Meteorology

Aerosol radiative forcing is a critical, though variable and uncertain, component of the global climate. Yet climate models rely on sparse information of the aerosol optical properties. In situ measurements, though important in many respects, seldom provide measurements of the undisturbed aerosol in the entire atmospheric column. Here, 8 yr of worldwide distributed data from the AERONET network of ground-based radiometers were used to remotely sense the aerosol absorption and other optical properties in several key locations. Established procedures for maintaining and calibrating the global network of radiometers, cloud screening, and inversion techniques allow for a consistent retrieval of the optical properties of aerosol in locations with varying emission sources and conditions. The multiyear, multi-instrument observations show robust differentiation in both the magnitude and spectral dependence of the absorption-a property driving aerosol climate forcing, for desert dust, biomass burning, urban-industrial, and marine aerosols. Moreover, significant variability of the absorption for the same aerosol type appearing due to different meteorological and source characteristics as well as different emission characteristics are observed. It is expected that this aerosol characterization will help refine aerosol optical models and reduce uncertainties in satellite observations of the global aerosol and in modeling aerosol impacts on climate.

The first part of this note provides all trigonometrical formulas which allow the direct spectral analysis and the computation of those long-term variations of the earth's orbital elements which are of primary interest for the computation of the insolation. The elements are the ...

Many ad hoc network protocols and applications assume the knowledge of geographic location of nodes. The absolute location of each networked node is an assumed fact by most sensor networks which can then present the sensed information on a geographical map. Finding location without the aid of GPS in each node of an ad hoc network is important in cases where GPS is either not accessible, or not practical to use due to power, form factor or line of sight conditions. Location would also enable routing in sufficiently isotropic large networks, without the use of large routing tables. We are proposing APS -a distributed, hop by hop positioning algorithm, that works as an extension of both distance vector routing and GPS positioning in order to provide approximate location for all nodes in a network where only a limited fraction of nodes have self location capability.

SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography) is a spectrometer designed to measure sunlight transmitted, reflected, and scattered by the earth's atmosphere or surface in the ultraviolet, visible, and near-infrared wavelength region (240-2380 nm) at moderate spectral resolution (0.2-1.5 nm, /⌬ ഠ 1000-10 000). SCIAMACHY will measure the earthshine radiance in limb and nadir viewing geometries and solar or lunar light transmitted through the atmosphere observed in occultation. The extraterrestrial solar irradiance and lunar radiance will be determined from observations of the sun and the moon above the atmosphere. The absorption, reflection, and scattering behavior of the atmosphere and the earth's surface is determined from comparison of earthshine radiance and solar irradiance. Inversion of the ratio of earthshine radiance and solar irradiance yields information about the amounts and distribution of important atmospheric constituents and the spectral reflectance (or albedo) of the earth's surface.

This paper presents a range of applications of the Regional Atmospheric Modeling System (RAMS), a comprehensive mesoscale meteorological modeling system. Applications discussed in this paper include large eddy simulations (LES) and simulations of thunderstorms, cumulus fields, mesoscale convective systems, mid-latitude cirrus clouds, winter storms, mechanically-and thermally-forced mesoscale systems, and mesoscale atmospheric dispersion. A summary of current RAMS options is also presented. Improvements to RAMS currently underway include refinements to the cloud radiation, cloud microphysics, cumulus, and surface soil/vegetative parameterization schemes, the parallelization of the code, development of a more versatile visualization capability, and research into meso-~-scale cumulus parameterization.

We propose a generalized split-window method for retrieving land-surface temperature (LST) from AVHRR and MODIS data. Accurate radiative transfer simulations show that the coefficients in the split-window algorithm for LST must vary with the viewing angle, if we are to achieve a LST accuracy of about 1 K for the whole scan swath range (1t5.5" from nadir) and for the ranges of surface temperature and atmospheric conditions over land, which are much wider than those over oceans. We obtain these coefficients from regression analysis of radiative transfer simulations, and we analyze sensitivity and error over wide ranges of surface temperature and emissivity and atmospheric water vapor abundance and temperature. Simulations show that when atmospheric water vapor increases and viewing angle is larger than 45", it is necessary to optimize the split-window method by separating the ranges of the atmospheric water vapor, lower boundary temperature, and the surface temperature into tractable subranges. The atmospheric lower boundary temperature and (vertical) column water vapor values retrieved from HIRS/2 or MODIS atmospheric sounding channels can be used to determine the range for the optimum coefficients of the split-window method. This new algorithm not only retrieves land-surface temperature more accurately, but is also less sensitive to uncertainty in emissivity and to instrument quantization error.

