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Sam Chang and Michael Ek

Abstract

Modifications to the formulations in the recently published paper by Ek and Mahrt are presented. One table and three figures in that paper have also been revised.

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L. Mahrt and Michael Ek

Abstract

The Penman relationship for potential evaporation is modified to simply include the influence of atmospheric stability on turbulent transport of water vapor. Explicit expressions for the stability-dependent, surface exchange coefficient developed by Louis are used. The diurnal variation of potential evaporation is computed for the stability-dependent and original Penman relationships using Wangara data.

The influence of afternoon instability increases the aerodynamic term of the modified Penman relationship by 50% or more on days with moderate instability. However, the unmodified Penman relationship predicts values of daily potential evaporation close to that of the stability-dependent relationship. This agreement is partly due to compensating overestimation during nighttime hours. Errors due to use of daily-averaged variables are examined in detail by evaluating the nonlinear interactions between the diurnal variation of the variables in the Penman relationship.

A simpler method for estimating the exchange coefficient is constructed from an empirical relationship between the radiation Richardson number and the Obukhov length. This method is less accurate, but it allows estimation of the stability-dependent exchange coefficient using only parameters already required for evaluation of the Penman relationship. Finally, the diurnal variation of the atmospheric resistance coefficient appearing in the Penman-Monteith relationship is presented.

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Rongqian Yang, Michael Ek, and Jesse Meng

Abstract

Surface water and energy budgets from the National Centers for Environmental Prediction–U.S. Department of Energy (NCEP–DOE) Atmospheric Model Intercomparison Project (AMIP-II) Global Reanalysis 2 (GR2), the North American Regional Reanalysis (NARR), and the NCEP Climate Forecast System Reanalysis (CFSR) are compared here with each other and with available observations over the Mississippi River basin. The comparisons in seasonal cycle, interannual variation, and annual mean over a 31-yr period show that there are a number of noticeable differences and similarities in the large-scale basin averages. Warm season precipitation and runoff in the GR2 are too large compared to the observations, and seasonal surface water variation is small. By contrast, the precipitation in both NARR and CFSR is more reasonable and in better agreement with the observation, although the corresponding seasonal runoff is very small. The main causes of the differences in both surface parameterization and approach used in assimilating the observed precipitation datasets and snow analyses are then discussed. Despite the discrepancies in seasonal water budget components, seasonal energy budget terms in the three reanalyses are close to each other and to available observations. The interannual variations in both water and energy budgets are comparable. This study shows that the CFSR achieves a large improvement over the GR2, indicating that the CFSR dataset can be used in climate variability studies. Nonetheless, improved land surface parameterization schemes and data assimilation techniques are needed to depict the surface water and energy climates better, in particular, the variation in seasonal runoff.

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Rongqian Yang, Kenneth Mitchell, Jesse Meng, and Michael Ek

Abstract

To examine the impact from land model upgrades and different land initializations on the National Centers for Environmental Prediction (NCEP)’s Climate Forecast System (CFS), extensive T126 CFS experiments are carried out for 25 summers with 10 ensemble members using the old Oregon State University (OSU) land surface model (LSM) and the new Noah LSM. The CFS using the Noah LSM, initialized in turn with land states from the NCEP–Department of Energy Global Reanalysis 2 (GR-2), Global Land Data System (GLDAS), and GLDAS climatology, is compared to the CFS control run using the OSU LSM initialized with the GR-2 land states. Using anomaly correlation as a primary measure, the summer-season prediction skill of the CFS using different land models and different initial land states is assessed for SST, precipitation, and 2-m air temperature over the contiguous United States (CONUS) on an ensemble basis.

