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Kenneth E. Mitchell
and
John B. Hovermale

Abstract

The structure of the thunderstorm gust front is investigated by a nonhydrostatic, two-dimensional (x z/) numerical model. In the model, which is dry, the production of negatively buoyant air by evaporation is parameterized via an externally imposed, local-cooling function. This parameterization sustains a steady cold downdraft, which drives the surface outflow and associated gust front.

It is shown that two dominant factors influencing gust front structure in the vertical plane are the solenoidal field coincident with the front and surface friction, modeled by means of a simple bulk aerodynamic drag formulation. The circulation theorem is invoked to illustrate how solenoidal accelerations oppose the deceleration by surface friction. After the onset of a downdraft in the model, these opposing tendencies soon reach a balance. Thus, following a brief transient stage, the model gust front exhibits a persistent configuration as it propagates rapidly forward. The essential features of this configuration are examined and compared with both tower observations of gust fronts and laboratory models of gravity currents.

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Yan Luo
,
Ernesto H. Berbery
, and
Kenneth E. Mitchell

Abstract

The surface hydrology of the United States’ western basins is investigated using the National Centers for Environmental Prediction operational Eta Model forecasts. During recent years the model has been subject to changes and upgrades that positively affected its performance. These effects on the surface hydrologic cycle are discussed by analyzing the period June 1995–May 2003. Prior to the model assessment, three gauge-based precipitation analyses that are potential sources of model validation are appraised. A fairly large disparity between the gridded precipitation analyses is found in the long-term area averages over the Columbia basin (∼23% difference) and over the Colorado basin (∼12% difference). These discrepancies are due to the type of analysis scheme employed and whether an orographic correction was applied.

The basin-averaged Eta Model precipitation forecasts correlate well with the observations at monthly time scales and, after 1999, show a small bias. Over the Columbia basin, the model precipitation bias is typically positive. This bias is significantly smaller with respect to orographically corrected precipitation analyses, indicating that the model’s large-scale precipitation processes respond reasonably well to orographic effects, though manifesting a higher bias during the cool season. Over the Colorado basin, the model precipitation bias is typically negative, and notably more so with respect to 1) the orographically corrected precipitation analyses and 2) the warm season, indicating shortfalls in the convection scheme over arid high mountains.

The mean fields of the hydrological variables in the Eta Model are in qualitative agreement with those from the Variable Infiltration Capacity (VIC) macroscale hydrologic model at regional-to-large scales. As expected, the largest differences are found near mountains and the western coastline. While the mean fields of precipitation, evaporation, runoff, and normalized soil moisture are in general agreement, important differences arise in their mean annual cycle over the two basins: snowmelt in the Eta Model precedes that of VIC by 2 months, and this phase shift is also reflected in the other variables. In the last 3–4 yr of the study period, notable improvements are evident in the quality of the model’s precipitation forecast and in the reduction of the residual term of the surface water balance, suggesting that at least similar (or better) quality will be found in studies based on NCEP’s recently completed Eta Model–based North American regional reanalysis.

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Yongkang Xue
,
Ratko Vasic
,
Zavisa Janjic
,
Fedor Mesinger
, and
Kenneth E. Mitchell

Abstract

This study investigates the capability of the dynamic downscaling method (DDM) in a North American regional climate study using the Eta/Simplified Simple Biosphere (SSiB) Regional Climate Model (RCM). The main objective is to understand whether the Eta/SSiB RCM is capable of simulating North American regional climate features, mainly precipitation, at different scales under imposed boundary conditions. The summer of 1998 was selected for this study and the summers of 1993 and 1995 were used to confirm the 1998 results. The observed precipitation, NCEP–NCAR Global Reanalysis (NNGR), and North American Regional Reanalysis (NARR) were used for evaluation of the model’s simulations and/or as lateral boundary conditions (LBCs). A spectral analysis was applied to quantitatively examine the RCM’s downscaling ability at different scales.

