Impact of Land Surface Snow Processes on the Arctic Stable Boundary Layer

Xiaodong Hong aNaval Research Laboratory, Monterey, California

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Qingfang Jiang aNaval Research Laboratory, Monterey, California

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Abstract

The impact of land surface snow processes on the Arctic stable boundary layer (ASBL) is investigated using the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to reduce the cold bias caused by decoupling between the land surface and atmosphere. The Noah land surface model (LSM) with improved snow processes is examined using COAMPS forecast forcing in the one-dimensional mode for one month. The new snow physics shows that the snow properties, roughness length, and sensible heat flux are modified as expected to compensate for the old LSM deficiency. These new snow processes are incorporated into the COAMPS Noah LSM, and the 48-h forecasts using both old and new Noah LSMs are performed for January 2021 with an every-6-h data assimilation update cycle. Standard verifications of the 48-h forecasts have used all available observational datasets and the snow depth from the Land Information System (LIS) analyses. The statistics have shown reduced monthly mean cold biases ∼1°C by the new snow physics. The weaker strength of surface inversion and stronger turbulence kinetic energy (TKE) from the new snow physics provides a higher boundary layer due to significantly stronger eddy mixing. The simulations have also shown the insignificant impact of different lateral boundary conditions obtained from the global forecasts or analyses on the results of the new snow physics. This study highlights the importance of the revised snow physics in Noah LSM for reducing the decoupling problem, improving the forecasts, and studying ASBL physics over the Arctic region.

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiaodong Hong, xiaodong.hong@nrlmry.navy.mil

Abstract

The impact of land surface snow processes on the Arctic stable boundary layer (ASBL) is investigated using the Navy’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to reduce the cold bias caused by decoupling between the land surface and atmosphere. The Noah land surface model (LSM) with improved snow processes is examined using COAMPS forecast forcing in the one-dimensional mode for one month. The new snow physics shows that the snow properties, roughness length, and sensible heat flux are modified as expected to compensate for the old LSM deficiency. These new snow processes are incorporated into the COAMPS Noah LSM, and the 48-h forecasts using both old and new Noah LSMs are performed for January 2021 with an every-6-h data assimilation update cycle. Standard verifications of the 48-h forecasts have used all available observational datasets and the snow depth from the Land Information System (LIS) analyses. The statistics have shown reduced monthly mean cold biases ∼1°C by the new snow physics. The weaker strength of surface inversion and stronger turbulence kinetic energy (TKE) from the new snow physics provides a higher boundary layer due to significantly stronger eddy mixing. The simulations have also shown the insignificant impact of different lateral boundary conditions obtained from the global forecasts or analyses on the results of the new snow physics. This study highlights the importance of the revised snow physics in Noah LSM for reducing the decoupling problem, improving the forecasts, and studying ASBL physics over the Arctic region.

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiaodong Hong, xiaodong.hong@nrlmry.navy.mil

1. Introduction

Over the Arctic area, the atmosphere is stably stratified due to the low temperature at the surface. The atmospheric stable boundary layer (ASBL) appears at night and during the daytime year around (Thorsten 2007; Steeneneld 2014). It is difficult to have robust parameterizations to represent the development and maintenance of the ASBL due to multiple physical processes involved and complex interactions among them. One of these essential and significant processes is the coupling between the lower atmosphere and the land surface with vegetation and soil. A realistic physical representation of the interdependence of the atmosphere and the underlying soil and vegetation can better model the surface temperature and sensible heat flux in the stable boundary layer. It is essential to have proper feedbacks between the surface temperature, the surface sensible heat flux, and the heat flux from the underlying soil and vegetation toward the surface (Steeneveld et al. 2006).

Strong cooling is found in some numerical weather prediction (NWP) models for stable boundary conditions due to unrealistic decoupling of the atmosphere from the surface, further stabilizing the boundary layer and leading to a shallower boundary layer (Walsh et al. 2008). It is described as a positive feedback between the stratification and the sensible heat flux and results in a collapse of turbulence and a decoupling of the actual ASBL from the surface (Steeneveld et al. 2006). In this case, Mahrt (1999) suggested that similarity theory and the traditional concept of a boundary layer break down under very stable conditions. The strongest turbulence may be detached from the surface and generated by shear associated with a low-level jet, gravity waves, or meandering motion. Delage et al. (2002) indicate that strong decoupling occurs during a weak wind when turbulent heat flux cannot oppose the net longwave radiation. Turbulence dies out from the ground upward, permits dramatic falls in surface temperature, and results in a positive feedback. Decoupling between the first model level and the surface is due to a missing process or some other model deficiency and is initially attributed to the turbulent transfer formulation. Such decoupling arises from the physics processes rather than the finite-difference schemes and appears to occur in the real atmosphere (Derbyshire 1999).

No theory is available to predict this phenomenon since it needs to be better understood and categorically satisfy traditional definitions of turbulence (Van de Wiel et al. 2004; Mahrt 2014). Some ASBL schemes derived from micrometeorological research seem to allow a “decoupling” behavior. Such decoupling of the surface from atmospheric fluxes can permit dramatic and possibly unrealistic falls in surface temperature. An inadequate formulation of turbulent transfer can result in the occurrence of decoupling in the model (Delage et al. 2002). Using downward longwave rather than net longwave radiation to drive the land surface process can benefit the model and prevent it from decoupling. This modification allows the model to produce a natural negative feedback between the surface air temperature and the turbulent heat flux and reduce the cold bias. A formulation giving greater downward flux values in situations of strong stability is better at preventing the occurrence of complete decoupling. More frequently observed in nature is the negative feedback when the wind speed stays above 5 m s−1 with the intermittency of turbulence.

For snow-covered surfaces, as in the Arctic land area, correct thermal insulation of the snowpack is essential to improve the realism of the near-surface atmospheric temperature (Dutra et al. 2012; Steeneneld 2014). Snowmelt and sublimation are important processes for manipulating the ground surface temperature and heat flux. Barlage et al. (2010) have modified the Noah land surface model (LSM) to improve snowpack prediction in the Colorado Rocky Mountains. Their modification includes time-varying snow albedo and increasing fresh snow albedo, adjusting solar radiation for terrain slope and orientation, reducing the surface exchange coefficient for the stable boundary layer, and adjusting surface roughness length when snow is present. It has diminished excessive sublimation and early snowmelt and improved the magnitude and timing of seasonal maximum snow water equivalent (SWE).

