Millimeter-wavelength Cloud Radar Research Articles

Microscale Updrafts within Northeast U.S. Coastal Snowstorms Using High-Resolution Cloud Radar Measurements

Abstract Limited knowledge exists about ∼100-m-scale precipitation processes within U.S. northeast coastal snowstorms because of a lack of high-resolution observations. We investigate characteristics of microscale updraft regions within the cyclone comma head and their relationships with snowbands, wind shear, frontogenesis, and vertical mass flux using high-spatiotemporal-resolution vertically pointing Ka-band radar measurements, soundings, and reanalysis data for four snowstorms observed at Stony Brook, New York. Updraft regions are defined as contiguous time–height plotted areas with upward Doppler velocity without hydrometeor sedimentation that is equal to or greater than 0.4 m s−1. Most updraft regions in the time–height data occur on a time scale of seconds (<20 s), which is equivalent to spatial scales < 500 m. These small updraft regions within cloud echo occur more than 30% of the time for three of the four cases and 18% for the other case. They are found at all altitudes and can occur with or without frontogenesis and with or without snowbands. The updraft regions with relatively large Doppler spectrum width (>0.4 m s−1) occur more frequently within midlevels of the storms, where there are strong wind shear layers and moist shear instability layers. This suggests that the dominant forcing for the updrafts appears to be turbulence associated with the vertical shear instability. The updraft regions can be responsible for upward mass flux when they are closer together in space and time. The higher values of column mean upward mass flux often occur during snowband periods. Significance Statement Small-scale (<500 m) upward motions within four snowstorms along the U.S. northeast coast are analyzed for the first time using high-spatiotemporal-resolution millimeter-wavelength cloud radar pointed vertically. The analysis reveals that updrafts appear in the storms regardless of whether snowbands are present or whether there is larger-scale forcing for ascent. The more turbulent and stronger updrafts frequently occur in midlevels of storms associated with instability from vertical shear and contribute to upward mass flux during snowband periods when they are closer together in space and time.

Classification and characteristic analysis of the clouds and dust in a dust-carrying precipitation process based on multi-source remote sensing observations

A dust-carrying precipitation event that took place on May 11, 2020 in North China was observed by remote sensing methods including a micropulse lidar (MPL), millimeter-wavelength cloud radar (MMCR) and microwave radiometer from Yanjiaping (YJP) Station, which is the center of the transmission path of this dust storm, and it is the only super station capable of vertical observation of aerosols, clouds and precipitation in North China. The study researched the macro and microphysical characteristics of clouds and sand dust, as well as the possible impact of sand dust on clouds. It is found that dust has a great influence on the CBH measurement of ceilometer but little on the MMCR. The normalized relative backscatter (NRB) and depolarization ratio (DR) of the MPL were comparatively analyzed in terms of the mass concentration of PM10, revealing that the mass concentration of PM10 is linearly positively correlated to the NRB, with the correlation coefficient up to 0.9273. It was also discovered that the 100 μg/m3 is a critical point for the mass concentration of dust particles, and when the PM10 was greater than 100 μg/m3, the DR would fluctuate within a narrow range of 0.32–0.38. A classification method that could be used to distinguish among clear sky, non-dust aerosols, clouds, dust, and cloud-dust mixing zones was proposed. Based on the classification results, the immersion freezing process that might occur in the cloud-dust mixing area was analyzed using Z-LDR and V-SW by reference to the vertical distribution of temperature and humidity.

A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

Abstract. Low-level clouds play a key role in the energy budget and hydrological cycle of the climate system. The accurate long-term observation of low-level clouds is essential for understanding their climate effect and model constraints. Both ground-based and spaceborne millimeter-wavelength cloud radars can penetrate clouds but the detected low-level clouds are always contaminated by clutter, which needs to be removed. In this study, we develop an algorithm to accurately separate low-level clouds from clutter for ground-based cloud radar using multi-dimensional probability distribution functions along with the Bayesian method. The radar reflectivity, linear depolarization ratio, spectral width, and their dependence on the time of the day, height, and season are used as the discriminants. A low-pass spatial filter is applied to the Bayesian undecided classification mask by considering the spatial correlation difference between clouds and clutter. The final feature mask result has a good agreement with lidar detection, showing a high probability of detection rate (98.45 %) and a low false alarm rate (0.37 %). This algorithm will be used to reliably detect low-level clouds at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site for the study of their climate effect and the interaction with local abundant dust aerosol in semi-arid regions.

