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Rain gauges and radars are the most popular sources for precipitation measurement and estimation, but their distribution is limited. In recent decades, satellite-based precipitation products have proven to be an [...]

Over most tropical land areas, the annual peak in precipitation occurs during summer, associated with the local monsoon circulation. However, in some regions of the tropics, the bulk of their [...]

PROBLEM: The North American Monsoon (NAM) can supply as much as 80% of the annual precipitation that falls in the desert Southwest. But monsoon rainfall is highly variable and difficult [...]

Precipitation data are crucial for extreme event risk assessment, decision-making, and adaptation strategies, and form a key input to hydrological, water resource, and ecological models. Global gridded precipitation datasets show [...]

Fog is a weather condition with significant socioeconomic impacts associated with increased hazards and constraints in road, maritime, and air traffic. While current numerical weather prediction models are able to forecast situations that are favorable to fog events, it is very difficult to determine the exact location and time of formation or dissipation. One-dimensional assimilation-forecast models have been implemented at a few airports and provide improved local predictions of fog events, but this approach is limited to specific locations. The occurrence, development, and dissipation of fog result from multiple processes (thermodynamical, radiative, dynamical, microphysical) that occur simultaneously, through a wide range of conditions, and that interact nonlinearly with each other. Hence, to advance our ability to forecast fog processes, we must gain a better understanding of how critical physical processes feed back on each other and improve their parametric representations in models. To provide a dataset suitable to study these processes simultaneously in continental fog, a suite of active and passive remote sensing instruments and in situ sensors was deployed at the Site Instrumental de Recherche en Teledetection Atmospherique (SIRTA; instrumented site for atmospheric remote sensing research) observatory, near Paris, France, for 6 months (winter 2006/07) to monitor profiles of wind, turbulence, microphysical, and radiative properties as well as temperature, humidity, aerosol, and fog droplet microphysics and chemical composition in the surface layer. This field experiment, called ParisFog, provides a comprehensive characterization of over 100 fog and near-fog events. The ParisFog dataset contains contrasted events of stratus-lowering fog and radiative cooling fog as well as a large number of situations considered as favorable to fog formation but where fog droplets did not materialize. The effect of hydrated aerosols on visibility, the role of aerosols' microphysical and chemical properties on super-saturation and droplet activation, and the role of turbulence and sedimentation on fog life cycles have been investigated using the ParisFog dataset. The interactions between these processes, however, remain to be explored.

We assess the response of pack ice penguins, Emperor (Aptenodytes forsteri) and Adelie (Pygoscelis adeliae), to habitat variability and, then, by modeling habitat alterations, the qualitative changes to their populations, size and distribution, as Earth's average tropospheric temperature reaches 2[degrees]C above preindustrial levels (ca. 1860), the benchmark set by the European Union in efforts to reduce greenhouse gases. First, we assessed models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) on penguin performance duplicating existing conditions in the Southern Ocean. We chose four models appropriate for gauging changes to penguin habitat: GFDL-CM2.1, GFDL-CM2.0, MIROC3.2(hi-res), and MRI-CGCM2.3.2a. Second, we analyzed the composited model ENSEMBLE to estimate the point of 2[degrees]C warming (2025-2052) and the projected changes to sea ice coverage (extent, persistence, and concentration), sea ice thickness, wind speeds, precipitation, and air temperatures. Third, we considered studies of ancient colonies and sediment cores and some recent modeling, which indicate the (space/time) large/centennial-scale penguin response to habitat limits of all ice or no ice. Then we considered results of statistical modeling at the temporal interannual-decadal scale in regard to penguin response over a continuum of rather complex, meso-to large-scale habitat conditions, some of which have opposing and others interacting effects. The ENSEMBLE meso/decadal-scale output projects a marked narrowing of penguins' zoogeographic range at the 2[degrees]C point. Colonies north of 70[degrees] S are projected to decrease or disappear: ~50% of Emperor colonies (40% of breeding population) and ~75% of Adelie colonies (70% of breeding population), but limited growth might occur south of 73[degrees] S. Net change would result largely from positive responses to increase in polynya persistence at high latitudes, overcome by decreases in pack ice cover at lower latitudes and, particularly for Emperors, ice thickness. Adelie Penguins might colonize new breeding habitat where concentrated pack ice diverges and/or disintegrating ice shelves expose coastline. Limiting increase will be decreased persistence of pack ice north of the Antarctic Circle, as this species requires daylight in its wintering areas. Adelies would be affected negatively by increasing snowfall, predicted to increase in certain areas owing to intrusions of warm, moist marine air due to changes in the Polar Jet Stream. Key words: Adelie Penguin; Antarctica; climate change; climate modeling; Emperor Penguin; habitat optimum; sea ice; 2[degrees]C warming.

This is the second of two companion papers on modeling of mesoscale cellular structures and drizzle in marine stratocumulus. In the first, aerosol-cloud-precipitation interactions and dynamical feedbacks were investigated to study the formation and evolution of open and closed cellular structures separately. In this paper, coexisting open and closed cells and how they influence one another are examined in a model domain of 180 x 60 x 1.5 [km.sup.3]. Simulations show that gradients in aerosol at the open-closed-cell boundary cause gradients in precipitation that generate a mesoscale circulation. The circulation promotes precipitation in the polluted closed cells but suppresses it in open cells by transporting water vapor to the closed-cell regime and carrying drier air and aerosol back to the open cells. The strength of this circulation depends on the contrast in precipitation under clean and polluted conditions at the boundary. Ship plumes emitted into clean, precipitating regions, simulated as a special case of a clean-polluted boundary, develop a similar circulation. Drizzle in the ship track is first suppressed by the increase in aerosol particles but later recovers and becomes even stronger because the local circulation enhances liquid water path owing to the convergence of water vapor from the region adjacent to the track. This circulation modifies the transport and mixing of ship plumes and enhances their dispersal. Finally, results show that whereas ship emissions do increase cloud albedo in regions of open cells, even the addition of very large aerosol concentrations cannot transform an open cellular structure to a closed one, for the case considered. DOI: 10.1175/2009JAS3120.1