To improve the stable surface layer in the NCEP Global Forecast System over the deep snowpack and snow-free conditions, Zheng et al. (2017) have implemented a stability parameter constraint for preventing the land–atmosphere system from fully decoupling with the modification of the roughness length formulation. It reduces the excessive near-surface cooling since the new modification prevents the collapse of turbulence in the stable surface layer over land and decoupling between the atmosphere and the surface. Consequently, it improves the forecast skill for light and medium precipitation amounts.

Wang et al. (2010) have found that the shading of the forest canopy is not considered for physical processes associated with deep snow under dense boreal forests in the Noah land surface model (LSM). It causes most solar radiation to reach the ground under the canopy and increases surface energy available for snow sublimation and melting. Furthermore, the downward sensible heat flux under very stable conditions is also overestimated, contributing to more snow sublimation and less snow depth and SWE. Snow physics under the canopy is added to Noah LSM to solve these issues to improve snow depth and SWE prediction. The new snow physics is verified with the observations from the high-altitude Niw Ridge forest site (40.03°N, 105.55°W) and a boreal forest site (53.9°N, 104.7°W). The new revisions improve simulations for all snow processes, including SWE, snow depth, and sensible and latent heat fluxes. As expected, the canopy shading effect on the underlying snow is most important for improving overall snow simulation. Erlandsen et al. (2017) have also included this snow physics in the Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) for studying the sensitivity of the terrestrial surface energy and water balance estimates for the south Norway snow area. It has shown decreased net downward surface radiation flux and sensible heat flux due to increasing ground snow by altering the snow/rain criterion.

The Naval Research Laboratory (NRL) Coupled Ocean/Atmosphere Prediction System (COAMPS; Hodur 1997; Hodur et al. 2002) has implemented the Noah LSM for the land surface processes. Operational forecasts have shown the cold bias over the Arctic land area with the older Noah LSM version (Noah3.2). The new revisions for the snow processes and physics (Wang et al. 2010) in the Noah LSM (Noah3.6) are added in COAMPS to improve Arctic forecasts. The initial conditions for COAMPS LSM are derived from the National Aeronautics and Space Administration (NASA) Land Information System (LIS; Kumar et al. 2006) analysis. The LIS is a high-resolution land data assimilation system integrating advanced LSM and high-resolution satellite and observational data (Kumar et al. 2008). It uses the Noah LSM for the analyses, and so does the COAMPS forecast. Therefore the two systems provide consistency in the LSM.

A description of the new revision for the snow processes in LSM Noah3.6 is briefly provided in section 2. The Noah version 3.6 in one-dimensional mode is examined in section 3 using atmospheric forcing from the COAMPS Arctic forecasts for October 2015. The application of LSM Noah3.6 for the COAMPS Arctic forecasts is discussed and verified in section 4. Finally, the concluding remarks of this study are presented in section 5.

2. Description and applications of the new snow physics

This section briefly describes the main changes related to the new snow physics, and more details can be found in Wang et al. (2010). The control version of LSM (Noah3.2) in COAMPS is without the new snow physics.

a. Adding vegetation shading effect on snow surface

The new snow physics aims to reduce the overestimation in potential evapotranspiration so that snow sublimation and snowmelt are not excessive. For this purpose, the new snow physics includes the fraction of vegetation with snow below (Fvb):
Fvb=GVF×Fg,sn(1Fbur),
Fg,sn=sneqvsneqvcr,g,
Fbur=HsnZbotZtopZbot,
where GVF is the green vegetation fraction; Fg,sn is the fraction of ground covered with snow and is between 0 and 1; sneqv is the SWE; sneqvcr,g = 0.02 m is the critical SWE for fully snow-covered ground; Fbur is the snow burial fraction and is between 0 and 1; Hsn is the snow depth; and Ztop and Zbot are the canopy top and bottom heights, respectively, prescribed as a function of the vegetation type.
Since the vegetation shading effect for the undercanopy snow is important for the solar radiation during the day, the net shortwave radiation (SWnet) is changed to be a new weighted average (SWnetn) between the vegetation shaded fraction Fvb and the nonshaded fraction 1 − Fvb:
SWnetn={(1αυ)(1αusn)×SW×γ}×Fvb+(1α)SW×(1Fvb),
where SW↓ is the downward solar radiation, α is the grid cell average albedo with vegetation albedo αυ ≈ 0.13 and undercanopy snow albedo αusn ≈ 0.5, and γ = exp(−LAI) with LAI being the leaf area index. The LAI is related to the GVF as
LAI=(1GVF)×LAImin(vegt)+GVF×LAImax(vegt),
where vegt denotes the vegetation type, and LAImin(vegt) and LAImax(vegt) are from a table of vegetation parameters for Noah3.6. The LAI is adjusted for vertical burying by snow in the Noah3.6:
LAI=LAI×(1Fbur).
The SWnetn is combined with the net longwave radiation LWnet for the new net radiative flux received by the surface Rnetn = SWnetn + LWnet, which is used to compute potential evapotranspiration (Ep) using the Penman equation (Penman 1948; Mahrt and Ek 1984):
Ep=Δ1+Δ(RnetnG)+ρLυra(1+Δ)(q*q),
Δ=0.622pLυcpde*(T)dT,
where G is the ground soil heat flux, ρ is the surface air density, Lυ is the latent heat of condensation, cp is the specific heat of air, ra is the aerodynamic resistance [ra = 1/(Chu)] with Ch being the turbulent exchange coefficient and u the wind speed, q* is the saturated atmospheric specific humidity, e* is the saturated vapor pressure, p is surface pressure, and T is near-surface air temperature.
The new Ep is used to determine the snow sublimation rate (Esn):
Esn=EpFsn,
Fsn=αssneqvsneqvmaxexp[αs(sneqv/sneqvmax)]+1sneqvmaxexp(αs),
where Fsn is the fractional snow coverage of the model grid cell (0 ≤ Fsn ≤ 1), sneqvmax is the vegetation type-dependent maximum SWE for complete snow fraction, and αs is a distribution shape parameter.
The new Rnetn is also used in computing the snowmelt rate (Ms) based on mass and energy balance in the snowpack,
Ms=ResLf=1Lf(RnetnGLHSHF1F2),
where Res > 0 is the energy available for the snowmelt, Lf is the latent heat of fusion, LH is the latent heat flux, SH is the sensible heat flux, F1 is the heat flux from newly accumulating precipitation, and F2 is the freezing rain latent heat flux.

