Southern Great Plains Site Research Articles

Determination of 3D Cloud Ice Water Contents by Combining Multiple Data Sources from Satellite, Ground Radar, and a Numerical Model

Abstract This study aims at determining the three-dimensional distribution of ice water content over a broad area near the Atmospheric Radiation Measurement Program Southern Great Plains site, where cloud radar and meteorological observations have been routinely conducted. Together with wind fields from other measurements, the ice water content retrievals can be used to derive the cloud ice water advective tendency terms needed for single-column model simulations. In this study, a Bayesian retrieval algorithm has been developed that combines multiple data sources from satellite high-frequency microwave radiometry, ground cloud radar observations, and mesoscale numerical model analysis. The cloud radar observations allow the characteristics of vertical ice water content structures to be inferred. The numerical model data are used to locate the cloud height. The satellite data provide information on the integrated ice water path, its horizontal distribution over a broad area, and, to a lesser extent, the vertical structure of ice water content. The approach herein is to retrieve the three-dimensional cloud ice water content in a 10° × 10° area surrounding the cloud radar site by combining all the information contained in the above datasets through a Bayesian framework. Validation of the algorithm has been done by comparing the retrievals with measurements from two ground radars. The comparison shows that the mean ice water content profiles and the two-dimensional (height–ice water content) probability density functions retrieved for 19 coincident cases agree fairly well with the validation data. However, the retrieved ice water contents generally lack detailed vertical structures because of the low sensitivity of satellite data to the vertical variation of cloud ice.

Observational evidence of changes in water vapor, clouds, and radiation at the ARM SGP site

Characterizing water vapor and cloud effects on the surface radiation budget is critical for understanding the current climate because water vapor is the most important greenhouse gas in the atmosphere and clouds are one of the largest sources of uncertainty in predicting potential future climate change. Several studies have shown that insolation over land declined until 1990 then increased until the present. Using 8 years of data collected at the ARM Southern Great Plains (SGP) surface site, we found that the insolation increased from 1997 to 2000, but significantly decreased from 2001 to 2004, changes that exactly mirror the variation in the second‐order fit of cloud fraction. Under clear‐sky conditions, the rates of change of water vapor, insolation and downwelling longwave (LW) flux are −0.0166 cm/yr, 0.48 Wm−2/yr, and −1.16 Wm−2/yr, respectively, indicating that water vapor changes are more important for LW flux than for insolation.

Temporal variation of aerosol properties at a rural continental site and study of aerosol evolution through growth law analysis

Aerosol size distributions were measured by a Scanning Mobility Particle Sizer (SMPS) on board the CIRPAS Twin Otter aircraft during 16 flights at the Southern Great Plains (SGP) site in northern central Oklahoma as part of the Aerosol Intensive Operation period in May 2003. During the same period a second SMPS was deployed at a surface station and provided continuous measurements. Combined with trace gas measurements at the SGP site and back trajectory analysis, the aerosol size distributions provided insights into the sources of aerosols observed at the SGP site. High particle concentrations, observed mostly during daytime, were well correlated with the sulfur dioxide (SO2) mixing ratios, suggesting nucleation involving sulfuric acid is likely the main source of newly formed particles at the SGP. Aerosols within plumes originating from wildfires in Central America were measured at the surface site. Vertically compact aerosol layers, which can be traced back to forest fires in East Asia, were intercepted at altitudes over 3000 m. Analyses of size‐dependent particle growth rates for four periods during which high cloud coverage was observed indicate growth dominated by volume controlled reactions. Sulfate accounts for 50% to 72% of the increase in aerosol volume concentration; the rest of the volume concentration increase was likely due to secondary organic species. The growth law analyses and meteorological conditions indicate that the sulfate was produced mainly through aqueous oxidation of SO2 in clouds droplets and hydrated aerosol particles.

