Base Height Research Articles

Numerical simulations of the effects of green infrastructure on PM2.5 dispersion in an urban park in Bangkok, Thailand

Traffic emission has been identified as one of the dominant sources of fine particulate matter (PM2.5) in Thailand, and urban green spaces have the capacity to mitigate air pollution. Taking Bangkok as the study area, one of the most polluted cities in Thailand, this study investigated the effect of vegetation on PM2.5 concentration at three different sites with different vegetation characteristics in Chatuchak Park, an urban park located in Bangkok. Sensors were installed at the park to measure PM2.5 and metrological parameters at the roadside and different distances inside the park away from the road, and the ENVI-met model was run to simulate PM2.5 concentration in the three study sites. The result shows that PM2.5 concentration behind the vegetation barrier was reduced 34% on average compared to the concentration next to the road at the three sites. The effect of vegetation on meteorological factors was clearly seen near the park border with a hedgerow grown along the border. The order of influence of meteorological factors on PM2.5 concentration was relative humidity > potential temperature > wind speed > wind direction. Two scenarios including changes in weather patterns and types of vegetation that affect PM2.5 concentration were studied. Changing in the wind direction from oblique to perpendicular to the park had no significant effect on PM2.5 concentration as long as there is a dense hedgerow along the park border. Comparing to the current vegetation, sparse vegetation with less leaf area density and higher crown base heights had lower impact to mitigate PM2.5 concentration in the park. Our study provides information on vegetation and landscape strategies which can provide good air quality in the urban parks for better park design in the future.

Transferability of Airborne LiDAR Data for Canopy Fuel Mapping: Effect of Pulse Density and Model Formulation

Canopy fuel characterization is critical to assess fire hazard and potential severity in forest stands. Simulation tools provide useful information for fire prevention planning to reduce wildfire impacts, provided that reliable fuel maps exist at adequate spatial resolution. Free airborne LiDAR data are becoming available in many countries providing an opportunity to improve fuel monitoring at large scales. In this study, models were fitted to estimate canopy base height (CBH), fuel load (CFL) and bulk density (CBD) from airborne LiDAR in a pine stand area where four point-cloud datasets were acquired at different pulse densities. Best models for CBH, CFL and CBD fitted with LiDAR metrics from the 1 p/m2 dataset resulted in an adjusted R2 of 0.88, 0.68 and 0.58, respectively, with RMSE (MAPE) of 1.85 m (18%), 0.16 kg/m2 (14%) and 0.03 kg/m3 (20%). Transferability assessment of fitted models indicated different level of accuracy depending on LiDAR pulse density (both higher and lower than the calibration dataset) and model formulation (linear, power and exponential). Best results were found for exponential models and similar pulse density (1.7 p/m2) compared to lower (0.5 p/m2) or higher return density (4 p/m2). Differences were also observed regarding the canopy fuel attributes.

Abrupt emission reduction during COVID-19 intensified the spring 2020 rainfall over India

The high level of aerosol pollution in South Asia has a measurable impact on clouds, radiation, and precipitation. Here, exploring multiple observational data sets and simulations of the state-of-the-art ECHAM6-HAMMOZ chemistry-climate model, we report that the reduction in anthropogenic emissions during the COVID-19 lockdown period has enhanced precipitation by 5–25% over India. This precipitation enhancement is the result of the combined effect of an enhancement in cloud cover, a reduction in aerosol induced cloud invigoration and dynamical changes. We observed that the increase in cloud cover was associated with a reduction in cloud base height and an increase in the effective radius of cloud particles which led to an increase in cloud water content. In response to sudden emission reduction, an anomalous northward moisture transport was observed adding convection and precipitation over the Indian region. Importantly, we show that there is an advantage of anthropogenic pollution reduction for water availability in addition to benefits of air quality, human health, and crop yield.

