Partial Cloud Cover Research Articles

Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition

Abstract. Comparing the output of general circulation models to observations is essential for assessing and improving the quality of models. While numerical weather prediction models are routinely assessed against a large array of observations, comparing climate models and observations usually requires long time series to build robust statistics. Here, we show that by nudging the large-scale atmospheric circulation in coupled climate models, model output can be compared to local observations for individual days. We illustrate this for three climate models during a period in April 2020 when a warm air intrusion reached the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the central Arctic. Radiosondes, cloud remote sensing and surface flux observations from the MOSAiC expedition serve as reference observations. The climate models AWI-CM1/ECHAM and AWI-CM3/IFS miss the diurnal cycle of surface temperature in spring, likely because both models assume the snowpack on ice to have a uniform temperature. CAM6, a model that uses three layers to represent snow temperature, represents the diurnal cycle more realistically. During a cold and dry period with pervasive thin mixed-phase clouds, AWI-CM1/ECHAM only produces partial cloud cover and overestimates downwelling shortwave radiation at the surface. AWI-CM3/IFS produces a closed cloud cover but misses cloud liquid water. Our results show that nudging the large-scale circulation to the observed state allows a meaningful comparison of climate model output even to short-term observational campaigns. We suggest that nudging can simplify and accelerate the pathway from observations to climate model improvements and substantially extends the range of observations suitable for model evaluation.

A comparison of steady-state and transient modelling approaches for the performance prediction of solar air heaters

• A transient solar air heater model using high resolution weather data is developed. • Transient model is compared to stationary model using hourly averaged weather data. • Steady approach results in large deviations, especially under variable cloud cover. • Large differences between approaches in useful energy above threshold temperatures. • Potential implications for latent heat storage, solar drying and other processes. Due to the nature of solar energy, the operation of solar air heaters is fundamentally transient, but most mathematical modelling published to describe their behavior has used stationary models coupled with hourly-averaged weather data. Little attention has been paid to the transient behavior of solar air heaters under real, highly variable weather conditions. In this study, the common modelling approach using a steady-state description of solar air heaters is compared to predictions of using a transient model to which minute resolution weather data is given as input. The steady-state approach was found satisfactory, although not optimal, only under clear sky conditions, with relatively small outlet air temperature errors compared to the results obtained with the transient model. Under less favorable conditions with partial cloud cover, the outlet air temperature with the transient model presented large variations due to the sudden changes in solar radiation not captured in the steady-state approach. With the solar air heater parameters employed, instantaneous deviations of almost 15 °C were found, as well as errors in the predicted useful energy above threshold temperature levels. These results support the use of transient models of solar air heaters and high resolution weather data for accurate, short-term modelling of solar drying, latent heat storage and other highly temperature dependent processes.

3D Convection-resolving Model of Temperate, Tidally Locked Exoplanets

Abstract A large fraction of known terrestrial-size exoplanets located in the habitable zone of M-dwarfs are expected to be tidally locked. Numerous efforts have been conducted to study the climate of such planets, using in particular 3D global climate models (GCMs). One of the biggest challenges in simulating such an extreme environment is to properly represent the effects of sub-grid convection. Most GCMs use either a simplistic convective-adjustment parameterization or sophisticated (e.g., mass flux scheme) Earth-tuned parameterizations. One way to improve the representation of convection is to study convection using numerical convection-resolving models (CRMs), with a fine spatial resolution. In this study, we developed a CRM coupling the non-hydrostatic dynamical core Advanced Research Weather-Weather Research and Forecast model with the radiative transfer and cloud/precipitation models of the Laboratoire de Météorologie Dynamique generic climate model to study convection and clouds on tidally locked planets, with a focus on Proxima b. Simulations were performed for a set of three surface temperatures (corresponding to three different incident fluxes) and two rotation rates, assuming an Earth-like atmosphere. The main result of our study is that while we recover the prediction of GCMs that (low-altitude) cloud albedo increases with increasing stellar flux, the cloud feedback is much weaker due to transient aggregation of convection leading to low partial cloud cover.