A new modeling framework is used to investigate aerosol-cloud-precipitation interactions and dynamical feedbacks at the mesoscale. The focus is on simulation of the formation and evolution of cellular structures that are commonly seen in satellite images of marine stratocumulus clouds. Simulations are performed at moderate resolution in a 60 x 60 [km.sup.2] domain for 16 h to adequately represent the mesoscale organization associated with open cells and precipitation. Results support the emerging understanding that precipitation plays a critical role in the formation and evolution of open cells. Evaporation of raindrops generates a dynamic response that manifests itself in cellular organization of updrafts and downdrafts and promotes and sustains the formation of an open cellular structure in cloud fields. Vertical motion in open-cell centers with thin clouds is minimal. It is shown that a mean surface rain rate as low as 0.02 mm [day.sup.-1] is, for the case considered, sufficient to promote the formation of open cells. The maximum dimension of individual open cells ranges between 5 and 30 kin. Individual cells grow at a mean rate of between 5 and 10 km [h.sup.-1]. Irregularity in the shape of open cells is caused by formation of new precipitating regions at the cell walls and interference with neighboring cells, which erode, and eventually eliminate, the old cells. The typical lifetime of large individual open cells is about 2 h, close to that observed by radar, although a collection of open cells as a whole may last for tens of hours. DOI: 10.1175/2009JAS3022.1

Global warming is expected to lead to a large increase in atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. The intensity of precipitation extremes is widely held to increase proportionately to the increase in atmospheric water vapor content. Here, we show that this is not the case in 21st-century climate change scenarios simulated with climate models. In the tropics, precipitation extremes are not simulated reliably and do not change consistently among climate models; in the extratropics, they consistently increase more slowly than atmospheric water vapor content. We give a physical basis for how precipitation extremes change with climate and show that their changes depend on changes in the moist-adiabatic temperature lapse rate, in the upward velocity, and in the temperature when precipitation extremes occur. For the tropics, the theory suggests that improving the simulation of upward velocities in climate models is essential for improving predictions of precipitation extremes; for the extratropics, agreement with theory and the consistency among climate models increase confidence in the robustness of predictions of precipitation extremes under climate change. global warming | hydrological cycle | rainfall | extreme events

The SeaWinds scatterometer was originally designed to measure wind vectors over the ocean by exploiting the relationship between wind-induced surface roughening and the normalized radar backscatter cross section. Rain can degrade scatterometer wind estimation; however, the simultaneous wind/ rain (SWR) algorithm was developed to enable SeaWinds to simultaneously retrieve wind and rain rate data. This algorithm is based on colocating data from the Precipitation Radar on the Tropical Rainfall Measuring Mission and SeaWinds on QuikSCAT. This paper develops a new wind and rain radar backscatter model for SWR using colocated data from the Advanced Microwave Scanning Radiometer (AMSR) and SeaWinds aboard the Advanced Earth Observing Satellite II. This paper accounts for rain height in the model in order to calculate surface rain rate from the integrated rain rate. The performance of SWR using the new wind/rain model is measured by comparison of wind vectors and rain rates to the previous SWR algorithm, AMSR rain rates, and National Center for Environmental Prediction numerical weather prediction winds. The new SWR algorithm produces more accurate rain estimates and improved winds, and detects rain with a low false alarm rate. Index Terms--Atmospheric measurements, rain, scattering, wind.

Byline: Holger Gottel (1), Jorn Alexander (1), Elke Keup-Thiel (1), Diana Rechid (1), Stefan Hagemann (1), Tanja Blome (1), Annett Wolf (2), Daniela Jacob (1) Abstract: In the context of the EU-Project BALANCE ( http://balance-eu.info ) the regional climate model REMO was used for extensive calculations of the Barents Sea climate to investigate the vulnerability of this region to climate change. The regional climate model REMO simulated the climate change of the Barents Sea Region between 1961 and 2100 (Control and Climate Change run, CCC-Run). REMO on ~50 km horizontal resolution was driven by the transient ECHAM4/OPYC3 IPCC SRES B2 scenario. The output of the CCC-Run was applied to drive the dynamic vegetation model LPJ-GUESS. The results of the vegetation model were used to repeat the CCC-Run with dynamic vegetation fields. The feedback effect of the modified vegetation on the climate change signal is investigated and discussed with focus on precipitation, temperature and snow cover. The effect of the offline coupled vegetation feedback run is much lower than the greenhouse gas effect. Author Affiliation: (1) Max Planck Institute for Meteorology, Bundesstr. 53, 20146, Hamburg, Germany (2) Abisko Scientific Research Station, Royal Swedish Academy of Sciences, 98107, Abisko, Sweden Article History: Registration Date: 04/10/2007 Received Date: 05/07/2006 Accepted Date: 03/10/2007 Online Date: 27/11/2007

