Exploring best practice procedures for optimal use of climate forecast for regional hydrological applications
Project PIs: Lifeng Luo and Pang-Ning Tan
Project Period: Aug 1, 2012 to July 31, 2016
Funding Agency: NOAA MAPP Program
Decisions regarding water resource management, agricultural practice, and energy allocation often require information about future climate conditions weeks to months in advance. Skillful and reliable seasonal climate prediction can significantly facilitate and benefit the decision making process. However, it is a challenge to make skillful predictions about the climate system at these time scales because the processes that contribute to the seasonal predictability are not fully understood thus not adequately represented in climate models. Research is needed to assess the current prediction skills from the state-of-the-art climate forecast systems, and to develop “best practice” procedures for optimal use of climate forecasts in applications that are directly relevant to decision making. This proposed project addresses those very issues.In this project, we will carry out research activities to 1) evaluate the state-of-the-art climate forecast systems, quantify their seasonal prediction quality, and assess factors that contribute to the prediction skill; 2) assess the optimal choice of ensemble members and scales, and to develop best practice procedures for combining and post-processing multiple forecasts to achieve better forecast quality; and 3) demonstrate the usefulness of seasonal climate prediction and evaluate the new post-processing procedures with seasonal drought prediction. The innovation of the proposed research is mainly reflected in the second activity. We will develop two innovative methods (multiscale Bayesian merging and structured output regression) in parallel to combine forecast information across multiple characteristic spatial and temporal scales. These methods will address outstanding issues like spatial and temporal dependence (or correlation structure) that is practically ignored when combining forecast members in an ensemble or multimodel ensemble system currently. It is our intention to make comparative evaluation of these two methods that grew out of two research communities. These statistical methods have the potential to significant advance the seasonal climate forecast skills. We will demonstrate the improvement in prediction skills and usefulness of climate prediction in regional hydrological applications by performing seasonal drought forecast for selected drought events in the US using these new methods.
We have assessed the feasibility of the project and have a clear understanding of possible difficulties. The proposed methods are new to seasonal climate prediction, but have been used in research fields of data assimilation, data mining and machine learning. The research team has experience in dealing with these methods, so we expect this project will progress smoothly.
The project is relevant to MAPP program because it directly responds to the first priority area solicited by MAPP for FY2012, i.e., advance intro-seasonal to decadal climate prediction. In particular, this proposed research focuses on objective assessment of climate prediction skill from state-of-the-art climate forecast systems, and development of “best-practice” procedures for post-processing the predictions for hydrological applications. The outcome of this research will contribute to NOAA’s operation in seasonal climate forecasting.
Developing an automated weekly probabilistic and categorical drought outlook based on U.S. Drought Monitor and ensemble prediction
Project PI: Lifeng Luo and Youlong Xia
Project Period: Aug 1, 2017 to July 31, 2021
Funding Agency: NOAA MAPP Program
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.