Tree species classification using Sentinel-2 imagery and Bayesian inference

The increased temporal frequency of optical satellite data acquisitions provides a data stream that has the potential to improve land cover mapping, including mapping of tree species. However, for large area operational mapping, partial cloud cover and different image extents can pose challenges. Therefore, methods are needed to assimilate new images in a straightforward way without requiring a total spatial coverage for each new image. This study shows that Bayesian inference applied sequentially has the potential to solve this problem. To test Bayesian inference for tree species classification in the boreo-nemoral zone of southern Sweden, field data from the study area of Remningstorp (58°27′18.35″ N, 13°39′8.03″ E) were used. By updating class likelihood with an increasing number of combined Sentinel-2 images, a higher and more stable cross-validated overall accuracy was achieved. Based on a Mahalanobis distance, 23 images were automatically chosen from the period of 2016 to 2018 (from 142 images total). An overall accuracy of 87% (a Cohen’s kappa of 78.5%) was obtained for four tree species classes: Betula spp., Picea abies, Pinus sylvestris, and Quercus robur. This application of Bayesian inference in a boreo-nemoral forest suggests that it is a practical way to provide a high and stable classification accuracy. The method could be applied where data are not always complete for all areas. Furthermore, the method requires less reference data than if all images were used for classification simultaneously.

Status and Current Distribution of the Pine Barrens Treefrog (Hyla andersonii) in Florida

We conducted roadside call surveys at 111 historical Hyla andersonii (Pine Barrens Treefrog) sites in 2013–2016. We detected the species at 49 sites (44%), which is consistent with surveys conducted in the early 1980s. We detected the species throughout its historical range except Holmes County, which only has 4 historical sites. We also identified 33 new sites on public lands and 4 new sites on private lands. We used multi-season occupancy models to determine the influence of site and survey covariates on species occupancy and detection rates in Blackwater River State Forest. Species detection was positively associated with air temperature and partial cloud-cover and negatively associated with clear skies. Pine Barrens Treefrog occupancy rates were lower at survey sites surrounded by hardwood-dominated wetlands, suggesting that hardwood encroachment has negatively impacted this species on some areas of Blackwater River State Forest. The best model had an estimated detection probability of 0.43 (95% CI = 0.29–0.47) and an estimated occupancy of 0.63 (95% CI = 0.43–0.79). We recommend that future call surveys include a minimum of 5 visits to each survey site before inferring species absence. Our surveys suggest that the status and distribution of the species remain stable despite some habitat degradation.

The Impact of Cross‐track Infrared Sounder (CrIS) Cloud‐Cleared Radiances on Hurricane Joaquin (2015) and Matthew (2016) Forecasts

AbstractHyperspectral infrared (IR) sounders provide high vertical resolution atmospheric sounding information that can improve the forecast skill in numerical weather prediction. Commonly, only clear radiances are assimilated, because IR sounder observations are highly affected by clouds. A cloud‐clearing (CC) technique, which removes the cloud effects from an IR cloudy field of view (FOV) and derives the cloud‐cleared radiances (CCRs) or clear‐sky equivalent radiances, can be an alternative yet effective way to take advantage of the thermodynamic information from cloudy skies in data assimilation. This study develops a Visible Infrared Imaging Radiometer Suite (VIIRS)‐based CC method for deriving Cross‐track Infrared Sounder (CrIS) CCRs under partially cloudy conditions. Due to the lack of absorption bands on VIIRS, two important quality control steps are implemented in the CC process. Validation using VIIRS clear radiances indicates that the CC method can effectively obtain the CrIS CCRs for FOVs with partial cloud cover. To compare the impacts from assimilation of CrIS original radiances and CCRs, three experiments are carried out on two storm cases, Hurricane Joaquin (2015) and Hurricane Matthew (2016), using Gridpoint Statistical Interpolation assimilation system and Weather Research and Forecasting‐Advanced Research Version models. At the analysis time, more CrIS observations are assimilated when using CrIS CCRs than with CrIS original radiances. Comparing temperature, specific humidity, and U/V winds with radiosondes indicates that the data impacts are growing larger with longer time forecasts (beyond 72 h forecast). Hurricane track forecasts also show improvements from the assimilation of CrIS CCRs due to better weather system forecasts. The impacts of CCRs on intensity are basically neutral with mixed positive and negative results.