Byline: Annett Wolf (1,2,5), Eleanor Blyth (3), Richard Harding (3), Daniela Jacob (4), Elke Keup-Thiel (4), Holger Goettel (4), Terry Callaghan (5,6) Abstract: We tested the sensitivity of a dynamic ecosystem model (LPJ-GUESS) to the representation of soil moisture and soil temperature and to uncertainties in the prediction of precipitation and air temperature. We linked the ecosystem model with an advanced hydrological model (JULES) and used its soil moisture and soil temperature as input into the ecosystem model. We analysed these sensitivities along a latitudinal gradient in northern Russia. Differences in soil temperature and soil moisture had only little influence on the vegetation carbon fluxes, whereas the soil carbon fluxes were very sensitive to the JULES soil estimations. The sensitivity changed with latitude, showing stronger influence in the more northern grid cell. The sensitivity of modelled responses of both soil carbon fluxes and vegetation carbon fluxes to uncertainties in soil temperature were high, as both soil and vegetation carbon fluxes were strongly impacted. In contrast, uncertainties in the estimation of the amount of precipitation had little influence on the soil or vegetation carbon fluxes. The high sensitivity of soil respiration to soil temperature and moisture suggests that we should strive for a better understanding and representation of soil processes in ecosystem models to improve the reliability of predictions of future ecosystem changes. Author Affiliation: (1) Department of Physical Geography and Ecosystem Analyses, Lund University, Lund, Sweden (2) Department of Environmental Science, Universitatsstr. 22, CH-8092, Zurich, Switzerland (3) Centre for Ecology and Hydrology, Wallingford, UK (4) Max-Planck-Institut fur Meteorologie (MPI-M), Hamburg, Germany (5) Abisko Scientific Research Station, Abisko, Sweden (6) Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, UK Article History: Registration Date: 04/10/2007 Received Date: 06/07/2006 Accepted Date: 03/10/2007 Online Date: 27/11/2007

Two monthly gridded precipitation datasets of the Global Precipitation Climatology Project (GPCP; the multisatellite product) and the Global Precipitation Climatology Centre (GPCC) Variability Analysis of Surface Climate Observations (VASClimO; rain gauge data) are compared for a 22-yr period, from January 1979 to December 2000, over land areas (i.e., latitudes 4[degrees]-20[degrees]N and longitudes 18[degrees]W-15[degrees]E). The two datasets are consistent with respect to the spatial distribution of the annual and seasonal rainfall climatology over the domain and along latitudinal bands. However, the satellite generally overestimates rainfall. The inability of the GPCC data to capture the bimodal rainfall pattern along the Guinea coast (i.e., south of latitude 8[degrees]N) is an artifact of the interpolation of the rain gauge data. For interannual variability, the gridded multisatellite and gridded gauge datasets agree on the sign of the anomaly 15 out of the 22 yr (68% of the time) for region 1 (between longitude 5[degrees] and 18[degrees]W and north of latitude 8[degrees]N) and 18 out of the 22 yr (82% of the time) for region 2 (between longitude 5[degrees]W and 15[degrees]E and north of latitude 8[degrees]N). The datasets agreed on the sign of the anomaly 14 out of the 22 yr (64% of the time) over the Guinea Coast. The magnitudes of the anomaly are very different in all years. Most of the years during which the two datasets did not agree on the sign of the anomaly were years with El Nino events. The ratio of the seasonal root-mean-square differences to the seasonal mean rainfall range between 0.24 and 2.60. The Kendall's tau statistic indicated statistically significant trends in both datasets, separately.

Currently, operational weather forecasting systems use observations to optimize the initial state of a forecast without considering possible model deficiencies. For precipitation assimilation, this could be an issue since precipitation observations, unlike conventional data, do not directly provide information on the atmospheric state but are related to the state variables through parameterized moist physics with simplifying assumptions. Precipitation observation operators are comparatively less accurate than those for conventional data or observables in clear-sky regions, which can limit data usage not because of issues with observations, but with the model. The challenge lies in exploring new ways to make effective use of precipitation data in the presence of model errors. This study continues the investigation of variational algorithms for precipitation assimilation using column model physics as a weak constraint. The strategy is to develop techniques to make online estimation and correction of model errors to improve the precipitation observation operator during the assimilation cycle. Earlier studies have shown that variational continuous assimilation (VCA) of tropical rainfall using moisture tendency correction can improve Goddard Earth Observing System 3 (GEOS-3) global analyses and forecasts. Here results are presented from a 4-yr GEOS-3 reanalysis assimilating Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Special Sensor Microwave Imager (SSM/I) tropical rainfall using the VCA scheme. Comparisons with NCEP operational analysis and the 40-yr ECMWF Re-Analysis (ERA-40) show that the GEOS-3 reanalysis is significantly better at replicating the intensity and variability of tropical precipitation systems ranging from a few days to interannual time scales. As a further refinement of rainfall assimilation using the VCA scheme, a variational algorithm for assimilating TMI latent heating retrievals using semiempifical parameters in the model moist physics as control variables is described and initial test results are presented.

The assimilation of observations indicative of quantitative cloud and precipitation characteristics is desirable for improving weather forecasts. For many fundamental reasons, it is a more difficult problem than the assimilation of conventional of clear-sky satellite radiance data. These reasons include concerns regarding nonlinearity of the required observation operators (forward models), nonnormality and large variances of representativeness, retrieval, or observation-operator errors, validation using new measures, dynamic and thermodynamic balances, and possibly limited predictability. Some operational weather prediction systems already assimilate precipitation observations, but much more research and development remains. The apparently critical, fundamental, and peculiar nature of many issues regarding cloud and precipitation assimilation implies that their more careful examination will be required for accelerating progress.

To date, the assimilation of satellite measurements in numerical weather prediction (NWP) models has focused on the clear atmosphere. But satellite observations in the visible, infrared, and microwave provide a great deal of information on clouds and precipitation. This special collection describes how to use this information to initialize clouds and precipitation in models. Since clouds and precipitation often occur in sensitive regions for forecast impacts, such improvements are likely necessary for continuing to acquife significant gains in weather forecasting. This special collection of the Journal of the Atmospheric Sciences is devoted to articles based on papers presented at the International Workshop on Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models, in Lansdowne, Virginia, in May 2005. This introduction summarizes the findings of the workshop. The special collection includes review articles on satellite observations of clouds and precipitation (Stephens and Kummerow), parameterizations of clouds and precipitation in NWP models (Lopez), radiative transfer in cloudy/precipitating atmospheres (Weng), and assimilation of cloud and precipitation observations (Errico et al.), as well as research papers on these topics.