Long-Term Retrievals of Cloud Type and Fair-Weather Shallow Cumulus Events at the ARM SGP Site

AbstractA long-term climatology of classified cloud types has been generated for 13 years (1997–2009) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for seven cloud categories: low clouds, congestus, deep convection, altocumulus, altostratus, cirrostratus/anvil, and cirrus. The classification was based on the cloud macrophysical quantities of cloud top, cloud base, and physical thickness of cloud layers, as measured by active sensors such as the millimeter-wavelength cloud radar (MMCR) and micropulse lidar (MPL). Climate variability of cloud characteristics has been examined using the 13-yr cloud-type retrieval. Low clouds and cirrus showed distinct diurnal and seasonal cycles. Total cloud occurrence followed the variation of low clouds, with a diurnal peak in early afternoon and a seasonal maximum in late winter. Additionally, further work has been done to identify fair-weather shallow cumulus (FWSC) events for 9 years (2000–08). Periods containing FWSC, a subcategory of clouds classified as low clouds, were produced using cloud fraction information from a total-sky imager and ceilometer. The identified FWSC periods in our study show good agreement with manually identified FWSC, missing only 6 cases out of 70 possible events during the spring to summer seasons (May–August).

Spatial and temporal variability of snowfall over Greenland from CloudSat observations

Abstract. We use the CloudSat 2006–2016 data record to estimate snowfall over the Greenland Ice Sheet (GrIS). We first evaluate CloudSat snowfall retrievals with respect to remaining ground-clutter issues. Comparing CloudSat observations to the GrIS topography (obtained from airborne altimetry measurements during IceBridge) we find that at the edges of the GrIS spurious high-snowfall retrievals caused by ground clutter occasionally affect the operational snowfall product. After correcting for this effect, the height of the lowest valid CloudSat observation is about 1200 m above the local topography as defined by IceBridge. We then use ground-based millimeter wavelength cloud radar (MMCR) observations obtained from the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland (ICECAPS) experiment to devise a simple, empirical correction to account for precipitation processes occurring between the height of the observed CloudSat reflectivities and the snowfall near the surface. Using the height-corrected, clutter-cleared CloudSat reflectivities we next evaluate various Z–S relationships in terms of snowfall accumulation at Summit through comparison with weekly stake field observations of snow accumulation available since 2007. Using a set of three Z–S relationships that best agree with the observed accumulation at Summit, we then calculate the annual cycle snowfall over the entire GrIS as well as over different drainage areas and compare the derived mean values and annual cycles of snowfall to ERA-Interim reanalysis. We find the annual mean snowfall over the GrIS inferred from CloudSat to be 34±7.5 cm yr−1 liquid equivalent (where the uncertainty is determined by the range in values between the three different Z–S relationships used). In comparison, the ERA-Interim reanalysis product only yields 30 cm yr−1 liquid equivalent snowfall, where the majority of the underestimation in the reanalysis appears to occur in the summer months over the higher GrIS and appears to be related to shallow precipitation events. Comparing all available estimates of snowfall accumulation at Summit Station, we find the annually averaged liquid equivalent snowfall from the stake field to be between 20 and 24 cm yr−1, depending on the assumed snowpack density and from CloudSat 23±4.5 cm yr−1. The annual cycle at Summit is generally similar between all data sources, with the exception of ERA-Interim reanalysis, which shows the aforementioned underestimation during summer months.