The simulations indicated that choice of domain size, LBCs, and grid spacing were crucial for the DDM. Several tests with different domain sizes indicated that the model in the North American climate simulation was particularly sensitive to its southern boundary position because of the importance of moisture transport by the southerly low-level jet (LLJ) in summer precipitation. Among these tests, only the RCM with 32-km resolution and NNGR LBC or with 80-km resolution and NARR LBC, in conjunction with appropriate domain sizes, was able to properly simulate precipitation and other atmospheric variables—especially humidity over the southeastern United States—during all three summer months—and produce a better spectral power distribution than that associated with the imposed LBC (for the 32-km case) and retain spectral power for large wavelengths (for the 80-km case). The analysis suggests that there might be strong atmospheric components of high-frequency variability over the Gulf of Mexico and the southeastern United States.

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Yu-Tai Hou
,
Kenneth A. Campana
,
Kenneth E. Mitchell
,
Shi-Keng Yang
, and
Larry L. Stowe

Abstract

CLAVR [cloud from AVHRR (Advanced Very High Resolution Radiometer)] is a global cloud dataset under development at NOAA/NESDIS (National Environmental Satellite, Data, and Information Service). Total cloud amount from two experimental cases, 9 July 1986 and 9 February 1990, are intercompared with two independent products, the Air Force Real-Time Nephanalysis (RTNEPH), and the International Satellite Cloud Climatology Project (ISCCP). The ISCCP cloud database is a climate product processed retrospectively some years after the data are collected. Thus, only CLAVR and RTNEPH can satisfy the real-time requirements for numerical weather prediction (NWP) models. Compared with RTNEPH and ISCCP, which only use two channels in daytime retrievals and one at night, CLAVR utilizes all five channels in daytime and three at night from AVHRR data. That gives CLAVR a greater ability to detect certain cloud types, such as thin cirrus and low stratus. Designed to be an operational product, CLAVR is also compared with total cloud forecasts from the National Meteorological Center (NMC) Medium Range Forecast (MRF) Model. The datasets are mapped to the orbits of NOAA polar satellites, such that errors from temporal sampling are minimized. A set of statistical scores, histograms, and maps are used to display the characteristics of the datasets. The results show that the CLAVR data can realistically resolve global cloud distributions. The spatial variation is, however, less than that of RTNEPH and ISCCP, due to current constraints in the CLAVR treatment of partial cloudiness. Results suggest that if the satellite cloud data is available in real time, it can be used to improve the cloud parameterization in numerical forecast models and data assimilation systems.

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Curtis H. Marshall
,
Kenneth C. Crawford
,
Kenneth E. Mitchell
, and
David J. Stensrud

Abstract

On 31 January 1996, the National Centers for Environmental Prediction/Environmental Modeling Center (NCEP/EMC) implemented a state-of-the-art land surface parameterization in the operational Eta Model. The purpose of this study is to evaluate and test its performance and demonstrate its impacts on the diurnal cycle of the modeled planetary boundary layer (PBL). Operational Eta Model output from summer 1997 are evaluated against the unique observations of near-surface and subsurface fields provided by the Oklahoma Mesonet. The evaluation is partially extended to July 1998 to examine the effects of significant changes that were made to the operational model configuration during the intervening time.

Results indicate a severe positive bias in top-layer soil moisture, which was significantly reduced in 1998 by a change in the initialization technique. Net radiation was overestimated, largely because of a positive bias in the downward shortwave component. Also, the ground heat flux was severely underestimated. Given energy balance constraints, the combination of these two factors resulted in too much available energy for the turbulent fluxes of sensible and latent heat. Comparison of model and observed vertical thermodynamic profiles demonstrates that these errors had a marked impact on the model PBL throughout its entire depth. Evidence also is presented that suggests a systematic underestimation of the downward entrainment of relatively warmer, drier air at the top of the PBL during daylight hours.

