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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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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. 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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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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Gang Hong
,
Ping Yang
,
Bo-Cai Gao
,
Bryan A. Baum
,
Yong X. Hu
,
Michael D. King
, and
Steven Platnick

Abstract

This study surveys the optical and microphysical properties of high (ice) clouds over the Tropics (30°S–30°N) over a 3-yr period from September 2002 through August 2005. The analyses are based on the gridded level-3 cloud products derived from the measurements acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard both the NASA Earth Observing System Terra and Aqua platforms. The present analysis is based on the MODIS collection-4 data products. The cloud products provide daily, weekly, and monthly mean cloud fraction, cloud optical thickness, cloud effective radius, cloud-top temperature, cloud-top pressure, and cloud effective emissivity, which is defined as the product of cloud emittance and cloud fraction. This study is focused on high-level ice clouds. The MODIS-derived high clouds are classified as cirriform and deep convective clouds using the International Satellite Cloud Climatology Project (ISCCP) classification scheme. Cirriform clouds make up more than 80% of the total high clouds, whereas deep convective clouds account for less than 20% of the total high clouds. High clouds are prevalent over the intertropical convergence zone (ITCZ), the South Pacific convergence zone (SPCZ), tropical Africa, the Indian Ocean, tropical America, and South America. Moreover, land–ocean, morning–afternoon, and summer–winter variations of high cloud properties are also observed.

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F. Giorgi
,
E.-S. Im
,
E. Coppola
,
N. S. Diffenbaugh
,
X. J. Gao
,
L. Mariotti
, and
Y. Shi

Abstract

Because of their dependence on water, natural and human systems are highly sensitive to changes in the hydrologic cycle. The authors introduce a new measure of hydroclimatic intensity (HY-INT), which integrates metrics of precipitation intensity and dry spell length, viewing the response of these two metrics to global warming as deeply interconnected. Using a suite of global and regional climate model experiments, it is found that increasing HY-INT is a consistent and ubiquitous signature of twenty-first-century, greenhouse gas–induced global warming. Depending on the region, the increase in HY-INT is due to an increase in precipitation intensity, dry spell length, or both. Late twentieth-century observations also exhibit dominant positive HY-INT trends, providing a hydroclimatic signature of late twentieth-century warming. The authors find that increasing HY-INT is physically consistent with the response of both precipitation intensity and dry spell length to global warming. Precipitation intensity increases because of increased atmospheric water holding capacity. However, increases in mean precipitation are tied to increases in surface evaporation rates, which are lower than for atmospheric moisture. This leads to a reduction in the number of wet days and an increase in dry spell length. This analysis identifies increasing hydroclimatic intensity as a robust integrated response to global warming, implying increasing risks for systems that are sensitive to wet and dry extremes and providing a potential target for detection and attribution of hydroclimatic changes.

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S. Sorooshian
,
X. Gao
,
K. Hsu
,
R. A. Maddox
,
Y. Hong
,
H. V. Gupta
, and
B. Imam

Abstract

Recent progress in satellite remote-sensing techniques for precipitation estimation, along with more accurate tropical rainfall measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) instruments, have made it possible to monitor tropical rainfall diurnal patterns and their intensities from satellite information. One year (August 1998–July 1999) of tropical rainfall estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system were used to produce monthly means of rainfall diurnal cycles at hourly and 1° × 1° scales over a domain (30°S–30°N, 80°E–10°W) from the Americas across the Pacific Ocean to Australia and eastern Asia.

The results demonstrate pronounced diurnal variability of tropical rainfall intensity at synoptic and regional scales. Seasonal signals of diurnal rainfall are presented over the large domain of the tropical Pacific Ocean, especially over the ITCZ and South Pacific convergence zone (SPCZ) and neighboring continents. The regional patterns of tropical rainfall diurnal cycles are specified in the Amazon, Mexico, the Caribbean Sea, Calcutta, Bay of Bengal, Malaysia, and northern Australia. Limited validations for the results include comparisons of 1) the PERSIANN-derived diurnal cycle of rainfall at Rondonia, Brazil, with that derived from the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) radar data; 2) the PERSIANN diurnal cycle of rainfall over the western Pacific Ocean with that derived from the data of the optical rain gauges mounted on the TOGA-moored buoys; and 3) the monthly accumulations of rainfall samples from the orbital TMI and PR surface rainfall with the accumulations of concurrent PERSIANN estimates. These comparisons indicate that the PERSIANN-derived diurnal patterns at the selected resolutions produce estimates that are similar in magnitude and phase.