An improvement of the retrieval of temperature and relative humidity profiles from a combination of active and passive remote sensing

This paper focuses on an improvement of the retrieval of atmospheric temperature and relative humidity profiles through combining active and passive remote sensing. Ground-based microwave radiometer and millimeter-wavelength cloud radar were used to acquire the observations. Cloud base height and cloud thickness determinations from cloud radar were added into the atmospheric profile retrieval process, and a back-propagation neural network method was used as the retrieval tool. Because a substantial amount of data are required to train a neural network, and as microwave radiometer data are insufficient for this purpose, 8 years of radiosonde data from Beijing were used as the database. The monochromatic radiative transfer model was used to calculate the brightness temperatures in the same channels as the microwave radiometer. Parts of the cloud base heights and cloud thicknesses in the training data set were also estimated using the radiosonde data. The accuracy of the results was analyzed through a comparison with L-band sounding radar data and quantified using the mean bias, root-mean-square error (RMSE), and correlation coefficient. The statistical results showed that an inversion with cloud information was the optimal method. Compared with the inversion profiles without cloud information, the RMSE values after adding cloud information reduced to varying degrees for the vast majority of height layers. These reductions were particularly clear in layers with clouds. The maximum reduction in the RMSE for the temperature profile was 2.2 K, while that for the humidity profile was 16%.

An improved hydrometeor detection method for millimeter-wavelength cloud radar

Abstract. A modified method with a new noise reduction scheme that can reduce the noise distribution to a narrow range is proposed to distinguish clouds and other hydrometeors from noise and recognize more features with weak signal in cloud radar observations. A spatial filter with central weighting, which is widely used in cloud radar hydrometeor detection algorithms, is also applied in our method to examine radar return for significant levels of signals. Square clouds were constructed to test our algorithm and the method used for the US Department of Energy Atmospheric Radiation Measurements Program millimeter-wavelength cloud radar. We also applied both the methods to 6 months of cloud radar observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University and compared the results. It was found that our method has significant advantages in reducing the rates of both failed negative and false positive hydrometeor identifications in simulated clouds and recognizing clouds with weak signal from our cloud radar observations.

Cloud macrophysical properties from KAZR at the SACOL

Clouds are one of the most important components in the climate system through their profound effects on the radiative energy budget. However, due to the lack of accurate representation of clouds in global climate models, they are the major source of uncertainties for the future climate projections. Millimeter wavelength Cloud Radars (MMCRs) are powerful instruments that can resolve vertical structure as well as microphysical properties of clouds. The wavelength of MMCRs, which is shorter than those of weather radars, allows an excellent sensitivity to cloud droplets and ice crystals while being much longer than lidar wavelengths enables MMCRs to penetrate multiple cloud layers. Ka-Band Zenith Radar (KAZR), which has been deployed at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) since July 2013, has been continuously operated for three years. By applying a cloud mask algorithm that is operationally employed in the Atmospheric Radiation Measurement Program to distinguish radar echoes from noise, we analyze the macrophysical properties of clouds above the SACOL site by using the KAZR observations through 2014. It is shown that the occurrence of cloud base peaks at both 1.5 and 5.5 km while the cloud top peaks at 2.5 and 8.5 km. About 67% of the cloud thickness is within 2 km. The heights of cloud base and top in spring and summer are higher than autumn and winter. The diurnal cycles of cloud top and base are significant in summer and autumn but not in spring and winter. In summer, both the cloud base and top heights increase after sunrise and then decrease from mid-afternoon to midnight, while the cloud base and cloud top heights increase from noon to midnight in autumn. These seasonal features of diurnal changes may be closely related to the cloud formation mechanism and seasonal variations of solar radiation. Clouds over the SACOL can be classified as low, middle and high clouds based on the cloud height and thickness. The monthly mean occurrence of all clouds varies between 44% and 76%. The occurrence of low, middle and high clouds is 13%, 30%, 34%, respectively. The occurrence of middle and high clouds does not show a significant difference except for spring when the high clouds occur more often than middle clouds. Low clouds, however, occur less frequently throughout the year than the other two cloud types. Clouds can also be classified based on the number of cloud layers. Clouds with layers less than three contribute to ~98% of the total clouds and multi-layer clouds occur frequently in summer and autumn. This may be a result of the convection that is more likely to happen in summer and autumn and will induce a vertical distribution of clouds. The cloud base and top heights detected by KAZR, CloudSat and CALIPSO are also compared in this study. We find that the differences in cloud base and top heights between CloudSat and KAZR are less than 0.12 km. The reflectivities detected by CloudSat are comparable with those from KAZR. CloudSat cannot detect clouds with weak signal less than −30 dBZ due to the limitation of its sensitivity. This part of missing clouds can be captured by CALIPSO but CALIPSO lidar signals can be attenuated in thick clouds leading to an overestimation of cloud base.

