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J. Li
,
X. Gao
, and
S. Sorooshian

Abstract

This study downscaled more than five years of data (1999–2004) for hydrometeorological fields over the upper Rio Grande basin (URGB) to a 4-km resolution using a regional model [fifth-generation Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5, version 3)] and two forcing datasets that include National Centers for Environmental Prediction (NCEP)–NCAR reanalysis-1 (R1) and North America Regional Reanalysis (NARR) data. The long-term high-resolution simulation results show detailed patterns of hydroclimatological fields that are highly related to the characteristics of the regional terrain; the most important of these patterns are precipitation localization features caused by the complex topography. In comparison with station observational data, the downscaling processing, on whichever forcing field is used, generated more accurate surface temperature and humidity fields than the Eta Model and NARR data, although it still included marked errors, such as a negative (positive) bias toward the daily maximum (minimum) temperature and overestimated precipitation, especially in the cold season.

Comparing the downscaling results forced by the NARR and R1 with both the gridded and station observational data shows that under the NARR forcing, the MM5 model produced generally better results for precipitation, temperature, and humidity than it did under the R1 forcing. These improvements were more apparent in winter and spring. During the warm season, although the use of NARR improved the precipitation estimates statistically at the regional (basin) scale, it substantially underestimated them over the southern upper Rio Grande basin, partly because the NARR forcing data exhibited warm and dry biases in the monsoon-active region during the simulation period and improper domain selection. Analyses also indicate that over mountainous regions, both the Climate Prediction Center’s (CPC’s) gridded (0.25°) and NARR forcings underestimate precipitation in comparison with station gauge data.

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X. H. Gao
and
J. L. Stanford

Abstract

Equatorial low-frequency oscillations with periods of 1–2 months are being intensively studied by many investigators. A strong equatorial “dipole” pattern is observed in which atmospheric variables such as temperate, wind, and pressure are out of phase between the Indian Ocean-Indonesia region and the western Pacific. While it is generally thought that the oscillations make a complete circuit around the earth from their excitation region in the equatorial Indian Ocean-western Pacific, the signal is more difficult to observe over the South America-Atlantic-Africa sector. Using analyses of four year of satellite-derived microwave radiance data, evidence is presented here for the possibility of a feedback route in the Southern Hemisphere. The observed propagation path extends from the central equatorial Pacific across lower South America and heads equatorward after passing south of Africa. The response route finally reenters the equatorial Indian Ocean with the correct phase to enhance the primary equatorial dipole structure. The Southern Hemisphere propagation path migrates northward in April-September and southward in October-March. The largest correlation with the equatorial dateline region occurs at the turning point of the feedback route, in the South Atlantic. This propagation path appears to constitute a feedback mechanism which could aid in stabilizing the low frequency oscillations through positive feedback.

The correlations are shown to be statistically significant by several methods, including Monte Carlo simulations.

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J. Li
,
R. A. Maddox
,
X. Gao
,
S. Sorooshian
, and
K. Hsu

Abstract

Severe flash flood storms that occurred in Las Vegas, Nevada, on 8 July 1999, were unusual for the semiarid southwest United States because of their extreme intensity and the morning occurrence of heavy convective rainfall. This event was simulated using the high-resolution Regional Atmospheric Modeling System (RAMS), and convective rainfall, storm cell processes, and thermodynamics were evaluated using Geostationary Operational Environmental Satellite (GOES) imagery and a variety of other observations. The simulation agreed reasonably well with the observations in a large-scale sense, but errors at small scales were significant. The storm's peak rainfalls were overestimated and had a 3-h timing delay. The primary forcing mechanism for storms in the simulation was clearly daytime surface heating along mountain slopes, and the actual trigger mechanism causing the morning convection, an outflow from nighttime storms to the northeast of Las Vegas, was not captured accurately. All simulated convective cells initiated over and propagated along mountain slopes; however, cloud images and observed rainfall cell tracks showed that several important storm cells developed over low-elevation areas of the Las Vegas valley, where a layer of fairly substantial convective inhibition persisted above the boundary layer in the simulation. The small-scale errors in timing, location, rain amounts, and characteristics of cell propagation would seriously affect the accuracy of streamflow forecasts if the RAMS simulated rainfall were used in hydrologic models. It remains to be seen if explicit storm-scale simulations can be improved to the point where they can drive operationally useful streamflow predictions for the semiarid southwest United States.