Analyses of the monthly mean bias of 2-m temperature and specific humidity revealed a cool, moist bias over western Oklahoma, and a warm, dry bias over the eastern portion of the state. A very sharp transition existed across central Oklahoma between these two regimes. The sharp spatial gradient in both the air temperature and humidity bias fields is strikingly correlated with a sharp west–east gradient in the model vegetation greenness database. This result suggests too much (too little) latent heat flux over less (more) vegetated areas of the model domain.

A series of sensitivity tests are presented that were designed to explore the reasons for the documented error in the simulated surface fluxes. These tests have been used as supporting evidence for changes in the operational model. Specifically, an alternative specification for the soil thermal conductivity yields a more realistic ground heat flux. Also, the alternative thermal conductivity, when combined with a slight adjustment to the thermal roughness length, yields much better internal consistency among the simulated skin temperature and surface fluxes, and better agreement with observations.

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Yan Luo
,
Ernesto H. Berbery
,
Kenneth E. Mitchell
, and
Alan K. Betts

Abstract

This study examines the recently released National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) products over diverse climate regimes to determine the regional relationships between soil moisture and near-surface atmospheric variables. NARR assimilates observed precipitation, as well as near-surface observations of humidity and wind, while seeking a balance of the surface water and energy budgets with a modern land surface model. The results of this study indicate that for most basins (of approximate size of 0.5–1.0 × 106 km2) the NARR surface water budgets have relatively small residuals (about 0.2 mm day−1), and slightly larger residuals (about 0.4 mm day−1) for basins with complex terrain like those in the western United States.

Given that the NARR is an assimilation system (especially one that assimilates observed precipitation), the NARR does not include feedbacks between soil moisture and precipitation. Nonetheless, as a diagnostic tool anchored to observations, the NARR does show that the extent of positive correlation between anomalies of soil moisture and anomalies of precipitation in a given region depends on that region’s dryness. The existence of correlations among all variables is a necessary—but not sufficient—condition for land–atmosphere feedbacks to exist, as a region with no correlations would not be expected to have feedbacks. Likewise, a high degree of persistence of soil moisture anomalies in a given basin does not by itself guarantee a positive correlation between anomalies of soil moisture and precipitation.

Land surface–atmosphere relationships at monthly time scales are identified by examining the associations between soil moisture and surface and boundary layer variables. Low soil moisture is consistently associated with increased net shortwave radiation and increased outgoing longwave radiation through the effects of less cloud cover and lower atmospheric humidity. No systematic association is revealed between soil moisture and total net surface radiation, as this relation varies substantially between different basins. Low soil moisture is positively correlated with increased sensible heat and lower latent heat (reflected in a smaller evaporative fraction), decreased low-cloud cover, and higher lifting condensation level. The relation between soil moisture anomalies and precipitation anomalies is found to be quite variable between the basins, depending on whether availability of surface water exceeds the available energy for evaporation, or vice versa. Wetter basins, like the Columbia and Ohio, display weak or no correlations between soil moisture anomalies and precipitation anomalies. On the other hand, transitional regions between wet and dry regions, like the central Great Plains, manifest a positive correlation between soil moisture anomalies and precipitation anomalies. These results further the understanding of previous predictability studies (in coupled land–atmosphere prediction models), which indicates that in order for precipitation anomalies to emerge in response to soil moisture anomalies in a given region, it is necessary that the region’s seasonal climate be neither too dry nor too wet.

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Alan K. Betts
,
Fei Chen
,
Kenneth E. Mitchell
, and
Zaviša I. Janjić