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X. Liang
,
S. Miao
,
J. Li
,
R. Bornstein
,
X. Zhang
,
Y. Gao
,
F. Chen
,
X. Cao
,
Z. Cheng
,
C. Clements
,
W. Dabberdt
,
A. Ding
,
D. Ding
,
J. J. Dou
,
J. X. Dou
,
Y. Dou
,
C. S. B. Grimmond
,
J. E. González-Cruz
,
J. He
,
M. Huang
,
X. Huang
,
S. Ju
,
Q. Li
,
D. Niyogi
,
J. Quan
,
J. Sun
,
J. Z. Sun
,
M. Yu
,
J. Zhang
,
Y. Zhang
,
X. Zhao
,
Z. Zheng
, and
M. Zhou

Abstract

Urbanization modifies atmospheric energy and moisture balances, forming distinct features [e.g., urban heat islands (UHIs) and enhanced or decreased precipitation]. These produce significant challenges to science and society, including rapid and intense flooding, heat waves strengthened by UHIs, and air pollutant haze. The Study of Urban Impacts on Rainfall and Fog/Haze (SURF) has brought together international expertise on observations and modeling, meteorology and atmospheric chemistry, and research and operational forecasting. The SURF overall science objective is a better understanding of urban, terrain, convection, and aerosol interactions for improved forecast accuracy. Specific objectives include a) promoting cooperative international research to improve understanding of urban summer convective precipitation and winter particulate episodes via extensive field studies, b) improving high-resolution urban weather and air quality forecast models, and c) enhancing urban weather forecasts for societal applications (e.g., health, energy, hydrologic, climate change, air quality, planning, and emergency response management). Preliminary SURF observational and modeling results are shown (i.e., turbulent PBL structure, bifurcating thunderstorms, haze events, urban canopy model development, and model forecast evaluation).

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Xin-Zhong Liang
,
Min Xu
,
Xing Yuan
,
Tiejun Ling
,
Hyun I. Choi
,
Feng Zhang
,
Ligang Chen
,
Shuyan Liu
,
Shenjian Su
,
Fengxue Qiao
,
Yuxiang He
,
Julian X. L. Wang
,
Kenneth E. Kunkel
,
Wei Gao
,
Everette Joseph
,
Vernon Morris
,
Tsann-Wang Yu
,
Jimy Dudhia
, and
John Michalakes

The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approach to improve weather or climate prediction along with a reliable uncertainty estimate. The CWRF also emphasizes the societal service capability to provide impactrelevant information by coupling with detailed models of terrestrial hydrology, coastal ocean, crop growth, air quality, and a recently expanded interactive water quality and ecosystem model.

This study provides a general CWRF description and basic skill evaluation based on a continuous integration for the period 1979– 2009 as compared with that of WRF, using a 30-km grid spacing over a domain that includes the contiguous United States plus southern Canada and northern Mexico. In addition to advantages of greater application capability, CWRF improves performance in radiation and terrestrial hydrology over WRF and other regional models. Precipitation simulation, however, remains a challenge for all of the tested models.

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M. Rodell
,
H. K. Beaudoing
,
T. S. L’Ecuyer
,
W. S. Olson
,
J. S. Famiglietti
,
P. R. Houser
,
R. Adler
,
M. G. Bosilovich
,
C. A. Clayson
,
D. Chambers
,
E. Clark
,
E. J. Fetzer
,
X. Gao
,
G. Gu
,
K. Hilburn
,
G. J. Huffman
,
D. P. Lettenmaier
,
W. T. Liu
,
F. R. Robertson
,
C. A. Schlosser
,
J. Sheffield
, and
E. F. Wood

Abstract

This study quantifies mean annual and monthly fluxes of Earth’s water cycle over continents and ocean basins during the first decade of the millennium. To the extent possible, the flux estimates are based on satellite measurements first and data-integrating models second. A careful accounting of uncertainty in the estimates is included. It is applied within a routine that enforces multiple water and energy budget constraints simultaneously in a variational framework in order to produce objectively determined optimized flux estimates. In the majority of cases, the observed annual surface and atmospheric water budgets over the continents and oceans close with much less than 10% residual. Observed residuals and optimized uncertainty estimates are considerably larger for monthly surface and atmospheric water budget closure, often nearing or exceeding 20% in North America, Eurasia, Australia and neighboring islands, and the Arctic and South Atlantic Oceans. The residuals in South America and Africa tend to be smaller, possibly because cold land processes are negligible. Fluxes were poorly observed over the Arctic Ocean, certain seas, Antarctica, and the Australasian and Indonesian islands, leading to reliance on atmospheric analysis estimates. Many of the satellite systems that contributed data have been or will soon be lost or replaced. Models that integrate ground-based and remote observations will be critical for ameliorating gaps and discontinuities in the data records caused by these transitions. Continued development of such models is essential for maximizing the value of the observations. Next-generation observing systems are the best hope for significantly improving global water budget accounting.

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