Results from these CFS experiments indicate that upgrading from the OSU LSM to the Noah LSM improves the overall CONUS June–August (JJA) precipitation prediction, especially during ENSO neutral years. Such an enhancement in CFS performance requires the execution of a GLDAS with the very same Noah LSM as utilized in the land component of the CFS, while improper initializations of the Noah LSM using the GR-2 land states lead to degraded CFS performance. In comparison with precipitation, the land upgrades have a relatively small impact on both of the SST and 2-m air temperature predictions.

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Weizhong Zheng, Michael Ek, Kenneth Mitchell, Helin Wei, and Jesse Meng

Abstract

This study examines the performance of the NCEP Global Forecast System (GFS) surface layer parameterization scheme for strongly stable conditions over land in which turbulence is weak or even disappears because of high near-surface atmospheric stability. Cases of both deep snowpack and snow-free conditions are investigated. The results show that decoupling and excessive near-surface cooling may appear in the late afternoon and nighttime, manifesting as a severe cold bias of the 2-m surface air temperature that persists for several hours or more. Concurrently, because of negligible downward heat transport from the atmosphere to the land, a warm temperature bias develops at the first model level. The authors test changes to the stable surface layer scheme that include introduction of a stability parameter constraint that prevents the land–atmosphere system from fully decoupling and modification to the roughness-length formulation. GFS sensitivity runs with these two changes demonstrate the ability of the proposed surface layer changes to reduce the excessive near-surface cooling in forecasts of 2-m surface air temperature. The proposed changes prevent both the collapse of turbulence in the stable surface layer over land and the possibility of numerical instability resulting from thermal decoupling between the atmosphere and the surface. The authors also execute and evaluate daily GFS 7-day test forecasts with the proposed changes spanning a one-month period in winter. The assessment reveals that the systematic deficiencies and substantial errors in GFS near-surface 2-m air temperature forecasts are considerably reduced, along with a notable reduction of temperature errors throughout the lower atmosphere and improvement of forecast skill scores for light and medium precipitation amounts.

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Joseph A. Santanello Jr., Mark A. Friedl, and Michael B. Ek

Abstract

The convective planetary boundary layer (PBL) integrates surface fluxes and conditions over regional and diurnal scales. As a result, the structure and evolution of the PBL contains information directly related to land surface states. To examine the nature and magnitude of land–atmosphere coupling and the interactions and feedbacks controlling PBL development, the authors used a large sample of radiosonde observations collected at the southern Atmospheric Research Measurement Program–Great Plains Cloud and Radiation Testbed (ARM-CART) site in association with simulations of mixed-layer growth from a single-column PBL/land surface model. The model accurately predicts PBL evolution and realistically simulates thermodynamics associated with two key controls on PBL growth: atmospheric stability and soil moisture. The information content of these variables and their influence on PBL height and screen-level temperature can be characterized using statistical methods to describe PBL–land surface coupling over a wide range of conditions. Results also show that the first-order effects of land–atmosphere coupling are manifested in the control of soil moisture and stability on atmospheric demand for evapotranspiration and on the surface energy balance. Two principal land–atmosphere feedback regimes observed during soil moisture drydown periods are identified that complicate direct relationships between PBL and land surface properties, and, as a result, limit the accuracy of uncoupled land surface and traditional PBL growth models. In particular, treatments for entrainment and the role of the residual mixed layer are critical to quantifying diurnal land–atmosphere interactions.

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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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Robert J. Zamora, Edward P. Clark, Eric Rogers, Michael B. Ek, and Timothy M. Lahmers

Abstract

The NOAA Hydrometeorology Testbed (HMT) program has deployed a soil moisture observing network in the Babocomari River basin located in southeastern Arizona. The Babocomari River is a major tributary of the San Pedro River. At 0000 UTC 23 July 2008, the second-highest flow during the period of record was measured just upstream of the location where the Babocomari River joins the main channel of the San Pedro River.

