Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/45060
Title: Understanding and improving snow processes in Noah-MP over the Northeast United States via the New York State Mesonet
Authors: Letcher, Theodore W.
Minder, Justin R.
Naple, Patrick
Keywords: Hydrology
Snow--runoff
Streamflow--Mathematical models
Northeastern States
Meteorology
Meteorological stations
Publisher: Engineer Research and Development Center (U.S.)
Series/Report no.: Technical Report (Engineer Research and Development Center (U.S.)) ; no. ERDC/CRREL TR-22-9
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.
Description: Technical Report
Gov't Doc #: ERDC/CRREL TR-22-9
Rights: Approved for Public Release; Distribution is Unlimited
URI: https://hdl.handle.net/11681/45060
http://dx.doi.org/10.21079/11681/45060
Appears in Collections:Technical Report

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