Electric Field Variations Caused by Low, Middle and High-Altitude Clouds over the Negev Desert, Israel

Ground-based measurements of the electric field from a station located in the arid Negev region of southern Israel have been conducted continuously since 2013. We present here results of observations of the electric field (Potential Gradient, PG) variability during 22 cloudy days, with varying cloud types and cloud base heights, and compare the measured values with the mean fair-weather PG. The results show an increase of PG (~+10 to +70 V m−1) from mean fair weather values during times of low clouds. During times of mid-altitude (alto) clouds or during a superposition of low and high clouds, there were small departures in the PG values (~0 to −30 V m−1) compared to mean fair weather PG values. During times of high-altitude cirrus clouds there is a clear decrease of the PG (~−40 to −90 V m−1). The data was compared with the Israeli meteorological service cloud data and with MODIS 7 satellite cloud top height maps. In addition, AERONET aerosol optical depth values and wind speed magnitude from a local meteorological station were analyzed.

Comparing tree attributes derived from quantitative structure models based on drone and mobile laser scanning point clouds across varying canopy cover conditions

Complex canopy cover conditions often challenge the accurate measurement of many individual tree attributes that are pivotal to the sustainable management of forest resources. Advances in drone laser scanning (DLS) and mobile laser scanning (MLS) have enabled the acquisition of high-density point clouds with the potential to better resolve detailed tree structures. Yet, the quality of DLS and MLS data can be limited by occlusions and environmental complexities. To quantify the impacts of canopy cover on the tree attribute estimation, this study investigated the utility of DLS and MLS data both individually and combined. Considering the scanning characteristics, we examined direct fusion and a new strategy using a relative weighting scheme based on the probability density of vertical point distribution. We compared the accuracy of seven tree attributes derived from quantitative structure models (QSMs) based on (1) DLS, (2) MLS, (3) fused, and (4) weighted point clouds under low, moderate, and high canopy cover levels. We found that the weighted data improved the modelling efficiency of QSMs by ∼ 20% on average, compared to fused and MLS data. Across canopy cover levels, the fused and weighted data achieved comparable results and outperformed DLS/MLS data in estimating tree attributes. Specifically, diameter at breast height and crown base height were accurately extracted from the fused, weighted, and MLS data under low canopy cover with the concordance correlation coefficient (CCC) > 0.80. As canopy cover increased, they were best estimated using the fused data (CCC > 0.90, RRMSE < 22%). Height was accurate regardless of canopy cover, which was independent of data collection platforms (CCC > 0.80, RRMSE < 16%). The crown diameter was also well estimated by fused, weighted, and MLS data across canopy cover levels (CCC > 0.82, RRMSE < 19%). The total, stem, and branch volumes could be best modelled by the fused data with increasing canopy cover. Overall, the fusion of DLS and MLS point clouds allowed the retrieval of comprehensive tree-level information. However, forestry practitioners still need to evaluate the trade-offs in selecting the most appropriate platform for laser scanning data based on their needs. Future studies should also enhance the modelling of trees with complex branching structures to strengthen the extraction of diverse attributes.

Accurate prophecy of photovoltaic-segmented thermoelectric generator’s performance using a neural network that feeds on finite element-generated data

To further enhance the photovoltaic–thermoelectric system efficiency, this paper proposes a new hybrid system design comprising a segmented thermoelectric generator and aluminum heat sink directly lapped to the back plate of a photovoltaic cell operating under Nigerian transient and fluctuating weather conditions. The performance evaluation and optimization of the hybrid system design is conducted using a numerical model setup in ANSYS software and the optimized parameters include the thermoelectric leg height and cross-sectional area, skutterudite content, ceramic height, fin and fin base heights, convective film coefficient, and solar concentration ratio while the performance indices are the system power generation rate and the system efficiency. Finally, a Bayesian regularized artificial neural network with 10 neurons in the hidden layer is proposed to overcome the lengthy computational time and energy needed to conduct the numerical-inspired system optimization. Results are that the proposed system design improved the efficiency of the conventional photovoltaic cell integrated with regular unsegmented thermoelectric generators by 96% at a solar concentration of 5. Additionally, the numerical-inspired optimization was able to improve the conventional system power output and efficiency by 45.7% and 45.9%, respectively, compared to the unoptimized system when operated under peak sunshine conditions. Finally, the Bayesian regularized neural network with a low mean squared error of 9.7 × 10−14 after 471 iterations and perfect regression correlations for training, testing, and all processes provided a perfect fit of the numerical-generated data, being 201 times faster than the conventional numerical optimization scheme.

