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  • Author or Editor: Michael Barlage x
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Anil Kumar
,
Fei Chen
,
Michael Barlage
,
Michael B. Ek
, and
Dev Niyogi

Abstract

The impact of 8-day-averaged data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor—namely, the 1-km leaf area index, absorbed photosynthetic radiation, and land-use data—is investigated for use in the Weather Research and Forecasting (WRF) model for regional weather prediction. These high-resolution, near-real-time MODIS data are hypothesized to enhance the representation of land–atmosphere interactions and to potentially improve the WRF model forecast skill for temperature, surface moisture, surface fluxes, and soil temperature. To test this hypothesis, the impact of using MODIS-based land surface data on surface energy and water budgets was assessed within the “Noah” land surface model with two different canopy-resistance schemes. An ensemble of six model experiments was conducted using the WRF model for a typical summertime episode over the U.S. southern Great Plains that occurred during the International H2O Project (IHOP_2002) field experiment. The six model experiments were statistically analyzed and showed some degree of improvement in surface latent heat flux and sensible heat flux, as well as surface temperature and moisture, after land use, leaf area index, and green vegetation fraction data were replaced by remotely sensed data. There was also an improvement in the WRF-simulated temperature and boundary layer moisture with MODIS data in comparison with the default U.S. Geological Survey land-use and leaf area index inputs. Overall, analysis suggests that recalibration and improvements to both the input data and the land model help to improve estimation of surface and soil parameters and boundary layer moisture and led to improvement in simulating convection in WRF runs. Incorporating updated land conditions provided the most notable improvements, and the mesoscale model performance could be further enhanced when improved land surface schemes become available.

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Margaret A. LeMone
,
Bingcheng Wan
,
Michael Barlage
, and
Fei Chen

Abstract

During the 2010 Bio–Hydro–Atmosphere Interactions of Energy, Aerosols, Carbon, H2O, and Nitrogen (BEACHON) experiment in Colorado, nighttime temperatures over a site within the 2002 “Hayman” fire scar were considerably warmer than over the “Manitou” site that was located outside the fire scar. Temperature differences reached up to 7 K at the surface and extended to an average of 500 m AGL. Afternoon temperatures through the planetary boundary layer (PBL) were similar at the two locations. PBL growth during the day was more rapid at Manitou until 1300 local time, after which the two daytime PBLs had similar temperatures and depths. Observations were taken in fair weather, with weak winds. Runs of the Advanced Research version of the Weather Research and Forecasting model (ARW-WRF) coupled to the Noah-MP land surface model suggest that the fire-induced loss of surface and soil organic matter accounted for the 3–4-K warming at Hayman relative to its prefire state, more than compensating for the cooling due to the fire-induced change in vegetation from forest to grassland. Modeled surface fluxes and soil temperature and moisture changes were consistent with observational studies comparing several-year-old fire scars with adjacent unaffected forests. The remaining difference between the two sites is likely from cold-air pooling at Manitou. It was necessary to increase vertical resolution and replace terrain-following diffusion with horizontal diffusion in ARW-WRF to better capture nighttime near-surface temperature and winds. Daytime PBL growth and afternoon temperature profiles were reasonably reproduced by the basic run with postfire conditions. Winds above the surface were only fairly represented, and refinements made to capture cold pooling degraded daytime temperature profiles slightly.

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Andrew J. Monaghan
,
Martyn P. Clark
,
Michael P. Barlage
,
Andrew J. Newman
,
Lulin Xue
,
Jeffrey R. Arnold
, and
Roy M. Rasmussen

Abstract

Weather and climate variability strongly influence the people, infrastructure, and economy of Alaska. However, the sparse observational network in Alaska limits our understanding of meteorological variability, particularly of precipitation processes that influence the hydrologic cycle. Here, a new 14-yr (September 2002–August 2016) dataset for Alaska with 4-km grid spacing is described and evaluated. The dataset, generated with the Weather Research and Forecasting (WRF) Model, is useful for gaining insight into meteorological and hydrologic processes, and provides a baseline against which to measure future environmental change. The WRF fields are evaluated at annual, seasonal, and daily time scales against observation-based gridded and station records of 2-m air temperature, precipitation, and snowfall. Pattern correlations between annual mean WRF and observation-based gridded fields are r = 0.89 for 2-m temperature, r = 0.75 for precipitation, r = 0.82 for snow-day fraction, r = 0.55 for first snow day of the season, and r = 0.71 for last snow day of the season. A shortcoming of the WRF dataset is that spring snowmelt occurs too early over a majority of the state, due partly to positive 2-m temperature biases in winter and spring. Strengths include an improved representation of the interannual variability of 2-m temperature and precipitation and accurately simulated (relative to regional station observations) winter and summer precipitation maxima. This initial evaluation suggests that the 4-km WRF climate dataset robustly simulates meteorological processes and recent climatic variability in Alaska. The dataset may be particularly useful for applications that require high-temporal-frequency weather fields, such as driving hydrologic or glacier models. Future studies will provide further insight on its ability to represent other aspects of Alaska’s climate.

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