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- Author or Editor: Martin C. Todd x
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Abstract
The development of a combined infrared and passive microwave satellite rainfall estimation technique is outlined. Infrared data from geostationary satellites are combined with polar-orbiting passive microwave estimates to provide 30-min rainfall estimates. Collocated infrared and passive microwave values are used to generate a database, which is accessed by a cumulative histogram matching approach to generate an infrared temperature–rain-rate relationship. The technique produces initial estimates at 30-min and 12-km resolution ready to be aggregated to the user requirements. A 4-month case study over Africa has been chosen to compare the results from this technique with those of some existing rainfall techniques. The results indicate that the technique outlined here has statistical scores that are similar to other infrared/passive microwave combined algorithms. Comparison with the Geostationary Operational Environmental Satellite (GOES) precipitation index shows that while these algorithms result in lower correlation scores, areal statistics are significantly better than either the infrared or passive microwave techniques alone.
Abstract
The development of a combined infrared and passive microwave satellite rainfall estimation technique is outlined. Infrared data from geostationary satellites are combined with polar-orbiting passive microwave estimates to provide 30-min rainfall estimates. Collocated infrared and passive microwave values are used to generate a database, which is accessed by a cumulative histogram matching approach to generate an infrared temperature–rain-rate relationship. The technique produces initial estimates at 30-min and 12-km resolution ready to be aggregated to the user requirements. A 4-month case study over Africa has been chosen to compare the results from this technique with those of some existing rainfall techniques. The results indicate that the technique outlined here has statistical scores that are similar to other infrared/passive microwave combined algorithms. Comparison with the Geostationary Operational Environmental Satellite (GOES) precipitation index shows that while these algorithms result in lower correlation scores, areal statistics are significantly better than either the infrared or passive microwave techniques alone.
Abstract
Equatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with preemptive action; however, currently available early warnings are limited to a few days’ lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However, before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper-quintile rainfall within a season, at lead times of 2 weeks and beyond. We demonstrate the link between the Madden–Julian oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia River in western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper-quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed.
Abstract
Equatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with preemptive action; however, currently available early warnings are limited to a few days’ lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However, before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper-quintile rainfall within a season, at lead times of 2 weeks and beyond. We demonstrate the link between the Madden–Julian oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia River in western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper-quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed.
