This research is designed to investigate how convective instability influences monthly mean precipitation in Texas in the summertime and to examine the modulation of convective instability and precipitation by local and regional forcings. Since drought results from the accumulated effects of deficient precipitation over time, this study is expected to shed light on the physical and dynamical mechanisms of the initiation and maintenance of serious droughts as well. The focus in Part I of this two-part study is on identification of the controlling convective parameters and, in turn, the surface-based processes that cause variations in these parameters. NCEP-NCAR reanalysis data and observed precipitation data, correlation analysis, multiple linear regression analysis, and back-trajectory analysis are used to reveal the underlying dynamics of their linkage and causality.Monthly mean precipitation is modified mainly by convective inhibition (CIN) rather than by convective available potential energy (CAPE) or by precipitable water. Excessive CIN is caused by surface dryness and warming at 700 hPa, leading to precipitation deficits on a monthly time scale. While the dewpoint temperature and thermodynamics at the surface are greatly affected by the soil moisture, the temperature at 700 hPa was found to be statistically independent of the surface dewpoint temperature since the 700-hPa temperature represents free-atmospheric processes. (These free-atmospheric processes are the focus of the companion paper.) Finally, the strong correlations among precipitation, soil moisture, and CIN, as well as their underlying physical processes, suggest that the tight linkage between precipitation and soil moisture is not only due to the impacts of precipitation on soil moisture but also to the feedbacks of soil moisture on precipitation by controlling CIN.

Extreme rainfall, with storm total precipitation exceeding 500 mm, occurs several times per decade in Texas. According to a compositing analysis, the large-scale weather patterns associated with extreme rainfall events involve a northward deflection of the tropical trade winds into Texas, with deep southerly winds extending into the middle troposphere. One such event, the July 2002 South-Central Texas flood, is examined in detail. This particular event was associated with a stationary upper-level trough over central Texas and northern Mexico that established a steady influx of tropical moisture from the south. While the onset of the event was triggered by destabilization caused by an upper-level vortex moving over the northeast Mexican coast, a succession of upper-level processes allowed the event to become stationary over south-central Texas and produce heavy rain for several days. While the large-scale signatures of such extreme rain events evolve slowly, the many interacting processes at smaller scales make numerical forecasts highly sensitive to details of the simulations. [

Abstract. Optimization of land surface models has been challenging due to the model complexity and uncertainty. In this study, we performed scheme-based model optimizations by designing a framework for coupling "the micro-genetic algorithm" (micro-GA) and "the Noah land surface model with multiple physics options" (Noah-MP). Micro-GA controls the scheme selections among eight different land surface parameterization categories, each containing 2-4 schemes, in Noah-MP in order to extract the optimal scheme combination that achieves the best skill score. This coupling framework was successfully applied to the optimizations of evapotranspiration and runoff simulations in terms of surface water balance over the Han River basin in Korea, showing outstanding speeds in searching for the optimal scheme combination. Taking advantage of the natural selection mechanism in micro-GA, we explored the model sensitivity to scheme selections and the scheme interrelationship during the micro-GA evolution process. This information is helpful for better understanding physical parameterizations and hence it is expected to be effectively used for further optimizations with uncertain parameters in a specific set of schemes.

Identifying dynamical and physical mechanisms controlling variability of convective precipitation is critical for predicting intraseasonal and longer-term changes in warm-season precipitation and convectively driven large-scale circulations. On a monthly basis, the relationship of convective instability with precipitation is examined to investigate the modulation of convective instability on precipitation using the Global Historical Climatology Network (GHCN) and NCEP-NCAR reanalysis for 1948-2003. Three convective parametersconvective inhibition (CIN), precipitable water (PW), and convective available potential energy (CAPE)-are examined. A lifted index and a difference between low-tropospheric temperature and surface dewpoint are used as proxies of CAPE and CIN, respectively.A simple correlation analysis between the convective parameters and the reanalysis precipitation revealed that the most significant convective parameter in the variability of monthly mean precipitation varies by regions and seasons. With respect to region, CIN is tightly coupled with precipitation over summer continents in the Northern Hemisphere and Australia, while PW or CAPE is tightly coupled with precipitation over tropical oceans. With respect to seasons, the identity of the most significant convective parameter tends to be consistent across seasons over the oceans, while it varies by season in Africa and South America. Results from GHCN precipitation data are broadly consistent with reanalysis data where GHCN data exist, except in some tropical areas where correlations are much stronger (and sometimes signed differently) with reanalysis precipitation than with GHCN precipitation.