High-quality, daily meteorological data at high spatial resolution are essential for a variety of hydrologic and ecological modeling applications that support environmental risk assessments and decision making. This paper describes the development, application, and assessment of methods to construct daily highresolution (~50-m cell size) meteorological grids for the 2003 calendar year in the Upper South Santiam Watershed (USSW), a 500-[km.sup.2] mountainous catchment draining the western slope of the Oregon Cascade Mountains. Elevations within the USSW ranged from 194 to 1650 m. Meteorological elements modeled were minimum and maximum temperature; total precipitation, rainfall, and snowfall; and solar radiation and radiation-adjusted maximum temperature. The Parameter-Elevation Regressions on Independent Slopes Model (PRISM) was used to interpolate minimum and maximum temperature and precipitation. The separation of precipitation into rainfall and snowfall components used a temperature-based regression function. Solar radiation was simulated with the Image-Processing Workbench. Radiation-based adjustments to maximum temperature employed equations developed from data in the nearby H. J. Andrews Experimental Forest. The restrictive terrain of the USSW promoted cold-air drainage and temperature inversions by reducing large-scale airflow. Inversions were prominent nearly all year for minimum temperature and were noticeable even for maximum temperature during the autumn and winter. Precipitation generally increased with elevation over the USSW. In 2003, precipitation was nearly always in the form of rain at the lowest elevations but was about 50% snow at the highest elevations. Solar radiation followed a complex pattern related to terrain slope, aspect, and position relative to other terrain features. Clear, sunny days with a large proportion of direct radiation exhibited the greatest contrast in radiation totals, whereas cloudy days with primarily diffuse radiation showed little contrast. Radiation-adjusted maximum temperatures showed similar patterns. The lack of a high-quality observed dataset was a major issue in the interpolation of precipitation and solar radiation. However, observed data available for the USSW were superior to those available for most mountainous regions in the western United States. In this sense, the methods and results presented here can inform others performing similar studies in other mountainous regions.

Byline: Jose Villanueva-Diaz (1), David W. Stahle (2), Brian H. Luckman (3), Julian Cerano-Paredes (1), Mathew D. Therrell (4), Malcom K. Cleaveland (2), Eladio Cornejo-Oviedo (5) Abstract: The understanding of historic hydroclimatic variability is basic for planning proper management of limited water resources in northeastern Mexico. The objective of this study was to develop a network of tree-ring chronologies to reconstruct hydroclimate variability in northeastern Mexico and to analyze the influence of large-scale circulation patterns, such as ENSO. Precipitation sensitive tree-ring chronologies of Douglas-fir were developed in mountain ranges of the Sierra Madre Oriental and used to produce winter-spring precipitation reconstructions for central and southern Nuevo Leon, and southeastern Coahuila. The seasonal winter-spring precipitation reconstructions are 342 years long (1659--2001) for Saltillo, Coahuila and 602 years long (1400--2002) for central and southern Nuevo Leon. Both reconstructions show droughts in the 1810s, 1870s, 1890s, 1910s, and 1970s, and wet periods in the 1770s, 1930s, 1960s, and 1980s. Prior to 1800s the reconstructions are less similar. The impact of ENSO in northeastern Mexico (as measured by the Tropical Rainfall Index) indicated long-term instability of the Pacific equatorial teleconnection. Atmospheric circulation systems coming from higher latitudes (cold fronts or `nortes') and others developed in the Gulf of Mexico (tropical storms, hurricanes) also influence the climatic conditions characterizing this region. The recent development of new and longer tree-ring chronologies for the region will contribute to a better understanding of the interannual and multidecadal climatic variability of northeastern Mexico. Author Affiliation: (1) Instituto Nacional de Investigaciones Forestales y Agropecuarias, Centro Nacional de Investigacion Disciplinarioa en Relacion Agua, Suelo, Planta. Km 6.5 Margen Derecha del Canal Sacramento. Gomez Palacio, Durango, 35140, Mex. (2) Tree-Ring Laboratory, Department of Geosciences, University of Arkansas, fayetteville, Arkansas, 72701, USA (3) Department of Geography, University of Western Ontario, London, Ontario, N6A5C3, Canada (4) Department of Environmental Sciences, University of Virginia, Charlottesville, VA, 29904, USA (5) Departamento Forestal, Universidad Autonoma Agraria 'Antonio Narro, Saltillo, Coahuila, Mex. Article History: Registration Date: 22/05/2006 Received Date: 03/10/2005 Accepted Date: 09/05/2006 Online Date: 01/03/2007

The link between realism in simulation of the seasonal mean precipitation and summer tropical intraseasonal oscillations and their dependence on cumulus parameterization schemes is investigated using the Florida State University Global Spectral Model (FSUGSM). Forty-member model ensemble simulations of the northern summer season are generated for three different cumulus parameterization schemes [namely, Arakawa-Schubert (Naval Research Laboratory; NRL), Zhang and McFarlane (National Center for Atmospheric Research; NCAR), and Emanuel (Massachusetts Institute of Technology; MIT)]. The MIT scheme simulates the regional pattern of seasonal mean precipitation over the Indian monsoon region well but has large systematic bias in simulating the precipitation over the western Pacific and the Maritime Continent. Although the simulation of details of regional distribution of precipitation over the Indian monsoon region by the NRL and NCAR schemes is not accurate, they simulate the spatial pattern of precipitation over the tropical Indo-Pacific domain closer to observation. The NRL scheme seems to captures the observed northward and eastward propagation of intraseasonal precipitation anomalies realistically. However, the simulations of the NCAR and MIT schemes are dominated by a westward propagating component. The westward propagating mode seen in the model as well as observations is indicated to be an equatorial Rossby wave modified by the northern summer mean flow. An examination of the relationship between simulation of the model climatology and eastward propagating character of monsoon intraseasonal oscillations (ISOs) in a limited sample shows that the scheme that simulates better seasonal mean pattern of rainfall over the tropical Indo-Pacific domain also simulates better intraseasonal variance and more realistic eastward propagation of monsoon ISOs. Among the parameters known to be important for meridional propagation of the summer monsoon ISOs, the meridional gradient of mean humidity in the lower atmosphere seems to be crucial in determining the northward propagation in the equatorial Indian Ocean (between 10[degrees]S and 10[degrees]N). For better prediction of the seasonal mean Indian monsoon, therefore, the model climatology should have minimum bias not only over the Indian monsoon region but also over the entire Indo-Pacific basin.

