletion. Reduced rates of water loss can be observed through the use of land surface temperature before it can be observed through decreases in vegetation health or “greenness.” The ESI describes soil moisture across the landscape without using observed rainfall data. This is critical in developing regions and other parts of the world lacking sufficient ground-based observations of rainfall. The ESI is based on satellite observations of land surface temperature, which are used to estimate water loss due to evapotranspiration (ET), the loss of water via evaporation from soil and plant surfaces and via transpiration through plant leaves. Generally, healthy green vegetation with access to an adequate supply of water warms at a much slower rate than does dry and/or stressed vegetation. Based on variations in land surface temperature, the ESI indicates how the current rate of ET compares to normal conditions. Negative ESI values show below normal ET rates, indicating vegetation that stressed due to inadequate soil moisture. (Plants’ first response when stressed from lack of water is to reduce their transpiration to conserve water within the plant.)
Global Ensemble Forecast System (GEFS)¶
The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. GEFS was developed by the National Centers for Environmental Prediction (NCEP), a group within NOAA. NCEP started the GEFS to address the nature of uncertainty in weather forecasts – GEFS quantifies the amount of uncertainty in a forecast by generating an ensemble of multiple forecasts, each minutely different, or perturbed, from the original observations. GEFS consist of 21 different models that each produce a 16-day forecast every 6 hours. Each model has the spatial resolution that ranges from 25 to 100km, and often each model has a systematic bias from reality, one that is different from other model biases, making inter-comparison among models difficult. University of With SERVIR’s guidance, FEWS NET/UCSB takes the GEFS data (the 21 GEFS model ensembles) and bias-adjusts each model using the historical CHIRPS data. Thus the merged CHIRPS-adjusted output dataset removes the inter-model biases, and results in a more consistent forecast dataset. University of California at Santa Barbara’s Climate Hazards Group, produces the new CHIRPS-GEFS rainfall dataset and the forecasts go back to 1985. The CHIRPS-GEFS’s forecast is updated every five days at a spatial resolution of 5 km across the globe. Figure 1 shows the forecast update cycle. The “first forecast” is the earliest time at which the 10-day rainfall can be forecasted. The “last forecast” shows the latest date on which that 10-day rainfall forecast can be produced. The data on ClimateSERV is the compilation of the latest available rainfall forecast for a given date per this production cycle.
Integrated Multi-satellitE Retrievals Precipication (IMERG)¶
Precipitation data from the Integrated Multi-satellitE Retrievals (IMERG) for Global Precipitation Mission (GPM).The IMERG algorithm intercalibrates, merges and interpolates “all” satellite passive microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, monthly precipitation gauge analyses, and potentially other precipitation estimators at fine time and space scales for the TRMM and GPM eras over the entire globe.