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The authors investigate the short-term relationship between aerosol concentrations and summer rainfall frequency in China using the daily surface observations of particulate matters with a diameter of less than 10 [micro]/m (PM10) mass concentration, rainfall, and satellite-observed cloud properties. Results in this study reveal that on the time scale of a few days aerosol concentration is positively correlated with the frequency of moderate-rainfall (10-20 mm [day.sup.-1]) days but is negatively correlated with the frequency of light-rainfall (

The effects of preeipitable water vapor (PWV) retrievals from the Southern California Integrated GPS Network (SCIGN) on quantitative precipitation forecast (QPF) skill are examined over two flood-prone regions of Southern California: Santa Barbara (SB) and Ventura County (VC). Two sets of QPFs are made, one using the initial water vapor field from the NCEP 40-km Eta initial analysis, and another in which the initial Eta water vapor field is modified by incorporating the PWV data from the SCIGN receivers. Lateral boundary data for the QPFs, as well as the hydrostatic component of the GPS zenith delay data, are estimated from the Eta analysis. Case studies of a winter storm on 2 February during the 1997/98 El Nifio, and storms leading up to the La Conchita, California, landslide on 10 January 2005, show notably improved QPFs for the first 3-6 h with the addition of GPS PWV data. For a total of 47 winter storm forecasts between February 1998 and January 2005 the average absolute QPF improvement is small; however, QPF improvements exceed 5 mm in several underpredicted rainfall events, with GPS data also improving most cases with overpredicted rainfall. The GPS improvements are most significant (above or near the 2[sigma] level) when the low-level winds off the coast of Southern California are from the southern (SW to SE) quadrant. To extend the useful forecast skill enhancement beyond six hours, however, additional sources of water vapor data over broader areas of the adjacent Pacific Ocean are needed.

The momentum flux by orographic gravity waves and the turbulent heat flux in wave-breaking regions are estimated from aircraft data from ALPEX. The fluxes on 6 March 1982 are controlled by low-level directional shear of the mean flow and associated critical level with wave stress decreasing toward the critical level. On 25 March 1982 a critical level does not occur and wave stress is approximately constant with height within the observational domain. The calculation of these fluxes appears to be the first direct comparison between simple models of gravity-wave momentum flux and observed atmospheric fluxes. To develop a simple formulation of gravity wave drag for large-scale models, the gravity-wave stress supersaturation theory by Lindzen is generalized for the application to vertically varying mean flows. The wave momentum flux estimated from the generalized model agrees well with the observations for the two ALPEX days. For the 6 March case, the vertical divergence of wave momentum flux below the critical level is comparable to the Coriolis term in the momentum equation. The effective height of the surface topography required for the formulation of the wave momentum flux at the ground surface is estimated from the data and found to agree with the formulation of Stem and Pierrehumbert. Wave breaking below the critical level leads to a convectively unstable region 10-20 km wide where well-organized turbulent-scale convection occurs. The magnitude of the observed upward turbulent heat flux can be approximated by using the flux gradient relationship in which the mixing length and modified shear are derived from the generalized wave-stress supersaturation condition. However, the net turbulent heat flux across the entire width of the mountain waves appears to be small due to cancellation between the upward heat flux in the convectively unstable region and the downward heat flux at the back of the wave. The spatially averaged wave-scale heat flux is also small for the data analyzed here.

A geostatistical framework for integrating lower-atmosphere state variables and terrain characteristics into the spatial interpolation of rainfall is presented. Lower-atmosphere state variables considered are specific humidity and wind, derived from an assimilated data product from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP-NCAR reanalysis). These variables, along with terrain elevation and its gradient from a 1-km-resolution digital elevation model, are used for constructing additional rainfall predictors, such as the amount of moisture subject to orographic lifting; these latter predictors quantify the interaction of lower-atmosphere characteristics with local terrain. A 'first-guess' field of precipitation estimates is constructed via a multiple regression model using collocated rain gauge observations and rainfall predictors. The final map of rainfall estimates is derived by adding to this initial field a field of spatially interpolated residuals, which accounts for local deviations from the regression-based first-guess field. Several forms of spatial interpolation (kriging), which differ in the degree of complexity of the first-guess field, are considered for mapping the seasonal average of daily precipitation for the period from 1 November 1981 to 31 January 1982 over a region in northern California at 1-km resolution. The different interpolation schemes are compared in terms of cross-validation statistics and the spatial characteristics of cross-validation errors. The results indicate that integration of low-atmosphere and terrain information in a geostatistical framework could lead to more accurate representations of the spatial distribution of rainfall than those found in traditional analyses based only on rain gauge data. The magnitude of this latter improvement, however, would depend on the density of the rain gauge stations, on the spatial variability of the precipitation field, and on the degree of correlation between rainfall and its predictors.

Regional-scale projections of climate change signals due to increases in atmospheric CO[sub 2] are generated for the western United States using a regional climate model (RCM) nested within two global scenarios from a GCM. The downscaled control climate improved the local accuracy of the GCM results substantially. The downscaled control climate is reasonably close to the results of an 8-yr regional climate hindcast using the same RCM nested within the NCEP–NCAR reanalysis, despite wet biases in high-elevation regions along the Pacific coast. The downscaled near-surface temperature signal ranges from 3 to 5 K in the western United States. The projected warming signals generally increase with increasing elevation, consistent with earlier studies for the Swiss Alps and the northwestern United States. In addition to the snow–albedo feedback, seasonal variations of the low-level flow and soil moisture appear to play important roles in the spatial pattern of warming signals. Projected changes in precipitation characteristics are mainly associated with increased moisture fluxes from the Pacific Ocean and the increase in elevation of freezing levels during the cold season. Projected cold season precipitation increases substantially in mountainous areas along the Pacific Ocean. Most of the projected precipitation increase over the Sierra Nevada and the Cascades is in rainfall, while snowfall generally decreases except above 2500 m. Projected changes in summer rainfall are small. The snow budget signals are characterized by decreased (increased) cold season snowfall (snowmelt) and reduced snowmelt during spring and summer. The projected cold season runoff from high-elevation regions increases substantially in response to increased cold season rainfall and snowmelt, while the spring runoff decreases due to an earlier depletion of snow, except above 2500 m. [ABSTRACT FROM AUTHOR]