Cloud base heights retrieved during night‐time conditions with MODIS data

The capability to retrieve cloud base heights was developed under the US National Polar‐orbiting Operational Environmental Satellite System (NPOESS) programme as one of 27 data products to be created from data collected by the Visible Infrared Imager Radiometer Suite (VIIRS). First launch of the VIIRS sensor, which is the high‐resolution Earth imager of the NPOESS programme, comes on National Aeronautics & Space Administration's (NASA) NPOESS Preparatory Project (NPP). In preparation for this launch, extensive testing of the VIIRS cloud algorithms was completed to verify that product performance will satisfy system requirements before the cloud algorithms were hosted in the NPOESS ground processing centre. The approach taken to retrieve cloud base height converts cloud optical properties into a geometric thickness which is then subtracted from the cloud top height. Performance of the cloud base height algorithms has been verified recently using MODIS data, together with temporarily and spatial coincident observations of cloud thickness values made with the millimetre cloud radar operated at the US Department of Energy Atmospheric Radiation Measurement (ARM) Program Southern Great Plains site in Oklahoma. Of particular significance is the clear demonstration that both cloud optical properties and cloud base heights are retrieved accurately during night‐time conditions with the VIIRS algorithms, since neither of these products is currently produced by the NASA EOS programme. Based upon these analyses, the VIIRS cloud algorithms are expected to satisfy NPOESS requirements, making VIIRS the first operational satellite sensor capable of retrieving three‐dimensional cloud fields.

Assessment of multispectral ATSR2 stereo cloud-top height retrievals

The Along-Track Scanning Radiometer 2 (ATSR2) instrument has a dual view capability that allows for stereo height retrievals. Stereo heights were retrieved for selected scenes over the United Kingdom Chilbolton Facility for Atmospheric and Radio Research and the United States Atmospheric Radiation Measurement program Southern Great Plains site from 1997 to 2000. Stereo cloud-top heights obtained with the 11 μm and 1.6 μm channels of ATSR2 were compared with ground-based millimeter-wave cloud radar measurements at both sites. On average ATSR2 11 μm channel cloud-top height retrievals were 350 m higher than those from radar with a standard deviation of 1 km. This difference increased with decreasing cloud-top height. One major problem found in the 11 μm channel cloud-top height retrievals was poor delineation between surface (i.e., clear) and low-altitude cloud pixels, though this tends to lower cloud-top heights rather than raise them. The ATSR2 1.6 μm channel stereo cloud-top heights had large discrepancies compared to radar because of the 1.6 μm channel sensitivity to a lower layer in the case of multilayer clouds or the terrain in the case of optically thin single-layer clouds. For single-layer clouds, the agreement between the ATSR2 1.6 μm channel and the radar cloud-top heights was similar to that between ATSR2 11 μm channel and radar cloud-top heights.

Cloud radiative forcing at the Atmospheric Radiation Measurement Program Climate Research Facility: 2. Vertical redistribution of radiant energy by clouds

Documentation of the effects of clouds on the radiant energy balance of the surface and atmosphere represents a shortcoming in the set of observations that are needed to ascertain the validity of climate model simulations. While clouds are known to cool the climate system from top of atmosphere (TOA) radiation budget studies, the redistribution of energy between the surface and atmosphere and within the atmosphere by clouds has not been examined in detail with observations. Using data collected at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site, we use measurements of cloud occurrence and structure together with a scheme to characterize the cloud microphysical and radiative properties to estimate the uncertainty in our ability to calculate the radiative forcing and effect of clouds at the top of atmosphere, the surface and within the atmosphere. We find that overcast clouds during 2000 tended to have a small net influence on the atmosphere (6 W m−2 ± 3 W m−2 of heating) with net TOA and surface cooling (25 W m−2 ± 3 W m−2 and 32 ± 3 W m−2, respectively). These statistics mask a significant redistribution of radiant energy within the atmosphere by clouds where low overcast clouds resulted in strong atmospheric cooling (37 W m−2 ± 9 W m−2), and thin high clouds resulted in warming (21 W m−2 ± 6 W m−2) suggesting that accurate prediction of the phasing of these cloud types within meteorological features is important for capturing the essential feedbacks by clouds to the general circulation.