Development and Applications of ARM Millimeter-Wavelength Cloud Radars

Development and Applications of ARM Millimeter-Wavelength Cloud Radars

The annual cycle of snowfall at Summit, Greenland

AbstractWhile snow accumulation over central Greenland has been extensively studied, interannual variability of snowfall in the region is not well understood due to a dearth of observations. The Integrated Characterization of Energy, Clouds, Atmospheric state and Precipitation at Summit (ICECAPS) project at Summit, Greenland, offers a unique, ground‐based opportunity to study precipitation in central Greenland where the surface mass balance is positive. Combining data from a Precipitation Occurrence Sensor System (POSS), Millimeter‐wavelength Cloud Radar (MMCR), and snow stake field, the annual cycle of precipitation at Summit is examined. Average daily snowfall is higher by a factor of 3 from June to October compared to November to May, while surface height change is only higher by 15% during the same timeframes. This reduced variability in surface height is explained by the seasonally varying nature of latent heat flux, compaction, and wind contributions. The ICECAPS remote sensors and stake field measurements do not agree as far as total annual water equivalent. This discrepancy is likely due to a low bias in the POSS and MMCR snowfall retrievals for Summit. To further examine the seasonal cycle, snowfall measurements by the POSS were linked to local meteorological parameters, including wind direction, liquid water path (LWP), 2 m temperature, and precipitable water vapor. An observed wind direction and moisture dependence are consistent with snowfall being linked to pulses of moist air that originate over nearby, ice‐free ocean, a resource that becomes more readily available in summertime as the winter sea ice retreats. LWP is shown to have little relationship to snowfall, indicating that ice‐phase precipitation processes are quite important for snowfall at Summit.

Relationship between cloud characteristics and radar reflectivity based on aircraft and cloud radar co-observations

Cloud properties were investigated based on aircraft and cloud radar co-observation conducted at Yitong, Jilin, Northeast China. The aircraft provided in situ measurements of cloud droplet size distribution, while the millimeter-wavelength cloud radar vertically scanned the same cloud that the aircraft penetrated. The reflectivity factor calculated from aircraft measurements was compared in detail with simultaneous radar observations. The results showed that the two reflectivities were comparable in warm clouds, but in ice cloud there were more differences, which were probably associated with the occurrence of liquid water. The acceptable agreement between reflectivities obtained in water cloud confirmed that it is feasible to derive cloud properties by using aircraft data, and hence for cloud radar to remotely sense cloud properties. Based on the dataset collected in warm clouds, the threshold of reflectivity to diagnose drizzle and cloud particles was studied by analyses of the probability distribution function of reflectivity from cloud particles and drizzle drops. The relationship between reflectivity factor (Z) and cloud liquid water content (LWC) was also derived from data on both cloud particles and drizzle. In comparison with cloud droplets, the relationship for drizzle was blurred by many scatter points and thus was less evident. However, these scatters could be partly removed by filtering out the drop size distribution with a large ratio of reflectivity and large extinction coefficient but small effective radius. Empirical relationships of Z-LWC for both cloud particles and drizzle could then be derived.

A comparison of observations in the tropical western Pacific from ground‐based and satellite millimeter‐wavelength cloud radars

Millimeter‐wavelength cloud radar (MMCR) can provide information on the vertical structure of cloud fields and thereby improve our understanding of the spatial distribution of clouds and their role in the climate system. Here we consider the representativeness of ground‐based vertically pointing MMCR observations, which have been used in numerous climate studies. MMCR cloud statistics collected at Darwin, Australia, are compared against CloudSat (spaceborne) observations gathered in the near vicinity of the ground site. Overall, the total cloud occurrence vertical profiles observed by CloudSat and the ground‐based MMCR agree on a spatial scale of 4° × 4°, although CloudSat is found to observe more high reflectivity cloud than the ground‐based MMCR. Computed radar reflectivity using idealized atmospheric profiles suggests that rain (especially below the melting level) influences the observed reflectivities, and this appears to account for much of the differences in the observed distributions of radar reflectivity. After removal of precipitation profiles, CloudSat and ground‐based MMCR observations show reasonable agreement. Sampling uncertainty in the CloudSat observations makes comparison at smaller region spatial scales (e.g., 2.5°) difficult and unfeasible for analysis at the time scale of months. Comparison of CloudSat observations with the ground‐based data on scales of 4° and 7.5° works well. Comparison of total cloud occurrence and reflectivity distribution of nonprecipitating cloud from the MMCR and CloudSat at spatial scales from 4° to 7.5° show good agreement. This suggests that the properties of the nonprecipitating cloud are relatively homogeneous at this large scale.