This paper describes a publicly available, long-term , hydrologically consistent dataset for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of 1 /168 latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset has 1) extended the period of analysis (1915-2011 versus 1950-2000), 2) increased the spatial resolution from 1 /88 to 1 /168, and 3) used an updated version of VIC. The previous dataset has been widely used in water and energy budget studies, climate change assessments, drought reconstructions, and for many other purposes. It is anticipated that the spatial refinement and temporal extension will be of interest to a wide cross section of the scientific community. , 2013: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions. J. Climate, 26, 9384-9392.

In this paper, three methods to retrieve the land surface temperature (LST) from thermal infrared data supplied by band 6 of the Thematic Mapper (TM) sensor onboard the Landsat 5 satellite are compared. The first of them lies on the estimation of the land surface temperature from the radiative transfer equation using in situ radiosounding data. The others two are the mono-window algorithm developed by Qin et al. [International Journal of Remote Sensing 22 (2001) 3719] and the single-channel algorithm developed by Jiménez-Muñoz and Sobrino [Journal of Geophysical Research 108 ]. The land surface emissivity (LSE) values needed in order to apply these methods have been estimated from a methodology that uses the visible and near infrared bands. Finally, we present a comparison between the LST measured in situ and the retrieved by the algorithms over an agricultural region of Spain (La Plana de Requena-Utiel). The results show a root mean square deviation (rmsd) of 0.009 for emissivity and lower than 1 K for land surface temperature when the Jiménez-Muñoz algorithm is used.

This paper reviews methods for estimating evaporation from landscapes, regions and larger geographic extents, with remotely sensed surface temperatures, and highlights uncertainties and limitations associated with those estimation methods. Particular attention is given to the validation of such approaches against ground based flux measurements. An assessment of some 30 published validations shows an average root mean squared error value of about 50 W m -2 and relative errors of 15-30%. The comparison also shows that more complex physical and analytical methods are not necessarily more accurate than empirical and statistical approaches. While some of the methods were developed for specific land covers (e.g. irrigation areas only) we also review methods developed for other disciplines, such as hydrology and meteorology, where continuous estimates in space and in time are needed, thereby focusing on physical and analytical methods as empirical methods are usually limited by in situ training data. This review also provides a discussion of temporal and spatial scaling issues associated with the use of thermal remote sensing for estimating evaporation. Improved temporal scaling procedures are required to extrapolate instantaneous estimates to daily and longer time periods and gap-filling procedures are needed when temporal scaling is affected by intermittent satellite coverage. It is also noted that analysis of multi-resolution data from different satellite/sensor systems (i.e. data fusion) will assist in the development of spatial scaling and aggregation approaches, and that several biological processes need to be better characterized in many current land surface models.

An overview of the Regional Atmospheric Modeling System (RAMS) is presented. We focus on new developments in the RAMS physics and computational algorithms since 1992. We also summarize some of the recent applications of RAMS that includes synoptic-scale weather systems and climate studies, to small-scale research using RAMS configured as a large eddy simulation model or to even flow around urban buildings. The applications include basic research on clouds, cloud systems, and storms, examination of interactions between tropical deep convective systems and ocean circulations, simulations of tropical cyclones, extreme precipitation estimation, regional climatic studies of the interactions between the atmosphere and the biosphere or snow-covered land-surfaces, prototype realtime mesoscale numerical weather prediction, air pollution applications, and airflow around buildings. Computer aspects -Operating systems: UNIX, LINUX, MS Windows 95=98=NT=2000 -Parallel processing on distributed and shared memory platforms using Message Passing Interface (MPI) Ã Option only in research versions at Colorado State University ÃÃ Option under development RAMS 2001: Current status and future directions 7

The Multi-angle Imaging SpectroRadiometer (MISR) instrument is scheduled for launch aboard the first of the Earth Observing System (EOS) spacecraft, EOS-AM1. MISR will provide global, radiometrically calibrated, georectified, and co-registered imagery at nine discrete viewing angles and four visiblehear-inf rared spectral bands. Algorithms developed specifically to capitalize on this measurement strategy will be used to retrieve geophysical products for studies of clouds, aerosols, and surface radiation. This paper provides an overview of the as-built instrument characteristics and the application of MISR to remote sensing of the Earth.

