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

Abstract

Estimating the water budgets in a small-scale basin is a challenge, especially in the mountainous western United States, where the terrain is complex and observational data in the mountain areas are sparse. This manuscript reports on research that downscaled 5-yr (1999–2004) hydrometeorological fields over the upper Rio Grande basin from a 2.5° NCEP–NCAR reanalysis to a 4-km local scale using a regional climate model [fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3]. The model can reproduce the terrain-related precipitation distribution—the trend of diurnal, seasonal, and interannual precipitation variability—although poor snow simulation caused it to overestimate precipitation and evapotranspiration in the cold season. The outcomes from the coupled model are also comparable to offline Variable Infiltration Capacity (VIC) and Land Data Assimilation System (LDAS)/Mosaic land surface simulations that are driven by observed and/or analyzed surface meteorological data.

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

Abstract

This study examines the capabilities and limitations of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) in predicting the precipitation and circulation features that accompanied the 2004 North American monsoon (NAM). When the model is reinitialized every 5 days to restrain the growth of modeling errors, its results for precipitation checked at subseasonal time scales (not for individual rainfall events) become comparable with ground- and satellite-based observations as well as with the NAM’s diagnostic characteristics. The modeled monthly precipitation illustrates the evolution patterns of monsoon rainfall, although it underestimates the rainfall amount and coverage area in comparison with observations. The modeled daily precipitation shows the transition from dry to wet episodes on the monsoon onset day over the Arizona–New Mexico region, and the multiday heavy rainfall (>1 mm day−1) and dry periods after the onset. All these modeling predictions agree with observed variations. The model also accurately simulated the onset and ending dates of four major moisture surges over the Gulf of California during the 2004 monsoon season. The model reproduced the strong diurnal variability of the NAM precipitation, but did not predict the observed diurnal feature of the precipitation peak’s shift from the mountains to the coast during local afternoon to late night. In general, the model is able to reproduce the major, critical patterns and dynamic variations of the NAM rainfall at intraseasonal time scales, but still includes errors in precipitation quantity, pattern, and timing. The numerical study suggests that these errors are due largely to deficiencies in the model’s cumulus convective parameterization scheme, which is responsible for the model’s precipitation generation.

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Kuo-lin Hsu
,
Tim Bellerby
, and
S. Sorooshian

Abstract

A new satellite-based rainfall monitoring algorithm that integrates the strengths of both low Earth-orbiting (LEO) and geostationary Earth-orbiting (GEO) satellite information has been developed. The Lagrangian Model (LMODEL) algorithm combines a 2D cloud-advection tracking system and a GEO data–driven cloud development and rainfall generation model with procedures to update model parameters and state variables in near–real time. The details of the LMODEL algorithm were presented in Part I. This paper describes a comparative validation against ground radar rainfall measurements of 1- and 3-h LMODEL accumulated rainfall outputs. LMODEL rainfall estimates consistently outperform accumulated 3-h microwave (MW)-only rainfall estimates, even before the more restricted spatial coverage provided by the latter is taken into account. In addition, the performance of LMODEL products remains effective and consistent between MW overpasses. Case studies demonstrate that the LMODEL provides the potential to synergize available satellite data to generate useful precipitation measurements at an hourly scale.

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

Abstract

Rainfall evolution and diurnal variation are important components in the North American monsoon system (NAMS). In this study these components are numerically studied using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) with high resolution (12-km grids) in contrast to most previous model studies that used relatively coarse spatial resolutions (>25 km grids). The model was initialized at the start of each month and allowed to run for 31 days.

The study shows that, in general, the model results broadly matched the patterns of satellite-retrieved rainfall data for monthly rainfall accumulation. The rainfall timing evolution in the monsoon core region predicted by the model generally matched the gauge observations. However, the differences among the three precipitation estimates (model, satellite, and gauge) are obvious, especially in July. The rainfall diurnal cycle pattern was reproduced in the monsoon core region of western Mexico, but there were differences in the diurnal intensity and timing between modeled and observed results. Furthermore, the model cannot capture the diurnal variation over Arizona.

Modeling results showed heavy monsoon rains shift northward along the western Mexico coast in association with the northward evolution of the subtropical highs. This is consistent with previous data analyses. The rainfall diurnal cycle was associated mainly with sea–land/mountain–valley circulations over western Mexico and adjacent oceans.

The simulations show that the model has deficiencies in predicting precipitation over the Gulf of Mexico. The model cannot reproduce the low-level inversion above the marine boundary layers and thus does not generate enough convective inhibition (CIN) to suppress the convection. The model also cannot produce realistic variations of day-to-day atmospheric conditions with only a single initialization at the start of the month.

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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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Terri S. Hogue
,
Soroosh Sorooshian
,
Hoshin Gupta
,
Andrea Holz
, and
Dean Braatz

Abstract

Operational flood forecasting models vary in complexity, but nearly all have parameters for which values must be estimated. The traditional and widespread manual calibration approach requires considerable training and experience and is typically laborious and time consuming. Under the Advanced Hydrologic Prediction System modernization program, National Weather Service (NWS) hydrologists must produce rapid calibrations for roughly 4000 forecast points throughout the United States. The classical single-objective automatic calibration approach, although fast and objective, has not received widespread acceptance among operational hydrologists. In the work reported here, University of Arizona researchers and NWS personnel have collaborated to combine the strengths of the manual and automatic calibration strategies. The result is a multistep automatic calibration scheme (MACS) that emulates the progression of steps followed by NWS hydrologists during manual calibration and rapidly provides acceptable parameter estimates. The MACS approach was tested on six operational basins (drainage areas from 671 to 1302 km2) in the North Central River Forecast Center (NCRFC) area. The results were found to compare favorably with the NCRFC manual calibrations in terms of both visual inspection and statistical measures, such as daily root-mean-square error and percent bias by flow group. Further, implementation of the MACS procedure requires only about 3–4 person hours per basin, in contrast to the 15–20 person hours typically required using the manual approach. Based on this study, the NCRFC has opted to perform further testing of the MACS procedure at a large number of forecast points that constitute the Grand River (Michigan) forecast group. MACS is a time-saving, reliable approach that can provide calibrations that are of comparable quality to the NCRFC’s current methods.

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