Internal variability and pattern identification in cirrus cloud structure: The Fokker‐Planck equation approach

Using 35‐GHz millimeter wave radar observations collected at the Southern Great Plains site of the Atmospheric Radiation Measurements (ARM) program, we study physical processes in cirrus cloud layers having different stratifications. The time‐dependent probability distribution functions of the backscattering cross section within different layers in the cloud describe the dynamics of the pertinent physical processes. The time‐dependent tails of the probability distribution functions provide the signature of intermittency characterizing turbulent flows. In the framework of the Fokker‐Planck equation approach, we derive the behavior of the drift D(1) and diffusion D(2) terms that characterize the deterministic and stochastic parts, respectively, of the dynamics of the probability distribution functions directly from the time series of observed backscattering cross section. We derive the Langevin equation that governs the temporal evolution of the backscattering cross‐section signal in cloud layers having different stratification. Finally, we ascertain that the drift γ and diffusion β coefficients of the backscattering cross‐section signal in the upper well‐mixed, neutrally stratified layer of the cirrus cloud satisfy the relationship γ = 2β, which is valid for fully developed turbulence owing to the Kolmogorov −4/5 law.

Application of aerosol hygroscopicity measured at the Atmospheric Radiation Measurement Program's Southern Great Plains site to examine composition and evolution

A Differential Mobility Analyzer/Tandem Differential Mobility Analyzer (DMA/TDMA) was used to measure submicron aerosol size distributions, hygroscopicity, and occasionally volatility during the May 2003 Aerosol Intensive Operational Period (IOP) at the Central Facility of the Atmospheric Radiation Measurement Program's Southern Great Plains (ARM SGP) site. Hygroscopic growth factor distributions for particles at eight dry diameters ranging from 0.012 μm to 0.600 μm were measured throughout the study. For a subset of particle sizes, more detailed measurements were occasionally made in which the relative humidity or temperature to which the aerosol was exposed was varied over a wide range. These measurements, in conjunction with backtrajectory clustering, were used to infer aerosol composition and to gain insight into the processes responsible for evolution. The hygroscopic growth of both the smallest and largest particles analyzed was typically less than that of particles with dry diameters of about 0.100 μm. It is speculated that condensation of secondary organic aerosol on nucleation mode particles is largely responsible for the minimal hygroscopic growth observed at the smallest sizes considered. Growth factor distributions of the largest particles characterized typically contained a nonhygroscopic mode believed to be composed primarily of dust. A model was developed to characterize the hygroscopic properties of particles within a size distribution mode through analysis of the fixed size hygroscopic growth measurements. The performance of this model was quantified through comparison of the measured fixed size hygroscopic growth factor distributions with those simulated through convolution of the size‐resolved concentration contributed by each of the size modes and the mode‐resolved hygroscopicity. This transformation from size‐resolved hygroscopicity to mode‐resolved hygroscopicity facilitated examination of changes in the hygroscopic properties of particles within a size distribution mode that accompanied changes in the sizes of those particles. This model was used to examine three specific cases in which the sampled aerosol evolved slowly over a period of hours or days.

Characterization of ambient aerosol from measurements of cloud condensation nuclei during the 2003 Atmospheric Radiation Measurement Aerosol Intensive Observational Period at the Southern Great Plains site in Oklahoma

