Interaction between cloud-radiation, atmospheric dynamics and thermodynamics based on observational data from GoAmazon 2014/15 and a Cloud Resolving Model

Observational meteorological data from the field experiment GoAmazon 2014/15 and data from numerical simulations with the Cloud-Resolving Model (CRM) called System for Atmospheric Modeling (SAM) are used to study the interaction between the cloudiness-radiation and the atmospheric dynamics and thermodynamics variables for a site located in the central Amazon region (−3.2° S, −60.6° W) during the wet and dry periods. The main aims are to (a) analyze the temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux; and (b) to determine the relationship between the integrated cloud fraction, radiative fluxes, and large-scale variable anomalies as a function of the previous day's average. The temporal series of the integrated cloud fraction, precipitation rate and downward shortwave flux from SAMS simulations showed physical consistency with the observations from GoAmazon 2014/15. Shallow and deep convection clouds show to have meaningful impact on radiation fluxes in the Amazon region during wet and dry periods. Anomalies of large-scale variables (relative to the previous day's average) are physically associated with cloud formation, evolution and dissipation. SAM consistently simulated these results, where the cloud fraction vertical profile shows a pattern very close to the observed data (cloud type). Additionally, the integrated cloud fraction and large-scale variable anomalies, as a function of the previous day's average, have a good correlation. These results suggest that the memory of the large-scale dynamics from previous day can be used to estimate the clouds fraction. As well as the water content, which is a variable of the cloud itself. In general, the SAM satisfactorily simulated the interaction between cloud-radiation and dynamic and thermodynamic variables of the atmosphere during the periods of this study, being indicated to obtain atmospheric variables that are impossible to obtain in an observational way.

Factors Controlling Tropical Cyclone Intensification Over the Marginal Seas of China

Tropical cyclone (TC) intensification over marginal seas, especially rapid intensification (RI), often poses great threat to lives and properties in coastal regions and is subject to large forecast errors. It is thus important to understand the characteristics of TC intensification and the involved key factors affecting TC intensification over marginal seas. In this study, the 6-hourly TC best-track data from Shanghai Typhoon Institute of China Meteorological Administration, ERA-Interim reanalysis data, and TRMM satellite rainfall products are used to analyze and compare the climatological characteristics and key factors of different intensification stratifications over the marginal seas of China (MSC) and the western North Pacific (WNP) during 1980–2018. The statistical results show that TC intensification over the MSC is more likely to occur when TCs experience relatively large intensities, weak vertical wind shear, small translation perpendicular to the coastline, relatively high fullness, strong upper-level divergence, low-level relative vorticity, and high inner-core precipitation rate. The box difference index method is used to quantify the relative contributions of these factors to TC RI. Results show that the initial (relative) intensity contributes the most to TC RI over both the MSC and the WNP. The inner-core precipitation rate and translation perpendicular to the coastline are of second importance to TC RI over the MSC, while both vertical wind shear and TC fullness are crucial to TC RI over the WNP. These findings may help understand TC activity over the MSC and provide a basis for improving intensity prediction of TCs in the MSC.

Idealized simulation study of the relationship of disdrometer sampling statistics with the precision of precipitation rate measurement

Due to the discretized nature of rain, the measurement of a continuous precipitation rate by disdrometers is subject to statistical sampling errors. Here, Monte Carlo simulations are employed to obtain the precision of rain detection and rate as a function of disdrometer collection area and compared with World Meteorological Organization guidelines for a 1 min sample interval and 95 % probability. To meet these requirements, simulations suggest that measurements of light rain with rain rates R ≤ 0.50 mm h−1 require a collection area of at least 6 cm × 6 cm, and for R = 1 mm h−1, the minimum collection area is 13 cm × 13 cm. For R = 0.01 mm h−1, a collection area of 2 cm × 2 cm is sufficient to detect a single drop. Simulations are compared with field measurements using a new hotplate device, the Differential Emissivity Imaging Disdrometer. The field results suggest an even larger plate may be required to meet the stated accuracy, likely in part due to non-Poissonian hydrometeor clustering.

