12 Aug 2025

Evaluating and Improving Snow in the National Water Model, Using Observations from the New York State Mesonet

Abstract: This study leverages observations from NYSM to evaluate and improve representation of snow within the NWM and its associated land surface model. Distributed NWM simulations were ran and analyzed, forced by gridded meteorological analyses, and Noah-MP point simulations, forced by NYSM observations. Distributed NWM runs, with a baseline configuration, show substantial SWE biases caused by biases in meteorological forcing used, imperfect representation of snow processes, and mismatches between land cover in the model and NYSM station locations. Noah-MP point simulations, using baseline configuration, reveal a systematic positive bias in SWE accumulation. Noah-MP point simulations, with improved precipitation phase partitioning, reveal a systematic negative bias in SWE ablation rates. Sensitivity experiments highlight uncertain parameters within Noah-MP that strongly affect ablation rates and show particularly large sensitivity to snow albedo decay time-scale parameter, which modulates snow albedo decay rates. Distributed NWM experiments, with precipitation phase partitioning and TAU0 adjusted based on Noah-MP point simulation results, show qualitatively similar sensitivities. However, the distributed experiments do not show clear improvements when compared to SWE and streamflow observations. This is likely due to some combination of sources of bias in the baseline-distributed run and biases in other parameterized processes unrelated to snow in the NWM.

6 Nov 2024

Time-Series Forecasting Methods: A Review

Abstract: Time-series forecasting techniques are of fundamental importance for predicting future values by analyzing past trends. The techniques assume that future trends will be similar to historical trends. Forecasting involves using models fit on historical data to predict future values. Time-series models have wide-ranging applications, from weather forecasting to sales forecasting, and are among the most effective methods of forecasting, especially when making decisions that involve uncertainty about the future. To evaluate forecast accuracy and to compare among models fitted to a time series, three performance measures were used in this study: mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE).

15 Sep 2023

Real-Time Forecasting Model Development Work Plan

Abstract: The objective of the Lowermost Mississippi River Management Program is to move the nation toward more holistic management of the lower reaches of the Mississippi River through the development and use of a science-based decision-making framework. There has been substantial investment in the last decade to develop multidimensional numerical models to evaluate the Lowermost Mississippi River (LMMR) hydrodynamics, sediment transport, and salinity dynamics. The focus of this work plan is to leverage the existing scientific knowledge and models to improve holistic management of the LMMR. Specifically, this work plan proposes the development of a real-time forecasting (RTF) system for water, sediment, and selected nutrients in the LMMR. The RTF system will help inform and guide the decision-making process for operating flood-control and sediment-diversion structures. This work plan describes the primary components of the RTF system and their interactions. The work plan includes descriptions of the existing tools and numerical models that could be leveraged to develop this system together with a brief inventory of existing real-time data that could be used to validate the RTF system. A description of the tasks that would be required to develop and set up the RTF system is included together with an associated timeline.