Measurements were made by a new cloud condensation nuclei (CCN) instrument (CCNC3) during the Atmospheric Radiation Measurement (ARM) Program's Aerosol Intensive Observational Period (IOP) in May 2003 in Lamont, Oklahoma. An inverse aerosol/CCN closure study is undertaken, in which the predicted number concentration of particles available for activation (NP) at the CCNC3 operating supersaturations is compared to that observed (NO). NP is based on Köhler Theory, with assumed and inferred aerosol composition and mixing state, and the airborne aerosol size distribution measured by the Caltech Dual Automatic Classified Aerosol Detector (DACAD). An initial comparison of NO and NP, assuming the ambient aerosol is pure ammonium sulfate ((NH4)2SO4), results in closure ratios (NP/NO) ranging from 1.18 to 3.68 over the duration of the IOP, indicating that the aerosol is less hygroscopic than (NH4)2SO4. NP and NO are found to agree when the modeled aerosol population has characteristics of an external mixture of particles, in which insoluble material is preferentially distributed among particles with small diameters (<50 nm) and purely insoluble particles are present over a range of diameters. The classification of sampled air masses by closure ratio and aerosol size distribution is discussed in depth. Inverse aerosol/CCN closure analysis can be a valuable means of inferring aerosol composition and mixing state when direct measurements are not available, especially when surface measurements of aerosol composition and mixing state are not sufficient to predict CCN concentrations at altitude, as was the case under the stratified aerosol layer conditions encountered during the IOP.

Evaluation of daytime measurements of aerosols and water vapor made by an operational Raman lidar over the Southern Great Plains

Raman lidar water vapor and aerosol extinction profiles acquired during the daytime over the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in northern Oklahoma (36.606 N, 97.50 W, 315 m) are evaluated using profiles measured by in situ and remote sensing instruments deployed during the May 2003 Aerosol Intensive Operations Period (IOP). The automated algorithms used to derive these profiles from the Raman lidar data were first modified to reduce the adverse effects associated with a general loss of sensitivity of the Raman lidar since early 2002. The Raman lidar water vapor measurements, which are calibrated to match precipitable water vapor (PWV) derived from coincident microwave radiometer (MWR) measurements were, on average, 5–10% (0.3–0.6 g/m3) higher than the other measurements. Some of this difference is due to out‐of‐date line parameters that were subsequently updated in the MWR PWV retrievals. The Raman lidar aerosol extinction measurements were, on average, about 0.03 km−1 higher than aerosol measurements derived from airborne Sun photometer measurements of aerosol optical thickness and in situ measurements of aerosol scattering and absorption. This bias, which was about 50% of the mean aerosol extinction measured during this IOP, decreased to about 10% when aerosol extinction comparisons were restricted to aerosol extinction values larger than 0.15 km−1. The lidar measurements of the aerosol extinction/backscatter ratio and airborne Sun photometer measurements of the aerosol optical thickness were used along with in situ measurements of the aerosol size distribution to retrieve estimates of the aerosol single scattering albedo (ωo) and the effective complex refractive index. Retrieved values of ωo ranged from (0.91–0.98) and were in generally good agreement with ωo derived from airborne in situ measurements of scattering and absorption. Elevated aerosol layers located between about 2.6 and 3.6 km were observed by the Raman lidar on 25 and 27 May. The airborne measurements and lidar retrievals indicated that these layers, which were likely smoke produced by Siberian forest fires, were primarily composed of relatively large particles (reff ∼ 0.23 μm) and that the layers were relatively nonabsorbing (ωo ∼ 0.96–0.98). Preliminary results show that major modifications that were made to the Raman lidar system during 2004 have dramatically improved the sensitivity in the aerosol and water vapor channels and reduced random errors in the aerosol scattering ratio and water vapor retrievals by an order of magnitude.