We design, apply, and validate a methodology for correcting climate model output to produce internally consistent fields that have the same statistical intensity distribution as the observations. We refer to this as a statistical bias correction. Validation of the methodology is carried out using daily precipitation fields, defined over Europe, from the ENSEMBLES climate model dataset. The bias correction is calculated using data from 1961 to 1970, without distinguishing between seasons, and applied to seasonal data from 1991 to 2000. This choice of time periods is made to maximize the lag between calibration and validation within the ERA40 reanalysis period. Results show that the method performs unexpectedly well. Not only are the mean and other moments of the intensity distribution improved, as expected, but so are a drought and a heavy precipitation index, which depend on the autocorrelation spectra. Given that the corrections were derived without seasonal distinction and are based solely on intensity distributions, a statistical quantity oblivious of temporal correlations, it is encouraging to find that the improvements are present even when seasons and temporal statistics are considered. This encourages the application of this method to multi-decadal climate projections.

Climate and weather constitute a typical example where high dimensional and complex phenomena meet. The atmospheric system is the result of highly complex interactions between many degrees of freedom or modes. In order to gain insight in understanding the dynamical/physical behaviour involved it is useful to attempt to understand their interactions in terms of a much smaller number of prominent modes of variability. This has led to the development by atmospheric researchers of methods that give a space display and a time display of large space-time atmospheric data. Empirical orthogonal functions (EOFs) were first used in meteorology in the late 1940s. The method, which decomposes a space-time field into spatial patterns and associated time indices, contributed much in advancing our knowledge of the atmosphere. However, since the atmosphere contains all sorts of features, e.g. stationary and propagating, EOFs are unable to provide a full picture. For example, EOFs tend, in general, to be difficult to interpret because of their geometric properties, such as their global feature, and their orthogonality in space and time. To obtain more localised features, modifications, e.g. rotated EOFs (REOFs), have been introduced. At the same time, because these methods cannot deal with propagating features, since they only use spatial correlation of the field, it was necessary to use both spatial and time information in order to identify such features. Extended and complex EOFs were introduced to serve that purpose. Because of the importance of EOFs and closely related methods in atmospheric science, and because the existing reviews of the subject are slightly out of date, there seems to be a need to update our knowledge by including new developments that could not be presented in previous reviews. This review proposes to achieve precisely this goal. The basic theory of the main types of EOFs is reviewed, and a wide range of applications using various data sets are also provided. Copyright

Space and airborne sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. Confusion about fire intensity, fire severity, burn severity, and related terms can result in the potential misuse of the inferred information by land managers and remote sensing practitioners who require unambiguous remote sensing products for fire management. The objective of the present paper is to provide a comprehensive review of current and potential remote sensing methods used to assess fire behavior and effects and ecological responses to fire. We clarify the terminology to facilitate development and interpretation of comprehensible and defensible remote sensing products, present the potential and limitations of a variety of approaches for remotely measuring active fires and their post-fire ecological effects, and discuss challenges and future directions of fire-related remote sensing research.