Lightning-ignited wildfires and long continuing current lightning in the Mediterranean Basin: preferential meteorological conditions

Lightning is the major cause of the natural ignition of wildfires worldwide and produces the largest wildfires in some regions. Lightning strokes produce about 5 % of forest fires in the Mediterranean Basin and are one of the most important precursors of the largest forest fires during the summer. Lightning-ignited wildfires produce significant emissions of aerosols, black carbon, and trace gases, such as CO, SO2, CH4, and O3, affecting air quality. Characterization of the meteorological and cloud conditions of lightning-ignited wildfires in the Mediterranean Basin can serve to improve fire forecasting models and to upgrade the implementation of fire emissions in atmospheric models. This study investigates the meteorological and cloud conditions of lightning-ignited wildfires (LIWs) and long continuing current (LCC) lightning flashes in the Iberian Peninsula and Greece. LCC lightning and lightning in dry thunderstorms with a low precipitation rate have been proposed to be the main precursors of the largest wildfires. We use lightning data provided by the World Wide Lightning Location Network (WWLLN), the Earth Networks Total Lightning Network (ENTLN), and the Lightning Imaging Sensor (LIS) on board the International Space Station (ISS), together with four databases of wildfires produced in Spain, Portugal, southern France, and Greece, respectively, in order to produce a climatology of LIWs and LCC lightning over the Mediterranean Basin. In addition, we use meteorological data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis data set and by the Spanish State Meteorological Agency (AEMET), together with the Cloud Top Height product (CTHP) derived from Meteosat Second Generation (MSG) satellites measurements to investigate the meteorological conditions of LIWs and LCC lightning. According to our results, LIWs and a significant amount of LCC lightning flashes tend to occur in dry thunderstorms with weak updrafts. Our results suggest that LIWs tend to occur in clouds with a high base and with a vertical content of moisture lower than the climatological value, as well as with a higher temperature and a lower precipitation rate. Meteorological conditions of LIWs from the Iberian Peninsula and Greece are in agreement, although some differences possibly caused by the highly variable topography in Greece and a more humid environment are observed. These results show the possibility of using the typical meteorological and cloud conditions of LCC lightning flashes as proxy to parameterize the ignition of wildfires in atmospheric or forecasting models.

Assessing the change of groundwater quality compared with land-use change and precipitation rate (Zrebar Lake's Basin)

Population, salinity, and increasing water consumption have caused high pressure on groundwater resources in Iran. The study reported here investigates the change of groundwater quality in Zrebar lake basin and the relationship between it with land-use change and precipitation rate from 1992 to 2018. To achieve the intended goal, chemical parameters of water from wells around the lake, bicarbonate (HCO3−), sulfate (SO4), sodium absorption ratio (SAR), and electrical conductivity (EC) were analyzed. Then, four methods including interpolation in the ArcGIS environment, Wilcox and Schoeller Diagram in Aq.QA software and Ground Water Quality Index (GWQI) were used to indicate the trend of water quality from 1992 to 2018. To detect land-use changes from 1992 to 2018, three Landsat satellite images covering the study area were used to identify land uses and their changes during the period that shows a significant area of forests that has been replaced by agricultural use, the dominant cover in 2018, while the area of forest has declined sharply. In this study, the precipitation patterns over the past years were showed to assess the relationship between rainy and low rainfall years with water quality. The results showed that forest area in 1992, 2003 and 2018 was 70.6, 62.5 and 50.2 hectares, respectively, which shows a significant reduction, 22%, during this study period. On the other hand, the area of farmlands and human-made constructions has increased by 20% and 200%, respectively. This study additionally revealed that although there was a decreasing trend in the rate of rainfall and the agricultural lands have increased, the quality of water was still suitable for drinking and agriculture consumptions. Changes in groundwater quality were not justifiable by rainfall rate and land-use change because there was no significant relationship between them with all the groundwater quality parameters.

Molecular Dynamics Research on the Impact of Vacancies on Cu Precipitation in BCC-Fe

The molecular dynamics (MD) simulation method was used to explore the impact of vacancy concentration (0 at%, 0.1 at% and 0.2 at%) on the diffusion and precipitation rate of Cu atoms in the Fe-3.5Cu alloy and the growth of Cu precipitation during the aging process of the alloy. The mechanism of the influence of Cu precipitation relative to the tensile properties of Fe-3.5Cu alloy was investigated. The results showed that the presence of vacancies will promote the diffusion and precipitation of Cu atoms in the Fe-3.5Cu alloy, but the diffusion and precipitation rate of Cu atoms does not always increase with the increase in vacancies. In the alloy containing 0.2 at% vacancies, the diffusion and precipitation rate of Cu atoms is lower than that in the alloy containing 0.1 at% vacancies. During the aging process, when the alloy contains no vacancies, no Cu precipitates will be produced. In the alloy containing 0.1 at% vacancies, the size of the Cu precipitates produced is larger than the size of the Cu precipitates produced in the alloy containing 0.2 at% vacancies, but the number of precipitates is less than that in the alloy with 0.2 at% vacancies. During the tensile process, the Cu precipitates will promote early occurrence of phase transition of the internal crystal structure in the Fe-3.5Cu alloy system, and lead to the generation of vacancy defects in the system, thus weakening the yield strength and strain hardening strength of the alloy.

