Sensitivity analysis of snow depth and surface air temperature to various WRF/Noah-MP model configurations in Central Europe

This study addresses snow depth overestimation and temperature underestimation in the WRF/Noah-MP regional climate model by producing eight sensitivity simulations at a 10 km grid spacing for the 2001–2006 period, driven by the ERA5 reanalysis. The experiments differ either in the parameterization of snow-related processes within the Noah-MP land surface model, the physical parameterization schemes of the atmospheric model (planetary boundary layer, surface layer, microphysics), or the WRF model version (v4.2 or v4.3, with the latter incorporating snow-related code enhancements). The best-performing configuration is further tested by conducting a hindcast simulation spanning an extended climatological period (1985–2010). The study area includes parts of Western, Central, and Eastern Europe, focusing on the Carpathian Basin. Snow depth is evaluated using in situ observations from low-elevation stations and ERA5 reanalysis data. Temperature and precipitation biases are assessed using the CARPATCLIM dataset, consisting of gridded surface meteorological observations. With the appropriate choice of physical parameterizations in the WRF/Noah-MP model, the median snow depth bias is reduced from 3.4 cm/day to 0.7 cm/day across all stations during the 2001–2006 period (November to March), with a corresponding decrease in RMSE from 8.4 to 3.9 cm/day and an increase in the correlation coefficient from 0.68 to 0.82. Concurrently, the underestimation of average daily minimum temperatures decreases from 2.5 °C to 1.6 °C over a plain subregion within the Carpathian Basin. Further efforts are required to eliminate the cold bias above snow cover in the WRF/Noah-MP model. The results suggest that exploring sensitivities to physical processes within the land surface scheme is equally important as testing different physical parameterizations in the atmospheric model itself. • Certain physics schemes greatly reduce snow depth overestimation in the WRF/Noah-MP regional climate model. • Temperature underestimation in the cold season decreases but is not completely alleviated. • WRF/Noah-MP parameterization options that enhance snow-related model performance in low-elevation regions are identified.

Authors

Institutions

Publication Details

Journal
Atmospheric Research
Published
2024-08-30
DOI
https://doi.org/10.1016/j.atmosres.2024.107659
Citations
2
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
0.75

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sensitivity analysis of snow depth and surface air temperature to various WRF/Noah-MP model configurations in Central Europe

Ákos János Varga, Hajnalka Breuer
2 citations
Atmospheric Research
Meteorological Phenomena and Simulations
0.75
article

Sensitivity analysis of snow depth and surface air temperature to various WRF/Noah-MP model configurations in Central Europe

Ákos János Varga, Hajnalka Breuer
article en
2 citations

Abstract

This study addresses snow depth overestimation and temperature underestimation in the WRF/Noah-MP regional climate model by producing eight sensitivity simulations at a 10 km grid spacing for the 2001–2006 period, driven by the ERA5 reanalysis. The experiments differ either in the parameterization of snow-related processes within the Noah-MP land surface model, the physical parameterization schemes of the atmospheric model (planetary boundary layer, surface layer, microphysics), or the WRF model version (v4.2 or v4.3, with the latter incorporating snow-related code enhancements). The best-performing configuration is further tested by conducting a hindcast simulation spanning an extended climatological period (1985–2010). The study area includes parts of Western, Central, and Eastern Europe, focusing on the Carpathian Basin. Snow depth is evaluated using in situ observations from low-elevation stations and ERA5 reanalysis data. Temperature and precipitation biases are assessed using the CARPATCLIM dataset, consisting of gridded surface meteorological observations. With the appropriate choice of physical parameterizations in the WRF/Noah-MP model, the median snow depth bias is reduced from 3.4 cm/day to 0.7 cm/day across all stations during the 2001–2006 period (November to March), with a corresponding decrease in RMSE from 8.4 to 3.9 cm/day and an increase in the correlation coefficient from 0.68 to 0.82. Concurrently, the underestimation of average daily minimum temperatures decreases from 2.5 °C to 1.6 °C over a plain subregion within the Carpathian Basin. Further efforts are required to eliminate the cold bias above snow cover in the WRF/Noah-MP model. The results suggest that exploring sensitivities to physical processes within the land surface scheme is equally important as testing different physical parameterizations in the atmospheric model itself. • Certain physics schemes greatly reduce snow depth overestimation in the WRF/Noah-MP regional climate model. • Temperature underestimation in the cold season decreases but is not completely alleviated. • WRF/Noah-MP parameterization options that enhance snow-related model performance in low-elevation regions are identified.

Atmospheric ResearchVol. 311
Eötvös Loránd University (HU)
European Commission, Nemzeti Kutatási és Technológiai Hivatal
Climate action
Openalex Percentile: Top 31%
Meteorological Phenomena and Simulations
0.75
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.