The mesoscale organization of precipitating convection is highly relevant to next-generation global numerical weather prediction models, which will have an intermediate horizontal resolution (grid spacing about 10 km). A primary issue is how to represent dynamical mechanisms that are conspicuously absent from contemporary convective parameterizations. A hybrid parameterization of mesoscale convection is developed, consisting of convective parameterization and explicit convectively driven circulations. This kind of problem is addressed for warm-season convection over the continental United States. although it is argued to have more general application. A hierarchical strategy is adopted: cloud-system-resolving model simulations represent the mesoscale dynamics of convective organization explicitly and intermediate resolution simulations involve the hybrid approach. Numerically simulated systems are physically interpreted by a mechanistic dynamical model of organized propagating convection. This model is a formal basis for approximating mesoscale convective organization (stratiform heating and mesoscale downdraft) by a first-baroclinic heating couplet. The hybrid strategy is implemented using a predictor-corrector strategy. Explicit dynamics is the predictor and the first-baroclinic heating couplet the corrector. The corrector strengthens the systematically weak mesoscale downdrafts that occur at intermediate resolution. When introduced to the Betts-Miller-Janjic convective parameterization, this new hybrid approach represents the propagation and dynamical structure of organized precipitating systems. Therefore, the predictor-corrector hybrid approach is an elementary practical framework for representing organized convection in models of intermediate resolution.

The Keys Area Microphysics Project (KAMP), conducted as part of NASA's Fourth Convective and Moisture Experiment (CAMEX-4) in the lower Keys area, deployed a number of ground radars and four arrays of rain gauge and disdrometer clusters. Among the various instruments is an X-band dual-polarization Doppler radar on wheels (XPOL), contributed by the University of Connecticut. XPOL was used to retrieve rainfall rate and raindrop size distribution (DSD) parameters to be used in support of KAMP science objectives. This paper presents the XPOL measurements in KAMP and the algorithm developed for attenuation correction and estimation of DSD model parameters. XPOL observations include the horizontal polarization reflectivity [Z.sub.H], differential reflectivity [Z.sub.DR], and differential phase shift [[PHI].sub.DP]. Here, [Z.sub.H] and [Z.sub.DR] were determined to be positively biased by 3 and 0.3 dB, respectively. A technique was also applied to filter noise and correct for potential phase folding in [[PHI].sub.DP] profiles. The XPOL attenuation correction uses parameterizations that relate the path-integrated specific (differential) attenuation along a radar ray to the filtered-[[PHI].sub.DP] (specific attenuation) profile. Attenuation-corrected [Z.sub.H] and specific differential phase shift (derived from filtered [[PHI].sub.DP] profiles) data are then used to derive two parameters of the normalized gamma DSD model, that is, intercept ([N.sub.w]) and mean drop diameter ([D.sub.0]). The third parameter (shape parameter [mu]) is calculated using a constrained [mu]-A relationship derived from the measured raindrop spectra. The XPOL attenuation correction is evaluated using coincidental nonattenuated reflectivity fields from the Key West Weather Surveillance Radar-1988 Doppler (WSR-88D), while the DSD parameter retrievals are statistically assessed using DSD parameters calculated from the measured raindrop spectra. Statistics show that XPOL DSD parameter estimation is consistent with independent observations. XPOL estimates of water content and [N.sub.w] are also shown to be consistent with corresponding retrievals from matched ER-2 Doppler radar (EDOP) profiling observations from the 19 September airborne campaign. Results shown in this paper strengthen the applicability of X-band dual-polarization high resolution observations in cloud modeling and precipitation remote sensing studies.

Microphysical schemes based on the scaling normalization of the particle size distribution (PSD) are cast into a variational data assimilation method to assess their ability to retrieve the precipitation structure and humidity from moments of the PSD that can be derived from radar- and ground-based disdrometer measurements. The sedimentation and evaporation, which are the main processes below the cloud base, are examined. Various identical twin experiments are presented in the context of a column time-dependent model used to simulate the passage of precipitating cells over a short period of time. The relative humidity profile is assumed constant. The feedback of the microphysical processes on the thermodynamic fields is ignored. Observations are generated from a three-moment scheme having the zeroth, third, and sixth moments of the PSD as prognostic variables. The model is discretized in terms of the logarithms of the predictive moments, which render the adjustment of the model variables easier to the observations. An upper bound for the characteristic diameter for the sixth moment is however necessary to prevent numerical instabilities from developing during the data assimilation process. The tangent linear model of the three-moment scheme reproduces well the difference between two nonlinear integrations over the assimilation window (8 min), which validates the use of its adjoint in the minimization of the cost function that measures the misfit between observations and corresponding model variables. A weak smoothness penalty function should be added to the cost function when noisy observations are assimilated. When all the predicted moments are observed and assimilated, the minimization converges very well, even with 40% observation error. In this case, the reflectivity factor, which is related to the sixth moment, can be retrieved with 0.2-dB accuracy. When only the sixth moment is observed, the total number of concentration (related to the zeroth moment) cannot be recovered. However, the constant relative humidity can be obtained with 1% accuracy. When simpler one-moment and two-moment schemes are used to retrieve the precipitation structure from the observed sixth moment, the model error strongly projects on the nonobserved moments of the PSD.