Hybrid Bermudagrass and Tall Fescue Turfgrass Irrigation in Central California: I. Assessment of Visual Quality, Soil Moisture and Performance of an ET-Based Smart Controller

Research-based information regarding the accuracy and reliability of smart irrigation controllers for autonomous landscape irrigation water conservation is limited in central California. A two-year irrigation research trial (2018–2019) was conducted in Parlier, California, to study the response of hybrid bermudagrass and tall fescue to varying irrigation scenarios (irrigation levels and irrigation frequency) autonomously applied using a Weathermatic ET-based smart controller. The response of turfgrass species to the irrigation treatments was visually assessed and rated. In addition, turfgrass water response functions (TWRFs) were developed to estimate the impact of irrigation scenarios on the turfgrass species based on long-term mean reference evapotranspiration (ETo) data. The Weathermatic controller overestimated ETo between 5and 7% in 2018 and between 5 and 8% in 2019 compared with California Irrigation Management Information System values. The controller closely followed programmed watering-days restrictions across treatments in 2018 and 2019 and adjusted the watering-days based on ETo demand when no restriction was applied. The low half distribution uniformity and precipitation rate of the irrigation system were 0.78 and 28 mm h−1, respectively. The catch-cans method substantially underestimated the precipitation rate of the irrigation system and caused over-irrigation by the smart controller. No water-saving and turfgrass quality improvement was observed owing to restricting irrigation frequency (watering days). For the hybrid bermudagrass, the visual rating (VR) for 101% ETo treatment stayed above the minimum acceptable value of six during the trial. For tall fescue, the 108% ETo level with 3 d wk−1 frequency kept the VR values in the acceptable range in 2018 except for a short period in mid-trial. The TWRF provided a good fit to experimental data with r values of 0.79 and 0.75 for tall fescue and hybrid bermudagrass, respectively. The estimated VR values by TWRF suggested 70–80% ETo as the minimum irrigation application to maintain the acceptable hybrid bermudagrass quality in central California during the high water demand months (i.e., May to August) based on long-term mean ETo data. The TWRF estimations suggest that 100% ETo would be sufficient to maintain the tall fescue quality for only 55 days. This might be an overestimation impacted by the relatively small tall fescue VR data in 2019 owing to minimal fertilizer applications and should be further investigated in the future.

A Deep Learning Multimodal Method for Precipitation Estimation

To improve precipitation estimation accuracy, new methods, which are able to merge different precipitation measurement modalities, are necessary. In this study, we propose a deep learning method to merge rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network, composed of an encoder–decoder architecture, performs a multiscale analysis of the three input modalities to estimate simultaneously the rainfall probability and the precipitation rate value with a spatial resolution of 2 km. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Our results for instantaneous precipitation detection, instantaneous precipitation rate estimation, and for daily rainfall accumulation estimation show that the best accuracy is obtained for the model combining all three modalities. The ablation study, done to compare every possible combination of the three modalities, shows that the combination of rain gauges measurements with radar data allows for a considerable increase in the accuracy of the precipitation estimation, and the addition of satellite imagery provides precipitation estimates where rain gauge and radar coverage are lacking. We also show that our multi-modal model significantly improves performance compared to the European radar composite product provided by OPERA and the quasi gauge-adjusted radar product RADOLAN provided by the DWD for precipitation rate estimation.

Electrochemical Properties of LiNi0.6Co0.2Mn0.2O2 Cathode Materials Prepared with Different Ammonia Content

The content of ammonia coordination agent in initial aqueous solution is one of important factors which greatly influences the morphologies and electrochemical performances of layered LiNi6Mn2Co2O2 (NCM622). The spherical morphologies, contributing to higher specific capacity of NCM622, ascribe to the same precipitation rate of transition metal ions (Ni2+, Co2+, Mn2+) during co-precipitation. Hence, the effects of different amounts of ammonia in initial solution on the hydroxide equilibrium constant and properties of NCM622 were discussed. With the ammonia content of 70 mL, the spherical morphology with more perfect layered structure and higher discharge capacity are obtained. The necessity of ammonia content in initial solution are also demonstrated from electrochemical performances of NCM622, such as the initial discharge capacity of 199.8 mAh g−1 at 0.1 C, the specific capacity of 150.0 mAh g−1 after 100th cycles, and the capacity retention rate of 89.6% at 3 C. If other metal ions are anticipated to be chemically doped with NCM622, the hydroxide equilibrium constant and precipitation rate need to be considered.