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Ismail Yucel
,
W. James Shuttleworth
,
X. Gao
, and
S. Sorooshian

Abstract

This study investigates the extent to which assimilating high-resolution remotely sensed cloud cover into the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) provides an improved regional diagnosis of downward shortwave surface radiation fluxes and precipitation and enhances the model's ability to make short-range prediction. The high-resolution (4 km × 4 km) clear- and cloudy-sky radiances derived using a cloud-screening algorithm from visible band Geostationary Operational Environmental Satellite (GOES) data were used in the University of Maryland Global Energy and Water Cycle Experiment's Surface Radiation Budget (UMD GEWEX/SRB) model to infer the vertically integrated cloud mass via cloud optical thickness. Three-dimensional cloud fields were created that took their horizontal distribution from the satellite image but derived their vertical distribution, in part, from the fields simulated by MM5 during the time step immediately prior to assimilation and, in part, from the observed cloud-top height derived from the infrared band of GOES. Linear interpolation was used to derive 1-min cloud images between 15-min GOES samples, and the resulting images were ingested every minute. Comparisons were made between modeled and observed data taken from the Arizona Meteorological Network (AZMET) in southern Arizona for model runs with and without cloud ingestion. Cloud ingestion substantially improved the ability of the MM5 model to capture temporal and spatial variations in surface fields associated with cloud cover. Experiments in which the model was operated in forecast mode suggest that cloud ingestion gave some limited enhancement in MM5 short-term prediction ability for up to 3 h. However, an analysis suggests that, in order to get additional forecasting capability, it will be necessary to modify the atmospheric dynamics and thermodynamics in the model to be consistent with the ingested cloud fields.

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J. Li
,
X. Gao
,
R. A. Maddox
,
S. Sorooshian
, and
K. Hsu

Abstract

Accurate summertime weather forecasts, particularly the quantitative precipitation forecast (QPF), over the semiarid southwest United States pose a difficult challenge for numerical models. Two case studies, one with typical weather on 6 July 1999 and another with unusual flooding on 8 July 1999, using the Regional Atmospheric Modeling System (RAMS) nested inside the regional Eta Model, were conducted to test numerical weather prediction capabilities over the lower Colorado River basin. The results indicate that the rapid changes in synoptic patterns during these two cases strongly affect the weather and rainfall situation in the basin. The model illustrates that the midlevel sinking over the low elevation of the southwest area of the basin “capped” the development of deep convection in case 1; meanwhile, in case 2, a shear line and convergence over the Las Vegas area valley stimulated intense convective storms in the region. In both cases, the low-level jet (LLJ) stream from the Gulf of California was the major source of atmospheric moisture for the basin. Local topography and thermodynamics also play a significant role in the formation of the weather features. The “thermal low” over the Sonoran Desert is responsible for the LLJ stream, which led to the valley of the Colorado River becoming the warmest and moistest area in the basin. By nesting fine-resolution grids over the Las Vegas area, the representation of local topography in the region was improved in the RAMS model, compared with that in the relatively coarse resolution Eta Model. This appears to be the major reason that the RAMS model could predict intense convective storms over Las Vegas, while the operational Eta forecast could not.