b. Snow density

To avoid the abrupt change of the snow density when the surface temperature is near melting point (i.e., 0°C), the portion of liquid water (DW) stored in the snowpack during snowmelt (Ms) is limited by
DW=0.13Δt/Dhr,
DW=min[DW,0.13Ms/(sneqv+0.13Ms)],
where DW is the portion of liquid water stored in the snowpack during snowmelt, Dhr = 24 h and Δt is the time step in hours. For both conditions T1 ≥ 0°C and Tsoil(1) ≥ 0°C, the snow density (ρsn) is
ρsn=ρsn(1DW)+DW.
The change in snow density modifies snow depth (snowd):
snowd=sneqvρsn.
The limitation setting of snow water helps the maintenance of snow depth.

c. Undercanopy resistance and sensible heat flux

The new snow physics has increased the undercanopy aerodynamic resistance ru to prevent overestimation of downward sensible heat flux (SH) under stable conditions (i.e., Ta > T1). The new formula of undercanopy aerodynamic resistance is based on the study of Sakaguchi and Zeng (2009):
ru=rumax×GVF×Fg,snΔT×[1exp(LAIeff)],
ΔT=min(TaT15,1),
LAIeff=LAI(1Fbur),
where rumax is 100 s m−1. The terms Fg,sn and Fbur are defined in Eqs. (1b) and (1c), respectively. The surface exchange coefficient (CH) for heat and moisture is then modified according to the change of the undercanopy aerodynamic resistance:
Chn=CH/(1+ruCH).
This, combined with the vegetation shading effect, leads to a modified sensible heat flux (SH) as shown below:
SH=ChnCpPsRTυ(TaT1)+SHc,
where Cp is the heat capacity of dry air, R = 287.04 J K−1 kg−1 is the gas constant for dry air, Ps is surface pressure, Ta is air potential temperature, Tυ is virtual potential temperature, T1 is surface skin temperature, and SHc represents the vegetation shading effect:
SHc=RnetRnetn.
Most solar heating for forests with underlying snow is used to heat the atmospheric boundary layer through sensible heat. To maintain the energy balance, the different net radiation (SHc) between the nonshaded (Rnet) and shaded effect (Rnetn) is added to the sensible heat flux (SH). Since the SH is also constrained by the solar energy absorbed by the surface (vegetation, snow, and soil), the SHc is restrained to be less than half of the solar energy absorbed by the surface (vegetation, snow, and soil) in absolute value in Eq. (2a) (Wang et al. 2010).

d. Roughness length for momentum under snow conditions

The roughness length for momentum is modified for vegetation under snow conditions to differ from the snow-free roughness length (z0). The new effective roughness length (z0n) for the grid cell includes the impact of snow coverage on vegetation:
lnz0n=[(1Fg,sn2)lnz0g+Fg,sn2lnz0s](1Fυ)2+[1(1Fυ)2]lnz0υ,
where z0g = 0.01 m is for bare soil, z0s = 0.001 m for snow, and z0υ is a vegetation type-dependent roughness length for momentum and is independent of snow coverage. Value Fυ is the exposed maximum GVF and is between 0 and 1 as
Fυ=GVFmax(1Fbur),
where GVFmax is the maximum GVF prescribed for each grid cell based on satellite data.

3. 1D new snow physics with COAMPS forcing

Before implementing Noah3.6 to the COAMPS Noah LSM, we examined it using a time series of atmospheric forcing and compared the results with those from Noah3.2. The atmospheric forcing is obtained from COAMPS forecasts during 1–31 October 2015, with every 12-h data assimilation cycle. They are the outputs from the innermost nest with a horizontal resolution of 5 km. The time series have a 1-h time interval and are taken at a location (66.2192°N, 150.96°W) inside Alaska. The forcings include wind speed and direction, air temperature and mixing ratio, surface pressure, shortwave and longwave radiation, and precipitation at the first model level (10 m). Figure 1 displays variations of these variables during the whole month of October 2015. There are a few high wind periods with the most significant and longest duration during 7–14 October (Fig. 1a) associated with significant drops in the surface pressure (Fig. 1f). Air temperature (Fig. 1b) and mixing ratio (Fig. 1c) show cold and dry trends during this particular period. The shortwave radiation (Fig. 1d) shows a strong diurnal cycle. Figure 1g illustrates a few occasions of precipitation during October.

Fig. 1.
Fig. 1.

Atmospheric forcings from the lowest COAMPS model level: (a) wind speed, (b) air temperature, (c) mixing ratio, (d) shortwave radiation, (c) longwave radiation, (d) atmospheric pressure, and (e) precipitation rate.

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

The COAMPS forcings are applied to LSM Noah3.2 and Noah3.6 to run with 20 different options of MODIS vegetation types to examine the effect of the new snow physics with various vegetation heights. As an example, the predicted variables from the two LSM versions are displayed in Fig. 2 for two vegetation types: a tall evergreen broadleaf forest and a short wooded tundra. Similar variation features are found for other vegetation types. By adding the vegetation shading effect, the net shortwave radiation [Eq. (2)] and the net radiative flux received by the surface are reduced in the calculation of potential evapotranspiration [Eq. (3)]. A decrease of the potential evapotranspiration directly reduces the snow sublimation [Eq. (4)] along with the reduction of the snowmelt rate associated with the change of net radiative flux [Eq. (5)]. It leads to more snow being retained when using the new snow physics, indicating considerable deeper snow depth than the old LSM, as seen in Fig. 2a. After combining it with the limitation on liquid water stored in the snow [Eq. (6b)], the snow density is reduced in Noah3.6 (Fig. 2b).

Fig. 2.
Fig. 2.

Time series of (a) snow depth (m), (b) snow density (%), and (c) roughness length (m) for a tall (evergreen broadleaf forest; blue) and short (wooded tundra; red) vegetation type using Noah3.2 (solid) and Noah3.6 (dashed). (d) Sensible heat flux (W m−2) is displayed only for evergreen broadleaf forests using Noah3.2 (blue) and Noah3.6 (red) due to similarity to the features from other types of vegetation. Only time series for 6–12 Oct are displayed for (d) since there are not significant modifications for the other period of October for this particular situation.