Since November 1999, the hydrostatic meteorological Bologna Limited-Area Model (BOLAM) has been running operationally at the National Observatory of Athens. The assessment of the model forecast skill during the 2-yr period included (a) calculation of the root-mean-square errors (model vs gridded analyses) of geopotential height and temperature at 850 and 500 hPa, (b) evaluation of the model's quantitative precipitation forecast skill for the most important events, and (c) evaluation of the model skill in the prediction of surface winds in comparison with buoy observations. Comparison of the verification results with those provided in the literature showed that BOLAM has a high forecast skill, even for precipitation, which is the most difficult parameter to forecast. Especially for precipitation, the comparison between coarse (~21 km) and fine (~6.5 km) grid spacing forecasts showed that for the low and medium precipitation amounts, the finer-grid forecasts are not as good as the coarse-grid forecasts. For the large precipitation amounts, the calculated statistical scores provide only little support of the idea that the fine-grid forecasts are better than those of the coarse grid because the fine-grid forecasts give better scores only for the quantity bias and the mean absolute error.

Recent studies of trends in Midwestern precipitation show marked increases over the last 50 years of the 20th Century, and most climate models project that future rainfall in the Corn Belt will be increased further. During five years, 1988-1991 and 1994, field tests were conducted on agricultural test plots in central Illinois, an area typical of the Corn Belt, to discern how corn yields reacted to varying levels of added rainfall (+10%, +25%, and +40%) during the growing season. The best treatment over the five years was a 40% rain increase, with an average yield increase of 9%. Its yield increase was up to 34% in a hot-dry year, but below that of natural rainfall in a wet year as were the yields of the other lesser increases. The average yield changes from the three treatments were not statistically significantly different. Major interannual yield differences were found in the yields for each rain treatment, reflecting how rain timing and temperatures also have major effects on yields. A 40% summer rain increase has little influence if natural rains do not occur in the high stress period of mid summer. The plots results show that only small average increases in corn yields occur from growing season rain additions in the 10% to 40% range, except in dry years. Weathercrop yield regression models incorporating the same rain increases predict greater yield increases than found in these field tests. This suggests that future yields projected for a wetter climate using yield-weather models may be over-estimated. The plot sample size is small but conditions sampled in the five years represented 43% of all past 97 growing seasons in central Illinois and extremely good and bad weather years, which resulted in large between-year yield differences. Hence, the experimental results provide useful information about how increased rainfall may affect future corn yields, especially since the sample included three of the five types of dry growing seasons found in the area's climate since 1900.

Six different convective-stratiform separation techniques are compared and evaluated using 2D numerical simulations of a tropical and a midlatitude continental squall line. The techniques used include a texture algorithm applied to surface rainfall, a similar algorithm but with additional criteria applied to vertical velocity and cloud, a texture algorithm applied to vertical velocities below the melting layer, a simple approach that assumes a constant characteristic width for the convective region, a more sophisticated texture algorithm applied to radar reflectivities below the melting layer, and a new technique based on the premise that the fall speed of precipitation particles is large relative to air velocity in regions of stratiform precipitation. Comparisons are made in terms of rainfall, mass fluxes, apparent heating and moistening, hydrometeor contents, reflectivity and vertical-velocity contoured-frequency-with-altitude diagrams (CFAD), microphysics, and latent heating retrieval. Overall, it was found that the different separation techniques produced results that qualitatively agreed. However, the quantitative differences were significant. The texture algorithm applied to surface rain consistently produced the most stratiform rain while the texture algorithm applied to radar reflectivities below the melting layer and the new method comparing air velocities to terminal velocities consistently produced the most convective rain. The simple constant-area method performed comparably to the others in this squall line setting. Observational comparisons within the context of the model were unable to identify a superior technique. However, all of the methods were able to generate CFADs that were consistent with observations. Latent heating retrieval was shown to be sensitive to the use of separation technique mainly as a result of differences in the stratiform region. Methods that found very little stratiform rain resulted in exaggerated rain-normalized stratiform heating profiles.

Herein preliminary findings are reported from a radar-based climatology of warm season precipitation 'episodes.' Episodes are defined as time-space clusters of heavy precipitation that often result from sequences of organized convection such as squall lines, mesoscale convective systems, and mesoscale convective complexes. Episodes exhibit coherent rainfall patterns, characteristic of propagating events, under a broad range of atmospheric conditions. Such rainfall patterns are most frequent under 'weakly forced' conditions in midsummer. The longevity of episodes, up to 60 h, suggests an intrinsic predictability of warm season rainfall that significantly exceeds the lifetime of individual convective systems. Episodes are initiated primarily in response to diurnal and semidiurnal forcings. Diurnal forcing is dominant near the Rocky and Appalachian Mountains, whereas semidiurnal forcing is dominant between these cordilleras. A most common longitude of origin is at or near the east slope of the Continental Divide (105 [degrees] W). These observations are consistent with a condition of continual thermal forcing, widespread hydrodynamic instability, and the existence of other processes that routinely excite, maintain, and regenerate organized convection. The propagation speed of major episodes is often in excess of rates that are easily attributable either to the phase speeds of large-scale forcing or to advection from low- to midlevel 'steering' winds. It is speculated that wavelike mechanisms, in the free troposphere and/or the planetary boundary layer, may contribute to the rates of motion observed. Once understood, the representation of such mechanisms in forecast models offers the opportunity for improved predictions of warm season rainfall.