A new three-dimensional cloud resolving model (CRM) has been developed to study the statistical properties of cumulus convection. The model was applied to simulate a 28-day evolution of clouds over the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains site during the summer 1997 Intensive Observation Period. The model was forced by the large-scale advective tendencies and surface fluxes derived from the observations. The sensitivity of the results to the domain dimensionality and size, horizontal grid resolution, and parameterization of microphysics has been tested. In addition, the sensitivity to perturbed initial conditions has also been tested using a 20-member ensemble of runs. The model captures rather well the observed temporal evolution of the precipitable water and precipitation rate, although it severely underestimates the shaded cloud fraction possibly because of an inability to account for the lateral advection of clouds over the ARM site. The ensemble runs reveal that the uncertainty of the simulated precipitable water due to the fundamental uncertainty of the initial conditions can be as large as 25% of the mean values. Even though the precipitation rates averaged over the whole simulation period were virtually identical among the ensemble members, the timing uncertainty of the onset and reaching the precipitation maximum can be as long as one full day. Despite the predictability limitations, the mean simulation statistics are found to be almost insensitive to the uncertainty of the initial conditions. The overall effects of the third spatial dimension are found to be minor for simulated mean fields and scalar fluxes but are quite considerable for velocity and scalar variances. Neither changes in a rather wide range of the domain size nor the horizontal grid resolution have any significant impact on the simulations. Although a rather strong sensitivity of the mean hydrometeor profiles and, consequently, cloud fraction to the microphysics parameters is found, the effects on the predicted mean temperature and humidity profiles are shown to be modest. It is found that the spread among the time series of the simulated cloud fraction, precipitable water, and surface precipitation rate due to changes in the microphysics parameters is within the uncertainty of the ensemble runs. This suggests that correlation of the CRM simulations to the observed long time series of the aforementioned parameters cannot be generally used to validate the microphysics scheme.

We applied a multiple linear regression (MLR) model to study the correlations of total PM 2.5 and its components with meteorological variables using an 11-year (1998e2008) observational record over the contiguous US. The data were deseasonalized and detrended to focus on synoptic-scale correlations. We find that daily variation in meteorology as described by the MLR can explain up to 50% of PM 2.5 variability with temperature, relative humidity (RH), precipitation, and circulation all being important predictors. Temperature is positively correlated with sulfate, organic carbon (OC) and elemental carbon (EC) almost everywhere. The correlation of nitrate with temperature is negative in the Southeast but positive in California and the Great Plains. RH is positively correlated with sulfate and nitrate, but negatively with OC and EC. Precipitation is strongly negatively correlated with all PM 2.5 components. We find that PM 2.5 concentrations are on average 2.6 mg m À3 higher on stagnant vs. non-stagnant days. Our observed correlations provide a test for chemical transport models used to simulate the sensitivity of PM 2.5 to climate change. They point to the importance of adequately representing the temperature dependence of agricultural, biogenic and wildfire emissions in these models.

This paper examines the effects of the relationship between cirrus cloud ice water content and cloud temperature on climate change. A simple mechanistic climate model is used to study the feedback between ice water content and temperature. The central question studied in this paper concerns the extent to which both the radiative and microphysical properties of cirrus cloud influence such a feedback. To address this question, a parameterization of the albedo and emissivity of clouds is introduced. Observations that relate the ice water content to cloud temperature are incorporated in the parameterization to introduce a temperature dependence to both albedo and emittance. The cloud properties relevant to the cloud feedback are expressed as functions of particles size re, asymmetry parameter g and cloud temperature and analyses of aircraft measurements, lidar and ground based radiometer data are used to select re and g. It was shown that scattering calculations assuming spherical particles with a distribution described by r, = 16 pm reasonably matched the lidar and radiometer data. However, comparison of cloud radiation properties measured from aircraft to those parameterized in this study required values of g significantly smaller than those derived for spheres but consistent with our understanding of nonspherical particle scattering. The climate simulations revealed that the influence of cirrus cloud on climate was strongly affected by the choice of re and g: parameters that are both poorly known for cirrus. It was further shown that the effect of ice water feedback on a COZ warming simulation could be either positive or negative depending on the value of re assumed. Based on these results, it was concluded that prediction of cirrus cloud feedback on climate is both premature and limited by our lack of understanding of the relationship between size and shape of ice crystals and the gross radiative properties of cirrus.