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J. Li
,
X. Gao
,
R. A. Maddox
, and
S. Sorooshian

Abstract

In this article, four continually processed sea surface temperature (SST) datasets, including the Reynolds SST (RYD), the global final analysis of skin temperature at oceans (FNL), and two Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua SSTs retrieved from thermal infrared imagery (TIR) and midinfrared imagery (MIR), were compared. The results show variations from each other. In comparison with the RYD SST, the FNL data have −0.5° ∼ 0.5°C perturbations, while the TIR and MIR SSTs possess larger deviations of −2° ∼ 1°C, mainly due to algorithm and/or sensor differences in these SST datasets.

A regional model, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5), was used to investigate whether model atmospheric predictions, especially those concerning precipitation during the North American monsoon season, are sensitive to these SST variations. A comparison of rainfall, atmospheric height, temperature, and wind fields produced by model results, reanalysis data, and observations indicates that, at monthly scale, the model shows changes in the simulations for three consecutive years; in particular, rainfall amounts, timing, and even patterns vary at some specific regions. Forced by the MODIS Aqua midinfrared SST (MIR), which includes large regions with SST values lower than the conventional Reynolds SST, the MM5 rain field predictions show reduced errors over land and oceans compared to when the model is forced by other SST data. Specifically, rainfall estimates are improved over the offshore of southern Mexico, the Gulf of Mexico, the coastal regions of southern and eastern Mexico, and the southwestern U.S. monsoon active region, but only slightly improved over the monsoon core and the high-elevated Great Plains. Using MIR SST data, one is also capable of improving geopotential height and temperature fields in comparison with the reanalysis data.

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J. Xu
,
X. Gao
,
J. Shuttleworth
,
S. Sorooshian
, and
E. Small

Abstract

In this study, the seasonal development of the North American monsoon system (NAMS), as simulated by a mesoscale model during a 22-yr simulation from 1980 through 2001, is assessed. Comparison between model simulations and observations shows that the model simulation reproduces the precipitation, skin temperature, and wind field patterns in the seasonal development (May–July) of the NAMS reasonably well and that the mesoscale features and spatial heterogeneity of the NAMS are described correctly. The onset of the monsoon in the central and southern Sierra Madre Occidental (SMO) in Mexico occurs on 20 June, about 2 weeks earlier than the onset in Sonora, Mexico (6 July), the Sonoran Desert, and central Arizona and New Mexico (8 July). The temperature in Mexico is highest after the onset of the monsoon and then decreases with the increasing monsoon rainfall. However, the temperature in the Sonoran Desert and central Arizona and New Mexico is highest just prior to the onset of the monsoon, and high temperatures may then persist throughout July. The lower-level (700 hPa) zonal wind field reverses from westerly to easterly over the central and southern SMO just before the onset of rain in these regions; this is associated with the abrupt northward movement of the subtropical high over this region. The progression of the subtropical high into central Arizona and New Mexico results in a local reduction in the westerly flow, and although the southwesterly flow weakens, atmospheric moisture is still mainly from the Gulf of California and the eastern Pacific Ocean.

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J. Li
,
S. Sorooshian
,
W. Higgins
,
X. Gao
,
B. Imam
, and
K. Hsu

Abstract

Diurnal variability is an important yet poorly understood aspect of the warm-season precipitation regime over southwestern North America. In an effort to improve its understanding, diurnal variability is investigated numerically using the fifth-generation Pennsylvania State University (PSU)–NCAR Mesoscale Model (MM5). The goal herein is to determine the possible influence of spatial resolution on the diurnal cycle.

The model is initialized every 48 h using the operational NCEP Eta Model 212 grid (40 km) model analysis. Model simulations are carried out at horizontal resolutions of both 9 and 3 km. Overall, the model reproduces the basic features of the diurnal cycle of rainfall over the core monsoon region of northwestern Mexico and the southwestern United States. In particular, the model captures the diurnal amplitude and phase, with heavier rainfall at high elevations along the Sierra Madre Occidental in the early afternoon that shifts to lower elevations along the west slopes in the evening. A comparison to observations (gauge and radar data) shows that the high-resolution (3 km) model generates better rainfall distributions on time scales from monthly to hourly than the coarse-resolution (9 km) model, especially along the west slopes of the Sierra Madre Occidental. The model has difficulty with nighttime rainfall along the slopes, over the Gulf of California, and over Arizona.