Classification of Precipitation Types Based on Machine Learning Using Dual-Polarization Radar Measurements and Thermodynamic Fields

An accurate classification of the precipitation type is important for forecasters, particularly in the winter season. We explored the capability of three supervised machine learning (ML) methods (decision tree, random forest, and support vector machine) to determine ground precipitation types (no precipitation, rain, mixed, and snow) for winter precipitation. We provided information on the particle characteristics within a radar sampling volume and the environmental condition to the ML model with the simultaneous use of polarimetric radar variables and thermodynamic variables. The ML algorithms were optimized using predictor selection and hyperparameter tuning in order to maximize the computational efficiency and accuracy. The random forest (RF) had the highest skill scores in all precipitation types and outperformed the operational scheme. The spatial distribution of the precipitation type from the RF model showed a good agreement with the surface observation. As a result, RF is recommended for the real-time precipitation type classification due to its easy implementation, computational efficiency, and satisfactory accuracy. In addition to the validation, this study confirmed the strong dependence of precipitation type on wet-bulb temperature and a 1000–850 hPa layer thickness. The results also suggested that the base heights of the radar echo are useful in discriminating non-precipitating area.

Intercomparison of Cloud Vertical Structures over Four Different Sites of the Eastern Slope of the Tibetan Plateau in Summer Using Ka-Band Millimeter-Wave Radar Measurements

The eastern slope of the Tibetan Plateau is a crucial corridor of water-vapor transport from the Tibetan Plateau to Eastern China. This is also a region with active cloud initiation, and the locally hatched cloud systems have a profound impact on the radiation budget and hydrological cycle over the downstream Sichuan Basin and the middle reach of the Yangtze River. It is noteworthy that there is a strong diversification in the characteristics and evolution of the ESTP cloud systems due to the complex terrain. Therefore, in this study, ground-based Ka-band millimeter-wave cloud radar measurements collected at the Ganzi (GZ), Litang (LT), Daocheng (DC), and Jiulong (JL) sites of the ESTP in 2019 were analyzed to compare the vertical structures of summer nonprecipitating clouds, including cloud occurrence frequency, radar reflectivity factor, cloud base height, cloud top height, and cloud thickness. The occurrence frequency exhibits two peaks on the ESTP with maximum values of ~20% (2–4 km) and 15% (7–9 km), respectively. The greatest (smallest) occurrence frequency occurs in the JL (GZ). The cloud occurrence frequency of all sites increases rapidly in the afternoon, and the occurrence frequency of the DC presents larger values at 2–4 km. In contrast, the occurrence frequency in the JL shows another increase from 2000 LT to midnight at 7–11 km. Stronger radar echoes occur most frequently in the LT at 5–7 km, and hydrometeor sizes and phase states vary dramatically in mixed-phase clouds. A small number of radar echoes occur at midnight in the JL. A characteristic bimodality of the cloud base height and top height for single-layer, double-layer, and triple-layer clouds was observed. Clouds show a higher base height in the GZ and higher top height in the JL. The ESTP is dominated by thin clouds with thicknesses of 200–400 m. The cloud base height, top height, and thickness exhibit an increase in the afternoon, and higher top height occurs more frequently from midnight to the next early morning in the JL because of its mountain-valley terrain.

One-Stage Repair of Alveolar Cleft and Nasal Deformities Using Grafts From Nasal Septum: Application of Vomer, Ethmoid, and Septal Cartilage

Background: Patients with alveolar cleft unrepaired suffer from nasal deformities of different magnitude. Bone and cartilage grafts are harvested through several incisions. In this study, we present a method to simultaneously correct nasal deformities and repair alveolar cleft using grafts from the nasal septum. Patients and Methods: All 6 patients with unilateral cleft lip and palate have alveolar cleft unrepaired combined with nasal deformity. Computed tomography scans and 3-dimensional-printed models of vomer and ethmoid bone were used for the purpose of preoperative design and for assessing the magnitude of deformity. Grafts of bone and cartilage from deviated septum were harvested by septoplasty through which dorsum deviation was corrected. Bone grafts from vomer and ethmoid were then fixed to the prepared alveolar cleft to repair the defect and elevate the alar base. Septal cartilage was adjusted into different shapes of grafts and deformities of nasal tip, nostrils, and columella were then corrected by rhinoplasty to restore the symmetry of the nose. Results: Symmetry of nostrils was improved. The height of alar base on the cleft side was elevated to the level close to the noncleft side. Deviation of the septum, nasal dorsum, and columella was corrected. Projection of the nasal tip was adjusted to facial midline. Midface aesthetics was generally improved. Conclusion: Application of septal grafts reduce the number of incisions. One-stage repair of alveolar cleft and nasal deformities, with the aid of digital design, improves the postoperative experience and the general outcome of the surgery.