Numerous models of evapotranspiration have been published that range in data-driven complexity, but global estimates require a model that does not depend on intensive field measurements. The Priestley-Taylor model is relatively simple, and has proven to be remarkably accurate and theoretically robust for estimates of potential evapotranspiration. Building on recent advances in ecophysiological theory that allow detection of multiple stresses on plant function using biophysical remote sensing metrics, we developed a bio-meteorological approach for translating Priestley-Taylor estimates of potential evapotranspiration into rates of actual evapotranspiration. Five model inputs are required: net radiation (R n ), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), maximum air temperature (T max ), and water vapor pressure (ea). Our model requires no calibration, tuning or spin-ups. The model is tested and validated against eddy covariance measurements (FLUXNET) from a wide range of climates and plant functional types-grassland, crop, and deciduous broadleaf, evergreen broadleaf, and evergreen needleleaf forests. The model-to-measurement r 2 was 0.90 (RMS = 16 mm/month or 28%) for all 16 FLUXNET sites across 2 years (most recent data release). Global estimates of evapotranspiration at a temporal resolution of monthly and a spatial resolution of 1°during the years 1986-1993 were determined using globally consistent datasets from the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP-II) and the Advanced Very High Resolution Spectroradiometer (AVHRR). Our model resulted in improved prediction of evapotranspiration across water-limited sites, and showed spatial and temporal differences in evapotranspiration globally, regionally and latitudinally.

This paper reports an intercomparison study on undisturbed trade wind cumulus convection under steadystate conditions as observed during the Barbados Oceanographic and Meteorological Experiment (BOMEX) with 10 large eddy simulation (LES) models. A main objective of this study is to obtain a quantitative assessment of the quality of the turbulent dynamics for this type of boundary layer clouds as produced by the different LES codes. A 6-h simulation shows excellent model-to-model agreement of the observed vertical thermodynamical structure, reasonable agreement of variances and turbulent fluxes, and good agreement of quantities conditionally sampled within the model clouds, such as cloud cover, liquid water, and cloud updraft strength. In the second part of this paper the LES dataset is used to evaluate simple models that are used in parameterizations of current general circulation models (GCMs). Finally, the relation of this work to subsequent LES studies of more complicated regimes is discussed, and guidance is given for the design of future observational studies of shallow cumulus boundary layers. * Deceased.

Aerosol measurements were made as a part of the Barbados Oceanographic and Meteorological Experiment (Bomex) during May, June, and July 1969. Maximum dust concentrations occurred between the altitudes of 1.5 km and 3.7 km, a region which we call the Saharan air layer. The average concentration of mineral aerosol within this layer was 61 t•g m-a; in contrast, the average concentration in the low-level air was 22 t•g m -a. These dust concentrations are comparable to those found in continental surface air. Because of the presence of a strong inversion at the base of the Saharan layer, sea. salt was confined to te lower altitudes where the average concentration was 10 tzg m -8. Thus, sea salt appears to be a relatively minor constituent of the trade wind aerosol during much of the year. On the basis of these measurements and of a model describing the movement of Sararan air outbreaks, we estimate that 25 to 37 million tons of dust are transported through the longitude of Barbados each year. This quantity of dust is sufficient to supply all the material required to maintain the present rate of pelagic sedimentation across the entire northern equatorial Atlantic Ocean. The mineral aerosol concentration in sealevel, trade wind air at Barbados, West Indies (13øN, 59øW), has been measured from the fall of 1965 to the present [Delany et al., 1967; Prospero, 1968; Prospero et al., 1970; Carlson and Prospero, 1972]. During the first four years of this program, the air sampling was essentially continuous. These measurements have shown that large quantities of dust are transported across the northern equatorial Atlantic from the deserts of North Africa during the late spring, summer, and early fall. The average surface air concentration of mineral aerosol at Barbados during the dusty season varies from year to year but generally is of the order of 10/•g m -8 of air; the concentration during the rest of the year is more than an order of magnitude less [Prospero, 1968; Carlson and Prospero, 1972]. equatorial North Atlantic and of the relationship of the aerosol distribution to meteorological parameters. The meteorological aspects of this work, a study of the large-scale movement of Sararan air outbreaks over the northern equatorial Atlantic, are presented by us elsewhere [Carlson and Prospero., 1972]