Rapid post-earthquake modelling of coseismic landslide intensity and distribution for emergency response decision support

Abstract. Current methods to identify coseismic landslides immediately after an earthquake using optical imagery are too slow to effectively inform emergency response activities. Issues with cloud cover, data collection and processing, and manual landslide identification mean even the most rapid mapping exercises are often incomplete when the emergency response ends. In this study, we demonstrate how traditional empirical methods for modelling the total distribution and relative intensity (in terms of point density) of coseismic landsliding can be successfully undertaken in the hours and days immediately after an earthquake, allowing the results to effectively inform stakeholders during the response. The method uses fuzzy logic in a GIS (Geographic Information Systems) to quickly assess and identify the location-specific relationships between predisposing factors and landslide occurrence during the earthquake, based on small initial samples of identified landslides. We show that this approach can accurately model both the spatial pattern and the number density of landsliding from the event based on just several hundred mapped landslides, provided they have sufficiently wide spatial coverage, improving upon previous methods. This suggests that systematic high-fidelity mapping of landslides following an earthquake is not necessary for informing rapid modelling attempts. Instead, mapping should focus on rapid sampling from the entire affected area to generate results that can inform the modelling. This method is therefore suited to conditions in which imagery is affected by partial cloud cover or in which the total number of landslides is so large that mapping requires significant time to complete. The method therefore has the potential to provide a quick assessment of landslide hazard after an earthquake and may therefore inform emergency operations more effectively compared to current practice.

Effective cloud optical depth and enhancement effects for broken liquid water clouds in Valencia (Spain)

Partly cloudy skies with liquid water clouds have been analysed, founding that it is essential to distinguish data if the Sun is obstructed or not by clouds. Both cases can be separated considering simultaneously the Cloud Modification Factor (CMF) and the clearness index (kt). For partly cloudy skies and the Sun obstructed the effective cloud optical depth (τ) has been obtained by the minimization method for overcast skies. This method was previously developed by the authors but, in this case, taking into account partial cloud cover. This study has been conducted for the years 2011–2015 with the multiple scattering model SBDART and irradiance measurements for the UV Erythemal Radiation (UVER) and the broadband ranges. Afterwards a statistical analysis of τ has shown that the maximum value is much lower than for overcast skies and there is more discrepancy between the two spectral ranges regarding the results for overcast skies. In order to validate these results the effective cloud optical depth has been correlated with several transmission factors, giving similar fit parameters to those obtained for overcast skies except for the clearness index in the UVER range. As our method is not applicable for partly cloudy skies with the visible Sun, the enhancement of radiation caused by clouds when the Sun is visible has been studied. Results show that the average enhancement CMF values are the same for both ranges although enhancement is more frequent for low cloud cover in the UVER and medium-high cloud cover in the broadband range and it does not depend on the solar zenith angle.

Influence of 3D effects on 1D aerosol retrievals in synthetic, partially clouded scenes

An important challenge in aerosol remote sensing is to retrieve aerosol properties in the vicinity of clouds and in cloud contaminated scenes. Satellite based multi-wavelength, multi-angular, photo-polarimetric instruments are particularly suited for this task as they have the ability to separate scattering by aerosol and cloud particles. Simultaneous aerosol/cloud retrievals using 1D radiative transfer codes cannot account for 3D effects such as shadows, cloud induced enhancements and darkening of cloud edges. In this study we investigate what errors are introduced on the retrieved optical and micro-physical aerosol properties, when these 3D effects are neglected in retrievals where the partial cloud cover is modeled using the Independent Pixel Approximation. To this end a generic, synthetic data set of PARASOL like observations for 3D scenes with partial, liquid water cloud cover is created. It is found that in scenes with random cloud distributions (i.e. broken cloud fields) and either low cloud optical thickness or low cloud fraction, the inversion algorithm can fit the observations and retrieve optical and micro-physical aerosol properties with sufficient accuracy. In scenes with non-random cloud distributions (e.g. at the edge of a cloud field) the inversion algorithm can fit the observations, however, here the retrieved real part of the refractive indices of both modes is biased.