Observations of cold-cloud properties in the Norwegian Arctic using ground-based and spaceborne lidar

Abstract. The role of clouds in the surface radiation budget is particularly complex in the rapidly changing Arctic. However, despite their importance, long-term observations of Arctic clouds are relatively sparse. Here, we present observations of cold clouds based on 7 years (2011–2017) of ground-based lidar observations at the Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) in Andenes in the Norwegian Arctic. In two case studies, we assess (1) the agreement between a co-located cirrus cloud observations from the ground-based lidar and the spaceborne lidar aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and (2) the ground-based lidar's capability to determine the cloud phase in mixed-phase clouds from depolarization measurements. We then compute multiyear statistics of cold clouds from both platforms with respect to their occurrence, cloud top and base height, cloud top temperature, and thermodynamic phase for the 2011–2017 period. We find that satellite- and ground-based observations agree well with respect to the coincident cirrus measurement and that the vertical phase distribution within a liquid-topped mixed-phase cloud could be identified from depolarization measurements. On average, 8 % of all satellite profiles were identified as single-layer cold clouds with no apparent seasonal differences. The average cloud top and base heights, combining the ground-based and satellite measurements, are 9.1 and 6.9 km, respectively, resulting in an average thickness of 2.2 km. Seasonal differences between the average top and base heights are on the order of 1–2 km and are largest when comparing fall (highest) and spring (lowest). However, seasonal variations are small compared with the observed day-to-day variability. Cloud top temperatures agree well between both platforms, with warmer cloud top temperatures in summer. The presented study demonstrates the capabilities of long-term cloud observations in the Norwegian Arctic from the ground-based lidar at Andenes.

The Transition and Spread of a Chaparral Crown Fire: Insights from Laboratory Scale Wind Tunnel Experiments

Fire occurring in the chaparral behaves as a crown fire, a dual-layer fire that typically ignites in a dead surface fuel layer and transitions to an elevated live crown layer where it continues to spread. In chaparral fuels including chamise, a dominant species in southern California, flame transition to live crown fuels is associated with higher spread rates and greater fire intensity. Despite the relative importance of surface-to-crown transition and crown fire spread, most fire models represent chaparral fire as surface fire, therefore omitting key behavior processes driving this fire system. The purpose of this study was to characterize transition and spread behavior in chaparral fires modeled experimentally as crown fires. We examined heat release rate in the surface and crown fuel layers, time to transition, flame height, and rate of spread in wind-driven and nonwind-driven fires at two crown base heights. Our results showed that wind increased heat release rate, rate of spread, and flame height. A marked increase in heat release rate was observed in wind-driven fires, where adding wind produced an increase from 328 kW to 526 for a crown base height of 0.6 m and from 243 kW to 503 kW for a crown base height of 0.7 m. Further, crown base height served to decrease heat release rate and rate of spread for wind-driven and nonwind-driven fires.

Soil splash erosion: An overlooked issue for sustainable rubber plantation in the tropical region of China

Soil erosion by water is known to be a major driver of land degradation. Splash erosion is the initial stage of water erosion and directly contributes to the detachment of soil particles on sloping land. Splash erosion may be exacerbated or mitigated by ongoing land use/cover change. To estimate the effects of various land use and management systems on splash erosion in the humid tropics, we measured the actual splash erosion under natural rainfall conditions in a tropical rainforest (TR), rubber monoculture (RM), and four rubber agroforestry ecosystems in Xishuangbanna, Southwestern China. The average cumulative splash mass in the RM was 48.7 times higher compared to that in the TR, indicating that the replacement of TR by RM results in severe splash erosion. In the rubber agroforestry systems, the throughfall kinetic energy and splash mass were 34–76% and 86–97% lower, respectively, than that in the RM, indicating that intercropping crops with rubber trees can effectively alleviate erosive power of throughfall and thus splash erosion. Under all land use regimes, in the plots where the litter layer was removed, splash erosion was significantly (2.0–12.1 times) higher and more spatially heterogeneous than in those with litter cover. This highlighted the key role of the forest litter layer in protecting the soil from raindrop detachment. In the study area, the actual splash erosion was strongly correlated with the conditions of precipitation (amount and intensity (I10) of rainfall) and the vegetation structure (especially the crown base height, leaf area index, and canopy cover), but weakly correlated with the soil properties. Given the importance of near-ground intercrops (height <5 m) for improving the vegetation structure, surface cover, and soil conditions, the intercropping of Camellia sinensis and Theobroma cacao with rubber trees is a promising approach for controlling splash erosion and also has benefits on the sustainable development of rubber plantation.

