15 Aug 2022

Understanding and Improving Snow Processes in Noah-MP over the Northeast United States via the New York State Mesonet

Abstract: Snow is a critical component of the global hydrologic cycle and is a key input to river and stream flow forecasts. In 2016, the National Oceanic and Atmospheric Administration launched the National Water Model (NWM) to provide a high-fidelity numerical forecast of streamflow integrated with the broader atmospheric prediction modeling framework. The NWM is coupled to the atmospheric model using the Noah-MP land surface modeling framework. While snow in Noah-MP has been consistently evaluated in the western United States, less attention has been paid to understanding and optimizing its performance in the Northeast US (NEUS). The newly installed New York State Mesonet (NYSM), a network of high-quality surface meteorological stations distributed across New York State, provides a unique opportunity to evaluate Noah-MP performance in the NEUS. In this report, we document the methodology used to perform single-column simulations using meteorological inputs from the NYSM and compare the point evaluations against baseline NWM performance. We further discuss how enhanced surface energy balance measurements at a selection of NYSM sites can be used to evaluate specific components of Noah-MP and present initial results.

2 Sep 2021

Spatial and Temporal Variance of Soil and Meteorological Properties Affecting Sensor Performance—Phase 2

ABSTRACT: An approach to increasing sensor performance and detection reliability for buried objects is to better understand which physical processes are dominant under certain environmental conditions. The present effort (Phase 2) builds on our previously published prior effort (Phase 1), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried-object detection. The study utilized a 3.05 × 3.05 m test plot in Hanover, New Hampshire. Unlike Phase 1, the current effort involved removing the soil from the test plot area, homogenizing the material, then reapplying it into eight discrete layers along with buried sensors and objects representing targets of interest. Each layer was compacted to a uniform density consistent with the background undisturbed density. Homogenization greatly reduced the microscale soil temperature variability, simplifying data analysis. The Phase 2 study spanned May–November 2018. Simultaneous measurements of soil temperature and moisture (as well as air temperature and humidity, cloud cover, and incoming solar radiation) were obtained daily and recorded at 15-minute intervals and coupled with thermal infrared and electro-optical image collection at 5-minute intervals.