Observations of backscattered near-ultraviolet radiation from the Total Ozone Mapping Spectrometer (TOMS) on board the Nimbus-7 (1979-92) and the Earth Probe (mid-1996 to present) satellites have been used to derive a long-term record of aerosol optical depth over oceans and continents. The retrieval technique applied to the TOMS data makes use of two unique advantages of near-UV remote sensing not available in the visible or near-IR: 1) low reflectivity of all land surface types (including the normally bright deserts in the visible), which makes possible aerosol retrieval over the continents; and 2) large sensitivity to aerosol types that absorb in the UV, allowing the clear separation of carbonaceous and mineral aerosols from purely scattering particles such as sulfate and sea salt aerosols. The near-UV method of aerosol characterization is validated by comparison with Aerosol Robotic Network (AERONET) ground-based observations. TOMS retrievals of aerosol optical depth over land areas (1996)(1997)(1998)(1999)(2000) are shown to agree reasonably well with AERONET sun photometer observations for a variety of environments characterized by different aerosol types, such as carbonaceous aerosols from biomass burning, desert dust aerosols, and sulfate aerosols. In most cases the TOMS-derived optical depths of UV-absorbing aerosols are within 30% of the AERONET observations, while nonabsorbing optical depths agree to within 20%. The results presented here constitute the first long-term nearly global climatology of aerosol optical depth over both land and water surfaces, extending the observations of aerosol optical depth to regions and times (1979 to present) not accessible to ground-based observations.

A global inventory has been constructed for emissions of black carbon from fossil fuel combustion and biomass burning. This inventory has been implemented in a threedimensional global transport model and run for 31 model months. Results for January and July have been compared with measurements taken from the literature. The modeled values of black carbon mass concentration compare within a factor of 2 in continental regions and some remote regions but are higher than measured values in other remote marine regions and in the upper troposphere. The disagreement in remote regions can be explained by the coarse grid scale of the model (10 ø x 10ø), the simplicity of the current deposition scheme, and possibly too much black carbon being available for transport, which would also account for the disagreement in the upper troposphere. The disagreement may also be due to problems associated with the measurement of black carbon. Emissions from this database appear to provide a reasonable estimate of the annual emissions of black carbon to the atmosphere. Biomass burning emissions amount to 5.98 Tg and that from fossil fuel amounts to 7.96 Tg. A local sensitivity analysis has been carded out and shows that black carbon has a lifetime between 6 and 10 days, depending on the transformation rate between hydrophobic and hydrophilic black carbon.

. Vegetation cover, surface moisture availability wetness and radiant surface temperature constitute microclimatic variables derivable from multi-spectral satellite imagery. In addition, fraction impervious surface cover and urban-induced Ž . surface runoff RO are obtainable from such imagery when it is combined with a conventional image classification. Using AVHRR and Landsat TM data, we illustrate how these parameters respond to urbanization with a case study of Chester County, PA -a region that has undergone rapid urban development over the past decade. Specific factors affecting the change in the surface microclimatic variables are discussed. Based on a statistical analysis of the Chester County data, future values of these microclimatic parameters can be predicted, given the characteristics of the initial land surface and an estimate of the change in vegetation and urban coverage. q

In this paper, we first describe the current status of the Advanced Regional Prediction System of the Center for Analysis and Prediction of Storms at the University of Oklahoma. A brief outline of future plans is also given. Two rather successful cases of explicit prediction of tornadic thunderstorms are then presented. In the first case, a series of supercell storms that produced a historical number of tornadoes was successfully predicted more than 8 hours in advance, to within tens of kilometers in space with initiation timing errors of less than 2 hours. The general behavior and evolution of the predicted thunderstorms agree very well with radar observations. In the second case, reflectivity and radial velocity observations from Doppler radars were assimilated into the model at 15-minute intervals. The ensuing forecast, covering a period of several hours, accurately reproduced the intensification and evolution of a tornadic supercell that in reality spawned two tornadoes over a major metropolitan area. These results make us optimistic that a model system such as the ARPS will be able to deterministically predict future severe convective events with significant lead time. The paper also includes a brief description of a new 3DVAR system developed in the ARPS framework. The goal is to combine several steps of Doppler radar retrieval with the analysis of other data types into a single 3-D variational framework and later to incorporate the ARPS adjoint to establish a true 4DVAR data assimilation system that is suitable for directly assimilating a wide variety of observations for flows ranging from synoptic down to the small nonhydrostatic scales.