Performance of a geostationary mission, geoCARB, to measure CO<sub>2</sub>, CH<sub>4</sub> and CO column-averaged concentrations

Abstract. GeoCARB is a proposed instrument to measure column averaged concentrations of CO2, CH4 and CO from geostationary orbit using reflected sunlight in near-infrared absorption bands of the gases. The scanning options, spectral channels and noise characteristics of geoCARB and two descope options are described. The accuracy of concentrations from geoCARB data is investigated using end-to-end retrievals; spectra at the top of the atmosphere in the geoCARB bands are simulated with realistic trace gas profiles, meteorology, aerosol, cloud and surface properties, and then the concentrations of CO2, CH4 and CO are estimated from the spectra after addition of noise characteristic of geoCARB. The sensitivity of the algorithm to aerosol, the prior distributions assumed for the gases and the meteorology are investigated. The contiguous spatial sampling and fine temporal resolution of geoCARB open the possibility of monitoring localised sources such as power plants. Simulations of emissions from a power plant with a Gaussian plume are conducted to assess the accuracy with which the emission strength may be recovered from geoCARB spectra. Scenarios for "clean" and "dirty" power plants are examined. It is found that a reliable estimate of the emission rate is possible, especially for power plants that have particulate filters, by averaging emission rates estimated from multiple snapshots of the CO2 field surrounding the plant. The result holds even in the presence of partial cloud cover.

The performance of vegetation indices for operational monitoring of CORINE vegetation types

Vegetation monitoring has been performed using remotely sensed images to secure food production, prevent fires, and protect natural ecosystems. Recent satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), provide frequent wide-scale coverage in multiple areas of the spectrum, allowing the estimation of a wide range of specialized vegetation indices (VIs), each offering several advantages. It is not, however, clear which VI performs better during operational monitoring of wide-scale vegetation patches, such as CORINE Land Cover (CLC) classes. The aim of this work was to investigate the performance of several VIs in operational monitoring of vegetation condition of CLC vegetation types, using Terra MODIS data. Comparison among the VIs within each CLC class was conducted using the sensitivity ratio, a statistical measure that has not been used to compare VIs and does not require calibration curves between each VI and a biophysical parameter. In addition, the VI’s sensitivity to factors such as the aspect, viewing angle, signal saturation, and partial cloud cover was estimated with correlation analysis in order to identify their operational monitoring ability. Results indicate the enhanced vegetation index as superior for monitoring vegetation condition among CLC types, but not always optimum in performance tests for operational monitoring.

Simulation of leaf photosynthesis of C3 plants under fluctuating light and different temperatures

An induction-dependent empirical model was developed to simulate the C3 leaf photosynthesis under fluctuating light and different temperatures. The model also takes into account the stomatal conductance when the light intensity just exceeds the compensation point after a prolonged period of darkness (initial stomatal conductance, $$ g_{{{\text{S}}_{\text{ini}} }} $$ ). The model was parameterized for both Chrysanthemum morifolium and Spinacia oleracea by artificially changing the induction states of the leaves in the climate chamber. The model was tested under natural conditions that were including frequent light flecks due to partial cloud cover and varying temperatures. The temporal course of observed photosynthesis rate and the carbon gain was compared to the simulation. The ability of the current model to predict the carbon assimilation rate was assessed using different statistical indexes. The model predictions were accurate but the model slightly underestimated the actual overall carbon gain. The accuracy of the simulation was largely dependent on the parameters that were calculated for the particular plant species, of which the simulation is intended for. In particular, the rate of change of induction and the initial stomatal conductance were found to be highly important and these were species-specific parameters for the predictions. The model is suitable for estimating instantaneous leaf CO2 assimilation for different herbaceous plant species under dynamic environmental conditions. It can be simply calibrated for other crops, by estimating the individual parameters.

