Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-316
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
06 Feb 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
TAMSAT-ALERT v1: A new framework for agricultural decision support
Dagmawi Asfaw1, Emily Black1, Matt Brown2, Kathryn Jane Nicklin3, Frederick Otu-Larbi4, Ewan Pinnington1, Andrew Challinor3, Ross Maidment1, and Tristan Quaife1 1Department of Meteorology, University of Reading, Reading, UK
2Department of Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK
3School of Earth and Environment, University of Leeds, Leeds, UK
4Ghana Meteorological Agency, Accra, Ghana
Abstract. Early warning of weather related hazards enables farmers, policy makers and aid agencies to mitigate their exposure to risk. We present a new operational framework, Tropical Applications of Meteorology using SATellite data and ground based measurements-AgricuLtural EaRly warning sysTem (TAMSAT-ALERT), which provides early warning of meteorological risk to agriculture. TAMSAT-ALERT combines information on land surface properties, seasonal forecasts and historical weather to quantitatively assess the likelihood of adverse weather–related outcomes, such as low yield. This article describes the modular TAMSAT-ALERT framework and demonstrates its application to risk assessment for low maize yield in Northern Ghana. The modular design of TAMSAT-ALERT enables it to accommodate any impact/land surface model driven with meteorological data. The implementation described here uses the well-established General Large Area Model for annual crops (GLAM) to provide probabilistic assessments of the meteorological hazard to maize yield in northern Ghana throughout the growing season. The results show that climatic risk to yield is poorly constrained in the beginning of the season, but as the season progresses, the uncertainty rapidly reduces. The TAMSAT-ALERT methodology implicitly weights forecast and observational inputs according to their relevance to the metric being assessed. TAMSAT-ALERT can thus be used as a test-bed for the value of probabilistic seasonal forecast information. Here, we show that in northern Ghana, the tercile seasonal forecasts of cumulative rainfall and mean temperature, which are routinely issued to farmers, are of limited value for decision making.

Citation: Asfaw, D., Black, E., Brown, M., Nicklin, K. J., Otu-Larbi, F., Pinnington, E., Challinor, A., Maidment, R., and Quaife, T.: TAMSAT-ALERT v1: A new framework for agricultural decision support, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-316, in review, 2018.
Dagmawi Asfaw et al.
Dagmawi Asfaw et al.

Model code and software

TAMSAT-ALERT version 1.0 D. Asfaw, E. Black, M. Brown, K. J. Nicklin, F. Out-Larbi, E. Pinnington, A. Challinor, R. Maidment, and T. Quaife https://doi.org/10.5281/zenodo.1164603
Dagmawi Asfaw et al.

Viewed

Total article views: 221 (including HTML, PDF, and XML)

HTML PDF XML Total Supplement BibTeX EndNote
177 36 8 221 3 1 8

Views and downloads (calculated since 06 Feb 2018)

Cumulative views and downloads (calculated since 06 Feb 2018)

Viewed (geographical distribution)

Total article views: 214 (including HTML, PDF, and XML)

Thereof 210 with geography defined and 4 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 24 Feb 2018
Publications Copernicus
Download
Short summary
TAMSAT-ALERT is a framework for combining observational and forecast information into continually updated assessments of the likelihood of some user-defined adverse events like low cumulative rainfall or lower than average crop yield. It is easy to use and flexible to accommodate any impact model that use meteorological data. The results showed it can be used to monitor meteorological impact on yield within growing season and to test the value of routinely issued tercile seasonal forecasts.
TAMSAT-ALERT is a framework for combining observational and forecast information into...
Share