Detection of supercooled liquid in mixed‐phase clouds using radar Doppler spectra

Cloud phase identification from active remote sensors in the temperature range from 0 to −40°C, where both liquid and ice hydrometeor phases are sustainable, is challenging. Millimeter wavelength cloud radars (MMCR) are able to penetrate and detect multiple cloud layers. However, in mixed‐phase conditions, ice crystals dominate the radar signal, rendering the detection of liquid droplets from radar observables more difficult. The technique proposed here overcomes this fundamental limitation by using morphological features in MMCR Doppler spectra to detect supercooled liquid droplets in the radar sampling volume in the presence of ice particles. High lidar backscatter and near‐zero lidar depolarization measurements (good indicators of the presence of liquid droplets) from the Mixed‐Phase Arctic Clouds Experiment (MPACE) conducted in Barrow, Alaska, are used to train the technique and evaluate its potential for detecting mixed‐phase conditions. Ceilometer, microwave radiometer, and radiosonde measurements provide additional independent validation. Because of the ability of MMCRs to penetrate multiple liquid layers, this radar‐based technique does not suffer from the extinction limitations of lidars and is thus able to expand cloud phase identification methods to cloud regions beyond where lidars can penetrate, providing output at the native radar resolution. The technique is applicable to all profiling radars that have sufficient sensitivity to observe the small amount of liquid in mixed‐phase clouds.

Large-Eddy Observation of Post-Cold-Frontal Continental Stratocumulus

Abstract More studies on the dynamics of marine stratus and stratocumulus clouds have been performed than comparable studies on continental stratocumulus. Therefore, to increase the number of observations of continental stratocumulus and to compare marine and continental stratocumulus to each other, the approach of large-eddy observation (LEO) was applied to a case of nocturnal continental stratocumulus observed over the Atmospheric Radiation Measurement Program (ARM) Climate Research Facility (ACRF) in the central United States on 8 April 2006. The stratocumulus occurred in cold-air and dry-air advection behind a surface cold front. LEOs were obtained from millimeter-wavelength cloud radar and micropulse lidar, whereas traditional meteorological observations described the synoptic environment. This study focuses on a 9-h period of a predominantly nonprecipitating stratocumulus layer 250–400 m thick. A slight thinning of the cloud layer over time is consistent with dry-air advection. A deep layer of descent overlaid a shallower layer of ascent from the surface up to 800 mb, providing a mechanism for strengthening the inversion at cloud top. Time series of Doppler velocity indicate vertically coherent structures identifiable throughout much of the cloud layer. The magnitude of turbulence, as indicated by the variance of the vertical velocity, was weak relative to typical marine stratocumulus and to the one other case of continental stratocumulus in the literature. Conditional sampling of the eddy structures indicate that strong downdrafts were more prevalent than strong updrafts, and negative skewness of vertical velocity in the cloud implies an in-cloud circulation driven by longwave cooling at cloud top, similar to that in marine stratocumulus.

