Drought monitoring and prediction are critical components of the NOAA-led National Integrated Drought Information System (NIDIS). The U.S. Drought Monitor (USDM) has played an important role in gathering, synthesizing and disseminating drought information to a range of users and stakeholders. The USDM is reasonably realistic with multiple sources of information; it is simple and easy to understand with five intensity categories; and it is up-to-date with a fixed weekly release. These features have made the USDM very successful and popular among drought users and stakeholders. It is the USDM drought map that policymakers and media use in discussion of drought and in allocating drought relief. However, on the prediction side, there is currently no suitable drought outlook that matches up with the USDM although monthly and seasonal drought outlooks are issued each month by CPC.
Our team has identified five important gaps in NOAA’s drought prediction capability, and this proposed project will develop an automated, weekly probabilistic and categorical drought outlook to fill these gaps. The proposed research builds off existing operational products, such as the USDM, LIS-based NLDAS, CFSv2 and NMME seasonal forecast, and research outcomes such as the weekly ensemble drought prediction system developed at MSU and the statistical modeling framework for producing probabilistic forecast of USDM drought categories from monthly drought indicators. All the model and data products are readily available; the key methods for utilizing these model and data products to produce a probabilistic and categorical drought outlook have been developed and tested in recent research. This project is a natural step towards integration of the them to produce the desired drought outlook that is multiple model- based, objective, probabilistic, categorical, and can be run on a weekly schedule to match exactly the USDM schedule. To achieve this, the project comprises of six well designed tasks from multimodel offline simulation and seasonal forecast with the LIS-based NLDAS framework to the development and evaluation of the ordinal regression model for predicting the USDM drought categories. The combination of dynamical modeling and statistical modeling is the major strength of this project. These tasks are well connected to ensure the success of the project. This proposed drought outlook system can potentially be seamlessly integrated with the current USDM to provide simple, easy-to-understand and up-to-date drought forecast information to users of USDM.
This proposal responds directly to Competition 1 (Advancing drought understanding, monitoring and prediction) of the MAPP program for FY2017. More specifically, the objectives of this project are in line with several priority areas highlighted in the MAPP information sheet. The project will help to advancing drought prediction system and outlooks operated, used, and produced by NOAA that contribute to the Drought Early Warning System effort. It will also contribute to the development of new national-scale monitoring and forecast products that can help integrate the results of research advances into improved information for managers and communities. From a practical perspective, the automated system with improved skill will provide additional assistance to CPC forecasters to improve their drought outlook.
Climate Risk Area: Water Resources