A PATCHY CLOUD MODEL FOR THE L TO T DWARF TRANSITION

One of the mechanisms suggested for the L to T dwarf spectral type transition is the appearance of relatively cloud-free regions across the disk of brown dwarfs as they cool. The existence of partly cloudy regions has been supported by evidence for variability in dwarfs in the late L to early T spectral range, but no self-consistent atmosphere models of such partly cloudy objects have yet been constructed. Here we present a new approach for consistently modeling partly cloudy brown dwarfs and giant planets. We find that even a small fraction of cloud holes dramatically alter the atmospheric thermal profile, spectra, and photometric colors of a given object. With decreasing cloudiness objects briskly become bluer in J - K and brighten in J band, as is observed at the L/T transition. Model spectra of partly cloudy objects are similar to our models with globally homogenous, but thinner, clouds. Hence spectra alone may not be sufficient to distinguish partial cloudiness although variability and polarization measurements are potential observational signatures. Finally we note that partial cloud cover may be an alternative explanation for the blue L dwarfs.

A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat

Abstract. We present a six-year global climatology of cloud properties, obtained from observations of the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) combined with CloudSat observations, both missions launched as part of the A-Train in 2006, provide a unique opportunity to evaluate the retrieved AIRS cloud properties such as cloud amount and height. In addition, they permit to explore the vertical structure of different cloud types. AIRS-LMD cloud detection agrees with CALIPSO about 85% over ocean and about 75% over land. Global cloud amount has been estimated from 66% to 74%, depending on the weighting of not cloudy AIRS footprints by partial cloud cover from 0 to 0.3. 42% of all clouds are high clouds, and about 42% of all clouds are single layer low-level clouds. The "radiative" cloud height determined by the AIRS-LMD retrieval corresponds well to the height of the maximum backscatter signal and of the "apparent middle" of the cloud. Whereas the real cloud thickness of high opaque clouds often fills the whole troposphere, their "apparent" cloud thickness (at which optical depth reaches about 5) is on average only 2.5 km. The real geometrical thickness of optically thin cirrus as identified by AIRS-LMD is identical to the "apparent" cloud thickness with an average of about 2.5 km in the tropics and midlatitudes. High clouds in the tropics have slightly more diffusive cloud tops than at higher latitudes. In general, the depth of the maximum backscatter signal increases nearly linearly with increasing "apparent" cloud thickness. For the same "apparent" cloud thickness optically thin cirrus show a maximum backscatter about 10% deeper inside the cloud than optically thicker clouds. We also show that only the geometrically thickest opaque clouds and (the probably surrounding anvil) cirrus penetrate the stratosphere in the tropics.

Automatic cloud removal from multi-temporal SPOT images

Partial cloud cover is a severe problem in the optical remote sensing images. The problem can be mostly overcome by mosaicking the cloud-free areas of the multi-temporal images. In this paper, multidisciplinary methods are proposed to generate cloud-free mosaic images from multi-temporal SPOT images in three steps. At first, the original images are enhanced in both brightness and chromaticity. Secondly, the linear spectral unmixing method (LSU) is used to extract all cloud cover regions. Then, we choose the base image that has the least thin-cloud cover and divide the base image into grid zones. We find the thin-cloud and cloud-shadow zones in the eight neighbors of the thick-cloud zones based on the relative locations and the sun elevation angle. At last, the cloud and cloud-shadow zones of the base image are replaced by the same-location cloud-free zones on other images. Between each two zones of the base image and the replacing image, we create a transition zone. The multiscale wavelet-based fusion method is then used to fuse the pixels in the zones to generate cloud-free satellite images. Based on our complete and sophisticated approach, the high-quality fused results are produced from the source images that have variant brightness.