A Technique for the Automatic Detection of Insect Clutter in Cloud Radar Returns

Abstract The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program operates 35-GHz millimeter-wavelength cloud radars (MMCRs) in several climatologically distinct regions. The MMCRs, which are centerpiece instruments for the observation of clouds and precipitation, provide continuous, vertically resolved information on all hydrometeors above the ARM Climate Research Facilities (ACRF). However, their ability to observe clouds in the lowest 2–3 km of the atmosphere is often obscured by the presence of strong echoes from insects, especially during the warm months at the continental midlatitude Southern Great Plains (SGP) ACRF. Here, a new automated technique for the detection and elimination of insect-contaminated echoes from the MMCR observations is presented. The technique is based on recorded MMCR Doppler spectra, a feature extractor that conditions insect spectral signatures, and the use of a neural network algorithm for the generation of an insect (clutter) mask. The technique exhibits significant skill in the identification of insect radar returns (more than 92% of insect-induced returns are identified) when the sole input to the classifier is the MMCR Doppler spectrum. The addition of circular polarization observations by the MMCR and ceilometer cloud-base measurements further improve the performance of the technique and form an even more reliable method for the removal of insect radar echoes at the ARM site. Recently, a 94-GHz Doppler polarimetric radar was installed next to the MMCR at the ACRF SGP site. Observations by both radars are used to evaluate the potential of the 94-GHz radar as being insect free and to show that dual wavelength radar reflectivity measurements can be used to identify insect radar returns.

The Atmospheric Radiation Measurement Program Cloud Profiling Radars: Second-Generation Sampling Strategies, Processing, and Cloud Data Products

Abstract The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program operates millimeter-wavelength cloud radars in several climatologically distinct regions. The digital signal processors for these radars were recently upgraded and allow for enhancements in the operational parameters running on them. Recent evaluations of millimeter-wavelength cloud radar signal processing performance relative to the range of cloud dynamical and microphysical conditions encountered at the ARM Program sites have indicated that improvements are necessary, including significant improvement in temporal resolution (i.e., less than 1 s for dwell and 2 s for dwell and processing), wider Nyquist velocities, operational dealiasing of the recorded spectra, removal of pulse compression while sampling the boundary layer, and continuous recording of Doppler spectra. A new set of millimeter-wavelength cloud radar operational modes that incorporate these enhancements is presented. A significant change in radar sampling is the introduction of an uneven mode sequence with 50% of the sampling time dedicated to the lower atmosphere, allowing for detailed characterization of boundary layer clouds. The changes in the operational modes have a substantial impact on the postprocessing algorithms that are used to extract cloud information from the radar data. New methods for postprocessing of recorded Doppler spectra are presented that result in more accurate identification of radar clutter (e.g., insects) and extraction of turbulence and microphysical information. Results of recent studies on the error characteristics of derived Doppler moments are included so that uncertainty estimates are now included with the moments. The microscale data product based on the increased temporal resolution of the millimeter-wavelength cloud radars is described. It contains the number of local maxima in each Doppler spectrum, the Doppler moments of the primary peak, uncertainty estimates for the Doppler moments of the primary peak, Doppler moment shape parameters (e.g., skewness and kurtosis), and clear-air clutter flags.

A Bootstrap Technique for Testing the Relationship between Local-Scale Radar Observations of Cloud Occurrence and Large-Scale Atmospheric Fields

Abstract A classification scheme is created to map the synoptic-scale (large scale) atmospheric state to distributions of local-scale cloud properties. This mapping is accomplished by a neural network that classifies 17 months of synoptic-scale initial conditions from the rapid update cycle forecast model into 25 different states. The corresponding data from a vertically pointing millimeter-wavelength cloud radar (from the Atmospheric Radiation Measurement Program Southern Great Plains site at Lamont, Oklahoma) are sorted into these 25 states, producing vertical profiles of cloud occurrence. The temporal stability and distinctiveness of these 25 profiles are analyzed using a bootstrap resampling technique. A stable-state-based mapping from synoptic-scale model fields to local-scale cloud properties could be useful in three ways. First, such a mapping may improve the understanding of differences in cloud properties between output from global climate models and observations by providing a physical context. Second, this mapping could be used to identify the cause of errors in the modeled distribution of clouds—whether the cause is a difference in state occurrence (the type of synoptic activity) or the misrepresentation of clouds for a particular state. Third, robust mappings could form the basis of a new statistical cloud parameterization.