In November 2006, Montana's Glacier National Park experienced an extreme precipitation event, which caused major flooding throughout the area. We examined the potential impacts of projected temperature changes on a [...]

Natural rainstorms have dynamic intensities; thus, a large portion of total precipitation may be received during a short time. Natural rainstorms also have both variable drop sizes and kinetic energy. Accurate studies of sediment transport and infiltration require realistic duplication of natural rain kinetic energy and intensities. Rain simulator designs must be capable of producing dynamic multiple intensities and corresponding multiple drop sizes to simulate variable kinetic energies. Many simulators are capable of producing rain with multiple and, in some cases, dynamic intensities, but a simulator that can vary both the intensity and kinetic energy is needed. Our objective was to design and build a rain simulator for field use that realistically duplicated natural rain storms. A computer-controlled solenoid-valve multiple nozzle type rain simulator was developed. The computer not only controls solenoid valve activation of the rain simulator during operation but also measures runoff and records rain application and runoff data to a log file for processing. The simulator has control software that controls and monitors all processes. The rain simulator is capable of uniformly applying water (coefficient of uniformity 84-94%) with a dynamically changing intensity of up to 200 mm [h.sup.-1] and kinetic energies ranging from 15.0 to 23.5 J [m.sup.-2] [mm.sup.-1]. Uniformity of application was achieved by oscillating the nozzles in two directions. Field operation of the simulator is enhanced by its portability. The measurement area is easily modified using in-line handrail connectors to change support frame dimensions, thus accommodating infiltration and chemical or sediment transport studies.

A technique is described that adds diabatic forcing from stratiform precipitation to a vertical normal-mode initialization of a mesoscale model. The technique uses observed precipitation amounts and cloud-top height estimations with analyzed thermodynamic and kinematic fields to vertically distribute diabatic heating that arises from stratiform precipitation. Simulation experiments reveal the importance of incorporating this heating into the initialization. An adiabatic initialization recovered about 65%-75% of the maximum upward vertical motions, whereas adiabatic initialization, with respect to stratiform precipitation, recovered nearly all the original vertical motions. A real-data case study is presented using combined rain gauge - satellite precipitation analyses with cloud-top heights estimated from Geostationary Operational Environmental Satellite infrared brightness temperatures. The short-term precipitation forecasts from a diabatically initialized model, with respect to stratiform precipitation, demonstrate improvement over forecasts from an adiabatically initialized model.

Numerical experiments were performed with a meso-[Beta]-scale meteorological model to investigate the influence of subgrid-scale surface heterogeneity on the prediction of evapotranspiration, cloud, and precipitation formation. The results of simulations using different horizontal grid resolutions and assuming the dominant land-use type within a grid box as the representative surface type for the entire grid element are compared with those obtained from model runs considering subgrid-scale heterogeneity by separately determining the fluxes of the respective subgrid-scale land-use types. The same surface parameterization scheme was applied in both cases. All of these numerical experiments show that the surface characteristics and, hence, the subgrid-scale surface processes strongly affect the predicted microclimate close to the ground. Furthermore, the model results also provide evidence that in the case of applying dominant land-use types the grid resolution may strongly affect the calculated water and energy fluxes because a subgrid-scale land-use type on a coarse grid is of minor importance and may be dominant on a finer grid. Moreover, if surface heterogeneity was considered, the simulation with coarser-grid width also predicted many features provided by the run with a finer-grid resolution with a sufficient degree of accuracy. The results substantiate that the degree of heterogeneity especially affects evapotranspiration, clouds, precipitation, and soil wetness.

Evidence is presented, from a composite analysis of a 14-year general circulation model simulation, that persistent North Pacific (PNP) circulation anomalies during boreal winter are part of a larger-scale meridional development extending into the Tropics and the Southern Hemisphere. Lagged composites suggest that the development is initiated over the tropical Pacific by anomalous convection (characterized by an east-west dipole structure centered at the date line) one to two weeks prior to the extratropical onset time. Relatively weak wave trains, extending from the region of anomalous convection into the extratropics, appear to set the stage for the subsequent rapid development of the PNP anomalies. After onset, the PNP anomalies extend into the Tropics and enhance moisture transports that tend to supply moisture to, and thus reinforce, the associated tropical precipitation anomalies. The mature stage is characterized by a strong coupling between hemispheres, including twin low-level cyclonic (anticyclonic) circulations straddling the equator with westerly (easterly) wind 'bursts' on their equatorward flanks. The tropical precipitation anomalies and the extratropical PNP anomalies evolve coherently with tropical intraseasonal oscillations reminiscent of the Madden-Julian oscillation. Results from a similar composite analysis of a shorter (5 year) assimilated atmospheric dataset are generally consistent with the simulated results, despite the substantially smaller sample size. The assimilation, however, positions the tropical heating dipole farther west, in better agreement with previous observational studies of intraseasonal tropical/extratropical teleconnections. As a consequence, the pre-onset extratropical 'response' to the tropical anomalies in the simulation has significant phase errors. The remarkably similar evolution in the extratropics after onset suggests that the tropical forcing acts primarily as a catalyst for the development of the PNP anomalies and that the most useful predictors of PNP events may lie not in the extratropics but in the tropical western and central Pacific.

A simple physical model of the melting layer of precipitation is presented. It is able to simulate the polarization-dependence of radar reflections and radio signals propagating through the melting layer. The radar observables are calculated in the Rayleigh regime. The propagation observables are given in the range of 5-50 GHz, and are calculated with an extended Rayleigh approximation. The model requires the rain intensity and the mass density of the snowflakes as input parameters. Radar observations, made with the Delft Atmospheric Research Radar, are used to discuss physical processes in the melting layer. Finally, model simulations are compared with radar data obtained with the Chilbolton radar of the Rutherford Appleton Laboratories.