A comparison of surface wind data from three NCAR Integrated Sounding System (ISS) stations and the Quick Scatterometer (QuikSCAT) to the model reveals a low bias in the strength of the Gulf of California low-level jet, even at high resolution. The model results indicate that outflow from convection over northwestern Mexico can modulate the low-level jet, though the extent to which these relationships occur in nature was not investigated.

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Z. Wang
,
X. Zeng
,
M. Barlage
,
R. E. Dickinson
,
F. Gao
, and
C. B. Schaaf

Abstract

The land surface albedo in the NCAR Community Climate System Model (CCSM2) is calculated based on a two-stream approximation, which does not include the effect of three-dimensional vegetation structure on radiative transfer. The model albedo (including monthly averaged albedo, direct albedo at local noon, and the solar zenith angle dependence of albedo) is evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) and albedo data acquired during July 2001–July 2002. The model monthly averaged albedos in February and July are close to the MODIS white-sky albedos (within 0.02 or statistically insignificant) over about 40% of the global land between 60°S and 70°N. However, CCSM2 significantly underestimates albedo by 0.05 or more over deserts (e.g., the Sahara Desert) and some semiarid regions (e.g., parts of Australia). The difference between the model direct albedo at local noon and the MODIS black-sky albedo for the near-infrared (NIR) band (with wavelength > 0.7 μm) is larger than the difference for the visible band (with wavelength < 0.7 μm) for most snow-free regions. For eleven model grid cells with different dominant plant functional types, the model diffuse NIR albedo is higher by 0.05 or more than the MODIS white-sky albedo in five of these cells. Direct albedos from the model and MODIS (as computed using the BRDF parameters) increase with solar zenith angles, but model albedo increases faster than the MODIS data. These analyses and the MODIS BRDF and albedo data provide a starting point toward developing a BRDF-based treatment of radiative transfer through a canopy for land surface models that can realistically simulate the mean albedo and the solar zenith angle dependence of albedo.

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J. Jin
,
X. Gao
,
Z.-L. Yang
,
R. C. Bales
,
S. Sorooshian
,
R. E. Dickinson
,
S. F. Sun
, and
G. X. Wu

Abstract

A comparative study of three snow models with different complexities was carried out to assess how a physically detailed snow model can improve snow modeling within general circulation models. The three models were (a) the U.S. Army Cold Regions Research and Engineering Laboratory Model (SNTHERM), which uses the mixture theory to simulate multiphase water and energy transfer processes in snow layers; (b) a simplified three-layer model, Snow–Atmosphere–Soil Transfer (SAST), which includes only the ice and liquid-water phases;and (c) the snow submodel of the Biosphere–Atmosphere Transfer Scheme (BATS), which calculates snowmelt from the energy budget and snow temperature by the force–restore method. Given the same initial conditions and forcing of atmosphere and radiation, these three models simulated time series of snow water equivalent, surface temperature, and fluxes very well, with SNTHERM giving the best match with observations and SAST simulation being close. BATS captured the major processes in the upper portion of a snowpack where solar radiation provides the main energy source and gave satisfying results for seasonal periods. Some biases occurred in BATS surface temperature and energy exchange due to its neglecting of liquid water and underestimating snow density. Ice heat conduction, meltwater heat transport, and the melt–freeze process of snow exhibit strong diurnal variations and large gradients at the uppermost layers of snowpacks. Using two layers in the upper 20 cm and one deeper layer at the bottom to simulate the multiphase snowmelt processes, SAST closely approximated the performance of SNTHERM with computational requirements comparable to those of BATS.

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