Improving forecast skill by assimilation of quality‐controlled AIRS temperature retrievals under partially cloudy conditions

The National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) on board the Aqua satellite is now recognized as an important contributor towards the improvement of weather forecasts. At this time only a small fraction of the total data produced by AIRS is being used by operational weather systems. In fact, in addition to effects of thinning and quality control, the only AIRS data assimilated are radiance observations of channels unaffected by clouds. Observations in mid‐lower tropospheric sounding AIRS channels are assimilated primarily under completely clear‐sky conditions, thus imposing a very severe limitation on the horizontal distribution of the AIRS‐derived information. In this work it is shown that the ability to derive accurate temperature profiles from AIRS observations in partially cloud‐contaminated areas can be utilized to further improve the impact of AIRS observations in a global model and forecasting system. The analyses produced by assimilating AIRS temperature profiles obtained under partial cloud cover result in a substantially colder representation of the northern hemisphere lower midtroposphere at higher latitudes. This temperature difference has a strong impact, through hydrostatic adjustment, in the midtropospheric geopotential heights, which causes a different representation of the polar vortex especially over northeastern Siberia and Alaska. The AIRS‐induced anomaly propagates through the model's dynamics producing improved 5‐day forecasts.

A practical approach for the assimilation of cloudy infrared radiances and its evaluation using airs simulated observations

A variational estimation procedure for the simultaneous retrieval of cloud parameters and thermodynamic profiles from infrared radiances is proposed. The method is based on a cloud emissivity model which accounts for the frequency dependence of cloud absorption and scattering and possible mixed phase situations. An effective cloud top height and emissivity are assumed. Monte Carlo experiments performed in a 1D‐var assimilation context using simulated Atmospheric Infrared Radiance Sounder (AIRS) observations from 100 channels demonstrate the substantial added value, in theory, of cloudy radiance assimilation as opposed to clear‐channel assimilation. Improved temperature and humidity retrievals are obtained for a broad layer above the cloud as well as below cloud level under partial cloud cover conditions. The impact is most pronounced in broken to overcast situations involving mid‐level clouds. In these situations, the effective cloud top height and emissivity are retrieved with estimated rms errors typically lower than 30 hPa and 3%, respectively. Expected relative errors on the retrieved effective particle size are of the order of 30–50%. The methodology is directly applicable to real hyperspectral infrared data upon inclusion, for local estimation, of the cloud parameters in the Canadian 4D‐var assimilation system.

A CCD Spectroradiometer for Ultraviolet Actinic Radiation Measurements

Abstract A new spectroradiometer for spectral measurements of ultraviolet (UV) atmospheric radiation (290–400 nm) using a charge coupled device (CCD) as a detector is introduced. The instrument development is motivated by the need for measurements with (a) high accuracy in the UV-B spectral range (290–315 nm) for photochemistry applications and (b) high temporal resolution in quickly changing atmospheric conditions such as partial cloud cover. The new CCD instrument is mainly intended for airborne use. It allows fast data collection (<300 ms time resolution for each spectrum) with improved sensitivity in the UV spectral range. The instrumental setup and its characterization in terms of stray light, dark current, noise, and detection limits are described and compared to a spectroradiometer with a photodiode array (PDA) detector. The new CCD spectroradiometer has a one order of magnitude greater sensitivity than the PDA-based spectroradiometer. However, the stray light of the CCD instrument is wavelength dependent, which requires a more complicated data evaluation procedure than the PDA instrument. Comparison with other UV spectroradiometers (a PDA spectroradiometer and two ground-based double monochromators) shows the advantages of the CCD system for UV-B measurements of actinic flux densities and photolysis frequencies of ozone and nitrogen dioxide, and the improved performance compared to PDA spectroradiometers.