Cloud macrophysical characteristics in China mainland and east coast from 2006 to 2017 using satellite active remote sensing observations

AbstractUsing the CloudSat and CALIPSO active remote sensing dataset from 2006 to 2017, this study investigates the spatial and temporal distribution along with vertical distribution of cloud macrophysical characteristics in six study areas over China mainland and east coast (15°–55°N, 70°–140°E). The results show that the cloud top height and cloud base height have significant regional, seasonal and zonal variations. The regional differences in the seasonal average vertical distribution of cloud cover are especially large, which are particularly reflected by the differences of heights with peak value of cloud cover, the curve characteristics of cloud cover with height, and the standard deviation of cloud cover. The cloud spacing, which is the distance between two cloud layers for cloud system with layers no less than 2, is found greater in the ocean area than in the land area and is greater in the low‐latitude area than in the high‐latitude area. Moreover, with the increase of the number of cloud layers in the cloud system, the cloud spacing decreases gradually. It is worth noting that the frequency distribution of cloud layers is the same as that of cloud spacing. By dividing clouds into eight types according to CloudSat data, the occurrence frequency of different cloud types shows great differences, ranking from high to low as high cloud &gt; altostratus &gt; altocumulus &gt; stratus &gt; stratocumulus &gt; cumulus &gt; nimbostratus &gt; deep convective cloud. Altostratus mainly occurs in northern regions, and high cloud mostly occurs in Tibetan Plateau, southern China mainland and eastern ocean. The vertical distributions of cloud macrophysical characteristics over the six typical study areas have been further demonstrated by constructing Pc‐τ diagram.

Very-Short-Term Variations of Sea Surface and Atmospheric Parameters Before the Ms 6.2 Zhangbei (China) Earthquake in 1998

The Ms 6.2 Zhangbei earthquake occurred at 3:53 (UTC) on 10 January 1998, with its epicenter located at about 150 km northwest of Beijing, China. Over the past 2 decades, many studies have reported that there was a positive thermal infrared (TIR) anomaly appearing along the direction from the Bohai Sea to Zhangbei within 2–3 days preceding the earthquake, which was considered to be caused by the activity of the great Zhangbei-Bohai fault (ZBF) extending across the Bohai Sea to the southeast, while neither the TIR anomaly is convinced nor the mechanism is clear. A collaborative analysis of the atmospheric disturbances several days before, during, and after the earthquake was conducted by using satellite observations and reanalysis datasets with multiple parameters, including sea surface roughness, evaporation rate, atmospheric CO concentration, atmospheric sea salt concentration, and cloud base height above the sea surface, as well as satellite infrared cloud images. Through individual analysis of the change of each parameter and synergic analysis of multiple parameters, particular atmospheric disturbances, including the formation of strip-shaped clouds on January 7 and 9, were revealed over the ZBF and another great fault named Tancheng-Lujiang fault (TLF), which extends across the Bohai Sea to the northeast. After careful investigation and attribution analysis of the spatio-temporal evolutions of the atmospheric disturbances every hour above and around the Bohai Sea from January 7 to 12, we came to the conclusion that the particular strip-shaped clouds were low-level clouds caused by the seismic activity and submarine gas release from TLF but not ZBF and was forced by particular wind field and lowering boundary layer. As an aftereffect of the gas release from TLF and the formation of the localized low-level clouds of higher brightness temperature than that of the land surface, a positive TIR anomaly, thus, appeared above TLF and by chance along ZBF, preceding the Zhangbei earthquake.