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

The new snow roughness length with the vegetation shading effect is compared with the one without the vegetation impact and displayed in Fig. 2c. The roughness lengths increase when the vegetation shading effect is considered. The roughness length increases by about 30% for both short and tall vegetation. Without adding vegetation effect, the roughness length mainly depends on climatology under snow condition that tends to be smaller. The roughness length of evergreen broadleaf remains unchanged, likely because the broadleaf evergreens usually hold their foliage year-round.

Wang et al. (2010) showed that downward sensible heat can be significantly overestimated under stable conditions with strong winds when snow physics is not included. Increasing undercanopy resistance in the new snow physics aims to correct this overestimation. As a result, the significant negative sensible heat flux during the intense wind periods (i.e., 7–14 October) from Noah3.2 is reduced by at least 30% from Noah3.6 (Fig. 2d). There is no apparent difference between short and tall vegetation, so the sensible heat flux is shown for only one vegetation type for clarity. The downward sensible heat flux reduction ranges are 20–60 W m−2 for all vegetation types.

4. Application of the new snow physics in COAMPS Arctic forecast

The new snow physics (Noah3.6) is implemented in COAMPS LSM to replace the old version LSM Noah3.2 for improving Arctic forecasts. Verification of COAMPS real case forecasts over the Arctic area with Noah3.6 is provided using all the available land surface observations. Various relationships associated with new snow physics and stable boundary layers are also discussed in this section.

a. COAMPS and experiments

The COAMPS atmospheric component is a three-dimensional, fully compressible, nonhydrostatic, primitive equation model (Hodur 1997; Hodur et al. 2002). Subgrid-scale physical parameterizations for COAMPS include Kain–Fritch (Kain and Fritsch 1990) scheme for convection, Rutledge and Hobbs (1983) single-moment scheme for cloud microphysics, Fu–Liou scheme for shortwave and longwave radiation (Fu and Liou 1992; Liu et al. 2009). The planetary boundary layer and free-atmospheric turbulent mixing and diffusion are modeled using a prognostic equation for the turbulent kinetic energy (TKE) budget based on a method derived from the level 2.5 formulation of Mellor and Yamada (1982). Atmospheric data assimilation is the NRL Atmospheric Variational Data Assimilation System (NAVDAS; Daley and Barker 2001) used to assimilate the observations and provide the initial conditions for the COAMPS forecasts. The lateral boundary conditions are derived from the Navy Global Environmental Model (NAVGEM; Hogan et al. 2014) atmospheric forecast fields. The sea surface temperatures used in the COAMPS forecasts are analyses from the Navy Coupled Ocean Data Assimilation (NCODA; Cummings 2005).

The surface layer (SL) parameterization in COAMPS follows a modified Monin–Obukhov similarity theory (MOST; Monin and Obukhov 1954) in Louis (1979) and Louis et al. (1982). The Louis scheme matches the MOST with gustiness effects for convective regimes (Bradley et al. 2000; Fairall et al. 2003). An analytical method is developed to solve the stability parameter z/L (z is height and L is Monin–Obukhov length) in the MOST for stable condition (Holtslag and De Bruin 1988). Detailed formulations can be found in Wang et al. (2002). The surface exchange coefficients for momentum (Cd), heat (Ch), and moisture (Cq) are formulated as follows:
Cd=u*2U2=k2ln(1+za/z0M)2FM(za/z0M,Rb),
Ch=u*θ*UΔθ=k2ln(1+za/z0M)ln(1+za/z0H)FH(za/z0M,H,Rb),
Cq=u*q*UΔq=k2ln(1+za/z0M)ln(1+za/z0q)Fq(za/z0M,q,Rb),
where U, θ, and q indicate the total wind speed, potential temperature, and water vapor mixing ratio at the lowest model level za, respectively. The differences in potential temperature and moisture deficit between za and the surface are denoted as Δθ and Δq. The z0M,H,q terms are the aerodynamic, thermal, and moisture roughness lengths. The bulk Richardson number (Rb) is calculated by
Rb=gΔzΔθυθυ¯ΔU2,
where Δθυ and ΔU are the differences in virtual potential temperature and wind speed between the top and bottom of the layer of thickness Δz. Solutions of FM, FH, and Fq are solved for stable and unstable conditions as illustrated in Wang et al. (2002). In the Noah LSM, it, like most numerical models, assumes that the surface exchange coefficient for moisture (Cq) is assumed to be identical to the heat exchange coefficient (Ch) (Chen et al. 1997). Therefore, the surface exchange coefficient for heat (Ch) is passed to the Noah LSM subroutines to calculate sensible and latent heat flux in the COAMPS. Before providing it to the LSM subroutines, Ch has been multiplied by wind speed:
CH=UCh,
where CH is technically a conductance. The formulation of sensible heat flux has been discussed in Eq. (8) to include the vegetation shading effect. Total evaporation (E) for latent heat flux is summed from the direct evaporation of bare soil (Es), the evaporation of precipitation intercepted by the canopy (Ew), and transpiration via the canopy and roots (Ec) as follows:
E=Es+Ew+Ec,
in which only the Ec formulation has used the moisture exchange coefficient (Cq) to calculate the plant coefficient (Pc) for the reduction in transpiration due to internal plant physiology:
Ec=σf(1βc)EpPc.
Here Pc is a function of the thermodynamic properties (Δ and Rr), the surface exchange coefficient for moisture (Cq), and the canopy resistance (Rc) as
Pc=1+(Δ/Rr)1+(Δ/Rr)+RcCq.
The detailed formulations for computing these variables can be found in Chen et al. (1997) and Koo et al. (2017).

The Noah LSM, along with the surface layer parameterization in COAMPS as described above, is initialized using global analyses from the NASA LIS. The U.S. Air Force 557th Weather Wing (557WW) produced the analyses and transferred daily to the Fleet Numerical Meteorology and Oceanography Center (FNMOC) for COAMPS operational forecasts. The analyses were generated using the LIS7.0 with a horizontal resolution of 0.25° in the past and updated using the LIS7.2 with the LSM Noah3.6 and a horizontal resolution of ∼10 km currently. Therefore, the COAMPS LSM is updated to Noah3.6 to match the physics with the LIS7.2 analyses.