Upper-air and surface meteorological observations and Special Sensor Microwave Imager (SSM/I) satellite images of integrated water vapor were used to establish the synoptic and mesoscale conditions that existed before the flood occurred. The analysis indicates that a weak Gulf of California surge initiated by Hurricane Fausto transported a warm moist tropical air mass into the lower troposphere over southern Arizona, setting the stage for the intense, deep convection that initiated the flooding on the Babocomari River. Observations of soil moisture and precipitation at five locations in the basin and streamflow measured at two river gauging stations enabled the documentation of the hydrometeorological conditions that existed before the flooding occurred. The observations suggest that soil moisture conditions as a function of depth, the location of semi-impermeable layers of sedimentary rock known as caliche, and the spatial distribution of convective precipitation in the basin confined the flooding to the lower part of the basin. Finally, the HMT soil moisture observations are compared with soil moisture products from the NOAA/NWS/NCEP Noah land surface model.

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Ben Livneh, Youlong Xia, Kenneth E. Mitchell, Michael B. Ek, and Dennis P. Lettenmaier

Abstract

A negative snow water equivalent (SWE) bias in the snow model of the Noah land surface scheme used in the NCEP suite of numerical weather and climate prediction models has been noted by several investigators. This bias motivated a series of offline tests of model extensions and improvements intended to reduce or eliminate the bias. These improvements consist of changes to the model’s albedo formulation that include a parameterization for snowpack aging, changes to how pack temperature is computed, and inclusion of a provision for refreeze of liquid water in the pack. Less extensive testing was done on the performance of model extensions with alternate areal depletion parameterizations. Model improvements were evaluated through comparisons of point simulations with National Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) SWE data for deep-mountain snowpacks at selected stations in the western United States, as well as simulations of snow areal extent over the conterminous United States (CONUS) domain, compared with observational data from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). The combination of snow-albedo decay and liquid-water refreeze results in substantial improvements in the magnitude and timing of peak SWE, as well as increased snow-covered extent at large scales. Modifications to areal snow depletion thresholds yielded more realistic snow-covered albedos at large scales.

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Gary M. Lackmann, Kermit Keeter, Laurence G. Lee, and Michael B. Ek

Abstract

During episodes of sustained moderate or heavy precipitation in conjunction with near-freezing temperatures and weak horizontal temperature advection, the latent heat released (absorbed) by the freezing (melting) of falling precipitation may alter thermal profiles sufficiently to affect the type and amount of freezing or frozen precipitation observed at the surface. Representation of these processes by operational numerical weather prediction models is incomplete; forecaster knowledge of these model limitations can therefore be advantageous during winter weather forecasting. The Eta Model employs a sophisticated land surface model (LSM) to represent physical processes at the lower-atmospheric interface. When considering the thermodynamic effect of melting or freezing precipitation at the surface, it is shown that limitations in the current version of the Eta LSM can contribute to biases in lower-tropospheric temperature forecasts. The Eta LSM determines the precipitation type reaching the surface from the air temperature at the lowest model level; subfreezing (above freezing) temperatures are assumed to correspond to snow (rain) reaching the surface. There is currently no requirement for consistency between the LSM and the Eta grid-scale precipitation scheme. In freezing-rain situations, the lowest model air temperature is typically below freezing, and the Eta LSM will therefore determine that snow is falling. As a result, a cold bias develops that is partly caused by the neglected latent heat release accompanying the freezing of raindrops at the surface. In addition, alterations in surface characteristics caused by erroneous snowfall accumulation in the model may also contribute to temperature biases. In an analogous fashion, warm biases can develop in cases with melting snow and above-freezing air temperatures near the surface (the LSM assumes rain). An example case is presented in which model misrepresentation of freezing rain is hypothesized to have contributed to a lower-tropospheric cold bias. A simple temperature correction, based on the first law of thermodynamics, is applied to lower-tropospheric model temperature forecasts; the neglect of latent heat released by freezing rain in the model is shown to contribute substantially to a cold bias in near-surface temperature forecasts. The development of a spurious snow cover likely exacerbated the bias.

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