Relationships between radiosonde and RUC‐2 meteorological conditions and cloud occurrence determined from ARM data

Relationships between modeled and measured meteorological state parameters and cloudy and cloud‐free conditions are examined using data taken over the ARM (Atmospheric Radiation Measurement) Southern Great Plains Central Facility between 1 March 2000 and 28 February 2001. Cloud vertical layering was determined from the Active Remotely Sensed Cloud Location product based on the ARM active sensor measurements. Both temperature and relative humidity (RH) observations from balloon‐borne Vaisala RS80‐15LH radiosonde (SONDE) and the Rapid Update Cycle (RUC) 40‐km resolution model are highly correlated, but the SONDE RHs generally exceed those from RUC. Inside cloudy layers, the RH from SONDE is 2–14% higher than the RH from RUC at all pressure levels. Although the layer mean RH within clouds is much greater than the layer mean RH outside clouds or in clear skies, RH thresholds chosen as a function of temperature can more accurately diagnose cloud occurrence for either data set than a fixed RH threshold. For overcast clouds (cloud amount greater than or equal to 90%), it was found that the 50% probability RH threshold for diagnosing a cloud, within a given upper tropospheric layer, is roughly 90% for the SONDE and 80% for RUC data. For partial cloud cover (cloud amount is less than 90%), the SONDE RH thresholds are close to those for RUC at a given probability in upper tropospheric layers. Cloud probability was found to be only minimally dependent on vertical velocity. In the upper troposphere, SONDE ice‐supersaturated air occurred in 8 and 35% of the clear and cloudy layers, respectively. The RH was distributed exponentially in the ice supersaturated layers as found in previous studies. The occurrence of high‐altitude, ice‐supersaturated layers in the RUC data was roughly half of that in the SONDE data. Optimal thresholds were derived as functions of temperature to define the best RH thresholds for accurately determining the mean cloud cover. For warm clouds the typical SONDE threshold exceeds 87%, while the RH thresholds for cold clouds are typically less than 80% and greater than 90% with respect to liquid and ice water, respectively. Preliminary comparisons with satellite data suggest that the relationships between cloudiness and RH and T determined here could be useful for improving the characterization of cloud vertical structure from satellite data by providing information about low‐level clouds that were obscured by high‐level clouds viewed by the satellite. The results have potential for improving computations of atmospheric heating rate profiles and estimates of aircraft icing conditions. Similar analyses are recommended for later versions of the RUC analyses and forecasts.

Lidar remote sensing for modeling gross primary production of deciduous forests

The influence of foliage vertical distribution on vegetation gross primary production (GPP) is investigated in this study. A new photosynthesis model has been created that combines the standard sunlit/shaded leaf separation (two-leaf) and the multiple layer approaches and uses vertical foliage profiles measured by SLICER (the Scanning Lidar Imager of Canopies by Echo Recovery). Daily gross carbon assimilation rates calculated by this model were compared with the rates calculated by two other models, the two-leaf model and the combined two-leaf multilayer model utilizing uniform foliage profiles. The comparison was made over a wide range of profiles and weather conditions for two mixed deciduous forest stands in eastern Maryland, measured by SLICER in September 1995. Incident radiation pattern, environmental parameters and total amounts of sunlit and shaded leaves were the same for all three models. The difference was in the distributions of radiation and sunlit/shaded leaves inside the canopy. For the combined models, these distributions were calculated based on the vertical foliage profiles, while for the two-leaf model, empirical equations were used to account for the average amounts of absorbed radiation. The simulations showed that: (1) the use of a uniform foliage distribution instead of the actual one results in large differences in the calculated GPP values, up to 46.4% and 50.7% for the days with partial and total cloud cover; (2) the performance of the two-leaf model is extremely sensitive to the absorbed radiation pattern, its disagreement with the proposed model becomes insignificant when the average amounts of absorbed radiation are the same; (3) days with partial cloud cover and a greater fraction of diffuse radiation are characterized by higher GPP rates. These findings highlight the importance of vertical foliage profile and separate treatments of diffuse and direct radiation for photosynthesis modeling.