Intercomparison of multiple years of MODIS, MISR and radar cloud-top heights

Abstract. Radar cloud-top heights were retrieved at both the Chilbolton Facility for Atmospheric and Radio Research, UK (CFARR) and the ARM Southern Great Plain site, USA (SGP), using millimetre wave cloud radars and identical algorithms. The resulting cloud-top heights were used for comparison with MODIS and MISR retrieved cloud-top heights, from March 2000 to October 2003. Both imaging instruments reside on the NASA Earth Observing System (EOS) Terra platform launched in 1999. MODIS and MISR cloud-top products were from the recent collections (4 and 3, respectively) that cover the entire mission. The cloud characteristics are different at each ground site, with clouds generally residing at higher altitudes at SGP, but with a greater occurrence of broken or multilayered clouds at CFARR. A method is presented to automatically eliminate scenes where clouds are of a broken nature, since it is difficult in these conditions to ensure that ground-based and satellite measurements refer to the same cloud deck. The intercomparison between MODIS and radar cloud-top heights reveals that MODIS cloud-top heights agree with radar within about 1km for mid- and high-level clouds. However, this accuracy is degraded to nearly 3 km for low-level clouds. MISR cloud-top heights are found to agree with radar cloud-top heights to within 0.6 km, which is in line with theoretical expectations. In single-level cloud situations MODIS and MISR cloud-top heights tend to agree within 1 km. This comparison also reveals that the loss of radar sensitivity during 2001 resulted in the CFARR instrument being less accurate for high-level cloud-top height measurements. Keywords. Atmospheric composition and structure (Instruments and techniques)

Assessment of variability in continental low stratiform clouds based on observations of radar reflectivity

The variability of overcast low stratiform clouds observed over the ARM Climate Research Facility Southern Great Plains (ACRF SGP) site is analyzed, and an approach to characterizing subgrid variability based on assumed statistical distributions is evaluated. The analysis is based on a vast (>1000 hours) radar reflectivity database collected by the Millimeter‐Wave Cloud Radar at ACRF SGP site. The radar data are classified into two low cloud categories and stratified by scale and the presence of precipitation. Cloud variability is analyzed by studying statistical distributions for the first two moments of the probability distribution functions (PDF) of radar reflectivity. Results indicate that variability for a broadly defined low‐altitude stratiform cloud type exhibits on average 40% greater standard deviation than canonical boundary layer clouds topped by an inversion. Cloud variability also dramatically depends on microphysical processes (as manifested in radar reflectivity) and increases by 2–5 times within a typical reflectivity range. Finally, variability is a strong function of scale and almost doubles in the 20–100 min temporal scale range. Formulations of subgrid variability, based on PDFs of reflectivity, are evaluated for the two cloud types and two scales of 10 and 30 km, taken to be representative of mesoscale and NWP model grid sizes. The results show that for these cloud types and scales the PDF of reflectivity can be reasonably well approximated by a truncated Gaussian function, specified by mean and standard deviation with the latter parameterized as a linear function of the mean.

Monitoring of rainfall by ground-based passive microwave systems: models, measurements and applications

Abstract. A large set of ground-based multi-frequency microwave radiometric simulations and measurements during different precipitation regimes are analysed. Simulations are performed for a set of frequencies from 22 to 60 GHz, representing the channels currently available on an operational ground-based radiometric system. Results are illustrated in terms of comparisons between measurements and model data in order to show that the observed radiometric signatures can be attributed to rainfall scattering and absorption. An inversion algorithm has been developed, basing on the simulated data, to retrieve rain rate from passive radiometric observations. As a validation of the approach, we have analyzed radiometric measurements during rain events occurred in Boulder, Colorado, and at the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains (SGP) site in Lamont, Oklahoma, USA, comparing rain rate estimates with available simultaneous rain gauge data.

Combining Microwave Radiometer and Wind Profiler Radar Measurements for High-Resolution Atmospheric Humidity Profiling

Abstract A self-consistent remote sensing physical method to retrieve atmospheric humidity high-resolution profiles by synergetic use of a microwave radiometer profiler (MWRP) and wind profiler radar (WPR) is illustrated. The proposed technique is based on the processing of WPR data for estimating the potential refractivity gradient profiles and their optimal combination with MWRP estimates of potential temperature profiles in order to fully retrieve humidity gradient profiles. The combined algorithm makes use of recent developments in WPR signal processing, computing the zeroth-, first-, and second-order moments of WPR Doppler spectra via a fuzzy logic method, which provides quality control of radar data in the spectral domain. On the other hand, the application of neural network to brightness temperatures, measured by a multichannel MWRP, can provide continuous estimates of tropospheric temperature and humidity profiles. Performance of the combined algorithm in retrieving humidity profiles is compared with simultaneous in situ radiosonde observations (raob’s). The empirical sets of WPR and MWRP data were collected at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) site. Combined microwave radiometer and wind profiler measurements show encouraging results and significantly improve the spatial vertical resolution of atmospheric humidity profiles. Finally, some of the limitations found in the use of this technique and possible future improvements are also discussed.