The influence of various environmental factors on tropical cyclone intensity is explored using a simple coupled ocean-atmosphere model. It is first demonstrated that this model is capable of accurately replicating the intensity evolution of storms that move over oceans whose upper thermal structure is not far from monthly mean climatology and that are relatively unaffected by environmental wind shear. A parameterization of the effects of environmental wind shear is then developed and shown to work reasonably well in several cases for which the magnitude of the shear is relatively well known. When used for real-time forecasting guidance, the model is shown to perform better than other existing numerical models while being competitive with statistical methods. In the context of a limited number of case studies, the model is used to explore the sensitivity of storm intensity to its initialization and to a number of environmental factors, including potential intensity, storm track, wind shear, upper-ocean thermal structure, bathymetry, and land surface characteristics. All of these factors are shown to influence storm intensity, with their relative contributions varying greatly in space and time. It is argued that, in most cases, the greatest source of uncertainty in forecasts of storm intensity is uncertainty in forecast values of the environmental wind shear, the presence of which also reduces the inherent predictability of storm intensity.

Convectively coupled equatorial waves, as previously detected in studies of wavenumber-frequency spectra of tropical clouds, are studied in more detail. Composite dynamical structures of the waves are obtained using linear regression between selectively filtered satellite-observed outgoing longwave radiation (OLR) data, and various fields from a global reanalysis dataset. The selective filtering of the OLR was designed to isolate the convective variations contributing to spectral peaks that lie along the equatorial wave dispersion curves for equivalent depths in the range of 12-50 m. The waves studied are the Kelvin, n ϭ 1 equatorial Rossby (ER), mixed Rossby-gravity, n ϭ 0 eastward inertio-gravity, n ϭ 1 westward inertio-gravity (WIG), and n ϭ 2 WIG waves. The horizontal structures of the dynamical fields associated with the waves are all generally consistent with those calculated from inviscid equatorial ␤-plane shallow water theory. In the vertical, there are statistically significant structures spanning the depth of the troposphere, and for all but the ER wave there are associated vertically propagating signals extending into the equatorial stratosphere as well. In zonal cross sections, the vertical structure of the temperature anomaly field appears, for all but the ER wave, as a ''boomerang''-like shape, with the ''elbow'' of the boomerang occurring consistently at the 250-hPa level. The tilts of the boomerang imply upward phase propagation throughout most of the troposphere, and downward phase propagation above. The deep convection of the waves occurs in regions of anomalously cold temperatures in the lower troposphere, warm temperatures in the upper troposphere, and cold temperatures at the level of the tropopause. Such a vertical structure appears to indicate that waves of relatively short vertical wavelengths (L z ϳ 10 km) are indeed important for the coupling of large-scale dynamics and convection. The deeper structure of the convectively coupled ER wave, on the other hand, is thought to be an indication of the effects of basic-state vertical shear. Finally, the scales of the waves in the equatorial lower stratosphere that are forced by the convectively coupled equatorial waves are quite consistent with those found in many previous studies.

Physics-based coupled fire-atmosphere models are based on approximations to the governing equations of fluid dynamics, combustion, and the thermal degradation of solid fuel. They require significantly more computational resources than the most commonly used fire spread models, which are semi-empirical or empirical. However, there are a number of fire behaviour problems, of increasing relevance, that are outside the scope of empirical and semi-empirical models. Examples are wildland-urban interface fires, assessing how well fuel treatments work to reduce the intensity of wildland fires, and investigating the mechanisms and conditions underlying blow-up fires and fire spread through heterogeneous fuels. These problems are not amenable to repeatable full-scale field studies. Suitably validated coupled atmosphere-fire models are one way to address these problems. This paper describes the development of a three-dimensional, fully transient, physics-based computer simulation approach for modelling fire spread through surface fuels. Grassland fires were simulated and compared to findings from Australian experiments. Predictions of the head fire spread rate for a range of ambient wind speeds and ignition line-fire lengths compared favourably to experiments. In addition, two specific experimental cases were simulated in order to evaluate how well the model predicts the development of the entire fire perimeter.