Numerical simulations are conducted to examine the impact of the new snow physics on the COAMPS forecast under stable atmospheric conditions during a winter month. The COAMPS domain is designed for two nests with horizontal resolutions of 45 and 15 km, respectively. Distributions of vegetation and soil types, sea ice, and land ice, are shown in Fig. 3, with a large variety of vegetation types from the shortest to the tallest. It indicates the vegetation shading effect on the snow surface must be included in the LSM model for the Arctic forecasts. The soil types are mostly loam and sandy loam. The different vegetation and soil types, and sea and land ice are considered in the computation of the surface fluxes in Noah LSM. The simulation period is from 1 to 31 January 2021, with every 6-h data assimilation cycle. The forecast length for each simulation initiated every 6 h is 48 h. Two experiments are conducted with the control run using Noah3.2 and the sensitivity run using Noah3.6.

Fig. 3.
Fig. 3.

The two-nested domain with MODIS (Moderate Resolution Imaging Spectroradiometer) (a) vegetation type and (b) soil type. Horizontal resolutions are 45 and 15 km for nests one and two, respectively.

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

b. Surface conditions

According to Wang et al. (2010), the most significant benefit of adding the new snow physics in LSM is substantial improvement in snow water equivalent and snow depth forecasts. In comparing results from one-dimensional simulations using Noah3.2 and Noah3.6 (Fig. 2), the new snow physics reduces the potential evapotranspiration and snow density. As a result, snow depth increases since the net shortwave radiation and snowmelt decrease. The monthly mean 48-h forecast of snow depth shows that the snow depth is better retained in Noah3.6 with the new snow physics (Fig. 4b) than in Noah3.2 (Fig. 4a). The new net shortwave radiation in Noah3.6 is the weighted average of fluxes between the vegetation shaded and nonshaded areas. The reduction of the net radiation results in less potential evapotranspiration in Eq. (3a) and decreases the snowmelt rate due to the available net heat flux in Eq. (5). Snow sublimation depends on potential evapotranspiration; therefore, it is reduced as described in Eq. (4). Snow water stored in the snowpack during snowmelt has set a limitation so that the snow density is not encountering an abrupt change as the adjustment in Eq. (8). The decreased snowmelt and limited snow water in Noah3.6 increase snow depth. The domain-averaged snow depth has shown that the mean snow depth from Noah3.6 (∼279 mm) has increased by 4.2% (∼11 mm) over the land area, as compared to Noah3.2 (∼268 mm).

Fig. 4.
Fig. 4.

Monthly mean 48-h forecasts of (top) snow depth and (bottom) sensible heat flux from COAMPS nest two over the land area for (a),(d) control run using Noah3.2 and (b),(e) sensitivity run using Noah3.6. (c),(f) The differences between the two experiments. The domain-averaged values are also included under each corresponding plot.

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

Adding the vegetation shading effect on undercanopy aerodynamic resistance reduces the downward sensible heat flux for the stable condition, as discussed in Fig. 2c. The downward sensible heat flux reduction can be seen from the monthly mean of COAMPS 48-h forecasts using the LSM Noah3.6 (Fig. 4e) compared to Noah3.2 (Fig. 4d). The difference between using Noah3.6 and Noah3.2 show the decrease of the downward sensible heat flux over the most land area (Fig. 4f). The increased snow depth from Naoh3.6 (Fig. 4b) prevents a reduction in the ground temperature and reduces the surface stability. Therefore, it reduces the overestimate of the downward sensible heat flux compared to Noah3.2.

Corresponding to the less downward sensible heat flux, the 10-m air temperature (Fig. 5a) and surface temperature (Fig. 5b) are warmer from Noah3.6 than Noan3.2. On the other hand, compared to the 10-m surface air temperature, the surface temperature appears significantly colder from Noah3.2, which is consistent with less snow depth, as shown in Fig. 4a. The difference between the 10-m air and surface temperature is about 1°–4°C more from Noah3.2, which leads to more significantly larger downward sensible heat fluxes in Fig. 4d. It also implies a more robust surface temperature inversion that results from a more stable surface condition from Noah3.2 (Fig. 5d) than Noah3.6 (Fig. 5e). Here we refer to the surface temperature inversion as the atmosphere at the surface (Ts) is colder than the layer above it (T10m). The strength of the surface temperature inversion is defined as the magnitude of the temperature difference (T10mTs).

Fig. 5.
Fig. 5.

(top) Scatterplots for 10-m air temperature (first atmospheric level) and surface temperature from (a) control run using Noah3.2, (b) sensitivity run using Noah3.6, and (c) the difference between (a) and (b). (bottom) The mean temperature differences between 10 m and surface for (d) from Noah3.2, (e) from Noah3.6, and the differences between (e) and (d).

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

c. Verification and discussion

Standard verification for the COAMPS forecasts uses the observational dataset. The observations include pressure, geopotential height, temperature, dewpoint temperature, wind direction and speed. All available observations are operationally collected from the surface station, radiosonde, buoy, pibal, and aircraft reports from the Global Telecommunications System (GTS), radar, SSM/I, and satellite data (https://doi.org/10.5065/Z83F-N512). Fig. 6 shows mean biases and root-mean-square (RMS) errors of 48-h COAMPS forecasts from 30 model–observation pairs for all surface station locations. Reduction of cold biases can be found by the sensitivity run using Noah3.6 (Fig. 6b), compared to the control run using Noah3.2 (Fig. 6a). Differences in the RMS error between using Noah3.6 and Noah3.2 in Fig. 6c show more negative values, indicating smaller RMS errors by using Noah3.6. Similar reductions of the RMS error are also evident for dewpoint temperature (Fig. 6d), sea level pressure (Fig. 6e), and 10-m wind speed (Fig. 6f). The new snow physics has improved COAMPS 48-h forecast skills for surface temperature, moisture, pressure, and wind.

Fig. 6.
Fig. 6.

Biases of 48-h COAMPS 2-m air temperature forecasts with station observations from using (a) Noah3.2 and (b) Noah3.6. (c) The corresponding differences in the root-mean-square (RMS) errors between control run using Noah3.2 and sensitivity run using Noah3.6. Differences in the RMS errors for 2-m dewpoint temperature, sea level pressure, and 10-m wind speed are displayed in (d), (e), and (f), respectively.

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

The verification also shows domain-averaged time series of mean biases and the RMS errors in Fig. 7. The new snow physics in Noah3.6 has significantly reduced the biases and RMS errors for all forecast lengths from 6 to 48 h. Confidence levels reach 99% for the biases and RMS errors, indicating that the verifications are statistically significant.