Wide-Angle Imaging Lidar Deployment at the ARM Southern Great Plains Site: Intercomparison of Cloud Property Retrievals

Abstract The Wide-Angle Imaging Lidar (WAIL), a new instrument that measures cloud optical and geometrical properties by means of off-beam lidar returns, was deployed as part of a multi-instrument campaign to probe a cloud field at the Atmospheric Radiation Measurement (ARM) Southern Great Plain (SGP) site on 25 March 2002. WAIL is designed to determine physical and geometrical characteristics using the off-beam component of the lidar return that can be adequately modeled within the diffusion approximation. Using WAIL data, the extinction coefficient and geometrical thickness of a dense cloud layer is estimated, from which optical thickness is inferred. Results from the new methodology agree well with counterparts obtained from other instruments located permanently at the SGP ARM site and from the WAIL-like airborne instrument that flew over the site during our observation period.

THOR—Cloud Thickness from Offbeam Lidar Returns

Abstract Conventional wisdom is that lidar pulses do not significantly penetrate clouds having an optical thickness exceeding about τ = 2, and that no returns are detectible from more than a shallow skin depth. Yet optically thicker clouds of τ ≫ 2 reflect a larger fraction of visible photons and account for much of the earth’s global average albedo. As cloud-layer thickness grows, an increasing fraction of reflected photons are scattered multiple times within the cloud and return from a diffuse concentric halo that grows around the incident pulse, increasing in horizontal area with layer physical thickness. The reflected halo is largely undetected by narrow field-of-view (FOV) receivers commonly used in lidar applications. Cloud Thickness from Offbeam Returns (THOR) is an airborne wide-angle detection system with multiple FOVs, capable of observing the diffuse halo as a wide-angle signal, from which the physical thickness of optically thick clouds can be retrieved. This paper describes the THOR system, demonstrates that the halo signal is stronger for thicker clouds, and presents a validation of physical thickness retrievals for clouds having τ > 20, from NASA’s P-3B flights over the Department of Energy’s Atmospheric Radiation Measurement Southern Great Plains site, using the lidar, radar, and other ancillary ground-based data.

Cluster analysis of cloud regimes and characteristic dynamics of midlatitude synoptic systems in observations and a model

Global climate models typically do not correctly simulate cloudiness associated with midlatitude synoptic systems because coarse grid spacing prevents them from resolving dynamics occurring at smaller scales and there exist no adequate parameterizations for the effects of these subgrid‐scale dynamics. Comparison of modeled and observed cloud properties averaged over similar regimes (e.g., compositing) aids the diagnosis of simulation errors and identification of meteorological forcing responsible for producing particular cloud conditions. This study uses a k‐means clustering algorithm to objectively classify satellite cloud scenes into distinct regimes based on grid box mean cloud fraction, cloud reflectivity, and cloud top pressure. The spatial domain is the densely instrumented southern Great Plains site of the Atmospheric Radiation Measurement Program, and the time period is the cool season months (November–March) of 1999–2001. As a complement to the satellite retrievals of cloud properties, lidar and cloud radar data are analyzed to examine the vertical structure of the cloud layers. Meteorological data from the constraint variational analysis is averaged for each cluster to provide insight on the large‐scale dynamics and advective tendencies coincident with specific cloud types. Meteorological conditions associated with high and low subgrid spatial variability are also investigated for each cluster. Cloud outputs from a single‐column model version of the GFDL AM2 atmospheric model forced with meteorological boundary conditions derived from observations and a numerical weather prediction model were compared to observations for each cluster in order to determine the accuracy with which the model reproduces attributes of specific cloud regimes.