An algorithm has been developed to identify precipitation features (Ն75 km 2 in size) in two land and two ocean regions during August, September, and October 1998. It uses data from two instruments on the Tropical Rainfall Measuring Mission (TRMM) satellite: near-surface precipitation radar (PR) reflectivities, and TRMM Microwave Imager (TMI) 85.5-GHz polarization corrected temperatures (PCTs). These features were classified by size and intensity criteria to identify mesoscale convective systems (MCSs), precipitation with PCTs below 250 K, and other features without PCTs below 250 K. By using this technique, several hypotheses about the convective intensity and rainfall distributions of tropical precipitation systems can be evaluated. It was shown that features over land were much more intense than similar oceanic features as measured by their minimum PCTs, maximum heights of the 30-dBZ contour, and 6-km reflectivities. The diurnal cycle of precipitation features showed a strong afternoon maximum over land and a rather flat distribution over the ocean, quite similar to those found by others using infrared satellite techniques. Precipitation features with MCSs over the ocean contained significantly more rain outside the 250-K PCT isotherm than land systems, and in general, a significant portion (10%-15%) of rainfall in the Tropics falls in systems containing no PCTs less than 250 K. Volumetric rainfall and lightning characteristics (as observed by the Lightning Imaging Sensor aboard TRMM) from the systems were classified by feature intensity; similar rain amounts but highly differing lightning flash rates were found among the regions. Oceanic storms have a bimodal contribution of rainfall from two types of systems: very weak systems with little ice scattering and moderately strong systems that do not produce high lightning flash rates. Continental systems that produce the bulk of the rainfall (as sampled) are likely to have higher lightning flash rates, which are shown to be linked to stronger radar and ice-scattering intensities.

Mapping of total suspended matter concentration (TSM) can be achieved from space-based optical sensors and has growing applications related to sediment transport. A TSM algorithm is developed here for turbid waters, suitable for any ocean colour sensor including MERIS, MODIS and SeaWiFS. Theory shows that use of a single band provides a robust and TSM-sensitive algorithm provided the band is chosen appropriately. Hyperspectral calibration is made using seaborne TSM and reflectance spectra collected in the southern North Sea. Two versions of the algorithm are considered: one which gives directly TSM from reflectance, the other uses the reflectance model of to take account of bidirectional effects. Applying a non-linear regression analysis to the calibration data set gave relative errors in TSM estimation less than 30% in the spectral range 670-750 nm. Validation of this algorithm for MODIS and MERIS retrieved reflectances with concurrent in situ measurements gave the lowest relative errors in TSM estimates, less than 40%, for MODIS bands 667 nm and 678 nm and for MERIS bands 665 nm and 681 nm. Consistency of the approach in a multisensor context (SeaWiFS, MERIS, and MODIS) is demonstrated both for single point time series and for individual images.

1] We investigate the impact of climate change on wildfire activity and carbonaceous aerosol concentrations in the western United States. We regress observed area burned onto observed meteorological fields and fire indices from the Canadian Fire Weather Index system and find that May-October mean temperature and fuel moisture explain 24-57% of the variance in annual area burned in this region. Applying meteorological fields calculated by a general circulation model (GCM) to our regression model, we show that increases in temperature cause annual mean area burned in the western United States to increase by 54% by the 2050s relative to the present day. Changes in area burned are ecosystem dependent, with the forests of the Pacific Northwest and Rocky Mountains experiencing the greatest increases of 78 and 175%, respectively. Increased area burned results in near doubling of wildfire carbonaceous aerosol emissions by midcentury. Using a chemical transport model driven by meteorology from the same GCM, we calculate that climate change will increase summertime organic carbon (OC) aerosol concentrations over the western United States by 40% and elemental carbon (EC) concentrations by 20% from 2000 to 2050. Most of this increase (75% for OC and 95% for EC) is caused by larger wildfire emissions with the rest caused by changes in meteorology and for OC by increased monoterpene emissions in a warmer climate. Such an increase in carbonaceous aerosol would have important consequences for western U.S. air quality and visibility.