The Atmospheric Radiation Measurement Program Cloud Profiling Radars: An Evaluation of Signal Processing and Sampling Strategies

Abstract The U.S. Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) program operates millimeter-wavelength cloud radars (MMCRs) in several specific locations within different climatological regimes. These vertically pointing cloud profiling radars supply the three most important Doppler spectrum moment estimates, which are the radar reflectivity (or zero moment), the mean Doppler velocity (or first moment), and the Doppler spectrum width (or second moment), as a function of time and height. The ARM MMCR Doppler moment estimates form the basis of a number of algorithms for retrieving cloud microphysical and radiative properties. The retrieval algorithms are highly sensitive to the quality and accuracy of the MMCR Doppler moment estimates. The significance of these sensitivities should not be underestimated, because the inherent physical variability of clouds, instrument-induced noise, and sampling strategy limitations all potentially introduce errors into the Doppler moment estimates. In this article, the accuracies of the first three Doppler moment estimates from the ARM MMCRs are evaluated for a set of typical cloud conditions from the three DOE ARM program sites. Results of the analysis suggest that significant errors in the Doppler moment estimates are possible in the current configurations of the ARM MMCRs. In particular, weakly reflecting clouds with low signal-to-noise ratios (SNRs), as well as turbulent clouds with nonzero updraft and downdraft velocities that are coupled with high SNR, are shown to produce degraded Doppler moment estimates in the current ARM MMCR operational mode processing strategies. Analysis of the Doppler moment estimates and MMCR receiver noise characteristics suggests that the introduction of a set of quality control criteria is necessary for identifying periods of degraded receiver performance that leads to larger uncertainties in the Doppler moment estimates. Moreover, the temporal sampling of the ARM MMCRs was found to be insufficient for representing the actual dynamical states in many types of clouds, especially boundary layer clouds. New digital signal processors (DSPs) are currently being developed for the ARM MMCRs. The findings presented in this study will be used in the design of a new set of operational strategies for the ARM MMCRs once they have been upgraded with the new DSPs.

Clouds and Shortwave Fluxes at Nauru. Part II: Shortwave Flux Closure

Abstract The datasets currently being collected by the Atmospheric Radiation Measurement (ARM) program on the islands of Nauru and Manus represent the longest time series of ground-based cloud measurements in the tropical western Pacific region. In this series of papers, a shortwave flux closure study is presented using observations collected at the Nauru site between June 1999 and May 2000. The first paper presented frequency of occurrence of nonprecipitating clouds detected by the millimeter-wavelength cloud radar (MMCR) at Nauru and statistics of their retrieved microphysical properties. This paper presents estimates of the cloud radiative effect over the study period and results from a closure study in which retrieved cloud properties are input to a radiative transfer model and the modeled surface fluxes are compared to observations. The average surface shortwave cloud radiative forcing is 48.2 W m−2, which is significantly smaller than the cloud radiative forcing estimates found during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) field project. The difference in the estimates during the two periods is due to the variability in cloud amount over Nauru during different phases of the El Niño–Southern Oscillation (ENSO). In the closure study, modeled and observed surface fluxes show large differences at short time scales, due to the temporal and spatial variability of the clouds observed at Nauru. Averaging over 60 min reduces the average root-mean-square difference in total flux to 10% of the observed flux. Modeled total downwelling fluxes are unbiased with respect to the observed fluxes while direct fluxes are underestimated and diffuse fluxes are overestimated. Examination of the differences indicates that cloud amount derived from the ground-based measurements is an overestimate of the radiatively important cloud amount due to the anisotropy of the cloud field at Nauru, interpolation of the radar data, uncertainty in the microwave brightness temperature measurements for thin clouds, and the uncertainty in relating the sixth moment of the droplet size distribution observed by the radar to the more radiatively important moments.

Clouds and Shortwave Fluxes at Nauru. Part I: Retrieved Cloud Properties

Abstract The datasets currently being collected at the Atmospheric Radiation Measurement (ARM) program's sites on the islands of Nauru and Manus represent the longest time series of ground-based cloud measurements available in the tropical western Pacific region. In this and a companion paper, a shortwave flux closure study is presented using observations collected at the Nauru site between June 1999 and May 2000. This paper presents frequency of occurrence of nonprecipitating liquid and ice clouds detected by the millimeter wavelength cloud radar (MMCR) and statistics of retrieved microphysical properties. The companion paper presents results from a closure study in which the retrieved cloud properties are input to a radiative transfer model, and the modeled surface fluxes are compared to observations. The liquid cloud properties are retrieved from MMCR and microwave radiometer (MWR) measurements using a Bayesian retrieval technique. Properties of ice phase clouds are retrieved from MMCR measurements usi...