Fig. 7.
Fig. 7.

Mean bias (dashed lines) and RMS (solid lines) of COAMPS surface forecasts from 1 to 31 Jan 2021, of (a) 2-m air temperature, (b) 2-m dewpoint temperature, (c) 2-m relative humidity, (d) sea level pressure, (e) 10-m wind speed, and (f) 10-m wind direction for control run using Noah3.2 (blue color) and sensitivity run using Noan3.6 (red color). Statistic confidence levels are marked as solid circles for bias and squares for RMS.

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

The reduction of bias and RME errors for 2-m air temperature (Fig. 7a) is consistent with the effect of the new snow physics, as discussed in section 4b. The mean cold bias of ∼1°C using Noah3.2 is reduced to ∼0°C using Noah3.6. It is the most beneficial variable from the increased snow depth (Fig. 4b) and decreased downward sensible heat flux (Fig. 4e) with the new snow physics in Noah3.6. The 2-m dewpoint temperature (Fig. 7b) and relative humidity (Fig. 7c), sea level pressure (Fig. 7d), and 10-m wind speed (Fig. 7e) and wind direction (Fig. 7f) in the sensitivity run using the new snow physics in Noah3.6 (Fig. 7) have also shown improvements.

Figure 8 shows the time series of domain-averaged snow depth from 48-h forecasts using Noah3.2 (blue line) and Noah3.6 (black line), compared to the LIS analyses (red line). The LIS data assimilation integrates observations with model forecasts to generate improved estimates of land surface conditions, including snow coverage (Kumar et al. 2006). The same snow depths from the LIS analyses are used to initialize LSM Noah3.2 and Noah3.6. However, the 48-h forecasted snow depths from Noah3.6 are closer to the LIS analyses than Noah3.2. The better match from Noah3.6 is reasonable since the LIS analyses have also used the same LSM Noah3.6. It confirms that without a good LSM, using the improved initial conditions does not ensure the improvement of model forecasts.

Fig. 8.
Fig. 8.

Domain-averaged 48-h forecast snow depth from LIS analyses (red line), COAMPS LSM using Noah3.2 (blue line), and Noah3.6 (black line).

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

d. Turbulent mixing in the surface layer

Noah3.2 (Fig. 9a) than Noah3.6 (Fig. 9b). It provides detailed information for the frequency range of decreased air temperature. Since the 2-m air temperature is derived from the 10-m air temperature using the similarity theory, they have similar features and appear colder than the 2-m air temperature. It is consistent with the stronger and higher inversion from Noah3.2, as presented in Fig. 5. Response to this change is the decreased boundary layer height (BLH); as seen in Fig. 9e, its frequency is higher under 100 m and lower above 100 m.

Fig. 9.
Fig. 9.

Histogram of 48-h (a),(b) 2- and (c),(d) 10-m air temperature, and (e) differences of boundary layer height between the control run using Noah3.2 and the sensitivity run using Noah3.6. All values are over the land points only.

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

Scatterplots for eddy mixing coefficients for momentum (Km) and heat (Kh) at 1000 hPa show stronger mixing from the sensitivity run using Noah3.6 than the control run using Noah3.2 (Figs. 10a,b). The correlations of eddy mixing coefficients between Noah3.6 and Noah3.2 are only 0.49 for Km and 0.51 for Kh. Both coefficients are around 1.0 m2 s−1 for Noah3.2 and in the range of 1.0–3.2 m2 s−1 for Noah3.6, indicating that much weaker turbulent mixing occurs in Noah3.2 than in Noah3.6. Turbulence kinetic energy (TKE) has a consistent change in magnitude and a high correlation between these two experiments. However, more substantial turbulence is from Noah3.6 (Fig. 10c), consistent with the warmer air temperature, as shown in Fig. 6.

Fig. 10.
Fig. 10.

Scatterplots between the sensitivity run using Noah3.6 and the control run using Noah3.2 for eddy mixing coefficients (m2 s−1) of (a) momentum and (b) heat and for (c) turbulence kinetic energy (×10 m2 s−2) at 1000 hPa over the land points.

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

e. Influences on the lower boundary layer

Time series of vertical profiles for potential temperature and TKE show warmer temperatures and more vigorous vertical mixing from using the new snow physics (Fig. 11b) than the old LSM (Fig. 11a). Correspondingly, the boundary layer is about 100–500 m higher (Fig. 11c), consistent with the higher frequency for the deeper boundary layer (Fig. 9c). The warmer surface temperature and less stable boundary layer promote vertical mixing and prevent the decoupling between the land surface and the atmospheric surface layer. The improvement is significant in the second half of the month when cold weather is more pronounced.

Fig. 11.
Fig. 11.

Time series of COAMPS forecasts of vertical profiles for potential temperature (K; color shaded), TKE (m2 s−2; white contours), and boundary layer height (m; black contour) from using (a) the control run using Noah3.2, (b) the sensitivity run using Noah3.6, and (c) difference between (b) and (a) at a location (72.6804°N, 77.3677°E).

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

Vertical profiles of potential temperatures further prove the overall less stable lower boundary layer from the sensitivity run using Noah3.6 than the control run using Noan3.2 (Fig. 12a). Influences of modifying the land surface model with the new snow physics have reached ∼300-m height. Vertical mixing is about 1–2 times stronger from Noah3.6 than Noah3.2 (Fig. 12b). The bulk Richardson number (Rb) shows a significantly larger value from Noah3.2 than Noah3.6 (Fig. 12c), indicating less mixing and results in much more stable lower boundary condition. It leads to very stable (Fig. 12a) and robust stratified conditions (Fig. 11a) without using the new snow physics.

Fig. 12.
Fig. 12.

Vertical profiles of (a) potential temperature (K), (b) TKE (m2 s−2), and (c) bulk Richardson number (Rb) from the control run using Noah3.2 (blue) and the sensitivity run using Noah3.6 (red) at 0600 UTC 11 Jan 2021.

Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0040.1

5. Impact of lateral boundary conditions

Benefits from larger-area forecasts to obtain smaller-area, higher-quality forecasts using lateral boundary conditions (LBCs) derived from the larger-area model have been recognized decades ago (Mesinger 2001). The values of the dependent variables at the lateral boundary grid points are used to solve the governing equations for the limited area model (LAM) (Davies 1976; Perkey and Krietzberg 1976). For operational atmospheric forecasts, the LBCs are obtained by interpolation of dependent variables at lateral grid points from global atmospheric forecasts. It has become a standard procedure for various operational centers to provide LBCs to a LAM with global model forecasts (Chandrasekar 2022). The operational COAMPS forecasts conducted at FNMOC have used the most recent global forecasts from NAVGEM to obtain the LBCs (Hodur 1997). As presented in section 4, the newly implemented snow physics has been verified in the standard manner using NAVGEM forecasts for COAMPS LBCs.

For nonoperational research purposes, researchers have the option of using archived regional or global analyses to provide LBCs for LAM forecasts, since the analyses have combined the optimal atmospheric model output with all possible atmospheric observations (Chandrasekar 2022). It is widely thought that the LBCs are a source of significant errors in LAMs, but the LBC errors are shown to be a tiny part of the overall error (Davies 2014). To examine the impact of the LBCs on the COAMPS forecast errors, we have performed two additional simulations with identical model settings to the two simulations presented in section 4 except that the LBCs have been obtained from the global NAVGEM analyses. The results of using different LBCs between the NAVGEM forecasts in the previous two simulations and the NAVGEM analyses in the additional two simulations are compared and discussed below.

Standard verifications using the observational dataset are performed for these two additional simulations using the method described in Figs. 6 and 7. Table 1 displays the mean biases and RMS errors of COAMPS 48-h forecasts for all four simulations. They include simulations using the LBCs obtained from the global NAVGEM forecasts (previous two simulations in section 4) and analyses (additional two simulations) with the LSM Noah3.2 and Noah3.6, respectively. Differences in the biases and RMS errors between using the NAVGEM forecasts and analyses for the LBCs are displayed in columns 4 and 7 for using the LSM Noah3.2 and Noah3.6, respectively. Overall, the differences in 48-h forecast biases and RMS errors between using two different LBCs are insignificant, which agrees with previous studies (e.g., Davies 2014). The results also show that using the LBCs from the analyses only sometimes produces smaller forecast biases and RMS errors than using the LBCs from the forecasts.

Table 1.

Mean biases and RMS errors of COAMPS 48-h forecasts using LBCs from the global NAVGEM forecasts (fcst) and analyses (anal) with the LSM Noah3.2 and Noah3.6. Abbreviations are as follows: tt2m = 2-m temperature, slpr = sea level pressure, wspd = 10-m wind speed, dwpt = 2-m dewpoint temperature, relh = 2-m relative humidity; 1 mb = 1 hPa.

Table 1.

The impact of the LBCs on the forecasts of a stable boundary layer is examined using the results of the four simulations, as displayed in Table 2. Potential temperatures within the boundary layer, as shown in Fig. 7, have minimum values on the surface indicating the strength of the stable condition. The potential temperatures are warmer using the LSM Noah3.6 than Noah3.2, and the results are consistent, whether using the LBCs with NAVGEM forecasts or analyses. The differences in minimum potential temperature between the two LBCs are negligible for both Noah3.2 and Noah3.6. The maximum bulk Richardson number within the boundary layer and the mean boundary layer height further explain the improvements using Noah3.6 through the increased turbulent and eddy mixing. They also show no significant impact when using two different LBCs obtained from the forecasts and analyses.

Table 2.

Minimum of potential temperature (Min_pott), maximum of bulk Richardson number (Max_Rich) within the boundary layer and mean boundary layer height (Mean_blht) during the simulation periods.

Table 2.

Further comparison between the BLCs derived from the global model forecasts and reanalysis for the four simulations find that there were noticeable quantitative differences between the two (not shown), suggesting that the COAMPS forecasted boundary layer characteristics were more dictated by processes taking place inside the COAMPS domain than the LBCs. This is likely due to the following reasons: First, a large coarse COAMPS mesh is used. As shown in Fig. 3a, the 45-km COAMPS domain covers the whole Arctic and beyond. Accordingly, the features examined in this study were more influenced by processes taking place inside the COAMPS domain rather than the outer LBCs. Second, this study focuses on the Arctic surface and boundary layer characteristics. The majority of the datasets used for the evaluation were obtained from surface observations, presumably subject to the strong influence of local surface processes. Furthermore, the two sets of LBCs were qualitatively consistent over the time periods examined. For example, if the prevailing wind directions over the area of interest are dramatically different due to the two different BLCs, the resulting COAMPS forecasted boundary layer features likely differ as well.

6. Concluding remarks

The forecast over the Arctic region remains a great challenge to the NWP community due to constant stable boundary layer conditions during day and night. Decoupling between the land surface and atmosphere due to inadequate turbulent heat flux often causes cold bias and induces positive feedback on the surface air temperature. To prevent the decoupling and reduce the forecast errors over the Arctic region, the new snow physics is added to the COAMPS Noah LSM to improve the surface heat flux. The impact of the new snow physics on the stable boundary layer is also investigated over the Arctic land surface.

The major additions in the new snow physics include adding the vegetation shading effect on snow sublimation and snowmelt, limiting the liquid water stored in the snowpack during the snowmelt, differing the roughness length for vegetation under snow and snow-free conditions, and modifying undercanopy resistance to prevent overestimating downward sensible heat flux.

The new snow physics is examined using COAMPS forecasts during October 2015 as surface forcing in one-dimensional mode and compared with the old Noah LSM. The results confirm the expected improvements by the new snow physics for all MODIS 20 vegetation types. The vegetation shading effect increases the snow depth and decreases the snow density, reducing excessive snowmelt in the old LSM. The downward sensible heat flux decrease is consistent with the increased undercanopy resistance for the snow condition. The new roughness length has shown the impact of snow coverage on vegetation to avoid small values under snow conditions.

The new snow physics is used in COAMPS Arctic forecasts for the winter of January 2021 to verify its improvement on the persistent cold bias over the land surface. Monthly mean 48-h predictions have confirmed the consistent results from the original paper on the new snow physics (Wang et al. 2010). Deeper snow depth and less downward sensible heat flux have resulted in warmer surface air and ground temperature. In addition, it has led to a more negligible temperature difference between surface air and ground and weaker surface inversion. Verification using all available surface land observations has shown a significant reduction of cold bias in 2-m air temperature, with the mean value changed from −1° to 0°C. The improvements can also be seen for variables such as 2-m dewpoint temperature and relative humidity, sea level pressure, and 10-m wind speed and direction. The 48-h forecast snow depths from Noah3.6 better match the Land Information System (LIS) analyses.