The analyses of data recorded during the past eight years with two Swiss radars, a network of rain gauges, and river flow measurements have helped to quantify the vertical profile of reflectivity and the influences of topography, meteorology, and radar parameters on the precision of radar precipitation estimation. The influence of the topography around the radar, the width of the radar beam, and the vertical echo structure produces a complex error distribution in space and time, with errors dependent upon storm type, distance from the radar, and the radar horizon. In spite of excellent agreement between amounts estimated by the 5-cm radar at close ranges and gauges located below the radar volume, underestimation of rainfall increases with range from the radar. The authors' experience dramatically shows how significantly errors are reduced when precipitation can be estimated close to the ground, a task made easier by choosing a radar site with a good view and by rigorously eliminating echoes contaminated by ground clutter and anomalous propagation without, however, reducing the detection capability of the radar for precipitation. Several methods of clutter detection are used together to ensure that precipitation estimates are not biased by clutter. A physical model can correct for a large part of these errors, including brightband effects, or at least tell us something about the validity of the results, if the causes of the long-range underestimation are understood. This paper proposes a two-step approach to error correction: first, a three-dimensional map of the 'visibility' from the radar of each observation point is made, initially assumed constant with time. The vertical profile of precipitation is then estimated (in real time where possible and from climatological values if not) and used together with a topographical database to estimate the precipitation reaching the (usually obscured) ground from a weighted function of all rain-rate estimates made above each point on the surface. The results of this analysis, especially appropriate for the Alps but also valuable in ordinary terrain, are being applied to the Swiss Meteorological Institute's new generation of weather radars in order to provide improved quantitative precipitation information to support the preparation of operational flood warnings in the Swiss Alps. An optimized scan strategy with simultaneous reflectivity and Doppler processing and automatic calibration is used to allow corrections in real time and to produce products to satisfy a wide variety of user needs.

Collisions between small precipitation drops in free fall were analyzed for sizes applicable to self-collection, the process that controls the spreading of precipitation drops to larger sizes. Results from 45 laboratory experiments were generalized using dimensionless parameters to scale the coalescence efficiency, the temporary coalescence probability, and the satellite occurrence frequency. The coalescence efficiency for uncharged drops ([[Epsilon].sub.0]) was found to be highly correlated ([Rho] = 0.99) with a simple combination of factors that scale the tendency for colliding drops to bounce apart as a function of the Weber number (We) and size ratio (p). Charge-induced coalescence was scaled by the electric field between the drops, assuming charged conducting spheres. The coalescence efficiency was obtained as a function of the normalized charge using a semiempirical formula ([Rho] = 0.95) for the amount of charge required to eliminate bounce and temporary coalescence. The occurrence of temporary coalescence is predicted by p We > 4 with a lower limit of p We > 1 for charge-induced coalescence. The fraction of collisions resulting in temporary coalescences increased with (1 - [[Epsilon].sub.0])p We, whereas the fraction of collisions producing satellites increased with (1 - [[Epsilon].sub.0]) [We.sup.2]. Both fractions were highly correlated with their respective scaling parameters ([Rho] = 0.99). Satellite drop radii were found to increase linearly with the geometric mean radius of the parent drops. Mass transfer in collisions involving temporary coalescence and satellite generation was estimated for use in modeling studies. Contour diagrams are provided for coalescence efficiency, temporary coalescence probability, and satellite occurrence frequency over a wide range of drop sizes for comparison with formulas based on previous laboratory results in the accretion and breakup regimes. Recommendations are given for applying present formulas to self-collection, as well as extending our findings to accretion and breakup.

A method for calculating the effect of cloud seeding is based on the use of a historical logarithmic model that compares unseeded and seeded rainfall over time. Two seeding experiments were carried out in Israel, out of which the first one resulted in an enhancement in rainfall by 15% while the second one produced a 13% increase compared to historical normal rainfall.

The nonlinear open system cloud is analyzed in this basic study in the context of the theory of self-organization. Emphasis is placed on the microphysical processes of riming, accretion, and sedimentation in a supercooled cloud containing several types of precipitation particles. These processes are mathematically described using a parameterization scheme of the Kessler type. This model of the competition of precipitation particle types for cloud water is analogous to the famous predator-prey model in population dynamics. The models differ, however, in the role of the exponents in the transformation rates, which can be interpreted as control parameters for the cloud physics model. The number as well as the type of attractors depend on a set of parameters including, for example, the attributes of the chosen type of precipitation particles and the prescribed external source rates. If only spherically shaped precipitation particles are considered, the system is characterized by a single point attractor. If flat precipitation particles are allowed, self-organization in time, in the form of a periodic attractor and multistability, may occur depending on the strength of the external forcing, and the dynamics permits different long-term evolution patterns. In the case of multistability, the initial conditions decide which one of the attractors will be reached. Over a wide range of external source rates, one of the precipitation species will dominate in the long term. As an example, suppression of rain in the presence of supercooled cloud water and precipitation ice particles is reproduced. With a vanishing precipitation source rate only a single precipitation species finally remains in the system (selection or hyperselection). Characteristic timescales depend on the chosen parameter values; in the examples presented they are of the order of an hour or less. This study elucidates how special assumptions in the parameterization scheme influence the long-term behavior of the system 'cloud' and gives an example of a structurally unstable system.

A model for estimating the occurrence and rate of precipitation has been helps in the determination of the monthly percentage of time of occurrence of precipitation and the minimum precipitation rate for a one-minute time period. The two components of this model are the threshold precipitation rate and cumulative distribution function (CDF) for precipitation. The variables used for the model are mean monthly temperature, mean monthly precipitation mean, monthly temperature range, and the mean number of days per month with threshold precipitation.