A microphysical parameterization for convective clouds in the ECHAM5 climate model: Single‐column model results evaluated at the Oklahoma Atmospheric Radiation Measurement Program site

The microphysical parameterization used for stratiform clouds in the ECHAM5 climate model is now extended for simulations of convective clouds. The performance of the newly implemented parameterization in simulating midlatitude continental summertime convective cloud systems is evaluated in this paper at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site in Oklahoma using the single‐column mode (SCM) of ECHAM5. Three ARM intensive operating periods (IOPs), including two summer ones and a late spring one, are used for the evaluation. Results show that the SCM simulated cloud cover fraction agrees well with observations. The SCM also captures most of the precipitation events. With the new microphysical parameterization, the model performs at least as well as with the original model setup in simulating almost all the fields examined in this study. Significant improvement is shown in the simulations of outgoing longwave radiation and net incoming solar radiation at the top of the atmosphere revealing the feasibility of the new parameterization. Sensitivity studies show that a 10‐fold increase in cloud droplet number concentration significantly increases the simulated liquid water content. More interestingly, this increase in cloud droplet number leads to an increase in the total amount of precipitation in two of the three IOPs.

Modeling and measurement of rainfall by ground-based multispectral microwave radiometry

The potential of ground-based multispectral microwave radiometers in retrieving rainfall parameters is investigated by coupling physically oriented models and retrieval methods with a large set of experimental data. Measured data come from rain events that occurred in the USA at Boulder, Colorado, and at the Atmospheric Radiation Measurement (ARM) Program's Southern Great Plains (SGP) site in Lamont, OK. Rain cloud models are specified to characterize both nonraining clouds, stratiform and convective rainfall. Brightness temperature numerical simulations are performed for a set of frequencies from 20 to 60 GHz at zenith angle, representing the channels currently deployed on a commercially available ground-based radiometric system. Results are illustrated in terms of comparisons between measurements and model data in order to show that the observed radiometric signatures can be attributed to rainfall scattering and absorption. A new statistical inversion algorithm, trained by synthetic data and based on principal component analysis is also developed to classify the meteorological background, to identify the rain regime, and to retrieve rain rate from passive radiometric observations. Rain rate estimate comparisons with simultaneous rain gauge data and rain effect mitigation methods are also discussed.

Moisture and temperature balances at the Atmospheric Radiation Measurement Southern Great Plains Site in forecasts with the Community Atmosphere Model (CAM2)

We compare the balance of terms in moisture and temperature prediction equations during short forecasts by the Community Atmosphere Model (CAM2) with observed estimates at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site for two intensive observing periods (IOPs). The goal is to provide insight into parameterization errors which ultimately should lead to model improvements. The atmospheric initial conditions are obtained from high‐resolution numerical weather prediction (NWP) analyses. The land initial conditions are spun up to be consistent with those analyses. Three cases are considered: (1) June/July 1997 when the atmosphere is relatively moist and surface evaporation corresponds to 90% of the precipitation with advection accounting for the remainder; (2) rainy days in April 1997 when the atmosphere is less moist and horizontal advection accounts for much of the precipitation with a small contribution from surface evaporation and the balance being derived from the water already present in the column; and (3) nonrainy days of the April 1997 when the moist process parameterizations are inactive and the planetary boundary layer (PBL) parameterization is dominant. For the first case the Zhang‐McFarlane deep convective parameterization drives the model to a wrong state. For the second the Hack shallow convective parameterization appears to be not acting deep enough. During both periods inconsistencies between CAM2 and ARM surface fluxes, land surface conditions and the net surface radiative fluxes indicate that the exchange parameterizations should be examined further. For the third case the PBL parameterization does not appear to create the correct vertical structure. In addition, the individual components of the dynamical tendency are very different between CAM2 and ARM, although the total dynamical tendency is similar in the two. Although these observations do not imply that those components are themselves wrong since they may be responding to other errors, each of these components should be examined further to determine the cause of their behaviors.