On the other hand, the histogram analysis indicates a higher, colder temperature frequency occurring from −25° to −35°C when using the old LSM. Response to this change is the higher frequency of the boundary layer larger than 100 m from the new snow physics. Further analysis shows more significant surface eddy mixing coefficients for momentum and heat and more extensive turbulence kinetic energy produced by the new snow physics. Time series of vertical profiles have shown that warmer temperatures, more vigorous vertical mixing, and higher boundary layer have been forecasted when using the new snow physics, especially during the cold period of the second half of the simulation month. The analyses of vertical profiles for TKE and bulk Richardson numbers denote the impact of the snow physics reaching ∼300 m in the lower boundary layer.

Two additional simulations are performed to investigate the impact of the LBCs on the forecasts. Instead of using the NAVGEM forecasts for the LBCs, the two new simulations use the LBCs obtained from the NAVGEM analyses. Overall, the differences in 48-h forecast biases and RMS errors from using two different LBCs are insignificant, which agrees with the previous investigation (Davies 2014), suggesting substantial influence on the Arctic boundary layer from local processes in the relatively large COAMPS domains. The results also show that using the LBCs from the analyses only sometimes provides smaller forecast biases and RMS errors than using the LBCs from the forecasts. No significant differences in the stable boundary layer prediction are found from using the two LBCs since the impact of the LBCs on the turbulent and eddy mixings is similar.

The implemented new snow physics in COAMPS Noah LSM has passed the operational scorecard and transited to FNMOC for operational forecasts. It helps researchers investigate stable boundary layers’ formation and persistency over higher-latitude areas. It also has the benefit of gaining insight into the interaction between the atmosphere and land surface processes for stable and very stable boundary layer conditions during the snow season.

Acknowledgments.

This research is supported by the Chief of Naval Research through the NRL Base Program (Program Element 0601153N). Computational resources were partly supported by a grant of HPC time from the Navy Department of Defense (DoD) Supercomputing Resource Center (Navy DSRC). COAMPS is a registered trademark of the U.S. Naval Research Laboratory.

Data availability statement.

The authors were unable to find a valid data repository for the data used in this study. The data are enormous, archived at the Navy DSRC Archive System https://navydsrc.hpc.mil/docs/archiveUserGuide.html, and will be available upon request to all interested parties.

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  • Fig. 1.

    Atmospheric forcings from the lowest COAMPS model level: (a) wind speed, (b) air temperature, (c) mixing ratio, (d) shortwave radiation, (c) longwave radiation, (d) atmospheric pressure, and (e) precipitation rate.

  • Fig. 2.

    Time series of (a) snow depth (m), (b) snow density (%), and (c) roughness length (m) for a tall (evergreen broadleaf forest; blue) and short (wooded tundra; red) vegetation type using Noah3.2 (solid) and Noah3.6 (dashed). (d) Sensible heat flux (W m−2) is displayed only for evergreen broadleaf forests using Noah3.2 (blue) and Noah3.6 (red) due to similarity to the features from other types of vegetation. Only time series for 6–12 Oct are displayed for (d) since there are not significant modifications for the other period of October for this particular situation.

  • Fig. 3.

    The two-nested domain with MODIS (Moderate Resolution Imaging Spectroradiometer) (a) vegetation type and (b) soil type. Horizontal resolutions are 45 and 15 km for nests one and two, respectively.

  • Fig. 4.

    Monthly mean 48-h forecasts of (top) snow depth and (bottom) sensible heat flux from COAMPS nest two over the land area for (a),(d) control run using Noah3.2 and (b),(e) sensitivity run using Noah3.6. (c),(f) The differences between the two experiments. The domain-averaged values are also included under each corresponding plot.

  • Fig. 5.

    (top) Scatterplots for 10-m air temperature (first atmospheric level) and surface temperature from (a) control run using Noah3.2, (b) sensitivity run using Noah3.6, and (c) the difference between (a) and (b). (bottom) The mean temperature differences between 10 m and surface for (d) from Noah3.2, (e) from Noah3.6, and the differences between (e) and (d).

  • Fig. 6.

    Biases of 48-h COAMPS 2-m air temperature forecasts with station observations from using (a) Noah3.2 and (b) Noah3.6. (c) The corresponding differences in the root-mean-square (RMS) errors between control run using Noah3.2 and sensitivity run using Noah3.6. Differences in the RMS errors for 2-m dewpoint temperature, sea level pressure, and 10-m wind speed are displayed in (d), (e), and (f), respectively.

  • Fig. 7.

    Mean bias (dashed lines) and RMS (solid lines) of COAMPS surface forecasts from 1 to 31 Jan 2021, of (a) 2-m air temperature, (b) 2-m dewpoint temperature, (c) 2-m relative humidity, (d) sea level pressure, (e) 10-m wind speed, and (f) 10-m wind direction for control run using Noah3.2 (blue color) and sensitivity run using Noan3.6 (red color). Statistic confidence levels are marked as solid circles for bias and squares for RMS.

  • Fig. 8.

    Domain-averaged 48-h forecast snow depth from LIS analyses (red line), COAMPS LSM using Noah3.2 (blue line), and Noah3.6 (black line).

  • Fig. 9.

    Histogram of 48-h (a),(b) 2- and (c),(d) 10-m air temperature, and (e) differences of boundary layer height between the control run using Noah3.2 and the sensitivity run using Noah3.6. All values are over the land points only.

  • Fig. 10.

    Scatterplots between the sensitivity run using Noah3.6 and the control run using Noah3.2 for eddy mixing coefficients (m2 s−1) of (a) momentum and (b) heat and for (c) turbulence kinetic energy (×10 m2 s−2) at 1000 hPa over the land points.

  • Fig. 11.

    Time series of COAMPS forecasts of vertical profiles for potential temperature (K; color shaded), TKE (m2 s−2; white contours), and boundary layer height (m; black contour) from using (a) the control run using Noah3.2, (b) the sensitivity run using Noah3.6, and (c) difference between (b) and (a) at a location (72.6804°N, 77.3677°E).

  • Fig. 12.

    Vertical profiles of (a) potential temperature (K), (b) TKE (m2 s−2), and (c) bulk Richardson number (Rb) from the control run using Noah3.2 (blue) and the sensitivity run using Noah3.6 (red) at 0600 UTC 11 Jan 2021.

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