Abstract

Data from the 1987 summer FIFE experiment for four pairs of days are compared with corresponding 48-h forecasts from two different versions of the Eta Model, both initialized from the NCEP–NCAR (National Centers for Environmental Prediction–National Center for Atmospheric Research) global reanalysis. One used the late 1995 operational Eta Model physics, the second, with a new soil and land surface scheme and revisions to the surface layer and boundary layer schemes, used the physics package that became operational on 31 January 1996. Improvements in the land surface parameterization and its interaction with the atmosphere are one key to improved summer precipitation forecasts. The new soil thermal model is an improvement over the earlier slab soil model, although the new skin temperature generally now has too large a diurnal cycle (whereas the old surface temperature had too small a diurnal cycle) and is more sensitive to net radiation errors. The nighttime temperature minima are often too low, because of a model underestimate of the downwelling radiation, despite improvements in the coupling of the surface and boundary layer at night. The daytime incoming solar radiation has a substantial high bias in both models, because of some coding errors (which have now been corrected), insufficient atmospheric shortwave absorption, and underestimates of cloud.

The authors explore evaporation before and after a midsummer heavy rain event with the two models. The late 1995 operational model uses a soil moisture bucket physics, with a specified annual-mean fixed field soil moisture climatology, so the surface evaporation responds primarily to the atmospheric forcing. While the surface fluxes in the new model show this strong rain event more dramatically, because its soil moisture comes from the global reanalysis rather than climatology, there remain problems with soil moisture initialization. It appears that a fully continuous Eta data assimilation system (which is under development), likely with more than two soil layers and assimilation of observed hourly precipitation, will be needed to get an adequate soil moisture initialization. Evaporation in the new two-layer soil model falls too much from forecast day 1 to day 2, as the first shallow 10-cm layer dries out (as it also does in the 1995 model with the bucket physics). This appears to be related to the specified low vegetation fraction and the bare soil evaporation model. Although the new boundary layer scheme is better coupled to the surface at night, both versions underestimate entrainment at the top of the mixed layer. The improvement in the surface evaporation resulting from using a climatological green vegetation fraction (derived from satellite data) and a revised bare soil evaporation formulation are shown. These changes were incorporated in a model physics revision in February 1997. An encouraging result from one case study, when it rained in the model, shows that the interaction between the surface, boundary layer, and convection schemes during precipitation is satisfactory, although the model underestimates the impact of cloud cover on the incoming solar radiation.

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Cheng-Hsuan Lu
,
Masao Kanamitsu
,
John O. Roads
,
Wesley Ebisuzaki
,
Kenneth E. Mitchell
, and
Dag Lohmann

Abstract

This study compares soil moisture analyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (R-1) and the later NCEP– Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP) global reanalysis (R-2). The R-1 soil moisture is strongly controlled by nudging it to a prescribed climatology, whereas the R-2 soil moisture is adjusted according to differences between model-generated and observed precipitation. While mean soil moisture fields from R-1 and R-2 show many geographic similarities, there are some major differences. This study uses in situ observations from the Global Soil Moisture Data Bank to evaluate the two global reanalysis products. In general, R-2 does a better job of simulating interannual variations, the mean seasonal cycle, and the persistence of soil moisture, when compared to observations. However, the R-2 reanalysis does not necessarily represent observed soil moisture characteristics well in all aspects. Sometimes R-1 provides a better soil moisture analysis on monthly time scales, which is likely a consequence of the deficiencies in the R-2 surface water balance.

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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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David J. Stensrud
,
Geoffrey S. Manikin
,
Eric Rogers
, and
Kenneth E. Mitchell

Abstract

The cold pool, a pool of evaporatively cooled downdraft air that spreads out horizontally along the ground beneath a precipitating cloud, is often a factor in severe weather and heavy precipitation events. Unfortunately, cold pools are not well sampled by the present observational network and are rarely depicted in numerical model initial conditions. A procedure to identify and insert cold pools into the 29-km Eta Model is developed and tested on seven cases during 1995. Results suggest that when the large-scale forcing is strong, the inclusion of cold pools produces only slight changes in the forecasts. However, for the one case in which the large-scale forcing is relatively weak, the inclusion of cold pools produces significant changes in many of the model fields. These initial results, while not conclusive, suggest that the incorporation of cold pools, and other mesoscale features, may be important to the improvement of numerical guidance for severe weather and heavy precipitation forecasting.

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