Transition of Large‐Scale Environmental Conditions and Characteristics of Four Rainfall Types Observed by S‐PolKa During the MJO‐1 Active Phase of DYNAMO/CINDY/AMIE

AbstractAnalyses of National Center for Atmospheric Research (NCAR) S‐PolKa dual‐polarization radar data and ERA5 reanalysis fields indicate gradual changes in convection characteristics and large‐scale environmental conditions during a central Indian Ocean Madden‐Julian Oscillation (MJO) event observed by the DYNAMO/CINDY/AMIE field campaigns in late October 2011 (MJO‐1). Examination of four rainfall types (isolated convective cores, convective, mixed, and stratiform) reveals a transition of convection characteristics (i.e., areal coverage and depth) between distinct 5‐day environmental periods at the beginning and end of this ∼2‐week MJO active phase. A shift toward less frequent rainfall covering less of the radar domain for all four rainfall types occurs when large‐scale lower‐tropospheric dry air advects into the region with the westerly wind burst (WWB). Drier and warmer lower‐free‐tropospheric conditions associated with the WWB contribute to increased large‐scale surface‐based convective inhibition (CIN), surface‐based convective available potential energy (CAPE), and cloud base heights. A thermodynamic budget analysis indicates reduced surface heat fluxes contribute to the increased surface‐based CAPE. Greater CAPE at the end of MJO‐1 coincides with deeper 50‐dBZ convective echoes, while decreased 10‐dBZ depth for all rainfall types corresponds in time with WWB‐related dry‐air advection. Increased (decreased) reflectivity values in the lower‐level vertical reflectivity distribution of convective (stratiform) precipitation indicate increased (decreased) convective (stratiform) intensities when the WWB is present. The opposite depth changes for convective echoes and opposite shifts in convective and stratiform precipitation intensities underscore how the WWB can have differing impacts at different reflectivity thresholds and stages of the deep convection lifecycle.

Comparison of Cloud Amounts Retrieved with Three Automatic Methods and Visual Observations

Four methods have been used for the estimation of the total cloud amount and cloud amount for low clouds: visual observations, the Long method applied on pyranometer measurements, the Automatic Partial Cloud Amount Detection Algorithm (APCADA) method applied on pyrgeometers measurements, and ceilometer measurements of the cloud base height. Records from meteorological observers indicate that clear days (0–1 octa) represent the most frequent cloud amount for low clouds. In contrast, the total cloud amount is more aleatory. Results obtained from the Long method show maximum frequency in the extreme cloud amount values. The APCADA method also indicates the predominance of cloudless skies. The ceilometer method shows a predominance of completely clear skies, but the completely cloudy (8 octas) is the second most frequent case. Automatic methods report more cloudless and overcast skies than the observer. Automatic methods agree with the visual method or differ in ±1 octa for 60–76% cases for low cloud amount and for 56–63% cases for total cloud amount. In general, low cloud amount agrees more with observer measurements than total cloud amount and the automatic methods underestimated total cloud amount observer values possibly due to the difficulty in monitoring high clouds.

Comparison of microphysics parameterization schemes on cloud macrophysics forecasts for mixed convective-stratiform cloud events

In this study, the performances and uncertainties of cloud microphysics parameterization (MP) schemes for simulating cloud macrophysics associated with mixed convective-stratiform cloud (MCSC) events are investigated. MCSC system is the primary object of cloud seeding for the purpose of rain enhancement in North China. Understanding the forecast uncertainties of cloud macrophysics, such as cloud top temperature (CTT), cloud base height (CBH), and cloud optical thickness (COT), is of great significance for cloud seeding operations. Simulations of cloud macrophysics are verified using cloud data retrieved from the Feng-Yun-2F geostationary meteorological satellite, radiosondes and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The performances of several MP schemes to accurately simulate cloud macrophysics within ensemble forecasts is explored using both grid-wise and object-based metrics. Large differences are found among schemes in simulated cloud macrophysics compared with similar performances in simulated precipitation. In most cases, the ensemble mean simulates more realistically than each single scheme. The ensemble spread varies somewhat according to the simulated variables. For CBH, the ensemble shows adequate ensemble spread, while relative underdispersion occurs for CTT and COT. Detailed COT comparisons of reveal that COT bias mainly comes from the contribution of cloud droplets and snow. The causes of COT bias are further investigated by a detailed comparison of the cloud water path (CWP) and cloud effective radius (EFFR). The study establishes a baseline for ensemble forecasting of cloud macrophysics.

Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions

This work proposes a new method for estimating downwelling surface longwave radiation (DSLR) under cloudy-sky conditions based on a parameterization method and a genetic algorithm–artificial neural network (GA-ANN) algorithm. The new method establishes a GA-ANN model based on simulated data, and then combines MODIS satellite data and ERA5 reanalysis data to estimate the DSLR. According to the validation results of the field sites, the bias and RMSE are –9.18 and 34.88 W/m2, respectively. Compared with the existing research, the new method can achieve reasonable accuracy. Parameter analysis using independently simulated data shows that the near-surface air temperature (Ta) and cloud base height (CBH) have an important influence on DSLR estimation under cloudy-sky conditions. With an increase in CBH, DSLR gradually decreases; however, with an increase in Ta, DSLR shows a trend of gradual increase. When estimating DSLR under cloudy-sky conditions, under the influence of clouds, except for cirrus, the change in DSLRs with CBH and Ta is greater than 20 W/m2.

Diurnal Variation in Cloud and Precipitation Characteristics in Summer over the Tibetan Plateau and Sichuan Basin

The diurnal variation in precipitation and cloud parameters and their influencing factors during summer over the Tibetan Plateau (TP) and Sichuan Basin (SB) were investigated using the Hydro-Estimator satellite rainfall estimates, ground observations, and ERA5 dataset. The precipitation and cloud parameters show diurnal propagation over the SB during the mei-yu period in contrast to such parameters over the TP. The diurnal maximum precipitation from the Hydro-Estimator satellite and cloud ice and liquid water content (cloud LWC and IWC) from the ERA5 dataset are concentrated in the early evening, while their diurnal minimums manifest in the morning. Cloud LWC accounts for more than 60% of the total water during almost the entire diurnal cycle over the inner TP and SB during the mei-yu period. The IWC accounts for more than 60% of the total water in the late afternoon over the edge of the SB and TP. The cloud base height (CBH) above ground level (AGL), the lifting condensation level (LCL) AGL, and the zero degree level AGL are almost equal over the TP during the summer period. The zero degree level AGL over the SB is higher than that over the TP because the air temperature lapse rate over the TP is larger. The thickness of liquid water cloud over the SB is larger than that over the TP. The correlation analysis shows that the CBH AGL and LCL AGL over the TP are related to the dewpoint spread, but less so over the SB because of the stronger turbulence and lower air density over the TP than the SB. Convective available potential energy has a larger impact on precipitation over the TP than the SB. The cloud LWC makes a larger contribution to the precipitation over the SB than over the TP, which is related to the mean zonal wind and diurnal cycle of low-level winds. The precipitation at the edge of the TP and SB (i.e., the steep downstream slope) is largely influenced by the ice water contained within clouds owing to the convergence rising motion over the slopes.

Comparing Mobile Laser Scanner and manual measurements for dendrometric variables estimation in a black pine (Pinus nigra Arn.) plantation

The growing demand of ecosystem services provided by forests increased the need for fast and accurate field survey. The recent technological innovations fostered the application of geomatic tools and processes to different fields of the forestry sector. In this study we compared the efficiency and the accuracy of Mobile Laser Scanner (MLS), combined with Simultaneous Localization and Mapping (SLAM) technology, and traditional field survey for the mensuration of main forest dendrometric variables like stem diameter at breast height (DBH), individual tree height (H), crown base height (CBH) and branch-free stem volume (VOL). With ground truth measurements taken from 50 felled trees, we tested the applicability of MLS technology for individual tree parameters estimation in a conifer plantation in central Italy. Our results showed no bias of DBH estimates and the corresponding RMSE was equal to 10.8% (2.7 cm). H and CBH measured with MLS were underestimated compared to the ground truth (bias of −8.6% for H and −13.3% for CBH). VOL values showed a bias and a RMSE of −4.1% (−0.01 m3) and 12.4% (0.04 m3) respectively. Tree height is not perfectly estimated due to laser obstruction by crowns layer, but the acquisition speed of this survey, joined with a suitable accuracy of parameters extraction, suggests sufficient suitability of the method for operational applications in simple forest structures (e.g. one-layered stands).