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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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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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Randal D. Koster
,
Zhichang Guo
,
Rongqian Yang
,
Paul A. Dirmeyer
,
Kenneth Mitchell
, and
Michael J. Puma

Abstract

The soil moisture state simulated by a land surface model is a highly model-dependent quantity, meaning that the direct transfer of one model’s soil moisture into another can lead to a fundamental, and potentially detrimental, inconsistency. This is first illustrated with two recent examples, one from the National Centers for Environmental Prediction (NCEP) involving seasonal precipitation forecasting and another from the realm of ecological modeling. The issue is then further addressed through a quantitative analysis of soil moisture contents produced as part of a global offline simulation experiment in which a number of land surface models were driven with the same atmospheric forcing fields. These latter comparisons clearly demonstrate, on a global scale, the degree to which model-simulated soil moisture variables differ from each other and that these differences extend beyond those associated with model-specific layer thicknesses or soil texture. The offline comparisons also show, however, that once the climatological statistics of each model’s soil moisture variable are accounted for (here, through a simple scaling using the first two moments), the different land models tend to produce very similar information on temporal soil moisture variability in most parts of the world. This common information can perhaps be used as the basis for successful mappings between the soil moisture variables in different land models.

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Jesse Meng
,
Rongqian Yang
,
Helin Wei
,
Michael Ek
,
George Gayno
,
Pingping Xie
, and
Kenneth Mitchell

Abstract

The NCEP Climate Forecast System Reanalysis (CFSR) uses the NASA Land Information System (LIS) to create its land surface analysis: the NCEP Global Land Data Assimilation System (GLDAS). Comparing to the previous two generations of NCEP global reanalyses, this is the first time a coupled land–atmosphere data assimilation system is included in a global reanalysis. Global observed precipitation is used as direct forcing to drive the land surface analysis, rather than the typical reanalysis approach of using precipitation assimilating from a background atmospheric model simulation. Global observed snow cover and snow depth fields are used to constrain the simulated snow variables. This paper describes 1) the design and implementation of GLDAS/LIS in CFSR, 2) the forcing of the observed global precipitation and snow fields, and 3) preliminary results of global and regional soil moisture content and land surface energy and water budgets closure. With special attention made during the design of CFSR GLDAS/LIS, all the source and sink terms in the CFSR land surface energy and water budgets can be assessed and the total budgets are balanced. This is one of many aspects indicating improvements in CFSR from the previous NCEP reanalyses.

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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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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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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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Joseph G. Alfieri
,
Dev Niyogi
,
Peter D. Blanken
,
Fei Chen
,
Margaret A. LeMone
,
Kenneth E. Mitchell
,
Michael B. Ek
, and
Anil Kumar

Abstract

Vegetated surfaces, such as grasslands and croplands, constitute a significant portion of the earth’s surface and play an important role in land–atmosphere exchange processes. This study focuses on one important parameter used in describing the exchange of moisture from vegetated surfaces: the minimum canopy resistance (r c min ). This parameter is used in the Jarvis canopy resistance scheme that is incorporated into the Noah and many other land surface models. By using an inverted form of the Jarvis scheme, r c min is determined from observational data collected during the 2002 International H2O Project (IHOP_2002). The results indicate that r c min is highly variable both site to site and over diurnal and longer time scales. The mean value at the grassland sites in this study is 96 s m−1 while the mean value for the cropland (winter wheat) sites is one-fourth that value at 24 s m−1. The mean r c min for all the sites is 72 s m−1 with a standard deviation of 39 s m−1. This variability is due to both the empirical nature of the Jarvis scheme and a combination of changing environmental conditions, such as plant physiology and plant species composition, that are not explicitly considered by the scheme. This variability in r c min has important implications for land surface modeling where r c min is often parameterized as a constant. For example, the Noah land surface model parameterizes r c min for the grasslands and croplands types in this study as 40 s m−1. Tests with the coupled Weather Research and Forecasting (WRF)–Noah model indicate that the using the modified values of r c min from this study improves the estimates of latent heat flux; the difference between the observed and modeled moisture flux decreased by 50% or more. While land surface models that estimate transpiration using Jarvis-type relationships may be improved by revising the r c min values for grasslands and croplands, updating the r c min will not fully account for the variability in r c min observed in this study. As such, it may be necessary to replace the Jarvis scheme currently used in many land surface and numerical weather prediction models with a physiologically based estimate of the canopy resistance.

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Raymond W. Arritt
,
Thomas D. Rink
,
Moti Segal
,
Dennis P. Todey
,
Craig A. Clark
,
Mark J. Mitchell
, and
Kenneth M. Labas

Abstract

Hourly wind profiler observations from the NOAA Profiler Network were used to develop a climatology of the low-level jet (LLJ) over the Great Plains of the central United States from April to September of 1993. The peak precipitation episode of the 1993 flood was associated with a sustained period of high incidence of strong low-level jets (over 20 m s−1). Consistent with previous studies, strong low-level jets were found to be promoted in the warm sector of an extratropical cyclone. Comparison of datasets formulated using velocity variance thresholds with unthresholded data similar to the operational hourly data suggests that the profiler observations often were contaminated by radar returns from migrating birds, especially during the months of April and May.

The strong low-level jets during the peak precipitation episode of the 1993 flood over the upper Mississippi River basin were associated with a high-amplitude upper-level wave pattern over and upstream of the continental United States. Separating the composite 850-mb wind for strong low-level jets into geostrophic and ageostrophic components showed that the magnitudes of the ageostrophic component and the anomalous geostrophic component were comparable.

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